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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Bhat, Shreenidhi H. Hareesh, K.S. Kamath, Abhay T. Kudva, Adarsh Manipal Institute of Technology Manipal Academy of Higher Education Dept. Data Science and Computer Applications Manipal India Consultant-Oral and Maxillofacial Surgery Kmc Hospitals Mangaluru India Manipal College of Dental Sciences Manipal Academy of Higher Education Dept. Oral and Maxillofacial Surgery Manipal India

ISBN: (纸本)9798350335095

Virtual reality (VR) requires dynamic and responsive rendering technologies to offer an immersive user experience. Failing, users may experience motion sickness due to motion lag in virtual space. This work aims to create a framework with a higher framerate to decrease the motion sickness in turn increasing the virtual world viewing quality. Essentially, an interactive environment is developed between existing volume rendering engines OpenGL and GPU-based ray casting techniques for rendering the dentofacial data volume. The developed visualization methods are incorporated into Head Mount Device(HMD) to offer immersive effects. The performance of the VR rendering application measured in terms of framerate was observed to be 125 frames per second, significantly higher than the current HMDs framerate. In addition, the adaptation of rendered image quality is activated by adjusting the sampling distance and jitter to complete the rendering process in the allotted time. The achieved result is expected to offer satisfactory comfort to the viewer with reduced motion sickness, particularly in pre-surgical planning for dentofacial surgery. © 2023 IEEE.

关键词： Virtual reality

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Automatic type Identification of Sub - Axial Vertebral Column Fractures using Deep learning Technique 2

Automatic type Identification of Sub - Axial Vertebral Colum...

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2nd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2023

作者： Sindhura, D.N. Pai, Radhika M Bhat, Shyamasunder N Manohara Pai, M.M. Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Data Science and Department of Orthopaeadics Kasturba Medical College Manipal Manipal Academy of Higher Education Manipal India Department of Data Science Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Traumas like falls, sports injuries, or vehicle accidents are the main causes of Vertebral Column Fracture(VCFs) or spine fracture. The symptoms of VCF include back pain, swelling, numbness, and height decrease. VCF causes extreme pain, paralysis, difficulty in movement, etc. CT scans are inexpensive and effective in providing precise and quick VCF type detection. The determination of type VCF, however subject to inter-observer variability. To address this limitation this work introduces an automatic system for the detection of VCF type based on an ensemble of deep fine tuned models. This can assist the orthopaedicians in type identification of VCF which decreases the image interpretation time and increases the patient care time. For type identification, a total of two fine-tuned CNN architectures are used. Then, to further enhance the type identification performance, we developed an ensemble model of fine tuned VGG16 and ResNet50 deep learning models, which average the outputs of the models during final prediction. We used a dataset of 2820 CT images from eight different VCF types that were collected from Kasturba Medical college, manipal Academy of Higher education, manipal. The results shows that the ensemble model performs well compare to individual fine-tuned models with accuracy of 82%, precision of 0.84, recall of 0.82 and F1-score of 0.81 for VCF type identification from CT scans. This study shows that type identification of VCF in CT scans may be successfully accomplished using an ensemble deep transfer learning system with various fine tuned CNN architectures. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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Behavioral Analytics for Predictive Modeling of Mental Health Disorders: A Review of Machine Learning Techniques and Challenges

Behavioral Analytics for Predictive Modeling of Mental Healt...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Vats, Prashant Kshirsagar, Pravin R. Upreti, Kamal Lalit, Keshav Tak, Tan Kuan Mahajan, Shubham Manipal University Jaipur Dept. of Computer Science and Engg Jaipur India J D College of Engineering & Management Department of Electronics & Telecommunication Engineering Nagpur India Dept. of Computer Science Delhi NCR India Manipal Institute of Technology Dept. of Computer Science and Engg Karnataka Manipal India Singapore Institute of Technology Singapore Amity University Gurugram India

ISBN: (纸本)9798331527495

Mental health issues, including anxiety, stress, and depression, may remain untreated until they escalate to a severe level. The issues significantly impact an individual's overall well-being and productivity. Timely identification is crucial for the effectiveness of both intervention and therapy. The application of machine learning techniques makes behavioral analytics a powerful tool for mental health disease prediction modeling. By analyzing behavioral data, this technology facilitates the early detection of various illnesses. This work aims to provide a thorough overview of the use of machine learning techniques, including models that employ Deep structured learning as well as both unsupervised as well as supervised learning, to behavioral data, including activity levels, speech patterns, and facial movements, in order to identify signs of mental health. The benefits and drawbacks of a broad range of machine learning algorithms are examined, with a focus on how these computer algorithms may be applied to identify patterns linked to illnesses like stress, anxiety, and emotional depression. This study looks into the problems that this business encounters as well. These difficulties include combining behavioral data with extra environmental issues and physiological features from the immediate surroundings, the necessity for large and diverse datasets, the need for security of information, and the capacity to understand models. © 2025 IEEE.

关键词： Supervised learning

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Resting-State Network Transitions in Temporal Lobe Epilepsy: Insights from MEG Based Dynamic Functional Connectivity

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IEEE Transactions on Neural Systems and Rehabilitation Engineering 2025年 33卷 2286-2298页

作者： Suhas, M.V. Sinha, Sanjib Kotegar, Karunakar Ravindranadh Chowdary, M. Raghavendra, K. Asranna, Ajay Viswanathan, L.G. Mariyappa, N. Anitha, H. Manipal Academy of Higher Education Manipal Institute of Technology Department of Electronics and Communication Engineering Karnataka Manipal 576104 India National Institute of Mental Health and Neurosciences (NIMHANS) Department of Neurology Karnataka Bangalore 560029 India Manipal Academy of Higher Education Manipal Institute of Technology Department of Data Science and Computer Applications Karnataka Manipal 576104 India National Institute of Mental Health and Neurosciences (NIMHANS) MEG Research Centre Department of Neurology Karnataka Bangalore 560029 India Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science and Engineering Karnataka Manipal 576104 India

Temporal Lobe Epilepsy (TLE), a common form of focal epilepsy, is associated with recurrent seizures originating in the temporal lobe, often leading to cognitive and psychological impairments. This study explores dynamic functional connectivity (dFC) patterns in TLE patients compared to Healthy Controls (HC) using resting-state Magnetoencephalography (MEG) data. dFC, which captures the temporal variability of brain networks, was analyzed across eight frequency bands (delta, theta, alpha, beta, low gamma, mid gamma, high gamma, and broadband) in 21 TLE patients and 21 HC. Nine dFC metrics, including state transitions, connectivity strength, network stability, and overall network movement, were derived using amplitude envelope correlations between 68 brain regions mapped to resting-state networks. Results reveal heightened variability in beta band transitions and increased entropy in delta band transitions, indicating unstable and diverse network configurations. TLE patients showed reduced dwell time in visual networks and increased dwell time in the dorsal attention network, suggesting compensatory mechanisms. Reduced connectivity in alpha and beta bands, coupled with increased variability in theta and low gamma bands, highlights widespread network instability. These findings emphasize dFC metrics as potential biomarkers for TLE, offering insights for targeted therapeutic interventions to stabilize brain dynamics. © 2001-2011 IEEE.

关键词： Dynamic functional connectivity magnetoencephalography resting state network temporal lobe epilepsy

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Predicting State of Health of Lithium Ion Batteries

Predicting State of Health of Lithium Ion Batteries

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IEEE Conference on Industrial Electronics and applications (ICIEA)

作者： Aiswarya Anand Manohara Pai M M Radhika M Pai Dept. of Information and Communication Technology Manipal Institute of Technology MAHE Manipal India Dept. of Data Science and Computer Applications Manipal Institute of Technology MAHE Manipal India

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

The paper focuses on predicting the state of health of Lithium Ion batteries used in Electric Vehicles to make predictive analytics on Remaining Useful Life as well as understand the battery dynamics for recycling enabling sustainable development. The work makes use of three different datasets, namely the Calce dataset, the lithium-ion battery model dataset, and the Oxford Battery Degradation dataset. Multiple machine learning and deep learning algorithms are adopted to predict the state of the health of the batteries in the datasets and are compared with respect to various metrics.

关键词： Lithium-ion batteries Measurement Machine learning algorithms Voltage Prediction algorithms Ions Recycling

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Real-Time Subtitle Generation for Live Videos Using AI and Machine Learning

Real-Time Subtitle Generation for Live Videos Using AI and M...

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Artificial Intelligence and Machine Learning applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： Sahil Anand Shristi Priya SK SS Shameem Department of Data Science and Computer Applications Manipal Institute of Technology Manipal India

ISBN: (数字)9798331538965

ISBN: (纸本)9798331538972

Real-time subtitling of live video streams is still a major challenge because of the difficulties involved in processing spontaneous speech, dealing with multiple speakers, and coping with varying audio conditions with low latency. This research compares the performance of an end-to-end automatic subtitling system that integrates state-of-the-art automatic speech recognition (ASR) with sophisticated natural language processing. Processed 500 hours of live broadcast content in news, sports, and entertainment segments with a hybrid architecture that combined bidirectional LSTM networks and transformer models. Results indicate a 94.2%-word accuracy rate with an average latency of 1.2 seconds, which is a 27 % improvement over current systems. The system was especially strong in dealing with domain-specific vocabulary and subtitle synchronization during rapid speaker changes. Our results show that deep learning-based methods can attain near-human quality in real-time subtitle generation and can fulfill broadcast industry latency demands, rendering automatic subtitling ever more feasible for live shows.

关键词： Vocabulary Accuracy computer architecture Streaming media Transformers Real-time systems Natural language processing Synchronization Streams Automatic speech recognition

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Exploring Design Optimisation Techniques of a Radio Telescope Using Fixed Costing Constraints 2

Exploring Design Optimisation Techniques of a Radio Telescop...

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2nd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2023

作者： Iyer, Aditya Arun Prabhu, Gautham Manuru Gupta, Tanay Deshmukh, Shrey Rivankar, Rushit Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Information Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Cost optimization is a common problem encountered in the design of telescopes. This paper comprehensively discusses various radio telescope designs worldwide, focusing on their design and utilities. It contextualizes the Pulsar data challenge and subsequently discusses and develops a mathematical model for designing radio telescopes. The model assumes a fixed-cost budget. This paper expands current ideas of modeling the system using figure-of-merit equations and optimizing them based on a fixed budget to obtain optimal and affordable radio telescope designs. © Published under licence by IOP Publishing Ltd.

关键词： Radio telescopes

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Exploring CNN Architectures for Seizure Detection from MRI during Pregnancy 3

Exploring CNN Architectures for Seizure Detection from MRI d...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Nayak, Geetanjali Padhy, Neela Madhab Mishra, Tusar Kanti Giet Unviersity Dept. of Computer Science and Engineering Odisha Gunupur India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Karnataka Manipal India

ISBN: (纸本)9798350375190

Deep Learning (DL) represents a subset that has revolutionized traditional machine learning. Unlike earlier methods relying on handcrafted feature extraction, DL automates both feature extraction and classification. This innovation has significantly advanced applications in various medical domains, including the diagnosis of epileptic seizures. This research provides a comparative overview of studies concentrating on automated epileptic seizure detection using leading convolutional neural networks (CNN) architectures. It explores diverse architectures proposed for the automatic diagnosis of epileptic seizures through MRI, encompassing an analysis of rehabilitation systems utilizing DL. The proposed work investigates the application of four primitive CNN architectures on the identification of onset of seizure. The proposed architectures are validated through benchmark MRI samples. Experimental evaluation is made on samples of pregnant females. Overall rate of accuracy stands at 85%, 80%, 90%, and 95% respectively. © 2024 IEEE.

关键词： Network architecture

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A Review: Recent Advancements in Online Private Mode Multi-Object Tracking

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Bilakeri, Shavantrevva Kotegar, Karunakar A. Manipal Institute of Technology Manipal Academy of Higher Education Department of Data Science and Computer Applications Manipal576104 India

Multi-object tracking (MOT) aims to establish connections between target objects throughout consecutive video frames to capture the complete trajectories of these moving objects. Due to the progress made in deep neural networks (DNN) and the growing need for intelligent video analysis, MOT has garnered considerable attention in "computer vision". MOT exhibits scholarly and economic promise in various domains, including urban public security, military applications, self-driving technology, etc. Despite the diverse range of approaches researchers propose to address this challenge, it doesn’t remain easy due to changing objects’ appearances, significant occlusion, and non-linear motion patterns. Our review process provides fundamental insights into multi-object tracking. It covers essential aspects such as the basic workflow, different approaches to conducting the MOT task, challenges encountered, dataset requirements, evaluation metrics, and procedure for result submission to MOT and CodaLab servers. This study analyses the benefits and constraints of current strategies, techniques, and methods through a systematic review of the latest advancements in object detection and online private detection-based MOT algorithms published. Additionally, we present a quantitative comparison and analysis of experimental evidence of the top-performing MOT algorithms across various metrics. Finally, we propose future research directions by analyzing the commonalities among several methods demonstrating excellent performance. © 2020 IEEE.

关键词： Object detection

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A Blockchain-Based Credentials for Food Traceability in Agricultural Supply Chain

A Blockchain-Based Credentials for Food Traceability in Agri...

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2023 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2023

作者： Manoj, T. Makkithaya, Krishnamoorthi Narendra, V.G. Manipal Academy of Higher Education Manipal Institute of Technology Dept. of Computer Science & Engg. Manipal India

ISBN: (纸本)9798350340129

In recent years the prominence of food product traceability has significantly increased due to growing concerns regarding food safety and ensuring equitable market access for farmers. Presently, supply chain information systems collect, transmit and store records in centralized systems. These conventional digital systems create fragmented data silos vulnerable to a single point of failure. These limitations pose significant challenges in establishing trust factors such as transparency, traceability, integrity, and privacy among the various actors involved in the agri-supply chain. To overcome these challenges, a decentralized technology like blockchain has immense potential to enhance traceability by addressing security and privacy aspects. This study proposes a blockchain-based framework called AgriFoodCredChain for food product traceability using Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). Initially, the study outlines a secure mechanism for establishing DID communication channels, assuring robust and reliable data exchange. Furthermore, methods for privacy-preserved issuance and verification of credentials for food traceability are developed and presented. The implementation of the framework is conducted utilizing the identity blockchain named Hyperledger Indy and Aries library, considering particular workflow in the food supply chain. Evaluating the framework w.r.t credential issue and verifying time proves that the solution implemented is scalable and satisfies the requirements for real-time adoption. © 2023 IEEE.

关键词： Blockchain

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