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检索条件"任意字段=2023 3rd International Conference on Electrical Engineering and Computer Technology, ICEECT 2023"

共 11233 条记录，以下是161-170 订阅

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Advanced Attendance Management System with Independent Yet Unified Face Recognition Pipeline 3rd

Advanced Attendance Management System with Independent Yet U...

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3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Shantilal, Sankhala Bhavik Singh, Amaresh Chandra Gupta, Nidhi Research and Development Isnartech PVT. LTD. Saharanpur India Department of Computer Applications National Institute of Technology Kurukshetra Kurukshetra India

ISBN: (纸本)9789819743582

An attendance management system allows us to track the attendance of students, teachers, employees, and other staff. It assists us in saving time and effort. Digital attendance systems have been used by some schools and companies in the past, but their accuracy and chances of locating an unidentified face aren’t all that significant. This attendance system is particularly effective for preventing errors while doing manual data entry and it comprises three main stages named Face Detection, Recognition, and Verification. Behavior analysis is combined with the attendance system. Although face detection and recognition have several applications in security, biometrics, and medicine, their best implementation in companies or schools is still in the early stages of research. The proposed approach is the most efficient alternative after comparing its accuracy and computational cost to those of preceding algorithms. This paper describes how an attendance management system integrates various facial recognition algorithms and object recognition techniques. The proposed model’s experimental results, which integrate detection, two-stage recognition, and analysis, are likely to be effective in modern schools and businesses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Attendance management system Behavior analysis Face detection Face recognition Face recognition pipeline FaceNet model

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Design Analysis of Vertical Axis Wind Turbine in Jaipur & Thiruvananthapuram 3rd

Design Analysis of Vertical Axis Wind Turbine in Jaipur & Th...

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3rd international conference on Emerging Electronics and Automation, E2A 2023

作者： Rizvi, Zoya Malpande, Amit Tekchandani, Rohit Garbyal, Kushagra Maheshwari, Ashray Mukesh Patel School of Technology Management & Engineering SVKM’s NMIMS India Mumbai India

ISBN: (纸本)9789819768011

This study employs ANSYS software, ANOVA, SolidWorks to design and analyze Vertical Axis Wind Turbines (VAWTs) in a methodical and scientific manner for Jaipur & Thiruvananthapuram region. The study examined a Savonius VAWT’s design and pinpointed its flaws. A new helical VAWT design was created to overcome these flaws and put to the test for power, TSR, and torque calculations. The study discovered that in Thiruvananthapuram, a windy coastal region with strong and consistent winds, the helical VAWT design performed significantly better. The more effective design was discernible using the ANOVA and Post Hoc tests. The paper emphasizes how crucial it is to take into account regional wind patterns when developing and deploying VAWTs. Furthermore, testing and optimization, incorporating the design into bigger renewable energy systems, looking into alternative materials and manufacturing processes, and performing a lifecycle analysis are all part of the project’s future scope. The paper offers a solid framework for additional study and advancement in the area of VAWT design and optimization. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Vertical axis wind turbine

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Optimized Task Scheduling in Diverse Distributed Systems with a Hybrid Genetic Algorithm Approach 3rd

Optimized Task Scheduling in Diverse Distributed Systems wit...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Muthusamy, Ramesh Kumar Kadu, Swati Ayyasamy, Karthikeyan Pichuka Veera Venkata Satya, Srinivas Arumugam, Ramachandran Ammasai, Viswanathan Sengan, Sudhakar Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of AI&DS AISSMS IOIT Pune411001 India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai602105 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627012 India

ISBN: (纸本)9789819783540

In Heterogeneous Distributed Systems (HDS), efficient task scheduling (TS) across diverse resources remains a pressing challenge. This paper introduces a Hybrid Genetic Algorithm (HGA) as a solution, blending heuristic techniques with the evolutionary strengths of Genetic Algorithms (GAs) to optimize Task Scheduling (TS). Traditional algorithms often fall short in balancing computational speed with solution quality, especially in large-scale environments. This proposed HGA addresses this by leveraging domain-specific heuristics to provide a promising starting point for the GA's optimization process. The TS dilemma has been successfully defined by an all-encompassing logical system, which ensures the accuracy of the design of algorithms. In comparison tests, the HGA frequently comes out on top, demonstrating less makespan and a more excellent distribution of tasks among the resources available. Also, resources with more efficient processing capabilities will be provided priority in this recommended heuristic method, which focuses on tasks with higher computational demands. The findings of the present investigation demonstrate that the HGA may transform TS approaches by providing high-quality solutions while increasing performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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A Portable Diagnostic and Medication System for Rural Areas Using IoT 3rd

A Portable Diagnostic and Medication System for Rural Areas ...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Islam, Md. Reazul Hossain, Arman Arafah, Sayefa Rabbi, M. M. Fazle Oliullah, Khondokar Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka1216 Bangladesh

ISBN: (纸本)9789819739363

In rural regions of Bangladesh, the scarcity of healthcare resources poses a substantial obstacle, frequently resulting in delayed diagnoses and inadequate medical care. This paper introduces an inventive solution, the "IoT-Based Rural Area Portable Diagnostic and Medication System," designed to address these healthcare disparities by harnessing the capabilities of Internet of Things (IoT) technology. This system leverages IoT sensors and devices to enable remote health monitoring and diagnostics for residents of rural and underserved communities. It comprises a network of portable medical devices, such as wearable sensors, diagnostic tools, and medication dispensers, seamlessly connected through MQTT protocol to a centralized healthcare platform. Through this system, healthcare providers can remotely monitor patients’ vital signs, track disease progression, and deliver timely medical interventions. Moreover, the system ensures the secure and confidential exchange of sensitive medical data and enhances transparency between physicians and patients through the utilization of blockchain technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Remote patient monitoring

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Dual Cues Dual Stream of Dilated Convolution Layers for Early Prediction of Alzheimer’s Disease 3rd

Dual Cues Dual Stream of Dilated Convolution Layers for Earl...

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3rd international conference on Signal and Data Processing, ICSDP 2023

作者： Tripathy, Santosh Kumar Panigrahi, Susant Kumar Rambabu, Medara Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna 800005 India School of Electrical & Electronics Engineering VIT Bhopal University Kothrikalan Sehore Madhya Pradesh Bhopal 466114 India Department of Computer Science and Engineering Gandhi Institute of Technology and Management Visakhapatnam 530045 India

ISBN: (纸本)9789819795772

Recently, a considerable number of investigations on deep learning methodologies for early identification of Alzheimer's disease (AD) have been reported in the literature. For accurate diagnosis, the currently used feature modelling algorithms must be improved. Such the gap is addressed by proposing a dual stream of dilated convolutional neural networks. The architecture uses Canny edge maps and MRI scans as two samples’ cues to extract fine-grained spatial characteristics. To enhance the quality of feature modelling process, the high-level features from the dual streams are combined. To predict illness stages, the fused features are flattened and tightly connected with 256 neurons. These neurons are then fully connected to an output layer for AD diagnosis. For testing and validation, the publicly accessible AD dataset is considered in this article to verify the performance of the proposed architecture. With a 95.93% accuracy rate, the suggested model outperforms the current best practices. Additionally, an ablation study on the architecture is also included to illustrate how each stream behaves for each sample cue. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Alzheimer’s disease Canny edge Deep learning Dementia Dilated CNN Medical image analysis

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Machine Learning Based Citrus Fruit Disease Prediction Using Support Vector Machine Algorithm 3rd

Machine Learning Based Citrus Fruit Disease Prediction Using...

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3rd international conference on Emerging Electronics and Automation, E2A 2023

作者： Mohanapriya, D. Kabilesh, S.K. Swaranambigai, R. Saritha, B. Deepikapriya, S.V.K. Jai Shriram Engineering College Tamil Nadu Tiruppur638660 India KGISL Institute of Technology Tamil Nadu Coimbatore641035 India Jansons Institute of Technology Tamilnadu Coimbatore641659 India Erode Sengunthar Engineering College Tamilnadu Erode638057 India

ISBN: (纸本)9789819730896

The recognition of fruit diseases is rapidly becoming a significant area in computer vision. Besides reducing fruit production, plant diseases also have a profound impact on the country's economy. Therefore, predicting plant illnesses and implementing remedial actions are of utmost importance. Plant illness directly affects the crop’s quality and overall productivity. To achieve swift and accurate disease prediction, an automated method is used to identify and diagnose affected leaves within a short span of time. Through the appropriate use of diagnosis and classification techniques, potential losses can be prevented. The features used for identifying plant diseases include color, strength, area of attack, and dimension. The current research primarily focuses on guava leaf disease prediction using K-means clustering techniques. In this proposed study, we aim to identify orange fruit and leaf diseases. To achieve this, K-means clustering is applied to segment the orange fruit images into distinct clusters. We leverage the gray-level co-occurrence matrix (GLCM) approach with the clustered data to extract the global features as feature vectors, and the orientation gradient approach is utilized to uncover real features. Implementing machine learning algorithms has significantly enhanced the accuracy of the system, achieving a remarkable 96% accuracy rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Machine Learning-Based Approach to Predict Heart Diseases Using Fused Dataset 3rd

Machine Learning-Based Approach to Predict Heart Diseases Us...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Uddin, Khandaker Mohammad Mohi Mohamed, Abdirahman Biswas, Nitish Mostafiz, Rafid Department of Computer Science and Engineering Southeast University Dhaka1208 Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka1205 Bangladesh Institute of Information Technology Noakhali Science and Technology University Noakhali Bangladesh

ISBN: (纸本)9789819739363

Cardiovascular disease is currently a prominent sickness that kills the majority of sufferers. The medical evaluation of cardiac disease presents significant problems. This diagnostic method is complex, requiring precision and efficiency. Early detection of heart disease can significantly lower the chance of death. In light of the high occurrence of cardiac issues in contemporary times, the prediction of heart disease has emerged as one of the most challenging endeavors within the medical field in recent years. Scientists examined a plethora of closely related characteristics to identify the most trustworthy predictors of these diseases. In this work, we use Machine Learning (ML) approaches to detect the existence of cardiac problems. The suggested method predicts the likelihood of heart disease and classifies people into risk categories. This is performed by utilizing several machine learning algorithms such as Support Vector Machine (SVM), Gradient Boosting, Random Forest (RF), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Logistic Regression (LR). The developed system is trained and evaluated using a composite dataset derived from two separate sources. The results of the experiments show that, when compared to other machine learning algorithms, the Random Forest approach achieves the greatest accuracy rate, achieving an amazing 99.99%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Tamper-Proof Smart Contract Metamodel for Blockchain to Optimise Computational Latency 3rd

A Tamper-Proof Smart Contract Metamodel for Blockchain to Op...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Sengupta, Ratul Srivastava, Ruchika Mishra, Sushruta Abualigah, Laith Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al-Albayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Gelugor11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

ISBN: (纸本)9789819726707

In recent years, blockchain technology has garnered considerable interest on account of its peer-to-peer, distributed consensus, and anonymity functionalities. A smart contract functions as a verifiable, self-executing, and self-validating computing layer. This study seeks to identify the key concepts and associated future research goals pertaining to smart contracts based on the blockchain. Smart contracts enable the implementation of programmable assets, including money, and the automation of business logic processes that were previously executed manually. In response to this disclosure, a comprehensive examination of smart contracts employing blockchain technology is undertaken. In this article, we propose an innovative and practical metamodel for ensuring the integrity of protocols within smart contracts facilitated by blockchain technology. This article begins with an explanation of smart contracts’ operational models, functionalities, and applications. Analysis of the results reveals that the utilisation of the model alleviates the workload associated with numerous measurements. It was determined that the mean response latency, computation time, and overhead were each 1.47, 1.19, and 3.04 s. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart contract

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Bangla License Plate Detection and Recognition Approach Based on computer Vision for Authentic Vehicle Identification 3rd

Bangla License Plate Detection and Recognition Approach Base...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Dhar, Rajib Kumar Khanom, Fahmida Uddin, Khandaker Mohammad Mohi Imam, Md. Hasan Mostafiz, Rafid Institute of Information Technology Noakhali Science and Technology University Noakhali Bangladesh Department of Mathematics American International University—Bangladesh 408/1 Kuratoli Khilkhet Dhaka1229 Bangladesh Department of Computer Science and Engineering Southeast University Dhaka1208 Bangladesh

ISBN: (纸本)9789819739363

Vehicle license plates (LPs) detection and recognition is a fundamental technique in computer vision that plays a crucial role to extract, localize, and authenticate the information of a vehicle. This paper presents an efficient method for Bangla LPs recognition and vehicle authentication using edge analysis and pattern matching algorithm in real-time nature. This method performs four modules as: preprocessing, license plate identification, character recognition, vehicle authentication. A multi-step preprocessing technique has been applied to extract and understand the potential information in case of any uneven images. To identify edge connectivity and uniformity, Canny edge detector provides more promising in contour analysis and character localization. The segmented characters are converted into texts and verified using the pattern matching algorithm by a predefined database. For this research, a database consisting of 3000 automobile license plate photos captured under different climatic circumstances has been compiled. Additionally, we have curated two additional databases consisting of templates for recognition and registered automobiles for the purpose of authentication. The proposed model can achieve accuracy of 96% in LPs detection, 99% in character recognition, and 100% in vehicle authentication by extracted texts from LPs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication

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IoT-Driven Agricultural Monitoring Architecture with Novel Light Machine Learning for Enhanced Precision Crop Health Analysis 3rd

IoT-Driven Agricultural Monitoring Architecture with Novel L...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Andi, Hari Krishnan Ramasamy, Lokeshkumar Nramban Kannan, Senthil Kumar Murugesan, Sasikumar Benarji, Bejjam S. N. Manogaran, Umamaheswari Sengan, Sudhakar Centre for Postgraduate Studies Asia Metropolitan University Johor81750 Malaysia School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering Deemed to Be University Vel Tech Rangarajan Dr Sagunthala R&H12D Institute of Science and Technology Tamil Nadu Chennai600062 India C. Abdul Hakeem College of Engineering & Technology Tamil Nadu Melvisharam Ranipet632509 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

In the age of digital transformation, agriculture is harnessing the potential of advanced technologies to address global challenges related to food security and environmental sustainability. While Smart Agriculture and Precision Agriculture lay the foundation, there remains a pressing need for real-time, granular data processing to realize their full potential. Edge computing, coupled with Machine Intelligence (MI), promises a solution, yet existing models often lack the adaptability and real-time efficiency demanded by diverse agricultural scenarios. Addressing these challenges, this paper proposes the Precision Intensive Crop Health Observer (PICHO). PICHO is an innovative three-tier architecture comprising the Field Data Interface Unit (FDIU) for seamless sensor data collection, the Edge Analytics Processor (EAP) for real-time edge-based analytics, and the Cloud Coordination Hub (CCH) for data storage and in-depth analysis. Central to the EAP's efficacy is the lightweight learning model LENet, which is designed with dual pipelines for processing sensor and image data. Specifically crafted to be trained on resource-constrained edge devices, LENet ensures that PICHO remains adaptable, efficient, and poised to redefine the future of precision agriculture. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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