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2025 IEEE International Students' Conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Baskar, R. Mohanraj, E. Jaisurya, K. Janani, S. Kesavan, M. K. S. Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode637 215 India K. S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tiruchengode637 215 India K. S. Rangasamy College of Technology Computer Science and Engineering Tiruchengode637 215 India

ISBN: (纸本)9798331529833

The growing prevalence of Internet of Things (IoT) devices necessitates the strategic allocation of computational activities inside cloud-fog networks to achieve low latency, diminished energy consumption, and optimal resource utilisation. This study introduces a new Human Evolutionary Optimisation Algorithm (HEOA) designed for optimising IoT job placement inside hybrid cloud-fog environments. The suggested method emulates human decision-making to provide adaptive and intelligent job allocation, optimising computational load distribution between centralised cloud data centres and decentralised fog nodes. HEOA employs evolutionary processes, including selection, mutation, and crossover, to dynamically identify the ideal configuration for job offloading, therefore reducing response time and energy expenditures. Simulation studies indicate that HEOA surpasses conventional optimisation methods by attaining quicker convergence, enhanced resource efficiency, and superior scalability. The HEOA diminished energy usage by 12% and cut response time by 15%, rendering it highly compatible with contemporary IoT applications. This study enhances job scheduling frameworks in diverse cloud-fog settings, facilitating improved performance and sustainability in IoT ecosystems. © 2025 IEEE.

关键词： Energy utilization

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Optimized Transfer Learning Model for Lung Cancer Stage Classification Using Computed Tomography Images 5

Optimized Transfer Learning Model for Lung Cancer Stage Clas...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Deepika, R. Shanmugam, P. Moorthi, K. Kumar, P. K. Manoj Swarna, S.L. Ramnath, K. B v Raju Institute of Technology Department of Artificial Intelligence and Data Science Telangana Narsapur India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Sri Sairam Engineering College Department of Electronics and Communication Engineering Chennai India School of Computational Intelligence Joy University Tirunelveli India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India K.L.N College of Information Technology Department of Computer Science and Engineering Tamilnadu Madurai India

ISBN: (纸本)9798331518097

Lung cancer has become one of the primary causes of death worldwide due to cancer, which indicates the necessity of early diagnosis. In this regard, the present work proposes an optimized model of transfer learning based on characterizing different stages of lung cancer by using computed tomography images acquired from the IQ-OTH/NCCD lung cancer database downloadable from Kaggle. It makes use of a two-step process: first, a U-Net model is used on the patient's CT images to reconstruct anatomical features and reduce noise;second, a ShuffleNet architecture is used to predict the patient's CT stage using the reconstructed image. In addition, to enhance classification, PSO was incorporated into the model so that it can optimize the hyperparameters. The accuracy of the model was at 97.85 percent, and it suggests that the model can become a very useful tool for clinicians to bring about earliest identification and staging for lung cancer. © 2024 IEEE.

关键词： Lung cancer

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Quantitative Assessment in Dynamic PET/CT for Hepatocellular Carcinoma through Bayesian Prior information-based Actor-Critic algorithm 24

Quantitative Assessment in Dynamic PET/CT for Hepatocellular...

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13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Li, Siming Huang, Jingchun He, Jianfeng Wang, Shaobo Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming Yunnan China PET/CT Center Hospital of Kunming University of Science and Technology First People's Hospital of Yunnan Kunming Yunnan China

ISBN: (纸本)9798400717482

Dynamic positron emission tomography/computed tomography (PET/CT) is a promising medical imaging technique that enables the visualization of biological processes via tracer activity. Analyzing the time-activity curves (TACs) of PET using pharmacokinetic models allows for the extraction of quantifiable physiological information relevant to hepatocellular carcinoma (HCC). Estimation of the pharmacokinetic model parameters requires advanced algorithms. The challenge lies in achieving accurate parameter estimation and obtaining parameter distributions that align with physiological characteristics. This paper introduces a reinforcement learning-based Actor-Critic (AC) algorithm and proposes a Bayesian prior information-based Actor-Critic (BPAC) algorithm. A dynamic parameter estimation environment for agent interaction was established, incorporating Bayesian optimization. The preceding information obtained through Bayesian optimization was used to design the reward and penalty signals in the interaction environment, and sampled as the initial value for the agent to estimate the parameters of 24 real HCC cases. Receiver operating characteristic (ROC) analyses were performed, using P-values and areas under the curve (AUC) to evaluate diagnostic performance between normal and tumor groups. Compared to the AC, the BPAC demonstrates statistically significant differences in K1, k2, k3, fa, and vb (all P © 2024 Copyright held by the owner/author(s).

关键词： Pharmacokinetics

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Improving the Quality of Diabetic data with Large Language Model-driven Cleaning Techniques

Improving the Quality of Diabetic Data with Large Language M...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Biradar, Divya Dattangire, Rahul Vaidya, Ruchika Inti, NagaSuryaShivani University of Texas at Arlington Computer Science ArlingtonTX76013 United States Data Engineering HoustonTX77002 United States Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Wardha442001 India University of Texas at Arlington Computer and Information Science ArlingtonTX76013 United States

ISBN: (纸本)9798331539948

The data-cleaning approach applies the capabilities of large language models to reduce the noise in the extracted and received data from healthcare sources. The aim will be to clean the collected and extracted data by looking up and dealing with the inaccuracies encountered during the project like missing or duplicated records, outliers, and data that will not add any value to the project. The 'Diabetes' data set from Kaggle is divided into implementing the combined approach data-cleaning process such as removing duplicate entries, and emptying fields by a Large Language Model, to improve the accuracy is assessed. Three components will increase the reliability of the collected data: 1. Gateway, 2. Storage 3. Cleaning. The outcomes demonstrated the LLMbased approach's effective impact in correcting and consistent data within the healthcare dataset. © 2024 IEEE.

关键词： Electronic health record

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A Multimodal Diagnostic Framework for Autism Spectrum Disorder Using Deep Learning: An In-Depth Exploration 4

A Multimodal Diagnostic Framework for Autism Spectrum Disord...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Tamizhmalar, D. Subbiah, Sankari Premkumar, R. Sri Sai Ram Engineering College Department of Artificial Intelligence and Data Science Chennai India Sri Sai Ram Engineering College Department of Information Technology Chennai India Sri Sai Ram Engineering College Department of Electronics and Instrumentation Engineering Chennai India

ISBN: (纸本)9798331508845

Autism Spectrum Disorder (ASD) represents a multifaceted neurodevelopmental condition, characterised by persistent and pervasive challenges in social communication, behavioural patterns, and sensory processing. The early and accurate diagnosis of ASD is imperative for timely intervention and support. This case study delineates a sophisticated deep learning-based multimodal diagnostic framework designed to augment the detection and analysis of ASD. By amalgamating various data modalities - encompassing neuro imaging, facial expressions, and speech patterns - this framework harnesses the capabilities of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to enhance diagnostic precision. Furthermore, we delve into the intricacies of the challenges encountered, the preprocessing methodologies employed, and the comparative performance of the model vis-à-vis traditional diagnostic approaches. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing the Prediction Accuracy of Brain Tumor Detection by using Fuzzy C-Means Segmentation and Support Vector Machine 5

Enhancing the Prediction Accuracy of Brain Tumor Detection b...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Revathi, M. Kalaiarasi, P. Jeevitha, S. Akilandasowmya, G. St. Joseph's Institute of Technology Artificial Intelligence & Data Science Chennai India Kalasalingam University Department of Machine Learning Krishnankoil India Jerusalem College of Engineering Information Technology Chennai India Sri Sairam Institute of Technology Computer and Communication Engineering Chennai India

ISBN: (纸本)9798331518097

Brain is an important organ of the human body that helps in the proper functioning of the all the organs. Without brain, the functions of other organs will not happen since they cannot run without the commands of brain. Brain tumor refers to a situation where an abnormal group of cells are found in some part of the brain. There are many types of brain tumor that are identified. The tumors in brain is independent of age and it can grow at anytime. The symptoms that is exhibited by the brain tumor depends on the size of the tumor cell as well as its location. Machine learning algorithms helps in the earlier identification of the disease which can be used to give proper treatment. In this paper, a fuzzy c means based segmentation of image is done. Then classification of the segmented image is done by using Support Vector Machine. The results shows 98% percentage of accuracy. © 2024 IEEE.

关键词： Support vector machines

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Intelligent wearable vision systems for the visually impaired in Saudi Arabia

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Neural Computing and Applications 2025年第14期37卷 8571-8591页

作者： Talaat, Fatma M. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Abd Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Navigating the world with visual impairments presents unique challenges, often limiting independence and safety. This research introduces SafeStride, a novel algorithm designed to empower visually impaired individuals through real-time obstacle detection and navigation assistance. SafeStride functions through five interconnected phases: sensor data acquisition, data preprocessing, obstacle detection, path planning, and feedback and guidance. A combination of ultrasonic sensors, cameras, and inertial measurement units (IMUs) feed comprehensive environmental data into the system, enabling accurate obstacle detection and safe path planning. Evaluating SafeStride's performance involved the MNIST dataset for image recognition and a dedicated indoor object detection dataset. Additionally, comparisons were made against three deep learning models—convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The results showcased SafeStride's high accuracy and effectiveness in both obstacle detection and classification. Beyond its impressive performance, SafeStride represents a significant leap forward in navigation aids for the visually impaired. By offering a comprehensive solution that enhances safety and independence in diverse environments, it holds immense potential to improve the lives of countless individuals. Future efforts will focus on further optimizing the algorithm and testing its real-world capabilities, paving the way for a future where safe and independent navigation becomes a reality for all. All three deep learning models (CNN, LSTM, GRU) excelled in the experiment, achieving high accuracy above 0.98, strong recall and F1-scores near 0.99, and exceptional AUC-ROC scores exceeding 0.9998. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion planning

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Improving Sign Language Recognition for Deaf and Hard of Hearing Individuals using the VGG16 Model 3

Improving Sign Language Recognition for Deaf and Hard of Hea...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Benjamin, Minu Inba Shanthini Watson Patan, Yaseen Kumar, Ansilin S.M. Reddy, Pocharam Nithyanandh Ahamad, Syed Faheez Puneeth, Muvvala Sai Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331532420

Persons who are deaf and who cannot speak face a variety of challenges, which emphasises the need for greater research to offer appropriate assistance. Interaction and communication with others are a big challenge. Since sign language is the major means of communication for these people, there is a significant knowledge gap among hearing individuals when it comes to understand and interpret sign language. As a result, there has been a notable expansion in the field of sign language study to close this gap. This work highlights the importance of sign language detection and offers a thorough analysis of relevant studies carried out in this domain using deep learning technique. It approaches the issue from multiple perspective, including translation, sign language recognition, and access to relevant datasets. Understanding these facets is intended to create a meaningful impact on sign language literature and its practical implementation. VGG16 deep learning model for sign language recognition offers the best solution for solving communication challenges faced by deaf and muted individuals. The results show that the VGG16 based technique achieves accuracy of around 95.8%. The VGG16 model uses a Convolutional Neural Network (CNN) architecture, which applies many layers of convolutional filters to identify patterns and attributes in the input images. Recognizing sign language using VGG16. © 2024 IEEE.

关键词： Convolutional neural networks

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Continuous Output Feedback Stabilization for a Class of Nonlinear Systems with Unknown Measurement Sensitivity 42

Continuous Output Feedback Stabilization for a Class of Nonl...

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42nd Chinese Control Conference, CCC 2023

作者： Zhang, Daoyuan Lin, Dong Guangzhou Xinhua University Department of Artificial Intelligence and Data Science Guangdong Guangzhou510520 China Liaoning Technical University Faculty of Electrical and Control Engineering Liaoning Huludao125100 China

ISBN: (纸本)9789887581543

This paper addresses the problems of output feedback stabilization for a class of nonlinear systems with a kind of truncation protocol. Based on the sampled measurements, an output feedback controller is proposed to globally stabilize the nonlinear systems. Then, sufficient conditions are given to ensure that the estimation errors are globally uniformly ultimately bounded. At last, an illustrative example is given to show the effectiveness of the proposed methods. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Nonlinear systems

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Unlocking the Potential of Generative AI in Large Language Models

Unlocking the Potential of Generative AI in Large Language M...

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engineering and technology (PICET), Parul International Conference on

作者： Sahil Warudkar Rugved Jalit Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

In the ever-evolving realm of artificial intelligence, the fusion of Generative AI with Large Language Models (LLMs) represents a ground-breaking leap forward in natural language processing (NLP), sparking a revolution in human-machine interactions. This comprehensive review delves into the transformative potential of Generative AI within LLMs, unveiling its profound impact on content creation, language translation, and conversational interfaces. Through a meticulous analysis of recent advancements, the review illuminates how Generative AI is reshaping the NLP landscape, empowering LLMs to craft captivating narratives and bridge linguistic barriers with unparalleled finesse. However, ethical considerations loom large amidst this innovation, underscoring the imperative of model interpretability and data privacy in ensuring responsible deployment across domains like healthcare and finance. By navigating these challenges, Generative AI in LLMs heralds a new era of collaborative innovation and global connectivity, where human ingenuity and machine intelligence converge to unlock boundless possibilities.

关键词： Technological innovation Ethics data privacy Generative AI Reviews Large language models Collaboration Natural language processing data models Problem-solving

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