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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Kosha, Katsumasa Miyazaki, Tetsuro Teranishi, Kaoru Kogiso, Kiminao Kawashima, Kenji The University of Tokyo Department of Information Physics and Computing 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan The University of Texas at Austin Oden Institute for Computational Engineering and Sciences AustinTX United States Japan Society for the Promotion of Science Tokyo Japan The University of Electro-Communications Department of Mechanical and Intelligent Systems Engineering 1-5-1 ChofugaokaChofu-shi Tokyo182-8585 Japan

ISBN: (纸本)9798331531614

Teleoperation of remote assist robots has recently advanced owing to the development of communication technology. However, anonymous malicious attacks may intercept or falsify the network control system. Therefore, it is necessary to improve the security against cyber-attacks. As a countermeasure, an encrypted control method has been applied to prevent intercepting and detect the falsification of control parameters, as well as control signals by performing control operations with encrypted signals and parameters. Furthermore, we have proposed the algorithm to detect and restore the attack exploiting a vulnerability of encryption, which falsifies the plaintext by multiplying a constant factor. However, the algorithm is only effective for periodic motions with a fixed operating frequency and amplitude. If the frequency or amplitude of the motion is smaller than those of the base motion, the algorithm cannot detect the attack. To solve the problem, we propose an improved algorithm to detect and restore the attack in periodic motion with variable operating frequency and amplitude. In the proposed method, the operating frequency and amplitude are obtained through the position frequency analysis. They contribute to update the base energy, which is the average energy of corresponding periodic motion. We verified the proposed method for the bilateral control system using ElGamal encryption and experimentally confirmed its effectiveness against the FDI attack in various periodic motion. © 2025 IEEE.

关键词： Cyber attacks

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Enhancing Diabetic Retinopathy Diagnosis with Inception v4: A Deep Learning Approach

Enhancing Diabetic Retinopathy Diagnosis with Inception v4: ...

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International Conference on Inventive Research in computing Applications (ICIRCA)

作者： R. Athilakshmi R. Jansi Roshan Upadhyay Department of Computational Intelligence Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

An automated system for the classification of Diabetic Retinopathy (DR) is introduced in this study. DR is a prominent cause of blindness globally, primarily impacting individuals aged 20 to 65. The proposed system can be an essential tool in the sustainable healthcare industry, providing timely and accurate diagnosis and treatment to patients with DR. Current manual screening by ophthalmologists can be time-consuming. Accurate categorization of DR is crucial to provide appropriate care and reduce the likelihood of permanent blindness. The use of big data and deep learning models in DR detection has the potential to revolutionize screening and diagnosis, leading to improved patient outcomes and reduced healthcare costs. This study compares traditional and CNN models, including Decision Trees, Random Forest, KNN, SVM, and Inception V4, to identify the best model for classifying retinal images into five stages of DR severity: No DR, Mild, Moderate, Severe, and Proliferative DR. Among the models tested, Inceptionv4 demonstrated the highest accuracy of 92.83% in classifying DR data. These findings demonstrate the potential of an automated system to improve DR diagnosis and management.

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A Symplectic Discretization Based Proximal Point Algorithm for Convex Minimization

arXiv

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arXiv 2024年

作者： Yuan, Ya-Xiang Zhang, Yi Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

The proximal point algorithm plays a central role in non-smooth convex programming. The Augmented Lagrangian Method, one of the most famous optimization algorithms, has been found to be closely related to the proximal point algorithm. Due to its importance, accelerated variants of the proximal point algorithm have received considerable attention. In this paper, we first study an Ordinary Differential Equation (ODE) system, which provides valuable insights into proving the convergence rate of the desired algorithm. Using the Lyapunov function technique, we establish the convergence rate of the ODE system. Next, we apply the Symplectic Euler Method to discretize the ODE system to derive a new proximal point algorithm, called the Symplectic Proximal Point Algorithm (SPPA). By utilizing the proof techniques developed for the ODE system, we demonstrate the convergence rate of the SPPA. Additionally, it is shown that existing accelerated proximal point algorithm can be considered a special case of the SPPA in a specific manner. Furthermore, under several additional assumptions, we prove that the SPPA exhibits a finer convergence *** Codes 49J52, 65B99, 65K10, 65K15, 68Q25, 90C25, 90C26 © 2024, CC BY.

关键词： Lyapunov functions

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SYMPLECTIC EXTRA-GRADIENT TYPE METHOD FOR SOLVING GENERAL NON-MONOTONE INCLUSION PROBLEM

arXiv

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arXiv 2024年

In recent years, accelerated extra-gradient methods have attracted much attention by researchers, for solving monotone inclusion problems. A limitation of most current accelerated extra-gradient methods lies in their direct utilization of the initial point, which can potentially decelerate numerical convergence rate. In this work, we present a new accelerated extra-gradient method, by utilizing the symplectic acceleration technique. We establish the inverse of quadratic convergence rate by employing the Lyapunov function technique. Also, we demonstrate a faster inverse of quadratic convergence rate alongside its weak convergence property under stronger assumptions. To improve practical efficiency, we introduce a line search technique for our symplectic extra-gradient method. Theoretically, we prove the convergence of the symplectic extra-gradient method with line search. Numerical tests show that this adaptation exhibits faster convergence rates in practice compared to several existing extra-gradient type *** Codes 47J20, 47H05, 65K10, 65K15, 65Y20, 90C30, 90C52 © 2024, CC BY.

关键词： Lyapunov functions

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Development of A Multipurpose Healthcare Mobile App Using JAVA

Development of A Multipurpose Healthcare Mobile App Using JA...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： R K Pongiannan S. Nikkath Bushra Syed Ismail Abdul Lathif Saahil Pradhan R Brindha J Franklin Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India Data science and business systems School of computing SRM Institute of Science and Technology Kattankulathur India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur India

Health care has become an essential part of present human life. The preliminary requirement to select the basic information about medical facilities through a mobile app will serve the needs of the common people. This paper presents an integrated mobile app developed using JAVA. This app is user-friendly in terms of login, registration, and bookings. This caters to the facility to select information regarding doctors, medical lab facilities, pharmacy details, and health articles. This app will provide communication.

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Multi-Verse Optimization for Node Localization in Wireless Sensor Networks

Multi-Verse Optimization for Node Localization in Wireless S...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： B. Pitchaimanickam P. Muthuvel R. Rajasekar Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Department of ECE Saveetha Engineering College Thandalam Chennai Department of CSE School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

Wireless Sensor Networks (WSN) are focused on increasing attention to develop their potential applications in healthcare, agriculture, object tracking, and energy management. Sensor nodes are placed in random locations. The information was gathered from the unknown positions of the sensor nodes, and this information may be useless for the applications. The accurate position of the node is required for more sensitive applications of WSN. This work proposes multi-verse optimization as a method for locating unknown nodes. Particle swarm optimization and firefly algorithms are contrasted with the suggested method. The experimental findings demonstrate that, compared to alternative algorithms, the suggested technique yields superior outcomes.

关键词： Location awareness Wireless sensor networks Accuracy Medical services Agriculture Information and communication technology Object tracking Particle swarm optimization Energy management Optimization

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Blockchain-Enabled Precision Agricultural System Using IoT and Edge computing 7th

Blockchain-Enabled Precision Agricultural System Using IoT a...

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7th International Conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Sakthi, U. Thangaraj, K. Anuradha, M. Kirubakaran, M.K. Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu TN Chennai603203 India Department of Information Technology Sona College of Technology Junction Main Road TN Salem636 005 India Department of Computer Science and Engineering S.A. Engineering College Thiruverkadu TN Chennai77 India Department of Information Technology St. Joseph’s Institute of Technology TN Chennai119 India

ISBN: (纸本)9789819907687

With the improvement of Internet of Things (IoT) and edge computing, the smart agricultural system is driven by data produced by the different sensors and smart computing devices in the agricultural land. A new methodological paradigm high-performance edge computing is incorporated with blockchain implemented precision agriculture system to improve the data processing operation related with resource management. Edge computing nodes collect and analyze the sensor data locally without transforming to the remote centralized cloud server, which rises data processing speed and reduce the network latency. The blockchain technology is incorporated with machine learning algorithm to maintain secured and protected distributed database for storing smart farm details like pH, soil moisture, temperature, crop management, humidity, and water irrigation level. The proposed system improves the productivity of food items and performance of the smart agricultural system by providing useful information to the farmers to make time-based decision about the land and increase the profit. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Crop Yield Prediction Using Spatio Temporal CNN and Multimodal Remote Sensing 2

Crop Yield Prediction Using Spatio Temporal CNN and Multimod...

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2nd International Conference on Edge computing and Applications, ICECAA 2023

作者： Arunarani, A.R. Selvanayaki, S. Saleh Al Ansari, Mohammed Ala Walid, Md. Abul Devireddy, Nagamalleswari Keerthi, Murugesan Mohana School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computational Intelligence Kattankulatur Campus Tamilnadu Chennai India Saveetha Engineering College Artificial Intelligence and Data Science Tamilnadu Chennai India College of Engineering University of Bahrain Department of Chemical Engineering Bahrain Department of Cse Khulna9203 Bangladesh Mohan Babu University Department of Freshman Andhra Pradesh Tirupathi India School of Agriculture Sr University Telangana Warangal India

ISBN: (纸本)9798350347579

Crop yield forecasting has been thoroughly studied recently and has become more important in preserving the safety of food. The majority of studies have concentrated on obtaining fixed spatial data from remote sensing images. Crop development, however, is a very complicated attribute influenced by a variety of factors. This work is intended to concurrently use spatial and temporal information from multimodal remotely sensed images in order to fully investigate these diverse characteristics. With the goal to make use of their supportive nature, Spatio temporal Convolutional Neural Networks (STCNN), is proposed as a revolutionary deep learning architecture for agricultural production prediction. To recognize the combined spatial-temporal representation, the STCNN specifically combines a spatial training segment and a temporal dependent segment into the convolutional network. The innovative spatial training segment begins by extracting enough spatial information from the multimodal spatial images. Then, in order to determine the temporal connection from the lengthy time-series images, the temporal dependent segment is concatenated on top of the spatial training segment. Both common machine learning techniques and cutting-edge deep learning techniques are compared to the outcomes of the proposed method. The experimental findings show that the STCNN can offer superior prediction performance of 0.64 RMSE and 12.03 MAPE values compared to the rival methods with minimized errors. © 2023 IEEE.

关键词： Remote sensing

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Predicting Average Tomato Prices Using Support Vector Machine with Polynomial Features

Predicting Average Tomato Prices Using Support Vector Machin...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： R K Pongiannan Swetanshu Agrawal Samudra Banerjee R Brindha Richard Pravin A Franklin J Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur India

Every economy depends on agriculture, and research in this area is primarily focused on boosting food supply and quality while minimizing costs and maximizing profit. Numerous factors can have an impact on tomato pricing, which serve as a crucial indicator of market trends for vegetables. Making educated decisions could be facilitated by predicting tomato average prices. To forecast the typical prices of tomatoes, we used the Support Vector Machine (SVM) model together with polynomial feature modification in this study. For our projections, we used the ‘Minimum’ and ‘Maximum’ pricing attributes. To guarantee consistency and improved performance, the dataset was standardized. With crossvalidation, we used a Grid Search technique to refine the parameters of our SVM model. The Mean Squared Error (MSE) and R2 score metrics were used to assess the model's performance. Our findings were shown graphically by scatter plots that contrasted real and forecasted prices and a histogram that showed the prediction errors. The SVM model has the potential to forecast average tomato prices when used in conjunction with polynomial feature transformation and parameter optimization. The agricultural industry's stakeholders may use this technique to gather knowledge of price trends and guide their decisions. More research may be done to increase prediction accuracy by incorporating more features and using other algorithm.

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Methodology to Maximize the Output Voltage Harvested from Thermoelectric Generator by Using Luo Converter

Methodology to Maximize the Output Voltage Harvested from Th...

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： Sivagami. P Harikrishnan. R Srideviponmalar. P Shripad V Deshpande Department of Electrical and Electronics Engineering Sathyabama Institute of Science & Technology Chennai India Symbiosis Institute of Technology Pune Campus Symbiosis International Deemed University Pune India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

In this technical epoch we are dealing with sensible issues such as global warming, pollution, depletion of available resources, increase in power cost etc. Researchers are focusing to reduce the effects due to sensible issues in present time by looking out for neoteric energy harvesters, that is power generators. A physical phenomenon that converts heat energy into electrical energy is thermoelectric effect. Thermal energy is transformed into electrical energy by thermoelectric generators (TEGs) using the Seebeck effect. Energy harvested from TEG have no moving parts, can be integrated on other devices, produces no noise, can be positioned anywhere that is position independent. The operating life time is also high and has no maintenance. TEG can harness energy for 1-2K thermal discrepancy in the environment. Using any wearable application in the body thermo electric generator can forge power in microwatts and voltage in mv. TEG has low conversion efficiency that it produces voltage in millivolt. To mold this trifling amount of voltage to some electrical appliances mv is boosted using LUO converter. Luo-converters boosts the voltage in geometric progression fed from the thermoelectric generator. Thermoelectric generator fed Luo converter is controlled by PID controller to proliferate output volt-age at the LUO converter side. Thus, TEG has raised as emerging green technology to power devices. This paper discusses about TEG application for charging devices.

关键词： Pollution Noise Green products Focusing Global warming Maintenance Voltage control Thermoelectricity Thermal energy Resistance heating

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