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4th International Conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Kumar, M. Praveen Raja, S. Charles Dharssini, A.C.Vishnu Keerthi, P. Thiagarajar College of Engineering Department of Eee Madurai India School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

ISBN: (纸本)9798331529635

The study aims to address a comprehensive energy optimization approach for a 2BHK residential building located in Bodinayakanur, Theni, Tamil Nadu, equipped with a 2 kW solar panel. The study focuses on minimizing energy costs by integrating day-ahead energy forecasting using a Long Short-Term Memory (LSTM) model with rule-based meta-heuristic optimization for load scheduling. Two scenarios are analyzed: the first optimizes the scheduling of household appliances, and the second incorporates an electric vehicle (EV) into the load profile. The optimization strategy shifts secondary loads, such as EV charging and non-essential appliances, to off-peak hours while maximizing solar energy utilization. Efficient management of energy demand and load scheduling results in significant cost savings and enhanced grid stability. The findings demonstrate the practical viability of the energy optimization framework for reducing electricity costs in real-world residential settings, especially in areas with renewable energy integration. © 2024 IEEE.

关键词： Costs

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A Subthreshold Time-Domain Analog Spiking Neuron With PLL-Based Leak Circuit and Capacitive DAC Synapse

IEEE Solid-State Circuits Letters

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IEEE Solid-State Circuits Letters 2024年 7卷 143-146页

作者： Barton, Taylor Smith, Shea Hao, Yu Watson, Ryan Rogers, Kyle Allred, Parker Sahoo, Bibhu Datta Fulda, Nancy Yorgason, Jordan T. Warnick, Karl F. Chang, Mau-Chung Frank Kuan, Yen-Cheng Chiang, Shiuh-Hua Wood Brigham Young University Electrical and Computer Engineering Department ProvoUT84602 United States State University of New York University at Buffalo Department of Electrical Engineering BuffaloNY14260 United States University of California at Los Angeles Electrical and Computer Engineering Department Los AngelesCA90095 United States National Yang Ming Chiao Tung University International College of Semiconductor Technology Hsinchu30010 Taiwan

The design and measurement of a time-domain analog spiking neuron is described. The proposed neuron leverages time-domain processing using voltage-controlled oscillators (VCOs) and a time-domain comparator to integrate the input spike and trigger the output spike. A novel leaky circuit uses a phase-locked loop (PLL) to drive the phase difference between the two VCOs toward zero. A weighted capacitive digital-to-analog converter (CDAC) synapse merges the input spikes and phase-frequency detector (PFD) outputs to generate the VCO control voltage. The neuron is implemented in a 28-nm CMOS technology and operates under a subthreshold supply voltage of 0.35 V. Occupying 154~mu {mathrm{ m}}{2} , measurement shows a maximum spike rate of 5.5 MHz and energy consumption of 159 fJ/spike. © 2018 IEEE.

关键词： Time domain analysis

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Achieving Seamless Semantic Interoperability and Enhancing Text Embedding in Healthcare IoT: A Deep Learning Approach with Survey

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SN computer Science 2024年第1期5卷 99页

作者： Purushothaman, Sony Shanmugam, G. Siva Nagarajan, Sureshkumar School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science Govt. Model Engineering College Kerala Kochi India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Krishnakovil India

Achieving semantic interoperability in healthcare is one of the significant challenges in the rapidly expanding healthcare sector. On the other hand, IoT in healthcare can provide patient-centric treatment mechanisms to help resolve these issues. When several healthcare organizations are generating heterogeneous medical information, semantic interoperability becomes essential to develop better treatment plans and better medical research. In this article, we discuss the applications of healthcare IoT, a comparative study of existing solution paradigms for achieving semantic interoperability and its enabling technologies. The comprehensive analysis of semantic interoperability has versatile dimensions to meet this, and it establishes deep learning-based directions. In continuing the background work, we proposed a new deep learning model called MedRelBERT with six embeddings to remove linguistic ambiguities in medical documents. The survey also examines the bibliographic evaluation of Web of Science articles discussing semantic interoperability solutions. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Internet of medical things Privacy Semantic interoperability

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Short-term load forecasting of mixed industrial users in distribution networks using DARTS-LSTM method

Short-term load forecasting of mixed industrial users in dis...

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2024 International Symposium on New Energy Technologies and Power Systems, NETPS 2024

作者： Shi, Wenyu Yang, Dechang Dehghaniang, Payman College of Information and Electrical Engineering China Agricultural University Beijing100083 China Department of Electrical and Computer Engineering George Washington University Washington United States

Load forecasting has perennially stood as a fundamental pillar within the power industry, with precision in prognostication constituting a paramount necessity for critical functions including grid management, optimization of energy storage, and response to demand fluctuations. In the present study, we introduce a comprehensive global modeling prediction framework aimed at forecasting large-scale power demand within time series data, alongside the development of a novel short-term power load forecasting model. By integrating all time series data pertaining to the same regression task and employing a unified univariate prediction function across the entire time series dataset, we efficaciously forecast large-scale heterogeneous loads aggregated at various levels within distribution networks. The proposed framework leverages the cutting-edge DARTS (Differentiable Architecture Search) deep learning framework for implementation. Through comprehensive experimentation on real-world data, our model framework demonstrates superior performance in terms of overall prediction accuracy, scalability, and robustness in handling missing and non-stationary time series data. © Published under licence by IOP Publishing Ltd.

关键词： Demand response

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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A novel approach to detect, analyze and block adversarial web pages

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3617-3622页

作者： Kumar, M. Ranjith Naik, Darshana A. Kapila, Neha Mohan, Chinnem Rama Prasad, Ch Raghava Shelke, Chetan Rao, C. V. Guru School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering M S Ramaiah Institute of Technology Department of Electronics and Electrical Engineering Lovely Professional University Department of Computer Science and Engineering Narayana Engineering College Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Alliance College of Engineering and Design Alliance University School of CS & amp AI SR Universtity

The phenomenon of distraction is very common, and its adverse effects are seen among people. The major cause underlying this issue is the ease with which adversarial web sites and web pages can be accessed. It is of utmost importance to locate, evaluate, and actively block such web pages in an effort to comprehensively and globally solve this societal issue. Thus, the given paper proposes an extension or plug-in to detect, analyze, and block websites smartly. The proposed approach takes keywords entered by the user as input into consideration, which eventually leads to the generation of web pages and web links. The filtering of web links is done by the proposed extension, followed by the extraction of features, the utilization of support vector machines (SVM) for binary classification, and the summarization of textual data using natural language processing (NLP). Lastly, the precise results corresponding to relevant web links are presented to the users, which will increase their productivity, thereby reducing their distraction levels under all working circumstances. The performance of the proposed approach is validated against existing recent studies based on evaluation metrics such as adversarial website detection time (ms) and accuracy (%). © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adversarial web pages Distraction Natural language processing (NLP) Support vector machine (SVM) Text summarization

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Detection and Quantification of TB Bacilli in Sputum Smear Images 1

Detection and Quantification of TB Bacilli in Sputum Smear I...

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1st International Conference for Women in computing, InCoWoCo 2024

作者： Rithani, M. Jeevanantham Sona, B. Syamdev, R.S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India New Horizon College of Engineering Department of AI \& ML Bangalore India

ISBN: (纸本)9798331518943

Early and precise detection is essential to stop the spread of tuberculosis (TB) and enhance patient outcomes. This research aims to create an automated system that employs cutting-edge image processing and machine-learning approaches to identify tuberculosis (TB) bacilli in sputum smear microscopy pictures. An automated solution is required to increase diagnostic accuracy and efficiency because traditional diagnostic methods are labor-intensive, time-consuming, and prone to human mistakes. In the end, we optimize the best-performing model to reduce processing time while preserving diagnostic accuracy. Our goal is to assess the performance of several models in terms of training time, testing time, and accuracy. By enabling the use of sophisticated diagnostic technologies in environments with limited resources, the suggested method supports international efforts to combat tuberculosis. © 2024 IEEE.

关键词： Convolutional neural networks

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Edge detection using the Prewitt operator with fractional order telegraph partial differential equations (PreFOTPDE)

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Multimedia Tools and Applications 2025年第13期84卷 12329-12345页

作者： Tenekeci, Mehmet Emin Abdulazeez, Sadeq Taha Karadağ, Kerim Modanli, Mahmut Department of Computer Engineering Faculty of Engineering Harran University Şanlıurfa Turkey Department of Mathematics College of Basic Education University of Duhok Duhok Iraq Department of Electrical and Electronics Engineering Faculty of Engineering Harran University Şanlıurfa Turkey Depertmants of Mathematics Faculty of Arts and Sciences Harran University Sanliurfa Turkey

Detecting edges in image processing is an important process in image analysis or enhancement. Many methods detected edge information based on the differences in brightness values. Prewitt, the most widely used edge detection method, performs the extraction of edges by partial differential calculation. This study performed fractional order partial differential calculation with the Telegraph equation in the Prewitt method calculation. Filter coefficients are determined by using Grumwald-Letnikov series expansion for fractional partial differential equation calculation. To determine the most appropriate fraction value, five different coefficients were tried. The proposed method is compared with the conventional edge detection methods Sobel, Canny and standard Prewitt. The obtained result showed that the proposed method detects the edges more accurately. In addition, it has been compared with Prewitt fractional order edge detection (FOED) and edge detection method based on ant colony optimization (ACO) methods developed in recent years. Pratt's figure of merit (FOM) metric was used to compare these studies. It was realized that the FOM values calculated with the proposed method were more accurate than the others methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Edge detection

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An Optically Transparent Near-Zero-Index Grating Metamaterial for Enhanced On-Glass Millimeter-Wave Radiation

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 4092-4097页

作者： Zhou, Ziheng Zhang, Yongjian Kuang, Zhicheng Li, Yue Chen, Zhizhang David Fuzhou University College of Physics and Information Engineering Fuzhou350108 China Tsinghua University Department of Electronic Engineering Beijing100084 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

An optically transparent antenna (OTA) based on a grating metamaterial is proposed for launching high-gain millimeter-wave radiation on glasses. This design incorporates a glass-based dielectric image line with a grating made of a transparent conductive film (TCF), which provides an effective near-zero-index response through the precise manipulation of spatial harmonic dispersion. To effectively overcome the intrinsic loss issue of TCF and control the power leakage, the field confinement in the grating metamaterial is engineered for optimizing the purely leakage-induced and total modal attenuations at the zero phase-progress state. The prototyped grating metamaterial antenna features an average optical transmittance of 76% over the visible spectrum. The antenna is validated to generate a near-broadside directive beam, achieving a realized gain exceeding 23 dBi and a total efficiency higher than 60% from 21.1 GHz to 22.3 GHz. Through leaky mode confinement control and complex dispersion relationship engineering, this work offers a promising solution to realizing OTAs with a substantially improved gain and aperture efficiency. © 2025 IEEE.

关键词： Millimeter waves

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Efficient-Cost Task Offloading Scheme in Fog-Internet of Vehicle Networks

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computer Systems Science & engineering 2023年第5期45卷 2223-2234页

作者： Alla Abbas Khadir Seyed Amin Hosseini Seno Baydaa Fadhil Dhahir Rahmat Budiarto Department of Electrical Power Techniques Engineering Al-Hussain University CollegeKarbalaIraq Department of Computer Engineering Ferdowsi University of MashhadMashhadIran Department of Computer Science Thi-Qar UniversityThi-QarIraq Department of Computer Science Mercu Buana UniversityJakartaIndonesia

Fog computing became a traditional OffLad Destination(OLD)to compute the offloaded tasks of the Internet of Vehicles(IoV).Nevertheless,the limited computing resources of the fog node leads to re-offload these tasks to the neighboring fog nodes or the ***,the IoV will incur additional offloading *** this paper,we propose a new offloading scheme by utilizing RoadSide Parked Vehicles(RSPV)as an alternative OLD for *** idle computing resources of the RSPVs can compute large tasks with low offloading costs compared with fog nodes and the ***,a performance evaluation of the proposed scheme has been presented and discussed with other benchmark offloading schemes.

关键词： RoadSide parked vehicles offloading cost deadline budget

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