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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Jain, Somya Diwakar, Shikha Yadav, Neha G.L. Bajaj Institute of Technology and Management Engineering/ Computer Science Artificial Intelligence Department of Applied Computational Science India School of Information & Communication Technology Gautam Buddha University Engineering/Artificial Intelligence India G.L. Bajaj Institute of Technology and Management Department of Applied Computational Science India

ISBN: (纸本)9798350371055

This research aims to break communication barriers for the deaf and hard-of-hearing by pioneering a real-time, dynamic sign language translator. Unlike existing apps, it uses a CNN to translate simultaneously between English and American Sign Language (ASL). It features an animated avatar for interactive communication, and goes beyond translation with chat, video calls, and precise hand motion capture. This innovative approach paves the way for inclusive and secure communication within the deaf community. © 2024 IEEE.

关键词： ASL avatar CNN communication barriers deaf inclusive real-time Sign language translation

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Privacy-Preserving Human Activity Recognition in Smart Homes using Deep Learning and Edge Computing for Real-Time Processing 5

Privacy-Preserving Human Activity Recognition in Smart Homes...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Mariappan, K. Gopal, Baskar Parthasarathy, J. Sreekanth, G.R. Nanmaran, R. Jegajothi, B. SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India College of Engineering University of Technology & Applied Science Nizwa Oman Computer Science & Engineering Jayalakshmi Institute Of Technology Dharmapuri India Information Technology Nandha Engineering College Erode52 India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India SRS Tech Solutions Chennai India

ISBN: (纸本)9798331530013

The rapid advancement of smart home technologies necessitates efficient human activity recognition (HAR) systems while ensuring user privacy. This research presents a novel architecture that integrates deep learning and edge computing for real-time HAR, emphasizing privacy protection. By employing differential privacy methods and federated learning, the approach minimizes personal data exposure risks. Using the UCI HAR dataset, the model achieves an accuracy of 96.50%, with precision, recall, and Fl-score metrics of 95.75%, 96.20%, and 95.97%, respectively. The results indicate high accuracy and adaptability to varying smart home environments, while edge computing ensures minimal latency. This work highlights the importance of balancing performance and privacy in HAR systems, paving the way for secure smart home applications. Additionally, it points to the potential of deep learning in medical diagnostics, suggesting future research could enhance diagnostic capabilities across various medical imaging modalities, including CT scans and MRIs, to improve healthcare outcomes. © 2024 IEEE.

关键词： Smart homes

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An Ensembled Real-Time Hand-Gesture Recognition using CNN 15

An Ensembled Real-Time Hand-Gesture Recognition using CNN

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kavitha, M.N. Saranya, S.S. Prasad, M. Kaviyarasu, S. Ragunath, N. Rahul, P. Kongu Engineering College Department of Computer Science and Design Tamilnadu Erode India PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Tamilnadu Coimbatore India Controller of Examinations Tamilnadu Namakkal India Kongu Engineering College Bachelor of Science - Information Systems Tamilnadu Erode India

ISBN: (纸本)9798350370249

Hand sign recognition is a vital technology in the human-computer interaction, enabling individuals to communicate with machines naturally and effectively. An innovative approach for real-time hand sign identification with the help of CNN and OpenCV is introduced with the fusion of computer vision and deep learning that can accurately interpret and classify an extensive range of hand signs and gestures. This research contributes significantly to the fields of computer vision and human-computer interaction, offering a practical and efficient solution for hand sign recognition. The combination of CNN and OpenCV presents a promising avenue for enhancing accessibility and communication, especially in environments where verbal communication is limited or non-existent. The model is trained with multiple data so that the system can recognize the hand gestures more precisely. Pre-trained architectures like ResNet and MobileNet are combined with the CNN model using ensemble learning and the performance is improved when compared to all the three CNN architectures individually. The ensemble model provides better accuracy of 96 %. The potential applications of this technology are vast, from assisting the hearing-impaired in understanding sign language to more immersive and intuitive interactions. Overall, the approach holds the promise of bridging the gap between human gestures and machine understanding, opening new doors for meaningful interactions between individuals and intelligent systems. © 2024 IEEE.

关键词： Intelligent systems

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mLBOA:A Modified Butterfly Optimization Algorithm with Lagrange Interpolation for Global Optimization

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Journal of Bionic Engineering 2022年第4期19卷 1161-1176页

作者： Sushmita Sharma Sanjoy Chakraborty Apu Kumar Saha Sukanta Nama Saroj Kumar Sahoo Department of Mathematics National Institute of Technology AgartalaAgartalaTripura 799046India Department of Computer Science and Engineering Iswar Chandra Vidyasagar CollegeBeloniaTripura 799155India Department of Computer Science and Engineering National Institute of Technology AgartalaAgartalaTripura 799046India Department of Applied Mathematics Maharaja Bir Bikram UniversityAgartalaTripura 799004India

Though the Butterfly Bptimization Algorithm(BOA)has already proved its effectiveness as a robust optimization algorithm,it has certain ***,a new variant of BOA,namely mLBOA,is proposed here to improve its *** proposed algorithm employs a self-adaptive parameter setting,Lagrange interpolation formula,and a new local search strategy embedded with Levy flight search to enhance its searching ability to make a better trade-off between exploration and ***,the fragrance generation scheme of BOA is modified,which leads for exploring the domain effectively for better *** evaluate the performance,it has been applied to solve the IEEE CEC 2017 benchmark *** results have been compared to that of six state-of-the-art algorithms and five BOA ***,various statistical tests,such as the Friedman rank test,Wilcoxon rank test,convergence analysis,and complexity analysis,have been conducted to justify the rank,significance,and complexity of the proposed ***,the mLBOA has been applied to solve three real-world engineering design *** all the analyses,it has been found that the proposed mLBOA is a competitive algorithm compared to other popular state-of-the-art algorithms and BOA variants.

关键词： Butterfly optimization algorithm Lagrange interpolation Levy flight search IEEE CEC 2017 functions Engineering design problems

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Sequence-to-Sequence Knowledge Graph Completion Based on Gated Attention Unit 29

Sequence-to-Sequence Knowledge Graph Completion Based on Gat...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yi, Fengge Wei, Xiumei Liu, Xiaojing Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

ISBN: (纸本)9798350330717

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5's proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment. © 2023 IEEE.

关键词： gated attention unit knowledge graph completion sequence-to-sequence model

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Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

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Journal of Bionic Engineering 2023年第3期20卷 1263-1295页

作者： Jeffrey O.Agushaka Absalom E.Ezugwu Oyelade N.Olaide Olatunji Akinola Raed Abu Zitar Laith Abualigah School of Mathematics Statisticsand Computer ScienceUniversity of KwaZulu-NatalKing Edward AvenuePietermaritzburg CampusPietermaritzburg3201KwaZulu-NatalSouth Africa Department of Computer Science Federal University of LafiaLafia950101Nigeria Unit for Data Science and Computing North-West University11 Hoffman StreetPotchefstroom2520South Africa Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044Abu DhabiUnited Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia

This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.

关键词： Improved dwarf mongoose Nature-inspired algorithms Constrained optimization Unconstrained optimization Engineering design problems

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A Comprehensive Approach of Algorithmic Trading System based on Technical Analysis

A Comprehensive Approach of Algorithmic Trading System based...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Jongpermwattanapol, Apisan Jongkohpo, Khathathorn Worasucheep, Chukiat Hiruncharoenvate, Chaya Krongtong, Tassanee King Mongkut's University Of Technology Engineering Science Classroom Bangkok Thailand King Mongkut's University Of Technology Applied Computer Science Bangkok Thailand Securities And Exchange Commission Bangkok Thailand The Institute For The Promotion Of Teaching Science The Institute And Technology Technology Unit Bangkok Thailand

ISBN: (纸本)9798350370096

Machine Learning researchers have been attempting to accurately predict stock price trends. Moreover, financial researchers use Technical Analysis with optimization models [1] to achieve the same task by extracting and combining important features for the model. Despite many Algorithmic Trading systems (ATS) available, modelling the stock market has still been proven difficult since stock prices are nonlinear and often appear random [2]. This work focuses on leveraging the advantages of both by employing the Technical Analysis approach to create features for training Machine Learning models. The models were evaluated using data from nine stocks listed on the Stock Exchange of Thailand (SET) from 2019 to 2023. The performances of three machine learning models: Logistic Regression, K-Nearest Neighbors, and Support Vector Machines were compared with Differential Evolution and the buy-and-hold strategies. The results showed that the Differential Evolution can significantly improve the return on investment in comparison to the Machine Learning models and the buy-and-hold strategies. © 2024 IEEE.

关键词： Financial markets

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Artificial Intelligence-Enabled Cooperative Cluster-Based Data Collection for Unmanned Aerial Vehicles

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computers, Materials & Continua 2022年第11期73卷 3351-3365页

作者： R.Rajender C.S.S.Anupama G.Jose Moses E.Laxmi Lydia Seifedine Kadry Sangsoon Lim Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Engineering Sungkyul UniversityAnyang14097Korea

In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile *** encompasses several heterogeneous resource and communication standard in ensuring incessant availability of *** the same time,the development of 6G enables the Unmanned Aerial Vehicles(UAVs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,*** UAV networks,energy efficiency and data collection are considered the major process for high quality network *** these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted *** issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAVs in the 6G *** this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAV)in 6G *** proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets *** presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization(QOSSO)algorithm for selecting the CHs and construct *** QOSSO algorithm derives a fitness function involving three input parameters residual energy of UAVs,distance to neighboring UAVs,and degree of *** performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.

关键词： 6G unmanned aerial vehicles resource allocation energy efficiency artificial intelligence clustering data collection

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Enhanced Intrusion Detection with Advanced Deep Features and Ensemble Classifier Techniques

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SN computer science 2025年第4期6卷 1-17页

作者： Toralkar, Pawan Mainalli, Kavita Allagi, Shridhar Debnath, Sanjoy Kumar Bagchi, Susama Leong, Wai Yie Khan, Muhammad Numan Ali Department of Artificial Intelligence Madanapalle Institute of Technology and Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering KLEIT: KLE Institute of Technology Karnataka Hubballi India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India INTI International University and Colleges Negeri Sembilan Nilai Malaysia College of Computing and Applied Sciences University of Technology and Applied Sciences Shinas Oman

Intrusion detection is crucial for modern cyber-security, aiming to detect and mitigate unauthorized access and malicious activities within networks. Traditional systems often rely on linear models, which may fail to capture complex patterns in advanced threats. This study proposes a heuristic approach to select the best deep features through an ensemble framework consisting of Random Forest (RF) and Support Vector Machine (SVM) classifiers using soft voting. The novelty of this work is the deep feature extraction through Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Autoencoder for intrusion detection. These extracted features were fed separately for classification. In order to balance the explainability and accuracy, the ensemble classifier consisting of RF and SVM, uses feature importance ranking (RF) and unambiguous decision bounds (SVM) to provide partial interpretability. Evaluating the NSL-KDD and CICDDoS2019 datasets, our ensemble model with CNN features achieves near-perfect accuracy of 99.99% and 99.91% respectively. RNN and Autoencoder models also show high performance, each reaching 99.3% accuracy on the CICDDoS2019 dataset with Soft Voting classifiers. This work highlights the importance of the ensemble method with better feature selection in intrusion detection. The proposed system outperformed the standard methods by providing a robust, scalable solution for organizational security. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolution neural network (CNN) Intrusion detection Random forest (RF) Recurrent neural network (RNN) Voting classification (Voting)

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Enhancing Timely Graduations: An Explainable AI Approach to Predict Academic Risks in South African Students

Enhancing Timely Graduations: An Explainable AI Approach to ...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Bokgoshi, Lenny Sixhaxa, Khanyisile Jadhav, Ashwini Nyamane, Seipati Ajoodha, Ritesh School of Computer Science and Applied Mathematics University of Witwatersrand Johannesburg South Africa University of Witwatersrand Science Teaching and Learning Unit Faculty of Science Johannesburg South Africa Sol Plaatje University Department of Computer Science and Information Technology Kimberley South Africa

ISBN: (纸本)9798350327816

With rising dropout rates and extended degree completion times in South African institutions, there's a pressing need to better understand and address the hurdles faced by students during their academic journey. This research harnesses the power of explainable AI to predict and classify students based on their likelihood of not completing their degrees on time. Utilizing synthetic data generated via a Bayesian network, we used predictive models that categorize students into four distinct risk profiles. This clarity in prediction not only illuminates the underlying causes of academic delays but also empowers faculty, advisors, and student support services with actionable insights. The goal of this research is to facilitate timely interventions, tailored support, and seamless transitions for students transferring between universities, ensuring more students can realize their academic aspirations within expected time frames. © 2023 IEEE.

关键词： academic performance at-risk students classification models Machine learning student attrition

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