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Hybrid AI-Electronic Systems for Real-Time Edge Processing in IoT Networks 3

Hybrid AI-Electronic Systems for Real-Time Edge Processing i...

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Sweety Prasanna Kiruba, G. Esakki Madura, E. Kumar, Bura Vijay Kavitha, M. Reddy, T. Srinivas Athiraja, A. Bannari Amman Institute of Technology Department of Computer Technology Tamil Nadu Sathyamangalam India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Sathyamangalam India School of Computer Science and Artificial Intelligence Sr University Warangal India Kathir College of Engineering Department of Artificial Intelligence and Data Science Neelambur Coimbatore India Malla Reddy Engineering College Department of Ece Main Campus Maisammaguda Telangana Medchal India Hyderabad India

ISBN: (纸本)9798331515911

Real-time data processing is facing many issues as a result of the Internet of Things' rapid expansion, especially at the network's edge where latency, bandwidth, and compute resources are limited. The creation of hybrid AI-electronic systems for real-time edge processing is investigated as a solution to these problems. To improve the effectiveness and precision of data processing in Internet of Things contexts, the research combines machine learning algorithms with sophisticated electronic circuits. The techniques include creating a brand-new hybrid framework that blends edge computing and AI models and focuses for low latency and low power consumption. When compared to conventional edge processing systems, extensive simulations and real-world research show a 25% reduction in processing latency and a 15% gain in data categorization accuracy. According to the findings, the suggested hybrid AI-electronic system can successfully handle the requirements of contemporary IoT networks and offer a scalable real-time edge processing solution. This strategy has potential for use in smart city applications, industrial automation, and other fields in the future. © 2025 IEEE.

关键词： Adversarial machine learning

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Non-English Natural Language Interface to databases: A Systematic Review 13

Non-English Natural Language Interface to Databases: A Syste...

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13th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2022

作者： Karimi, Sadullah Rasel, Annajiat Alim Abdullah, Matin Saad Department of Computer Science and Engineering School of Data and Sciences Brac University Dhaka Bangladesh

ISBN: (数字)9781665463164

ISBN: (纸本)9781665463164

A natural language interface (NLI) facilitates communication between a user and a system using a natural language used by a human as a medium. Instead of a complex user interface with numerous controls, the user enters input via natural language in the form of text, speech, signs, etc. The system responds via natural language in the form of text, speech, signs, etc. The use of a natural language interface has been increasing. To interact with a dataset or a database, such a system or interface is not always available. To access data, users need the knowledge of Structured Query Language (SQL) or need to learn an alternative graphical tool or database browser with varying limitations. For non-English speakers, it may create further issues in accessing data. English users can access the database using the natural language interface to database (NLIDB) which converts natural language to English-like Structured Query Language (SQL). For non-English users, the query in non-English language needs to be converted to SQL directly or via the English language as an intermediary. Researchers have proposed NLIDB systems for native speakers of many languages. In this research, we present results from a systematic review of non-English NLIDB with their various approaches. This research aims to assist future NLIDB researchers in understanding the current state and to identify the research gaps. © 2022 IEEE.

关键词： User interfaces

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Probe Machine Based Computing Model for Maximum Clique Problem

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Chinese Journal of Electronics 2022年第2期31卷 304-312页

作者： CUI Jianzhong YIN Zhixiang TANG Zhen YANG Jing Department of Computer Huainan Union University School of Electronic and Information Engineering Anhui University of Science & Technology School of Mathematics Physics and Statistics Shanghai University of Engineering Science School of Mathematics and Big Data Anhui University of Science & Technology

Probe machine(PM) is a recently reported mathematic model with massive parallelism. Herein,we presented searching the maximum clique of an undirected graph with six vertices. We constructed data library containing n sublibraries, each sublibrary corresponded to a vertex in the given graph. Then, probe library according to the induced subgraph was designed in order to search and generate all maximal cliques. Subsequently,we performed probe operation, and all maximal cliques were generated in parallel. The advantages of the proposed model lie in two aspects. On one hand, solution to NP-complete problem is generated in just one step of probe operation rather than found in vast solution *** the other hand, the proposed model is highly *** work demonstrates that PM is superior to TM in terms of searching capacity when tackling NP-complete problem.

关键词： maximal cliques maximum clique problem searching capacity data library NP-complete problem computational complexity model lie induced subgraph probe operation undirected graph mathematic model graph theory parallel algorithms optimisation parallelism computing model vertex probe library probe machine

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Performance Analysis of TSP Training Instance Generation Methods in Policy-Based Hyper-Heuristic Optimization Framework 4

Performance Analysis of TSP Training Instance Generation Met...

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4th International Conference on Big data Analytics and Practices, IBDAP 2023

作者： Udomkasemsub, Orachun Sirinaovakul, Booncharoen Achalakul, Tiranee King Mongkut's University of Technology Thonburi Department of Computer Engineering Bangkok Thailand Government Big Data Institute Bangkok Thailand

ISBN: (纸本)9798350300192

The Policy-Based Hyper-Heuristic Optimization framework (PHH), which utilizes reinforcement learning to solve combinatorial optimization problems, has demonstrated promising results in prior studies. However, training PHH models with real problem instances can be resource-intensive and challenging to obtain. To address this issue, this paper explores the effects of different randomization methods for generating training instances in the context of the traveling salesman problem (TSP). Seven randomization methods, namely grid, uniform, square, circle, Gaussian, wave, and mixed, were employed to generate TSP instances. The PHH models were trained using these instances, and their performance was evaluated on unseen TSP instances from TSPLIB. Experimental results indicate that training with TSP instances generated by the square method consistently achieved the lowest travel distances across small, medium, and large test instances. This research contributes to the understanding of instance-generation techniques for combinatorial optimization problems and provides insights for designing efficient hyper-heuristic frameworks. © 2023 IEEE.

关键词： Combinatorial optimization

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Spectral Normalized-Cut Graph Partitioning with Fairness Constraints 26

Spectral Normalized-Cut Graph Partitioning with Fairness Con...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Li, Jia Wang, Yanhao Merchant, Arpit School of Data Science and Engineering East China Normal University Shanghai China Department of Computer Science University of Helsinki Helsinki Finland

ISBN: (纸本)9781643684369

Normalized-cut graph partitioning aims to divide the set of nodes in a graph into k disjoint clusters to minimize the fraction of the total edges between any cluster and all other clusters. In this paper, we consider a fair variant of the partitioning problem wherein nodes are characterized by a categorical sensitive attribute (e.g., gender or race) indicating membership to different demographic groups. Our goal is to ensure that each group is approximately proportionally represented in each cluster while minimizing the normalized cut value. To resolve this problem, we propose a two-phase spectral algorithm called FNM. In the first phase, we add an augmented Lagrangian term based on our fairness criteria to the objective function for obtaining a fairer spectral node embedding. Then, in the second phase, we design a rounding scheme to produce k clusters from the fair embedding that effectively trades off fairness and partition quality. Through comprehensive experiments on nine benchmark datasets, we demonstrate the superior performance of FNM compared with three baseline methods. © 2023 The Authors.

关键词： Embeddings

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Convolution Neural Network with Unsupervised Machine Learning Approach for Feature Extraction and Brain Tumor Detection in Human beings 1

Convolution Neural Network with Unsupervised Machine Learnin...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Kumar, G. Sathish Nivaashini, M. Maheshwari, G. Uma Rasi, D. Kumar, B. Magesh Arun, R. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India Dr. Mahalingam College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India Hindusthan Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350359756

Brain tumors are atypical progress of cells in the brain or the contiguous tissues. Tumors can be either non-cancerous or cancerous. The prognosis for a person with a brain tumor varies significantly. Benign tumors are generally less aggressive and may be curable with surgery. Malignant tumors can be more challenging to treat and may have a poorer prognosis. The outcome also depends on the stage at which the tumor is diagnosed. Detecting brain tumors using magnetic resonance imaging (MRI) images with convolutional neural networks (CNNs) is a common and effective approach in medical image analysis. In this research, the unsupervised machine learning approach called self-organizing map (SOM) is implemented for effective feature extraction and CNN with ResNet architecture is employed for brain tumor exposure efficiently. The proposed SOMResNet algorithm takes the MRI images as the input and perform feature extraction. Again, the mined essential features are given as the input for SOMResNet to identify the tumor in the human brain. The accuracy of the proposed SOMResNet is compared with softmax, ReLu, Tanh, decision tree classifiers. The accuracy of SOMResNet is 97.5%, Sensitivity is 98.0%, specificity rate is 97.1%, precision rate is 97.3% with 97.6% of F-Score value. The result shows that the SOMResNet algorithm outperforms than the traditional algorithms in brain tumor detection. © 2023 IEEE.

关键词： Feature extraction

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American Sign Language Recognition System for Numerical and Alphabets

American Sign Language Recognition System for Numerical and ...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Sathyanarayanan, D. Reddy, T. Srinivasa Sathish, A. Geetha, P. Arunkumar, J.R. Deepak, S. Prem Kumar Department of Computer Science and Engineering B V Raju Institute of Technology Telangana Narsapur India Department of Computer Science and Engineering Potti Sriramulu Chalivudi Mallikarjuna Rao College of Engineering and Technology vijayawada India Department of Computer Science and Engineering Roever Engineering College Perambalur621220 India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Samayapuram Trichy India Department of Computer Science and Engineering Modern Institute of Technology and research centre Alwar Rajasthan India

ISBN: (纸本)9798350305708

Sign language is a helpful visual technique that can be utilized by individuals who have difficulty communicating verbally. This language is used frequently by people as well. However, the vast majority of individuals don't understand sign language very well, and as a result, they frequently struggle when attempting to communicate with someone who is fluent in it. The community of people who have trouble speaking or hearing will benefit from an initiative called the Sign Language Recognition System. It is extremely challenging for deaf mutes to communicate, which is why we require a linguistic framework to understand what it is that they are attempting to express. In light of this, we present in this research an American Sign Language (ASL) handwriting translator that is built on convolutional neural networks. Instructing computers to recognize specific characters is one way to accomplish this goal. The Support Vector Machine (SVM) and the Convolutional Neural Network are two types of artificial neural networks that are being utilized in our project we have suggest the identification of American Sign Language. In addition, the Decision Tree and Voting Classifier are applied to the dataset in order to do an analysis. We have demonstrated the design and execution of an American Sign Language (ASL) translator that also incorporates fingerspelling as a direct consequence of this. © 2023 IEEE.

关键词： Convolution Neural Network Decision Tree Sign Language Support Vector Machine Voting Classifier

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Deep learning at the service of metaheuristics for solving numerical optimization problems

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Neural Computing and Applications 2025年 1-36页

作者： Oyelade, Olaide N. Ezugwu, Absalom E. Saha, Apu K. Thieu, Nguyen V. Gandomi, Amir H. School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Bangladesh Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 United States Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Faculty of Computer Science Phenikaa University Yen Nghia Hanoi Ha Dong12116 Viet Nam Department of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Integrating deep learning methods into metaheuristic algorithms has gained attention for addressing design-related issues and enhancing performance. The primary objective is to improve solution quality and convergence speed within solution search spaces. This study investigates the use of deep learning methods as a generative model to learn historical content, including global best and worst solutions, solution sequences, function evaluation patterns, solution space characteristics, population modification trajectories, and movement between local and global search processes. An LSTM-based architecture is trained on dynamic optimization data collected during the metaheuristic optimization process. The trained model generates an initial solution space and is integrated into the optimization algorithms to intelligently monitor the search process during exploration and exploitation phases. The proposed deep learning-based methods are evaluated on 55 benchmark functions of varying complexities, including CEC 2017 and compared with 13 biology-based, evolution-based, and swarm-based metaheuristic algorithms. Experimental results demonstrate that all the deep learning-based optimization algorithms achieve high-quality solutions, faster convergence rates, and significant performance improvements. These findings highlight the critical role of deep learning in addressing design issues, enhancing solution quality, trajectory, and performance speed in metaheuristic algorithms. © The Author(s) 2025.

关键词： Contrastive Learning

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Optimizing Federated Semantic Learning in Distributed AIGC-Enabled Human Digital Twins: A Multi-Criteria and Multi-Shard User Selection Framework

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5916-5933页

作者： Okegbile, Samuel D. Gao, Haoran Talabi, Oluwasegun Cai, Jun Niyato, Dusit Shen, Xuemin Concordia University Department of Electrical and Computer Engineering MontrealQCH3G 1M8 Canada University of the Fraser Valley School of Computing AbbotsfordBCV2S 7M8 Canada Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Artificial intelligence-generated content (AIGC) has been proposed as a solution to meet the requirements of ultra-reliable, secure, and privacy-preserving connectivity in human digital twin (HDT) networks. In such an AIGC-enhanced HDT, contents representing the true statuses of physical twins are generated in the virtual environment for the immediate update and evolution of the corresponding virtual twins (VTs). However, adopting a distributed AIGC in HDT presents several challenges, including the need for personalized VTs, data privacy concerns, and insufficient contextual understanding. This paper introduces a multi-layer federated semantic learning framework to address these challenges, incorporating batch learning to meet the training requirements for semantic-channel encoders and decoders. Furthermore, we introduce a novel user association framework to maximize the overall system performance under shard formation constraints. We then formulate a long-term joint optimization problem for user selection over finite learning periods. A novel Lyapunov-based online optimization strategy was proposed to mitigate the impact of time-varying and unpredictable training conditions. Additionally, we introduce a multi-arm bandit-based method and a context-centric user selection approach to solve the optimization problem. The results demonstrate that the proposed user association framework addresses the limitations of existing approaches, thereby improving the overall performance of the multi-shard AIGC-enhanced HDT. © 2002-2012 IEEE.

关键词： Federated learning

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OFDM with Generalized Cascade Index Modulation for Secure Communications

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China Communications 2024年第10期21卷 210-225页

作者： Hu Zeng Jin Xiaopin Yao Yudong Chen Fangjiong Wan Dehuan Liu Yun College of Information Science and Technology Zhongkai University of Agriculture and EngineeringGuangzhou 510225China College of Information Engineering China Jiliang UniversityHangzhou 310018China Department Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ 07030USA School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China Center for Data Science and Artificial Intelligence Guangdong University of FinanceGuangzhou 510521China

Cascade index modulation(CIM) is a recently proposed improvement of orthogonal frequency division multiplexing with index modulation(OFDM-IM) and achieves better error *** CIM, at least two different IM operations construct a super IM operation or achieve new functionality. First, we propose a OFDM with generalized CIM(OFDM-GCIM) scheme to achieve a joint IM of subcarrier selection and multiple-mode(MM)permutations by using a multilevel digital ***, two schemes, called double CIM(D-CIM) and multiple-layer CIM(M-CIM), are proposed for secure communication, which combine new IM operation for disrupting the original order of bits and symbols with conventional OFDM-IM, to protect the legitimate users from eavesdropping in the wireless communications. A subcarrier-wise maximum likelihood(ML) detector and a low complexity log-likelihood ratio(LLR) detector are proposed for the legitimate users. A tight upper bound on the bit error rate(BER) of the proposed OFDM-GCIM, D-CIM and MCIM at the legitimate users are derived in closed form by employing the ML criteria detection. computer simulations and numerical results show that the proposed OFDM-GCIM achieves superior error performance than OFDM-IM, and the error performance at the eavesdroppers demonstrates the security of D-CIM and M-CIM.

关键词： cascade index modulation low-complexity detector maximum likelihood OFDM physical layer security

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