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Learning and technology Conference (L&T)

作者： Mohamed Elgilani Eltahir Elshareif Nurul Aswani Ahmad Alias Nazean Jomhari Hazrina Sofian Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia

The use of assistive technology in the field of education is now a common practice in today’s tech-driven era. The implementation is quite rampant in all levels and sections of education, including by special needs students with hearing impairments. With the help of technology or Internet of Things (IoT), they are offered with better opportunities to improve their communication and the teaching and learning process especially when it comes to sign language. However, the situation is quite different for Muslim deaf individuals. Sign language is not only used for communication but also as a form for reciting the Quran. Yet, the utilization of technology in the area of sign language for Quranic recitation is still limited. Hence, the goal of this study was to design and develop a sensor-based sign language recognition device consisting of a sensory smart glove and mobile application to detect Arabic sign language alphabets used in Quran recitation. The experimental result shows that the gesture recognition rate of 28 different Arabic alphabet signs, performed by 7 different signers, is 79.08%. The significance of the system lies in its mobility and capability to recognize the hand gestures regardless of the background noise.

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Utilization of HEVC ChaCha20-Based Selective Encryption for Secure Telehealth Video Conferencing

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computers, Materials & Continua 2022年第1期70卷 831-845页

作者： Osama S.Faragallah Ahmed I.Sallam Hala S.El-sayed Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Engineering Faculty of EngineeringMenoufia UniversityShebin El-Kom32511Egypt

Coronavirus(COVID-19)is a contagious disease that causes exceptional effect on healthcare organizationsworldwide with dangerous impact on medical services within the *** of the fast spread of COVID-19,the healthcare facilities could be a big source of disease ***,healthcare video consultations should be used to decrease face-to-face communication between clinician and *** video consultations may be beneficial for some COVID-19 conditions and reduce the need for faceto-face contact with a potentially positive patient without *** conditions are like top clinicians who provide remote consultations to develop treatment methodology and follow-up remotely,patients who consult about COVID-19,and those who have mild symptoms suggestive of the COVID-19 *** consultations are a supplement to,and not a substitute for,telephone *** may also form part of a broader COVID-19 distance care strategy that contains computerized screening,separation of possibly infectious patients within medical services,and computerized video-intensive observing of their intensive care that helps reduce ***,the spread of the COVID-19 virus helps to expand the use of video healthcare consultations because it helps to exchange experiences and remote medical consultations,save costs and health procedures used to cope with the pandemic of the COVID-19 virus,and monitor the progress of treatment plans,moment by moment from a distance with precision,clarity and *** this perspective,this paper introduces a high-efficiency video coding(HEVC)ChaCha20-based selective encryption(SE)scheme for secure healthcare video *** proposed HEVC ChaCha20-based SE scheme uses the ChaCha20 for encrypting the sign bits of the Discrete Cosine Transform(DCT)and Motion Vector Difference(MVD)in the HEVC entropy *** main achievement of HEVC ChaCha20-based SE scheme is encrypting the most sensitive video bits with keeping low

关键词： Telehealth HEVC cahcha20 video conferencing

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Enhancing Anxiety Prediction: Leveraging Machine Learning with Functional Design Metrics 21

Enhancing Anxiety Prediction: Leveraging Machine Learning wi...

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21st International Conference on Electrical engineering, Computing science and Automatic Control, CCE 2024

作者： Javed, Abqa Ajmal, Sahar Tajammal, Taliah Aslam, Muhammad Martinez-Enriquez, A.M. Sheikh, Usman Sadiq University of Engineering and Technology Department of Computer Science Lahore Pakistan National University of Computer and Emerging Sciences Department of Software Engineering Pakistan Virtual University of Pakistan Department of Computer Science and Information Technology Lahore Pakistan CINVESTAV-IPN D.F. Department of Computer Science Mexico Medicine Bronglais General Hospital Clinical Fellow in Respiratory SY23 1ER United Kingdom

ISBN: (纸本)9798350377545

Fear, nervousness, and social phobia all contribute to anxiety episodes reaching their peak in seconds. Using data collected from patient questionnaires, this study seeks to anticipate and diagnose anxiety disorders effectively and precisely using machine learning approaches. However, current research offers standardized metrics for assessing machine learning approaches throughout the design process. This work presents a systematic method for efficiently extracting literature from digital sources and emphasizes the need for data engineers to evaluate the efficiency of machine learning approaches to assure forecast quality. The International Organization for Standardization (ISO) 9126 standard is used to assess machine learning approaches for anxiety prediction to address this gap. This evaluation considers parameters such as functionality, accuracy, resilience, throughput, explainability, privacy, and correctness. These defined characteristics serve as the benchmark to measure the efficacy of machine learning approaches in predicting anxiety disorders. © 2024 IEEE.

关键词： Adversarial machine learning

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Accurate Classification of Cervical Cancer Based on Multi-layer Perceptron Hunger Games search Optimization technique 21

Accurate Classification of Cervical Cancer Based on Multi-la...

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21st International Learning and technology Conference, L and T 2024

作者： Aly, Rabab Hamed M. Hussein, Aziza I. Youssef, Rasha Y. The Higher Institute for Management and Information Technology Minya Egypt Nahda University Faculty of Computer Science Computer Science Department Beni Suef Egypt Effat University College of Engineering Energy and Technology Research Center Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Cancer is distinguished by the presence of abnormal cellular proliferation and growth, both of which serve as signs and symptoms for this kind of illness. computer vision, deep learning, and metaheuristics optimization techniques are increasingly important for solving complex medical Artificial Intelligence (AI) problems such as cancer detection. This paper introduces a new methodology for training the Multi-Layer Perceptron (MLP) using the optimization algorithm known as Hunger Games search Optimization technique (HGO) and apply this method to classify the cervical cancer. The main goal of this method is to reduce the error and enhance the classification rate of cervical cancer. The outcomes show that the MLP with HGO algorithm performed better than other algorithms in terms of classification efficacy and accuracy rate. Simulation outcomes indicate that the proposed strategy performs better than previously published research in terms of effectiveness for the classification optimization methods. © 2024 IEEE.

关键词： Diseases

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Google Scholar University Ranking Algorithm to Evaluate the Quality of Institutional Research

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computers, Materials & Continua 2023年第6期75卷 4955-4972页

作者： Noor Ul Sabah Muhammad Murad Khan Ramzan Talib Muhammad Anwar Muhammad Sheraz Arshad Malik Puteri Nor Ellyza Nohuddin Department of Computer Science Government College UniversityFaisalabad 38000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Software Engineering Government College UniversityFaisalabad 38000Pakistan Institute of IR4.0 Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia

Education quality has undoubtedly become an important local and international benchmark for education,and an institute’s ranking is assessed based on the quality of education,research projects,theses,and dissertations,which has always been ***,this research paper is influenced by the institutes ranking all over the *** data of institutes are obtained through Google Scholar(GS),as input to investigate the United Kingdom’s Research Excellence Framework(UK-REF)*** this purpose,the current research used a Bespoke Program to evaluate the institutes’ranking based on their *** bespoke program requires changes to improve the results by addressing these methodological issues:Firstly,Redundant profiles,which increased their citation and rank to produce false ***,the exclusion of theses and dissertation documents to retrieve the actual publications to count for ***,the elimination of falsely owned articles from scholars’*** accomplish this task,the experimental design referred to collecting data from 120 UK-REF institutes and GS for the present year to enhance its correlation analysis in this new *** data extracted from GS is processed into structured data,and afterward,it is utilized to generate statistical computations of citations’analysis that contribute to the ranking based on their *** research promoted the predictive approach of correlational ***,experimental evaluation reported encouraging results in comparison to the previous modi-fication made by the proposed *** paper discussed the limitations of the current evaluation and suggested the potential paths to improve the research impact algorithm.

关键词： Google scholar institutes ranking research assessment exercise research excellence framework impact evaluation citation data

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LiDAR Image-Based Earth Carbon Emission Analysis and Its Impact on Public Health: A Machine Learning Model

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 455-464页

作者： Meghana, S. Padmaja, Dhyaram Lakshmi Gundu, Krishna Sriharsha Kudari, Rajeev Somasekar, J. Chandran, Likha Department of Computer Science Engineering Neil Gogte Institute of Technology Hyderabad India Information Technology Anurag University Hyderabad India Enterprise Technology Arizona State University Tempe AZ United States Department of CSE Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Andhra Pradesh Guntur 522502 India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka Bengaluru 562112 India Department of Chemistry VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

Afforestation is a practical way to absorb carbon dioxide in contemporary context of carbon neutrality. In the assessment of forest emission reduction projects, stock can be utilized as a crucial indicator of biomass and carbon storage estimation in addition to serving as an economic value index for forest wood resources. We suggest a framework and technique for data-driven machine learning that uses satellite remote sensing and aerial LiDAR to forecast plantation stock. This research proposes a novel method in LiDAR image-based carbon emission analysis with their health impact using a machine learning method. Here, the input is collected as LiDAR image-based healthcare data for processing and smoothening. Then, this image has been segmented and classified using convolutional contour graph cut equalization with VGG-net transfer encoder Gaussian neural networks. The experimental analysis for LiDAR image dataset has been carried out for analysis of carbon emission, and healthcare data analysis has been carried out in terms of training accuracy, average precision, recall, F-1 score, and AUC. The proposed technique attained a training accuracy of 98%, an average precision of 93%, a recall of 96%, an F-1 score of 92%, and an AUC of 94%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Carbon emission analysis Health impact LiDAR image Machine learning model Transfer encoder

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A Double Adaptive Random Spare Reinforced Sine Cosine Algorithm

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computer Modeling in engineering & sciences 2023年第9期136卷 2267-2289页

作者： Abdelazim G.Hussien Guoxi Liang Huiling Chen Haiping Lin Department of Computer and Information Science Linköing UniversityLinköing4189Sweden Faculty of Science Fayoum UniversityFayoum63514Egypt Department of Information Technology Wenzhou PolytechnicWenzhou325035China Artificial Intelligence Wenzhou UniversityWenzhou325035China College of Information Engineering Hangzhou Vocational&Technical CollegeHangzhou310018China

Many complex optimization problems in the real world can easily fall into local optimality and fail to find the optimal solution,so more new techniques and methods are needed to solve such *** algorithms have received a lot of attention in recent years because of their efficient performance and simple *** Cosine Algorithm(SCA)is a recent Metaheuristic algorithm that is based on two trigonometric functions Sine&***,like all other metaheuristic algorithms,SCA has a slow convergence and may fail in sub-optimal *** this study,an enhanced version of SCA named RDSCA is suggested that depends on two techniques:random spare/replacement and double adaptive *** first technique is employed in SCA to speed the convergence whereas the second method is used to enhance exploratory searching *** evaluate RDSCA,30 functions from CEC 2017 and 4 real-world engineering problems are ***,a nonparametric test called Wilcoxon signed-rank is carried out at 5%level to evaluate the significance of the obtained results between RDSCA and the other 5 variants of *** results show that RDSCA has competitive results with other metaheuristics algorithms.

关键词： Sine cosine algorithm global optimization swarm intelligence meta-heuristic algorithms

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Utilizing Machine Learning Algorithms for Effectively Detection IoT DDoS Attacks 1st

Utilizing Machine Learning Algorithms for Effectively Detect...

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1st International Conference on Advances in Computing Research, ACR’23

作者： Mughaid, Ala Alqahtani, Ali AlZu’bi, Shadi Obaidat, Ibrahim Alqura’n, Rabee AlJamal, Mahmoud AL-Marayah, Raid Department of Information Technology Faculty of Prince Al-Hussien Bin Abdullah for IT The Hashemite University Zarqa Jordan Department of Networks and Communication Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia Computer Science Department Faculty of Science and Information Technology Al Zaytoonah University of Jordan Amman Jordan

ISBN: (纸本)9783031337420

One of the most critical features of the Internet of Things (IoT) is that it allows a massive number of devices to communicate with each other at the same time, for example (cars, smoke alarms, watches, glasses, web cameras, etc.). Given that they can connect to the Internet, the huge demand to convert systems into smart systems that can be controlled remotely increases the chances of cyber-attacks that threaten security and availability becoming more frequent, severe, and in a different form. Distributed Denial of Service (DDoS) attacks have become the most common threats in IoT. These attacks make IoT systems unreliable because complex DDoS attacks cannot be detected by traditional security countermeasures such as firewalls and intrusion detection systems (IDS). In this research, we proposed a model dedicated to work on prediction and detection of DDoS attacks on networks IoT, where we run this model on a NF-BoT-IoT-v2 dataset, which contains 37,763,497 records divided into five categories (Benign, Reconnaissance, DDoS, DoS, and Theft), and then we process our dataset to be more business friendly so it only has two categories (100,000 records broken down into 23,906 benign attacks and 76,094 DDoS attacks). This study used WEKA and MATLAB tools to perform the classification efficiently and find the most important features through feature selection. We used a decision tree (J48) machine learning classifier, a Naive Bayes (NB) classifier, and a random forest (RF) classifier. However, the results were as expected and improved accuracy and efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Denial-of-service attack

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A Logical Evaluation of Predicting Immune System Strength by using Enhanced Deep Learning Strategy

A Logical Evaluation of Predicting Immune System Strength by...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Malathi, K. Ramkumar, M. Ruby, E. D. Kanmani Santhi, G.B. Korde, Vivek Aljawawdeh, Hamzeh J. Saveetha University Department of Cse Chennai India Veltech Rangarajan Dr Sagunthala R & D Institute of Science & Technology Department of Ece Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of information technology Department of software engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The immune system plays a crucial role in defending the body against diseases, making it vital to develop models that can accurately predict immune system strength. This study presents an Enhanced Deep Learning Strategy (EDLS) designed to predict immune system strength based on immune-related biomarkers such as White Blood Cell (WBC) count and Immunoglobulins. A dataset consisting of 23,654 records with 12 features was collected from clinical sources. The data underwent comprehensive preprocessing, including noise reduction using a Median Filter, feature selection with Random Forest, and data fusion using Kalman Filters. The EDLS model integrates AutoEncoder for dimensionality reduction and Cascaded Long Short-Term Memory (LSTM) for capturing temporal dependencies. The proposed model outperforms nine traditional and advanced machine learning models, including Logistic Regression, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), with an accuracy of 94.72%. Evaluation metrics such as Precision (92.01%), Recall (93.77%), and F1-Score (92.88%) highlight the model's effectiveness in predicting immune system strength. Feature importance analysis reveals that WBC count and Immunoglobulins are the most influential predictors. This study demonstrates the potential of EDLS for enhancing immune strength prediction, which can have significant implications for personalized healthcare and disease prevention. © 2024 IEEE.

关键词： Median filters

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Examining Students' Learning Styles Impacted on Learning Outcome in the MOOC Course: A Case Study 23

Examining Students' Learning Styles Impacted on Learning Out...

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9th International Conference on Frontiers of Educational Technologies, ICFET 2023

作者： Nguyen, Viet Anh Computer Science Department Faculty of Information Tecnhology VNU University of Engineering and Technology Vietnam National University Hanoi Viet Nam

ISBN: (纸本)9798400707353

This article presents the results of a study examining the influence of learning styles on students' learning outcomes in the MOOC course. In this study, we analyzed the data of interaction and student learning outcomes after they finished the course by using a quantitative method that combines machine learning and learning analytics to answer two research questions: 1) Which student's learning styles have better learning outcomes? 2) Which learning activities have the utmost influence on learning outcomes? The data of 264 students taking the course is collected automatically through the LMS system. We perform clustering, labeling, and classification techniques to determine the student's learning styles according to the VARK [1] model. Quantitative analysis of the student group data identified learning styles to answer two research questions. The results showed that the students who identified the learning style as Kinesthetic had better academic results, with 58% of the students in this group in the top 5% of students who have the highest score. When considering how learning styles affect learning outcomes, activities that interact with the learning system have more influence on learning outcomes than other learning activities. Although this is only the result of a case study, it can be applied to develop MOOC courses tailored to specific students by implementing appropriate learning content types for the learner can get a better result. © 2023 ACM.

关键词： Students

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