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2021
Organized by
Jahangirnagar University,
Bangladesh and South
Asian University, India
Technically Sponsored by
Soft Computing Research
Society
October 23-24, 2021
5th International Joint Conference
on Advances in Computational
Intelligence (IJCACI 2021)
Souvenir
ii
TABLE OF CONTENTS
Patron ............................................................................................................................... 1
General Chair ................................................................................................................... 1
Program Chair .................................................................................................................. 1
Publicity Chair ................................................................................................................. 1
Publication Committee ..................................................................................................... 2
Technical Program Committee ........................................................................................ 2
Advisory Board ................................................................................................................ 3
Abstract of Accepted Papers ............................................................................................ 8
Performance Analysis of Secure Hybrid Approach for Sharing Data Securely in
Vehicular Adhoc Network ............................................................................................... 9
A Review on Curvelets and Its Applications ................................................................... 9
Machine Learning Approaches for handling SQL Injection Attack .............................. 10
Assessing Usability of Mobile Applications Developed for Autistic Users through
Heuristic and Semiotic Evaluation ................................................................................. 10
Blockchain Implementations and Use Cases for Inhibiting COVID-19 Pandemic ....... 11
Random Forest based Legal Prediction System ............................................................. 11
Particle swarm optimization and computational algorithm based weighted fuzzy time
series forecasting method ............................................................................................... 12
Ant Colony Optimization to Solve the Rescue Problem as a Vehicle Routing Problem
with Hard Time Windows .............................................................................................. 12
Applying Opinion Leaders to Investigate the Best-of-n Decision Problem in
Decentralized Systems ................................................................................................... 13
Pathfinding in the Paparazzi Problem Comparing Different Distance Measures .......... 14
Empirical Evaluation of Motion Cue for Passive-Blind Video Tamper Detection Using
Optical Flow Technique ................................................................................................. 14
iii
Quantifying Changes in Sundarbans Mangrove Forest Through GEE Cloud Computing
Approach ........................................................................................................................ 15
Metaheuristics and Hyper-heuristics based on Evolutionary Algorithms for Software
Integration Testing ......................................................................................................... 16
Towards a static and dynamic features-based framework for android vulnerabilities
detection ......................................................................................................................... 16
A Comparative Study of Existing Knowledge based Techniques for Word Sense
Disambiguation .............................................................................................................. 17
Online Subjective Examination a Student Perspective .................................................. 18
An Insider Threat Detection Model Using One-Hot Encoding and Near-Miss Under-
sampling Techniques ...................................................................................................... 18
Problem Solution Strategy Assessment of a Hybrid Knowledge-Based System in
Teaching and Learning Practice ..................................................................................... 19
Towards Developing a Mobile Application for Detecting Intoxicated People through
Interactive UIs ................................................................................................................ 19
Novel Harris Hawks Optimization and Deep Neural Network Approach for Intrusion
Detection ........................................................................................................................ 20
Random forest classification and regression models for literacy data ........................... 21
Towards Robotic Knee Arthroscopy: Spatial and Spectral Learning Model for Surgical
Scene Segmentation ....................................................................................................... 21
Opposition-based Arithmetic Optimization Algorithm with Varying Acceleration
Coefficient for Function Optimization and Control of FES System .............................. 22
The Impact of Using Facebook on the Social Life of College Students ........................ 23
Power control of a Grid Connected Hybrid Fuel cell, Solar and Wind Energy Conversion
Systems by using Fuzzy MPPT Technique .................................................................... 23
Stabilizing Constrained Control for Discrete-Time Multivariable Linear Systems via
Positive Polyhedral Invariant Sets ................................................................................. 24
iv
Texture Feature Analysis for Inter-Frame Video Tampering Detection ........................ 24
Computer Vision-Based Algorithms on Zebra Crossing Navigation ............................ 25
Spider Monkey Optimization (SMO) algorithm based Innovative strategy for
strengthening of Reliability Indicators of Radial Electrical System .............................. 25
AI Based Multi Label Data Classification of social media ............................................ 26
Performance Analysis of Random Forest (RF) and Support Vector Machine (SVM)
algorithms in classifying Breast Cancer ......................................................................... 26
Prediction of Water Quality Index of Ground Water Using the Artificial Neural Network
and Genetic Algorithm ................................................................................................... 27
Improving Throttled Load Balancing Algorithm in Cloud Computing ........................ 28
Feature Extraction Based Landmine Detection Using Fuzzy Logic .............................. 28
IOT based Smart Parking System using NodeMCU and Arduino ................................. 29
Study on Intelligent Tutoring System for Learner Assessment Modeling Based on
Bayesian Network .......................................................................................................... 29
Finite Element Analysis of Prosthetic Hip Implant ....................................................... 30
A Comprehensive Study on Multi Document Text Summarization for Bengali Language
........................................................................................................................................ 31
Deep Learning-Based Lentil Leaf Disease Classification ............................................. 31
Framework for Diabetes Prediction using Machine Learning Techniques through Swarm
Intelligence ..................................................................................................................... 32
A Graphical Approach for Image Retrieval Based on Five Layered CNNs Model ....... 32
Statistical Post-processing Approaches for OCR Texts ................................................. 33
FPGA Implementation of Masked-AE$HA-2 for Digital Signature Application ......... 34
A Framework for Improving the Accuracy With Different Sampling Techniques for
Detection of Malicious Insider Threat in Cloud ............................................................ 34
v
Eliminating racial bias at the time of detection Melanoma using Convolution Neural
Network (CNN) .............................................................................................................. 35
Customer Churn Analysis using Machine Learning ...................................................... 35
A Comparative Study of Hyperparameter Optimization Techniques for Deep Learning
........................................................................................................................................ 36
Fault Location on Transmission Lines of Power Systems with Integrated Solar
Photovoltaic Power Sources ........................................................................................... 37
Emergency Vehicle Detection Using Deep Convolutional Neural Network ................. 37
Emotion Recognition from Speech using Deep Learning ............................................. 38
Secure predictive analysis on heart diseases using partially homomorphic machine
learning model ................................................................................................................ 38
Quality Analysis of PATHAO Ride-sharing Service in Bangladesh ............................. 39
Robot Path Planning using β Hill Climbing Grey Wolf Optimizer ............................... 39
An Image Steganography Technique based on Fake DNA Sequence Construction ..... 40
Artificial Intelligent Based Control of Improved Converter for Hybrid Renewable Energy
Systems .......................................................................................................................... 40
A Review on Unbalanced Data Classification ............................................................... 41
A Comparative Study of Meta-heuristic Algorithms based on the solution of VRPTW in
E-logistics ....................................................................................................................... 42
1
Patron
R. K. Mohanty, President (Acting), South Asian University, India
Farzana Islam, Vice Chancellor, Jahangirnagar University (JU),
Bangladesh
General Chair
Mohammad Shorif Uddin, Jahangirnagar University, Bangladesh
Jagdish Chand Bansal, South Asian University Delhi, India
Prashant Jamwal, Nazarbayev University, Kazakhstan
Program Chair
Akhil Ranjan Garg, Jai Narain Vyas University, Jodhpur, India
Deepa Sinha, South Asian University, New Delhi, India
Kapil Kumar Sharma, South Asian University, New Delhi, India
Mukesh Saraswat, Jaypee Institute of Information Technology, Noida,
India
Sandeep Kumar, CHRIST (Deemed to be University), Bangalore
Publicity Chair
Ashish Kumar Tripathi, Malaviya National Institute of Technology
Jaipur, India
Kusum Kumari Bharti, Indian Institute of Information Technology,
Design and Manufacturing, Jabalpur, India
Prashant Singh Rana, Thapar University, India
Saroj Kumar Sahani, South Asian University, New Delhi, India
2
Publication Committee
Mohammad Shorif Uddin, Jahangirnagar University, Bangladesh
Jagdish Chand Bansal, South Asian University Delhi, India
Sandeep Kumar, CHRIST (Deemed to be University), Bangalore
Prashant Jamwal, Nazarbayev University, Kazakhstan
Technical Program Committee
M. Shamim Kaiser, JU, Bangladesh
Mufti Mahmud, Nottingham Trent University, UK
Md. Imdadul Islam, JU, Bangladesh
Md. Ezharul Islam, JU, Bangladesh
Mohammad Hanif Ali, JU, Bangladesh
J. K. Das, JU, Bangladesh
Mohammad Zahidur Rahman, JU, Bangladesh
Md. Golam Moazzam, JU, Bangladesh
Israt Jahan, JU, Bangladesh
Md. Humayun Kabir, JU, Bangladesh
ASM Mustafizur Rahman, JU, Bangladesh
Md. Abul Kalam Azad, JU, Bangladesh
Md. Musfique Anwar, JU, Bangladesh
Morium Akter, JU, Bangladesh
3
Advisory Board
A K Verma, Western Norway University of Applied Sciences,
Haugesund, Norway
Rajveen Chandel, NIT Hamirpur
Miodrag potkonjak, UCLA ,467 Engineering VI, Los Angeles,
Nilanjan Dey, Techno India College of Technology, India
Neetesh Purohit, IIIT Allahabad
Nishchal K. Verma, Indian Institute of Technology Kanpur, India
Preetam Kumar, IIT, Patna
Nooritawati Md Tahir, University Technology MARA (UiTM), Malaysia
Priti Srinivas Sajja, Sardar Patel University Vallabh Vidyanagar Gujarat
Prena Gaur, NSUT, Dwarka, New Delhi
R. Gangopadhyay, LNMIIT, Jaipur
R. P. Yadav, MNIT Jaipur
Pushpendra Singh, NIT Hamirpur
S. Sundaram, IISc Bangalore
Mohd Muntjir, Taif University, Kingdome of Saudia arabia
Sandeep Sancheti, SRM University, India
Sanjeev Yadav, GWEC, Ajmer
Sanjay Singh, CEERI Pilani
Seemanti Saha, NIT Patna
4
Sanyog Rawat, Manipal University Jaipur
Shashi Shekhar Jha, IIT Ropar
Suneeta Agrawal, Motilal Nehru National Institute of Technology
Allahabad
Sudhir Kumar, IIT Patna
Surajit Kundu, NIT, Sikkim
Sureswaran Ramadass, USM University Penang, Malaysia
Swagatam Das, Indian Statistical Institute, Kolkata, India
Debasish Ghose, IISc Bangalore
Alok Kanti Deb, Indian Institute of Technology Kharagpur
Anand Nayyar, Scientist, Graduate School, Duy Tan University, Da
Nang, Viet Nam
Anand Paul, Kyungpook National University, South Korea
Aniruddha Chandra, NIT Durgapur
Anupam Yadav, National Institute of Technology Jalandhar
Aruna Tiwari, Indian Institute of Technology Indore
Atulya K. Nagar, Liverpool Hope University, UK
Ashvini Chaturvedi, NIT Suratkal
Carlos E. Palau, ETSI Telecommunication, UPV, Camino de Vera, Spain
Costin Badica, University of Craiova, Dolj, Romania
Dan Simon, Cleveland State University USA
Sushmita Das, NIT, Rourkela
5
Deepak Garg, Bennett University, India
Dinesh Goyal, Poornima Institute of Engineering & Technology, Jaipur
Dumitru Baleanu, Cankaya University
K. S. Nisar, Riyadh, Saudi Arabia
Kamran Iqbal, University of Arkansas at Little Rock, Little Rock,
Arkansas, United States
Kusum Deep, Indian Institute of Technology, Roorkee, India
Kuldeep Singh, MNIT, Jaipur
Lalit Lumar Goyal, NTU Nanyang, Singapore
Manoj K. Shukla, Harcourt Butler Technical University, Kanpur
Manoj Thakur, IIT Mandi
Marcin Paprzycki, Polish Academy of Sciences, Warsaw, Poland
Md. Abdur Razzaque, DIU
Sheikh Md. Monzurul Huq, Charles Darwin University, Australia
A. K. M. Fazlul Hoque, Treasurer, JU
Abdul Goffar Khan, DIU
Asadul Huq, Dean, JU
Ajit Kumar Mazumdar, RUET
Sheikh Anowarul Fattah, DU
Mohammad Shamsul Arefin, BUET
Celia Shahnaz, BUET
Mohammed Moshiul Hoque, JU
6
Mohammad Zahidur Rahman, CUET
Md Kabirul Islam, DIU
Hamidul Haque Khan, CUET
Xavier Fernando, LNM IIIT, India
Md. Amir Hussain, Pro-VC, JU
Md. Nurul Alam, DU
Md. Atiqur R. Ahad, Pro-VC, JU
Md. Golam Mowla Choudhury, DU
Md. Mohiuddin Ahmad, DIU
Md. Milan Khan, DIU
Md. Nurunnabi Mollah, DUET
Md. Nasim Akhtar, KUET
S.M. Mahbub Ul Haque Majumder, JU
Mohammad Hanif Ali, KUET
Shamsul Alam, DIU
Curtis R. Menyuk, MUET, Pakistan
T. Rama Rao, SRM Institute of Science & Technology, Chennai
Vimal Bhatia, IIT Indore
Wan young chung, Pukyong National University Busan, South Korea
Navnit Jha, South Asian University, India
Pankaj Jain, South Asian University, India
7
Saroj Sahani, South Asian University, India
Danish Lohani, South Asian University, India
Alamgir Hossain, University of Malaya, Malaysia
Abdullah Gani, Director, AIIT, Amity University, India
Sunil Kumar Khatri, South Asian University, India
Asik Paul, Anglia Ruskin University, UK
AHM Zahirul Alam, University of Calcuta, India
Syed Mizanur Rahman, DIU
B. S. Chowdhury, IIUM, Malaysia
Haris Haralambous, University of Maryland, USA
M Mahbubur Rashid, University of West Scotland, UK
K. Dahal, Federation University, Australia
J Kamruzzaman, Frederick University, Cyprus
M Nasir Uddin, Nottingham Trend University, Uk
Mahmud, IIUM, Malaysia
Ramjee Prasad, BU-CROCCS, Thailand
Poompat Saengudomlert, Lakehead University, Canada
Emeritus Ranjan Gangopadhyay, Aalborg University, Denmark
Jamal El-Den, Ryerson Comm. Lab, Canada
8
Abstract of Accepted Papers
9
Performance Analysis of Secure Hybrid Approach for
Sharing Data Securely in Vehicular Adhoc Network
Atul B. Kathole, Dinesh N.Chaudhari
Pimpri Chinchwad College of Engineering, Pune, India
Abstract. As Adhoc networks are essentially dynamic networks, several security
issues may occur with the different attacks in the network due to their dynamic
nature. Therefore, several mechanisms have been proposed to prevent packet
routing errors in these networks. The deployment scenario shows that the packet
transmission rate and performance are very slow when the Sybil attack is present in
the network. Our goal is to propose a clustering method to improve latency, packet
transfer rate, and other performance indicators. In the proposed approach, we will
use two phases: the first phase, based on the packet delivery rate, and then the second
phase checks the exact cause of the performance degradation to verify the node's
behaviour. To improve security, a program that authenticates the cluster network
should be used. For malicious entities, the false accusation algorithm provides
methods to revoke and revoke certificates. Using the proposed system, we are trying
to improve the system's performance by comparing it with the existing system. As
the number of shared nodes in the system increases, the system can exert its best
performance and prevent various attacks.
A Review on Curvelets and Its Applications
Shristi Mishra and Deepika Sharma
Department of Mathematics, Chandigarh University, Punjab
Abstract. Wavelets have a large impact on image and signal processing, but it is
observed that they fail to represent an object with highly anisotropic elements like
linear or curvilinear structure. To alleviate this problem, curvelets have been
introduced by E. J. Candes and L. Demanent. Curvelets have shown a great interest
in the field of image and signal processing over the past few years. The beauty of
curvelets over wavelets is that it can be constructed over general manifolds. In this
paper, we represent the review on curvelets transform, including its history from
wavelets, and so forth. Moreover, this paper will also demonstrate the numerous
applications of curvelets such as seismic data recovery, X-Ray computed
tomography, interferometry images, time-frequency analysis, processing of MRI for
local image enhancement and noise suppression of receiver’s function, which would
be fruitful for discovering the real-life applications and problems that can be solved
with the help of curvelets.
10
Machine Learning Approaches for handling SQL
Injection Attack
Neha Bhateja1, Sunil Sikka1 and Anshu Malhotra2
1Amity University Haryana, India
2The North Cap University, India
Abstract. In today’s world, there is a massive amount of information related to users,
businesses etc. that is available on the internet which provides a motive for malicious
users to steal by creating the attacks. The most dominant attack in the current
scenario is SQL injection attack that is performed by attackers on web applications.
As SQL Injection attacks are becoming more diverse and complex every day, a
variety of ways for preventing and detecting SQL attacks are implemented. Machine
learning provides a way for detecting and preventing such attacks using
sophisticated algorithms. The aim of the paper is to present different available
methods used for preventing and detecting SQL Injection attacks that use a machine
learning approach.
Assessing Usability of Mobile Applications Developed for
Autistic Users through Heuristic and Semiotic Evaluation
Sayma Alam Suha, Muhammad Nazrul Islam, Shammi
Akter, Milton Chandro Bhowmick, and Rathin Halder
Department of Computer Science and Engineering, Military Institute
of Science and Technology Mirpur Cantonment, Dhaka, Bangladesh
Abstract. Mobile applications using Augmented and Alternate Communication
(AAC) technologies have found to be an effective approach for autistic people
having communication disparity to enhancing their communication skills. These
applications, on the other hand, must be designed in such a way that they are usable
and intuitive for individuals with autism. Therefore, the objectives of this research
are to evaluate the usability of mobile applications developed for enhancing the
communication skills of autistic users according to their special needs and to assess
the applicability of heuristic evaluation (HE) and semiotic evaluation (SE)
techniques for evaluating the usability of such kind of mobile apps. To attain these
objectives, four applications for improving the communication skill of autistic
people were evaluated through heuristic evaluation and semiotic evaluation
techniques. Both evaluations found that a significant amount of usability and
interactivity flaws are exist in each application that need to be considered for making
these applications usable for the autistic user. The study also showed that different
types of usability problems were revealed by heuristic evaluation and also by the
semiotic evaluation; and an evaluation by integrating of both techniques could be an
effective approach for enhancing the usability and interactivity of applications
developed for improving the communication skill of autistic user.
11
Blockchain Implementations and Use Cases for Inhibiting
COVID-19 Pandemic
Amirul Azim1 and Muhammad Nazrul Islam2
1Department of Information and Communication Technology (ICT),
Bangladesh University of Professionals (BUP), Bangladesh
2Department of Computer Science and Engineering (CSE) Military
Institute of Science and Technology (MIST), Bangladesh
Abstract. The SARS COV-2 or COVID-19 epidemic has created a global health
crisis that is having a profound effect on our daily livelihood and globalization.
Hospitals across the globe are currently facing tremendous problems in delivering
treatment to COVID-19 patients. Clinical trials and research are always lengthy
processes and the exchanges of trailed data among the untrusted parties need a
trusted source that would provide immutable transactions with a minimum time
stamp. Thus, the objectives of this review are to explore the current focus of
Blockchain-based research for inhabiting the COVID-19 pandemic and to devise all
possible use cases that can be used to create a Blockchain-based pandemic data
sharing and management system. To attain these objectives, a total of 19 articles
have been reviewed following the Systematic Literature Review (SLR) process. As
outcomes, this study highlighted four focused objectives concurrently being
exposed by existing studies and revealed eight Blockchain use-cases to develop a
future system for COVID-19 pandemic data sharing and management system with
enhanced security.
Random Forest based Legal Prediction System
Riya Sil
Adamas University, Kolkata 700126, India
Abstract. The evolution of science and technology has abetted in the integration of
human intelligence into its computerized version using artificial intelligence. The
advancement of artificial intelligence has radically changed the 21st century in terms
of technology that can analyse any event to predict its outcome based on multiparty
argument. This approach may be implemented to transit from problem domain to
solution domain for any critical issue. As a result, it may prove to be beneficial to
solve any problem which is suffering due to lack of manpower engagement,
infrastructure, etc. The concept of artificial intelligence can be applied over the legal
domain to execute complex tasks in an efficient manner. Precisely, the legal field
together with artificial intelligence and machine learning can be used for legal
document generation, pre-diction, briefs, search, case outcomes, and many more. It
can predict the conclusion of any legal case by analysing the information provided
and also the previous records that have been gathered from legal case documents. In
this paper, authors have used Random Forest based legal judgement prediction
system for classification of the offender and manage the cases related to the Dowry
12
Prohibition Act. Using supervised learning, the authors have pro-posed a prediction
system to find the offender in an accurate manner thus assisting the legal
professionals to resolve cases.
Particle swarm optimization and computational algorithm
based weighted fuzzy time series forecasting method
Shivani Pant and Sanjay Kumar
Department of Mathematics, Statistics and Computer Science, G. B.
Pant University of Agriculture and Technology, Pantnagar,
Uttarakhand, India, 263145
Abstract. Numerous fuzzy time series (FTS) predictive models had been envisaged
in past decades to cope with complicated and undetermined circumstances. The key
elements: namely determination of intervals and modeling of fuzzy logical
relationships, affect the model’s forecasting accuracy. The manner in which proper
fuzzy relationships are generated is pivotal in establishing fuzzy interactions and
predictions. Using the prevalent swarm intelligence method of particle swarm
optimization (PSO), this work proposes a computational algorithm for forecasting
time series by optimizing the weights of fuzzy logical relations (FLRs) of high-order
weighted FTS. The relevance of each individual fuzzy relationship in predicting is
shown by the weights in FTS. The model's appropriateness was tested using the
University of Alabama enrolment dataset. In the context of average forecasting and
root mean square error, the suggested model's forecasting accuracy was
demonstrated to be better than the other models.
Ant Colony Optimization to Solve the Rescue Problem as
a Vehicle Routing Problem with Hard Time Windows
Mélanie Suppan1, Thomas Hanne2 and Rolf Dornberger3
1School of Life Sciences, University of Applied Sciences and Arts
Northwestern Switzer-land, Muttenz, Switzerland
2Institute for Information Systems, University of Applied Sciences
and Arts Northwestern Switzerland, Olten, Switzerland
3Institute for Information Systems, University of Applied Sciences
and Arts Northwestern Switzerland, Basel, Switzerland
Abstract. The rescue problem is an adaptation of a standard Vehicle Routing
Problem where a set of patients suffering from various medical conditions has to be
picked up by a set of ambulances and brought back to the hospital. Optimizing this
13
problem is important to improve the use of life emergency vehicles in daily or
disaster situations. Although this problem is usually modelled as a Capacitated
Vehicle Routing Problem, different formulations are proposed in the literature
including multi-objective optimization with shortest route and maximization of the
number of patients that will survive or remain stable. Ant Colony Optimization
(ACO) and Genetic Algorithms (GA) are frequent-ly used, where ACO performs
better on objectives specific to the rescue problem. We model the problem as a
single-objective Vehicle Routing Problem with Time Windows (VRPTW) using
hard time windows. Each patient is assigned a degree of injury and a corresponding
maximum time window. An immediate return to the hospital for critically injured
patients is also introduced. The rescue problem turns to a VRPTW with hard time
windows for different problem sizes and is solved with ACO. The results suggest
that with a sufficiently large fleet, it can be ensured that critically injured patients
are reached in good time.
Applying Opinion Leaders to Investigate the Best-of-n
Decision Problem in Decentralized Systems
Jan Kruta1, Urs Känel1, Rolf Dornberger2 and Thomas
Hanne3
1Medical Informatics, University of Applied Sciences and Arts,
Northwestern Switzerland, Muttenz, Switzerland
2Institute for Information Systems, University of Applied Sciences
and Arts Northwestern Switzerland, Basel, Switzerland,
3Institute for Information Systems, University of Applied Sciences
and Arts Northwestern Switzerland, Olten, Switzerland
Abstract. Decision-making is considered a key ability for any living organism or
artificial system. Finding a consensus on the most beneficial solution among a
collective in a decentralized system is a challenging task, especially when
individuals operate with incomplete knowledge and no central authority. This paper
investigates collective decision making using a best-of-n algorithm which is a
nature-inspired approach based on the behaviour of honeybees. We focus on the role
of opinion leaders and their influence. We investigate such an agent by adapting the
behavior of a swarm and changing its basic dynamics during different experiments.
Our results illustrate that it is possible to in-corporate new valuable features (such
as opinion leader effects and captain effects) into a proposed problem model. Our
results indicate that such opinion leader effects have a beneficial impact on
consensus finding in specific situations whereas in other situations the decision-
making process may get complicated.
14
Pathfinding in the Paparazzi Problem Comparing
Different Distance Measures
Kevin Schär1, Philippe Schwank1, Rolf Dornberger2 and
Thomas Hanne3
1Institute for Medical Engineering and Medical Informatics, School
of Life Sciences, FHNW, Muttenz, Switzerland
2Institute for Information Systems, University of Applied Sciences
and Arts Northwestern Switzerland, Basel, Switzerland,
3Institute for Information Systems, University of Applied Sciences
and Arts Northwestern Switzerland, Olten, Switzerland
Abstract. This paper compares different alternative path construction mechanisms
used in the A* algorithm applied to the Paparazzi problem. It investigates the
Manhattan, Euclidean and Chebyshev approaches for distance measurement using
four and eight neighbouring nodes in different maps. The maps consist of various
sizes and include fixed obstacles and different terrain structures represented by
weighted nodes. The Manhattan approach offers the best performance in terms of
the number of iterations and run time when using four neighbouring nodes in small
maps. In contrast, the Euclidean approach per-forms best at reasonable path costs
for large maps. The Chebyshev approach shows the lowest path costs in every map
regardless of the number of neighbouring nodes. However, the Chebyshev approach
as the gold standard for eight neighbouring nodes does not show the expected
superiority in pathfinding performance.
Empirical Evaluation of Motion Cue for Passive-Blind
Video Tamper Detection Using Optical Flow Technique
Poonam Kumari and Mandeep Kaur
University Institute of Engineering & Technology Panjab
University, Chandigarh
Abstract. The advances in multimedia processing technologies have led to an ever-
augmenting challenge to sustain the integrity and authenticity of digitized videos.
The domain of digital video forensics has been crucial in devising new
methodologies to counterfeit these attacks in a passive-blind manner. The paper
presents an empirical evaluation of motion cues computed using the Optical Flow
(OF) algorithm that enables automatic detection of inter-frame forgeries in digital
videos. The optical flow deals with the estimation of the true motion field. Though
various sparse OF methods are analysed in literature for detecting forgeries in digital
videos but study of Farneback OF for forensic applications has been very limited.
15
Farneback method calculates dense optical flow and thus the motion cue is exploited
as forensic footprint to detect copy-paste region in videos. The visual inspection of
the statistical detail plotted from optical flow images displays superfluous spikes in
forged videos that highlights the tampered region. Supervised machine learning was
therefore applied to automate the process of discriminating original and forged
videos. Region of interest is selected around the forged region to reduce overall
computational overhead and maintains uniformity in the feature vector length from
each sample. The proposed approach can detect and localize the frames where the
forgery attack is carried out. A Lin-ear SVC model is used that gives a classification
accuracy of 97%. The analysis is carried out on the benchmark REWIND dataset.
The study is significant in designing advanced video forensic algorithms based on
motion cues.
Quantifying Changes in Sundarbans Mangrove Forest
Through GEE Cloud Computing Approach
Chiranjit Singha and Kishore C. Swain
Department of Agricultural Engineering, Institte of Agriculture,
Visva-Bharati, Sriniketan, West Bengal-731236
Abstract. Precise information concerning mangrove ecosystem change is crucial for
their conservation and restoration in local to global scale. Due to natural events, like
cyclone, tsunami etc. causes disaster to the biodiversity in the coastal zone. This
study quantifies the changes in Sundarbans mangrove forest during 1996-2017
including changes during pre and post Bulbul cyclone in 2019. Google Earth Engine
(GEE) cloud computing approach using Remote Sensing based L-band ALOS
PALSAR 1 and 2 mosaic tiles, and Sentinel 1 dual-polarization C-band SAR
(Synthetic Aperture Radar) data with Sentinel 2 optical was used to estimate forest
loss and gain and above-ground biomass (AGB). The results showed that the long-
term mangrove loss area found 1.12 sq. km during 1996-2017 whereas, short-term
mangrove loss and the gain area is 0.78 sq. km and 0.25 sq. km, respectively, during
2007-2017. The densest mangrove elevation and total AGB were found mostly in
the eastern region of the study area. Mangrove elevation varies from -16 to 31m and
AGB ranges from 30.2 to 700 Mg·ha−1 in the study area. After the Bulbul cyclone,
the VH backscatter varies between -42.18 and -9.26dB where the VV backscatter
ranges from -31.80dB to -1.01dB. The cyclone causes tremendous losses of biomass
in the Sundarban. The study validated the suitability of SAR data for continuous
mapping, monitoring, and change detection of the mangrove forest. Our research
establishes the abilities of radar-based RS technology for change detection and
sustainable planning of mangrove forests in India.
16
Metaheuristics and Hyper-heuristics based on
Evolutionary Algorithms for Software Integration Testing
Valdivino Alexandre de Santiago Junior and Camila
Pereira Sales
Coordenacao de Pesquisa Aplicada e Desenvolvimento Tecnologico
(COPDT), Instituto Nacional de Pesquisas Espaciais (INPE),
Avenida dos Astronautas, 1758, Jardim da Granja - 12227-010, Sao
Jose dos Campos, SP, Brazil
Abstract. Hyper-heuristics have been identified as optimisation algorithms that
would have better generalisation capabilities than metaheuristics. In this article, we
present a controlled experiment that evaluates our metaheuristics (evolutionary
algorithms), two multi-objective (SPEA2, IBEA) and too many-objective (NSGA-
III, MOMBI-II), and three selection hyper-heuristics (HRISE R, HRISE M, Choice
Function) for the software integration testing problem. We relied on and improved
our previous method which aims at generating integration test cases based on C++
source code and optimisation algorithms. Considering three different quality
indicators and two types of evaluations (cross domain and statistical analyses),
results demonstrate that, for the algorithms and case studies considered in this
research, classical metaheuristics, such as SPEA2 and IBEA, performed better
compared to not only the most recent many-objective algorithms but also to the
hyper heuristics. This conclusion, based on empirical evidences, seems to be related
to the well-known no free lunch theorems which assert that any two algorithms are
equivalent when their performances are averaged across all possible problems.
Hence, we claim that it is needed to carry out more rigorous experiments, in the
context of optimisation, to better answer the question of generalisation in practical
terms.
Towards a static and dynamic features-based framework
for android vulnerabilities detection
Jigna Rathod1 and Dharmendra Bhatti2
1Babu Madhav Institute of Information Technology, UKA
TARSADIA UNIVERSITY, Gujarat, India
2Shrimad Rajchandra Institute of Management and Computer
Application, UKA TARSADIA UNIVERSITY, Gujarat, India
Abstract. Mobile phones are coming out as one of the governing computing
platforms in the contemporary world where android phones are the top pick for users
as well as developers due to their open-source nature. Such fame of android phone
17
usage comes with an elevation in malware targeting the Android operating system.
The proposed framework discovers vulnerabilities from an-droid applications by
performing dynamic analysis and uses the combination of static and dynamic
features such as permission, system calls, and network traffic. This paper analyzes
the impact of network traffic feature with system calls and permission to detect
vulnerability in android apps. To ascertain the vulnerability from real-world
applications, we trained our proposed frame-work by selecting attributes that are
obtained by implementing various attribute selection tactics. The Experiment was
performed on 2511 android applications. Our experimental results show that the
approach is remarkably accurate and the average accuracy is 94.57% for neural
networks and 98% for deep learning algorithms.
A Comparative Study of Existing Knowledge based
Techniques for Word Sense Disambiguation
Aarti Purohit and Kuldeep Kumar Yogi
Department of Computer Science and Engineering, Banasthali
University, Rajasthan, India
Abstract. Word Sense Disambiguation refers to the process of determining the
correct sense of a given word in a given sentence. It is a difficult problem to solve
because it necessitates gathering information from various sources. The human mind
can use cognition and world knowledge to resolve word sense ambiguities.
However, machine translation systems are in high demand today, and because
machines cannot use cognition and world knowledge to resolve such ambiguities,
they make semantic errors and generate incorrect interpretations. Handling sense
ambiguity is one of the most difficult challenges in natural language processing and
understanding; such words result in erroneous machine translation. WSD techniques
were used and implemented on various corpora for almost all languages some
Lexical knowledge sources are available used as machine readable dictionaries.
WSD algorithms were classified into two broad categories: knowledge-based,
machine learning based approaches. In this paper we have tried to compare various
used algorithms and methods like overlap based, selection preference, semantic
approach and heuristic approach under knowledge-based approach for WSD. By
comparison, we have concluded that Knowledge based resource plays a vital role in
processing of any language. Some knowledge-based techniques give high accuracy
while some give low results for WSD for various languages. In this paper we try to
focus on Knowledge based Techniques or algorithms used and their benefits and
drawbacks in terms of execution speed or accuracy level with different languages.
Some results are low due to low resource languages used for WSD needs a work to
prepare knowledge resource.
18
Online Subjective Examination a Student Perspective
Madhav A. Kankhar, C. Namrata Mahender
Department of Computer Science & Information Technology Dr.
Babasaheb Ambedkar Marathwada University Aurangabad (MS),
India
Abstract. Online education has become an integral part today mostly due to the
COVID-19 scenario. Teaching, learning and evaluating are the main component of
online education system. In examination online education also place very vital role.
If considering the online option of exam mostly objective (MCQ) based has high
priority compare to subjective form of exam due to lot of limitations during
conduction of online subjective exams. As researches educationist pay more
attention on subjective examination for overall evaluation of student. Present work
tries to get students perspective on subjective examination through a survey
conducted on graduate and post graduate level student on Marathawada region.
An Insider Threat Detection Model Using One-Hot
Encoding and Near-Miss Under-sampling Techniques
Rakan A. Alsowail
Deanship of Common First Year, King Saud University, Riyadh
11362, Saudi Arabia
Abstract. Insider threats are malicious acts (e.g., data theft, fraud, and sabotage)
which are very difficult to detect that are carried out by authorized users within an
organization. The existing research in the field of insider threat detection mostly
focused on general insider threat scenarios. Moreover, the skewed is-sue that could
occur during the encoding process and due to the imbalanced classes of the dataset
are not addressed. As an enhancement to the existing work, we propose an insider
data leakage detection model that focus on detecting the most serious attack scenario
where a malicious insider executes an attack before his/her leaving from an
organization. The model embeds multi-data granularity techniques (label encoding,
scaling, one-hot encoding, and Near Miss under sampling) for the aim of addressing
the possible bias of the encoding process and the imbalance issue of dataset classes.
Several ma-chine learning classifiers are also employed for detecting insider data
leakage instances utilizing different classification perspectives. The model is
validated using The CERT Insider Threat Dataset to assess its performance in com-
parison to the ground truth, as a proof of concept. The results show that our model
outperforms the existing work that was validated on the same dataset with an AUC
score of 0.94.
19
Problem Solution Strategy Assessment of a Hybrid
Knowledge-Based System in Teaching and Learning
Practice
Kamalendu Pal
City, University of London, London EC1V 0HB, United Kingdom
Abstract. This paper presents the main features of an assessment method for a
knowledge-based software system, which uses Socratic style teaching and learning
practice in the higher education environment. Software system assessment happens
in a hybrid legal intelligent tutoring system, Guidance for Business Merger and
Acquisition (GBMA). The legal knowledge for GBMA is presented in two forms,
as rules and previously decided cases. In addition, distinguishing the two different
forms of knowledge representation, the pa-per outlines the actual use of these forms
in a computational framework de-signed to generate a plausible solution for a given
case by using rule-based reasoning (RBR) and case-based reasoning (CBR) in an
integrated frame-work. The nature of a solution's suitability assessment is
considered a multiple criteria decision-making process in GBMA evaluation. The
assessment used discussions and questionnaires with different user groups in a
scenario-based teaching and learning practice. The answers to questionnaires use
fuzzy linguistic concepts. The finding suggests that fuzzy linguistic concept-based
assessment helps in evaluating knowledge-based systems.
Towards Developing a Mobile Application for Detecting
Intoxicated People through Interactive UIs
Ifath Ara, Tasneem Mubashshira, Fariha Fardina Amin,
Nafiz Imtiaz Khan, and Muhammad Nazrul Islam
Department of Computer Science and Engineering, Military Institute
of Science and Technology, Mirpur Cantonment, Dhaka-1216,
Bangladesh
Abstract. Alcohol and Cannabis are among the most frequently used drugs
worldwide. Excessive drinking is one of the leading lifestyle-related causes of death
across the whole world. Both alcohol and cannabis can cause short-term problems
with thinking, remembering, concentrating, and performing psycho-motor tasks.
Taking drugs like alcohol and cannabis can impair a person's ability to perform tasks
such as driving a car, flying an airplane, and making critical decisions. Clinical dope
test methods are time-consuming, and instant testing devices, such as breath alysers,
are only available to law enforcement personnel which is expensive. Therefore,
detecting intoxicated people using ubiquitous devices such as smartphones without
any use of external hardware can be a cost-effective, time-saving, and efficient
approach for ensuring safe performance in critical tasks. Hence, the objective of this
20
research is to propose a conceptual framework for developing an interactive mobile
application that detects intoxicated people by measuring behavioural abnormalities
caused by alcohol and cannabis consumption. To accomplish this objective, the
effects of alcohol and cannabis are investigated, followed by a review of the
available tests in the literature. The proposed conceptual model encompasses testing
of balancing capability, grip sense, simple reaction time, choice reaction time, short-
time memory, and measuring a person's heart rate using tasks based on the short-
term effects of alcohol and cannabis. Prototypes of the user interfaces are also
developed based on the proposed conceptual framework.
Novel Harris Hawks Optimization and Deep Neural
Network Approach for Intrusion Detection
Miodrag Zivkovic1, Nebojsa Bacanin1, Jelena
Arandjelovic1, Andjela Rakic1, Ivana Strumberger1, K.
Venkatachalam2, and P Mani Joseph3
1Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
2Department of Applied Cybernetics, Faculty of Science, University
of Hradec Kralove, 50003 Hradec Kralove, Czech Republic
3Department of Mathematics & Computer Science, Modern College
of Business and Science, PO Box 100, PC 133, Muscat, Sultanate of
Oman
Abstract. Intrusion detection systems attempt to identify assaults while they occur
or after they have occurred and they detect abnormal behavior in a network of
computer systems in order to identify whether the activity is hostile or unlawful,
allowing a response to the violation. Intrusion detection systems gather network
traffic data from a specific location on the network or computer system and utilize
it to safeguard hardware and software assets against malicious attacks. These
systems employ high-dimensional datasets with a high number of redundant and
irrelevant features and a large number of samples. One of the most significant
challenges from this domain is the analysis and classification of such a vast amount
of heterogeneous data. The utilization of machine learning models is necessary. The
method proposed in this paper represents a hybrid approach between recently
devised yet well-known, harris hawks optimization metaheuristics and deep neural
network machine learning model. Since the basic harris hawks optimization exhibits
some deficiencies, its improved version is used for dimensionality reduction,
followed by the classification executed by the deep neural network model. Proposed
approach is tested against well-known NSL-KDD and KDD Cup 99 Kaggle
datasets. Comparative analysis with other similar methods proved the robustness of
the presented technique when metrics like accuracy, precision, recall, F1-score are
taken into account.
21
Random forest classification and regression models for
literacy data
Mayur Pandya1 and Jayaraman Valadi2
1Savitribai Phule Pune University, Pune, India
2Vidyashilp University, Bengaluru, India
Abstract. In this study we analysed data provided by the Ministry of Human
Resources Development (MHRD), India to develop models for estimation of male,
female and Overall literacy rates. This study further examines the district wise
primary and secondary school education data. The data originally consisted of 819
input attributes. We employed exploratory data analysis for the removal of
redundant features. We also concatenated some features and we were left with 222
features. The output consisted of male and female literacy rates. We employed the
Random Forest regression algorithm for the prediction of these two output variables.
Further, we employed proper thresholding to convert these data into classification
models using two different classification models. The first one classified data into
two groups: males who are literates and illiterates. The second model classified
females into literates and illiterates. We employed random forest classifiers for the
classification tasks also. Further, we employed Gini importance ranking criteria
which are embedded in the random forest algorithm itself for selecting the most
informative attributes for both classification and regression tasks. Finally, we
carried out multi-label literacy classification for multi-label prediction of literacy
rates. We used the binary relevance method for the two-label prediction task. Our
prediction algorithms performed well in regression and classification tasks. We also
identified top-ranked features having a maximum correlation with the output
variables. This characterization provides important domain knowledge along with
model interpretability.
Towards Robotic Knee Arthroscopy: Spatial and Spectral
Learning Model for Surgical Scene Segmentation
Shahnewaz Ali and Ajay K. Pandey
School of Electrical Engineering and Robotics, Faculty of
Engineering, Queensland University of Technology, Brisbane, QLD
4001, AUSTRALIA
Abstract. Minimally invasive surgeries are complex to perform, and surgical
outcomes are varied due to limited view of the surgical scene. There is a lack of
reliable vision systems that can identify and segment different tissue types intra-
operatively. Here we introduce a novel approach towards overcoming this
limitation. Our approach extracts geometric and spectral information captured by a
22
miniaturized camera using deep learning algorithms. We have successfully
implemented a deep neural network and trained it for insitu labelling of soft and
hard tissues acquired from clinically relevant cadaveric studies. Although presented
and validated for the knee arthroscopy our approach can be implemented across
different endoscopic platforms. Intraoperative nature of tissue segmentation could
be easily implemented in medical imaging systems for achieving better outcomes in
minimally invasive procedures. In addition to segmenting different tissue types like,
ACL, femur, tibia, cartilage and meniscus, our network can also segment surgical
tools present inside the knee cavity. The achieved dice similarity score for the tissue
types femur, tibia, ACL, and meniscus were 0.91,0.71, 0.39, and 0.62.
Opposition-based Arithmetic Optimization Algorithm
with Varying Acceleration Coefficient for Function
Optimization and Control of FES System
Davut Izci1, Serdar Ekinci2, Erdal Eker3 and Laith
Abualigah4
1Batman University, Batman 72060, Turkey
2Batman University, Batman 72100, Turkey
3Mus Alparslan University, Mus 49250, Turkey
4Amman Arab University, Amman, Jordan 11953, Jordan
Abstract. In this paper, the integration of the arithmetic optimization algorithm
(AOA) with a modified opposition-based learning (mOBL) mechanism is presented.
The proposed novel mOBL based AOA (mAOA) was demonstrated to have better
capability for optimization problems by using four classical bench-mark functions.
For further evaluation, the proposed mAOA was also used to tune a proportional-
integral-derivative controller (PID) adopted in a functional electrical stimulation
(FES) system for the first time. The latter system is a challenging biomedical system
that helps revealing the potential of the mAOA for real-world engineering
optimization problems. The comparative transient response analysis was performed
for PID controlled FES system using the original arithmetic optimization algorithm
and Ziegler-Nichols based tuning schemes. The latter comparative analysis has
shown better capability of the proposed mAOA algorithm for such a biomedical
system.
23
The Impact of Using Facebook on the Social Life of
College Students
Deepali A. Mahajan, C. Namrata Mahender
Dr. Babasaheb Ambedkar Marathwada University, India
Abstract. The advancement in information technology brings new ways of
communication among the people, especially among the students. This changes the
social life of the students as they are continuously engaged on social networking
sites (SNS), they are far away from the actual relationships. This study finds the
addictive behavior of the students towards Facebook, as this application is more
popular among the students. We have conducted a survey for 484 students from
which 107 responses collected using offline survey and 381 responses were
collected by online survey. SPSS has been used for statistical analysis of the
questions. For the selected questions calculated the frequencies and percentage. This
gives us information about the variable Excess use and social life. Further, we found
a negative correlation between the variables which means excessive use of Face-
book is negatively related to social life. Students get disconnected from society
because of the excess use of Facebook.
Power control of a Grid Connected Hybrid Fuel cell,
Solar and Wind Energy Conversion Systems by using
Fuzzy MPPT Technique
Satyabrata Sahoo and K. Teja
Department of Electrical and Electronics Engineering, Nalla Malla
Reddy Engineering College, Hyderabad - 500088
Abstract. The main aim of this paper is integration and generation of quality power
through a grid-connected hybrid fuel cell, solar and wind energy conversion
(WECS) systems by using Fuzzy MPPT technique. In this paper the power sources
like fuel cell, solar energy and WECS are used for the generation of electrical power.
Furthermore, the wind, solar and fuel cell inputs have to be combined appropriately
to ensure that the load on demand is constantly continued and maintained. In fact,
all these power sources are connected to the dc bus through the buck-boost
converter. By using the fuzzy Maximum Power Point Tracking system, these
converters are managed to improve efficiency compared with Hill-Climbing Search
methods and P & O MPPT techniques. Using the MATLAB / Simulink platform,
simulation studies of the proposed system are carried out and the results are
presented.
24
Stabilizing Constrained Control for Discrete-Time
Multivariable Linear Systems via Positive Polyhedral
Invariant Sets
BOUREBIA Ouassila
Automatic and Robotics Laboratory, Faculty of Sciences and
Technology, Department of Electronics Constantine1 University,
Constantine, Algeria
Abstract. In this work we propose a numerical method to compute stabilizing state
feedback control laws and associated polyhedral invariant sets for multivariable
discrete systems. An MPC-based dual mode strategy and backward recursion from
invariant set approach guarantees the feasibility and stability of the controller are
investigated. The explicit solution subdivides the state space into regions, for each
section, the set of admissible control laws is determined, the objective is to
determine the region containing the admissible solutions starting the terminal region
and backtracking. An illustrative example, showing the effectiveness of the
proposed methods, is presented.
Texture Feature Analysis for Inter-Frame Video
Tampering Detection
Shehnaz, Mandeep Kaur
Department of Information Technology, University Institute of
Engineering and Technology, Panjab University, Chandigarh
Abstract. Inter-frame video forgeries can involve insertion, deletion, or duplication
of frames with malicious intentions. Most of the available passive methods follow a
pixel-correlation based approach that is computationally expensive as it compares
each pixel of video frames to identify forgery. In this paper, a histogram-based
approach is proposed that is computationally efficient and results in better
classification accuracy. It computes histograms of frames having texture
characteristics encoded with Local Binary Pattern (LBP). Histogram similarity of
adjacent LBP coded frames is measured through the Histogram Intersection
comparison metric. The differences of these adjacent metrics values provide
significant cues for forgery detection, that are further normalized and quantized to
obtain a fixed-length feature vector. It makes the proposed approach scalable and
hence enhances its applicability for variable-length videos. Training and testing are
done using SVM classifier with RBF kernel. The method is capable to detect
different kinds of interframe forgeries that include insertion, deletion and
duplication Due to lack of benchmark dataset of interframe video forgeries, a
25
customized dataset is prepared through MoviePy tool that comprises total 1370
videos with interframe forgeries (frame deletion, insertion and duplication).
Experimental results demonstrate an overall detection accuracy of 99% that can
efficiently detect various kinds of inter-frame video forgeries A comparative
analysis with existing interframe forgery detection approaches are also presented.
Computer Vision-Based Algorithms on Zebra Crossing
Navigation
Sumaita Binte Shorif , Sadia Afrin , Anup Majumder and
Mohammad Shorif Uddin
Department of Computer Science and Engineering, Jahangirnagar
University, Savar, Dhaka, Bangladesh
Abstract. A zebra crossing, which is specified as extensive white bands painted on
typical black roads, is a pathway for pedestrians to cross a road. Pedestrians need to
find a zebra crossing to negotiate a road. Finding zebra crossing is difficult for a
pedestrian if he/she is a blind or a visually impaired person. Hence, the detection of
zebra crossings is extremely crucial for ameliorating the agility of the visually
challenged and blind people as well as preventing precarious situations from taking
place in navigating a road. Several computer vision-based techniques are developed
to find the zebra patterns on a road surface. This paper tries to study the existing
vision-based techniques, their performances and also shows the future research
directions to develop an intelligent vision-based road crossing system for visually
impaired people.
Spider Monkey Optimization (SMO) algorithm based
Innovative strategy for strengthening of Reliability
Indicators of Radial Electrical System
Aditya Tiwary1, R. S. Mandloi2
1Fire Technology & Safety Engineering, IPS Academy, Institute of
Engineering & Science, Indore (M.P.), India
2Electrical Engineering, Shri G.S. Institute of Technology &
Science, Indore (M.P.), India
Abstract. A zebra crossing, which is specified as extensive white bands painted on
typical black roads, is a pathway for pedestrians to cross a road. Pedestrians need to
find a zebra crossing to negotiate a road. Finding zebra crossing is difficult for a
pedestrian if he/she is a blind or a visually impaired person. Hence, the detection of
zebra crossings is extremely crucial for ameliorating the agility of the visually
26
challenged and blind people as well as preventing precarious situations from taking
place in navigating a road. Several computer vision-based techniques are developed
to find the zebra patterns on a road surface. This paper tries to study the existing
vision-based techniques, their performances and also shows the future research
directions to develop an intelligent vision-based road crossing system for visually
impaired people.
AI Based Multi Label Data Classification of social media
Shashi Pal Singh1, Ajai Kumar1, Sanjeev Sharma2,
Snehil R Singh3
1AAIG, Center for development of Advanced Computing, Pune,
India
2Indian Institute of Information Technology Pune. IIIT Pune, India
3Banasthali Vidyapith, Banasthali, Rajasthan, India
Abstract. We have unlabelled data in different ways- news article, and countless
other types of documenting text. Large volumes of data are produced from many
online sources such as emails, www, organization's electronic health records, and
databases. These data must be classified to avoid information loss and to boost data
discovery and retrieval more quickly. This research is about classifying tweets, the
New York Times, or other social media. It classifies the data into predefined generic
classes, such as “business,” “sad,” “style,” “advertising,” “events,” “news,” etc.,
using author information and some features. The approach is to reduce the noise and
identify tweets or any data classes as it is evident that an article can fall in more than
one category.
Performance Analysis of Random Forest (RF) and
Support Vector Machine (SVM) algorithms in classifying
Breast Cancer
FHA. Shibly1,2, Uzzal Sharma1 and HMM. Naleer2
1Assam Don Bosco University, India
2South Eastern University of Sri Lanka
Abstract. Breast cancer is a serious disease cause of death among females. In cancer
diagnosis, accurate classification of breast cancer data is critical, and the distinction
between malignant and benign tumors can help patients avoid unnecessary
procedures. The classification of breast cancer can also be used to select the best
treatment options. The classification of patients into benign and malignant groups is
a well-known medical study topic. Machine learning is commonly employed in
27
Breast cancer prediction because it has the advantage of finding essential features
from a medical data collection. Several empirical researches have used machine
learning and soft computing techniques to treat breast cancer. Many people claim
that their algorithms are better than others' because they are faster, easier, or more
accurate. Therefore, which algorithm is more accurate in classifying breast cancer
was the research question. Furthermore, the main objective of this research study is
to calculate and compare the performance of SVM and RF algorithms in classifying
breast cancer more accurately. For the experimental analysis, the Wisconsin Breast
Cancer Data Set (WBCD) is employed. There are a total of 699 instances and 10
qualities to examine. Based on accuracy, recall, precision and F1 scores, RF has the
higher percentages in all four measurement scales as 92.98%, 93.65%, 88.05% and
90.67% accordingly. As a result, RFs have the best chance of successfully
diagnosing breast cancer.
Prediction of Water Quality Index of Ground Water
Using the Artificial Neural Network and Genetic
Algorithm
Mehtab Mehdi and Bharti Sharma
DIT University Dehradun, India
Abstract. This research work is related with artificial neural network and genetic
algorithm techniques to forecast the condition of groundwater at Amroha region
located in Uttar Pradesh location of India. For this, twelve (12) samples of
groundwater have been composed and investigated for main features during before
and after monsoon period. The physicochemical factor was considered that use for
calculating water quality index. Logical outcome established by which all the factors
are in satisfactory range however, EC, TDS, TH, Ca and Mg are greater than the
enviable boundary of the WHO values. The groundwater fitness for drinking was
determined by WQI technique. The WQI assessment limits from 24.76 to 128.07
and from 36.54 to 90.38 in pre- and post-monsoon period, respectively. Only one
test (DW5) shows 130.07 WQI assessments indicating bad quality for drinking
because of input of urban and rural waste. For generating reliable and exact
representation for forecast of groundwater excellence based on water quality index,
a multi-layer back propagation algorithm uses in the ANN. Moreover, GA model is
applied to make better results of ANN. The outcome confirmed the forecast of ANN
model are acceptable and corroborate constantly satisfactory presentation for each
term. The planned ANN technique might be helpful to predict the quality of
groundwater.
28
Improving Throttled Load Balancing Algorithm in Cloud
Computing
Worku Wondimu Mulat1, Sudhir Kumar
Mohapatra2, Rabinarayana Sathpathy2, Sunil Kumar
Dhal2
1 Addis Ababa Science & Technology University, ADDIS ABABA,
ETHIOPIA, P.O.Box: 16417
2Faculty of Emerging Technologies, Sri Sri University, Cuttack,
Odisha, India
Abstract. The service and resource delivery model through high-speed internet is
called as Cloud Computing. Users of Cloud Computing has increased exponentially
due to specific characteristics like pay per usage and use anywhere, any time without
human intervention. The emergence of Cloud Computing has reduced the initial
investment from the service providers' point of view and this, in turn, results in low-
cost service for service users. Cloud Compu-ting has many issues associated with it,
out of which performance is a major challenge. The elasticity of resources without
paying a premium increased the traffic on the internet rapidly. Such a rapid increased
workload overloads the server and leads to performance inefficiency. One of the
techniques to overcome this challenge is using load balancing. This needs to be done
cautiously because of failure in any one of the virtual machines can lead to the
unavailability. We compare performance of the three well-known algorithms:
Equally Spread Current Execution, Round Robin and Throttled load balancing and
the result shows Throttled performs better that the two but it still need improvement.
The proposed algorithm improves performance of Throttled algorithm by
introducing two queues. One of the queues is used to store available virtual machines
and the other queue, priority based, is used to store busy virtual machines. When a
request arrives the load balancer pop the front virtual machine from available queue,
assign the request to it and then push it to busy queue. Cloud Analyst simulator is
used for simulation and the result shows the proposed algorithm improves the
response time and resource utilization of the cloud computing environment.
Feature Extraction Based Landmine Detection Using
Fuzzy Logic
T.Kalaichelvi, S. Ravi
Department of Computer Science, School of Engineering and
Technology, Pondicherry University, Pondicherry-605014, India
Abstract. The detection of landmines plays a crucial role in saving soldiers' lives,
hu-man beings, and in general, animals. The researcher uses digital image pro-
29
cessing techniques to detect landmines using sensors. Many countries are striving
hard in landmine detection, and the fight is going on against the buri-al of landmines.
This paper gives a review of the landmine detection techniques using fuzzy logic
and wavelet transform. There are two feature-based techniques for landmine
detection: clustering method and subspace detection techniques. These techniques
collect the data from the various sensors from the vehicle-mounted to the ground-
penetrating radar using metrics to evaluate and improve the detection results to
minimize the false alarm rate. Each technique's performance depends on the
contaminated soil's nature, the depth of an object, and the type of material used.
IOT based Smart Parking System using NodeMCU and
Arduino
Amirineni Sai Venkata Dhanush and Kasukurthi
Rohit Sai
Department of Electronics and Communication Engineering, SRM
University, India
Abstract. Through this paper, we would like to explain the major issues that have
start-ed to take place in the world around us. With the growing need for
transportation in our daily lives, we have begun to see the increase in automotive
vehicles in the world around us. Although this drastic increase has resulted in
problems such as global warming another unseen issue in our day to day lives is
parking. Often, we are forced to search for parking hours on end when going to work
or an important event. This is because most parking lots are filled and there is no
way for us to identify vacant parking spaces. This is why we would like to work
towards creating a system that is capable of helping people identify vacant spaces
and eliminate the need to search for parking as a whole. Through this paper we
would like to explain our approach to the issue and how we have been able to
successfully create a prototype with the use of IoT to detect the presence of vehicles
and have this updated in our application for users to gain access to. We have split
our paper into various sections explaining the problem of our solution as well as the
drawbacks of the solutions which are already present in the modern world.
Study on Intelligent Tutoring System for Learner
Assessment Modeling Based on Bayesian Network
Rohit. B. Kaliwal and Santosh. L. Deshpande
Dept. of CSE, VTU, Belagavi, Karnataka, India
Abstract. The most crucial part of the educational system is the intelligent tutoring
system. A computer system that intends to give learners quick and customised
lessons or feedback, usually without the intervention of a professor, is known as an
30
intelligent tutoring system. Artificial intelligence technology is employed in an
intelligent tutoring system to provide a lot of help to learners in terms of acquiring
skills and knowledge. Human professors are not required to contribute to the
organisation in this pro-cess, and Bayesian Network has been employed to solve this
problem. An intelligent tutoring system’s heart is the beginner learner model. Using
a Bayesian network with high self-learning ability to build an intelligent tutoring
system for the novice concept can considerably improve the lev-el of comprehension
of the intelligent tutoring system. The core philosophy of an intelligent tutoring
system for the beginner concept will be the major focus. The elements of impact on
the learners’ learning method are then studied at this level, starting with the
perception of the beginner’s expertise in teaching, mutual with the state of learning,
and the features of the beginner. Based on Bayesian network, this work presented a
study model for constructing an intelligent tutoring system for learner assessment.
The tutoring system’s design model takes into account a client model and a learner
model. The Bayesian network was employed in an e-learning environment to assess
the learners’ current level of knowledge so that the model may evolve and offer new
knowledge to improve learner performance.
Finite Element Analysis of Prosthetic Hip Implant
Priyanka Jadhav, Swar Kiran, Tharinipriya T, T.
Jayasree
Department of Electronics and Communication Engineering, College
of Engineering Guindy, Anna University, Chennai- 600 025, India
Abstract. In the human body, hip joints are important shock absorbing and weight-
bearing structures. Individuals suffering from severe arthritis or hip bone fractures
are dependent on hip replacement joints. An artificial hip joint con-sists of a stem,
ball, and socket assembly. The stem is implanted in the femur, which is connected
to the femoral head that is replaced by the artificial ball, that is placed inside the
socket which resembles the acetabulum. In this study, a three-dimensional
computer-aided design (CAD) software, Solid-Works is used to design the artificial
hip joint. The functionality and longevity of the implant greatly depend on the design
of the implant. Biocompatible and robust materials are used in the designing process
for parts of the hip joint. The model was subjected to finite element analysis, and
the stress-strain distribution across the model was estimated for different loads to
determine the implant's endurance. SolidWorks is used to perform static analysis to
determine the optimum implant design. It calculates the characteristics of stress,
strain, and displacement in different directions. When von Mises stress values
exceed the yield strength of the implant material, it is said to fail. Thus, it is essential
to know the stress for implementing a proper implant design.
31
A Comprehensive Study on Multi Document Text
Summarization for Bengali Language
Nadira Anjum Nipa and Naznin Sultana
Daffodil International University, Ashulia, Dhaka
Abstract. Automatic text summarization is a useful and needed approach in which a
small subset of text is extracted concisely and pertinently from large text documents
where the extracted sentences may have significant and notable meaning com-pared
to other sentences in the document. Although there have been a lot of approaches to
English text summarization, very few works have been found in the literature on
automatic Bengali text summarization. Our work focuses on multi-text
summarization tasks based on data mining and some statistical approaches which
primarily employ the method on Bengali text documents as a basis for
summarization. We used a hybrid approach for extracting the most significant word
during tokenization and used some statistical methods to rearrange the sentences.
The TextRank algorithm is used to pick the top few sentences from the processed
text as the summary and finally we compared and evaluated our model with
benchmark standard summary text generated by a group of human contributors. Our
proposed hybrid model generates an average of 0.66 Precision, 0.59 Recall and 0.62
F-Score which indicates that our model can be used as an alternative system to
address multi-text summarization problems of Bengali text documents.
Deep Learning-Based Lentil Leaf Disease Classification
Kaniz Fatema1, Md. Awlad Hossen Rony1, Kazi
Mumtahina Puspita1, Md. Zahid Hasan1 and
Mohammad Shorif Uddin2
1Department of Computer Science and Engineering, Daffodil
International University, Dhaka, Bangladesh
2Department of Computer Science and Engineering, Jahangirnagar
University, Dhaka, Bangladesh
Abstract. Ascochyta blight, Anthracnose, Mold and Rust are the four most common
diseases of lentil leaves that extremely affect the lentil field. Nevertheless, current
research deficiencies a real-time detection tool for lentil leaf diseases, making it
impossible to ensure the health of lentil plants. In this chapter, histogram
equalization and gamma correction method are studied as image enhancement
methods with three deep transfer learning architectures VGG16, Inceptionv3, and
ResNet50 is investigated to perform the classification of lentil leaf diseases from the
images. Gradient Energy Measure (GEM) Filter is applied after image enhancement
to clearly visible the features from the im-age. The main focus of this chapter is to
find out the perfect image enhancement technique with gradient filter and accurate
deep learning architecture to classify lentil leaf diseases with the highest accuracy.
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A dataset with four different types of leaf disease images is collected from the lentil
field used for this experiment. The experimental result shows that the histogram
equalization techniques with Inceptionv3 outperform than the other methods. It
achieves an accuracy of 98.13% for classifying lentil leaf diseases.
Framework for Diabetes Prediction using Machine
Learning Techniques through Swarm Intelligence
C. Kalpana, B. Booba
Dept. of CSE VISTAS, Vel’s University, Chennai
Abstract. Diabetes is regarded as a lingering and fatal disease that affects people all
over the world. It curtails life anticipation and makes people more susceptible to
cardiovascular disease. An effective diabetes prediction can help people take
effective preventive measures. Medical data is complicated and unstructured,
making it difficult to accurately forecast disease. However, much research has been
conducted on diabetes-prediction, it remains a big challenge. This study purpose is
to characterize the issue and develop a machine learning model to tackle it. In this
work, different attributes like BMI, Age, Blood pressure, Blood sugar, and so on has
been used for diagnosing diabetes. Several machine learning techniques Support
Vector Machine (SVM), Naïve Bayes, XGBoost were deployed to predict diabetes.
Further, the ML algorithms were optimized by applying the Binary particle Swarm
optimization (BPSO) algorithm. ML algorithm achieved high accuracy with
lifestyle attributes. The ML algorithms were evaluated by deploying various
measures like Accuracy, F1-Measures, Recall and Precision. The XGBoost method,
when paired with other algorithms, may reliably predict the onset of diabetes by 82
percent. The research work main purpose is to develop a paradigm that employs
machine learning techniques to help medical practitioners predict diabetes early.
A Graphical Approach for Image Retrieval Based on Five
Layered CNNs Model
Mohammad Khalid Imam Rahmani
College of Computing and Informatics, Saudi Electronic University,
Riyadh, Saudi Arabia
Abstract. Image processing is an important field in the computer vision domain. A
lot of work has been done for the processing of image data in various fields like
science and technology, defence, medical, space science for satellite imagery
analysis, seismology, traffic control, crime control, publishing, and other emerging
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research areas. There are different levels of complexities for the accurate retrieval
of images as most of the images are affected by different kinds of noise and other
factors. In this proposed work, I have performed the work of image retrieval using
two methods: firstly, processing for de-noising and filtering of the dataset of images
taking density parameter 0.7 and adaptive gamma parameter constant value 0.5. The
obtained images are then processed by Convolutional neural networks (CNNs). The
5-layer convolutional neural network has been used for the best features extraction
and then the algorithm is finally optimized using GA (Genetic Algorithm). In my
work I have used 5*5 fold convolutional layers and compared the results with the
previous approach Deep Convolutional neural network (DCNN). Finally, the
Genetic Algorithm is implemented to obtain the best-optimized value. The proposed
work is validated with a graphical-based approach using the mathematical results in
terms of peak signal-to-noise ratio (PSNR), mean-squared error (MSE), and the
processing time of the algorithm. The result parameters of the proposed algorithm
clearly show better performance as com-pared to the previous approach.
Statistical Post-processing Approaches for OCR Texts
Quoc-Dung Nguyen1,5, Duc-Anh Le2, Nguyet-Minh
Phan3, Nguyet-Thuan Phan4 and Pavel Kromer5
1Van Lang University, 45 Nguyen Khac Nhu, Co Giang Ward,
District 1, Ho Chi Minh city, Vietnam
2The Institute of Statistical Mathematics, Tokyo 101-8430, Japan
3Sai Gon University, 273 An Duong Vuong, Ward 3, District 5, Ho
Chi Minh City, Vietnam
4University of Science, VNU-HCM, 227 Nguyen Van Cu, Ward 4,
District 5, Ho Chi Minh City, Vietnam
5Technical University of Ostrava, 17. listopadu 15, 708 33, Ostrava-
Poruba, Czech Republic
Abstract. Low quality of scanned documents and limitations in text recognition
methods result in different error types in OCR-generated texts. Hence, OCR error
detection and correction are essential OCR post-processing tasks for improving the
OCR text readability and usability. In this paper, we present and dis-cuss the
statistical linguistic features obtained from text corpora and OCR text datasets and
employed in OCR post-processing approaches. In addition, we show our two
statistical language models based on these linguistic features and their OCR error
correction performances on two published data-bases attracting research efforts in
text recognition and correction, one data-base in the ICDAR 2017 OCR post-
correction competition and the other database in the Vietnamese online handwriting
recognition competition.
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FPGA Implementation of Masked-AE$HA-2 for Digital
Signature Application
M M Sravani1, S Ananiah1, M Prathyusha Reddy1, G
Sowjanya1, Nabihah. A2
1School of Electronics Engineering, VIT Chennai, India
2Universiti Tun Hussein Onn Malaysia, Malaysia
Abstract. Authentication and data integrity are essential features in cryptographic
algorithms for generating the digital signature. It will enhance trusted
communication in a wireless network. Hash functions are the best choice for
generating the digital signatures at the transmitter end and validates the original data
at the receiver side in the open public access. These hash functions are prone to
advanced attacks such as Side-Channel Analysis (SCA), birthday, and pre-image
collision. A hybrid Masked AE$HA-2 hash function has been pro-posed to increase
the security strength of digital signature for effective shielding against such attacks.
The proposed architecture includes a masked Advanced Encryption Standard (AES-
256) followed by the secured hash algorithm (SHA 256) yielding a highly secured
Masked AE$HA-2 crypto-style. The masked AES algorithm has the advantage of
combining a false key with the real key to hide the original data, thus significantly
improving the security. Further, in conventional SHA-2, the message expansion has
been re-placed with a sliding window protocol to minimize the computational time.
Hardware implementation on Virtex 7 device with the help of Xilinx Vivado tool
achieves low computational time of 680 ns for generating the masked AE$HA-2
hashed value.
A Framework for Improving the Accuracy With Different
Sampling Techniques for Detection of Malicious Insider
Threat in Cloud
S. Asha1, D. Shanmugapriya2 and G. Padmavathi1
1Department of Computer Science, Avinashilingam Institute for
Home Science and Higher Education for Women, Coimbatore,
641043 Tamilnadu, India
2Department of Information Technology, Avinashilingam Institute
for Home Science and Higher Education for Women, Coimbatore,
641043 Tamilnadu, India
Abstract. Cloud computing provides more beneficial services to its users with
limited cost. Cloud is prone to many threats, and one of the major threats is the
malicious insider threat. Detection of malicious insider threats is more challenging,
35
and many cloud datasets are available to detect a malicious insider. In real-time data
collection, the data set is prone to a class imbalance problem. Minority class related
to insider threat events has a smaller number of in-stances, whereas majority class
related to non-insider threats has a minimum number of instances. Supervised
classification techniques provide a better result for the classification of the majority
class and a less accurate result for the minority class. Classification without treating
the imbalanced class data results in adverse effects in prediction. In this paper,
different sampling techniques are implemented to accurately handle the imbalanced
class data to detect malicious insider threats in cloud computing. The performance
of different sampling techniques is compared by implementing Support Vector
Machine (SVM) algorithm using the performance metrics such as accuracy, f-score,
precision and recall.
Eliminating racial bias at the time of detection Melanoma
using Convolution Neural Network (CNN)
Md. Abdullah Al Noman Majumder1, Eimon Hossain
Taief1, Md. Nurul Amin Bhuiyan1, M. F. Mridha2,
Aloke Kumar Saha1
1University of Asia Pacific, Bangladesh
2Bangladesh University of Business and Technology, Bangladesh
Abstract. Melanoma considers deadly cancer that can cause the death of a person if
not distinguished at an initial stage. Although Melanoma is most common in white
skin people and can be detected at an early stage using AI, white skin people have a
much lower death rate from this cancer. But when black skin people have
Melanoma, AI can’t detect it at an early stage because most of the time, the machines
are trained with Dermoscopic pictures of white people, which leads to a higher
mortality rate for black skin people. As a result, people don’t want to trust the AI
system at the time of Melanoma detection. In this paper, we proposed a model with
whatever black or white skin it can easily detect using machine learning. In this case,
we will use the Convolution Neural Network (CNN) of machine learning to detect
Melanoma at an early stage so that the death rate caused by Melanoma cancer can
be reduced. The proposed method can detect Melanoma with an accuracy of 88.9%
for both skin people which may significantly decrease the mortality rate.
Customer Churn Analysis using Machine Learning
Ritika Tyagi and Sindhu K
Department of ISE, BMS College of Engineering, Bangalore, India
Abstract. Many companies lack awareness about the different kinds of customer
deviations that exist in today’s world. There can be many reasons that factor the
churn rate of a company, ranging from the success of a product, reputation of the
36
brand, extra services, accessibility, price range and many others. It’s usually very
tedious to shortlist a particular reason that is causing a higher churn rate than the
others manually. Recognizing this problem, this paper answers some of the churn
analysis questions through the development of an efficient churn analysis machine
learning based model that performs various functions. The proposed work is broken
down into two phases. First being, data analysis followed by churn prediction. For
data analysis, multiple graphs are plotted with different features to gain interesting
insights on the shape and nature of the company’s churn rate and to narrow down
on which combination of features might be more heavily correlated with the
predictor variable ‘churn’. For churn prediction, a classification model was built that
comprised of six algorithms. Further on, cross validation and hyperparameter tuning
was performed on all the models. An ensemble model was also built to increase
model accuracy and finally, performance evaluation was done to check the best built
model. Ultimately, the model giving the best results in the performance evaluation
phase is chosen to be used for the end-to-end model use. In the proposed work, XG
Boost Classifier proves to be the best performing algorithm for the prediction of
customer churn.
A Comparative Study of Hyperparameter Optimization
Techniques for Deep Learning
Anjir Ahmed Chowdhury, Argho Das, Khadija
Kubra Shahjalal Hoque, and Debajyoti Karmaker
Department of Computer Science, American International
University-Bangladesh
Abstract. Algorithms for deep learning (DL) have been widely employed in a variety
of applications and fields. The hyperparameters of a deep learning model must be
optimized to match different challenges. For deep learning models, choosing the
optimum hyperparameter configuration has a direct influence on the model's
performance. It typically involves a thorough understanding of deep learning
algorithms and their hyperparameter optimization (HPO) techniques. Although
there are various automatic optimization approaches available, each has its own set
of advantages and disadvantages when applied to different datasets and
architectures. In this paper, we analysed which algorithm takes the longest
optimization time to optimize an architecture and whether the performance of HPO
algorithms is consistent across different datasets and architectures. We selected
VGG16 and ResNet50 architectures, CI-FAR10 and Intel Image Classification
Dataset, as well as Grid search (GS), Genetic algorithm (GA), Bayesian
optimization (BO), Random search (RS), Hyperband (HB) and Particle swarm
optimization (PSO) HPO algorithms for comparison. Due to the lack of pattern, it is
challenging to determine which approach obtains the best performance on different
datasets and architecture. The results show that all of the algorithms have similar
results in terms of optimization time. This research is expected to aid DL users,
developers, data analysts, and researchers in their attempts to use and adapt DL
models utilizing appropriate HPO methodologies and frameworks. It will also help
37
to better understand the challenges that currently exist in the HPO field, allowing
future research into HPO and DL applications to move forward.
Fault Location on Transmission Lines of Power Systems
with Integrated Solar Photovoltaic Power Sources
Thanh H. Truong1, Duy C. Huynh1, and Matthew W.
Dunnigan2
1Ho Chi Minh City University of Technology (HUTECH), Ho Chi
Minh City, Vietnam
2Heriot-Watt University, Edinburgh, United Kingdom
Abstract. This paper proposes a fault location technique on transmission lines of a
power system with integrated solar photovoltaic (PV) power sources that is based
on a solution of an optimization problem. It is realized that the power systems are
increasingly complicated as renewable energy power sources are integrated, of
which wind and solar power sources are the most popular. This leads to a great
challenge in the fault location on the transmission line. An advanced cuckoo search
(ACS) algorithm is proposed to improve the searching performance and applied to
solve this problem. The obtained results of applying the ACS algorithm are
compared to those of applying the cuckoo search (CS) and particle swarm
optimization (PSO) algorithms. The comparison is to confirm the effectiveness of
the proposals.
Emergency Vehicle Detection Using Deep Convolutional
Neural Network
Samiul Haque, Shayla Sharmin and Kaushik Deb
Department of CSE, Chittagong University of Engineering &
Technology (CUET), Chattogram-4349, Bangladesh
Abstract. In densely populated cities, emergency vehicles getting caught in traffic is
a regular occurrence. As a result, emergency vehicles arrive late, resulting in asset
and human life losses. It is critical to treat emergency vehicles differently to avoid
losses. The purpose underlying this research is to preserve human lives and reduce
losses. For this, an automated method for detecting emergency vehicles is
implemented. Ambulance and fire trucks are considered an emergency, and other
vehicles are considered non-emergency vehicles in the proposed method. Initially,
it identifies several vehicles from an image. The YOLOv4 object detector
accomplished this part of the method. The identified vehicles are the region of
interest for the rest of the research. Finally, the method classifies the vehicles into
emergencies or non-emergencies. This study contributes by developing a model
38
based on rigorous testing and analysis and includes a viral algorithm in deep
learning: convolutional neural network (CNN). Furthermore, the transfer learning
technique with VGG16's fine-tuned model is employed for emergency vehicle
detection. On the Emergency Vehicle Identification v1 dataset, this model had an
average accuracy of 82.03%.
Emotion Recognition from Speech using Deep Learning
MD. Muhyminul Haque1 and Kaushik Deb
Department of CSE, Chittagong University of Engineering &
Technology (CUET), Chattogram-4349, Bangladesh
Abstract. For more than a decade, emotion recognition from speech has been a major
research topic, following in the footsteps of its "big brothers," speech and speaker
recognition. It's currently a growing field of study targeted at improving human-
machine interaction. Our main goal is to propose a method that allows computers to
identify eight emotions in speech. Initially, the audio files are passed through a
Noise Reduction algorithm based on Spectral gating. 15 different spectral & Timbre
features are then extracted from this audio data. Finally, the method classifies the
audio to a certain emotion class. This research contributes significantly by
developing two models based on rigorous testing, fine-tuning, and analysis that use
two of the most popular deep learning algorithms: Artificial Neural Network (ANN)
and Long Short-Term Memory (LSTM) Network. The ANN and LSTM models
exhibited an average accuracy of 88.3 percent and 87.4 percent using five different
datasets.
Secure predictive analysis on heart diseases using
partially homomorphic machine learning model
M.D. Boomija, S.V. Kasmir Raja
Department of CSE, SRM Institute of Science & Technology,
Kattankulathur, Tamil Nadu, India
Abstract. Cardiovascular disease is the most important reason for death worldwide
and significant public health distress. Timely prevention and treatment are possible
by early prediction of the disease. However, there is necessary to include or vary
new risk factors to improve the prediction models' performance. It is one of the best
ways to make development towards human-level AI. The machine learning (ML)
algorithms PCA (Principal Component Algorithm) and XGboost classification are
used to process the user queries and send the prediction. A major constraint is to
secure the user queries submitted to the prediction models in order that the patient
can submit their questions in an encrypted format to ensure security. It motivates us
to develop a predictive model PHML which combines Partial Homomorphic
Encryption and Ma-chine Learning algorithms PCA and XGboost classification.
39
The model is implemented in Amazon SageMaker with the dataset stored in Amazon
S3 using the above algorithms. The patient query was submitted to the cloud with
the encrypted format by the proposed partial homomorphic encryption algorithm.
The machine learning algorithms predict the user queries, which are in encrypted
form. The dataset includes multiple attributes like age, height, weight, gender,
smoking, alcohol intake, physical activity, systolic blood pressure, cholesterol,
glucose, diastolic blood pressure, and medical notes. With all features, doctors try
to predict whether our individual has a high risk of cardiovascular.
Quality Analysis of PATHAO Ride-sharing Service in
Bangladesh
Md. Biplob Hosen1, Nusrat Jahan Farin2, Mehrin
Anannya1, Khadija Islam3, and Mohammad Shorif
Uddin1
1Jahangirnagar University, Savar, Dhaka, Bangladesh
2Stamford University Bangladesh, Dhaka, Bangladesh
3Sonargaon University, Dhaka, Bangladesh
Abstract. This research intends to analyse the factors that influence the user's
behavioural intention on one of the most popular ride-sharing services in
Bangladesh: PATHAO. These factors are derived from few speculations,
particularly diffusion of innovation, theory of planned behaviour, and technology
acceptance model. This study utilizes a quantitative methodology with a total of
1,535 respondents. The collected data is being analysed for the current state of
service quality by doing an analysis about the users' satisfaction using machine
learning algorithms and entropy techniques. By analysing this study, prediction can
be made about users' satisfaction i.e., by improving which criteria of service quality
can augment the users' satisfaction in future.
Robot Path Planning using β Hill Climbing Grey Wolf
Optimizer
Saniya Bahuguna and Ashok Pal
Chandigarh University, India
Abstract. Path planning is a computational problem called the navigation problem
or the piano mover's problem that requires establishing the sequence of viable
designs that takes an object from its origin towards its destination. Computational
modelling, computer animation, robotics, and computer gaming all use the phrase.
A competent path planning technology of mobile robots can save a ton of time, yet
40
diminish the wear and capital venture of mobile robots. Several approaches for
mobile robot path planning have been explored and published in the literature. This
study deployed a hybrid of the Gray Wolf Optimization (GWO) and β Hill Climbing
method called β Hill Climbing Grey Wolf Optimizer (β-HCGWO) to tackle the
problem of robot path planning. In the test simulations of the robot path planning,
we used a map with three circular obstacles. β-HCGWO algorithm was adapted to
this problem. Its performance compared to other metaheuristic algorithms was
evaluated for solving the robot path planning problem. The results obtained showed
a better optimal path found for the used test map.
An Image Steganography Technique based on Fake DNA
Sequence Construction
Subhadip Mukherjee1, Sunita Sarkar2, and Somnath
Mukhopadhyay2
1Department of Computer Science, Kharagpur College, Kharagpur
721305, India
2Department of Computer Science and Engineering, Assam
University, Silchar 788011, India
Abstract. Steganography is the process of using a cover or medium such as
photograph, audio, text, video etc. to shield information from the outer world. A
new approach based on DNA computing for hiding information within an image
using the least significant bit (LSB) is proposed in this paper. To do this, the DNA
is decomposed by four nucleotides namely adenine, thymine, guanine and cytosine.
Here, the confidential data bits are encrypted within the DNA sequence then
concealed within the cover image. This process transmutes the original cover image
into a stego-image which is completely trustworthy to avoid human visual system,
and the confidential data is impossible to detect. The empirical findings show the
effectiveness of the suggested approach by producing 0.784 bpp of hiding power
with an average 56.24 dB of peak-signal to noise-ratio (PSNR) which makes it a
strong image steganography technique.
Artificial Intelligent Based Control of Improved
Converter for Hybrid Renewable Energy Systems
L. Chitra and Kavitha Kumari. K. S
Department of Electrical and Electronics Engineering, Aarupadai
Veedu Institute of Technology, Vinayaka Missions Research
Foundation, Chennai-603104, India
Abstract. Advancements of renewable energy technology and consequent rise in
petroleum prices result in popularity of hybrid renewable energy systems (HRES).
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Due to utilization of RES to generate electricity, solar PV energy generation systems
have been assessed as a leading energy system by power providers all over the
world. In addition, owing to higher efficiency and independent management of
active and reactive power employing converters with partial capacity, DFIGs are
more often used in the generation of wind energy. Even though the above-mentioned
renewable energy systems are thought to be potential power generators, one
disadvantage of these energy solutions is their unpredictability and reliance on
weather and climatic circumstances. Hence an efficient approach is designed
utilizing landsman converter for DFIG and PV systems performing noise free
voltage stress reduction. A PWM based PI controller is exploited that adopts fire fly
algorithm for controlling the maxi-mal power tracking point. The DC voltage is
inverted to AC by a grid synchronized 3ϕ VSI with LC filter ensuring smooth
operation reducing harmonics. Simulation of the proposed approach is carried out
in MATLAB and obtained outputs revealed minimal THD value of 1.8%.
A Review on Unbalanced Data Classification
Arvind Kumar1, Shivani Goel1, Nishant Sinha2, and
Arpit Bhardwaj3
1Computer Science Engineering Department, Bennett University
Greater Noida, India
2Pitney Bowes Software, Noida, India
3Computer Science and Engineering Department. Mahindra
University, Hyderabad, India
Abstract. Classification is a supervised machine learning technique to categorize
data into a predefined and distinct number of classes. Again, in the real world, most
of these data set are unbalanced. If one of its classes contains significantly fewer
samples than other classes, this class is called minority class and this data-set is
called the unbalanced data-set. The imbalanced property of the data set highly
influenced the performance of traditional classification techniques, and classifiers
become biased toward the majority class. For the classification of an unbalanced
data-set, different machine-learning techniques are presented by various
researchers. In this paper, an attempt is made to summarize popular ML
classification techniques to handle an unbalanced data set. This paper classifies the
existing techniques into three groups: data level approach, algorithm level approach,
and classifier's ensemble. This paper also discusses the brief technical details,
advantages and disadvantages of these methods. Finally, some of the popular
unbalanced data sets available on the UCI repository are also summarized.
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A Comparative Study of Meta-heuristic Algorithms based
on the solution of VRPTW in E-logistics
Nesrine Bidani1, Hela Moalla Frikha1 and Adnan
Yassine2
1OLID Laboratory, ISGI Sfax, University of Sfax, Tunisia
2LMAH, University of Havre, France
Abstract. Vehicle Routing Problem (VRP) is very important in operational research
and in logistic domain. Also, it is an NP-hard problem and has many variants
considering some different criteria’s. Indeed, our context focuses in the case of VRP
with Time Windows (VRPTW) in E-logistic and E-commerce fields based on multi-
objective optimization. This optimization is based on requests for transport between
industry and customers for the reason of cost, capacity, satisfying precedence, and
time constraints. In this paper, to solve this problem and evaluate the performance
of our optimization approach, we present a comparative analysis that compare
between existing resolution methods generally and metaheuristics algorithms
especially ac-cording to an overview of these optimization techniques based on
finding the best solution of VRP and VRPTW. According to the literature review of
these algorithms, we provide the best method to be applied when we would solve
our studied problem. So, the goal of this work is to compare meta-heuristics methods
that are presented in literature to obtain an optimal solution by applying a best
algorithm using different optimization techniques to solve our problem using some
instances. After analysing many existing studies, we can see and conclude that our
proposed approach is able to solve our studied problem. In order to achieve the
objectives of this proposed approach, we should facilitate the routine work in
numerous online sales companies by applying optimal approaches. So, we want
realize our two main objectives which are: maximizing of quality of customer
service and minimizing the cost of transport.
