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3rd international conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

3rd International Conference on Machine Learning, Internet o...

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3rd international conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

ISBN: (纸本)9789819939312

The proceedings contain 52 papers. The special focus in this conference is on Machine Learning, Internet of Things and Big Data. The topics include: Smart Skin-Proto: A Mobile Skin Disorders Recognizer Model;machine Learning Approach Using Artificial Neural Networks to Detect Malicious Nodes in IoT Networks;real Time Air-Writing and Recognition of Tamil Alphabets Using Deep Learning;a Fuzzy Logic Based Trust Evaluation Model for IoT;supervised Learning Approaches on the Prediction of Diabetic Disease in Healthcare;solar Powered Smart Home Automation and Smart Health Monitoring with IoT;seasonal-Wise Occupational Accident Analysis Using Deep Learning Paradigms;MLFP: Machine Learning Approaches for Flood Prediction in Odisha State;vision-Based Cyclist Travel Lane and Helmet Detection;machine Learning Algorithms Aided Disease Diagnosis and Prediction of Grape Leaf;design and Experimental Analysis of Spur Gear–A Multi-objective Approach;Chest X-Ray Image Classification for COVID-19 Detection Using Various Feature Extraction Techniques;computer vision and Image Segmentation: LBW Automation Technique;a Mixed Collaborative Recommender System Using Singular Value Decomposition and Item Similarity;hybrid Clustering-Based Fast Support Vector Machine Model for Heart Disease Prediction;forecasting and Analysing Time Series Data Using Deep Learning;intelligent Blockchain: Use of Blockchain and Machine Learning Algorithm for Smart Contract and Smart Bidding;Weed Detection in Cotton Production Systems Using Novel YOLOv7-X Object Detector;smart Healthcare System Management Using IoT and Machine Learning Techniques;Automatic Code Clone Detection Technique Using SDG;Optimized Fuzzy PI Regulator for Frequency Regulation of Distributed Power System;simulated Design of an Autonomous Multi-terrain Modular Agri-bot;customer Segmentation Analysis Using Clustering Algorithms.

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Enhancing Rider Safety-A computer vision based Approach for Helmet Detection 3

Enhancing Rider Safety-A Computer Vision based Approach for ...

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3rd international conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Brintha, N.C. Ananda Kumar, V. Rama Krishna, G. Yadav, G Nithish Kumar Sai, P. Nikhil Kalasalingam Acadamy of Research and Education Department of Computer Science and Engineering Tamilnadu Virudhunagar India

ISBN: (纸本)9798350396157

Bike riders have been rapidly rising in numerous countries throughout time. Motorcycles are popular among residents from many social strata for a variety of reasons, including their low cost. Helmets are required by law, yet the majority of people choose not to wear them. The primary purpose of the helmet is to protect the riders. This study collects and annotates a variety of datasets, including photographs and video frames with and without helmets. Following that, this study modifies a trained object identification model such that it can recognise helmets in a variety of locations and lighting conditions. This study uses the trained model to analyse real-Time video data from cameras positioned in locations. When helmets are recognised, they are marked in the video feed with bounding boxes, allowing spectators to easily identify persons, who follow safety requirements. This study incorporates processing techniques such as non-maximum suppression to improve accuracy. Furthermore, this technology allows you to establish a confidence threshold which will activate warnings or messages if helmets do not achieve the specified degree of confidence or are not identified at all. This study employs cutting-edge deep learning approaches for object detection, allowing persons wearing helmets in video feeds to be identified. To build a customised object identification model particularly built for helmet recognition, we use transfer learning with trained convolutional neural networks (CNNs). The goal of this research work is to create a deep learning-based helmet detection system using trained models and datasets, which will be useful for the police branch in implementing regulations for the good of society. The main aim of this research work is to detect the biker's helmets to enhanced road safety by using deep learning and CNN based algorithms. The proposed model enhances biker's safety and its helpful for the traffic police to check whether they wear the helmet or not while d

关键词： Object detection

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Enhancing Smart Grid Security with Quantum Cryptography: A BB84-based Framework 3

Enhancing Smart Grid Security with Quantum Cryptography: A B...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Garg, Shail Gayatri, Yerukola Surya Kausthub, A. Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The study investigates how quantum cryptography can be employed to improve security in smart grid communication networks. Attempting to use the BB84 QKD protocol, it allows secure creation and management of those cryptographic keys that cannot be broken with the help of quantum and traditional hacking techniques. It depends on symmetric encryption mechanisms like AES to provide high data confidentiality during transmission. Regarding the key management, the system provides a key management server that distributes keys to the grid nodes and performs key updating to ensure control with the managing authority. It also presents reliability under practical conditions through performance analogs such as Quantum Bit Error Rates (QBER), and system latency. This approach shows that quantum cryptography can provide a highly secure and efficient communication network for the next-generation smart grid. © 2025 IEEE.

关键词： Smart power grids

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Research on Network Security Protection Techniques for the Central Station Side of Distributed Power Dispatch and control Systems 3

Research on Network Security Protection Techniques for the C...

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2023 IEEE 3rd international conference on Data Science and computer Application, ICDSCA 2023

作者： Zhang, Xiao Wang, Youhuai Zhang, Wenlu State Grid Hubei Electric Power Co. Ltd. Wuhan China

ISBN: (纸本)9798350341546

This research focuses on the study of network security protection techniques for the central station side of distributed power dispatch and control systems. It covers various aspects such as vulnerability discovery, information interaction security, device management, and threat tracing. The aim is to propose an integrated, multi-dimensional, and multi-level security protection system that combines active and passive data security measures and security analysis methods to ensure the safety and reliability of the distributed power dispatch and control systems. © 2023 IEEE.

关键词： Device Management Distributed Power Dispatch and control System Information Interaction Security Network Security Vulnerability Discovery

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Detection and Prevention of Tomato Leaf Disease using Convolutional Neural Network and Inception Net 3

Detection and Prevention of Tomato Leaf Disease using Convol...

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3rd IEEE international conference on Intelligent Techniques in control, Optimization and Signal Processing, INCOS 2024

作者： Ajitha, E. Nivedha, V. Parvathi, B. St.Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350361186

A state-of-the-art technique for analyzing images that is still relatively new but yields consistent results is Deep Learning(DL). Many DL techniques are used for leaf disease categorization. Regardless of the cuisine, tomatoes are a staple in every kitchen and are one of the most popular vegetables. It ranks third in terms of widespread production, behind sweet potatoes and potatoes. The number and quality of tomato crops are, however, impacted by several illnesses. An approach to crop disease diagnosis by deep learning is used. For disease categorization and detection, a Convolutional Neural Network is employed. Two pooling and two Convolutional layers are employed inside the model. The suggested model beat pre-trained InceptionV3, and VGGI6, according to the experiment findings. 98% training accuracy and 88.17% testing accuracy were attained by the CNN model. © 2024 IEEE.

关键词： Convolutional neural networks

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The Leader-Follower Joint Space Synchronous Impedance control for Coordinated Operation in Nuclear Industry 3

The Leader-Follower Joint Space Synchronous Impedance Contro...

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3rd international conference on computer, control and Robotics, ICCCR 2023

作者： Zhao, Xiaoyu Tang, Yuyao Yang, Yapeng Lv, Qi China Institute for Radiation Protection Nuclear Emergency and Nuclear Safety Department Taiyuan China

ISBN: (纸本)9781665492126

Coordinated operation is a common task for the dual-Arm robot system in nuclear industry, and the trajectory tracking performance and inner torque are both important indications for the coordinated operation. However, the radiation affects the application of end force/torque sensor and it is practicable to compromising the indications in the joint space. In this paper, leader-follower joint space synchronous impedance control is proposed to compromise the two indications. The dual-Arm system is controlled by the leader-follower mode and controller is designed to satisfy the pose and force constraints. Simulation is carried out and the results show considerable performance. © 2023 IEEE.

关键词： Nuclear industry

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Identifying Appropriate Probability Density Function for Wind Speed Profiles of Two Coastal Cities in Pakistan for Better Wind Power Assessment 3

Identifying Appropriate Probability Density Function for Win...

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3rd international conference on Emerging Trends in Electrical, control, and Telecommunication Engineering, ETECTE 2024

作者： Abbas, Ghulam Ali, Arshad Zubair, Muhammad Mushtaq, Zohaib Farooq, Umar Khan, Irfan A. Department of Electrical Engineering The University of Lahore Lahore Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Madinah Saudi Arabia College of Engineering and Technology University of Doha for Science and Technology Doha Qatar Department of Electrical Electronics and Computer Systems University of Sargodha Sargodha Pakistan IEECE University of the Punjab Lahore Pakistan Lab Texas A&M University GalvestonTX77553-1675 United States

ISBN: (纸本)9798331504861

To assess the wind power potential of a specific wind site, a suitable distribution function that best suits the wind data needs to be known. In this study, we employ four probability density functions (PDFs), namely Weibull, Rayleigh, Log-Normal, and Gamma, on wind speed data obtained from two coastal cities in Pakistan, Gwadar and Karachi, to check which one is the best suited. Cities along the coastline of Pakistan offer greater wind speeds and, thus, greater wind power potential. The maximum likelihood technique is used to estimate the parameters of the density distributions. Three statistical measures—Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2)—are used to assess the goodness of fit for every distribution. These measures give a complete picture of every distribution's correctness in terms of reflecting the recorded wind speed patterns. Numerical results reveal that the Weibull distribution fits well with the wind speeds of the two coastal cities compared to the other three probability distributions, providing insightful analysis for the evaluation of wind resources and possible local energy generation. The MATLAB environment is used to compare the distributions and validate the effectiveness of the proposed methodology. ©2024 IEEE.

关键词： Windmill

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A Robust Visually Secure Bit Plane Image Encryption Using Chaotic Map and SHA-512 3

A Robust Visually Secure Bit Plane Image Encryption Using Ch...

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3rd international conference on Emerging Trends in Electrical, control, and Telecommunication Engineering, ETECTE 2024

作者： Khan, Jan Sher Ahmed, Saygin Siddiq Haq, Farman Ul Tariq, Noshina Ahmad, Jawad Ashraf, Muhammad Department of Avionics Engineering Air University Islamabad Pakistan Department of Renewable Energy Techniques College of Oil and Gas Techniques Engineering Northern Technical University Iraq Department of Computer Sciences SZABIST Islamabad Pakistan Department of AI and DS National University of Computer and Emerging Sciences Islamabad Pakistan Cybersecurity Center Prince Mohammad Bin Fahd University Alkhobar Saudi Arabia

ISBN: (纸本)9798331504861

With the introduction of the latest digital technologies, the protection of images from unauthorized use has become increasingly important, necessitating the development of robust image encryption techniques. This paper proposes a novel bit plane image encryption method that incorporates chaos theory and cryptographic hash functions. To ensure a high degree of security, the key for the three-dimensional Intertwining Logistic chaotic map is generated (initial conditions) using the SHA-512 hash algorithm. The chaotic map is then utilized to generate two random vectors and one random matrix, respectively. In order to shuffle the original bit plane structure, random vectors are used to scramble the pixel positions across the bit planes. A bitwise XOR operation is then performed on the shuffle bit planes with the third random matrix. To avoid the attention of potential hackers, a visually secured ciphertext image is generated by embedding the noise-like ciphertext within a host image. Several tests, such as histogram, correlation coefficient, key sensitivity, key space, entropy, Number of Pixel Change Rate (NPCR), and Unified Average Change Intensity (UACI), were performed on the proposed visually secured bit plane image encryption scheme. As a result of the extensive tests, one can conclude that the proposed scheme is robust and can withstand a wide range of possible cryptographic attacks. ©2024 IEEE.

关键词： Pixels

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Enhancing the accuracy of fruit freshness detection by utilizing transfer learning and customizing convolutional neural network(CNN). 3

Enhancing the accuracy of fruit freshness detection by utili...

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3rd international conference on Computing and Machine Intelligence (ICMI)

作者： Rahman, Nabila Arefin, Mahira Rahman, Sabila Islam, Md Shamiul Khatun, Tammy Akter, Usha Bangladesh Univ Business & Technol Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9798350372977;9798350372984

Food quality and safety are paramount concerns in our modern world and perishable goods, especially fruits and vegetables, stand at the intersection of these concerns. The ability to accurately determine the freshness of these products not only impacts food safety but also holds the key to reducing waste in our food supply chain. To enhance the mean lifespan of humans, it is imperative to eradicate the potential for infectious illnesses. The majority of a high-risk community's diet consists of fruits and vegetables. Consequently, differentiating spoiled fruits from viable ones is critical for their preservation. Automation technology is an indispensable component of daily existence. The principal source of wealth is agriculture in the modern world. Daily growth is observed in the sales volume of fresh produce. People who prioritize their health select only high-quality, nutritious fresh fruits. In this paper, we present a novel approach that leverages state-of-the-art artificial intelligence and computer vision techniques, including Convolutional Neural Networks (CNN), ResNet50, VGG16 and InceptionV3 to tackle the challenge of assessing the quality of fruits and vegetables. By automating the evaluation process, our method goes beyond the traditional, subjective, and time-consuming ones. Using the power of deep learning, we present a complete framework that can tell with unprecedented accuracy whether a wide range of produce items are fresh and safe to eat.

关键词： Fresh Fruit CNN InceptionV3 RseNet50 Rotten Fruit Agriculture

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A Review of Vehicle Stability control Theory 3

A Review of Vehicle Stability Control Theory

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3rd international conference on Mechanical, Electronics, and Electrical and Automation control, METMS 2023

作者： Wu, Boan International Curriculum Center The High School Affiliated to Renmin University Beijing100191 China

ISBN: (纸本)9781510666689

The vehicle stability control system is an onboard computer that calculates the instantaneous dynamics and potential future movement trend of the vehicle through a series of algorithms and makes the vehicle in a relatively safe driving range through the control of vehicle components. Its main purpose is to assist the driver and play an effective role when the vehicle movement is in danger or the driver cannot effectively control the vehicle. This paper summarizes the concept and application of vehicle stability control, introduces the research status of vehicle stability control systems, summarizes its current research, and provides some references for related research. © 2023 SPIE. All rights reserved.

关键词： Vehicles

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