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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

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

ISBN: (纸本)9798350361537

Better patient outcomes and prompt care depend on early detection of heart attacks. In this current work, we use the infamous MIT-BIH Arrhythmia Dataset, a reference resource for cardiac abnormality recognition, to train and validate our models. We propose an improved method for heart attack diagnosis and detection which includes a CNN model that is optimized with the help of the Grey Wolf Optimization (GWO) technique. This novel integration aims to enhance feature extraction and classification processes by tackling the drawbacks of traditional deep learning methods. A comprehensive testing process has been conducted on the proposed CNN-GWO model versus well-known deep learning models, such as Artificial Neural Networks also known as ANN, as well as many state-of-the-art machine learning models used for heart attack detection such as SVM, Decision Tree, and other ensemble learning models such as XG Boost. Our findings demonstrate a significant increase in detection accuracy and efficiency when comparing the CNN-GWO model to its rivals. This work not only demonstrates the effectiveness of combining CNN with GWO, but also demonstrates how well the proposed model performs in detecting heart attacks. This work contributes to the advancement of medical diagnostic technology by offering a more reliable and accurate diagnostic instrument. It also opens new options for algorithmic advancements in the future that forecast heart attacks. © 2024 IEEE.

关键词： Diseases

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Automatic Query Generation Based on Adaptive Naked Mole-Rate Algorithm

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Multimedia Tools and Applications 2025年第16期84卷 16249-16280页

作者： Kinikar, Mayura Saleena, B. School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Pune India

In the growing information retrieval (IR) world, selecting suitable keywords and generating queries is important for effective retrieval. Modern database applications need a sophisticated interface for automatically updating the connections between users and databases. Most database applications are intelligent;however, some may be complex to understand when generating queries for effective retrieval. Therefore, this paper develops a new Adaptive Naked Mole-Rate algorithm (ANMR) for an automatic query generation (AQG) based IR system. The query generation approach primarily generates the query with expanded keywords to enhance IR. Modified sealion optimization (MSO) is applied to select several features. The selected features are combined in the feature fusion process using the Fuzzy based Search and Rescue approach (FSR). Similarity matching is performed using the hybrid cosine and Jaccard similarity measures. At last, the ranking process is performed using the Dynamic Global Local Attention Network based on Capsules (DGLANC). The developed AQG-ANMR system improves the performance of the entire information retrieval system. Then, to analyze the performance, the proposed approach is implemented in the python platform and evaluated in terms of accuracy, recall, precision, and F1-Score by employing TREC-3 and CISI datasets. Besides, the performance of the proposed approach is compared with that of state-of-the-art approaches. Finally, the simulated results clearly showed that the proposed approach outperformed the state-of-the-art approaches better. The maximum accuracy attained by the proposed approach is 97.2% with AQG-ANMR and 94.69% without AQG-ANMR for the TREC 3 dataset. In the same way, using the CISI dataset, the proposed approach reached a maximum accuracy of 98.6% with AQG-ANMR and 95.8% without AQG-ANMR, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Information retrieval

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Deep Learning Based Face Detection and Identification of Criminal Suspects

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computers, Materials & Continua 2023年第2期74卷 2331-2343页

作者： S.Sandhya A.Balasundaram Ayesha Shaik School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)Chennai600127India

Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past *** of the most tedious tasks is to track a suspect once a crime is *** most of the crimes are committed by individuals who have a history of felonies,it is essential for a monitoring system that does not just detect the person’s face who has committed the crime,but also their ***,a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network(DNN)model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been *** detection and extraction of the face in the image by face cropping,the captured face is then compared with the images in the *** comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity *** plotting the values in a graph to find the threshold value,we conclude that the confidence rate of the encoder model is 0.75 and above.

关键词： Deep learning opencv deep neural network single shot multi-box detector auto-encoder cosine similarity

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Home-based Detection and Prediction of Diabetic Foot Ulcers at Early Stage Using Sensor technology and Supervised Learning

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Journal of Harbin institute of technology(New Series) 2024年第1期31卷 26-37页

作者： Kamasamudram Bhavya Sai Rishi Raghu Sai Surya Varshith Nukala Jayashree Jayaraman Vijayashree Jayaraman School of Computer Science and Engineering Vellore Institute of TechnologyVellore 632014India

For years,foot ulcers linked with diabetes mellitus and neuropathy have significantly impacted diabetic patients’ health-related quality of life(HRQoL). Diabetes foot ulcers impact15% of all diabetic patients at some point in their lives. The facilities and resources used for DFU detection and treatment are only available at hospitals and clinics,which results in the unavailability of feasible and timely detection at an early stage. This necessitates the development of an at-home DFU detection system that enables timely predictions and seamless communication with users,thereby preventing amputations due to neglect and severity. This paper proposes a feasible system consisting of three major modules:an IoT device that works to sense foot nodes to send vibrations onto a foot sole,a machine learning model based on supervised learning which predicts the level of severity of the DFU using four different classification techniques including XGBoost,K-SVM,Random Forest,and Decision tree,and a mobile application that acts as an interface between the sensors and the patient. Based on the severity levels,necessary steps for prevention,treatment,and medications are recommended via the application.

关键词： diabetic foot ulcer podiatry diabetes mellitus healthcare footcare internet of things machine learning human⁃computer interaction

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EARA-PSOCAE: an energy aware resource allocation and particle swarm optimisation based cryptographic algorithm in e-healthcare cloud environment

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International Journal of Cloud Computing 2023年第5期12卷 497-513页

作者： Palani, S. Babu, K. Ramesh Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India

In cloud platforms, large amount of energy is consumed by execution of scientific workflow. So, VMs has to be deployed in energy efficient manner. Throughout the world, wide attention is attracted by cloud platform’s energy consumption. Cooling systems, light systems, network peripherals, monitors, console, processor’s cooling fan, server running consumes large amount of power in a cloud data centre. In order to face these issues, energy-aware resource allocation method is proposed in this work, which is termed as EnReal. For execution of scientific workflow, virtual machines are deployed dynamically which focus on current e-health standards and solutions. In general e-health systems, client platform security is not addressed by this method which is an important factor to be considered. In e-health domain infrastructures, for privacy establishment, new particle swarm optimisation (PSO) with cryptography-based security algorithm (PSOCA) is proposed in this work. Controlled environment can be created by this for data’s privacy easy management as well as for security. For experimentation, CloudSim framework is adapted in cloud simulation environment. Hence the proposed method is evaluated using various parameters like energy consumption and resource utilisation. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Resource allocation

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COVINet: A hybrid model for classification of COVID and Non-COVID pneumonia in CT and X-Ray imagery

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International Journal of Cognitive Computing in engineering 2023年第1期4卷 149-159页

作者： Mittal, Vasu Kumar, Akhil School of Computer Science & Engineering Vellore Institute of Technology Chennai India

The COVID-19 pandemic has resulted in a significant increase in the number of pneumonia cases, including those caused by the Coronavirus. To detect COVID pneumonia, RT-PCR is used as the primary detection tool for COVID-19 pneumonia but chest imaging, including CT scans and X-Ray imagery, can also be used as a secondary important tool for the diagnosis of pneumonia, including COVID pneumonia. However, the interpretation of chest imaging in COVID-19 pneumonia can be challenging, as the signs of the disease on imaging may be subtle and may overlap with normal pneumonia. In this paper, we propose a hybrid model with the name COVINet which uses ResNet-101 as the feature extractor and classical K-Nearest Neighbors as the classifier that led us to give automated results for detecting COVID pneumonia in X-Rays and CT imagery. The proposed hybrid model achieved a classification accuracy of 98.6%. The model's precision, recall, and F1-Score values were also impressive, ranging from 98-99%. To back and support the proposed model, several CNN-based feature extractors and classical machine learning classifiers have been exploited. The outcome with exploited combinations suggests that our model can significantly enhance the accuracy and precision of detecting COVID-19 pneumonia on chest imaging, and this holds the potential of being a valuable resource for early identification and diagnosis of the illness by radiologists and medical practitioners. © 2023

关键词： COVID-19

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Sign Language Recognition Using Convolutional Neural Network Based on VGG-16 Deep Learning Approach 5th

Sign Language Recognition Using Convolutional Neural Network...

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5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Athish, M.J. Sowmya, S. Lakshaya, E. Preetha, S. Rishiya Shankar, S.U. Sendhil Kumar, K.S. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819761050

Communication is a significant part of our lives, as it facilitates interaction among individuals. Language serves as a mode of communication for all individuals for expressing their thoughts and interpretations. However, hearing-impaired individuals often face hurdles in using spoken language. The technology for recognizing sign language made a commendable effort to improve communication levels among hearing-impaired individuals. In the current era of deep learning, which is a subset of machine learning, Convolutional Neural Network models have been developed and are now in use for gesture recognition. This recognition is based on the CNN model and VGG-16 model. It is used to hone the temporal features. The dataset used here is American Sign Language Dataset (A–Z and 0–9). The focus of this work is to develop a sign language recognition system to fulfill the need for communication of impaired people in society. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Stereotactic Needle Path Planning for Brain Biopsy Based on Volumetric Segmentation of MRI and Heuristic Approach 5th

Stereotactic Needle Path Planning for Brain Biopsy Based on ...

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5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Krishnan, Sushil Bhogale, Atharva Gawande, Aarya Jaisankar, N. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819761050

Any abnormal growth in brain cells refers to brain tumor. With large variety of tumors reported, accurate diagnosis is very essential for appropriate intervention. Traditionally, biopsies are preferred diagnostic tool, where a small piece of tissue or some cells are collected from suspected region and tested in a laboratory. Brain being the control and coordination center, biopsy in brain requires utmost precision and accuracy to avoid any damage to healthy tissues. Stereotactic needle biopsies are specialized minimally invasive technique for collecting the required samples. In such cases precise path planning is very essential. Availability of large medical image datasets and enormous computational capability encourages to train deep learning models that can accurately delineate and identify the tumor’s location, shape, size, and borders within the patient's brain. Volumetric segmentation combined with heuristic path planning algorithm can play a synergistic role in increasing the success rates of brain biopsies. The main objective of the work is to propose a framework for precise needle insertion for brain tumor biopsies. The work proceeds by comparing performances of various model for volumetric segmentation of brain MRI scans and identifying shortest path to the tumor, utilizing the segments obtained. This approach minimizes blood loss and the need for extensive medical intervention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biopsy

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Enhancing Multiclass Pneumonia Classification with Machine Learning and Textural Features

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Machine Graphics and Vision 2023年第3-4期32卷 83-106页

作者： Godbin, A. Beena Jasmine, S. Graceline School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The highly infectious and mutating COVID-19, known as the novel coronavirus, poses a substantial threat to both human health and the global economy. Detecting COVID-19 early presents a challenge due to its resemblance to pneumonia. However, distinguishing between the two is critical for saving lives. Chest X-rays, empowered by machine learning classifiers and ensembles, prove effective in identifying multiclass pneumonia in the lungs, leveraging textural characteristics such as GLCM and GLRLM. These textural features are instilled into the classifiers and ensembles within the domain of machine learning. This article explores the multiclass categorization of X-ray images across four categories: COVID-19-impacted, bacterial pneumonia-affected, viral pneumonia-affected, and normal lungs. The classification employs Random Forest, Support Vector Machine, K-Nearest Neighbor, LGBM, and XGBoost. Random Forest and LGBM achieve an impressive accuracy of 92.4% in identifying GLCM features. The network’s performance is evaluated based on accuracy, precision, sensitivity and F1-score. © 2023 institute of Information technology, Warsaw University of Life sciences - SGGW. All rights reserved.

关键词： COVID-19

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Crafting Captivating Narratives: Enhancing Image Captioning Through Encoder-Decoder Architectures 2

Crafting Captivating Narratives: Enhancing Image Captioning ...

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

作者： Sahaaya Arul Mary, S.A. Pandey, Adamya Kumar Jha, Aadarsh Sharma, Kartikay Shohel, Mohmad Natarajan, B. School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Information Technology Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350373110

This research work aims to develop an image captioning system utilizing deep learning techniques. The pre-trained VGG-16 model is employed to extract image features, while an innovative encoder-decoder architecture is used to generate descriptive captions. To ensure compatibility with the model, pre-processing techniques are applied to the dataset, which comprises images and their corresponding descriptions. Tokenization, vocabulary generation, and effectively utilizing a data generator are integral parts of the training process, facilitating the handling vast datasets. Multiple training epochs are employed to optimize the model's ability to associate captions with specific picture attributes. The model's performance is evaluated using BLEU-1 and BLEU-2 scores on a designated test set, providing quantitative insights into the quality of the generated captions. Additionally, this work includes a visualization component enabling users to input names of specific images, generating captions in real-time for comparative analysis with actual captions. This comprehensive approach integrates methodologies from computer vision and natural language processing, showcasing how deep learning can enhance the interpretability and comprehension of images akin to human perception. BLEU is a widely used, automated metric for evaluating image captioning models, offering objective and quantitative comparisons. © 2024 IEEE.

关键词： Deep learning

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