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Quantum-Improved Weather Forecasting: Integrating Quantum Machine Learning for Precise Prediction and Disaster Mitigation

Quantum-Improved Weather Forecasting: Integrating Quantum Ma...

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Suhas, S. Divya, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Centre for Advanced Data Science Chennai India

ISBN: (纸本)9798350314946

In numerous industries, weather forecasting is essential for making informed decisions and mitigating the effects of extreme weather events. The complexity and chaos of weather systems, however, place restrictions on standard procedures, leading to errors and significant threats. We suggest the Quantum Improved Weather Forecast framework, which combines quantum machine learning methods with conventional methodologies, to solve these issues. By using quantum algorithms including quantum support vector machines, quantum neural networks, and quantum clustering, the QWF framework seeks to improve accuracy. Despite a few drawbacks, the QWF framework provides a method to revolutionize weather forecasting by enhancing prediction accuracy and facilitating improved catastrophe preparedness. It may prevent fatalities, safeguard critical infrastructure, and promote sustainable growth. © 2023 IEEE.

关键词： Climate change

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Optimized Deep Learning Mechanism for Intrusion Detection: Leveraging RFE-Based Feature Selection and PCA for Improved Accuracy 5

Optimized Deep Learning Mechanism for Intrusion Detection: L...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rajkumar, M. Vs, Lakshi Karthik, R. Pavithra, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Smart Computing Tamil Nadu India B. Tech-Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India

ISBN: (纸本)9798331530013

With the growing demand for wireless networks, network traffic has surged, particularly involving large-scale data transmission. This results in an increased number of network security threats and the emergence of various cyber-attacks. To overcome these threats, the development of an efficient intrusion detection system (IDS) has become crucial. By leveraging the recent advancements in Artificial intelligence (AI), this project introduces an optimized deep learning mechanism utilizing a prevalent deep learning model, the Deep Neural Network (DNN) algorithm. This proposed method uses feature engineering techniques like feature selection and feature reduction. Given the high dimensionality of the selected datasets, these techniques are employed to prevent over-fitting. Recursive feature elimination performs feature selection followed by Principal Component Analysis for the feature reduction process, thus enhancing the model's efficiency. The system is assessed using two benchmark datasets: KDD and UNSW-NB15. The experimental results indicate that the presented DNN model on the KDD dataset has achieved 92.45% accuracy and 91.35% accuracy on the UNSW-NB15 dataset for intrusion classification. © 2024 IEEE.

关键词： Network intrusion

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IoT-Based Health Monitoring System for Post-Covid with Diabetes 3rd

IoT-Based Health Monitoring System for Post-Covid with Diabe...

引用

3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Pankaja Lakshmi, P. Sivagami, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu Chennai600127 India

ISBN: (纸本)9789819726707

Internet of Things (IoT) devices are proliferating in the modern world, and this has a significant impact on healthcare. It can lower the price of medical care and offer early diagnosis of health issues. A patient that needs to be watched over constantly needs a healthcare monitoring system. In order to save their lives in dire circumstances, elderly patients, in particular, should periodically have their health state checked and informed to the doctor. Quiet people suffer the negative effects of true medical issues because of a lack of adequate health monitoring. Many IoT devices are available today to autonomously monitor patient health in order to address this problem. As of now, there is no other significant and adequate framework, to predict the impact of Covid-19 infection in diabetes patients. The literature has shown a separate ML/DL model for Covid-19 prediction and diabetes severity analysis. This is mainly because of the large gap in the database model. This paper has a detailed study of the machine learning (ML) model combined with IoT devices and the proposed framework would have been used for diabetic patients who were affected with Covid-19 and other symptoms for early prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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Evaluating the Performance of Clinical data using Machine learning Approach- An Ensemble Model 2

Evaluating the Performance of Clinical data using Machine le...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Abdullah, A. Sheik Vinod, A Aashish Pranav, A G Vishal Govindarajan, Yeshwanth Selvakumar, Subramanian Vellore Institute of Technology School of Computer Science and Engineering Chennai India Bahir Dar University Faculty of Electrical and Computer Engineering Ethiopia

ISBN: (纸本)9798350372892

In the rapidly evolving field of healthcare, accurate clinical predictions are paramount for effective disease management and treatment planning. This paper introduces a novel ensemble machine learning model that utilizes the combined strengths of decision trees, neural networks, random forests, and support vector machines, integrated through a sophisticated linear regression meta-learner. This advanced integration strategy enhances predictive accuracy by optimally weighting the contributions of each base model based on their predictive reliability and effectiveness. The ensemble model amalgamates diverse algorithms with a meta-learning approach to harness a broader spectrum of data insights, thereby overcoming the limitations of traditional single-model approaches. Comparative analysis reveals that the ensemble model significantly outperforms existing methods, achieving superior accuracy in clinical predictions. These results underscore the potential of ensemble approaches in revolutionizing predictive analytics in healthcare, offering a promising tool for improving diagnostic precision and patient outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Threat Detection in VANET Communications 1

Threat Detection in VANET Communications

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Kandakatla, Akash Anbalagan, Sudha School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India Center for Smart Grid Technologies Scope Vellore Institute of Technology Chennai Chennai India

ISBN: (纸本)9798350377002

Communication plays a vital role in autonomous vehicles networks. Detecting the security threats in communication still acts as an open challenge. Leveraging the simulation environments such as OMNeT++ for network simulation and SUMO for traffic mobility visualization, we can show a realistic vehicular environment where vehicles communicate with each other and interact dynamically. The integration of Snort, an open-source intrusion detection system, allows for the detection and representation of various malicious attacks that commonly afflict Vehicular Adhoc Networks (VANETs). This work addresses the critical security challenges faced by VANETS, including the protection of communication integrity, privacy preservation, and resilience against malicious entities. By simulating realistic traffic scenarios and diverse attack vectors, the effectiveness of Snort in detecting and mitigating threats are assessed including black hole attacks, denial-of-service (DoS) attacks, and so forth. Through visualization techniques, we provide insights into the dynamics of VANETs and the impact of malicious activities on network performance and reliability. Real-time monitoring and alerting mechanisms are implemented to notify network administrators of detected threats, enabling timely response and mitigation actions. The outcomes of this research contribute to the development of robust security solutions for VANETs, enhancing the safety and trustworthiness of connected vehicle systems. By combining simulation environments with intrusion detection capabilities, the proactive defense mechanisms against evolving cyber threats in the context of intelligent transportation systems are addressed. © 2024 IEEE.

关键词： Network intrusion

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Sentiment Analysis of Covid-19 Twitter Data using Deep Learning Algorithm 2

Sentiment Analysis of Covid-19 Twitter Data using Deep Learn...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Valarmathi, B. Gupta, N. Srinivasa Karthick, V. Chellatamilan, T. Santhi, K. Chalicheemala, Dhanush Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Assistant Systems Engineer Tata Consultancy Services Chennai India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

A key method used in the study of Natural Language Processing (NLP) is sentiment analysis, or emotion analysis, plays a pivotal role in text analysis. Its primary function is to discern and categorize the underlying emotions within textual content, classifying them as positive, neutral, or negative sentiments. A variety of textual inputs can be used with this process, including entire documents, individual sentences, and more. Understanding the sentiments expressed by individuals is of paramount importance to organizations in today's digital age. Clients and customers now have the means to express their thoughts and emotions with greater ease and immediacy than ever before. This wealth of sentiment data can offer valuable insights that organizations can leverage to enhance their products, services, and overall customer experience. The present study focuses on the sentiment analysis of Twitter data related to the COVID-19 pandemic, employing the Long Short-Term Memory (LSTM) algorithm. To improve the accuracy of sentiment analysis, a pre-processing procedure is introduced, which involves the use of the Neat Text module in Python to clean the tweets. In this research endeavor, a dataset comprising 1,79,107 COVID-19-related tweets is subjected to sentiment analysis using the proposed pre-processing module and LSTM. The results demonstrate an impressive accuracy rate of 96% in accurately determining the sentiments conveyed in these COVID-19 tweets. In contrast, the existing algorithm, based on Artificial Neural Network (ANN), achieved a significantly lower accuracy rate of 76%. This research not only showcases the effectiveness of LSTM and pre-processing in sentiment analysis but also highlights the significance of sentiment analysis in gaining valuable insights from vast amounts of textual data, especially when important events like the COVID-19 pandemic are involved. © 2024 Elsevier B.V.. All rights reserved.

关键词： COVID-19

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Video Indexing and Retrieval Techniques: A Review 4th

Video Indexing and Retrieval Techniques: A Review

引用

4th International Conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Poovaraghan, R.J. Prabhavathy, P. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India School of Information Technology and Engg Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9789819914784

Content-based video retrieval is one of the foremost research problems in the fields of artificial intelligence, computer vision, digital image processing, and natural language understanding over the past decade. The exponential development of video retrieval has created several research problems and opportunities for the design and develop a variety of real-world applications such as face video retrieval/recognition, video tagging, video annotation, crime investigation, Web albums, and health care. Most of the research is to develop efficient content-based video retrieval techniques from large video databases. Even though a large number of video retrieval techniques have been increased, there are no universally accepted techniques available. The challenge in content-based video retrieval is to build a rapid solution for high-dimensional indexing, which is essential for constructing large-scale content-based video retrieval systems. This study presents an emphasis on the role of content-based video indexing and also highlights the challenges in designing content-based video retrieval and recognition systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Enhanced Public Safety through LSTM-Based Spatio-Temporal Crime Analytics 1

Enhanced Public Safety through LSTM-Based Spatio-Temporal Cr...

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

作者： Pimple, Sairam Mansoor Hussain, D. Karthikeyan, N. Vellore Institute of Technology Tamilnadu India Vellore Institute of Technolog School of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350359756

The study explores the application of Long Short-Term Memory (LSTM) neural networks in handling Space-Time Crime Analytics, with an aim to boost public security in city regions. Utilizing LSTM's potential for mapping intricate crime dynamics within both time and space dimensions, it seeks to deliver prompt and accurate forecasts of criminal activities. Main goals include marking danger zones prone to crimes, understanding temporal patterns & discerning emerging trends;aiding police departments by equipping them with active measures against future incidents & effective resource distribution strategies. This trailblazing advancement aims at transforming predictive policing methods towards secure urban societies while highlighting how deep learning can be pragmatically applied on a spatio-temporal basis regarding analyzing crimes that elevates tactics associated with digital-age safety. © 2023 IEEE.

关键词： Crime

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GIS and ML Integrated Techniques for Detection of Landslide-Prone Areas 2

GIS and ML Integrated Techniques for Detection of Landslide-...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Sul, Astha Patil, Sanjay Vellore Institute of Technology School of Computer Science and Engineering Vellore India Maharashtra Remote Sensing Applications Centre Maharashtra Pune India

ISBN: (纸本)9798350368413

Landslides are a common natural disaster that can result in substantial casualties. One of the region's most vulnerable to landslides in India is Western Ghats. The risk casualty is reduced with the use of the landslide susceptibility map (LSM) and prediction of landslide points. The aim of this research is to explore and compare the 4 ML algorithms (K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR)) for generating the most promising LSM. This study focuses on 12 Landslide conditioning factors (LCFs) such as Elevation, Slope, Lithology, LULC, Distance to Road, Topographic Wetness Index (TWI), Stream Power Index (SPI), Topographic Roughness Index (TRI), Drainage Networks, NDVI, Rainfall, Sediment Transport Index (STI). It has been found that the slope of the ground, the distance to the road, and the annual rainfall all have a significant impact on the occurrence of landslides. The models made using different approaches have been analysed and compared using the following metrics: prediction accuracy, precision, recall, F1-score, and True Skill Statistics (TSS). Overall accuracy for LR, SVM, KNN, RF are 82.30, 84.00, 82.50, 87.20 respectively. The findings of the study show that there are more landslide-prone locations in Bhor, Velhe, Ambegaon, and Junnar in Pune District, Maharashtra. © 2024 IEEE.

关键词： Landslides

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Post-operative Smart Healthcare Kit

Post-operative Smart Healthcare Kit

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Kanchana Devi, V. Umamaheswari, E. Gao, Xiao-Ghi Rathinavel, Nirupama School of Computer Science and Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India University of Eastern Finland Kuopio Finland

ISBN: (纸本)9789819613472

The Internet of Things is becoming an increasingly important tool for the healthcare sector. It is utilized in common healthcare settings, such as smart watches and oxygen monitors, as well as in hospitals for anything from routine checks to critical care units. One of the most crucial hospital departments is post-operative care, where patients are monitored to make sure they are getting better. Here, a constant watch is kept on the patient’s vital signs and the associated organs to detect any anomalies that might develop following the treatment. Utilizing IoT in post-operative treatment will guarantee patient contentment. This is ensured by the Post-Operative Smart Healthcare Kit (POSHK), presented in this paper, furnished with several sensors, including heartbeat, oxygen and carbon dioxide sensors, blood pressure, room, and patient temperature sensors, and more. During discharge, data is gathered and analyzed to look at possible future recovery trends. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electrotherapeutics

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