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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Neelamegam, G. Ashforn, Hermina J.M. Raja, Mohamed S. Priyadharshini, J. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. Vsb College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India K. Ramakrishnan College of Engineering Department of Information Technology Trichy India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350377002

IoT for smart microgrids is an innovative disruption at the cutting edge of energy management, relying on interconnectedness provided by smart sensors and devices to enhance power generation and distribution precision in metropolitan areas. The problems of the real-time operational capabilities required for efficient energy resource management thus progress, inhibit rapid control and management. The main goal of this research proposal consists of to use of the advanced technology of 5G NR Sidelink combined with MQTT to conduct a revolutionary communication system of Microgrid running through the IoT devices. The 5G NR (New Radio) Sidelink enables direct D2D communication, which leads to a dramatic lowering of the latency in the absence of traditional network infrastructures for communication, while the MQTT protocol improves the communication efficiency among networks of numerous IoT devices. The introduction of this system was followed by a 15% decrease in energy transmission losses and by 20% improvement in response times in comparison to conventional models specified in the quantitative evaluation thus increasing operational efficiency by far. These efficiencies not only reduce operating expenses and power consumption but also increase the solution's scalability, pointing out the possibility of building the solution for bigger urban microgrid implementations. In consideration of that, the suggested mechanism with 5G NR Sidelink along with MQTT overcomes elaborate barriers of IoT-based microgrids and establishes a new potency in advanced energy management in smart cities which could make the distribution network in the urban areas on the edge of the verge of a change. © 2024 IEEE.

关键词： Microgrids

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Class Incremental Learning Method for Hyperspectral Images Based on Real Data Playback Mechanism and Classification Network Optimization 15th

Class Incremental Learning Method for Hyperspectral Images B...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Liu, Xiaodong Wang, Siqi Liu, Huanyu Li, Junbao Department of Automatic Test and Control School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China School of Computer Science and Technology Information Countermeasure Technique Institute Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9789819700677

In recent years, the hyperspectral image (HSI) classification has attracted great attention in the field of earth observation. With the expansion of application scenarios and the continuous improvement of application requirements, new classes of HSI continue to emerge. In the face of open and dynamic application scenarios, the model is required to be able to continuously learn new categories based on maintaining existing category knowledge. Class incremental learning algorithms have received extensive attention as key solutions to the problem above. In this paper, we use a regularization-based incremental learning algorithm, elastic weight consolidation (EWC) for class incremental learning for HSI classification. After data preprocessing to reduce data dimensions, we select new classes for research on class incremental learning. We combine 3D-2D convolutional structures and residual blocks as the classification network and use real data playback mechanism to improve class recognition accuracy and realize class incremental recognition of HSI data. We demonstrate the feasibility of the algorithm with extensive experiments based on three widely used hyperspectral datasets. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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Smart EV Charging to Mitigate Range Anxiety in VANET Backbone Guided by Named Data Networking and Block-Chain 2

Smart EV Charging to Mitigate Range Anxiety in VANET Backbon...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Vendan, Vetri Chaudhary, Anamika Galgotias University Department of Computer Science and Engineering Greater Noida India Noida Institute of Engineering and Technology Department of Information Technology Greater Noida India

ISBN: (纸本)9798350347456

A interesting area of research for future connected car application is the development of an effective charging management system for on-the-go EVs, which is especially important given the inherent unpredictability in EV mobility. The main technological hurdles here are the necessity for intelligent decision making when choosing a CS and the related communication infrastructure for exchanging data between the grid and mobile EVs. Switching to electric vehicles has the potential to reduce pollution, vehicle maintenance expenses, and increasing gas prices (EVs). The range anxiety issue is a significant impediment to the rapid adoption of EV. Range anxiety can be alleviated by guaranteeing that a charging point will be found within the vehicle's driving range, subject to other constraints such as waiting time, reliability, cost, and so on. In this research, we suggest building a P2P infrastructure on top of a VANET so that users may trade power with one another in a secure and reliable manner. Although the labour itself is decentralised, secure, and reliable, the associated financial transactions are not. The proposed framework for exchanging electric charge is based on the intersection of three concepts from the realm of automotive social networks: data connectivity, the bitcoin, and the anxiety of having insufficient battery power. We address reliability using an established collaborative paradigm rooted in the field of automotive social networks. Organizational verification previous to trading and encrypted parameter exchange provide safety. With the help of named data networking, reliable charging points may be located using a set of criteria. The simulation results demonstrate that the proposed technique decreases the amount of time it takes to complete an energy transaction by 17%, boosts coverage for picking charging providers by 13%, and decreases the amount of erroneous provider filtering by 18%. © 2023 IEEE.

关键词： Commerce

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CTC_BERT: A Chinese Text Correction Model With Multi-scale Semantic Feature Caption 3

CTC_BERT: A Chinese Text Correction Model With Multi-scale S...

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3rd International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Wang, Guoqi Li, Ran Li, Tianyu Xie, Tongtong Cao, Jingwei Jiang, Lifen Zheng, Fengbo College of Computer and Information Engineering Tianjin Normal University Tianjin China International School of Information Science&Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781510671485

Text correction aims to determine whether natural language text contains grammatical errors, text errors, etc., and correct sentences. Previous work usually adopts byte pair encoding (BPE), which may lead to semantically related Chinese characters being separated. In addition, the previous models can only extract the superficial semantic features, but cannot capture the global deep semantic relationships. In this paper, we introduce the time convolutional network (TCN) to capture multi-scale semantic information, so as to promote the development of global semantic information. The CTC_BERT model uses the synonym masking strategy to reduce the semantic segmentation of related words and adds a fully connection layer as the error detection layer. In order to verify the performance of the CTC_BERT model, a comparison experiment has been carried out on SIGHAN2015+Wang271K Chinese error correction dataset. The results show that the accuracy of this model can reach 81.4%, which is better than that of BERT, BART, ConvSeq2Seq and other conventional models, and effectively improves the performance of text error correction. © 2023 SPIE.

关键词： Error correction

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Predicting Lung Cancer Disease Using Optimized Weighting-Based Enhanced Neural Network Classification 3

Predicting Lung Cancer Disease Using Optimized Weighting-Bas...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Chitra, T. Hemanth, S.V. Karthikeyan, S. Reddy, Vallem Ranadheer Banupriya, K.M. Amirthayogam, G. Christian College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Oddanchatram624619 India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana Hyderabad501401 India PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Vaagdevi Engineering College Department of Computer Science and Engineering Telangana Bollikunta506005 India Arunai Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvannamalai606603 India Department of Information Technology Tamilnadu Chennai603103 India

ISBN: (纸本)9798350372120

Lung cancer is a highly lethal disease, claiming the lives of approximately 1 million individuals annually. Cancer is characterized by abnormal and rapid cell growth, making it difficult to control, though early diagnosis through Computed Tomography (CT) scan images can significantly improve chances of survival. However, detecting lung cancer early is challenging and can increase the risk of complications such as infection, inflammation, and tumor growth in the lungs. Moreover, the current methods for analyzing lung cancer prognosis have low accuracy. To solve this problem, we proposed an Optimized Weighting-Based Enhanced Neural Network (OWENN) method to classify patients based on attributes accurately. Furthermore, they utilize the Adaptive Median Filter (AMF) technique during image pre-processing to calculate the mean value of each pixel and remove noise. Moreover, Improved Particle Swarm Optimization (IPSO) can be implemented to extract tumors from lung images efficiently. Finally, the OWENN method improves the accuracy of cancer or non-cancer detection by classifying patients based on their selected attributes. Afterward, the experimental results indicate that the suggested OWENN approach can attain lung cancer prediction by evaluating sensitivity, precision, accuracy, and time delay. © 2024 IEEE.

关键词： Median filters

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TU-IR Apple Image Dataset: Benchmarking, Challenges, and Asymmetric Characterization for Bruise Detection in Application of Automatic Harvesting

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第1期2卷 105-124页

作者： Das, Dipak Hrishi Roy, Sourav Dey Saha, Priya Bhowmik, Mrinal Kanti Government Degree College Department of Information Technology Santirbazar 799144 India Department of Computer Science and Engineering Suryamaninagar 799022 India Department of Cyber Security and Digital Forensics Agartala799001 India

With the blooming interest in computer vision-based technologies for future automation of food producers, there is a need for incorporating an automatic bruise detection module in robotic apple harvesting because of decreasing accessibility and growing labor costs. Although numerous studies have been published for automatic quality inspection of fruit and other agricultural products, there is a lack of publicly available image-based datasets for quality inspection/automatic detection of bruises. Toward the aim of developing a bruise detection system for apple harvesting, especially at night time, this article describes the designing issues (i.e., protocol) and creation of a new infrared imaging-based dataset titled 'TU-IR Apple Image Dataset,' which contains 1375 infrared images of apples defining four major categories of bruises (i.e., fresh, slight, moderate, and severe). Along with the infrared images, ground truths (in the form of binary masks) and measurements of suspicious bruised regions are defined. This study also investigates the efficiency of infrared imaging technology for automatic bruise detection in apples by performing an analysis of temperature-based, intensity-based, texture-based, shape-based, and deep convolutional neural network-based features. The classification performance was evaluated using eight different feature sets. Based on the experimental results, considering the most outer-performed classifier, deep convolutional neural networks as a fixed feature extraction method were found to provide the highest prediction performance for discriminating between fresh and three categories of bruises in apples with an average accuracy, specificity, and sensitivity of 93.87%, 80.57%, and 92.02%, respectively. © 2023 IEEE.

关键词： Feature extraction

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Implementation of IoT Enabled RFID Driven Human Life Rescuer in Railways 2

Implementation of IoT Enabled RFID Driven Human Life Rescuer...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Kalaiselvi, V.K.G. Swathi, K. Sri Aishwarya, W. Rakshana, S. Shanmugasundaram, Hariharan Kukreja, Vinay Sri Sai Ram Engineering College Department of Information Technology Chennai India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9781665456302

As per the Data Compiled by the Ministry of Indian Railways, there are nearly 5413 people who lost their lives in Railway accidents from 2015-2020, most of which are due to careless and rushed boarding & de-boarding. Thus, a proposed model called 'RFID enabled Railway Rescuer' is introduced to minimize these railway accidents. The proposed Internet of Things (IoT) system consists of a Radio Frequency Identification (RFID) tag, that detects the passenger in ZOR (Zone of Risk - The Door Edge) which in turn triggers the RFID reader fixed in the compartment. The Activated RFID reader will sense all the RFID tags that are present in the ZOR by providing alerts in the form of sound beeps or lights on the Door Edge. Or in other cases, it sends quick notifications to the passengers who are holding respective RFID tags. These notifications alert the passenger to change their standing position. By this, the proposed model tends to minimize railway accidents & thus save many innocent lives. © 2023 IEEE.

关键词： Internet of things

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Stackelberg Game-Based Pricing and Offloading for the DVFS-Enabled MEC Systems

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2502-2515页

作者： Mei, Jing Zeng, Cuibin Tong, Zhao Yang, Zhibang Li, Keqin Hunan Normal University College of Information Science and Engineering Changsha410081 China Changsha University in Changsha Hunan Province Key Laboratory of Industrial Internet Technology and Security Hunan 410022 China Hunan University College of Information Science and Engineering Changsha410012 China National Supercomputing Center Hunan Changsha410082 China State University of New York Department of Computer Science New YorkNY12561 United States

Due to the limited computing resources of both mobile devices (MDs) and the mobile edge computing (MEC) server, devising reasonable strategies for MD task offloading, MEC server resource pricing, and resource allocation is crucial. In this paper, a scenario is considered, comprising multiple MDs and a single MEC server. Each MD has a divisible task in each time slot, allowing for partial offloading and the option to discard parts of the task. The MEC server contains multiple computing units with the same computing power, and its computing resources can be dynamically adjusted through dynamic voltage and frequency scaling (DVFS) according to the size of tasks offloaded by MDs. At any given time slice, a Stackelberg game is formulated based on the strategies of the MDs and the strategy of the MEC server. An iterative evolution algorithm is employed to explore the optimal strategies for MDs and the MEC server. Simulation results demonstrate that both parties can reach an equilibrium state through the game, and these experiments confirm that the algorithm effectively enhances system efficiency. © 2004-2012 IEEE.

关键词： Mobile edge computing

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A novel recursive privacy-preserving information retrieval approach for private retrieval

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International Journal of Intelligent information and Database Systems 2022年第3期15卷 269-274页

作者： Bhat, Radhakrishna Kumar, K.M. Mahesh Sunitha, N.R. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bangalore560064 India Department of Computer Science and Engineering Siddaganga Institute of Technology Karnataka Tumkur572102 India

The major drawback of existing information retrieval schemes in preserving user privacy is that they either exhibit computationally bounded privacy with intractability assumptions or perfect privacy with high bandwidth utilisation. Today, the essential requirement is to have a bandwidth efficient perfect privacy preserving information retrieval scheme in order to provide effective and guaranteed service to the information retrieving user. Therefore, in this paper, we have constructed a new single database perfect privacy preserving private block retrieval scheme called ‘pepperPBR’ using quadratic residuosity as the underlying primitive where private block retrieval (PBR) is a natural extension to private information retrieval (PIR). In this paper, user generates O(6k) bit query and server generates O(2ku + o(n)) bit response where n is the database size, o(n) is the non-trivial server communication cost, u is the number of database blocks, k is the security parameter. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： information retrieval

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MSIsensor-RNA:Microsatellite Instability Detection for Bulk and Single-cell Gene Expression Data

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Genomics, Proteomics & Bioinformatics 2024年第3期22卷 85-90页

作者： Peng Jia Xuanhao Yang Xiaofei Yang Tingjie Wang Yu Xu Kai Ye Department of Gynecology and Obstetrics Center for Mathematical MedicalThe First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute The First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China

Microsatellite instability(MSI)is an indispensable biomarker in cancer ***,MSI scoring methods by high-throughput omics methods have gained popularity and demonstrated better performance than the gold standard method for MSI ***,the MSI detection method on expression data,especially single-cell expression data,is still lacking,limiting the scope of clinical application and prohibiting the investigation of MSI at a single-cell ***,we developed MSIsensor-RNA,an accurate,robust,adaptable,and standalone software to detect MSI status based on expression values of MSI-associated *** demonstrated the favorable performance and promise of MSIsensor-RNA in both bulk and single-cell gene expression data in multiplatform technologies including RNA sequencing(RNA-seq),microarray,and single-cell ***-RNA is a versatile,efficient,and robust method for MSI status detection from both bulk and single-cell gene expression data in clinical studies and ***-RNA is available at https://***/xjtu-omics/msisensor-rna.

关键词： Microsatellite instability Gene expression Single-cell RNA-seq RNA-seq Microarray

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