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Rainfall Forecasting Using Machine Learning Algorithms for Localized Events

computers, Materials & Continua 2022年第6期71卷 6333-6350页

作者： Ganapathy Pattukandan Ganapathy Kathiravan Srinivasan Debajit Datta Chuan-Yu Chang Om Purohit Vladislav Zaalishvili Olga Burdzieva Centre for Disaster Mitigation and Management Vellore Institute of TechnologyVellore632014India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Service Systems Technology Center Industrial Technology Research InstituteHsinchuTaiwan School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Geophysical Institute of Vladikavkaz Scientific Centre Russian Academy of Sciences(RAS)VladikavkazRussian Federation

A substantial amount of the Indian economy depends solely on ***,on the other hand,plays a significant role in agriculture–while an adequate amount of rainfall can be considered as a blessing,if the amount is inordinate or scant,it can ruin the entire hard work of the *** this work,the rainfall dataset of the vellore region,of Tamil Nadu,India,in the years 2021 and 2022 is forecasted using several machine learning *** engineering has been performed in this work in order to generate new features that remove all sorts of autocorrelation present in the *** removal of autocorrelation,the data could be used for performing operations on the time-series data,which otherwise could only be performed on any other regular regression *** work uses forecasting techniques like the AutoRegessive Integrated Moving Average(ARIMA)and exponential smoothening,and then the time-series data is further worked on using Long Short Term Memory(LSTM).Later,regression techniques are used by manipulating the *** work is benchmarked with several evaluation metrics on a test dataset,where XGBoost Regression technique outperformed the *** uniqueness of this work is that it forecasts the daily rainfall for the year 2021 and 2022 in vellore *** work can be extended in the future to predict rainfall over a bigger region based on previously recorded time-series data,which can help the farmers and common people to plan accordingly and take precautionary measures.

关键词： Time-series ARIMA LSTM regression rainfall

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Automated Detection of Hate Speech in Hindi using Supervised Learning Models

Automated Detection of Hate Speech in Hindi using Supervised...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Rajak, Nikhil Phulre, Ajay Kumar Chandra Patel, Subhash Patel, Narottam Das Khekare, Ganesh Vit Bhopal University School of Computing Science Engineering and Artificial Intelligence Sehore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350375442

This study addresses the pressing issue of hate speech on online platforms. Traditional text-based analysis is no longer adequate for identifying hate speech due to the growth of internet platform data. The project aims to create scalable automated methods for web platforms to detect hate speech in real-time. To find the best algorithms for recognizing hate speech, the study compares many machine learning models using a large dataset in Hindi from Twitter. Where the study matters is in the creation of advanced content filtering systems that offer a more secure and friendly online environment. Support Vector Machine (SVM) has the best performance, with outstanding precision (0.9468) and F1-score (0.944), showcasing the top-performing ones and talking about the difficulties and ways ahead in this field. The study recommends improving Hindi hate speech detection systems. With problems such as imbalanced data sets and the dynamic nature of online language, language research using hybrid models, constant model adjustments, and language trend monitoring are all necessary. © 2024 IEEE.

关键词： Adversarial machine learning

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A novel filter for removing image noise and improving the quality of image

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International Journal of Cloud Computing 2022年第1期11卷 14-26页

作者： Prathik, A. Anuradha, J. Uma, K. School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India School of Advanced Sciences Vellore Institute of Technology Tamil Nadu Vellore India

This paper proposed a hybrid wavelet double window median filter (HWDWM) which is made by blending decision-based coupled window median filter and discrete wavelet transform (DWT) and review is made to increase the filters which are widespread for removing noise. In the proposed filter there are double window such as row window and column window. This proposed method takes the noisy image for processing and it moves row window for indexing from the 1st pixel of the noisy image up to the last pixel of the noised image then indexing is made by column window then decompose the signal of the image to provide the localisation. The noisy image is decomposed by DWT and then coefficients are transformed to independent distributed variables. The coefficients are then analysed on the basis of thresholding. The image is reconstructed using wavelet transform's inverse after the threshold. Experiments were executed in order to show the effect of noise removal filters on soil image. Two metrics are used to measure the quality of image;they are: peak signal to noise ratio (PSNR) and root mean square error (RMSE). Experimental results show the superiority of this filter over other noise removal filters. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Data mining

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An Approach to Analyze the Permanent Magnet Motor Torque Ripple for Improving Torque Power Curves used in Electric Vehicles 15

An Approach to Analyze the Permanent Magnet Motor Torque Rip...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Neelakandan, Varatharaj Prabakaran, B. Balaji, D. Suresh, P. Maheswaran, S. Indhumathi, N. Hindustan Institute of Technology and Science Chennai India Vellore Institute of Technology School of Computer Science and Engineering TamilNadu Vellore India Kongu Engineering College Department of Electronics and Communication Engineering TamilNadu Erode India

ISBN: (纸本)9798350370249

An electric car's permanent magnet synchronous motor (PMSM) will be designed and evaluated for performance in this research article. In order to satisfy the unique needs of the electric car and improve its overall performance, the goal is to achieve the highest torque density and the lowest torque ripple. The study's goal is to increase the electric vehicle's power density to do this, which calls for the use of a high-power-density motor. This is addressed by conducting an optimization procedure to increase the generated torque while making sure that the chosen motor stays within the defined size and volume restrictions. The efficient use of permanent magnet (PM) materials with strong thermal limiting capacity, which can enhance the power, is a critical component of this improvement. The efficient use of permanent magnet (PM) materials with high thermal limiting capacity, which may boost the power output under both peak and continuous loads, is a vital component of this optimization. The most important motor design parameters are carefully chosen throughout the optimization process to have the greatest influence on motor performance. Using the finite element software program 'CAE Software,' the research team tests and executes the suggested motor design. The overall goal of this project is to build a PMSM that is highly effective and adapted to the needs of electric vehicles. The goal of the study is to optimize critical motor parameters and use modern PM materials with higher thermal properties in order to increase torque density and reduce torque ripple of the motor. © 2024 IEEE.

关键词： Permanent magnets

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Enhancing Driver Safety: Real-Time Drowsiness Detection through Eye Aspect Ratio and CNN-Based Eye State Analysis 2

Enhancing Driver Safety: Real-Time Drowsiness Detection thro...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Rajasekaran, Rajkumar Narayanamoorthi, M. Solanki, Rashi Sanghavi, Veer Yilmaas, S. Vellore Institute of Technology Department of Analytics Tamil Nadu India Vellore Institute of Technology Department of IoT Tamil Nadu India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350306415

As technology rapidly advances, our reliance on automobiles for transportation has grown significantly. However, this increased use of vehicles has brought about a critical concern for road safety - drowsy driving. To address this issue, we intro- duce a novel Drowsiness Detection System (DDS) that harnesses the capabilities of machine learning, specifically OpenCV and Keras models. Our DDS adopts a comprehensive approach to identify driver drowsiness by integrating computer vision techniques and deep learning algorithms. OpenCV is employed for real-time video analysis, tracking crucial facial and eye-related indicators such as eye closure, blink rate, and head position. Moreover, the heart of our DDS relies on deep learning models implemented through Keras, trained on a diverse dataset of driver behavior examples. These Keras models can dynamically adapt to varying lighting conditions, driver appearances, and different vehicle environments, ensuring reliable performance. The DDS promptly alerts the driver upon detecting signs of drowsiness, using both audible alerts and visual prompts. © 2024 IEEE.

关键词： Deep Learning Drowsiness Detection System (DDS) OpenCV

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Fine-tuning Llama-2-7b Model for Conversation Summarization 15

Fine-tuning Llama-2-7b Model for Conversation Summarization

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nivethitha, V. Verma, Nirvik Bokadia, Ronit Jaju, Ronith Vellore Institute of Technology Chennai School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350370249

In this research paper, we explore the optimization for conversation summarization of the Llama 2.7 b model by quantization-aware fine-tuning, specifically exploiting QLORA quantization techniques. In natural language processing (NLP), large language models (LLMs) have become powerful tools for various text-processing tasks including dialogue summarization in a rapidly evolving landscape. Still, the computational needs of these model's present obstacles to their practical implementation in conversational interfaces and live applications. Quantization observance fine-tuning is an optimistic way of compressing model parameters while still enhancing performance. This analysis demonstrates through rigorous experimentation that the QLORA quantization is effective on fine tuning the Llama 2.7b model for conversation summarization. Fine-tuning the model on a well-rounded dataset with conversational data derived from different sources such as social media, chat logs, and customer interactions makes it more adaptable to any sort of dialog that takes place. The incorporation of QLORA quantization's also improves the attention mechanism of the model enabling it to obtain relevant information such as generating concise summaries. The latest version of Llama is 2.7. It is called Llama 2.7b and provides several practical advantages for summarizing dialogues such as improved computational efficiency, better user experience in conversational interfaces, and capturing key insights from long conversations. Its outputs are in line with the current research on LLM-based conversation summarization and exhibit the viability of optimization techniques like quantization-aware fine-tuning to maximize the performance of large language models for particular natural language processing tasks (NLP). © 2024 IEEE.

关键词： llama 2 LLM Natural Language Processing QLORA Quantization

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Key-Independent Image Encryption using DNA Encoding and Chaotic Dynamics 10

Key-Independent Image Encryption using DNA Encoding and Chao...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Maharaja Vannia Moorthi, V. Krishna, Megha R. Kumar, Gopika Anil Thanikaiselvan, V. Subashanthini, S. Amirtharajan, Rengarajan Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India Sastra Deemed University School of Electrical & Electronics Engineering Thanjavur613 401 India

ISBN: (纸本)9798350353068

Privacy violations are common in our technology-driven world, where almost everyone interacts with internet-connected electronic devices. To address this, cryptography has emerged, concealing data during transmission to deter hackers. Cryptography involves basic mathematical operations, exchanging data based on a shared key known only to the sender and receiver. However, this method suffers from key dependency at the receiver's end, requiring constant key knowledge. To overcome this issue, a novel image encryption method has been introduced in this paper that is independent of the key on the receiver side. This method leverages techniques such as DNA Encoding, Chaotic Dynamics, and Key Hiding Mechanisms to increase the security of the sent image. The proposed method has been tested experimentally, and the results demonstrate its effectiveness with entropy values exceeding 7.995 and correlation coefficient values below 1% for the vertical, horizontal, and diagonal directions. Overall, the proposed image encryption scheme offers a promising solution for protecting sensitive image data in various applications, combining advanced encryption techniques with key management enhancements to ensure confidentiality and integrity. © 2024 IEEE.

关键词： Cryptography

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Autonomous Wheelchair Robot for Enhanced Airport Accessibility

Autonomous Wheelchair Robot for Enhanced Airport Accessibili...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Luke, Blesson Mathews Tripathi, Achint Sampson, S. Abraham School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350319088

The paper presents a theoretical feasibility study of an autonomous wheelchair robot (AWR) designed to guide and assist the elderly and people with limited mobility, at various airports, by addressing the difficulties they face while traversing complex and crowded airport terminals. The proposed system is designed to automatically navigate through all check points, namely, check-in counters, security checks and finally the boarding gate, by simply obtaining destination information (like flight number), either from a source like bar code printed on the ticket, or direct input. Once the data is obtained, the AWR, using the gmapping module which generates detailed airport maps, determines the most efficient routes to reach its destination and automatically navigates to the destination through the check points. The motion detection module identifies and avoids obstacles, allowing passengers to adjust their speed and heading. A comprehensive performance evaluation of the AWR system assesses navigation accuracy and efficiency, revealing significant enhancements in airport transportation accessibility, safety, and convenience for travellers with limited mobiity. Beyond airports, other potential applications of AWR include a variety of public spaces, such as shopping centres, hospitals, and museums, offering flexible solutions for autonomous mobility. © 2023 IEEE.

关键词： Navigation

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Real-Time Fire Detection in Unmanned Ground Vehicles Integrating YoloV5 and AWS IoT

Real-Time Fire Detection in Unmanned Ground Vehicles Integra...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Banerjee, Saptajit Das, Ronodeep Rathinam, Rajesh Dhanalakshmi, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350315127

This paper aimed at the development of a remotely controllable 4-wheeled unmanned ground vehicle (UGV) capable of transmitting realtime sensor data and live video feeds over the internet. The central innovation lies in the utilization of an advanced object detection model, to detect fires within the live video feed. By leveraging a Raspberry Pi 4 with 2GB RAM, AWS IoT Core, MQTT, HTTP, and TCP protocols, the UGV achieves seamless data exchange. AWS IoT Core serves as both MQTT broker and subscriber, with the UGV as publisher for sensor readings and subscriber for control commands. The web app acts as the publisher for control commands, generating signals based on user input. The Remote. it software plays a pivotal role in facilitating a cloud-based HTTP service for live video streaming, thereby significantly enhancing global accessibility. Moreover, diverse remote operation methods including keyboard, onscreen, and speech controls, provide flexible means for user interaction. The integration of YOLOv5, Tensorflow, and OpenCV yields a robust fire detection capability, effectively triggering SMTPbased email alerts. This system effectively showcases the seamless fusion of robotics, IoT, web technologies and machine learning, thereby contributing to safety enhancement and inspiring a wave of future advancements. © 2023 IEEE.

关键词： Web services

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Mango Leaf Disease Detection and Classification Using Spatial Attention Enabled Ensemble Classification

Mango Leaf Disease Detection and Classification Using Spatia...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Pandiyaraju, V. Venkatraman, Shravan Abeshek, A. Aravintakshan, S.A. Pavan Kumar, S. School Of Computer Science And Engineering Vellore Institute Of Technology Tamil Nadu Chennai600127 India

ISBN: (纸本)9798350364828

Leaf diseases are growing to be a concerning threat which impacts both the biological conditions and economic conditions of human beings. The poor cultivation would result in world hunger as well as increase in cost of living since reduction in supply would lead to increase in demand especially with necessity commodities. Mango being a seasonal crop with high value would face the same issue as well. Therefore, this research work proposes an ensemble learning classifier which is based on spatial attention that incorporates two deep learning classifiers namely the VGG-16 model and EfficientNetV2-BO model for immediate identification as well as classification of various mango leaf diseases such as Cutting Weevil, Gall Midge, Die Back, Bacterial Canker, Powdery Mildew, Anthracnose and Sooty Mould. The proposed ensemble model classifies effectively resulting with high evaluation scores of 97.13%, 97.53%, 97.33% and 97.38% in terms of Accuracy, Precision, Recall and Fl-Score respectively. These results are better than ResN et50, TL-based diagnosis using AlexNet, ANN, AlexNet and VGG16 models. © 2024 IEEE.

关键词： Cultivation

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