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

作者： Kowsigan, M. Shetty, Ronit Vyas, Aryan School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chengalpattu603203 India

ISBN: (纸本)9798350377002

As global concerns about climate change intensify, accurately predicting and managing carbon dioxide (CO2) emissions becomes paramount for sustainable environmental practices. This project employs a machine learning-driven approach to forecast CO2 emissions, utilizing historical data and advanced predictive modeling techniques. The study integrates diverse datasets, including environmental parameters and temporal information, to enhance the accuracy of predictions. The predictive models, primarily based on linear regression, are trained and evaluated using a comprehensive dataset. The approach not only explores the performance of different machine learning models but also delves into feature engineering and model interpretability. Through this research, we aim to contribute to the growing body of knowledge in environmental science and provide valuable insights for policymakers and stakeholders. The results of this study are anticipated to facilitate informed decision-making for mitigating environmental impact and promoting sustainable practices. © 2024 IEEE.

关键词： Prediction models

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A Survey of Machine Learning Technique for Topic Modeling and Word Embedding 10

A Survey of Machine Learning Technique for Topic Modeling an...

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10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Saranya, M. Amutha, B. School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

'Word embedding' is a key phrase in natural language processing that describes how words are expressed as real-valued vectors for text analysis. A breakthrough in natural language processing (NLP) has improved computers' ability to understand textual content. It is thought to be among the most significant developments in deep learning for addressing challenging problems in natural language processing. Since words and texts are represented as numeric vectors, this technique permits related words to have comparable vector representations. The gathered features are integrated into a machine learning model to handle text data while preserving syntactic and semantic information. Once translated, these data are used by natural language processing (NLP) algorithms, which can process textual material and absorb these learned representations fast. This paper focuses on word embedding methods used to LDA, LSA, PLSA, Bag of Words, TF-IDF, Word2Vec, GloVe, and BERT, among other natural language processing applications. © 2024 IEEE.

关键词： Embeddings

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Court Judgment Prediction for Article6 Using Machine Learning 9

Court Judgment Prediction for Article6 Using Machine Learnin...

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9th International Conference on science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Iniyan, S. Raghuvanshi, Nishanth Maurya, Piyush School of Computing Srm Institute of Science And Technology Department of Computing Technologies Chengalpattu Kattankulathur India

ISBN: (纸本)9798350365092

The number of cases that get registered in courts is increasing day by day. The Judiciary system cannot work on all the cases at the same time. Even though there are lots of courts, panchayats and people working, each case requires a different time to get solved and for justice to be delivered we cannot neglect any detail. The aim of the current research is to develop an Artificial Intelligent model which will help in the proceedings of the court. We aim to train and integrate the model with the judiciary system. The primary goal of this project is to increase the efficiency of the courts. We wish to employ an AI model to speed up routine procedures that would ordinarily take a long time if done manually. Not only the judge but everyone connected with the law will benefit from this model. © 2024 IEEE.

关键词： Machine learning

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Survey on age-related macular degeneration detection in OCT image

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International Journal of Computers and Applications 2024年第2期46卷 101-112页

作者： Deepti, S. Renu Karthik, P.C. Department of Data Science and Business Systems School of computing SRM Institute of Science and Technology Kattankulathur India

Age-related Macular Degeneration (AMD) is the most common eye disease that causes visual impairment in elder people. Prevalently, AMD is detected by Spectral Domain Optical Coherence Tomography (SD-OCT) for diagnosis and monitoring purposes. In this work, 50 papers are reviewed to examine various approaches and their performance metrics. This paper reviewed various existing works related to AMD detection using different imaging modalities for quality images with high resolution. The investigation of case studies is analyzed on the basis of data collected from various patients undergoing several treatments and scans obtained from various imaging modalities. Furthermore, the analysis extends to the works that used different AI techniques, such as Deep Learning (DL), Machine Learning (ML), and Unsupervised Framework, for the detection of AMD-related diseases. Also, the analysis of different performance measures is done that shows the variations in terms of performance metrics such as accuracy, sensitivity, specificity, and so on. The outcome of this deep analysis shows that the system with the DL model reaches its utmost performance in AMD detection. Finally, gaps and challenges that are still lagging with this research area are summarized, which motivates the researchers to take away the work in an effective way. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Diagnosis

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Assessing Machine Learning Algorithms for Real-Time Fake Currency Detection 5

Assessing Machine Learning Algorithms for Real-Time Fake Cur...

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

作者： Lakkshmanan, Ajanthaa Grandhi, Revanth Sai Girish, V. School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai603203 India

ISBN: (纸本)9798331530013

Counterfeiting poses a significant threat as the circulation of fake currency diminishes the value of genuine notes, thereby disrupting the country's economic stability. Addressing this issue is critical before the proliferation of counterfeit notes becomes unmanageable. Manual detection methods are often unreliable since counterfeit notes are produced with materials and inks that closely resemble the original. Across the country, counterfeit detection is typically carried out using hardware-based systems. However, these methods are time-consuming and struggle to process large volumes efficiently. To address these challenges and streamline the process of counterfeit currency detection, this study proposes an image processing-based computational technique. The objective is to accurately determine whether a given note is genuine or counterfeit, with a high prediction rate. This detection is enhanced using deep learning algorithms, which analyze key attributes such as color, form, paper thickness, serial numbers, and image filters on the currency. The proposed model is trained on a real-time dataset consisting of both genuine and counterfeit notes. Experimental results demonstrate that this method achieves an overall accuracy of 96.41%. © 2024 IEEE.

关键词： Deep learning

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Leveraging Customer Sentiment Analysis with Deep Learning for Business Growth Forecasting and Product Flaw Identification 12

Leveraging Customer Sentiment Analysis with Deep Learning fo...

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12th International Conference on Current Trends in Advanced computing, ICCTAC 2024

作者： Tripathy, Varun Samanta, Samaresh Briskilal, J. School of Computing SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur603203 India

ISBN: (纸本)9798350395808

This study harnesses Convolutional Neural Networks (CNNs) alongside advanced Deep Learning models such as Long Short-Term Memory (LSTM), Bidirectional LSTM (bi-LSTM), and ResNet, incorporating Time Series Analysis and Word2Vec embeddings for sophisticated customer sentiment analysis within business contexts. We introduce a novel CNN-based framework that integrates these methodologies to simultaneously forecast business growth and identify product flaws, utilizing large-scale datasets comprising customer reviews, feedback, and social media data. By capturing hidden patterns and nuances within text data, our framework transcends traditional sentiment analysis, providing actionable insights for informed decision-making. Through rigorous evaluation, including the application of Time Series Analysis for predicting future sales trends, we demonstrate the effectiveness of our framework in facilitating customer acquisition and pinpointing areas for product improvement. These findings underscore its potential to optimize market performance and drive product development strategies in dynamic business landscapes. © 2024 IEEE.

关键词： Sentiment analysis

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Real Time Road Lane Detection and Vehicle Detection on YOLOv8 with Interactive Deployment 16

Real Time Road Lane Detection and Vehicle Detection on YOLOv...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Segu, Girish Sai Pavan Kumar Sivannarayana, Annam Devi Satya Naga Ramesh, S. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies India

ISBN: (纸本)9798331505264

Modern strides in autonomous vehicles and embedded advanced driver assistant part systems(ADAS) have forced the need of an efficient in addition to accurate system intended for clear road lane as in addition to vehicle detection. For road lane detection, this study introduces a new proposal through the combination of YOLOv8 (You Only Look Once), and YOLO for vehicle detection, creating a complete package for road understanding and navigation. To achieve accurate road lane detection, YOLOv8, which is the fastest and most accurate model to date, is used to ensure that it can keep track of these lanes in real-time with consistent accuracy whenever the camera is placed under various scenarios with insufficient lighting and occlusions as well. At the same time it uses YOLO for vehicle detection, which improves the recognition and interpretation of the presence and motion of vehicles in real time. The system uses Streamlit, which is an open-source app framework for Machine Learning and Data science projects to provide an intuitive user experience. Finally, we build an interface to provide results of lane and vehicle detection in realtime manner to allow ease of monitoring and evaluation on the overall system. The combination of YOLOv8 and YOLO with Streamlit provides a powerful, scalable, and deployable solution for practical computer vision applications. Combining well established object detection algorithms with a simplified deployment platform, the proposed system tackles some of the hardest and time consuming areas of autonomous driving. The goal is to make significant contributions to both the safety and efficiency of autonomous vehicles and pave the way for further inevitable advances in ADAS. © 2024 IEEE.

关键词： Advanced driver assistance systems

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OncoScan3D: Lung Tumor Detection with ResUNet 5th

OncoScan3D: Lung Tumor Detection with ResUNet

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Shukla, Tanmay Suri, Khushi Sindhu, C. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur603203 India

ISBN: (纸本)9789819760350

The diagnosis and treatment of lung diseases in Computed Tomography images represent an essential task, particularly concerning the segmentation of the lung and its tumors. The challenges in this domain arise from the uneven distribution, indistinct borders, varied densities, as well as diverse shapes and sizes of lesions. Addressing these complexities is pivotal for minimizing the time and energy expended in the diagnosis of lung diseases. The current study primarily concentrates on employing deep learning algorithms for the segmentation of the lung and its tumors from abdominal Computed Tomography scan images. The algorithm utilized is rooted in the modified Deep Residual UNET architecture and automated semantic segmentation and Convolutional Neural Networks to separate the lung from Computed Tomography data and detect lesions within the segmented lung region. The overall methodology to utilize this architecture performs quite well with a Dice Similarity Coefficient of 96.35% and a lung segmentation rate of 89.28% resulting in an overall accuracy of 99%. These significant results from abdomen Computed Tomography help in early stage detection and allow more precise clinical attention of lung disorders. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lung cancer

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CA-EBM3D-NET: a convolutional neural network combined framework for denoising with weighted alpha parameter and adaptive filtering

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International Journal of Information technology (Singapore) 2024年第8期16卷 4855-4867页

作者： Padmapriya, R. Jeyasekar, A. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai 603203 India

Block-matching and 3D filtering (BM3D) is a popular denoising technique, renowned for its efficiency in enhancing image quality. However, traditional BM3D method involves hard thresholding and Euclidean distance, which shows limitation in yielding effective results due to constant thresholding. Hence in this paper, we provide an improved variant of the BM3D algorithm that incorporates the cosine distance metric for patch similarity measurement and includes exponential moving average (EMA) of noise and non-zero similar patch. EMA acts as a weighting factor to balance the contributions of similar patches. The weighting parameter of EMA is adjusted according to the unique characteristics of the input image. This adaptability improves BM3D’s efficiency to maintain image details while effectively reducing noise. Further, when identifying similar patches for collaborative filtering, we also replace the Euclidean distance metric with the cosine distance, which is renowned for being robust to variations in brightness and contrast, making it particularly ideal for natural images with a variety of textures and contents. Our cosine adaptive enhanced BM3D (CA-EBM3D) delivers improved patch selection and enhanced denoising performance by using cosine distance. CA-EBM3D-NET also incorporates convolutional neural networks (CNNs), which can capture complex image features and improves denoising performance. CNNs have demonstrated remarkable proficiency in learning complex images and performing denoising tasks. Experimental results against benchmark images show that the proposed CA-EBM3D-NET consistently outperforms traditional BM3D and other cutting-edge denoising approaches. The evaluation measures are peak signal-to-noise ratio (PSNR), mean squared error (MSE), structural similarity index (SSIM), and entropy. The results consistently demonstrate improved performance, demonstrating the effectiveness of the proposed method in a variety of image denoising applications. The combined u

关键词： Adaptive weights BM3D Cosine distance Denoising Exponential moving average (EMA) Patch similarity Soft thresholding

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Medical image security by crypto watermarking using enhanced chaos and fruit fly optimization algorithm with SWT and SVD

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Multimedia Tools and Applications 2024年第27期83卷 70451-70476页

作者： Abirami, R. Malathy, C. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur603203 India

Telemedicine is the field that uses medical images for diagnosing various diseases. The transmitting and storing of medical images via the cloud-based network must meet several stringent criteria, including confidentiality, validity, and security, and are viewed as very sensitive, irrespective of the image processing context. Medical image copyright protection has been essential since little alterations can even put the lives of patients in danger. Hence numerous significant watermarking methods need to be developed. Watermarking conceals sensitive data by embedding it in a harmless medium, such as a cover. The challenging issue of quick and extremely safe image encryption can be solved more effectively with chaos-based cryptography. Thus the image is encrypted by using enhanced chaos with the Fruit Fly Optimization Algorithm (FFOA). The work proposes an efficient medical image watermarking approach using the Two-level Stationary Wavelet Transform (SWT) and Singular Value Decomposition (SVD) technique on chaotic encrypted medical images. Finally, the proposed method is compared with existing methods to demonstrate higher performance. To check robustness and imperceptibility results, the work is examined under various attacks and produces good results in Peak Signal-to-Noise Ratio (PSNR) and Normalized Cross Correlation (NCC) measures. For different kinds of medical images, the PSNR of the suggested methodology is greater than 40 dB, and NCC values are close to 1 illustrating the technique's superior efficacy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Singular value decomposition

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