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4th IEEE International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Kumar, Ravi Kumar, Ashish Kumar, Manikant Rai, Mithilesh Kumar Kumari, Shweta Mourya, Vishal Electronics and Communication Engineering Muzaffarpur Institute of Technology Muzaffarpur India Computer Science Engineering Muzaffarpur Institute of Technology Muzaffarpur India Leather Technology Muzaffarpur Institute of Technology Muzaffarpur India Information Technology Muzaffarpur Institute of Technology Muzaffarpur India National Institute of Electronics and Information Technology Patna India

ISBN: (纸本)9798331528614

- Underwater image enhancement has been employed in a variety of areas, such as terrain scanning, underwater microscopic detection, telecommunication cables, mine detection, and autonomous underwater vehicles. However, underwater imagery suffers from strong scattering, noise, color distortion, and absorption from artificial light sources, causing haziness, image blur, and a greenish or bluish texture. To address these issues, the proposed work introduces a new enhancement approach based on Contrast Limited Adaptive Histogram Equalization (CLAHE) used for images taken underwater. The proposed method employs CLAHE to adaptively enhance local contrast while constraining the amplification of noise and artifacts. The fusion method is proposed to enhance the complementary advantages of different color spaces in YIQ and HSI color models. The YIQ color space separates intensity from chrominance, allowing for independent processing of luminance information, which is crucial for preserving image details and contrast. The proposed algorithm improves low-contrast underwater images with intact color and a natural appearance with reliability according to the results of the qualitative analysis. The proposed method outperforms the state-of-the-art methods in terms of image performance measures like GMSD, VSI, MCSD, and PSNR. ©2024 IEEE.

关键词： Underwater imaging

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Predicting Lung Cancer Disease Using Optimized Weighting-Based Enhanced Neural Network Classification 3

Predicting Lung Cancer Disease Using Optimized Weighting-Bas...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Chitra, T. Hemanth, S.V. Karthikeyan, S. Reddy, Vallem Ranadheer Banupriya, K.M. Amirthayogam, G. Christian College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Oddanchatram624619 India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana Hyderabad501401 India PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Vaagdevi Engineering College Department of Computer Science and Engineering Telangana Bollikunta506005 India Arunai Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvannamalai606603 India Department of Information Technology Tamilnadu Chennai603103 India

ISBN: (纸本)9798350372120

Lung cancer is a highly lethal disease, claiming the lives of approximately 1 million individuals annually. Cancer is characterized by abnormal and rapid cell growth, making it difficult to control, though early diagnosis through Computed Tomography (CT) scan images can significantly improve chances of survival. However, detecting lung cancer early is challenging and can increase the risk of complications such as infection, inflammation, and tumor growth in the lungs. Moreover, the current methods for analyzing lung cancer prognosis have low accuracy. To solve this problem, we proposed an Optimized Weighting-Based Enhanced Neural Network (OWENN) method to classify patients based on attributes accurately. Furthermore, they utilize the Adaptive Median Filter (AMF) technique during image pre-processing to calculate the mean value of each pixel and remove noise. Moreover, Improved Particle Swarm Optimization (IPSO) can be implemented to extract tumors from lung images efficiently. Finally, the OWENN method improves the accuracy of cancer or non-cancer detection by classifying patients based on their selected attributes. Afterward, the experimental results indicate that the suggested OWENN approach can attain lung cancer prediction by evaluating sensitivity, precision, accuracy, and time delay. © 2024 IEEE.

关键词： Median filters

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AI-Powered IoT Framework for Enhancing Building Safety through Stability Detection 8

AI-Powered IoT Framework for Enhancing Building Safety throu...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Raja, S.R. Devi, T. Rathi Raj, J. Relin Francis Sankar, V. Krishna Krishnan, R. Santhana Venkatalakshmi, R. Hindustan Institute of Technology and Science Computer Applications Chennai India Christ University Department of Computer Science and Engineering Bangalore India Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Saveetha School of Engineering Chennai India Scad College of Engineering and Technology Department of Civil Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Psna College of Engineering and Technology Department of Information Technology Dindigul India

ISBN: (纸本)9798350376425

The rapid urbanization and increasing structural complexities of modern buildings have heightened the need for advanced monitoring systems to ensure building safety. The research presents an AI-powered IoT framework that enhances building safety through advanced stability detection mechanisms. The proposed framework employs a novel algorithm, Ensemble Learning with IoT Sensor Data Aggregation (EnIoT-SDA), which integrates ensemble learning techniques with aggregated sensor data to provide accurate and real-time stability assessments of building structures. The effectiveness of EnIoT-SDA was evaluated through a comprehensive simulation analysis, comparing its performance against existing algorithms, including Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Fuzzy Logic Systems (FLS). Simulation metrics, such as accuracy, false positive rate, computational time, and detection latency, were used to assess and compare the algorithms' performance. The results demonstrated that EnIoT-SDA outperformed the existing methods in several key areas, offering improved accuracy and reduced detection latency, thus establishing its potential as a robust solution for building safety monitoring. The study underscores the significant advancements brought by integrating ensemble learning with IoT sensor data and highlights areas for future research and development in this domain. © 2024 IEEE.

关键词： Support vector machines

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Parasitology Unveiled: Revolutionizing Microorganism Classification Through Deep Learning 6

Parasitology Unveiled: Revolutionizing Microorganism Classif...

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6th International Conference on Electrical engineering and information and communication Technology, ICEEICT 2024

作者： Hosen, Md Delwar Bin Mohiuddin, Arafath Sarker, Nayamul Sakib, Md Shadman Al Sakib, Abdullah Dip, Riyadot Hossain Ahmed, Md. Redwan Haque, Rezaul North South University Department of Electronics and Communication Engineering Dhaka Bangladesh WestcliffUniversity Department of Information Technology California United States East West University Department of Computer Science and Engineering Dhaka Bangladesh Kyungdong University Department of Computer Engineering Gangwon-do Korea Republic of

ISBN: (纸本)9798350385779

Microorganisms play a crucial role in multiple fields, such as medical diagnostics, food safety, and ecological research, accounting for a significant 60% of Earth's biomass. This paper explores the complex connection between microorganisms and ecosystems, with a focus on accurate parasite classification methods. Conventional approaches, although important, have their limitations, leading to a growing dependence on CNN s in image analysis to enhance accuracy. Utilizing advanced DL techniques, particularly pretrained CNNs, has completely transformed the field of microorganism classification, providing unparalleled accuracy and efficiency. This study delves deeper into the intersection of swarm intelligence and DL algorithms, with a specific focus on the complex task of parasite identification. The effort is backed by a comprehensive dataset consisting of 34,298 images from ten different parasite species. The study employs meticulous preprocessing techniques, including uniform scaling and normalization, to maintain the integrity and precision of the dataset. These techniques serve as the foundation for subsequent modeling efforts. In addition, a comprehensive examination of four pretrained CNN architectures offers valuable insights that are crucial for selecting a model. Dynamic visualizations provide a thorough representation of model convergence dynamics, helping to enhance understanding of classification performance. The XceptionNet model stands out as the highest performer, achieving an impressive accuracy and F1 score of 0.9489 and 0.9492, respectively. This research demonstrates notable progress in the intersection of AI and biology, leading to new possibilities in disease diagnosis and improvements in public health practices by refining microorganism classification methods. © 2024 IEEE.

关键词： Microorganisms

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Performance Analysis of Cooperative VLC with L-PPM Modulation and Diversity Combining Strategies under Perfect and Imperfect CSI for IoT Devices

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IEEE Sensors Journal 2025年第12期25卷 23028-23036页

作者： Dwivedy, Prashant Dixit, Vipul Kumar, Atul PDPM-Indian Institute of Information Technology Design and Manufacturing M.P. Jabalpur482005 India Bennett University School of Computer Science Engineering and Technology Greater Noida India Maulana Azad National Institute of Technology Department of Electronics and Communication Engineering Madhya Pradesh Bhopal462003 India

The rise of digital transformation is driven by the convenience of internet of things (IoT) wireless connectivity, facilitating the development of smarter, more interconnected, and efficient solutions across diverse sectors such as smart homes, cities, industrial automation, healthcare, transportation, agriculture, and environmental monitoring. Visible Light communication (VLC) emerges as a promising wireless data transmission technique utilizing the visible light spectrum. Integrating VLC with IoT devices (IDs) offers myriad benefits and potential *** a practical scenario, this study presents a novel approach for cooperative VLC (C-VLC) systems that use L-ary pulse position modulation (L-PPM) to establish long-range indoor communication across IDs, even in the presence of noisy channel state information (CSI). Three diversity combining procedures, including equal gain combining (EGC), maximum ratio combining (MRC) and selection-based combining (SBC), are employed and compared in the study. The study examines the bit error probability (BEP) of a C-VLC system using L-PPM modulation for IDs under perfect and imperfect CSI conditions for a M-IDs scenario. The BEP of all three diversity combining strategies is computed using extensive simulations and compared to the performance of direct transmission and on-off keying (OOK) C-VLC systems. These comparisons provide empirical support for the analytical BEP findings. © 2001-2012 IEEE.

关键词： Error statistics

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Overview of Versatile Video Coding (H.266/VVC) and Its Coding Performance Analysis

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IEIE Transactions on Smart Processing and Computing 2023年第2期12卷 122-154页

作者： Lee, Minhun Song, HyeonJu Park, Jeeyoon Jeon, Byeungwoo Kang, Jungwon Kim, Jae-Gon Lee, Yung-Lyul Kang, Je-Won Sim, Donggyu Department of Computer Engineering Kwangwoon University Seoul Korea Republic of Department of Computer Engineering Sejong University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Kyunggi-do Korea Republic of Communication and Media Research Laboratory Electronics and Telecommunications Research Institute Deajeon Korea Republic of School of Electronics and Information Engineering Korea Aerospace University Kyunggi-do Korea Republic of Department of Electronic and Electrical Engineering Graduate Program in Smart Factory Ewha Womans University Seoul Korea Republic of

Versatile Video Coding (H.266/VVC) is the newest video coding standard jointly developed by the Joint Video Experts Team (JVET), which is organized by the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG). H.266/VVC provides about 40% bitrate reduction compared with High Efficiency Video Coding (H.265/HEVC) for the same visual quality. This paper introduces in detail the core structure of H.266/VVC by highlighting its features within block partitioning structure, intra/inter prediction, transform, quantization, and in-loop filtering, compared to its predecessor (H.265/HEVC). H.266/VVC yields significantly improved the coding performance, but it increased the computational complexity, particularly for the encoder side, which remains a problem to be tackled for successful commercialization in the future. This paper examined the statistical performance of H.266/VVC coding tools from the bitstreams encoded by the VVC test model (VTM12.0) encoder through rate-distortion optimization under the JVET common test conditions. In addition, the complexity and performance analyses are conducted on the block partitioning structure and group of picture structure. It is expected that an optimized H.266/VVC encoder can be designed and developed by minimizing the coding loss based on the analysis data. © 2023 Institute of electronics and information Engineers. All rights reserved.

关键词： Image compression

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Innovative Approaches to Dental Age Estimation: A Comprehensive Review of CNN Technologies and Ethical Issue 5

Innovative Approaches to Dental Age Estimation: A Comprehens...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shyni, I. Domilin Sheeja Herobin Rani, C. Sheeba, I. Rexiline Mahizha, S.I. Dmi Engineering College Dept. Of Computer Science and Engineering Aralvoimozhi India St.Xavier's Catholic College of Engineering Dept. Of Electronics and Communication Engineering Nagercoil India Sathyabama Institute of Technology and Science Dept. Of Electronics and Communication Engineering Chennai India St.Xavier's Catholic College of Engineering Dept. Of Information Technology Nagercoil India

ISBN: (纸本)9798350389609

Estimating dental age is crucial in multiple disciplines, such as forensic science, orthodontics, and pediatric dentistry. Traditional methods, while effective, often involve manual processes and subjective judgment. Recent advancements in Convolutional Neural Networks (CNNs) have introduced innovative approaches that promise greater accuracy and efficiency. This comprehensive review examines the latest CNN technologies applied to dental age estimation, highlighting their methodologies, performance, and advancements. Key aspects covered include the development and optimization of CNN architectures, the role of data preprocessing and augmentation, and the impact of these technologies on predictive accuracy. Additionally, the review addresses critical ethical issues associated with the implementation of CNNs in dental age estimation, such as data privacy, potential biases, and the implications of automated decision-making in sensitive contexts. By integrating insights from current research and practical applications, this review provides a thorough understanding of both the technological innovations and ethical considerations driving progress in determining dental age. © 2024 IEEE.

关键词： Convolutional neural networks

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Artificial Intelligence-based Solution for the Prediction of Power Consumption in electronics and Software Applications

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IETE Journal of Research 2024年第1期70卷 356-371页

作者： C, Savitha Khampariya, Prabodh Singh, Kamred Udham Kumar, Ankit Singh, Teekam Swarup, Chetan Department of Electronics and Communication Engineering Sri Satya Sai University of Technology & Medical Sciences Sehore India Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Computer Engineering and Applications GLA University Mathura281 406 India School of Computer Science University of Petroleum and Energy Studies Dehradun India Basic Science College of Science and Theoretical Studies Saudi Electronic University Riyadh-Male Campus Riyadh11673 Saudi Arabia

As new computing paradigms such as mobile grids and clouds become more commonplace, mobile devices are becoming increasingly attractive to scientists and HPC users who need high-performance computing capabilities. There are still many challenges to design software that uses mobile devices, such as their restricted capabilities compared to traditional devices such as PCs and servers. It’s even more critical to remember that batteries are the primary power source for mobile devices. Thus people delete programmes that severely reduce their battery life. The upshot is that it is common for developers to have no idea how much power specific hardware and software components use. So even if they have been trained in software development, they still require specific standards and skills to design energy-efficient apps. This Paper investigates ways to minimize mobile device energy usage by restructuring source code. However, we did not overlook the prospect of applying our findings to other domains, such as recurring demanding computational cores in scientific applications. Specifically, this research contributes to the field of Green Computing and mobile software development. © 2024 IETE.

关键词： Energy efficiency

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Optimizing Emotion Recognition in EEG Data: A Genetic Algorithm Approach with XAI Insights 15

Optimizing Emotion Recognition in EEG Data: A Genetic Algori...

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

作者： Ali, Noman Asif, Mohammad Kaushal, Anshul Singh, Uphaar Tiwary, Uma Shanker Panjab University Department of Electronics and Communication Engineering Uiet H Panjab Chandigarh India Indian Institute of Information Technology Allahabad Department of Information Technology Prayagraj India Panjab University Department of Computer Science and Engineering Uiet H Panjab Chandigarh India

ISBN: (纸本)9798350370249

Emotion recognition using electroencephalogram data presents a burgeoning area of research, yet navigates through intricate optimization hurdles, alongside the persistent challenge of rendering results interpretable. In this study, we employed a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks and utilized a Genetic Algorithm (GA) for optimization and enhancing the model's performance and robustness in deciphering emotional states from EEG signals. Our methodology encompasses data pre-processing techniques, including Short-Time Fourier Transform (STFT) analysis, applied to EEG data for feature extraction and GA-driven hyperparameter optimization to identify an optimal neural network architecture. This architecture, consisting of Convolutional and Recurrent layers with dropout regularization, is adept at extracting temporal and spatial features from EEG signals while mitigating overfitting. Furthermore, we investigate explainable AI (XAI) strategies to get insight into the decision-making process of our GA-based optimized model. Additionally, rigorous cross-validation ensures the generalization performance of the optimized model across diverse datasets. Empirical results demonstrate the effectiveness of our approach, with the optimized CNN-LSTM hybrid model achieving an accuracy of 93.28% in classifying 24 different emotions. This study enhances our understanding of emotion recognition systems by examining the intricate interplay between EEG data analysis, CNN-LSTM networks, and Genetic Algorithm optimization. Additionally, it provides practical insights into the optimization of such systems, potentially influencing future advancements in affective computing technologies. © 2024 IEEE.

关键词： CNN-LSTM EEG Emotions Recognition Explainable AI Genetic Algorithm (GA) Optimization

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Exploring the Development of Flexible Network Subsystems in communications Software Design 15

Exploring the Development of Flexible Network Subsystems in ...

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

作者： Mamatha, G.N. Pandeya, Megha Saraswat, Bhawna School of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350370249

The development of cutting-edge telecommunications systems more and more call for the implementation of community subsystems which might be bendy and can unexpectedly reply to modifications in user visitors. Such flexibility requires the layout of a structure able to scaling to distinctive gadget demands while nevertheless being capable of maintain excessive overall performance, reliability, and availability. This paper gives a systematic exploration of the strategies, gear, and ideas important in designing a bendy community subsystem for telecommunications software. We speak the necessities and challenges of developing network structures, and have a look at the feasibility of employing current trendy protocols which includes SIP, SDP, and RTP as a part of a usual structure. Furthermore, we speak the advantages and downsides of using software program-defined networking (SDN) as a part of a flexible community subsystem, along with issues for deploying it. Updates to present community control structures to assist dynamic reconfiguration are explored, in addition to an assessment of in-house and 1/3-celebration answers for integration into a bendy community subsystem. Sooner or later, key layout issues for deploying and managing a flexible community subsystem are discussed and summarized. © 2024 IEEE.

关键词： Downsides Implementation Necessities Networking Nevertheless Telecommunications

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