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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Pijush Kanti Kumar, Indrajeet Kumar, Sanjeev Kumar, Padmanavan Harshith, John Dutta, Anurag Government College of Engineering and Textile Technology Department of Information Technology Serampore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India G L Bajaj Institute of Technology and Management Department of Master of Computer Applications Greater Noida India Kalinga Institute of Industrial Technology Department of Computer Science and Engineering Bhubaneswar India Vellore Institute of Technology Department of Computer Science and Engineering Vellore Campus Vellore India Government College of Engineering and Textile Technology Department of Computer Science and Engineering Serampore India

ISBN: (纸本)9798350323917

In reaction to recent technology breakthroughs, modern computational fields such as the IoT, Data science, Machine Learning, AI, and several more have arisen. Humans benefit greatly from these fields. In this study, we would employ parts of machine learning to forecast plant illnesses. Particularly amongst the potato leaves, severe Late Blight Disease was discovered. The potato plant, also known as Solanum tuberosum, is a significant crop that is planted all over the world and yields copious amounts of nutritious tubers. The potato is a frequent staple food and has several health benefits. When ingested in moderation, the fluid extracted from tubers can cure stomach ulcers and reduce inflammation and acidity. Both Early and Late Blight are dangerous and widespread potato diseases. We would concentrate on Late Blight in this effort. A fatal fungus that harms potatoes is called Late Blight. It is accessible everywhere in the world. It can be found in areas of the world where potatoes are grown. Winter is the primary growing season for potatoes in India. It is followed by sweltering summer months in the lowlands. Heat and dryness kill the fungus that lives in the soil. The fungus Phytophthora Infestans, the most prevalent agricultural disease that causes the Late blight of potatoes, can quickly result in crop failure if appropriate control measures are not adopted. Potato crop declines in epidemic years might be as high as 80%. We acquired a collection of roughly one thousand samples of Late Blight-affected leaves of potatoes for this investigation. With the help of conventional classifier algorithms, we have developed a machine-learning classification paradigm that may be able to forecast the onset of Late Blight disease. Medical science has developed significantly. Plant pathology could be able to prevent the threat from happening if we could perhaps predict the illness in its early stages. © 2023 IEEE.

关键词： Diseases

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Enhancing Image Super-Resolution with GANs Featuring Enhanced Deep Super-Resolution and Attention Mechanisms

Enhancing Image Super-Resolution with GANs Featuring Enhance...

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Emerging Techniques in Computational Intelligence (ICETCI), International Conference on

作者： G Aditya Kumar Aditi Kativar Kannan A Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387070

Improving the quality and resolution of low- resolution digital images is an important task with far-reaching implications for a variety of applications, including medical imaging, surveillance, and content retrieval. To solve the high- resolution problem of a single image, this study proposes a new Generative Adversarial Network (GAN) model that includes an Enhanced Deep Super-Resolution (EDSR) generator. Our model includes a sensitivity function and a Squeeze-and- Excitation (SE) block in an EDSR design. These SE blocks directly model channel connections to optimize channel-specific performance responses and improve network capabilities without incurring significant computational costs. After extensive training using complex data processing methods such as rotation, rotation, and rotation, our model shows an impressive ability to enhance image details and traditional culture more accurately. Our strategy outperforms existing models both statistically and qualitatively with better signal-to- noise ratios (PSNRs) on the DIV2K dataset. These results, together with deep learning models, demonstrate the role of attentional processes in improving image resolution and lay the groundwork for further work in this field.

关键词： Training Adaptation models Attention mechanisms Computational modeling Superresolution Urban planning Generative adversarial networks Computational efficiency Image enhancement Biomedical imaging

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Shape Organization for Enhanced Design Optimization in Generative Systems Using Clustering technology 6

Shape Organization for Enhanced Design Optimization in Gener...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Krishna, Raji Suganya, R. Yaganti, Mounika Sai Mohamed Buhari, Seyed Vellore Institute of Technology School of Electrical Engineering Chennai Campus Chennai India Vellore Institute of Technology School of Computer Science Engineering Chennai Campus Chennai India University Brunei Darussalam Department of Computer Science Gadong Brunei

ISBN: (纸本)9798350394702

The current research in design space articulation tries to solve the problem that comes up because computer generative systems are becoming more complex, and there are more design options. This study makes a contribution to the existing body of research by introducing a technique for form clustering that utilizes K-medoids, a Machine Learning approach. The suggested method uses a clustering method to group design shapes that are similar and then pick a representative body for each cluster in a 2D-Grid Based representation. The efficacy of the process has been empirically validated by applying it to a novel test case and comparing it with other clustering evaluation methods. The accuracy of the results of this evaluation is about the same as that seen in an outside study. This gives us essential information about how to judge machine learning techniques. © 2023 IEEE.

关键词： Hamming distance

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Leveraging Ensembles of Pre-trained CNNs for Improved Lung Cancer Detection and Classification

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Procedia computer science 2025年 260卷 151-159页

作者： Dasari Bhulakshmi Dharmendra singh rajput Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore 632014 India

Lung cancer is a serious global health concern, highlighting the importance of early identification to improve patient survival rates. We explore the potential of deep learning(DL) models to improve lung cancer diagnosis through detection and classification models. The performance of pre-trained ResNet50, VGG19, and AlexNet models is evaluated on an augmented lung cancer image dataset to determine their suitability for lung cancer classification. The fine-tuned models are evaluated for their ability to identify and classify lung cancer, achieving high accuracy of 92.88%, 93.06%, and 95.23%. While promising, this approach has limitations. The efficacy of DL models is significantly influenced by both the quality and volume of the training data. Additionally, the ”black box” nature of DL models can make it challenging to understand their decision-making process. However, the results of this study suggest that DL ensembles hold significant potential for lung cancer diagnosis. Further research is necessary to address limitations and explore interpretability techniques for wider clinical acceptance.

关键词： Deep Learning Machine Learning convolutional neural networks Artificial Intelligence

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Sentiment Analysis on Reddit Trading Data 2

Sentiment Analysis on Reddit Trading Data

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

作者： Govinda, K. Chintalapati, Akhil Senapati, Aparajita Enkhbat, Khashbhat School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Tokyo International University Economics Digital Business and Innovation Department Saitama Japan

ISBN: (纸本)9798350328202

In the volatile world of trading, success frequently depends on the ability to devise and implement effective methods. Understanding market sentiment has been increasingly important for stabilizing revenue and separating strategic trading from simply speculation since the advent of internet trading forums such as those on Reddit. This research offers a novel machine learning model that uses sentiment analysis to evaluate Reddit trading data, with the goal of providing traders with a better-informed decision-making base. This model tries to decipher the frequently complicated and trend-driven conversation within trading groups by merging techniques from machine learning, deep learning, and mathematical transformations-including VADER Analysis and Fourier Transforms, complemented by Long-Short Term Memory networks. The idea is to turn what often appears to be gambling into a more systematic approach to trading led by data-driven insights into market emotion. © 2024 IEEE.

关键词： Fourier transforms

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Prognostic Survival Analysis for AD Diagnosis and Progression Using MRI Data: An AI-Based Approach

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IEEE Access 2025年 13卷 89059-89078页

作者： Deenadayalan, T. Shantharajah, S.P. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore632014 India

Alzheimer’s Disease is a progressive neuro-degenerative disorder and a leading cause of dementia, marked by cognitive decline, memory loss, and behavioral changes. Despite advancements in medical imaging and Artificial Intelligence (AI), early detection and accurate prognostic modeling remain challenging, particularly in distinguishing overlapping disease stages. This study proposes a novel AI framework combining EfficientNetB0, a state-of-the-art deep learning model, with dual attention mechanisms for robust feature extraction and survival analysis using the Alzheimer’s MRI dataset hosted on the Kaggle platform. The model was trained and evaluated on a balanced dataset of 6,400 MRI images, classified into 4 AD stages: Non-Demented (NoD), very Mild-Demented (vMiD), Mild-Demented (MiD), and Moderate-Demented (MoD). The pre-processing steps, including resizing, normalization, and data augmentation, ensured robustness and generalizability. The framework achieved classification accuracies of 99. 93% (training), 93. 60% (validation) and 93. 59% (testing), with a perfect Concordance Index of 1.0 for survival predictions, demonstrating its ability to accurately rank survival times. By integrating survival analysis with deep learning, the framework addresses limitations in previous work, such as reliance on linear assumptions and limited classification capabilities, and provides actionable insights for clinical workflows, supporting personalized treatment and early intervention strategies. Future work will focus on integrating longitudinal data, exploring time-dependent co-variates, and enhancing explainability to promote adoption in diverse clinical settings. This approach represents a significant advancement in AI-driven diagnosis and progression modeling of AD, with the potential to transform patient care and outcomes. © 2013 IEEE.

关键词： Deep learning

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Secure Private Data Storage and File Sharing Using Homomorphic and Hybrid Hash-Based Cryptographic Technique in Cloud

Secure Private Data Storage and File Sharing Using Homomorph...

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International Conference on Computing and Machine Learning, CML 2024

作者： Senthil, G.A. Prabha, R. Monica, K.M. Priya, R. Mohana Kanna, R. Rajesh Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri SaiRam Institute of Technology Chennai India Department of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Salem India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Chennai India

ISBN: (纸本)9789819765874

Data is a crucial asset in any business, and for some businesses, it ranks among the most valuable. More and more businesses want to keep their data on the cloud and securely and efficiently distribute it among their authorized staff. Data duplication across numerous local devices is one factor in the enormous amount of data that is expanding daily. Both databases and data are made available on a large scale through the Cloud. Storage infrastructure is employed in many different locations. Security concerns have prevented a broad adoption of the cloud computing architecture. This is because of the extensive use of the internet throughout the world;advancements in cryptography have become vital in addressing some of these security issues. Two-phase algorithms have been introduced for a protocol for safe data storage. The hybrid technique that has been presented seeks to boost the power of cloud storage by decreasing computation time and ciphertext size. In this project, users may store all sorts of files, establish an effective directory structure, and navigate it in a way that is remarkably comparable to any conventional Operating system, all in a cloud area. The system also demonstrates a method for secure sharing of personal digital content. Users may transfer files via various methods, including links and QR codes, all inside an easy-to-use single-page Angular UI, with enhanced security provided by AWS services. Our solution can offer some extra features like making lightweight sticky notes and sharing folders utilising a Nodejs server and a MongoDB in addition to these basic functions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud storage

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YOLOv9 in Action: A Futuristic Approach to Real-Time License Plate Recognition and Analysis

YOLOv9 in Action: A Futuristic Approach to Real-Time License...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Kumar, M Ranjith Sendur, G Jeevan Kumar, S Praveen Annamalai, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379525

The automated recognition and identification of license plates is an essential element of intelligent transportation systems that enable effective traffic management, security measures, and the development of efficient e-payment algorithms in toll booths and parking facilities. Even though license plate recognition (LPR) technology is fairly advanced, significant efforts are being made to enhance its accuracy and resilience The growing adoption of deep learning technologies across multiple industry domains, particularly computer vision, presents a novel idea implemented in this paper. To develop an approach to improve detection accuracy using YOLOv9 and transfer learning. Through combining the power of YOLOv9 with transfer learning, it is feasible to tackle the traditional LPR system's inherent flaws. Character recognition, picture pre-processing, license plate extraction, and character segmentation are the four main parts of the proposed system. Each step involves unique methodologies, primarily focusing on character recognition methodologies using YOLOv9 which has not been adequately tackled in existing research. By thorough testing and analysis, this study seeks to further the current developments in LPR systems, ensuring discernibly better-established intelligent transportation solutions. We have achieved a mAP50 Score of 99.5% and a mAP50-95 score of 71 % © 2024 IEEE.

关键词： Image segmentation

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Intrusion Detection System in Internet of Things-Empowered Cybersecurity by Using Various Algorithms 2

Intrusion Detection System in Internet of Things-Empowered C...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Surisetti, Sivaleela Bhavanam, S. Nagakishore Oruganti, Vijageesh Devi, M Vasuja Acharya Nagarjuna University Department of Computer Science & Engineering Andhra Pradesh Guntur India Department of Computer Science & Engineering Jabalpur Barela India Vit University Department of Computer Science & Engineering Vellore Institute of Technology Andhra Pradesh India

ISBN: (纸本)9798350373110

Internet of Things (loT) is the new expertise for various significant application growth. To avoid adversarial attacks, network intrusion, and fraud, Intrusion Detection System (IDS) is one of a primary element of organization. An IDS in loT -empowered cybersecurity observes network traffic to alert and detect malicious activities or policy violations. It secures the devices of loT and networks by recognizing unauthorized access or anomalies. Recently, various researchers have identified significant ways for IDS by utilizing Artificial Intelligence (AI) techniques of Machine Learning (ML) and Deep Learning (DL) algorithms. This research represents several methodologies like Artificial Neural Network (ANN), K- Nearest Neighbor (KNN), Decision Tree (DT), Bagging, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Hybrid Deep Belief Network (HDBN) that are employed for IDS. Accuracy, precision, sensitivity, specificity, and so on, are utilized as the parameters in this study. © 2024 IEEE.

关键词： Cybersecurity

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Modeling and Analysis of Question Answering for Long Context and Compound Questions Using BERT Model

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Procedia computer science 2025年 258卷 2685-2694页

作者： Sivakumar S Meenakshi S P Department of Mathematics Vellore Institute of Technology Vellore and 632 014 India Department of Computer Science and Engineering Vellore Institute of Technology Vellore and 632 014 India

In the context of Indian Lok Sabha question answering, this study introduces a unique use of the Bidirectional Encoder Representations from Transformers (BERT) paradigm for managing long and compound questions. We introduce a specialized dataset of Lok Sabha questions, addressing the unique challenges of parliamentary language and complex queries. Our approach modifies the BERT architecture with hierarchical attention mechanisms to better process multi-part questions. The model was trained on a carefully curated dataset of Lok Sabha questions spanning 2014-2023, encompassing a wide range of topics relevant to Indian governance. We implemented data augmentation techniques, including question decomposition and combination, to enhance the model’s ability to handle compound queries. Our BERT-based model (BERT-VIndLok) outperformed existing baselines, achieving a 76.3% Exact Match score, 84.2% F1 score, and 72.1% Accuracy on the Lok Sabha dataset. These results demonstrate significant improvements over traditional question answering systems in handling the complexities of parliamentary inquiries. Through detailed error analysis and attention visualizations, we provide insights into the model’s behavior and its effectiveness in understanding question structure. We also identify areas for future research, including improving performance on explanatory questions. This work explores how transformer-based models can be used effectively in Natural Language Processing, particularly for handling compound, specialized queries related to Indian parliamentary discussions.

关键词： Question Answering Systems BERT Compound Questions Deep Learning Hierarchical Attention Transformers

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