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Image Analysis for E-Healthcare Systems using Multi-Biometric Recognition Model

Image Analysis for E-Healthcare Systems using Multi-Biometri...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Naman Bansal Pranav Arora Deepak Kumar Sharma Koyel Datta Gupta Chandana Kuntala Department of Instrumentation and Control Engineering Netaji Subhas Institute of Technology University of Delhi New Delhi India Department of Information Technology Indira Gandhi Delhi Technical University for Women New Delhi India Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of Information Technology Indira Gandhi Delhi Technical University for Women New Delhi India

With the dawn of e-Healthcare systems, Medical Record Management has become an important research problem. The storage and organization of medical records have made relatively little progress in a world of constantly emerging new technologies and continuous innovation. Many hospitals keep the records on paper, which raises many challenges, including but not limited to a significant amount of time for searching and retrieving a specific record, high maintenance costs, lack of backup, and limited security. Although the inclusion of technology has made this task far more efficient and secure, there is still much that can be done to improve it. This paper proposes a double index-based approach for mapping medical records directly to a patient’s biometrics which would take advantage of the uniqueness of biometrics to identify a patient. The proposed multi-biometric model achieves an accuracy of 97% and an F 1 score of 0.9814.

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An Intelligent Prediction Model of COVID-19 in India using Hybrid Epidemic Model

An Intelligent Prediction Model of COVID-19 in India using H...

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2020 International Conference on Smart Electronics and Communication, ICOSEC 2020

作者： Kumar, Manoj Bareja, Jatin Singh, Manjot Sharma, Rupanshu Delhi Technological University Department of Computer Science Engineering Delhi India Netaji Subhas Institute of Technology Manufacturing Process and Automation Engineering Delhi India

ISBN: (纸本)9781728154619

This article presents the current situation of COVID 19 spread in India and how it is impacted by various measures taken by the administration. Data source is taken (till 15th June 2020) from World Health organization (WHO) to study various trends and pattern. Hybrid epidemic susceptible-infected recovered model is used to make predictions at every stage of coronavirus. The basic reproduction number R0, is constructed using a logistic function. An improved or hybrid epidemic model is build based on various other factors to build a logistic infection rate to analyse the patterns and trend in the data. Secondly, ICU beds and ventilators available per 100, 000 inhabitants is also taken in account for critical. Also, the timeline for changing the stage of coronavirus is used from various reliable resources. Moreover, in comparison with the traditional SIR models, accuracy in the predictions has significantly increased with the R2 value of 96.8 percent for the future days in India.. © 2020 IEEE.

关键词： COVID-19

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Centrality Based Geocasting for Opportunistic Networks 33rd

Centrality Based Geocasting for Opportunistic Networks

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33rd International Conference on Advanced Information Networking and Applications, AINA-2019

作者： Singh, Jagdeep Dhurandher, Sanjay K. Woungang, Isaac Takizawa, Makoto CAITFS Division of Information Technology Netaji Subhas Institute of Technology University of Delhi New Delhi India CAITFS Department of Information Technology Netaji Subhas University of Technology New Delhi India Department of Computer Science Ryerson University TorontoON Canada Department of Advanced Sciences Faculty of Science and Engineering Hosei University Tokyo Japan

ISBN: (纸本)9783030150310

Opportunistic networks (OppNets) are a subclass of delay tolerant networks characterized by unstable topology, intermittent connectivity, and no guarantee of the existence of an end-to-end path to carry the messages from source to destination because of node’s mobility. For these reasons, the routing of data in such networks occurs in a store-carry-and-forward approach. Following this approach combined with geocasting as message transmission strategy, this paper proposes a geo-casting protocol for OppNets named Centrality based Geocasting for OPPortunistic networks (CGOPP for short). The proposed scheme combines uni-casting and flooding by first transferring a message to an individual geographic location, then intelligently flood the message to all nodes inside the area by using a degree centrality function. Simulation results are provided, demonstrating the efficiency of the proposed protocol against traditional geo-casting protocols for OppNets, in terms of average latency, delivery ratio, and number of messages forwarded, under varying number of nodes, buffer sizes, and TTL. © 2020, Springer Nature Switzerland AG.

关键词： Floods

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Detecting and Predicting Agitation Episodes in Dementia Patients Using IoT and Artificial Neural Networks

Detecting and Predicting Agitation Episodes in Dementia Pati...

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Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： S. Pragadeeswaran N. Mohankumar R Puviarasi N. Nandhini Audithan Sivaraman M. Rajmohan Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Information Technology P. S. V College of Engineering and Technology Krishnagiri Tamilnadu Department of Information Technology Netaji Subhas university of Technology (Govt of NCT Delhi) New Delhi India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

Dementia patients' agitation is a major problem for caregivers, who report greater rates of stress and costs connected to care. This research aims to determine if it can detect and predict agitation episodes in dementia patients by combining Internet of Things (IoT) devices with Artificial Neural Networks (ANNs). It continually monitors environmental and physiological variables like room temperature, noise levels, heart rate, and movement patterns by installing IoT sensors. A custom-built ANN is trained to detect patterns related to agitation episodes and feed the collected data. This method preprocesses raw sensor data using present techniques for signal processing to increase reliability and robustness. Data from multiple healthcare institutes helps the ANNs model recognize agitation. Early intervention may lessen the severity of these instances and forecast weight gain due to the model's ability to predict. The integration of IoT with ANNs may improve the quality of care for individuals with dementia and provide carers with actionable insights that enable enhanced, personalized therapy. This combination of technology has the potential to significantly enhance patient outcomes and optimize resource utilization in dementia care facilities.

关键词： Training Temperature sensors Accuracy Artificial neural networks Predictive models Data models Sensors Internet of Things Biomedical monitoring Dementia

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Passive Copy-Move Forgery Detection using Localized Intensity Feature 11

Passive Copy-Move Forgery Detection using Localized Intensit...

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11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020

作者： Dua, Shilpa Shahi, Dhruv P. Singh, Jyotsna Parthasarathy, Harish Multimedia Research Lab Netaji Subhas Institute of Technology Division of Electronics and Communication Engineering New Delhi India Delhi Technological University Division of Electronics and Electrical Engineering New Delhi India

ISBN: (纸本)9781728168517

This paper presents an approach for the detection and localization of copy-move forgery (CMF) in digital images using feature based on the localized intensity patterns of pixels. Initially, the test image is partitioned into overlapping blocks and local intensity feature descriptors are extracted from each of these blocks or frames. The matching of similar local frames is then performed in feature space using generalized 2NN (g2NN) test. The detection gets refined in the next stage by the application of agglomerative hierarchal clustering followed by RANdom SAmple Consensus (RANSAC) algorithm with the removal of unwanted outliers. Simulation results are demonstrated to show the efficacy of proposed technique even after numerous pre- and post-processing operations. © 2020 IEEE.

关键词： Digital forensics

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Optimizing Blood Bank Management with Cloud-Hosted Long Short-Term Memory Models for Inventory Forecasting and Utilization

Optimizing Blood Bank Management with Cloud-Hosted Long Shor...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： R. Aiyshwariya Devi Sree Southry S S. Rajanarayanan S. Chandra Sekaran Audithan Sivaraman S. Srinivasan Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamil Nadu India Department of Computer Science and Engineering Vinayaka Mission’s Research Foundation (DU) Vinayaka Mission’s Kirupananda Variyar Engineering College Salem Tamil Nadu India Department of Computer Science and Engineering P.S.V College of Engineering and Technology Krishnagiri Tamil Nadu India Department of Information Technology Netaji Subhas University of Technology (Govt of NCTDelhi) Jafferpur New Delhi India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This research presents an innovative method for blood bank management using Cloud-based Long Short-Term Memory (LSTM) models for precise inventory forecasting and optimization. The objective of this research is to increase blood bank efficiency by utilizing LSTM models to accurately estimate demand, optimize inventory levels, and enhance overall resource utilization. The system uses cloud computing to provide real-time demand forecasting, minimizing blood waste and assuring a reliable supply for healthcare requirements. LSTM models use historical data to identify long-term trends and variations in blood demand, facilitating accurate inventory forecasting. This predictive system optimizes the allocation process, resulting in improve resource utilization and increased operational efficiency. In compared to traditional techniques, the proposed system provides a more scalable and flexible solution by using cloud technology to provide continuous monitoring and dynamic modifications of inventory levels. It provides a robust framework for management of blood supply and enhancing patient care for improved resource management. Future enhancements may include broadening the system to incorporate real-time multi-source data integration and investigating hybrid machine learning models to better optimize demand forecasts.

关键词： Cloud computing Computational modeling Demand forecasting Medical services Predictive models Real-time systems Resource management Blood Long short term memory Optimization

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A decade of EEG Analysis: Prospects & Challenges in Biometric System

A decade of EEG Analysis: Prospects & Challenges in Biometri...

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International Conference on Confluence The Next Generation Information technology Summit (Confluence)

作者： Swati Aggarwal Nupur Chugh Computer Engineering Netaji Subhas University of Technology (Formerly Netaji Subhas Institute of Technology) New Delhi India

ISBN: (数字)9781728127910

ISBN: (纸本)9781728127927

EEG signals have been investigated in medical and entertainment applications for many years. Recently, researchers have investigated EEG signals as a biometric trait for person identification and authentication. EEG biometric system has achieved reasonable accuracy, yet some of the challenges hinder the development of ready to deploy EEG based biometric systems. This paper highlights the emerging challenges of the EEG biometric system. The authors present the interrelationships among identified challenges and review the published articles quantitatively based on publication time and online library.

关键词： Conferences Cloud computing Data science Electroencephalography Biomedical engineering computer science Identification of persons

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A Guided Learning Approach for Generative Adversarial Networks

A Guided Learning Approach for Generative Adversarial Networ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Sidhant Nagpal Siddharth Verma Shikhar Gupta Swati Aggarwal Department of Computer Engineering Netaji Subhas Institute of Technology Delhi India

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In this paper, we propose a novel technique for training Generative Adversarial Networks (GANs) using autoencoders. GANs, in recent years, have emerged as one of the most popular generative models. Despite their success, there are several challenges in maintaining the trade-off between diversity and quality of the generated distribution. Our idea stems from the fact that deeper layers of an autoencoder contain high-level feature representation of the input data distribution. Reusing these layers provides GAN with information about the representative characteristics of real data and hence can guide its adversarial training. We call our model Guided GAN since the autoencoder (guiding network) provides a direction to train the GAN (generative network). Guided GAN also minimizes both the forward and reverse Kullback-Leibler (KL) divergence in a single model, exploiting the complementary statistical properties of the two. We conduct extensive experiments and use various metrics for assessing the quality, diversity of generated images and convergence of the model. Our model is evaluated on two standard datasets: CIFAR-10 and CelebA demonstrating either superior or competitive performance compared to baseline GANs, especially in the earlier training stages. Our guided training procedure has been tested on different baseline GANs without any changes to their hyper-parameter configuration or architecture.

关键词： Gallium nitride Training Generators Decoding Generative adversarial networks Convolution Data models

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Detection of Epileptic Seizures using EEG Signals

Detection of Epileptic Seizures using EEG Signals

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Sarthak Gupta Siddhant Bagga Vikas Maheshkar M. P. S. Bhatia Division of Information Technology Netaji Subhas University of Technology New Delhi India Department of Computer Engineering Netaji Subhas University of Technology New Delhi India

ISBN: (数字)9781728144580

ISBN: (纸本)9781728144597

Epilepsy is a neurological disorder which causes abnormal brain activity such as seizures. Electroencephalogram (EEG) signals are recordings of the electrical activity of brain, which are used extensively in many medical applications, including detection of epileptic seizures. Traditionally, neurologists made inferences by visual inspection. However, this was usually very time consuming and the results are subject to the expertise of the reader. Hence, automatic epileptic seizure detection techniques are needed, which are able to provide high quality results in a short time. In this paper, an approach has been proposed that uses Discrete Wavelet Transform to convert the EEG signal into the time-frequency domain. Approximation and detail coefficients were obtained after the discrete wavelet transform of EEG signals. Then, various features are extracted and then classification is carried out on a number of classifiers including convolutional neural networks, random forests etc for the detection of epilepsy seizure. Results show that our processing technique and the combination of features extracted provide far superior results than those obtained by applying the classifiers on the EEG signals directly. In our work, an accuracy of 99.29 was achieved which outperformed the conventional epileptic seizure detection techniques. The proposed approach is tested on Bonn University's EEG signal dataset.

关键词： Electroencephalography Feature extraction Discrete wavelet transforms Time-frequency analysis Epilepsy Fourier transforms

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Reinforcement Learning Based Congestion Control Mechanism for Opportunistic Networks

Reinforcement Learning Based Congestion Control Mechanism fo...

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IEEE Globecom Workshops

作者： Jagdeep Singh Sanjay Kumar Dhurandher Isaac Woungang Periklis Chatzimisios Joel J. P. C. Rodrigues Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal Punjab India Department of Information Technology Netaji Subhas University of Technology New Delhi India Department of Computer Science Ryerson University Toronto ON Canada International Hellenic University Thessaloniki Greece College of Computer Science and Technology China University of Petroleum (East China) Qingdao China Instituto de Telecomunicações Covilhã Portugal

ISBN: (纸本)9781665459761

This work proposes a Reinforcement Learning-based Congestion Control protocol (called (RLCC)) for Opportunistic Networks to find the optimal number of message counts based on the real-time density in the network. (RLCC) jointly uses Q-learning and fuzzy logic to make the routing decision based on real attributes such as social status, centrality, activeness, message lifetime, hop count, and battery status. In the proposed (RLCC) scheme, the message priority is required to maintain the optimal count of messages in the network, and the fuzzy inference rules perform well in predicting the best hop for message transmission. All network nodes get inputs from the environment and take action accordingly. If the message is transferred successfully to the intended node, then the node receives the reward for the action, otherwise, the penalty will be assigned. Based on this, network nodes only select those nodes, which are capable to transmit the message from one node to another node. Simulation results demonstrate that RLCC is superior to the MARLCC and F-GSAF routing protocols using the infocom2006 real mobility data trace, in terms of delivery probability, latency, and overhead ratio.

关键词： Fuzzy logic Measurement Q-learning Mobility models Simulation Routing Routing protocols

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