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

作者： Dhabliya, Dharmesh Amudha, A. Kaur, Ashmeet Gayathri, C. Garg, Anvesha Vivek, V. Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Research Impact and Outcome Chitkara University Centre of Rajpura Punjab 140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

Radio Frequency identity (RFID) is a rising technology that uses radio waves to become aware of and tune gadgets. With advances in hardware and software, RFID technology gives many blessings to agencies by means of growing facts accuracy, improving asset tracking, inventory management, and different procedure automation. However, no matter the diverse blessings it could provide, there are several challenges that businesses have to consider while implementing the RFID era. Those demanding situations encompass capability security and privacy worries, technical complexity, price, and interference from other RF indicators. To capitalize on the advantages of RFID, agencies must carefully weigh the advantages against the potential risks to decide the end goals and whether RFID is the most viable solution. © 2024 IEEE.

关键词： Accuracy Advantages Agencies Automation Demanding Implementing Improving Indicators

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Systematic Study of Detection Mechanism for Network Intrusion in Cloud, Fog, and Internet of Things Using Deep Learning 1st

Systematic Study of Detection Mechanism for Network Intrusio...

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1st International Conference on Human-Centric Smart Computing, ICHCSC 2022

作者： Bhattacharya, Sanjukta Ghorai, Santanu Khan, Ajoy Kumar Department of Computer Engineering Mizoram University Mizoram Aizawal796004 India Department of Information Technology Techno International Newtown West Bengal Newtown700156 India Department of Applied Electronics and Instrumentation Engineering Heritage Institute of Technology West Bengal Anandapur Kolkata700107 India

ISBN: (纸本)9789811954023

Development of latest technologies creates human life more convenient and easier. However, along with such technological advancements, several complications are generated in various segments. Network security also experiences inconvenient situations those are literally originated from infinite number of complex intrusions. A network intrusion detection system (NIDS) is an advanced and revolutionary system that has been established to resolve the problematic behaviors of the networking environment through accurate detection of unidentified attacks. Several methods and techniques have been taken active part for the development of an ideal NIDS but merging with deep learning technologies, NIDS achieves miracle performance against various intrusive activities in the security domain. In this paper, we serialize and present an adequate number of existing deep learning-based NIDSs in the Internet of things (IoT), cloud, fog, and edge networks domain. Different NIDS approaches along with their utilization, advantages, and restrictions are perfectly described in this paper so that people can achieve proper and detailed knowledge of security issues in the above-mentioned networks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Modernizing Home Protection: An IoT-Driven Approach with Smart Lock and Android Application 14

Modernizing Home Protection: An IoT-Driven Approach with Sma...

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14th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2024

作者： Rajput, Summaiya Talpur, Noureen Boudville, Rozan Abro, Ghulam Mustafa E Talpur, Beenish Zahid, Faizan Mehran University of Engineering and Technology Department of Telecommunications Engineering Jamshoro Pakistan Computer Information Science Department Seri Iskandar32610 Malaysia Electrical Engineering Studies College of Electrical Engineering Universiti Teknologi MARA Permatang Pauh Pulau Pinang13500 Malaysia University of Petroleum and Minerals Dhahran Interdisciplinary Research Center for Aviation and Space Exploration King Fahd Dammam31261 Saudi Arabia University of Catania Electronics and Computer Engineering Department of Electrical Italy

ISBN: (纸本)9798350364507

In the field of access control locks, conventional methods often require users to download applications with Bluetooth connectivity, and biometric measures such as fingerprints or face recognition that introduce challenges like access to only authorized parties, limited device accessibility and intricate registration processes. To address these challenges, the research paper presents an innovative IoT-based Smart Door Lock System, aiming to redefine access control. Diverging from traditional approaches, this system eliminates the need for application downloads and simplifies the complexities tied to biometrics. The core innovation lies in the simplicity of a user-friendly 4-digit PIN for primary access, prioritizing ease of use. To overcome real-time access constraints, the system introduces One-time Password (OTP) and One Touch Access (OTA) modes. These modes eliminate the intricacies linked with traditional methods, ensuring immediate and hassle-free access for both authorized and unauthorized users. © 2024 IEEE.

关键词： Face recognition

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Attention U-Net: A Deep Learning Approach for Breast Cancer Segmentation

Attention U-Net: A Deep Learning Approach for Breast Cancer ...

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Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security (iQ-CCHESS), International Conference on

作者： Krishna Mridha Tasnim Sarker Suborno Deb Bappon Shahriar Mahmud Sabuj Computer Engineering Marwadi University Rajkot Gujarat India Electrical and Electronics Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Computer Science & Engineering Chittagong University of Engineering & Technology

One of the main causes of death for women globally is breast cancer. Early discovery can reduce the frequency of early deaths. Breast ultrasound images can help in diagnosis and treatment planning by helping to categories different types of breast cancer. To segment breast cancer pictures in this study, we employed an Attention U-Net. A deep learning model for picture segmentation is called the Attention U-Net. We used a dataset of 780 breast ultrasound pictures divided into three classes-normal, benign, and malignant-to train the Attention U-Net. The mean IoU, precision, recall, and F1 scores for the Attention U-Net were 0.840,0.815,0.848, and 0.831 respectively. These outcomes are comparable to those of other cutting-edge techniques for segmenting breast cancer. The findings of this study indicate that the Attention U-Net is a potential technique for segmenting breast cancer. The great accuracy that the Attention U-Net can attain can assist to increase the precision of diagnosis and treatment planning

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Ensemble Deep Learning Framework for the Traffic Accidents Duration Prediction 2

Ensemble Deep Learning Framework for the Traffic Accidents D...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Somnathe, Abhijit T. Reddy, Pundru Chandra Shaker Kiran, Ajmeera Gowda, Raghavendra Nithyapriya, S. Bai, K. Vinaya Sree Smt. Kashibai Navale College of Engineering Electronics and Telecommunication Engineering Maharashtra Pune India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Mlr Institute of Technology Dundigal Telangana Hyderabad India Vardhaman College of Engineering Telangana Hyderabad India Bannari Amman Institute of Technology Tamil Nadu India Lakireddy Bali Reddy College of Engineering AP Mylavaram India

ISBN: (纸本)9798350388916

Traffic management and disaster response rely heavily on accident duration anticipation on expressways. There is no way around the fact that data pertaining to traffic accidents is multi-mode data. Nevertheless, the vast majority of research has been on single-mode data, and there has been surprisingly little quantitative examination of how multi-mode data affects model prediction abilities. In response to these shortcomings, the current study suggests a heterogeneous deep learning architecture that makes use of multi-modal characteristics to enhance the precision of motorway traffic accident length prediction models. To begin, by combining structured and textual data, six distinct data modes are produced. Second, to construct classification models with less prediction error, a hybrid deep learning method is used. In the end, we use a number of deep learning models to thoroughly examine how multi-mode data affects the accuracy of accident length predictions. The survey data obtained from an expressway observing system of China, is utilized to gauge the proposed method. The experimental outcomes demonstrate that Word2Vec-BiGRU-CNN - with an F1-score of 0.3648 - is the most appropriate and superior for anticipating the duration of traffic accidents using text features. The results of this study show that deep learning algorithms' prediction abilities are much improved by using newly-established structured features retrieved from text data. © 2024 IEEE.

关键词： Deep learning

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High-Bandwidth,Low-Power CMOS Transistor Based CAB for Field Programmable Analog Array

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computers, Materials & Continua 2023年第3期74卷 5885-5900页

作者： Ameen Bin Obadi Alaa El-Din Hussein Samir Salem Al-Bawri Kabir Hossain Abdullah Abdulhameed Muzammil Jusoh Thennarasan Sabapathy Ahmed Jamal Abdullah Al-Gburi Mahmoud A.Albreem Advanced Communication Engineering(ACE) Centre of ExcellenceUniversiti Malaysia Perlis(UniMAP)Faculty of Electronic Engineering TechnologyKangar01000Malaysia Electrical Engineering King Fahd University of Petroleum and MineralsDhahranEastern31261Saudi Arabia Space Science Centre Climate Change InstituteUniversiti Kebangsaan MalaysiaBangi43600Malaysia Department of Electronics&Communication Engineering Faculty of Engineering&PetroleumHadhramout UniversityAl-MukallaHadhramout50512Yemen Faculty of Engineering Norwegian University of Science and Technology(NTNU)GjovikN-2815Norway Faculty of Electronic Engineering Technology Universiti Malaysia Perlis(UniMAP)Kampus Alam UniMAP Pauh PutraArau02600Malaysia Department of General Educational Development Faculty of Science and Information Technology(FSIT)Daffodil International UniversityDhaka1207Bangladesh Department of Electronics and Computer Engineering(FKEKK) Center for Telecommunication Research and Innovation(CeTRI)Universiti Teknikal Malaysia Melaka(UTeM)Durian Tungal76100Malaysia Department of Electrical Engineering University of SharjahSharjah27272United Arab Emirates

This article presents an integrated current mode configurable analog block(CAB)system for field-programmable analog array(FPAA).The proposed architecture is based on the complementary metal-oxide semiconductor(CMOS)transistor level design where MOSFET transistors operating in the saturation region are *** proposed CAB architecture is designed to implement six of thewidely used current mode operations in analog processing systems:addition,subtraction,integration,multiplication,division,and pass *** functionality of the proposed CAB is demonstrated through these six operations,where each operation is chosen based on the user’s selection in the CAB interface *** architecture of the CAB system proposes an optimized way of designing and integrating only three functional cells with the interface circuitry to achieve the six ***,optimized programming and digital tuning circuitry are implemented in the architecture to control and interface with the functional ***,these designed programming and tuning circuitries play an essential role in optimizing the performance of the proposed *** of the proposed CMOS Transistor Based CAB system is carried out using Tanner EDA Tools in 0.35μm standard CMOS *** design uses a±1.5 V power supply and results in maximum 3 dB bandwidth of 34.9 MHz and an approximate size of 0.0537 *** demonstrates the advantages of the design over the current state-of-the-art designs presented for comparison in this ***,the proposed design has a clear aspect of simplicity,low power consumption,and high bandwidth operation,which makes it a suitable candidate for mobile telecommunications applications.

关键词： CMOS field programmable analog array configurable analog block current mode circuit

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GroupedMixer: An Entropy Model with Group-wise Token-Mixers for Learned Image Compression

arXiv

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arXiv 2024年

作者： Li, Daxin Bai, Yuanchao Wang, Kai Jiang, Junjun Liu, Xianming Gao, Wen The School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China The School of Electronics Engineering and Computer Science Peking University Beijing100871 China

Transformer-based entropy models have gained prominence in recent years due to their superior ability to capture long-range dependencies in probability distribution estimation compared to convolution-based methods. However, previous transformer-based entropy models suffer from a sluggish coding process due to pixel-wise autoregression or duplicated computation during inference. In this paper, we propose a novel transformer-based entropy model called GroupedMixer, which enjoys both faster coding speed and better compression performance than previous transformer-based methods. Specifically, our approach builds upon group-wise autoregression by first partitioning the latent variables into groups along spatial-channel dimensions, and then entropy coding the groups with the proposed transformer-based entropy model. The global causal self-attention is decomposed into more efficient group-wise interactions, implemented using inner-group and cross-group token-mixers. The inner-group token-mixer incorporates contextual elements within a group while the cross-group token-mixer interacts with previously decoded groups. Alternate arrangement of two token-mixers enables global contextual reference. To further expedite the network inference, we introduce context cache optimization to GroupedMixer, which caches attention activation values in cross-group token-mixers and avoids complex and duplicated computation. Experimental results demonstrate that the proposed GroupedMixer yields the state-of-the-art rate-distortion performance with fast compression speed. © 2024, CC BY.

关键词： Probability distributions

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Parkinson's Disease Early Detection and Classification based on EMG Signal using Spherical Convolutional Neural Network 8

Parkinson's Disease Early Detection and Classification based...

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

作者： Nandankar, Praful V. Kothiyal, Arnav Kiran Kumar, D. Patil, Anuradha Patil, Harshal Maranan, Ramya Government College of Nagpur Department of Electrical Engineering Maharashtra 441108 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Bgs Institute of Technology Department of Information Science and Engineering Karnataka B G Nagar571448 India Sharnbasva University Faculty of Engineering and Technology Exclusively for Women Department of Electronics and Communication Engineering Karnataka Kalaburagi585103 India Iilm University School of Computer Science & Engineering Uttar Pradesh Greater Noida201 306 India Simats Saveetha School of Engineering Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350376425

Parkinson's Disease (PD) is a degenerative neurological condition that impairs motor function. Tremors, stiffness, sluggishness, and trouble balancing are its hallmarks. Degeneration of dopamine-producing neurons in the brain is the disease's primary cause, resulting in progressive motor and non-motor symptoms. But the existing methods have lot of disadvantages such as over fitting, quality control and limited data availability. To overcome the aforementioned problem, A Spherical Convolutional Neural Network with Mantis Search Algorithm (SCNN-MSA) is proposed for accurately classifying PD. In this input image is taken from two datasets such as ADL dataset and REMPARK dataset. The Wavelet Decomposition Threshold Anisotropic Filter is used for the pre-processing (WDTAF) is proposed, for the purpose of removing noises. Following that segmentation are done using Spike-Driven Transformer-based U-Net (SDT-Unet). After that classification and feature extraction are done using Spherical Convolutional Neural Network (SCNN) and optimization are done using Mantis Search Algorithm (MSA) for classifying the Parkinson's Disease. The efficiency of the proposed SCNN-MSA is analyzed using a dataset and attains 99.93% accuracy, 98.76% recall and attains better results compared with the existing methods. This demonstrates the approach's increased efficiency and potential advancement in the industry. The model's accuracy at identifying PD in EMG signals might allow doctors to treat patients considerably faster. © 2024 IEEE.

关键词： Convolutional neural networks

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AI-Driven IoT Refrigeration Management using SVM and Cloud Computing 5

AI-Driven IoT Refrigeration Management using SVM and Cloud C...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Swathi, G. Pavithra, M.R. Epsiba, P. Manivasagam, M.A. Mani, A. Murugan, S. Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India Rajalakshmi School of Business Department of Management Studies Tamil Nadu Chennai India Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Siddartha Institute of Science and Technology Department of Computer Science and Engineering Andhra Pradesh Puttur India Vinayaka Mission's Kirupananda Variyar Engineering College Department of Management 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)9798350376685

The paper presents an AI-Driven IoT Refrigeration Monitoring (IRM) using support vector machine algorithms (SVM). The improvement in food safety and environmental sustainability has resulted in a paradigm shift in the techniques used for refrigeration. IRM guarantees that refrigeration units have perfect temperature management by seamlessly combining modern sensors, real-time data analysis, and artificial intelligence. The innovative strategy stops food spoiling, improves food safety, cuts down on waste, and promotes environmental responsibility across the supply chain. The intuitive alarm mechanism of the system notifies temperature variations as quickly as possible, which enables immediate remedial steps to be taken. IRM becomes a crucial instrument for preserving fresh foods and promoting environmentally aware behaviors since it bridges the traditional refrigeration rules with the digital world. The structure of the system, its benefits, and its potential to redefine industry norms in terms of safety and sustainability are discussed in depth in the article. © 2024 IEEE.

关键词： Information management

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An Intelligent Energy Management System with an Efficient IoT based Deep Learning Framework

An Intelligent Energy Management System with an Efficient Io...

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2022 International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2022

作者： Bazil Wilfred, C. George, Santhi M Sivaranjani, S. Selvan, Saravana Feros Khan, J. Mohammed Beulah David, D Karunya Institute of Technology and Sciences Department of Mathematics Coimbatore India Rmk Engineering College Department of Electronics and Communication Engineering Chennai India Sri Krishna College of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India Aimst University Faculty of Engineering Computer Technology Kedah Malaysia Vickram College of Engineering Department of Eee Madurai India Institute of Information Technology Saveetha School of Engineering Chennai India

ISBN: (纸本)9781665478847

Efficient and economical energy utilization is ensured using green energy management systems that currently exist. However, integration of this technology with the Internet of Things (IoT) and edge intelligence is not completely explored. A smart energy management system with a deep learning framework is presented in this paper to address the requirements of energy management in smart industries, homes and grids. An efficient communication is established between the consumers and energy distributors while predicting the future energy consumptions over short time intervals. With least error rate and reduced time complexity, a smart energy management system with optimal normalization model selection and cloud-based data supervising server for energy forecasting in IoT and edge devices is introduced. Communication between the smart grids and the edge devices in the IoT networks connected to a common cloud server regarding efficient energy demand and response features occur in a secure and continuous manner. Short-term energy requirement forecasting is performed with an efficient decision making algorithm while using various preprocessing techniques to manage the electricity data which is of diverse nature. This model is implemented in resource constrained devices and shows promising outcomes. For commercial and residential datasets, the proposed system offers reduced mean-square error (MSE) and root MSE (RMSE) values. © 2022 IEEE.

关键词： Edge computing

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