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2024 International Conference on Intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Panda, Sanghamitra Sahu, Rekha Dash, Satya Ranjan Centurion University of Technology and Management Department of Computer Science and Engineering Odisha Bhubaneswar India Silicon University Department of Computer Science and Engineering Odisha Bhubaneswar India KIIT Deemed to Be University School of Computer Application Odisha Bhubaneswar India

ISBN: (纸本)9798350369175

Alzheimer's disease is one sort of neurological disorder which affects the daily activities and it is incurable. But, if it can be detected early, then it may be prevented or made to progress slowly. For detection, deep learning has played a great role now a days. In this paper, we have taken 6400 MRI images of both dementia and non-dementia subjects. The dataset is spilt into ${8 0 \%}$ for training a model and ${2 0 \%}$ for validating and accessing the achievement of the trained model. We have recommended a two-dimensional convolutional neural network architecture in order to apply on the MRI dataset to generate a model for classifying and predicting Alzheimer's disease patients. The model generated using the proposed two-dimensional architecture has given approximately $98 \%$ classification accuracy. Besides, we have presented roc-auc curve, f1-score, recall, support, precision and confusion matrix to show the execution of the model. Again, at training procedure, model loss and accuracy also depicted according to epoch times. The researches already performed on prediction of Alzheimer's disease has given poor performance of ${9 1 \%}$ classification accuracy in comparison to our model. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comprehensive Study of a Conversational AI-Driven Healthcare Bot with Pose Estimation for Precision Fitness Monitoring and Personalized Guidance

A Comprehensive Study of a Conversational AI-Driven Healthca...

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Sinha, Anurag Kumar, Biresh Kundu, Roumo Sharma, Amit Sharma, Mukesh Raza, Asif ICFAI University ICFAI Tech School Computer Science Department Jharkhand Ranchi India Amity University Department of Computer Engineering and Application Jharkhand India Amity Institute of Information Technology Amity University Jharkhand Jharkhand Ranchi India Lovely Professional University Department of Computer Science Phagwara India Amity University Department of Master of Computer Application Jharkhand Ranchi India

ISBN: (纸本)9798350374100

This research investigates a new healthcare paradigm through the analysis of a conversational AI-powered healthcare bot that has posture estimation capabilities for accurate fitness tracking and customized recommendations. User motions during workout routines are accurately and thoroughly assessed thanks to the incorporation of powerful posture estimation technology. With the help of individualized exercise advice, this thorough study explores the capabilities of the system and shows how it may transform wellness practices. The healthcare bot provides an innovative method of fitness tracking by using conversational AI and accurate posture prediction to customize recommendations based on user requirements. This novel combination promises to enhance wellbeing and provide consumers with a more efficient and customized workout experience. The study emphasizes this point. © 2024 IEEE.

关键词： Health care

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Exploiting Predictability of Random Vector Functional Link Networks in Forecasting Quality of Service (QoS) Parameters of IoT-Based Web Services Data

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International Journal of Sensors, Wireless Communications and Control 2023年第2期13卷 57-70页

作者： Lenka, Stitapragyan Pradhan, Sateesh Kumar Nayak, Samaleswari Prasad Nayak, Sarat Chandra Department of Computer Science Application Utkal University Odisha Bhubaneswar India Department of Computer Science and Engineering Silicon Institute of Technology Odisha Bhubaneswar India Department of Computer Science Yonsei University Seoul South Korea

Background: QoS parameters are volatile in nature and possess high nonlinearity, thus making the IoT-based service and recommendation process challenging. Methods: An efficient and accurate forecasting model is lacking in this area and needs to be explored. Though an artificial neural network is a prominent option for capturing such nonlinearities, its effi-ciency is limited by the structural complexity and iterative learning method. The random vector functional link network (RVFLN) significantly reduces the time complexity by randomly assigning input weights and biases without further modification. Only output layer weights are calculated iteratively by gradient methods or non-iteratively by least square methods. It is an efficient algorithm with low time complexity and can handle complex domain problems without compromising accuracy. Motivat-ed by these characteristics, this article develops an RVFLN-based model for forecasting QoS parameter sequences. Results: Two real-world IoT-enabled web service dataset series are used in developing and evaluating the effectiveness of RVFLN-based forecasts in terms of three performance metrics. Conclusion: Experimental results, comparative studies, and statistical tests are conducted to establish the superiority of the proposed approach over four other similar forecasting techniques. © 2023 Bentham Science Publishers.

关键词： artificial neural network IoT QoS parameter forecasting random vector functional link network time series forecasting web service search

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Automated Detection of Abnormalities in Ultrasound Images Using Deep Learning

Automated Detection of Abnormalities in Ultrasound Images Us...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kumar, Amit Bhuvana, J. Kharbas, Vikas Kumar College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

Deep getting to know has been utilized in scientific imaging studies to explore challenges with diagnosing ailments and detecting anomalies. This examination focused on the automated detection of bizarre tissues in ultrasound pix. Pics must adhere to a homogeneous, properly-described, predictable anatomy for hit-deep getting-to-know applications. It looks at the proposed artificial neural network (ANN) and lengthy quick-time period memory (LSTM) based version referred to as ALunoNet to detect anomalies in ultrasound pictures. The models were skilled on a 10,000 M-mode and B-mode ultrasound pictures dataset. After ten epochs, their checking out accuracy was eighty three.16%. They look at comparing the performance of ALunoNet with other popular CNN and LSTM models. The observed effects demonstrate that ALunoNet performs higher than the same old fashions and that its overall performance is consistent even on datasets with constrained information. It has a look at highlights the capability of deep-gaining knowledge to automatize the detection of strange tissues in ultrasound pics. © 2024 IEEE.

关键词： abnormal automatized homogeneous performance ultrasound

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Research on Model Predictive Control-Based Feedback Strategies for Autonomous Driving 6

Research on Model Predictive Control-Based Feedback Strategi...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Li, Ting Xu, Xianxia Liu, Xuejuan Liu, Jie Guangxi Vocational Normal University School of Computer and Information Engineering Nanning China Key Laboratory of Application Technology of Intelligent Connected Vehicle Nanning China

ISBN: (纸本)9798350316520

To address the issues of control stability and safety in autonomous vehicles, this study focuses on research related to Model Predictive Control (MPC) algorithms and algorithm optimization. By leveraging MPC control algorithms and system dynamics techniques, we have achieved stable autonomous driving. Simulation experiments based on Model Predictive Control have been conducted to validate the feasibility and effectiveness of MPC algorithms for feedback control in autonomous driving. Two distinct simulation scenarios have been designed to verify the MPC algorithm's performance under different road conditions. The results indicate that the MPC algorithm effectively enhances the control stability and safety of autonomous driving vehicles. © 2024 IEEE.

关键词： Model predictive control

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Investigation of player emotion and instinct on racing video game using 6-11 framework (case study: split second (2010) single player modes) 9th

Investigation of player emotion and instinct on racing video...

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9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： Pratama, Galih Dea Novika, Andien Dwi Ardiyanto, Elshad Ryan Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Binus Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Racing is one of the most prominent genres in the modern video game industry, where the games within the genre enable players to use any vehicle of their choice and win through doing series of action within gameplay. Thus, it made the genre popular over the years, spawning many famous titles such as Split Second back in 2010. Like every other video game title, Split Second also invokes the player emotion and instinct during its gameplay session. To understand the emotion and instinct aspect present in players of the game, we will use 6-11 framework which focuses on the two aspects. As part of the conclusion, Split Second game involves instincts such as Competition, Collecting, and Survival, with emotions including Fear, Pride, Joy, and Excitement, which can be found from its vast single player game modes. © 2024 The Authors.

关键词： 6-11 framework game analysis racing video games video games

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Double-Edged Agriculture 4.0: Hodiernal Expedient Technologies and Cyber-Security Challenges 6

Double-Edged Agriculture 4.0: Hodiernal Expedient Technologi...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Arya, Swati Tripathi, Shubham Srivastava, Archana Aggarwal, Shruti Soni, Nupur Ansar, Syed Anas School of Computer Application Babu Banarasi das University Lucknow India Thapar Institute of Engineering and Technology Punjab India

ISBN: (纸本)9798350304480

Digitalization and intelligent systems have engulfed all dimensions and vicinity of existence. The intense fusion of hodiernal information technology and archaic agriculture has ushered in the epoch of agriculture 4.0, invariably reputed as smart agriculture. Variegated operational or forthcoming technologies reckon in agriculture 4.0, e.g., algae feedstock, automation, hydroponics, nanotechnology, genome editing, cognitive computing, blockchain, and machine learning, may have far-reaching implications for future agriculture and food systems and radical rudimentary potential. Although the profusion benefits of this Fourth Revolution abound, many unresolved issues and stumbling blocks must be conquered to make this paradigm shift feasible. Future solutions will coerce data availability and accuracy as vital factors to assist farmers, also security will become indispensable for constructing vigorous and efficacious systems. Security deals with difficulties like compatibility, scarce funds, and gargantuan data as smart agriculture incorporate a huge gamut and volume of resources. In this frame of reference, the authors opine that the inclusion and exclusion impact of Agriculture 4.0 technologies require ancillary scrutiny. This paper articulates the core technologies of agriculture 4.0 with an explicit focus on its applications in veritable contexts whilst delving into security issues (chiefly cyber-security). This research is envisaged to have a pragmatic influence on agriculture 4.0, emphasizing paradigms and security measures to aid farm stakeholders to navigate the firm's transition to digitization. © 2023 IEEE.

关键词： Agriculture 4.0 Cloud Computing Cyber Security Data Analytics Internet of Things Sensor

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IoT based Smart wheelchair For Disabled Person

IoT based Smart wheelchair For Disabled Person

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar, Lalit Kushwaha, Ajay Shriram Sherawat, Kartikeya Siddique, Zishan IILM University School of Computer Scienceand Engineering Department of Computer Application Uttar Pradesh Greater Noida India Sharda University Sharda School of Engineringand Technology Department of Computer Scienceand Applications Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350378092

Smart wheelchairs are revolutionizing mobility for disabled individuals, offering enhanced independence and improved quality of life. These advanced devices incorporate cutting-edge technologies like obstacle avoidance systems, voice control, and self-navigation capabilities, transforming the traditional wheelchair into an intelligent mobility solution. Obstacle avoidance systems utilize sensors to detect and avoid obstacles, ensuring safe navigation in complex environments. Voice control enables users to operate the wheelchair hands-free, providing greater convenience and control. Self-navigation capabilities allow the wheelchair to autonomously navigate to pre-programmed destinations, reducing user burden and enhancing independence. Smart wheelchairs also offer connectivity features, enabling remote monitoring and control by caregivers. This provides an added layer of safety and support for users, ensuring timely assistance when needed. The integration of these advanced technologies into smart wheelchairs is transforming mobility for disabled individuals, empowering them with greater independence, safety, and control over their movement. As smart wheelchair technology continues to evolve, the possibilities for enhanced mobility and improved quality of life for disabled individuals are limitless. © 2024 IEEE.

关键词： Disabled persons

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Machine Leaming Models for Phenotype Prediction from Genotype 23

Machine Leaming Models for Phenotype Prediction from Genotyp...

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23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： Annan, Richard Qingge, Letu Yang, Pei Department of Computer Science North Carolina A&T State University Greensboro United States Department of Computer Technology and Application Qinghai University Xining China

ISBN: (纸本)9798350393118

Phenotype prediction refers to estimation or prediction of an organism's observable characteristics, or phenotypes, based on its genetic information or other relevant factors. The problem is important for several reasons. Phenotype prediction can contribute to a personalized medicine, prediction of disease risks, prognosis, and treatment response. In this paper, we develop quantitative phenotype regression prediction models to predict the values of phenotypes from various genotype features based on several machine learning and deep learning models, such as elastic net, a multilayer perceptron (MLP), convolutional neural network (CNN) and multi-task learning. The comparison results on yeast data show that our proposed elastic net and CNN model outperforms the existing models for 20 phenotype predictions. © 2023 IEEE.

关键词： Forecasting

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Improving energy efficiency in scalable WSNs through IoT-driven approach

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International Journal of Information technology (Singapore) 2025年第5期17卷 2659-2669页

作者： Singh, Omkar Kushwaha, Ajay Shriram Sharma, Col. Rahul Kumar, Lalit National Institute of Fashion Technology Bihar Patna India Department of Computer Science & Application Sharda School of Computing Sciences & Engineering Sharda University UP Greater Noida India School of Computer Science & Engineering ILM University Greater Noida India

A novel QoS-Driven Optimization for Energy-Efficient Clustering Routing (QDOEECR) protocol tailored for IoT-based Wireless Sensor Networks (WSNs), addressing the critical challenge of energy efficiency in sensor nodes with limited battery life. The protocol’s innovative approach lies in its advanced techniques for optimizing the selection of sub-sink nodes and identifying the optimal path to the sink node, ensuring improved adherence to Quality of Service (QoS) standards. Performance evaluations, conducted through MATLAB simulations, demonstrate the QDOEECR protocol's superior performance, achieving significant advancements over existing routing protocols. These include 30–35% reduction in end-to-end delay, 25–30% improvement in throughput, 35–40% decrease in energy consumption, 28–32% reduction in packet loss, and 32–38% extension in network lifetime. These results establish QDOEECR as a groundbreaking solution that not only enhances energy efficiency but also elevates network performance metrics beyond the capabilities of current state-of-the-art protocols like CBCCP, HCSM, and ZEAL. © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： CBCCP HCSM IoT QDOEECR WSN ZEAL

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