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Indoor Electromagnetic Radiation Intensity Relationship to Total Energy of Household Appliances

computers, Materials & Continua 2022年第3期70卷 5421-5435页

作者： Murad A.A.Almekhlafi Lamia Osman Widaa Fahd N.Al-Wesabi Mohammad Alamgeer Anwer Mustafa Hilal Manar Ahmed Hamza Abu Sarwar Zamani Mohammed Rizwanullah Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The rapid technological developments in the modern era have led to increased electrical equipment in our daily lives,work,and *** this standpoint,the main objective of this study is to evaluate the potential relationship between the intensity of electromagnetic radiation and the total energy of household appliances in the living environment within the building by measuring and analyzing the strength of the electric field and the entire electromagnetic radiation flux density of electrical devices operating at frequencies(5 Hz to 1 kHz).The living room was chosen as a center for measurement at 15 homes in three different environmental regions(urban,suburbs,and open areas).The three measurement methods are(Mode 1:people in a sitting position with electrical appliances *** 2:People in a standing position with electrical appliances *** 3:People are in the upright positionwhile turning off the electrical devices)in the living *** measurement methods and their results reinforce the importance of this *** results showed that the average electric field strengthmeasured inMode 2 ismuch greater than the two methods,and we also found less electromagnetic radiation in Mode 3 than in the two *** results remain within the recommended overall exposure developed by the International Committee for the Prevention of Non-Ionizing Radiation and the International Electrotechnical Commission.

关键词： Exposure assessment electromagnetic field household appliances indoor various environments epidemiology

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Identification of Wheat Leaf Diseases Based on Deep Learning Algorithms 7

Identification of Wheat Leaf Diseases Based on Deep Learning...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Therasa, P.R. Hemalatha, R. Sivajothi, E. Jayashankari, J. Anni Princy, B. Uma Maheswari, S. RMK Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India RMD Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of IT Tamilnadu Chennai India Panimalar Engineering College Department of Computer and Communication Engineering Tamilnadu Chennai India Saveetha School of Engineering Department of Biosciences Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India

ISBN: (纸本)9798350340600

Utilizing a convolutional neural network (CNN) architecture, the proposed method reliably extracts pertinent information from wheat leaf images for disease diagnosis. Preprocessing and data augmentation methods enhance the quality of the wheat-leaf image. CNNs are designed to detect and understand certain characteristics of the images they receive. The network is trained and optimized to classify illnesses more accurately. Standard performance evaluation indicators are utilized to demonstrate the accuracy of wheat leaf disease diagnosis using the suggested technique. Experiments demonstrate that solutions based on deep learning outperform more conventional methods. The proposed work demonstrates how a proposed CNN model outperformed existing techniques by a significant margin (98.31 percent) and how this enhancement would assist in the early diagnosis and treatment of wheat leaf diseases, thereby reducing the likelihood of catastrophic crop loss. © 2023 IEEE.

关键词： Convolutional neural networks

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A Perceptual Data Cleansing Model (SDCM) for Reducing the Dirty Data

A Perceptual Data Cleansing Model (SDCM) for Reducing the Di...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Al-Madi, Mohammad Azmi Abdel-Wahab, Ahmed Alshanty, Mohammed Bawazeer, Shaikhan Alzahrani, Mohammed Al-Zaytoonah University of Jordan Faculty of Science and Information Technology Department of Computer Sciences Amman Jordan Arab Open University Faculty of Computer Studies Riyadh Saudi Arabia Al-Azhar University Deprtment of Systems and Computers Engineering Cairo Egypt

ISBN: (纸本)9798350347050

Data cleansing approaches aim at revealing and reducing different types of outsourced errors. Such errors introduce a major issue as data cleansing often involves costly computations and time consumption. Data cleansing is complicated as most of the errors within the obtained data emerge in different forms, such as typos, duplicates, noncompliance with business rules, outdated data, and missing values. In this paper, the Supervised Dataset Cleaning Model (SDCM) is proposed in order to detect and reduce different types of outsourced errors that are stored in the data repository according to the supervised cleaning rules that are practiced from the previous cleaning processes. The findings indicates that the cleaning execution time can be reduced with this supervised model and the accuracy of the model is also increased when it covers the entire training rules and classified error types derived from the data warehouse. There are some expected future directions, which include: implementing full scale of SDCM and comparing the results obtained with different current methods. By comparing different data cleaning algorithms with SDCM, a knowledge gap is likely to arise in the search for further future improvements. © 2023 IEEE.

关键词： Errors

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BNIMI: Boolean Gene Regulatory Network Inference Based on Mutual Information

BNIMI: Boolean Gene Regulatory Network Inference Based on Mu...

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2024 Multimedia University engineering Conference, MECON 2024

作者： Barman, Shohag Farid, Fahmid Al Sarker, Md. Tanjil Hafiz, Md Ferdous Bin Khan, Niaz Ashraf Gope, Hira Lal Karim, Hezerul Abdul Mansor, Sarina Bari, Ahsanul University of Liberal Arts Bangladesh Department of Computer Science & Engineering Dhaka Bangladesh Multimedia University Centre for Digital Home Faculty of Engineering Cyberjaya63100 Malaysia Multimedia University PV Energy Stroage Lab Faculty of Engineering Cyberjaya63100 Malaysia Sylhet Agricultural University Faculty of Agricultural & Engineering Technology Sylhet Bangladesh

ISBN: (纸本)9798331530747

Network inference is a challenging task in Bioinformatics. Numerous network inference approaches have been proposed, but most of them face scalability challenges when inferring a gene regulatory network because they typically limit the regulatory gene within a range of 1 to 3. Our solution to this issue is the BNIMI network inference approach, which uses mutual information to infer a gene regulatory network. The pairwise mutual information between a target gene and a regulatory gene is computed in the BNIMI. The regulatory and target gene pair is taken into consideration in the gene regulatory network if the mutual information surpasses a large threshold value. We do 100 random shuffles on the time series gene expression data to find the highest mutual information for each target gene in order to establish the threshold value. This maximum value is then considered as the threshold. We tested BNIMI against two popular existing methods (Context Likelihood Relatedness and REVEAL) by running numerous simulations on both artificial and real gene expression data. Our findings show that BNIMI significantly outperforms these established methods in both types of datasets. This means BNIMI is a powerful and effective tool for figuring out how genes regulate each other based on time-series gene expression information. © 2024 IEEE.

关键词： Gene expression regulation

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Myocardial infarction detection based on deep neural network on imbalanced data

Myocardial infarction detection based on deep neural network...

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作者： Hammad, Mohamed Alkinani, Monagi H. Gupta, B.B. Abd El-Latif, Ahmed A. Information Technology Department Faculty of Computers and Information Menoufia University Shebin El-Koom32511 Egypt Department of Computer Science and Artificial Intelligence College of Computer Sciences and Engineering University of Jeddah Jeddah Saudi Arabia Department of Computer Engineering National Institute of Technology Kurukshetra India Department of Computer Science and Information Engineering Asia University Taichung City Taiwan Mathematics and Computer Science Department Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

Myocardial infarction (MI) is an acute interruption of blood flow to the heart, which causes the heart to suffer from a deficiency of blood and ischemia, so the heart muscle is damaged, and cells can die and lose their function. Despite the low incidence of MI in the world, it is still a common disease-causing death. Therefore, detecting the MI signals early can reduce mortality. This paper presented a method based on a deep convolutional neural network (CNN) for the detection of MI automatically. The proposed CNN is an end-to-end model without requiring any stages of machine learning and requires only one stage to detect MI from the input signals. In the case of imbalanced data, we optimize our deep model with a new loss function named the focal loss to deal with this case by constituting the loss indirectly the focus in those difficult classes. The Physikalisch-Technische Bundesanstalt (PTB) dataset was employed in the validation to classify the signals to normal and MI. The performance of our technique alongside state-of-the-art in the area shows an increase in terms of average accuracy and F1 score. Results show that focal loss improves the detection accuracy by 9% for detecting MI signals. In summary, the proposed method achieved an overall accuracy, precision, F1 score, and recall of 98.84%, 98.31%, 97.92%, and 97.63, respectively using focal loss and overall accuracy of 89.72%, a precision of 88.52%, a recall of 81.11% and F1 score of 83.02% without using focal loss. Our method using focal loss is an effective tool to perform a fast and reliable MI diagnosis to assist the cardiologists in detecting MI early. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

关键词： Convolutional neural networks

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Mobility and Behavior based Trustable Routing in Mobile Wireless Sensor Network 7

Mobility and Behavior based Trustable Routing in Mobile Wire...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Praveen Kumar, B. Umamageswaran, J. Kalpana, A.V. Dhanalakshmi, R. Department of Computer Science and Engineering KL University Andhra Pradesh Vijayawada India Department of Information Technology RMK Engineering College Tamil Nadu Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9781665464086

Mobile wireless sensor networks (MWSNs) allow the sensor nodes to move freely and transmit to each other without needing a fixed infrastructure. Usually, the routing process is very complex, and it becomes even more complicated as the sensor nodes are low-power, cost-effective mobile devices with minimum resources. MWSNs raises deployed in security-critical applications due to their resource-constrained features;they are prone to several security attacks that critically affect data transmission. The most significant innovation of trust is that it evades untrustable nodes by activating several detection routes to detect and obtain node trust, enhancing security quickly. Mobility and Behavior based Trustable Routing (MBTR) is proposed to solve these issues. This approach measures sensor node mobility based on the Received Signal Strength (RSS) between the two sensor nodes. The trust manager computes the node trust ability by the node behavior. Here, the node behavior is checked through the node energy, supportive account, and received packet account. The trust manager selects the security algorithm based on distance during data transmission. The simulation examination demonstrates lesser delay and lesser packet loss in the network. Furthermore, it improves the network throughput in the MWSN. © 2023 IEEE.

关键词： Sensor nodes

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Cyber Twin-Enabled Real-Time AI Orchestration for Advanced IoT Security and Dynamic Software Optimization 4

Cyber Twin-Enabled Real-Time AI Orchestration for Advanced I...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Sunkara, Krishna Chaitanya Barmola, Pareshwar Prasad Ravi, K.S. Shukla, Vishal Soni, Umang Sapatnekar, Amol Oracle Cloud Engineering Data Center Design & Modeling NC United States Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Vidyavardhaka College of Engineering Department of Mechanical Engineering Mysuru India Ramdeobaba University Department of Mechanical Engineering Nagpur India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electronics & Communication Engineering Gujarat India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The rapid expansion of Internet of Things (IoT) ecosystems has increased the complexity and vulnerability of software systems, making them more susceptible to security breaches and performance inefficiencies. Traditional security and optimization techniques struggle to keep up with the dynamic nature of IoT networks. This calls for the development of advanced frameworks that can adapt in real-time to evolving threats and operational requirements. This paper introduces Cyber Twins, an AI-driven framework designed for real-time security and software optimization in IoT environments. Cyber Twins leverage artificial intelligence and digital twin technology to create dynamic cyber representations of physical IoT devices and software processes. The framework integrates reinforcement learning for adaptive optimization, convolutional neural networks (CNNs) for anomaly detection, and graph neural networks (GNNs) for real-time threat analysis. By continuously synchronizing with live IoT data, Cyber Twins enable proactive threat detection and autonomous system optimization, ensuring a secure and high-performing IoT ecosystem. The proposed framework combines Federated Learning (FL) for distributed intelligence and Reinforcement Learning (RL) for adaptive decision-making. Key advancements include (1) a 35% improvement in intrusion detection accuracy compared to traditional methods, (2) a 40% reduction in false positive rates, and (3) a 25% increase in dynamic software adaptation speed. Experiments conducted on a simulated IoT environment with 1,000 devices demonstrated scalability and resilience under varying network conditions. Additionally, the framework achieved a 20% decrease in latency during AI model updates and a 30% reduction in computational overhead, ensuring efficient resource utilization. © 2024 IEEE.

关键词： Federated learning

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HPViT: A Hybrid Visual Model with Feature Pyramid Transformer Structure 8

HPViT: A Hybrid Visual Model with Feature Pyramid Transforme...

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8th International Conference on Control, Robotics and Cybernetics, CRC 2023

作者： Li, Jiaxiong Xiao, Hongguang Li, Yongzhou Changsha University of Science & Technology Faculty of Computer and Communication Engineering Changsha China Institute of Microelectronics Chinese Academy of Sciences Beijing Chaoyang China

ISBN: (纸本)9798350330588

Recently,the fusion design of Transformer and CNN has significantly improved the efficiency and accuracy of the model. In this work, we propose a hybrid backbone network model-Hybrid Pyramid Vision Transformer(HPViT), which can be used for dense prediction tasks. Compared with the ViT image classification design, HPViT introduces the Transformer structure into CNN and also adopts a pyramid structure, which allows various dense prediction tasks, detection and segmentation tasks, etc. Compared with ViT, HPViT has the following advantages: (1) Compared with the high computational complexity and high memory usage of ViT, HPViT can not only train high-resolution images for density division to capture enough detail information, but also converge faster, occupy less memory, and reduce the computation brought by the Transformer structure through the pyramid structure;(2) HPViT has the advantages of CNNs and Transformer and can be used as a general backbone. (3) Experiments show that HPViT performs well in image classification and object detection, with a top1 accuracy rate of 81.2% on the ImageNet1k dataset. In the task of object detection, RetinaNet+HPViT finetuned on COCO for 12 rounds reached 34.3%AP, while RetinaNet+ResNet50 only had 22.9%AP. © 2024 IEEE.

关键词： Object detection

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Elevating Robust ASR By Decoupling Multi-Channel Speaker Separation and Speech Recognition

Elevating Robust ASR By Decoupling Multi-Channel Speaker Sep...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yang, Yufeng Taherian, Hassan Kalkhorani, Vahid Ahmadi Wang, DeLiang Department of Computer Science and Engineering The Ohio State University United States Center for Cognitive and Brain Sciences The Ohio State University United States

ISBN: (纸本)9798350368741

Despite the tremendous success of automatic speech recognition (ASR) with the introduction of deep learning, its performance is still unsatisfactory in many real-world multi-talker scenarios. Speaker separation excels in separating individual talkers but, as a frontend, it introduces processing artifacts that degrade the ASR backend trained on clean speech. As a result, mainstream robust ASR systems train on noisy speech to avoid processing artifacts. In this work, we propose to decouple the training of the multi-channel speaker separation frontend and the ASR backend, with the latter trained only on clean speech. On SMS-WSJ, the proposed approach achieves a word error rate (WER) of 5.74%, outperforming the previous best by 14.3%. Furthermore, on recorded LibriCSS, we achieve the speaker-attributed WER of 3.86%, outperforming the previous best system trained on the same data by 24.8%. These state-of-the-art results suggest that decoupling speech separation and recognition is a potentially effective approach to robust ASR. © 2025 IEEE.

关键词： LibriCSS multi-channel robust ASR SMS-WSJ speaker separation

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RPF-ELD: Regional Prior Fusion using Early and Late Distillation for Breast Cancer Recognition in Ultrasound Images

RPF-ELD: Regional Prior Fusion using Early and Late Distilla...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Haosen Zhang, Gengyuan Zhao, Yingnan Lai, Fang Cui, Wenwei Xue, Jiexiao Wang, Qihang Zhang, Hao Lin, Yi Beijing Jiaotong University School of Computer Science and Information Technology Beijing China Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Harbin Engineering University School of Computer Science and Technology Harbin China University of Denver Department of Computer Science Denver United States University of York Wentworth College York United Kingdom Institute of Heilongjiang The Second Surveying and Mapping Harbin China Southeast University School of Information Science and Engineering Nanjing China Harbin Institute of Technology Faculty of Computing Harbin China Harbin Medical University School of Interdisciplinary Medicine and Engineering Harbin China

ISBN: (纸本)9798350386226

Breast cancer is one of the main factors responsible for the deaths of women worldwide. Ultrasound imaging is a key method for early detection of breast cancer, which can help patients gain valuable treatment time and improve their chances of survival. The computer-aided system of breast cancer recognition has started to receive attention due to the lack of experienced sonographers. Presently, most breast cancer recognition methods typically suffer from uncertain locations and proportions of tumor regions in ultrasound images. In this paper, we propose a novel Regional Prior Fusion framework using Early and Late Distillation (RPF-ELD), inspired by the knowledge distillation of the teacher-student framework, for breast cancer recognition in ultrasound images. Firstly, to enhance the concentration of the tumor regions, a high-performing prior-fused model is trained as the teacher model using ultrasound images with the corresponding regional prior information. Next, a diagnostic model is trained as the student model under the prior-fused model distillation using early and late features to implicitly obtain the regional prior knowledge. Finally, the diagnostic model recognizes the categories of breast cancer from only ultrasound images using the experience from distilled prior knowledge. Two publicly released datasets are used to evaluate the proposed RPF-ELD framework. Experimental results demonstrate that the proposed RPF-ELD surpasses current state-of-the-art methods. © 2024 IEEE.

关键词： Ultrasonic imaging

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