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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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Predicting Genetic Disorders: A Link Mining Approach 2

Predicting Genetic Disorders: A Link Mining Approach

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Sen, Madhura Rajasekaran, Rajkumar Jayaram Reddy, A. Govinda, K. Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350306415

Genetic diseases are conditions caused by a spontaneous alteration or mutation in an individual's DNA. People can inherit genetic disorders from parents, which means they are born with them, even if they are not initially noticeable. On the other hand, some illnesses are not inherited and arise due to disease-causing mutations occurring during cell division. In link mining, link prediction is a crucial task. Based on attribute information and observed existing link information, link prediction predicts if there will be a link between two nodes. Link prediction is applicable not just in the sphere of social networks but also in other fields. This project primarily aims to forecast a person's probability of being diagnosed with a genetic condition. To accomplish this, elements such as the person's family history, the environment in which they live, and a variety of others will be considered. The likelihood of a person being diagnosed with a specific genetic condition can be predicted using the Firefly algorithm. If a person is genetically connected to someone having a given ailment, they are more likely to be diagnosed. Pollution, radioactive radiation, and other environmental factors may also affect the disease's incidence. Considering these factors, we suggest a method for determining the likelihood of a person being diagnosed via link prediction. © 2024 IEEE.

关键词： firefly algorithm genetic disorders healthcare link prediction social network analysis

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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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Feature Extraction using Hybridized Transfer Learning Approach for Oral Cancer Diagnosis 5

Feature Extraction using Hybridized Transfer Learning Approa...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Bharanidharan, N. Abhinav, Kalluri Karthikeyagopala Lathvik, Cherukuri Krishna Chethan, Muppavarapu Reddy, Metta Manikanta Karthikeya Deepak, K. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331504403

Oral cancer diagnosis at earlier stage is very crucial to decide the treatment procedure and to avoid mortality due to this malignant disease. Histopathological imaging is one among modalities widely used by the clinicians to identify the oral cancer. This research focuses on utilizing the hybridized version of transfer learning to extract the deep features from the histopathological oral images for oral cancer detection. Three transfer learning approaches namely VGG16, MobileNetV2, and ResNet50 are used to extract the features individually and then variance based filter technique is used to choose the appropriate features. The experiment is done using 439 oral cancer images and 89 normal images which are obtained from an online repository. The suggested approach offers 97% accuracy while the accuracy attained by stand-alone transfer learning models are less than 90%. © 2024 IEEE.

关键词： Lung cancer

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Secure File Sharing System Using Hybrid Authentication and Secured Key Management Algorithm 4th

Secure File Sharing System Using Hybrid Authentication and S...

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Dokania, Aryan Bamb, Anshul Dheeba, J. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819751990

When web file sharing is used, users can safely receive and share any files or documents over the web using any of their preferred web browsers. Though the existing cryptographic algorithms perform well in end-to-end encryption, they are not resistant to attacks that extract information from the computer system, such as passwords and encryption/decryption keys. In this paper, a hybrid method is proposed for secure and efficient end-to-end encryption. This paper focuses on secure key exchange, efficient data encryption, and strengthening of passwords, which is achieved by a combination of four cryptographic algorithms. The proposed hybrid cryptographic method is secure against several attacks, including man-in-the-middle attacks and side-channel attacks. The proposed method offers many advantages over traditional end-to-end encryption methods, including security, efficiency, and resistance to side-channel attacks. Extensive analytical and experimental results show that the proposed method is efficient enough to be used in a file sharing system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication Protocol

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Parallelization of the Smith-Waterman Algorithm to Accelerate DNA Sequence Alignment 3

Parallelization of the Smith-Waterman Algorithm to Accelerat...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Dhar, Akanksha Chauhan, Ishita Azad, Hiteshwar Kumar Bamzai, Anusha Yadav, Takshu Vellore Institute of Technology Vellore Computer Science and Engineering India

ISBN: (纸本)9798350372120

The genome is a rapidly evolving topic that constantly offers researchers with new biological and computational application challenges. One of the biggest challenges is biological sequence comparison, which involves identifying similarities or identical segments between a query sequence and a reference sequence. Global and local alignments are two common types of sequence alignments. Global alignment identifies the optimal alignment across entire sequences, while local alignment focuses on matching specific segments rather than the entire length. Local alignment algorithms offer faster performance compared to global alignment since they only align specific portions of sequences. In this study, the Smith-Waterman method would be executed serially and in parallel for comparison and analysis. The results reveal that parallel execution has a significant advantage over serial execution, with a maximum improvement of 78.5 \% above serial performance. Furthermore, increasing the matrix size enhances parallel execution performance over serial processing. © 2024 IEEE.

关键词： DNA sequences

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An Intelligent Intrusion Detection using Rule-based CNN with PSO Optimizer 3

An Intelligent Intrusion Detection using Rule-based CNN with...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Karpurasundharapondian, P. Selvi, M. Thangaramya, K. Santhosh Kumar, S.V.N. Kannan, A. Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

Security and secure routing are important design issues in the design of Wireless Sensor Networks (WSNs). Intrusion Detection Systems (IDSs) are useful for securing the communication in WSNs. An IDS can be developed by using machine learning based approach. On the other hand, it can be developed using a deep learning classification algorithm. Optimization techniques including Particle Swarm Optimization (PSO) are useful for choosing optimal features from given dataset. This paper proposes an extended CNN algorithm by extending the existing CNN classifier with rules, SVM and PSO named Rule Extended CNN with SVM (RE-SVM-CNN) for developing this IDS which reduces the false alarm rate by enhanced detection accuracy. © 2024 IEEE.

关键词： Wireless sensor networks

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Design of a Scoring System for National Fitness Volunteer Services under Deep Learning 4

Design of a Scoring System for National Fitness Volunteer Se...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Singh, Abhimeet Singla, Raghav Sodhi, Pulkit Pandian, M. Thurai School of Computer Science and Engineering Vellore Institute of Technology Vellore India

This work is developed to establish a comprehensive, scientific, and reasonable national fitness volunteer service scoring system under a long short-term memory (LSTM) recurrent neural network (RNN) algorithm. The LSTM RNN algorithm-based architecture of a three-layer information processing system for human motion recognition is proposed, which includes a data acquisition layer, a data calculation layer, and a data application layer. The LSTM RNN model is verified on the public dataset PAMAP2. Under the LSTM RNN-based information processing system for human motion recognition, the national fitness volunteer service scoring system is established by using the literature method, the survey method, the analytic hierarchy process (AHP) method, and the Delphi method. The candidate indicators are screened regarding the dimension of "service quality", the indicators are screened by the Delphi method, and the judgment matrix weights and consistency of each included indicator are analyzed. It is found that the information processing system for human motion recognition based on the LSTM RNN algorithm can effectively identify different motion states, such as sitting, lying down, running, walking, and cycling. A national fitness volunteer service scoring system based on the quality of communal sports facilities, services, fitness environment, affiliated sports facilities, and mass fitness service personnel is established under the LSTM RNN-based human motion recognition information processing system. The system includes one number of(#) first-level indicator, five number of(#) second-level indicators, and thirty two number of (#) third-level indicators. In summary, the LSTM RNN-based human motion recognition information processing system can correctly mine the types of fitness exercises, and the established national fitness volunteer service scoring system provides a scientific reference for building a perfect national fitness service system. © 2025 The Authors. Published by Els

关键词： Recurrent neural networks

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Comparative Prediction Analysis for Credit Scoring System 3

Comparative Prediction Analysis for Credit Scoring System

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Rai, Anjali Mehfooza, M. Department of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

The importance of predictive modelling for green selection-making in a variety of fields has expanded because of the fast development of information-driven technologies. The basic and primary goal of this research is to analyse and find an appropriate model which gives the best results in order to perform credit scoring on a given dataset. This study gives a thorough investigation of predictive modelling strategies, assessing their makes use of, benefits, and disadvantages. We discover the theoretical underpinnings and actual-international programs of famous predictive algorithms, including ensemble tactics, choice trees, neural networks, and regression-based totally fashions. This emphasises how vital variables like feature choice, model tuning, and facts exceptional have an effect on predictive accuracy. The revelation of every method’s overall performance in various datasets and eventualities through empirical analysis. By supplying insights that can assist practitioners pick appropriate fashions ideal to precise problem contexts, this studies adds to ongoing efforts to maximise predictive accuracy. The efforts highlight the opportunities of hybrid and ensemble techniques for complicated prediction tasks, suggesting avenues for future research to refine those methodologies. © 2025 IEEE.

关键词： Prediction models

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