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International Symposium on Advanced Computing and Communication (ISACC)

作者： Neelamegam G Rajaram Venkatesan Ramya SR R.S. Ramya Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Builders Engineering College Tirupur Tamil Nadu India Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems.

关键词： Smart cities Reinforcement learning Medical services Throughput Dynamic scheduling Energy efficiency Real-time systems Resource management Optimization Multi-agent systems

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Priority Based Cyber Incident Handling: Work in Progress

Priority Based Cyber Incident Handling: Work in Progress

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International Conference on Electrical engineering, computer science and Informatics (EECSI)

作者： Mulyati Deris Stiawan Wijang Widhiarso Rahmat Budiarto Department of Information System Multi Data Palembang University & Faculty of Engineering Universitas Sriwijaya Palembang Indonesia Faculty of Computer Science Sriwijaya University Palembang Indonesia Department of Informatics Multi Data Palembang University Palembang Indonesia College of Computer Science & IT Albaha University Saudi Arabia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Effective and efficient cyber incident handling is crucial for maintaining the security of information systems and organizational data. This research aims to develop a priority-based cyber incident handling method by adopting the NIST 2.0 framework and utilizing machine learning classification algorithms. This method enables organizations to quickly and accurately identify and respond to incidents based on threat levels and impact, focusing resources on the most critical incidents. The research employs the Support Vector Machine (SVM) classification algorithm to determine incident priorities. The study is still developing for handling cyber incidents, followed by testing and evaluating the system prototype within organizations. Initial results indicate that the priority-based approach using the SVM classification algorithm achieves a True Positive (TP) Rate of 0.999 and a False Positive (FP) Rate of 0.008, with a correlation of 98.88%, resulting in excellent performance in identifying positive cases and a low error rate.

关键词： Support vector machines Error analysis Prototypes Focusing Organizations Machine learning NIST Classification algorithms Security Informatics

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A Knowledge Graph Completion Model with Path-Specific Learning

A Knowledge Graph Completion Model with Path-Specific Learni...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xiulin Zheng Zan Zhang Rui Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) and also with the School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Inspur Academy of Science and Technology Shandong China

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

Knowledge graphs (KGs) play an increasingly im-portant role in many knowledge-aware tasks. However, existing KGs are struggle with incompleteness, which motivates Knowledge graph completion (KGC), that is, predicting the lost links between entities based on observed triples. Reasoning over relation paths in incomplete KGs is popular. Nonetheless, some significant issues are still remained to be addressed, such as path noise and ambiguity of inferred relation. To address these problems, we propose a novel path augmented _Reasoning model with avoidance of Path noise and Disambiguation of inferred relation in this paper, referred to as RPD. In this model, we calculate the sum of resource allocation for each relation path to measure its reliability to avoid the inference of path noise. To address the ambiguity of an inferred relation, we introduce position embedding to denote the relation position along the path when learning path representation. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of our proposal RPD model in the handling of KGC tasks compared to SOTAs.

关键词： Heavily-tailed distribution Noise Knowledge graphs Benchmark testing Transformers Cognition Reliability Resource management Proposals Noise measurement

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AI based Mobile Machine Learning Framework for Edge Video Analytics

AI based Mobile Machine Learning Framework for Edge Video An...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Suresh Babu, Kamalesh Thoppaen Dhar Diwan, Tarun Lakshmi, G. Prasanna Narasimhan, Lakshmi Tingare, Bhagyashree Ashok Khatkale, Pravin B Globallogic Inc Augustine Drive Santa ClaraCA95054 United States Atal Bihari Vajpayee University Bilaspur India Sandip University School of Computer Science and Engineering Maharashtra Nashik India HCL America Solutions Inc Krishnamoorthy Senior Technical Lead SunnyvaleCA United States D Y Patil College of Engineering Akurdi Department of Artificial Intelligence and Data Science Pune India Sanjivani University Maharashtra Kopargaon India

ISBN: (纸本)9798350390049

The rapid advancements in mobile technology and edge computing have spurred the development of innovative video analytics solutions that leverage the computational power of mobile devices for real-time processing and decision-making. This paper presents an AI-based mobile machine learning framework tailored for edge video analytics, addressing the challenges of latency, bandwidth consumption, and privacy inherent in traditional cloud-based approaches. The proposed framework integrates advanced machine learning models optimized for mobile hardware, utilizing techniques such as model quantization, pruning, and hardware acceleration to deliver efficient and accurate video analytics directly on edge devices. Experimental results demonstrate the framework's effectiveness across various mobile platforms, showing significant improvements in latency reduction, battery efficiency, and data privacy compared to cloud-based solutions. The framework's scalability and adaptability make it a powerful tool for deploying sophisticated video analytics in diverse edge computing environments. © 2024 IEEE.

关键词： Mobile edge computing

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Enhancing Service Management in Industry 4.0: The Role of AI-Powered Chatbots

Enhancing Service Management in Industry 4.0: The Role of AI...

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Advances in science and engineering Technology International Conferences (ASET)

作者： Asraa Mohammed Albeshr Fady Alnajjar Hassan Abdulmouti Omar Mubin College of Information Technology UAE University Alain UAE Engineering Technology & Science Faculty of Engineering Higher College of Technology Sharjah UAE School of Computer Data and Mathmatical Science Westren Sydney University Sydney Australia

ISBN: (数字)9798350344134

ISBN: (纸本)9798350344141

In the era of Industry 4.0, where digital transformation is crucial, developing advanced chatbot system becomes essential to revolutionize service management and bolstering business continuity. By leveraging Artificial Intelligence (AI) within such industrial frameworks, smartchatbots promise to streamline operations, ensure seamless customer interactions, and drive efficiency, thereby fortifying businesses in today's competitive landscape. This study presents an adaptable smart chatbot model designed to mimic and absorb the expertise of technical officers within a factory setting, responding to employee technical queries. This approach aims to alleviate the burden on technical personnel while enhancing response efficiency. Unlike traditional systems, this adaptive model benefits from continuous learning capabilities, enabling it to refine its responses over time based on new interactions and corrections made by technical officers. Furthermore, the chatbot's 24/7 availability and the collaborative feedback mechanism for evaluating its responses ensure that it not only meets but exceeds user expectations. Through detailed analysis and empirical evidence, we demonstrate how this chatbot significantly enhances work quality and operational continuity, setting a new benchmark for digital service management in the context of Industry 4.0. The results from conducting surveys among the technical officers regarding the chatbot's usefulness were overwhelmingly positive. All participants expressed high satisfaction, confirming that the implementation of such a smart chatbot model significantly reduces their load and enhances the user experience.

关键词： Surveys Adaptation models Chatbots User experience Production facilities Fourth Industrial Revolution Personnel Artificial intelligence Business Load modeling

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Automatic Early Detection of Tomato Blight Disease Using Deep Neural Networks

Automatic Early Detection of Tomato Blight Disease Using Dee...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Anangsha Halder Subhrendu Guha Neogi Shibdas Dutta Soumallya Chandra Chandra Pritha Guha Majumdar Akash Sarkar Department of Computer Science and Engineering The Neotia University Sarisha West Bengal India Department of Computer Science and Engineering Brainware University Kolkata West Bengal India DCG Data-Core System India Pvt. Ltd Kolkata West Bengal India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Crop disease control is crucial for food security since plant illnesses diminish agricultural production. Crop productivity and quality depend on leaf diseases like tomatoes. Early plant disease identification utilizing computer vision and artificial intelligence can lessen disease severity and overcome the limitations of continuous human monitoring. Automatic Early detection and alert systems reduce disease and boost agricultural productivity by classifying tomato diseases from tomato leaf images using Deep Neural Network Models. Our study examined leaf photos to classify tomato diseases and healthy plants using AlexNet, ResNet-9, VGG-19, and a modified CNN model for tomato plant disease diagnosis using deep neural network architectures. The proposed CNN analyses images to identify edges, colours, and textures. The suggested model can recognize healthy and diseased tomato leaves with 98% accuracy, making it a valuable crop management tool that promotes sustainable agriculture and global food security.

关键词： Productivity Plant diseases Image color analysis Computational modeling Plants (biology) Image edge detection Crops Food security Artificial neural networks Monitoring

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AMSER: Accelerate Mobile Speech Emotion Recognition with Signal Compression

AMSER: Accelerate Mobile Speech Emotion Recognition with Sig...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yu Lu Ran Wang Dian Ding Han Zhang Liyun Zhang Lanqing Yang Yi-Chao Chen Guangtao Xue Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speech-based interaction systems are widely used in mobile devices like smartphones. With advances in deep neural networks, tasks such as speech emotion recognition (SER) enhance these systems’ user-friendliness. However, deploying SER models on mobile devices is challenging due to their complexity and computational demands. While pruning can reduce complexity, it often compromises accuracy, and hardware accelerators like FPGAs are difficult to integrate into mobile devices. This paper proposes AMSER, a real-time speech emotion recognition framework using signal compression and task offloading. AMSER utilizes logarithmic Mel-filter bank coefficients (Fbank) and singular value decomposition (SVD) for feature extraction and compression. The compressed signal is only 6.25% of the original size, achieving 2.24x faster transfer rates and 55.35% energy savings compared to raw audio transmission. Despite the compression, the features preserve key audio information for text and emotion recognition, performed server-side. Experiments show a WER of 4.68% (Librispeech), 10.69% (CommonVoice), and 69.83% emotion recognition accuracy (IEMOCAP).

关键词： Emotion recognition Accuracy Computational modeling Speech recognition Artificial neural networks Speech enhancement Real-time systems Complexity theory Smart phones Singular value decomposition

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Aspect category and sentiment polarity detection using a permutation language-based transformer model

Aspect category and sentiment polarity detection using a per...

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3rd International Conference on Machine Learning and data engineering, ICMLDE 2024

作者： Jayaraman, Ashok Kumar Trueman, Tina Ester Ananthakrishnan, Gayathri Murugappan, Abirami Cambria, Erik Manikandan, G. Mitra, Satanik HCLTech Engineering and R&D Services Tamil Nadu Chennai India Department of Computer Science University of the People CA United States School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Information Science and Technology Anna University Chennai India College of Computing and Data Science Nanyang Technological University Nanyang Singapore Singapore School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Management BITS Pilani India

Aspect-based sentiment analysis (ABSA) performs fine-grained analysis on text to determine a specific aspect category and a sentiment polarity. Recently, machine learning models have played a key role in ABSA tasks. In particular, transformer-based pre-trained models have achieved promising results in natural language processing tasks. Therefore, we propose a permutation based XLNet fine-tuning model for aspect category detection and sentiment polarity detection. Our model learns bidirectional contexts via positional encoding and factorization order. We evaluate the proposed permutation language model on three ABSA datasets, namely, SentiHood, SemEval 2015, and SemEval 2016. Specifically, we studied the ABSA tasks in a constrained system with a multi-class environment. Our result indicates that the proposed permutation language model achieves a better result. © 2025 The Authors. Published by Elsevier B.V.

关键词： Emotion Recognition

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A Novel Method for Classifying and Detecting Kidney Stone using MRI based on CNN and Attention Model

A Novel Method for Classifying and Detecting Kidney Stone us...

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Integration of Emerging Technologies for the Digital World (ICIETDW), International Conference on

作者： Gnanapoo Denosha T P Lalitha Mohammad Shahnawaz Nasir Mohammad Shabbir Alam N Aparna Rajasree Rajamohanan Department of Mathematics Vignan's Foundation for Science Technology and Research Guntur Andhra Pradesh India Geetanjali College of Engineering and Technology Hyderabad Telangana India Department of Computer Science College of Engineering and Computer Science Jazan University Jizan Riyadh Kingdom of Saudi Arabia Department of Biomedical Engineering Dhaanish Ahmed Institute of Technology Coimbatore Tamilnadu India Department of Computer Science (Artificial Intelligence & Data Science) Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

When training neural networks, the Back Propagation Network approach is frequently employed. It uses data and images to construct an automated kidney stone classification system. Classification of medical MRI kidney imaging and identification of stones typically involves human assessment. Due of the impracticality of dealing with massive data volumes, this technique is incorrect. Operator error is a potential source of noise contamination in Magnetic Resonance (MR) images. This leads to serious problems with image processing and inaccurate classification characteristics. On the other hand, the use AI, NN, and feature extraction to isolate the target area using back propagation network analysis has yielded impressive results. The objective of this endeavor was to detect kidney stones by use of the Back Propagation Network. In order to accomplish this, data image preprocessing acquires a framework, reduces noise, and removes bed-mats. area, volume of area, and stone's surface area are utilized for feature extraction after the momentum phrase and R proper term are employed for picture segmentation. All three of these models—CNN, LSTM, and AGCNN-LSTM were utilized to train the projected model. With a 96% accuracy rate, the suggested model surpassed its predecessor.

关键词： Backpropagation Training Accuracy Magnetic resonance imaging Noise Predictive models Feature extraction Convolutional neural networks Artificial intelligence Long short term memory

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GAN-Based Architecture for Low-dose Computed Tomography Imaging Denoising

arXiv

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arXiv 2024年

作者： Wang, Yunuo Yang, Ningning Li, Jialin College of Life Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science Data Science and Engineering New York University Shanghai Shanghai China School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China

Generative Adversarial Networks (GANs) have surfaced as a revolutionary element within the domain of low-dose computed tomography (LDCT) imaging, providing an advanced resolution to the enduring issue of reconciling radiation exposure with image quality. This comprehensive review synthesizes the rapid advancements in GAN-based LDCT denoising techniques, examining the evolution from foundational architectures to state-of-the-art models incorporating advanced features such as anatomical priors, perceptual loss functions, and innovative regularization strategies. We critically analyze various GAN architectures, including conditional GANs (cGANs), CycleGANs, and Super-Resolution GANs (SRGANs), elucidating their unique strengths and limitations in the context of LDCT denoising. The evaluation provides both qualitative and quantitative results related to the improvements in performance in benchmark and clinical datasets with metrics such as PSNR, SSIM, and LPIPS. After highlighting the positive results, we discuss some of the challenges preventing a wider clinical use, including the interpretability of the images generated by GANs, synthetic artifacts, and the need for clinically relevant metrics. The review concludes by highlighting the essential significance of GAN-based methodologies in the progression of precision medicine via tailored LDCT denoising models, underlining the transformative possibilities presented by artificial intelligence within contemporary radiological practice. © 2024, CC BY-SA.

关键词： Generative adversarial networks

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