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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Felix, A. Yovan Sharmila, V. Devi, S. Nandhini Deena, S. Yadav, Ajay Singh Jeyalakshmi, K. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering R.M.D. Engineering College Tamil Nadu Kavaraipettai India Department of Computer Science and Engineering VSB Engineering College Tamil Nadu Karur India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Mathematics SRM Institute of Science and Technology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad India Department of Physics PSNA College of Engineering and Technology Tamil Nadu Dindigul India

ISBN: (纸本)9781032669663

The Internet of Things (IoT) connects systems, software, cloud computing, activities and could provide new access points for cyber-attacks. Virus attacks and unauthorized downloading are the two main risks to IoT security at the moment. These dangers run the risk of obtaining private information, damaging their reputation and their finances. A frame is employed in this paper’s deep learning to identify threat avoidance in IoT. The cyber security detection systems following database has to be built, after which the benchmark variables are chosen and assessed in order to establish a cyber-security alert system in a large data set. The deep learning strategy to identifying threat avoidance was described for the simulation of this scenario. From the Mailing database, malware samples have been gathered for analysis. The research results demonstrate that the proposed method for assessing risks to cyber security in IoT provides highest accuracy to current approaches. Because of this, Deep Convolution Neural Network (DCNN) is utilized in IoT to detect attack avoidance. © 2024 The Author(s).

关键词： Internet of things

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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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Cascaded Approach for Image Segmentation and Classification for Skin Cancer Detection 5

Cascaded Approach for Image Segmentation and Classification ...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Pattanaik, Abhipsa Das, Leena Banerjee, Shobhan School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Department of Engineering Technology Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9798350361155

With advancements in medical sciences, the detection and classification of skin cancer in patients is a field where extensive efforts are been put in by researchers to improve real-time solutions. It is a state where the growth of skin cells goes out of control. Since cancer evolves with respect to time, hence, to classify the type of skin cancer we need to segment the image properly before classification, which can lead to more reliable results in the detection of the type of cancer that has occurred. In this paper, we have proposed a cascaded approach to detect the type of skin cancer where instead of directly feeding an input as an input for classification, we first input the original image to U-net for segmentation and derive the granular details from it. Followed by which, the segmented image is fed and forwarded to CNNs through which the classification of the cancer is performed. We have implemented and compared the performances of MobileNet, DenseNet121, and ResNet50 in the classification of skin cancer disease. © 2024 IEEE.

关键词： Image segmentation

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Early Detection and Classification of Tomato Leaf Blight Disease Using Deep Neural Networks 2

Early Detection and Classification of Tomato Leaf Blight Dis...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Halder, Anangsha Guha Neogi, Subhrendu Dutta, Shibdas Khamaru, Koustov Bhattacharya, Arghya Roy, Shovan The Neotia University Department of Computer Science and Engineering Sarisha India Dcg Data-Core System India Department of Information Technology Kolkata India University of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350373110

The world population relies on agricultural plants for food, and illnesses reduce yield, but proper plant disease monitoring can help to resolve such issues. computer vision and machine learning methods can detect plant illnesses early, reducing their negative consequences and overcoming the drawbacks of continual human monitoring. After pre-processing of input images, the target region needs to be isolated. The proposed models analyzes images for detection and classification to identify edges, colors, and textures. This research has agricultural applications for early disease identification. This study examined ResNet, MobileNet, DenseNet, InceptionV3 and modified CNN model on the PlantVillage dataset to classify tomato illnesses. The F1 score, accuracy, sensitivity, and specificity can evaluate models. This paper evaluates these models using a diverse collection of tomato leaf pictures, contributing to automated disease detection systems and may be helpful for improving farm management and sustainability. © 2024 IEEE.

关键词： Fruits

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Unveiling Hidden Patterns: Exploration of Data Dependencies in Disease Prediction

Unveiling Hidden Patterns: Exploration of Data Dependencies ...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Durga Devi, P. Akila, K. Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani India

ISBN: (纸本)9798350394580

In the current scenario, the possibility of getting affected by disease is high so it is important to predict in advance. Due to technological advancement, it is for early prediction. Hence, this paper is focused on analysis of various DL algorithms for prediction system. This work provides a comprehensive report for the disease prediction system with the analysis of test input parameters for different disease. Further, different algorithms useful for the prediction framework has been discussed. Studying the interdependence of these risk factors in patients' medical records and comprehending their respective contributions to stroke prediction is critical. Deep learning techniques are crucial in overcoming these issues since they enable automatic disease identification in healthcare facilities. The prediction framework is analyzed using data from the Electronic Health Record dataset. Parametric statistics are used to evaluate the results. © 2023 IEEE.

关键词： Electroencephalogram(EEG) Electronic Health Record(EHR) Gated Recurrent Unit(GRU) Long Short Term Memory(LSTM) Recurrent Neural Network(RNN)

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A Secure Remote Monitoring as a Service (MaaS) for Solar Power Plant 1

A Secure Remote Monitoring as a Service (MaaS) for Solar Pow...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Sophy Priya, C. Thangakumar, J. Sambath, M. Hindustan Institute of Technology and Science Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350336009

In the field of Renewable energy, data processing is occurring in the photovoltaic plant using the IoT data logger. A data logger is a piece of technology that records and stores data over time, where SCADA systems are also indulged in data transfer and monitoring. Using these systems, we are transferring data from the data logger to cloud where a cloud-based platform is allowed users to manage and monitors the data loggers and as the result of analysis of various plants, the issues will be reported in the ticketing system and the further storage will be done in another cloud. This will result a cloud-cloud data flow where we are concentrating in ensuring the security of the data logger, Ethernet, WIFI, router connectivity, external web pages where data is viewed should be protected and the data backup, storage should be a secured area. RS485/RS422 bus provides the highest data transmission rate in data logger. Data encryption technique that involves Internet-cloud data transfers. This project mainly focus on introducing some new technologies in MaaS, where MaaS is a collection of tools and apps used to monitor a specific aspect of an application, server, system, or IT component. In the solar industry, this implies combining hardware, software, and services to monitor, evaluate, and troubleshoot solar plant faults throughout the solar array's lifetime, where we can monitor the amount of energy and control it securely. And this will help to introduce a secure wide range of global monitoring and reporting techniques in the renewable energy sector where data and data loggers are rising along with the risks. Cyber threats have a significant impact on smart grid performance due to the fast proliferation of real system in power electronic systems for connecting renewable energy sources with cyber frameworks. These electrical equipment in systems are linked by communication networks, which may be vulnerable to major cyber-attacks by malicious attackers. We have shown the d

关键词： Data transfer

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Vehicle Motion Prediction Based on Selective Perception and Weak Lane Guidance 29

Vehicle Motion Prediction Based on Selective Perception and ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wang, Xiaoliang Zheng, Shiqi Zou, Junkang Liu, Yuzhen Hunan University of Science and Technology Department of Computer Science and Engineering Xiangtan China

ISBN: (纸本)9798350330717

Trajectory prediction is crucial for ensuring the safety and reliability of autonomous driving systems. Accurately predicting the future trajectory of a vehicle can aid drivers or automated driving systems in developing reasonable movement plans and avoiding potential collisions. In real-world scenarios, drivers selectively observe areas that influence their driving decisions based on real-time traffic scenarios, adapting to changing traffic situations to ensure safety. To simulate this behavior, we propose a selective perception module that utilizes historical trajectories and lane centerlines to extract vital information from the changing traffic scene. Furthermore, vehicles tend to follow lane guidance to some extent, we use a normalized flow decoder and small-scale guided loss to induce the distribution of predicted trajectories, slightly increasing the probability of lane centerlines in the predictions as a weak lane guidance. We conduct ablation studies using the public dataset nuScenes and comparative experiments with state-of-the-art methods, showcasing the predictive diversity of our approach while maintaining high accuracy. © 2023 IEEE.

关键词： autonomous driving motion prediction

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ExpACVO-Exponential Anti Corona Virus Optimization Enabled Hybrid Deep learning for Tongue Image Segmentation Towards Diabetes Mellitus Detection Via CNN-LSTM Network 1

ExpACVO-Exponential Anti Corona Virus Optimization Enabled H...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Mathew, Jimsha K Sathyalakshmi, S. Hindustan Institute of Technology & Science Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350330915

The surprising headways in biotechnology and public medical services foundations have prompted a pivotal creation of basic and touchy medical care information. By applying canny information examination methods, many fascinating examples are distinguished for the early and beginning discovery and avoidance of a few deadly infections. Diabetes Mellitus (DM) is a very dangerous illness since it adds to other deadly infections, i.e., heart, kidney, and nerve harm. The following stages have been proposed in this paper for the order, beginning phase ID, and expectation of diabetes: a) Data collection from tongue dataset of many images, b) Preprocessing using the gaussian filter for noise removal, c) Segmentation using U-Net-CRFRNN for segmenting region of interest and finally d) classification using the integration of Exponential Weighted Moving Average (EWMA) and Anti Corona Virus Optimization (ACVO) and also to increase the efficiency of the network, use of CNN-LSTM. The suggested architecture is more effective than other state-of-the-art models on many different parameters, based on the outcomes (accuracy: 0.94, sensitivity: 0.96, specificity: 0.96) © 2023 IEEE.

关键词： Anti Corona Virus Optimization Classification CNN-LSTM Deep learning Diabetes Mellitus Exponential Weighted Moving Average Tongue Segmentation

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Efficient Lane Detection and Keeping for Autonomous Vehicles in Real-World Scenarios 26

Efficient Lane Detection and Keeping for Autonomous Vehicles...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Azad, Farhana Islam, Yumna Ruslan, Ch Zakauddin Md Aye Mong Marma, C. Kalpoma, Kazi A. Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

In this study, we developed an auto-lane-keeping assist system to contribute to autonomous driving. The system will keep the vehicle in its designated lane to reduce road accidents. The main challenge of this research is to make the system suitable for real-world road conditions. To achieve an efficient lane-keeping system, we detected the lane lines automatically using canny edge detection and a sliding window algorithm. The proposed lane-keeping system has another stage: lane-keeping. The lane-keeping task includes the calculation of the offset value, meaning how much the vehicle deviates from its lane or if the vehicle is in the center line of the lane, and the steering angle correction of the vehicle to be in the lane. We use the Sliding Window Algorithm for the offset value calculation, which makes the detection of curved lane lines more accurate than the previous works and also performs well in various weather and lighting conditions. The proposed system's performance is evaluated using the IROADS dataset in this work. We implemented lane keeping in both software and hardware models, introducing the AURIX TC-375 microcontroller for hardware integration, which enhances overall lane-keeping system output through the integration of detection and keeping processes. © 2023 IEEE.

关键词： AURIX TC-375 Micro-controller Automotive ECU Lane Detection Lane Keeping Sliding Window Algorithm

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Dynamic Optimal Power Flow Method Based on Reinforcement Learning for Offshore Wind Farms Considering Multiple Points of Common Coupling

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Journal of Modern Power Systems and Clean Energy 2024年第6期12卷 1749-1759页

作者： Yang Fu Zixu Ren Shurong Wei Lingling Huang Fangxing Li Yang Liu Engineering Research Center of Offshore Wind Technology Ministry of Education(Shanghai University of Electric Power) Shanghai 200090China Department of Electrical Power Engineering Shanghai University of Electric PowerShanghai 200090China Department of Electrical Engineering and Computer Science The University of TennesseeKnoxvilleTN 37996USA

The widespread adoption of renewable energy sources presents significant challenges for power system *** paper proposes a dynamic optimal power flow(DOPF)method based on reinforcement learning(RL)to ad-dress the dispatching *** proposed method consid-ers a scenario where large-scale offshore wind farms are inter-connected and have access to an onshore power grid through multiple points of common coupling(PCCs).First,the opera-tional area model of the offshore power grid at the PCCs is es-tablished by combining the prediction results and the transmis-sion capacity limit of the offshore power *** upon this,a dynamic optimization model of the power system and its RL en-vironment are constructed with the consideration of offshore power dispatching ***,an improved algorithm based on the conditional generative adversarial network(CGAN)and the soft actor-critic(SAC)algorithm is *** analyzing an improved IEEE 118-node system,the proposed method proves to have the advantage of economy over a longer *** resulting strategy satisfies power system opera-tion constraints,effectively addressing the constraint problem of action space of RL,and it has the added benefit of faster so-lution speeds.

关键词： Offshore power grid optimal scheduling dy-namic optimal power flow(DOPF) reinforcement learning(RL) renewable energy

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