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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Mahire, Sarthak Balaji Saravanan, U.K. Murali, M. Prabakaran, N. Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India Sona College of Technology Department of Information Technology Salem India

ISBN: (纸本)9798331534950

Text mining and Natural Language Processing (NLP) have witnessed significant advancements in recent years, driven by the increasing availability of unstructured data and the development of sophisticated machine learning models. This review explores the evolution of text mining and NLP, highlighting the transition from rule-based systems to modern deep learning approaches. This paper reviews various techniques and its impact across various domains. Challenges such as data bias and model interpretability still remain. The paper also discusses future directions, emphasizing the need for fair, interpretable and sustainable NLP systems. This paper aims to provide insights into the current state of text mining and NLP, the challenges faced and potential pathways for future research. © 2024 IEEE.

关键词： Adversarial machine learning

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Identification of Data-Intensive Systems Requirements using Semantic Similarity Search

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Journal of engineering science and technology Review 2022年第1期15卷 199-211页

作者： Savarimuthu, Margret Anouncia Raymond, Renita School of Computer Science and Engineering Vellore Institute of Technology India

The phenomenal growth of big data in social applications and IT software platforms over the last few decades has emphasized the significance of a systematic requirement engineering strategy for analyzing the requirements of dataintensive systems, that deliver valuable insights to business entities. Classification of data-intensive requirements can aid in the development of a more systematic and transparent requirements engineering process, resulting in increased requirement compliance and software project completion. As a result, this paper provides a unique approach Word2Vector based Fast Similarity Search (WV-FaSS) for improving the process of software requirement categorization for dataintensive systems in two phases. Word2Vec begins by taking a corpus of software requirements as input and producing a well-trained high-dimensional vector space. Following that, the vectors that were extracted semantically are indexed. The query vector is then used to look for the most similar vectors within the index, and lastly, similar documents are obtained. Experiments on two benchmark datasets, PURE and WARC, as well as a dataset from the private IT industry, demonstrated that our model outperformed state-of-the-art techniques with precision, recall, and F1 values of 0.91, 0.9, and 0.9, respectively. Thus, the proposed model WV-FAISS enables developers to rapidly search for embeddings of similar requirements that are similar to one another, while also increasing the scalability of similarity search methods. © 2022. School of science, IHU. All rights reserved.

关键词： Requirements engineering

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High-Performance Computing for Efficient Transaction Processing: A Comparative Study of Parallel and Sequential Execution using SQLite 5

High-Performance Computing for Efficient Transaction Process...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Balaji, C.V.Roshan Shaman, K.R. Pritham Raj, V. Kumar, N.Nithish Kumaran, K. School of Computer Science and Engineering Vellore Institute of Technology Chennai campus India

ISBN: (纸本)9798331518097

High-performance computing (HPC) has become an essential tool for improving the efficiency and scalability of transaction processing systems, especially as data volumes continue to grow in fields like finance, e-commerce, and blockchain. This paper presents a comparative study of parallel and sequential transaction processing methods using an SQLite database. Specifically, the research investigates the impact of HPC techniques on transaction throughput, processing speed, and efficiency by simulating 1,000 user transactions. The study employs Python's multiprocessing module to simulate parallel execution, contrasting it with traditional sequential execution. Key performance indicators, including execution time, transaction success rate, and system efficiency, were analyzed to determine the advantages of parallel processing in a transaction-heavy environment. Our results reveal that parallel execution significantly reduces processing time, boosts throughput, and increases overall system efficiency compared to sequential processing. Additionally, the study discusses how parallel processing techniques can address common bottlenecks in transaction-heavy applications and provide solutions for improving the performance of large-scale, data-intensive systems. The findings demonstrate the potential for using HPC to optimize database operations, particularly in systems where high-volume transaction processing is a critical requirement. Future work will explore advanced parallelization strategies, fault tolerance mechanisms, and integrations with distributed databases and blockchain systems. This research contributes to the growing body of knowledge on optimizing transaction processing in high-performance computing environments, with potential applications across various sectors, including financial services, e-commerce, and blockchain technology. © 2024 IEEE.

关键词： Blockchain

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Enhancement in Context-Aware Recommender Systems - A Systematic Review 2

Enhancement in Context-Aware Recommender Systems - A Systema...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Abinaya, S. Ramya, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India

ISBN: (纸本)9798350328202

A Recommendation System is a crucial Machine Learning algorithm that provides suggestions for various applications and uses computer algorithms or software to analyse and predict user preferences between products, content, services, or information. Within the realm, Recommendation Systems have become vital tools, utilizing Machine Learning algorithms to provide valuable suggestions across various applications. However, traditional Recommendation System encounters challenges related to scalability, sparsity, cold start issues, lack of diversity, and context-based recommendation, which results in user dissatisfaction and limited engagement. Addressing these issues, this study embarks on a comprehensive survey to propose a Context-Aware Recommendation System that integrates contextual factors such as the user's geographical position, current timing, emotional state, company, social engagements, psychological well-being, atmospheric conditions, direction, life stage, sensory information, gender, and personal profile. The survey meticulously investigates the potential enhancement of the Context-Aware Recommendation System by applying Deep Learning methodologies. The survey critically assesses and explores how advanced Deep Learning techniques can synergize with Context-Aware Recommendation System technology, overcome the limitations of current contextual systems, and ultimately optimize the recommendation process for an enriched user experience and enhanced system efficiency. © 2024 IEEE.

关键词： Recommender systems

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Satellite Imagery Analysis Using Image Registration and Edge Detection 8

Satellite Imagery Analysis Using Image Registration and Edge...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Kiruthika, S. Athanesious, J. Joshan Kanna, Lakshmi Dathatreya Sch. of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331531539

In this research we evaluated multiple feature-based image registration techniques by leveraging keypoint detectors to extract robust and distinctive key points for matching across images. Various detectors and descriptors, including those used in the ratio test, FLANN parameters for approximate nearest neighbor searches, and the BF Matcher for brute-force matching, were employed to extract keypoints, which were then used to search for suitable matches. Various techniques, including warp Affine, estimateAffinePartial2D, and homography, were employed to estimate transformations. We have done a comparative analysis in which we used different combination of feature-based image registration algorithms and edge detection algorithms for the purpose of image alignment and boundary extraction. Subsequently, a performance assessment was performed by comparing the registered images with ground truth using Structural Similarity Index Measure (SSIM), Precision, Recall, Mean Square Error (MSE) and F1-Score metrics. Among the evaluated models, the BRISK model with the Canny edge detection algorithm consistently demonstrated superior performance, achieving the highest Precision (0.973), Recall (0.984), F1-Score (0.918), SSIM (0.0427), and the lowest MSE (0.0427) scores. These findings emphasize the efficacy of the BRISK model in image alignment tasks. © 2024 IEEE.

关键词： Satellite imagery

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Smart Classroom Power Automation: An Adaptive And Predictive Approach

Smart Classroom Power Automation: An Adaptive And Predictive...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Madhurima, Gangireddy Reddy, Panduga Venkata Jaya Srikanth Srinivasa Perumal, R. Vellore Institute of Technology School of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331529833

The growing demand for energy efficiency in educational institutions has led to the exploration of smart technologies for managing power consumption. This project introduces a "Power Automation for Classrooms"system that leverages long-range RFID (Radio Frequency Identification) technology to automate the activation of classroom lights and fans based on student attendance. The system operates by detecting student entries through RFID tags without the need for manual scanning, thus ensuring a seamless and instant process. The project aims to reduce energy wastage by activating the power in stages depending on the number of students present, with specific power allocation thresholds set according to the class strength. The data on students entered, as well as the power consumption for each classroom, is stored in the cloud, allowing for real-time monitoring, analysis and supports data-driven decision-making for future energy planning. © 2025 IEEE.

关键词： Decision making

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AI-Driven Space Solar Power: Pioneering the Next Frontier in Global Energy 2

AI-Driven Space Solar Power: Pioneering the Next Frontier in...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Varrshini, G.M. Vignesh, U. Parvathi, R. School of Computer Science Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9798331504106

This review provides a close insight into recent and significant technological developments that have emerged in this ever-changing field of Space Based Solar Power, which has enormous potential to alter the global energy supply landscape by harnessing solar energy from outer space through technical advances such as Energy Harvesting from Space and Wireless Power Transmission along with novel engineering solutions being developed to overcome emerging challenges. It also focuses on how AI came into play in optimizing various features of the generation, transmission, and distribution processes. In the context of SBSP, standing for Solar Power Satellite, the energy from the sun is generally collected by using either solar collectors or the advanced light framework of the solar arrays. This captured sun energy is then converted to another type of energy with the help of different electronic devices, such as microwave or laser energy, for transmission back to Earth to be useful in practical ways. To offer a different perspective on SBSP’s ability to meet future energy needs while staying eco-friendly, the review covers environmental, economic, & geopolitical issues related to space-based solar power. It dives into how artificial intelligence (AI) can change the game for SBSP systems by making them more efficient and reliable and helps enormously in growing SBSP technologies. ©2024 IEEE.

关键词： Space power generation

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Detection of Artificially Ripened Bananas Using Multispectral Images and Deep Learning

Detection of Artificially Ripened Bananas Using Multispectra...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Sai Vignesh, N. Harini, S. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378177

Identifying artificially ripened fruits, especially bananas, has become a crucial issue in today's modern culture because of growing worries about food safety. In addition to compromising the nutritional content and flavor of fruits, artificial ripening techniques, which frequently involve the use of dangerous chemicals like calcium carbide, also seriously endanger the health of the public. Even though current digital technologies provide significant improvements in agriculture and food processing, they often fail to distinguish between naturally ripened fruits and artificially ripened fruits, which could pose a risk to public health and result in financial losses for fruit growers. A ground-breaking answer to this urgent problem is offered by our initiative, which involves fusing cutting-edge computer analysis with sophisticated imaging techniques to transform the assessment of fruit ripening and guarantee consumer safety. State-of-the-art technologies, such as DJI Pro drones fitted with multispectral cameras for high-resolution imaging, are integrated into our methodology. With the help of this cutting-edge hardware and software combo, fruit ripening processes may be thoroughly monitored over vast agricultural landscapes, guaranteeing effective data collection and analysis. The usage of wavelength Signature graphs to spot unique patterns indicating the presence of heavy metals in calcium carbide in artificially ripened fruits is a crucial component of our research. Additionally, clustering algorithms are incorporated into our method to evaluate the consistency of chlorophyll content, a vital sign of organic ripening processes. The assessment of fruit ripening can be made more accurate and reliable by distinguishing between naturally and artificially ripened fruits based on physiological parameters through the analysis of spatial distribution patterns and intensity variations in chlorophyll levels. CNN is also used to produce supporting results for the classificati

关键词： Fruits

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Design of Crop Classification Model Based on Soil Information 3

Design of Crop Classification Model Based on Soil Informatio...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Sharma, Krish L Patel, Yashaswi Jain, Ankur School of Computer Science and Engineering Vellore Institute of Technology Bhopal Bhopal India

ISBN: (纸本)9798350396157

The adoption of contemporary techniques to boost agricultural output is unavoidable given worries about the growth of the world's population and the scarcity of food. Artificial neural networks (ANN) in particular have attracted interest recently for the analysis of agricultural data so that farmers may maximise resource allocation, reduce waste, and improve overall efficiency by accurately identifying their crops, giving farmers useful information for making data-driven crop production decisions. ANNs are capable of learning intricate correlations between input characteristics and output labels. The paper proposes an ANN model that classifies crops based on a variety of soil properties. 22 different crops are included in the data collection. The study provides a comparative study of different ANN models with respect to activation function and loss function yielding 8 different models. The proposed model correctly detects crop types using specific soil parameters. This study aids in the creation of simple farming solutions that facilitate decision-making and raise overall agricultural productivity. © 2024 IEEE.

关键词： Neural networks

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A Predictive Model for Marine Debris Prediction (A Comparative Case Study) 2

A Predictive Model for Marine Debris Prediction (A Comparati...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Venkatesh, T. Jenila Livingston, L.M. Rajkumar, S. School of Computer Science and Engineering Vellore Institute of Technology-chennai Chennai India

ISBN: (纸本)9798350328202

Efficient removal of marine debris is imperative for safeguarding public health and the environment, particularly marine ecosystems. This research addresses the limitations of conventional methods, such as image detection and satellite data, which often exhibit an accuracy below 85% in estimating waste levels in specific geographic areas. Our primary objective is to enhance predictive accuracy by leveraging machine learning and deep learning algorithms that capitalize on historical waste generation patterns. Through an exhaustive exploration of various algorithms documented in research papers, we aim to identify the most suitable model, whether multimodal or hybrid, to improve accuracy. Performance assessment will be conducted using key metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). This comprehensive approach holds the potential for significant advancements in marine debris management and environmental protection. © 2024 IEEE.

关键词： Environmental protection

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