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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Tummala, Manoj Pravin, A. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396638

Progressive technological development results in modern automotive technology such as self-driving automobiles. These require sensors to collect data about nearby objects and the surroundings in order to identify lanes and make decisions. Instead of LiDAR sensors (Light Detection and Ranging), Tesla, Waymo, and other successful businesses have employed cameras for developing autonomous vehicles. The disadvantage of camera performance changing with lighting conditions can be solved by using LiDAR. The LiDAR sensor aids in 3D mapping, allowing cars to travel in a predictable environment. The use of cameras for monitoring normal driving circumstances might be difficult because of the large range of features they include, but LiDAR contains infrared light, which is a solution for concerns such as fog, rainy weather, and varied textures. Developing sensors driven by computer vision can lead to more environmental sensing for self-driving automobiles in the future. This study discusses the use of LiDAR sensors in autonomous cars. © 2023 IEEE.

关键词： Autonomous vehicles

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Classification and Segmentation of Leaf Images based on Deep Learning for Peanut Plant Disease Detection 4

Classification and Segmentation of Leaf Images based on Deep...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Ebenezer, V. Daniel, Arsha Anna Getzi Jeba Lillipushpam, P. Edwin, E.Bijolin Roshni Thanka, M. Devassy, Rosebel Karunya Institute of Technology and Science Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350300093

Peanut is a key food commodity, and diseases of its leaves can have a negative impact on the quantity and quality of the product. The seed has high levels of potassium, magnesium, calcium, riboflavin, niacin, folic acid, vitamin E, resveratrol, and amino acids. It also contains 36% to 54% oil, 16% to 36% protein, and 10% to 20% carbs. Early, late, and rusty leaf spots, among other frequent illnesses, are recognized by this equipment. Techniques for image augmentation have been used, such as twisting, rotating, and scaling. The variant employs a Multi Class Convolutional neural network with 5 output which include Normal Leaf, Images Without Leaf and the images with the leaf been infected by the diseases. © 2023 IEEE.

关键词： Convolutional neural networks

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Tropical Cyclone Intensity Prediction Using Deep Learning Techniques- A Survey 4

Tropical Cyclone Intensity Prediction Using Deep Learning Te...

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4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Ilayaraja, V. Rajamohana, S.P. Jagadeesan, S. Aishwarya, V. Rakesh, M. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632 014 India School of Engineering & Technology Pondicherry University Department of Computer Science Puducherry Karaikal India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Tamil Nadu Vellore632 014 India

ISBN: (纸本)9798331528171

Tropical cyclones, characterized by strong winds and heavy rainfall, threaten human life in coastal regions crucial to the economy, including fisheries, agriculture, tourism, and infrastructure. Their frequent occurrence impacts communities reliant on these industries for livelihoods. Accurate estimation of tropical cyclone intensity is vital for disaster preparedness, risk assessment, and timely evacuations. Recent advancements in machine learning and deep learning have been applied to predict cyclone intensity from satellite images, providing insights into cyclone dynamics and enhancing disaster response. This paper analyzes recent research on intensity estimation using various machine learning algorithms and discusses future prospects for improving accuracy and reliability. © 2024 IEEE.

关键词： Tropical cyclone

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Test Suite Minimization for Regression Testing Using Keyword Match Technique 7th

Test Suite Minimization for Regression Testing Using Keyword...

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7th International Conference on Information and Communication technology for Competitive Strategies , ICTCS 2022

作者： Freeda, R. Adline Rajendran, P. Selvi Department of Computer Science and Engineering Hindustan Institute of Science and Technology Chennai India

ISBN: (纸本)9789811996375

Regression testing is a crucial phase of the testing practice as it ensures stability and functionality of the system. It repeats test cases from the previous versions to ensure that the original function of the system is not affected by the new changes. The agile methodologies had gained popularity in the recent years which stresses the key importance of regression testing in protecting software quality, and regression testing has made significant development in the recent years. In an agile framework, the ideal scenario for regression testing is to run a regression suite of test cases after each sprint and release, which consumes money and time. In the proposed system, using the keyword match technique, we provide a test case coverage mechanism that predicts the maximum coverage of test cases over the requirements in this work. Following that, test cases are chosen based on maximum coverage. The approach improves both prioritization and decision-making. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision making

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A robust solid electrolyte interphase enabled by solvate ionic liquid for high-performance sulfide-based all-solid-state lithium metal batteries

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Nano research 2023年第6期16卷 8411-8416页

作者： Jingguang Yi Chong Yan Dan Zhou Li-Zhen Fan Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China School of electrical engineering and automation Luoyang Institute of Science and TechnologyLuoyang 471023China Shanxi Research Institute for Clean Energy Tsinghua UniversityTaiyuan 030032China

All-solid-state lithium metal batteries(ASSLMBs)that incorporate solid electrolyte(SE)and lithium metal anode suggest considerable potential in addressing the security concerns and energy density limitation of conventional lithium-ion batteries(LIBs).However,the practical application of ASSLMBs is always restricted by the interfacial instability of lithium metal anode/electrolyte and inevitable lithium dendrites propagation in ***,a solvate ionic liquid is adopted to modify the interface stability of lithium metal anode/electrolyte and inhibit the growth of lithium dendrites via an in-situ formation of a robust solid electrolyte interphase(SEI)on the surface of lithium metal ***,the ASSLMBs assembled with Li_(6)PS_(5)Cl(LPSCl)electrolyte,lithium metal anode that protected by robust SEI layer,and LiNbO_(3)@NCM622 cathode exhibit high initial capacity of 126.5 mAh·g^(−1)and improved cycling stability with a capacity retention of 80.3%over 60 cycles at 0.1 *** work helps to provide a facile route for the design of robust SEI in the application of ASSLMBs.

关键词： all-solid-state lithium metal batteries solvate ionic liquid robust solid electrolyte interphase Li_(6)PS_(5)Cl(LPSCl)electrolyte

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Energy Consumption in Smart Buildings using Machine Learning 7

Energy Consumption in Smart Buildings using Machine Learning

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Kumar, Katti Bharath Abhay Gopireddy, Gangireddy Ranjana, P. Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

Energy is the indispensable factor of modern societies. From last few decades, there is rapid increase in world's energy consumption due to raise of population and their comfort demands. Buildings are accountable for 40% of global energy hence Building Energy Management has become crucial topic nowadays for reducing energy wastage The ability to forecast the amount of energy used in SB and plan for that use is essential for the implementation of energy-efficient management systems. The proposed method for estimating the amount of energy that is used in buildings, as well as offering suggestions for cutting down on energy use and lowering expenses. The systems use the information like temperature humidity air quality etc. This information is analyzed by machine learning forecast the amount of energy that is used by smart buildings. © 2023 IEEE.

关键词： Internet of things

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Road Damage Detection Using Real Time Video 8th

Road Damage Detection Using Real Time Video

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Gomathi, G. Niranjana, G. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9783031736162

This study presents a completely unique deep-learning- powered real-time road damage detection system, paving the way for more secure roads and smarter infrastructure. With the help of convolutional neural networks (CNNs) and an easy to use interface, the system can automatically and clearly identify different types of road damage, such as surface irregularities, detailed fractures, and dangerous potholes. This innovative method surpasses the drawbacks of laborious manual inspections and ushers in a brand new age of advanced road safety and efficient maintenance. A strong CNN model that has been painstakingly trained on an intensive collection of annotated pictures and videos forms the basis of the system. It, as it should be, detects different sorts of damage with an accuracy close to 89%, offering users useful visible information via highlighting particular places on output pictures. However, the creativity doesn’t stop there. The system has a clean-to-use Tkinter graphical consumer interface (GUI) that facilitates engagement. It also has real-time alarm triggers that, without delay, inform the appropriate authorities, ensuring timely restoration and decreasing any safety threats. To make the system work better, it is put through a radical testing process using common evaluation standards along with F1-rating, accuracy, precision, and recall. The findings are clear-cut, which is revolutionary pressure in addition to a useful tool for finding road degradation. Beyond the instantaneous safety advantages, it offers better infrastructure management and optimized useful resource allocation, opening the door to a future in which technology protects our lives and creates more secure, more convenient travel. © IFIP International Federation for Information Processing 2025.

关键词： Resource allocation

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The Use of Recurrent Neural Networks in the Optimization of computer science Algorithms 1

The Use of Recurrent Neural Networks in the Optimization of ...

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1st IEEE International Conference on Emerging research in Computational science, ICERCS 2023

作者： Franklin, Ramya G Ronald Doni, A. Poornima, D. Sabasti Prabu, S. Igni Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

A new era of computational efficacy and problem-solving abilities has begun with the combination of Recurrent Neural Networks (RNNs) and computer science methods. It is crucial in modern computing to combine RNNs with methods from the field of computer science. RNNs bring a dynamic learning paradigm, making algorithms more efficient and flexible by allowing them to process sequential input and complicated dependencies. Their usefulness goes beyond one specific area of study, and into others such as optimization issues, recommendation systems, image processing, and natural language processing. RNNs serve a vital role in addressing the severe challenges that algorithms confront, including processing sequential input, identifying temporal connections, and optimizing in high-dimensional environments. By providing dynamic and adaptive modeling, RNNs aid algorithms in navigating these obstacles. Recurrent Neural Network-Based Optimization Algorithm (RNN-OA) is an innovative novel approach presented in this research. RNN-OA is an approach to algorithmic optimization that makes use of RNNs' dynamic learning capability. Mechanisms for selective data processing with attention, regularization methods for model stability, and interpretability improvements for openness are all a part of it. Quickening the process of optimization in many different types of problems is transfer learning and fine-tuning approaches. There are a wide variety of computer science fields where RNN-OA can be useful. Time-series analysis, language modeling, picture recognition, and recommendation systems all benefit greatly from its ability to handle sequential data. The efficacy, scalability, and robustness of the method are thoroughly evaluated by simulating various problem scenarios and data streams. The results of these simulations provide illumination for the benefits and drawbacks of RNN-OA, paving the way for its development toward actual application in algorithmic optimization. © 2023 IEEE.

关键词： Recurrent neural networks

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IoT-Supported Indoor Navigation System Using Machine Learning Technique 12th

IoT-Supported Indoor Navigation System Using Machine Learnin...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Bhuvana, S. Vidhya, K. Sudharsan, S. Suhita, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819788354

Indoor Navigation System (INS) supports seamless movement of objects within confined spaces in smart environments. In this paper, a novel INS that relies on ESP32-based Received Signal Strength Indication (RSSI) measurements for precise estimation of distance using K-Nearest Neighbour (K-NN) algorithm is proposed. The system architecture includes three ESP32 nodes functioning as clients, and ESP8266 combined with ESP32 acting as a server. To overcome the limitations of Global Positioning System (GPS) within an indoor environment, Machine Learning (ML)-based prediction models are employed to estimate distance between nodes and predict user locations along with the distance between source and destination. This approach not only addresses the challenges posed by GPS in indoor settings, but also shows the potential of ESP32 devices and predictive algorithms for enhancing navigation accuracy and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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Mitigating Credit Card Fraud: A Machine Learning Perspective 2

Mitigating Credit Card Fraud: A Machine Learning Perspective

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Bhushan, Shivam Gupta, Indrajeet Angayarkanni, S.A. Balakiruthiga, B. Pandimurugan, V. Rajaram, V. School of Computing SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur India Acharya Institute of Technology Computer Science and engineering Bangalore India

ISBN: (纸本)9798350366570

In this day and age of online and digital transactions, credit cards have come up as a popular option of payment. This has led to the increase in the fraudulent credit card transactions and as a result the banks and the customers have to bear the loss. It is very vital for the credit card companies to identify and stop the fraud transactions. The main aim of this project is to create a model that can distinguish fraudulent credit card transactions from real ones. The proposed model uses the transaction patterns of previous credit card transaction data which contains both genuine and fraud data to train itself using suitable ML algorithms and identify the fraudulent transactions and nip it in the bud. We will be using using SMOTE to process the data considering the imbalance between legitimate and fraudulent transactions, hyperparameter tuning the model for increased effectiveness and using random forest classifier and decision tree classifier to compare results. By using the above mentioned techniques we saw an increase in accuracy and recall. These models offer a scaleable solution to the problem of real time fraud detection in an ever growing and vast financial landscape © 2024 IEEE.

关键词： Random forests

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