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An Efficient IoT-based Patient Monitoring System for Heart Disease Prediction and Categorization using Optimized Cell Attention based Spiking Neural Network

An Efficient IoT-based Patient Monitoring System for Heart D...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： P B Suryawanshi Ruchira Rawat Battu Deepa Subhra Chakraborty Aparajita Mohanty Syed Noeman Taqui Department of MBA IBMRD PO MIDC Nr College of Engineering Viladghat Ahmednagar Ahmednagar Maharashtra India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Electronics and Communication Engineering SV College of Engineering Tirupati Andhra Pradesh India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bangalore Karnataka India Faculty of Law Symbiosis Law School (SLS) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The smart healthcare patient monitoring system based on Internet of Things (IoTs) capabilities continuously gathers physiological information through wearable sensors attached to the person’s body. In this concern, the proposed study presents a novel smart healthcare patient monitoring system based on Cell Attention Spiking Neural Network (CASNN). The proposed CASNN model is obtained by integrating Cell Attention Network (CAN) with Spiking Neural Network (SNN) that provides enhanced performance during heart disease prediction and classification. Initially, preprocessing is performed to remove unwanted noises and duplicate values from the raw physiological data. Secondly, selection of optimal feature subsets is carried out by utilizing Hybrid Mud Ring Osprey Optimization Algorithm (HMROOA). Finally, cardiac disease prediction and categorization is accomplished with the help of CASNN, in which the hyper parameters are optimized using War Strategy Optimization (WSO). As a result, the proposed model accurately identifies the presence or absence of heart disease, which helps in timely diagnosis of heart illness. The physiological data used for analysis is taken from UCI Heart Disease Kaggle Dataset in which 75% of data is used for training and 25% of data are used for testing. The investigational results show that the developed system achieves greater detection accurateness of 99.7%, which is better than the conventional heart disease prediction models.

关键词： Heart Patient monitoring Spiking neural networks Medical services Predictive models Prediction algorithms Internet of Things Biomedical monitoring Optimization Diseases

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Efficient Method for Personality Prediction using Hybrid Method of Convolutional Neural Network and LSTM

Efficient Method for Personality Prediction using Hybrid Met...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： N Sujatha S Pramod Shruti Bhatla T Thulasimani Ravi Kant Amit Chauhan Department of Mathematics B. M. S. College of Engineering Bengaluru Karnataka India Department of Mathematics Nitte Meenakshi Institute of Technology Yelahanka Bangalore Karnataka India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu India CSE Department Shoolini University Solan Himachal Pradesh India Department of life Sciences CHRIST (Deemed to be University) Bengaluru Karnataka India

Users’ contributions and the emotions conveyed in status updates may prove invaluable to studies of human behavior and character. A number of other research have taken a similar approach, and the field itself is still growing. The goal of this proposed is to create a technique for deducing a user’s personality traits based on their social media profiles. Among the many customer services now available on SNSs are media and recommendations of user involvement. The need to give internet users with more specialized and customized services that meet their specific requirements, which sometimes depend heavily on the users’ inner personalities, is significant. However, there hasn’t been much work done on the psychological analysis that’s needed to deduce the user’s inner nature from their outward activities. In this instance, LSTM-CNN was fed pre-processed and vectorized text documents. SNF is used for feature extraction. The proposed method employs CFS for the purpose of Feature Selection. Finally, LSTM-CNN was used to train the model. While CNN is good at extracting features that are independent of time, LSTM is better at capturing long-term dependencies. combination of features for personality prediction, the LSTM-CNN model is superior to the individual models.

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AI based Solar Powered Railway Track Crack Detection and Notification System with Chatbot Support 3

AI based Solar Powered Railway Track Crack Detection and Not...

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3rd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2019

作者： Salvi, Sanket Shetty, Shashank Nitte Meenakshi Institute of Technology Department of Information Science Engineering Bangalore Karnataka India NMAM Institute of Technology Department of Computer Science Engineering Karkala Karnataka India

ISBN: (数字)9781728143651

ISBN: (纸本)9781728143651

India is one of the fastest developing countries with significant advancement in the field of the railway network. There have been several incidents of railway accidents due to derailments caused by cracks in the railway track. These cracks generally go unnoticed due to irregular maintenance and manual track monitoring. In this work, we have proposed an solar powered autonomous vehicle called 'Rail-Rakshak' for inspecting and detecting cracks in the railway line. The proposed robot moves along the path and detects the cracks. The exact location of the cracks is sent to the concerned authorities. The proposed robot leverages the concepts of Cloud Computing and Natural Language Processing (NLP) for better visualization and interaction with the robot. © 2019 IEEE.

关键词： Railroads

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Stress Recognition Using Facial Landmarks and Cnn (Alexnet)

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Journal of Physics: Conference Series 2021年第1期2089卷

作者： P Ramesh Naidu S Pruthvi Sagar K Praveen K Kiran K Khalandar Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore Karnataka India

Stress is a psychological disorder that affects every aspect of life and diminishes the quality of sleep. The strategy presented in this paper for detecting cognitive stress levels using facial landmarks is successful. The major goal of this system was to employ visual technology to detect stress using a machine learning methodology. The novelty of this work lies in the fact that a stress detection system should be as non-invasive as possible for the user. The user tension and these evidences are modelled using machine learning. The computer vision techniques we utilized to extract visual evidences, the machine learning model we used to forecast stress and related parameters, and the active sensing strategy we used to collect the most valuable evidences for efficient stress inference are all discussed. Our findings show that the stress level identified by our method is accurate is consistent with what psychological theories predict. This presents a stress recognition approach based on facial photos and landmarks utilizing AlexNet architecture in this research. It is vital to have a gadget that can collect the appropriate data. The use of a biological signal or a thermal image to identify stress is currently being investigated. To address this limitation, we devised an algorithm that can detect stress in photos taken with a standard camera. We have created DNN that uses facial positions points as input to take advantage of the fact that when a person is worried their eye, mouth, and head movements differ from what they are used to. The suggested algorithm senses stress more efficiently, according to experimental data.

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Cybertwin driven resource allocation using optimized proximal policy based federated learning in 6G enabled edge environment

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Digital Communications and Networks 2025年

作者： Sowmya Madhavan M.G. Aruna G.P. Ramesh Abdul Lateef Haroon Phulara Shaik Dhulipalla Ramya Krishna Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Yelahanka Bangalore India Department of Artificial Intelligence and Machine Learning Dayanand Sagar College of Engineering Bangalore India Department of Electronics and Communication Engineering St Peter's Institute of Higher Education and Research Chennai India Department of Electronics and Communication Engineering Ballari Institute of Technology and Management Ballari India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad India

Sixth-generation (6G) communication system promises unprecedented data density and transformative applications over different industries. However, managing heterogeneous data with different distributions in 6G-enabled multi-access edge cloud networks presents challenges for efficient Machine Learning (ML) training and aggregation, often leading to increased energy consumption and reduced model generalization. To solve this problem, this research proposes a Weighted Proximal Policy-based Federated Learning approach integrated with ResNet50 and Scaled Exponential Linear Unit activation function (WPPFL-RS). The proposed method optimizes resource allocation such as CPU and memory, through enhancing the Cyber-twin technology to estimate the computing capacities of edge clouds. The proposed WPPFL-RS approach significantly minimizes the latency and energy consumption, solving complex challenges in 6G-enabled edge computing. This make sures that efficient resource utilization and enhanced performance in heterogeneous edge networks. The proposed WPPFL-RS achieves a minimum latency of 8.20 s on 100 tasks, a significant improvement over the baseline Deep Reinforcement Learning (DRL), which recorded 11.39 s. This approach highlights its potential to enhance resource utilization and performance in 6G edge networks.

关键词： Cybertwin Federated learning ResNet50 Resource allocation Scaled exponential linear Unit Weighted proximal policy

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Alphabetic cryptography: Securing communication over cloud platform 1

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5th International Conference on Information System Design and Intelligent Applications, INDIA 2018

作者： Cowlessur, Sanjeev K. Annappa, B. Manoj Kumar, M.V. Thomas, Likewin Sneha, M.M. Puneetha, B.H. Department of Software Engineering Université des Mascareignes Swami Dayanand Campus Beau Plan Pamplemousses Mauritius Department of Computer Science and Engineering National Institute of Technology Karnataka SurathkalKarnataka India Department of Information Science and Engineering NITTE Meenakshi Institute of Technology YelahankaBengaluru India Department of Computer Science and Engineering P.E.S. Institute of Technology and Management Shivamogga India

ISBN: (数字)9789811333385

ISBN: (纸本)9789811333378

This paper introduces alphabetic cryptography inspired by bidirectional DNA encryption algorithm. Alphabetic cryptography first offers higher randomization and secure communication over the cloud computing platform, and second supports the exchange of complete UNICODE character set. Alphabetic cryptography has been implemented on mobile and desktop platforms. Through experimental studies, it has been observed that randomness of encryption increases exponentially with the increase in the number of alphabets of the alphabetic encryption scheme. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Cryptography

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Slow Heat-Based Hybrid Simulated Annealing Algorithm in Vehicular Ad Hoc Network

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Computational intelligence and neuroscience 2023年第1期2023卷 9918748页

作者： Pavan Kumar Pagadala P Lalitha Surya Kumari Deepak Thakur Vivek Bhardwaj Mohammad Shahid Abdulrajak Buradi Abdul Razak Abiot Ketema Computer Science and Engineering Koneru Lakshmaiah Educational Foundation Hyderabad Telangana India. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India. School of Computer Science and Engineering Lovely Professional University Jalandhar 144411 India. Department of Electrical Engineering Galgotias College of Engineering and Technology 1 Knowledge Park Phase II Noida 201306 UP India. Department of Mechanical Engineering Nitte Meenakshi Institute of Technology Bangalore India. Department of Mechanical Engineering P. A. College of Engineering (Affiliated to Visvesvaraya Technological University Belagavi) Mangaluru 574153 India. Department of Biosystems Engineering Institute of Technology Hawassa University Hawassa Ethiopia.

Vehicular ad hoc networks (VANETs) using reliable protocols of routing have become crucial in identifying the changes to topology on a continuous basis for a large collection of vehicles. For this purpose, it becomes important to identify an optimal configuration of these protocols. There are several possible configurations that have been preventing the configuration of efficient protocols that do not make use of automatic and intelligent design tools. It can further motivate using the techniques of metaheuristics like the tools, which are well-suited to be able to solve these problems. The glowworm swarm optimization (GSO), simulated annealing (SA), and slow heat-based SA-GSO algorithms have been proposed in this work. The SA is a method of optimization, which imitates the manner in which the thermal system has been frozen down to its lowest state of energy. In the GSO, there is guidance to the rules of feasibility, where the swarm converges to its feasible regions very fast. Additionally, for overcoming any premature convergence, there is a local search strategy that is based on the SA and is used for making a search that is near to its true optimum solutions. Finally, this sluggish temperature-based SA-GSO algorithm will be employed to solve routing problems and problems of heat transfer. There is a hybrid slow heat SA-GSO algorithm with a faster speed of convergence and higher precision of computation that is more effective in solving problems of constrained engineering.

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Noise Estimation and Type Identification in Natural Scene and Medical Images using Deep Learning Approaches

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Contrast Media & Molecular Imaging 2023年第1期2023卷

作者： G. Kavitha Chetana Prakash Majid Alhomrani N. Pradeep Abdulhakeem S. Alamri Piyush Kumar Pareek Musah Alhassan Department of Computer Science and Engineering Bapuji Institute of Engineering and Technology Davangere 577004 Karnataka Indiabietdvg.edu Department of Computer Science and Engineering University BDT College of Engineering Davangere 577004 Karnataka *** Department of Clinical Laboratories Sciences The Faculty of Applied Medical Sciences Taif University Taif Saudi Arabiatu.edu.sa Centre of Biomedical Sciences Research (CBSR) Deanship of Scientific Research Taif University Taif Saudi Arabiatu.edu.sa Department of Computer Science and Engineering and Head of IPR Cell Nitte Meenakshi Institute of Technology Bengaluru Indianmit.ac.in University for Development Studies Tamale Ghanauds.edu.gh

The image enhancement for the natural images is the vast field where the quality of the images degrades based on the capturing and processing methods employed by the capturing devices. Based on noise type and estimation of noise, filter need to be adopted for enhancing the quality of the image. In the same manner, the medical field also needs some filtering mechanism to reduce the noise and detection of the disease based on the clarity of the image captured; in accordance with it, the preprocessing steps play a vital role to reduce the burden on the radiologist to make the decision on presence of disease. Based on the estimated noise and its type, the filters are selected to delete the unwanted signals from the image. Hence, identifying noise types and denoising play an important role in image analysis. The proposed framework addresses the noise estimation and filtering process to obtain the enhanced images. This paper estimates and detects the noise types, namely Gaussian, motion artifacts, Poisson, salt-andpepper, and speckle noises. Noise is estimated by using discrete wavelet transformation (DWT). This separates the image into quadruple sub-bands. Noise and HH sub-band are high-frequency components. HH sub-band also has vertical edges. These vertical edges are removed by performing Hadamard operation on downsampled Sobel edge-detected image and HH sub-band. Using HH sub-band after removing vertical edges is considered for estimating the noise. The Rician energy equation is used to estimate the noise. This is given as input for Artificial Neural Network to improve the estimated noise level. For identifying noise type, CNN is used. After removing vertical edges, the HH sub-band is given to the CNN model for classification. The classification accuracy results of identifying noise type are 100% on natural images and 96.3% on medical images.

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Enhanced charge injection in quantum dot light-emitting diodes enabled by discotic liquid crystals

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Journal of Molecular Liquids 2025年 430卷

作者： Zebing Liao Xinyi Yu Mallem Kumar Chengbin Kang Jianxin Song Yiyang Gao Debjyoti Bhadra Maksym F. Prodanov Asmita Shah Dharmenda Pratap Singh Sandeep Kumar Abhishek Kumar Srivastava State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronics and Computer Engineering The Hong Kong University of Science and Technology 999077 Hong Kong China Centre for Display Research Department of Electronics and Computer Engineering The Hong Kong University of Science and Technology 999077 Hong Kong China UDSMM Unité de Dynamique et Structure des Matériaux Moléculaires Université du Littoral Côte d’Opale (ULCO) UR 4476 62228 Calais cedex France Raman Research Institute Soft Condensed Matter C. V. Raman Avenue Bangalore 560080 India Department of Chemistry Nitte Meenakshi Institute of Technology Yelahanka Bangalore 560064 India IAS Center for Quantum Technologies The Hong Kong University of Science and Technology Hong Kong China

Recent progress in quantum dot light-emitting diodes (QLEDs) has substantially improved their external quantum efficiency (EQE), nearing the theoretical limit of 25–30% for primary-color devices. Nevertheless, this peak performance is confined to low current densities and luminance levels, with a significant efficiency roll-off observed at elevated currents. This phenomenon has been primarily attributed to imbalanced charge carrier injection under high-current operation. While charge imbalance mitigation strategies hold critical importance, research on novel hole transport layer (HTL) materials addressing this issue remains relatively underexplored. Herein, we propose a discotic liquid crystals (DLCs) material, 2,3,6,7,10,11-hexabutoxytriphenylene (HAT-4), as an innovative HTL candidate for QLEDs. The HAT-4 system demonstrates enhanced hole mobility coupled with favorable energy level alignment for efficient hole injection into QD layers. However, its implementation is hindered by suboptimal film morphology characterized by excessive crystallinity. To address this limitation, we strategically introduced poly[(9,9-dioctylfluorenyl-2,7-diyl)-alt-(4,4′-(N-(4-butylphenyl))] (TFB) as a morphology-regulating agent. Notably, the optimized HAT-4/TFB (80:20 wt%) hybrid HTL enables remarkable device performance at 5 V, achieving 953 mA·cm −2 (2.2× enhancement vs. TFB control) and 380,000cd·m 2 (2.1× improvement vs. TFB control), respectively. These breakthroughs substantiate the effectiveness of DLC-based hybrid HTLs in suppressing efficiency roll-off while achieving high brightness, thus providing a viable pathway toward high-performance QLED commercialization.

关键词： Charge injection Discotic liquid crystal Quantum dot light-emitting diodes

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An IoT based railway security system for automated manning at level crossings 3

An IoT based railway security system for automated manning a...

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3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication technology, RTEICT 2018

作者： Chaithra, N. Shruthi, P.C. Rashmi, V. Akhil, V. Saqquaf, S.S.M. Telecommunication Engineering Dr. Ambedkar Institute of Technology Bangalore India Computer Science and Engneering Nitte Meenakshi Institute of Technology Bangalore India Digits Indian Institute of Science Bangalore India

ISBN: (纸本)9781538624401

Railways is considered as one of the widely spread mode of transportation all over the globe. Nowadays there is an enormous increase in road and railway traffic. This rapid growth has given rise to more and more accidents at the level crossings. This is a serious concern for both railway and road traffic users. There are no easy ways for tackling this problem, but the main concern is regarding its feasibility for the fluctuating environmental conditions. In this paper, we are proposing an IoT based technique as an alternative and efficient solution for manned and unmanned level crossings. To implement this technology, we are fixing two Infrared Sensors at a pre-calculated distance to calculate the speed of train and time taken by the train to reach level crossings. With this data we are trying to automate closing and opening of gates at level crossings and to regulate road traffic users waiting time. This real time information is sent to database server with the help of Wi-Fi module through Internet of Things (IoT). With the help of GSM module, we send the intrusion detection information to the concerned train driver, station master and control room for efficient monitoring. © 2018 IEEE.

关键词： Internet of things

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