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5th IEEE International Conference on Artificial Intelligence in engineering and technology, IICAIET 2023

作者： Qamar, Faizan Gachhadar, Anand Ali Kazmi, Syed Hussain Hassan, Rosilah Faculty of Information Science and Technology UKM Bangi Selangor 43600 Malaysia Institute of Engineering Tribhuvan University Department of Electronics and Computer Engineering Kathmandu44600 Nepal

ISBN: (纸本)9798350304152

The rapid evolution of wireless communication technologies has led to the emergence of Ultra-Dense Networks (UDNs) in Beyond 5G (B5G) systems, providing unprecedented connectivity and capacity. However, the dense deployment of base stations in UDNs results in severe interference, leading to considerable performance challenges. To tackle this issue, the Successive Interference Cancellation (SIC) approach has gained attention as a promising technique to enhance system performance. This research article explores the potential of SIC implementation in 5G UDN. The primary contribution of this work is summarized as follows. Firstly, an improved SIC technique is developed to validate the theoretical study of SIC in a multi-tier 5G UDN. This technique enhances the accuracy of outage probability and coverage probability calculations by employing a stochastic geometry model that accurately represents the real-world scenario, considering the random distribution of both users and base stations within the UDN. Secondly, numerical simulations are conducted to demonstrate the effectiveness of the proposed SIC technique, surpassing traditional methods. The simulation results are compared with Zero Forcing (ZF) to assess the validity and applicability of the concept and validate the theoretical findings. This research contributes to advancing the understanding and practical implementation of SIC in UDN, paving the way for improved network performance and capacity in future wireless communication systems. © 2023 IEEE.

关键词： Base stations

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A Hybrid Security Framework for Medical Image Communication

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computers, Materials & Continua 2022年第11期73卷 2713-2730页

作者： Walid El-Shafai Hayam A.Abd El-Hameed Ashraf A.M.Khalaf Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMinia61111Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

Authentication of the digital image has much attention for the digital *** image authentication can be verified with image watermarking and image encryption *** schemes are widely used to protect images against forgery attacks,and they are useful for protecting copyright and rightful *** on the desirable applications,several image encryption and watermarking schemes have been proposed to moderate this *** framework presents a new scheme that combines a Walsh Hadamard Transform(WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding(DRPE).First,on the sender side,the secret medical image is encrypted using *** the encrypted image is watermarking based on *** combination between watermarking and encryption increases the security and robustness of transmitting an *** performance evaluation of the proposed scheme is obtained by testing Structural Similarity Index(SSIM),Peak Signal-to-Noise Ratio(PSNR),Normalized cross-correlation(NC),and Feature Similarity Index(FSIM).

关键词： Walsh hadamard transform watermarking encryption double random phase encoding structural similarity index

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An Effective Automated Framework for Oral Cancer Detection by Enhanced Convolutional Neural Networks 12

An Effective Automated Framework for Oral Cancer Detection b...

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12th IEEE International Conference on Advanced Computing, ICoAC 2023

作者： Santhiya, M. Sindhuja, M. Jegatha, R. Manikandan, J. Rajalakshmi Engineering College Thandalam Department of Computer Science and Engineering Chennai India Faculty of Engineering and Technology Srm Institute of Science and Technology Kattankulathur Department of Computational Intelligence Chennai India Sri Sai Ram Institute of Technology Department of Information Technology Chennai India St. Joseph's College of Engineering Omr Department of Information Technology Chennai India

ISBN: (纸本)9798350318210

Oral cancer, an aggressive form of cancer affecting the mouth and throat, has been on an alarming worldwide increase due to tobacco use and smoking. Oral cancer presents various challenges such as high incidence rates, delayed diagnosis, inadequate treatment plans and late prognoses and outcomes;early detection is crucial in improving prognosis outcomes and survival prospects. Machine learning techniques have gained prominence for improving cancer diagnosis while decreasing mortality and morbidity;in recent years they were used to detect oral cancer, providing highly accurate classification tasks yet limited capabilities in terms of feature extraction capabilities. To address these limitations, we are currently employing a deep-learning approach. To this end, we have devised an automated framework encompassing feature extraction, preprocessing and classification. Contrast-limited adaptive Histogram Equalization (CLACHE), used for effective pre-processing purposes to increase image contrast and resolution, is one form of pre-processing while Gray Level Cooccurrence Matrix extracts statistical texture-based features;finally, a Modified Convolutional Neural Net (MCNN) was employed for accurate classification of oral cancer cases. Utilizing Kaggle Repository dataset, we used it to assess the performance of our framework, while comparing it against other high-performance methods. Our proposed framework had an accuracy rating of 99.13% which surpasses current state-of-the art approaches. © 2023 IEEE.

关键词： Feature extraction

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Fused Transformers: Fused information of Arabic long Article for Summarization 6

Fused Transformers: Fused Information of Arabic long Article...

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6th International Conference on AI in Computational Linguistics, ACLing 2024

作者： Ezzat, Zeyad Khalfallah, Ayman Khoriba, Ghadad Center for Informatics Science Information Technology and Computer Science School Nile University Giza Egypt Computer and Systems Department Faculty of Engineering Alexandria University Alexandria Egypt Applied Innovation Centre Egypt Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt

This paper introduces a novel approach for extending the applicability of pre-trained models to accommodate longer texts. Addressing the inherent limitation of quadratic performance in attention models within transformers, we propose the concept of fused transformers. By integrating new adaptor techniques, we enhance the encoding of lengthy text segments by breaking them into shorter spans. Subsequently, these segments are fused to increase the model's effectiveness in processing extended texts. This fusion mechanism serves to fortify the model's capacity for fine-tuning. Furthermore, we implement a length-based curriculum for expedited training Our experiments yielded 16 Rouge-2 points, representing a doubling of the score achieved by the vanilla fine-tuning method on the newly introduced "Mukhtasar" dataset for summarization. This highlights the effectiveness of managing complex relations among text segments and confirms that our method can outperform conventional training approaches. © 2024 Elsevier B.V.. All rights reserved.

关键词： Distribution transformers

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Exploring the Impact of Machine Learning on Real-Time Data Analysis 3

Exploring the Impact of Machine Learning on Real-Time Data A...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Singh, Swati Nanda, R.T. Sathya, S. Banchhor, Chitrakant O. Pandey, Pratik Rampal, Sourav Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Basic Science Karnataka Ramnagar India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

The emergence of system mastering has enabled actual-time information analysis to come to be extra accurate, efficient, and comfy. ML can rapidly examine and classify excessive-volume streams of raw records, allowing companies to reply to quickly changing environments. This technology has had a considerable impact on the way groups and developers manner complicated datasets and make decisions in realtime. It has revolutionized how agencies use and combine information to gain new insights and understand trends. ML performs a vital position in helping businesses efficiently technique large quantities of data while reducing human errors. At the same time, it provides security and integrity of the data, lowering the capacity for change and loss. In this paper, we discover the effect of ML on actual-time information evaluation and talk about its diverse applications. We additionally offer an overview of different methods of ML and their advantages and disadvantages. ultimately, we speak of future traits for mixing ML with actual-time records analytics and the consequences of this integration. © 2023 IEEE.

关键词： disadvantages environments organizations revolutionized understand

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Hybrid Convolutional Neural Networks for Enhanced Detection of Mango Leaf Diseases 6

Hybrid Convolutional Neural Networks for Enhanced Detection ...

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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Porna, Shweta Bhattacharjee Kabir, Md. Firoz Rana, Md. Imran Chowdhury Sajol, Md. Saiful Islam Roy, Tonmoy Khan, Mohammad Aman Ullah Adnan, Md. Al Bhavani, Girigula Durga Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh Master's in Information Technology University of the Cumberlands United States Bachelor of Business Administration International American university Los Angeles CA United States Louisiana State University Department of Computer Science and Engineering LA United States Data Analytics & Information Systems Utah State University Logan United States Brac University School of Data and Sciences Department of Computer Science and Engineering Dhaka1212 Bangladesh Noakhali Science And Technology University Bsc in software engineering Bangladesh Andhra Pradesh Amaravati522237 India

ISBN: (纸本)9798331505790

The classification of mango leaf diseases is critical for effective disease management and ensuring high-quality yields in mango cultivation. This paper presents a comprehensive study on using deep learning techniques to classify various mango leaf diseases, leveraging convolutional neural networks (CNNs) and hybrid models. A total of 7,524 images were used in our study. These included 4,000 training samples and 3,524 testing samples. The images were split into eight groups, which were powdery mildew, cutting weevil, anthracnose, bacterial canker, sooty mold, gall midge, healthy, and die back. The suggested method starts with feature extraction using VGG19 and MobileNetB1, then classification using both standalone models (ResNet50V2 + EfficientNetB1 and VGG16 + MobileNetB1). We employed data augmentation techniques like random brightness adjustment, rotation, and flipping to enhance the robustness of the model. We conducted hyperparameter tuning using hyperband and Bayesian optimization to optimize the model's performance. Experimental results demonstrate that the hybrid models achieved superior performance, with ResNet50V2 and EfficientNetB1 attaining a perfect accuracy of 100 % on the test set. These findings highlight the potential of deep learning techniques to improve the accuracy and reliability of mango leaf disease diagnosis, contributing significantly to the advancement of precision agriculture. © 2024 IEEE.

关键词： Fruits

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Intelligent Control Based in Neural-Fuzzy of Adaptive Backstepping for The Double Stator IPMSM Powered by a Five-Level NPC Inverter 13

Intelligent Control Based in Neural-Fuzzy of Adaptive Backst...

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13th IEEE International Conference and Expositions on Electric and Power engineering, EPEi 2024

作者： Benaissa, Rabie Mansouri, Smail Ouledali, Omar University of Adrar Faculty of Sciences and Technology Department of Electrical Engineering Laboratory for Sustainable Development and Computer Science 01000 Algeria University of Adrar Faculty of Sciences and Technology Department of Hydrocarbons and Renewable Energies Laboratory for Energy Environment and Information Systems 01000 Algeria

ISBN: (纸本)9798350356199

This article supplies a proposed approach Neuro Fuzzy Controller (NFC)-Adaptive Backstepping Controller (ABC)-Space Vector Modulation (SVM) for a five-level NPC inverter-Double Stator Interior Permanent Magnet Synchronous Motor (DSIPMSM). This paper's primary objective is to enhance DSIPMSM's performance by the use of strong control using the proposed approach. Given its benefits - including high torque density, high efficiency, high power density, and minimal maintenance - DSIPMSM, have recently been the focus of much research. Using the input-output linearization approach and Lyapunov stability theory, asymptotically stable trajectory-following dynamics are demonstrated using nonlinear adaptive backstepping control. The use of fuzzy logic and intelligence-based controllers are increasingly being used to enhance traditional control's performance and output. In addition, the fuzzy logic controller has a lot of flaws that are presently being fixed by the extremely effective NFC tool. The proposed method's worth was demonstrated by the simulation results in terms of durability, effective tracking dynamics, precision, and good disturbance rejection with little ripple. © 2024 IEEE.

关键词： Disturbance rejection

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Sustainable electric vehicles fault detection based on monitoring by deep Learning Architectures in feature extraction and classification

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2023年 57卷

作者： Wongchai, Anupong Aoudni, Yassine Yesubabu, Mannava Reegu, Faheem Ahmad Gowri, N. Vasantha Vijayakumar, P. [a]The Independent researcher and Social Worker Thailand [b]Department of Computers and Information Technology Faculty of Sciences and Arts Turaif Northern Border University Arar 91431 Saudi Arabia [c]Computer & Embedded Systems Laboratory National School of Engineers of Sfax (ENIS) University of Sfax BP 1173 3038 Sfax Tunisia [d]Department Of Computer Science & Engineering Vardhaman College Of Engineering Shamshabad Hyderabad 501218 India [e]College of Computer Science And Information Technology Jazan University Jazan 45142 Saudi Arabia [f]Department of EEE Chaitanya Bharathi Institute of Technology (A) Hyderabad India [g]School of Electronics Engineering Vellore Institute of Technology (VIT) Chennai Tamil Nadu India

Numerous industrial sector paradigms have been altered by the necessity to produce more competitive ma-chinery and the introduction of digital technologies from so-called Industry 4.0. This research proposes novel technique in electric vehicle fault detection based on monitoring data classification and feature extraction using deep learning architectures. The real Proton exchange membrane fuel cell (PEMFC) experiment dataset has been collected as sustainable electric vehicles data using multi-cell parallel electric vehicle and it has been processed for noise removal, dimensionality reduction and extraction using deep stacking auto gradient descent for-classifyingthrough monte Carlo regressive Gaussian naive bayes *** of experiments demonstrate that suggested model achieves over 99% accuracy in identifying flooding fault of fuel cell under load-varying situations. The experimental analysis has been carried out in terms of accuracy, robustness, reliability, preci-sion, recall. The proposed technique attained 99% of accuracy, 89% of robustness, 85% of Reliability, 95% of precision and 81% of recall.

关键词： Electric vehicles Fault detection Monitoring Data classification Feature extraction Deep learning

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Image Classification and Segmentation for Estimating Calories of Food 10

Image Classification and Segmentation for Estimating Calorie...

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10th International Conference on Digital Arts, Media and technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Chanthanuwatkun, Udomkan Jitpattanakul, Anuchit Hnoohom, Narit Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Nakhon Pathom Thailand Faculty of Applied Science King Mongkut's University of Technology Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics North Bangkok Thailand

ISBN: (纸本)9798331543273

Accurate estimation of calorie content in meals is essential for promoting healthy dietary practices and supporting healthcare efforts. However, the lack of nutritional knowledge poses challenges for individuals in estimating the calorie content of foods. This study aims to develop a deep learning-based model for estimating the calorie content of real foods through image classification and segmentation techniques. Image classification was employed to identify various one-dish Thai menus, while image segmentation categorized food components into fats, carbohydrates, and proteins. A new dataset, consisting of 15 one-dish Thai menus, was created using a combination of publicly available datasets (THFOOD-50 and FoodyDudy) and self-collected images. Nutritional analysis was based on the Thai nutritional table from the Bureau of Nutrition, department of Health, Ministry of Public Health. We compared the performance of YOLOv5x, YOLOv8x, and YOLOv9e on the ThaiFood dataset using identical hyperparameters. The results showed that YOLOv8x achieved the highest mAP@0.5 of 0.883, with an accuracy of 0.829 and a recall of 0.902. YOLOv5x closely followed with a mAP@0.5 of 0.881, an accuracy of 0.822, and a recall of 0.945. YOLOv9e demonstrated competitive performance, achieving a mAP@0.5 of 0.846, an accuracy of 0.775, and a recall of 0.91. These findings highlight YOLOv8x as the most suitable model for Thai food classification and detection tasks under the evaluated conditions. © 2025 IEEE.

关键词： Nutrition

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Improving Bandwidth Utilization for Underwater Fiber Optic Networks Through Intelligent Optimization Techniques

Improving Bandwidth Utilization for Underwater Fiber Optic N...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Sakhare, Nitin N. Yadav, Rakesh Kumar Rajesh, A. Hassan, Inzimam Ul Vinithra Banu, T. Maheswaran, S. Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka 562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350329773

Underwater Fiber Optic Networks (UFONs) are getting increasingly more important because of the emergence of the marine economic system, the enlargement of ocean exploration sports, and the need for reliable net get entry to. However, their bandwidth utilization is negative due to insufficient useful resource allocation techniques, constrained community design facts, and restricted expertise about consumer traffic styles. This paper proposes clever optimization techniques to improve the bandwidth usage of UFONs. These strategies are primarily based on gadget studying strategies along with neural networks and particle-swarm optimization. Numerous performance metrics are used to assess the proposed techniques. Simulations and experiments are performed to compare the overall performance of the proposed techniques with conventional techniques. Effects show the proposed smart optimization strategies are able to enhancing the bandwidth utilization of UFONs by means of as much as sixteen%. The proposed shrewd optimization strategies can provide significant enhancements for actual-time aid allocation, dynamic network control, and monitoring of underwater traffic styles. © 2023 IEEE.

关键词： Particle swarm optimization (PSO)

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