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Design of Pelletizer automation based Embedded System for Soap Oil Factory

Design of Pelletizer Automation based Embedded System for So...

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Device Intelligence, computing and communication Technologies, (DICCT), international conference on

作者： Sathish Kumar Shanmugam Koushik Nagarajan Ragavi Ravichandran Manjuragavi Ramasamy Sameera Sara Shajahan Department of Electrical and Electronics Engineering M. Kumarasamy College Of Engineering Karur India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

A SIBY Solvents Extraction Private Limited (Soap Oil factory) located near Chinna Salem has a major problem in Pelletizer control. This problem mainly depends on load control like handling coal and other raw materials by the *** embedded system based design consists of a controller which is used to measure the current and voltage rating for the Pelletizer. Based on the above parameters the power of the motors can be calculated simultaneously. The solution of the problem is solved by setting the lower limit and upper limit of current value for the motor. If the motor current is greater than set value the contactor, will cut the power supply to the motor. If the motor current less than set value it generates the on signal to the motor. In addition with that the on time and off time of the motor will be stored to the controller for the future reference. IoT based Monitoring is implemented here. The workers from the industry can't control the pelletizer within in a time due to its control panel distance and lack of language communication.

关键词： Temperature sensors Voltage measurement Power supplies Oils Motors Real-time systems Production facilities Safety Monitoring Standards

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Privacy and Security Concerns in Edge-Cloud Systems: An In-Depth Analysis 3

Privacy and Security Concerns in Edge-Cloud Systems: An In-D...

引用

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

作者： Kaki, Samuel Suhas Kantareddygari, Jagadeswara Reddy Aishwarya, R. School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

Edge-Cloud systems provide efficient computation and storage close to data sources, with lower latency, scalability, and application performance for various applications, such as IoT, autonomous vehicles, and real-time analytics. These systems are highly vulnerable to significant security and privacy challenges, which include the following: data confidentiality and integrity in the process of transmission, secure protection of sensitive data stored on the edge against physical and remote attacks, and maintaining availability under dynamic network conditions. More challenges related to ensuring users' private nature through anonymizing data, obtaining consent for all processes involved, and compliance with specific regulations like GDPR and CCPA. This work incorporates more enhanced cryptographic methods in the transfer of data between heterogeneous systems and environment-based anomaly detection, advanced privacy-preserving analytics to avoid extraction of private data, adaptive control mechanisms in place for Edge-Cloud for minimizing vulnerability with enhanced trust along with the maintenance of data integrity. The key benefits would be robust mitigation of threats, better interoperability between edge and cloud platforms, and alignment with evolving security standards, which promotes wider adoption across various sectors. © 2025 IEEE.

关键词： Cloud platforms

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

3rd International Conference on Emerging Electronics and Aut...

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

ISBN: (纸本)9789819768011

The proceedings contain 54 papers. The special focus in this conference is on Emerging Electronics and automation. The topics include: P4 Language-Based Load-Aware Efficient Resource Utilization in Software Defined Networks;implementation of Derived Quadratic Boost Dual Input Converter for Automotive Application;intelligent Solution for Voltage Profile Enhancement Using Dynamic Voltage Restorer;Optimizing Reliability of PV-Based Microgrid by Integrating Fuel Cell;secure communication in Vehicle-to-Grid Networks: A Study on Man in the Middle Attack Mitigation;Comparative Analysis of PID and LQR control for Autonomous Vehicle Platoons;effect of Mass on Parameters of Electric Vehicle;Modeling and Analysis of an Underactuated Underwater Vehicle Manipulator System (UVMS) for Underwater Intervention;modified Artificial Potential Field Algorithms for Mobile Robot Path Planning;Comparative Analysis of Advanced Driver Assistance Systems (ADAS) in Automobiles;innovative Acoustic Wave Fire Suppression System for Portability;a Comparative Study on the Various Aging Laws Governing Lithium-Ion Capacitor Cycling Aging;Modified Carrier Based Phase Shift Modulation Method for Modular Multilevel Converter for the Improvement of THD and Switching Losses;design Analysis of Vertical Axis Wind Turbine in Jaipur & Thiruvananthapuram;structural and Numerical Analysis of Bladeless Wind Turbine;classification of Poikilocytosis Abnormality Using Ensemble Deep Learning Technique;person Re-identification Using Convolutional Fuzzy Neural Network Classifier;demand Forecasting for Market Basket Analysis;framework to Evaluate Twitter Tweets Using K-Means Clustering;feature Selection Strategies for Accurate Crop Yield Prediction in Heterogeneous Datasets;deep Transfer Learning for Multiclass Dermatology Classification: A Comprehensive Study;depneumoNet: A Novel Model for Improved Pneumonia Diagnosis Through Chest X-Ray Imaging.

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Visual Cloud Processing System for Intelligent Management of Real-Time Laboratory Data 14th

Visual Cloud Processing System for Intelligent Management of...

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14th international conference on Frontier computing, FC 2024

作者： Chen, Licong School of Film Television and Communication Xiamen University of Technology Fujian Xiamen361024 China

ISBN: (纸本)9789819627974

The development of Internet of Things technology provides a real-time data collection platform for laboratory management, and data visualization provides new ideas for object-oriented behavior management. This article introduces an intelligent laboratory management system with a computer as the core and introduces it in detail. This article compares the performance of typical visualization systems at home and abroad, and discusses the development and implementation of a typical intelligent laboratory management system, the visual expression of the system, and its impact on the behavior of experimenters. The article empirically analyzes the role of behavior in laboratory learning and proposes ideas for further development (Before the system was put into use, employees in the position spent 2.18 h processing laboratory data every day, and after the system was put into use, it only took 1.02 h, indicating that the put into use of the system can effectively reduce the time spent on data processing). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： automation and Data Analysis Laboratory Intelligent Management Real-Time Data Visual Processing

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Federated Learning for Protecting Privacy in Big Data Networks 3

Federated Learning for Protecting Privacy in Big Data Networ...

引用

3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Pattanayak, Binod Kumar Samrat, Bhavuk Thomas, Shaji Sudha, F. V. Jaya Choudhary, Deeksha Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331542375

Due to the big data era, the use of cloud computing and Internet of Things devices has increased tremendously, which has resulted in the mass collection of personal data. This caused major privacy protection and safety issues, as sensitive personal data could be exploited and misused. Data encryption and similar techniques have largely needed to be improved as a privacy-protecting solution. A new approach to overcome the privacy and security issues in big data networks is federated learning. Federated Learning is a distributed approach to learning in which machine learning models are trained locally on each device (e.g., phone), and the raw data never leaves the local devices to reach a central server. With this device, however, only model updates are sent while the private data continues to be stored locally on the devices for privacy protection. FL has several benefits compared to normal privacy protection methods, such as reducing the risks of data breaches and minimizing computational and communication costs. In addition, FL allows better data management and greater control over personal data as users are involved in training. FL can be an important building block for privacy and security in large data networks, and it has been adopted by most of the major technology companies as part of their applications. They still have to solve problems like efficient convergence of the model, heterogeneous data and so on. FL is still being prepared for practical use in big data networks and needs more research based on its development to protect privacy. © 2025 IEEE.

关键词： Federated learning

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Real-Time Traffic Prediction And Optimization using Machine Learning 3

Real-Time Traffic Prediction And Optimization using Machine ...

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

作者： Vineetha, Kundrapu Tharun Reddy, M. Jaffer Ali, Md. Prathima, Narisetty Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The Real-Time Traffic Prediction and Optimization System may be described as enriched, spacious, algorithm-oriented, and designed to help contribute to the anti-symptomatic deprievement of increasingly congested urban traffic streams. The system with the help of integrating real time data of numerous sources like GPS tracker, traffic sensor as well as weather API, the system using advanced machine learning algorithm in order to predict traffic accurately and optimize routes based on it. This predictive capability then greatly improves the flow of vehicles, the time taken, and the occurrence of choke points. The structure of the system is fully based in the cloud, which makes it easy to scale and very compatible with other systems to amalgamate a vast array of data inputs with high dependability. Fully customizable and intuitive control panel gives live view of traffic, offers actionable stats, and instant control mechanisms to traffic operators to make correct decisions quickly. Congestion in urban areas has an influence on time taken to cover a certain distance, fuel consumption and emission to the atmosphere. On the basis of the above analysis, this work extends a Real-Time Traffic Prediction and Optimization System (RTPOPS) using machine learning approach to predict the traffic density and to further optimize traffic signals and routes. The used system demonstrated successful management and reduction of vehicle density by 30%, and increasing speed limits by 18% and proved that Intelligent Urban Mobility has the potential to be achieved. © 2025 IEEE.

关键词： Traffic congestion

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Proceedings of 2025 3rd international conference on Intelligent Systems, Advanced computing, and communication, ISACC 2025

Proceedings of 2025 3rd International Conference on Intellig...

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3rd international conference on Intelligent Systems, Advanced computing, and communication, ISACC 2025

ISBN: (纸本)9798331523893

The proceedings contain 222 papers. The topics discussed include: automation of traffic signal for better commutation of vehicles;an algorithmic and machine learning approach for yoga asanas feedback;potential of vision transformer in leukemia detection;integrative personalized oncology: leveraging multi-source data and drug solubility for optimal cancer treatment;generative adversarial networks for melanoma detection;improving English text machine translation quality with cutting-edge neural network systems;automated assessment of pizza quality using computer vision and deep learning techniques;15 puzzle problem solving by using heuristic-based approaches;and advancing phishing detection with random forest: a machine learning approach for enhanced cybersecurity.

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Collaborative Optimization Algorithm of Deep Learning and Compliant control in Online Trajectory Planning 14

Collaborative Optimization Algorithm of Deep Learning and Co...

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14th IEEE international conference on communication Systems and Network Technologies, CSNT 2025

作者： Liao, Junting Zhao, Chunjian Xie, Hui School of Computer and Artificial Intelligence Xiangnan University Hunan Chenzhou423000 China China Telecom Hunan Chenzhou Branch Hunan Chenzhou423000 China

ISBN: (纸本)9798331531935

With the rapid development of robotics and automation, online trajectory planning faces problems such as poor adaptability to dynamic environments and low computational efficiency. This study aims to propose a collaborative optimization algorithm that combines deep learning and compliant control to improve the efficiency and accuracy of online trajectory planning. First, CNN is used for feature extraction and a prediction model is established. Then a compliant control strategy is designed to deal with dynamic obstacles to ensure the flexibility and stability of the robot in trajectory planning. The trained CNN model is then used to generate real-time trajectory predictions. The robot motion is adjusted through a fuzzy logic controller to optimize the relative position of the trajectory and obstacles. The average obstacle avoidance success rate of the algorithm in dynamic environments reaches 97%. The collaborative optimization algorithm of deep learning and compliant control effectively improves the real-time and accuracy of online trajectory planning, providing a new solution for the application of intelligent robots in complex environments. © 2025 IEEE.

关键词： Intelligent robots

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Multi-class Classification of Wireless Capsule Endoscopy Images By Using the Fusion of Pre-trained Networks and Fusion Residual Block

Multi-class Classification of Wireless Capsule Endoscopy Ima...

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international conference on Systems, control and automation, ICSCA 2023

作者： Lavanya, Katteboina Aparna, P. Department of Electronics and Communication National Institute of Technology Karnataka Srinivasa Nagar Karnataka Mangalore575025 India

ISBN: (纸本)9789819773831

In order to identify diverse endoscopic images of gastrointestinal (GI) problems, this study presented a multi-fused residual convolutional neural network (MFuRe-CNN) with auxiliary fusion layers (AuxFL) and a fusion residual block (FuRB) with alpha dropouts (ADO). The implemented MFuRe-CNN dealt with five instances sourced from reputed databases such as the KVASIR database, the ETIS-Larib Polyp Database, and the Red Lesion Endoscopic Database, which included Esophagitis, Ulcerative colitis, Polyps, Healthy colon and Bleeding images. The proposed model was created by fusing three cutting-edge models fused into a single-feature extraction pipeline with its layers that are truncated and partially frozen. This model which despite using a small portion of the computing power of most current cutting-edge models, helped spread robust features and enhanced diagnostic performance. In addition, compared to those without the aforementioned components, the MFuRe-CNN in conjunction with AuxFLs, DOs and FuRB has significantly reduced overfitting and performance saturation. With merely 4.8 million parameters, this model achieved test accuracy of 93.79 percent, outperforming the majority of DCNNs that are trained in the traditional manner. Thus, the suggested MFuRe-CNN could improve GI tract diagnosis more cheaply than ensembles and outperform other traditional pre-trained as well as fine-tuned Deep convolutional neural networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Endoscopy

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Algae Based Air Purifier

Algae Based Air Purifier

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Advancements in Electrical, Electronics, communication, computing and automation (ICAECA), international conference on

作者： Shamyuktha V P Pavith P Kishore N M Sri Arunachaleeshwaran M Anitha N Kumaraguru College of Technology Coimbatore India Department of Mathematics School of Foundational Sciences Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The pollutant levels in the atmosphere have been drastically increasing over the past few decades. The air pollution causes health & environmental impacts, and climate change. This paper focuses on the use of biological method as a solution to combat this issue for which microalgae has been used due to its air purification property. Arthrospira maxima has been identified as the optimal species for this experiment. Winkler’s method for the estimation of dissolved oxygen and American Public Health Association method for the estimation of dissolved carbon dioxide of the algal medium were experimented for the verification of the functionality of the air purifier.

关键词： Air cleaners Climate change automation Purification Estimation Carbon dioxide Air pollution Biology Telecommunication computing Public healthcare

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