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IEEE Open Journal of Nanotechnology 2023年 4卷 229-238页

作者： Kola, Sekhar Reddy Li, Yiming National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Biomedical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

Characteristic variability induced by process variation effect (PVE) is one of technological challenges in semiconductor industry. In this work, we computationally study electrical characteristic and power fluctuations induced by six factors of PVE of the gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field-effect-transistors (CFETs) which are formed by vertically stacking n-FET on top of p-FET. Among the six factors, NS thickness (TNS), NS width (WNS), and gate length (LG) are identified as crucial factors contributing to large variations in device characteristics. The p-FET exhibits substantial off-state current fluctuation (about 151%) due to the bottom parasitic channel leakages. Compared with the magnitudes of dynamic and short circuit powers, the static power is marginal, but it possesses the largest fluctuation (up to 148%). If we assume that each factor of PVE has the same probability distribution as the others and all are mutually independent, the statistical sum of their power fluctuations will exhibit more than 50% overestimations, compared with the results when all factors are considered simultaneously. © 2020 IEEE.

关键词： III-V semiconductors

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Establishment of an Effective Brain Tumor Classification System through Image Transformations and Optimization Techniques 1

Establishment of an Effective Brain Tumor Classification Sys...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Arora, Gaurav Kumar Taj Mahaboob, Shaik Adilakshmi, S. Rani, Sasi Kala Kasthuri, A. Mamodiya, Udit IBM India Pvt. Ltd Uttar Pradesh Greater Noida India Jntua College of Engineering Department of ECE Andhra Pradesh Pulivendula India Vignana Bharathi Institute of Technology Department of Data Science Telangana India Sri Krishna College of Engineering and Technology Department Computer Science and Engineering Coimbatore India Vinayaka Mission's Kirupananda Variyar Engineering College Department Computer Science and Engineering Salem India Poornima Institute of Engineering and Technology Department of Electrical Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350345957

In recent years, researchers and health care professionals have been interested in medical image fusion, which combines images of the human body, organs, and cells with general information to address medical difficulties. computer-aided imaging techniques offer quantitative assessment of images under validation, which helps doctors make unbiased, objective decisions quickly. Multi-sensor and multi-source image fusion models also diversify healthcare analysis characteristics. It led to strong information processing that reveals otherwise invisible details. The fused pictures can help pinpoint anomalies. Multimodal image fusion combines complementing pictures into one. The fused image is ideal for visual perception or computer segmentation, feature extraction, and object recognition. The goal is to create a single fused image with more information than the source photos alone. The fused image combines the source images' complementary and shared elements, delivering enhanced subjective and objective detail. In biomedical imaging, fusing multimodal images has shown useful for disease characterisation. Medical image analysis uses CT, MRI, and PET (PET). In this research paper, we developed a Multimodality Image Fusion using Centre based genetic algorithm (CBGA) and fuzzy logic (FL) technique with lifting wavelet transform (LWT) for fusion of spatially registered MRI and CT images. We also proposed an efficient Multimodality Medical Image Fusion system based on DWT and binary crow search optimization (BCSO) algorithm for fusing MRI and CT images. The performance of the suggested models is validated against many benchmark medical datasets. The experiment showed that the proposed models outperformed all others. © 2023 IEEE.

关键词： Magnetic resonance imaging

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DFD-Net:lung cancer detection from denoised CT scan image using deep learning

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Frontiers of computer science 2021年第2期15卷 119-131页

作者： Worku J.SORI Jiang FENG Arero W.GODANA Shaohui LIU Demissie J.GELMECHA School of Electrical Engineering and Computing Department of Computer Science and EngineeringAdama Science and Technology UniversityAdama1888Ethiopia Faculty of Natural Computational Science Department of Mathematics Bule Hora UniversityBule HoraHagere Maryam144Ethiopia School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China Peng Cheng Laboratory Shenzhen518052China School of Electrical Engineering and Computing Department of Electronics and Communications EngineeringAdama Science and Technology UniversityAdama1888Ethiopia

The availability of pulmonary nodules in CT scan image of lung does not completely specify *** noise in an image and morphology of nodules,like shape and size has an implicit and complex association with cancer,and thus,a careful analysis should be mandatory on every suspected nodules and the combination of information of every *** this paper,we introduce a“denoising first”two-path convolutional neural network(DFD-Net)to address this *** introduced model is composed of denoising and detection part in an end to end ***,a residual learning denoising model(DR-Net)is employed to remove noise during the preprocessing ***,a two-path convolutional neural network which takes the denoised image by DR-Net as an input to detect lung cancer is *** two paths focus on the joint integration of local and global *** this end,each path employs different receptive field size which aids to model local and global *** further polish our model performance,in different way from the conventional feature concatenation approaches which directly concatenate two sets of features from different CNN layers,we introduce discriminant correlation analysis to concatenate more representative ***,we also propose a retraining technique that allows us to overcome difficulties associated to the image labels *** found that this type of model easily first reduce noise in an image,balances the receptive field size effect,affords more representative features,and easily adaptable to the inconsistency among nodule shape and *** intensive experimental results achieved competitive results.

关键词： medical image discriminant correlation analysis features fusion image detection denoising

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Distributed Estimation of Anomalies using a Low Complexity PSO Algorithm

Distributed Estimation of Anomalies using a Low Complexity P...

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2024 International Conference on Emerging Trends in Smart Technologies, ICETST 2024

作者： Saeed, Muhammad Omer Bin Khan, Salman A. Butt, Naveed R. Mohammad, Nazeeruddin Zerguine, Azzedine Gik Institute Of Engineering Sciences & Technology Faculty Of Engineering Sciences Topi Pakistan Karachi Institute Of Economics & Technology College Of Computing & Information Sciences Karachi Pakistan Prince Mohammad Bin Fahd University College Of Computer Engineering And Science AlKhobar Saudi Arabia King Fahd University Of Petroleum & Minerals Department Of Electrical Engineering Dhahran Saudi Arabia

ISBN: (纸本)9798331515850

Any wireless sensor network can experience anomalies that can hinder performance. This work presents a distributed estimation algorithm that estimates any such anomalies using a PSO algorithm. PSO algorithms are computationally expensive. This work presents a low complexity version of the algorithm that removes redundant computations and achieves excellent performance as well. Low complexity algorithms are essential for a longer battery life of wireless sensor so this algorithm provides an excellent alternative to the basic linear adaptive filtering algorithms that have low complexity but require a lot more time to achieve similar results. © 2024 IEEE.

关键词： Consensus algorithm

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Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm

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computers, Materials & Continua 2024年第9期80卷 4895-4916页

作者： Brij Bhooshan Gupta Akshat Gaurav Razaz Waheeb Attar Varsha Arya Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPuneMaharashtra411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Ronin Institute MontclairNJ 07043USA Management Department College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)HomantinHong KongChina

Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection *** machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 *** WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation *** results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.

关键词： Phishing detection Recurrent Neural Network(RNN) Whale Optimization Algorithm(WOA) cybersecurity machine learning optimization

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Large Language Model (LLM)-enabled Graphs in Dynamic Networking

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IEEE Network 2024年

作者： Sun, Geng Wang, Yixian Niyato, Dusit Wang, Jiacheng Wang, Xinying Poor, H. Vincent Letaief, Khaled B. Jilin University College of Computer Science and Technology Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States Hong Kong University of Science and Technology Department of Electrical and Computer Engineering Hong Kong

Recent advances in generative artificial intelligence (AI), and particularly the integration of large language models (LLMs), have had considerable impact on multiple domains. Meanwhile, enhancing dynamic network performance is a crucial element in promoting technological advancement and meeting the growing demands of users in many applications areas involving networks. In this article, we explore an integration of LLMs and graphs in dynamic networks, focusing on potential applications and a practical study. Specifically, we first review essential technologies and applications of LLM-enabled graphs, followed by an exploration of their advantages in dynamic networking. Subsequently, we introduce and analyze LLM-enabled graphs and their applications in dynamic networks from the perspective of LLMs in different roles. On this basis, we propose a novel framework of LLM-enabled graphs for networking optimization, and then present a case study on UAV networking, concentrating on optimizing UAV trajectory and communication resource allocation to validate the effectiveness of the proposed framework. Finally, we outline several potential future extensions. © 2024 IEEE.

关键词： Generative adversarial networks

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Building Intelligent Automated Real-Time data Analysis Systems Through Machine Learning 2

Building Intelligent Automated Real-Time Data Analysis Syste...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Rathee, Sahil Vigenesh, M. Priydarshini, Kala Vijaykumar, R. Singh, Shweta Seth, Jyoti Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350308846

This paper explores the capacity of using gadget-get-to-know-based facts evaluation structures to build intelligent, automated real-time statistics analysis systems. It examines the demanding situations present points evaluation systems pose and proposes a unique remedy. The proposed method leverages supervised and unsupervised devices and learning strategies to enable deep information on complex datasets. It then makes use of predictive skills to make clever selections with the aid of comparing input facts about databases. ultimately, the paper describes a case to demonstrate the effectiveness of this proposed machine and outlines implications for comparable implementations in the enterprise. This work is precious for information scientists and commercial enterprise decision-makers, as it outlines an ability to deploy reliable and accurate records analysis structures. © 2023 IEEE.

关键词： Accurate Deploying Enterprise Structures

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Real-time Agrochemical Management Solutions using Cloud computing and K-Nearest Neighbors Algorithm 8

Real-time Agrochemical Management Solutions using Cloud Comp...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Sivanesan, T.M. Mohankumar, N. Shibu, N.V. Nandagopal, V. Rajasekaran, B. Srinivasan, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Nagpur Campus Maharastra Pune India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India Mohan Babu University School of Engineering Department of Electrical and Electronics Engineering Andhra Pradesh Tirupathi India Vinayaka Missions Kirupananda Variyar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Salem India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350367904

Optimizing pesticides to ensure crop health and productivity while limiting environmental effect is one of the many issues faced by modern agriculture. In this research, it provides a cloud-based, K-Nearest Neighbors (KNN) algorithm-based real-time agrochemical management system. The proposed method allows for effective data processing and analysis by using the computing power of cloud platforms. Environmental factors, crop type, soil properties, and historical data are used to inform the KNN algorithm's prediction of appropriate pesticide application rates. To provide the most current insights and suggestions, the system incorporates real-time data from a variety of sources, including sensors, satellites, and weather predictions. Enhanced crop yields, decreased costs, and less environmental impact may result from farmers making informed decisions about the use of agrochemicals via continuous monitoring and analysis. The system can be easily deployed and adjusted to many agricultural contexts because of the scalability and flexibility of cloud computing. This method provides a viable strategy for improving pesticide management practices, which in turn promotes sustainable farming and increases food safety. © 2024 IEEE.

关键词： Smart agriculture

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An Investigation into Spectral Efficiency Enhancement of Fiber-Optic Communication via High-Speed Spectrum Management 2

An Investigation into Spectral Efficiency Enhancement of Fib...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Singh, Khushboo Lalitha, Sai Ravivarman, G. Sasirekha, R. Shinde, Swapnil K. Singh, Akhilendra Pratap Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350308846

This research appears into the effectiveness of high-pace spectrum management as a way to beautify the spectral performance of fiber-optic conversation. that is finished by means of forecasting a dynamic optical visitors arrival pattern to reduce carrier-degree blocking off opportunity, and with the aid of allocating the maximum green bandwidth to every request based totally on a non-blocking priority-based totally scheduling algorithm. This study assesses the overall performance of modern-day systems, analyzes their shortcomings, and uses experimental consequences to evaluate their effectiveness for improving spectral efficiency. The findings endorse that there's ability for improvement in spectral efficiency with excessive-speed spectrum control. © 2023 IEEE.

关键词： Control Efficiency Excessive Improvement Spectrum

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Deep Mapper: A Multi-Channel Single-Cycle Near-Sensor DNN Accelerator 8

Deep Mapper: A Multi-Channel Single-Cycle Near-Sensor DNN Ac...

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8th IEEE International Conference on Rebooting computing, ICRC 2023

作者： Morsali, Mehrdad Tabrizchi, Sepehr Liehr, Maximilian Cady, Nathaniel Imani, Mohsen Roohi, Arman Angizi, Shaahin New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ United States University of Nebraska-Lincoln School of Computing LincolnNE United States Suny Polytechnic Institute College of Nanoscale Science and Engineering AlbanyNY United States University of California Irvine Department of Computer Science IrvineCA United States

ISBN: (纸本)9798350382044

This work proposes the Deep Mapper, as a new near-sensor resistive accelerator architecture for Deep Neural Networks (DNN) inference that co-integrates the sensing and computing phases of resource-constrained edge devices. Deep Mapper is developed to intrinsically realize highly parallelized multi-channel processing of input frames supported by a new dense hardware-friendly mapping methodology. Our circuit-to-application simulation results on the DNN acceleration task show that Deep Mapper reaches an efficiency of 4.71 TOp/s/W outperforming state-of-the-art near-/in-sensor accelerators. © 2023 IEEE.

关键词： Deep neural networks

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