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3rd International Conference on Signal and Data Processing, ICSDP 2023

作者： Islam, Md. Aminul Nag, Anindya Chandra, Pretam Mishra, Bhupesh Kumar Fahim, S. M. Firoz Ahmed Hoque, Md Mozammel School of Engineering Computing and Mathematics Oxford Brookes University Oxford United Kingdom College of Engineering and Physical Sciences University of Birmingham Birmingham United Kingdom Computer Science & Engineering Discipline Khulna University Khulna9208 Bangladesh Department of Computer Science & Engineering Adamas University Kolkata700126 India Centre of Excellence for Data Science AI and Modelling University of Hull Hull United Kingdom Department of Computer Science & Engineering American International University Dhaka Bangladesh Gannon University Erie United States

ISBN: (纸本)9789819795772

The present study introduces a health insurance prediction system that leverages machine learning methodologies. In contemporary times, there has been a notable increase in endeavors focused on tackling this matter, since the significance of health insurance as a research topic has markedly escalated following the occurrence of the pandemic. The dataset employed in this research comprises 1338 observations, 7 columns, and corresponds to individual medical expenditures in the United States which is available at the Kaggle platform. The dataset encompasses a variety of variables utilized in the prediction of insurance prices, including age, gender, BMI, smoking status, and number of children. The researchers utilized machine learning models including neural network, XAI, auto modeling to determine the correlation between pricing and the aforementioned attributes. The training process involved partitioning the dataset into a 80–20 ratio for training and evaluation respectively. As a consequence, the system achieved an accuracy rate of 97% by Gradient Boosting but we corrected it as 92% by Gradient Boosting Regressor by encoding and hyper tuning. Also, among predictive machine learning models, Random Forest had the best accuracy i.e., of 83.44%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Health insurance

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Aatma Yoga: Automation of Yoga Pose Recognition and Recommendation using Deep Learning 7

Aatma Yoga: Automation of Yoga Pose Recognition and Recommen...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Duppala, Vijaya Raghava Marepalli, Harika Yadav Kirti Jain, S. Anusha, Koduru Thangavel, Senthil Kumar Kumar, B Senthil Bindu, Avadhani Satish, Latha Sekar, Jeeva Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Coimbatore India Amrita Vishwa Vidyapeetham Amrita Darshanam-International Centre For Spiritual Studies Coimbatore India Krishnamacharya Yoga Mandiram Research Department Chennai India Prana Yoga Studio Chennai India

ISBN: (纸本)9798350359299

Youngsters often face stress, pressure, and various challenges in today's fast-paced world. The need for yoga arises as a powerful tool to alleviate stress, promote mental well-being, and enhance resilience. The purpose of this research is to present an innovative approach to personalized yoga practice by leveraging deep learning and computer vision techniques for real-time monitoring and correction of yoga poses. The system consists of mood-based yoga recommendation, yoga pose detection, yoga pose estimation, and pose correction. Through the seamless integration of Singular Value Decomposition (SVD), YOLOv3, PoseNet, and an angle heuristic algorithm, this work ensures accurate analysis and correction of the user's yoga poses, facilitating an effective and personalized yoga experience. By addressing the lack of personalization and emotional oversight prevalent in traditional yoga practices, the work aims to promote emotional balance, stress relief, and physical wellness. Through this innovative approach, the work endeavors to transform the traditional paradigm of yoga practice, offering a more personalized, effective, and holistic wellness experience for practitioners. © 2024 IEEE.

关键词： Heuristic algorithms

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Deep Reinforcement Learning for Addressing Disruptions in Traffic Light Control

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computers, Materials & Continua 2022年第5期71卷 2225-2247页

作者： Faizan Rasheed Kok-Lim Alvin Yau Rafidah Md Noor Yung-Wey Chong School of Engineering and Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computing and Information Systems Sunway UniversitySubang Jaya47500Malaysia Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia National Advanced IPv6 Centre Universiti Sains MalaysiaUSMPenang11800Malaysia

This paper investigates the use of multi-agent deep Q-network(MADQN)to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning(MARL)*** proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions,particularly *** is based on deep Q-network(DQN),which is an integration of the traditional reinforcement learning(RL)and the newly emerging deep learning(DL)*** enables traffic light controllers to learn,exchange knowledge with neighboring agents,and select optimal joint actions in a collaborative manner.A case study based on a real traffic network is conducted as part of a sustainable urban city project in the Sunway City of Kuala Lumpur in *** is also performed using a grid traffic network(GTN)to understand that the proposed scheme is effective in a traditional traffic *** proposed scheme is evaluated using two simulation tools,namely Matlab and Simulation of Urban Mobility(SUMO).Our proposed scheme has shown that the cumulative delay of vehicles can be reduced by up to 30%in the simulations.

关键词： Artificial intelligence traffic light control traffic disruptions multi-agent deep Q-network deep reinforcement learning

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Accelerating fluid flow in Quantum computing using GPU

Accelerating fluid flow in Quantum Computing using GPU

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computer, Electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： Joel Dandin Satyadhyan Chickerur School of Computer Science and Engineering KLE Technological University Hubballi India Centre for High Performance Computing School of Computer Science and Engineering KLE Technological University Hubballi India

In the last few years, quantum computing has evolved as the next big technological breakthrough. Many big companies are actively researching this area and have built open-source frameworks which can be used to develop solutions for problems that connect be solved by classical computers. One such problem is the solution of Navier-Stokes equations which takes exponential time for simulation but using quantum computers, they can be solved in polynomial time. But the current state of quantum computation does not enable everyone to test their implementation on a real quantum computer. In this situation, a simulator enables rapid prototype and testing techniques and algorithms for when the real quantum computer is ready. This paper focuses on simulating fluid flow algorithms using a quantum framework, such as Navier- Stokes equations, with the lattice Boltzmann method (LBM) used to numerically solve the relevant system of equations on both CPU and GPU to speed up the process. The quantum framework used in this paper is Qiskit, an open-source software development kit created by IBM that allows developers to create and test quantum algorithms. NVIDIA cuQuantum is an SDK of optimized libraries and tools for expediting quantum computing operations used in this paper to solve the fundamental problem of modeling large-size quantum circuits using the power of GPU

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Adoption of Internet of Things for Smart Monitoring of Aquaponics 14

Adoption of Internet of Things for Smart Monitoring of Aquap...

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

作者： Joshi, Kapil Priya, Priya Kaushik, Vishal Anandaram, Harishchander Mohapatro, Arati Kumar, Anil Uttaranchal University Uttaranchal Institute of Technology Department of Cse Dehradun India Graphic Era Hill University School of Agriculture Uttarakhand Dehradun India Upes School of Computer Science Dehradun India Amrita Vishwa Vidyapeetham Centre for Computational Engineering and Networking Tamil Nadu Coimbatore India Dept of Computer Science Bengaluru India Dit University School of Computing Dehradun India

ISBN: (纸本)9798350335095

Aquaponics is a cutting-edge, clever, and ecological technology for agriculture that combines both aquaculture and hydroponics to produce vegetable products prototype. This method promises to be environmentally friendly and viable due to its high-water consumption efficacy, lack of use of pesticides, and decreased fertilizer use. The main task is to make aquaponics viable and dependable at a business scale because interest in it is growing. By recognizing, being listed, and thoroughly describing each of the characteristics detected in aquaponics, as well as the smart platforms and Internet of Things (IoT) devices in the examined literature, this article hopes to support study towards a feasible business aquaponics option. The suggested study also identifies future efforts needed for mechanized aquaponics and possible voids in the existing literature. This is anticipated the fact that the system for aquaponics backed by smart control units will benefit in terms of profitability, intelligence, accuracy, and effectiveness within the context of the research studies examined in this paper. © 2023 IEEE.

关键词： Water quality

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Comparative Analysis of Docker Image Files Across Various Programming Environments 3rd

Comparative Analysis of Docker Image Files Across Various Pr...

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3rd International Semantic Intelligence Conference, ISIC 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Choudhury, Tanupriya Kotecha, Ketan School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Bidholi Uttarakhand Dehradun248007 India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819773558

Docker technology plays an essential part in improving the process of software development while also efficiently solving the issues related to deployment and hosting. Docker is able to work independently of the underlying hardware infrastructure since it uses virtualisation as its mode of operation. Instead, it makes use of its own collection of code in order to carry out the implementations of applications. The concept of enclosing the whole of an application's environment together with its source code is fundamental to Docker's approach. Docker successfully avoids the problems that are often associated with having a variety of working environments by using this method. This method, on the other hand, might result in a larger file size since it includes the whole environment in its representation. Within the Docker environment, these data files are referred to as "Images," and the considerable size of these images may be affected by a wide variety of variables. In the course of this study, a number of tests were carried out, each of which included the use of Docker to run the same lines of code on a variety of different platforms. The major aim was to improve the feasibility of conducting a thorough comparison study. Software engineers were given the ability to evaluate and compare results by exploiting Docker's capabilities, which finally enabled them to arrive at well-informed conclusions about the most appropriate course of action. The software development community is able to get useful insights into the benefits and considerations of using Docker on multiple platforms as a result of this research, which paves the way for a journey that is more streamlined in terms of both development and deployment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software design

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Peak-Average-Power Ratio Techniques for 5G Waveforms Using D-SLM and D-PTS

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computer Systems science & engineering 2023年第5期45卷 1199-1210页

作者： Himanshu Sharma karthikeyan Rajagopal G.Gugapriya Rajneesh Pareek Arun Kumar HayaMesfer Alshahrani Mohamed K.Nour Hany Mahgoub Mohamed Mousa Anwer Mustafa Hilal Department of Computer Science&Engineering JECRC UniversityJaipurIndia Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia School of Electronics Engineering Vellore Institute of TechnologyChennaiIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multicarrier Waveform(MCW)has several advantages and plays a very important role in cellular *** generation(5G)MCW such as Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC)are thought to be important in 5G *** Peak to Average Power Ratio(PAPR)is seen as a serious concern in MCW since it reduces the efficiency of amplifier use in the user *** paper presents a novel Divergence Selective Mapping(DSLM)and Divergence Partial Transmission Sequence(D-PTS)for 5G *** is seen that the proposed D-SLM and PTS lower PAPR with low computational *** work highlighted a combination of multi-data block partial transmit schemes along with tone *** this,an overlapping factor is used to determine the number of data blocks for every ***,considering only those data blocks that have minimum signal power,the use of DSLM and DPTS are required to eliminate the segment’s *** results reveal that the suggested hybrid technique proves to be better than the conventional PTS ***,the power saving performance of FBMC and NOMA is compared with the Orthogonal Frequency Division Multiplexing(OFDM)waveform.

关键词： 5G PAPR D-SLM D-PTS

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Intelligent Mining of Association Rules Based on Nanopatterns for Code Smells Detection

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Scientific Programming 2023年第1期2023卷

作者： Juliet Thessalonica, D. Khanna Nehemiah, H. Sreejith, S. Kannan, A. Ramanujan Computing Centre Anna University Chennai600025 India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Software maintenance is an imperative step in software development. Code smells can arise as a result of poor design as well as frequent code changes due to changing needs. Early detection of code smells during software development can help with software maintenance. This work focuses on identifying code smells on Java software using nanopatterns. Nanopatterns are method-level code structures that reflect the presence of code smells. Nanopatterns are extracted using a command-line interface based on the ASM bytecode analysis. Class labels are extracted using three tools, namely inFusion, JDeodorant, and iPlasma. Rules are extracted from nanopatterns using the Apriori algorithm and mapped with the extracted class labels. Best rules are selected using the Border Collie Optimization (BCO) algorithm with the accuracy of the k-NN classifier as the fitness function. The selected rules are stored in the rule base to detect code smells. The objective is to detect a maximum number of code smells with a minimum number of rules. Experiments are carried out on Java software, namely jEdit, Nutch, Lucene, and Rhino. The proposed work detects code smells, namely data class, blob, spaghetti code, functional decomposition, and feature envy, with 98.78% accuracy for jEdit, 97.45% for Nutch, 95.58% for Lucene, and 96.34% for Rhino. The performance of the proposed work is competitive with other well-known methods of detecting code smells. © 2023 D. Juliet Thessalonica et al.

关键词： computer software maintenance

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SoulNourish: A Comprehensive Machine learning Based Recommendation Yoga Prediction System Based on Health and Lifestyle Data 4

SoulNourish: A Comprehensive Machine learning Based Recommen...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Kirankumar, M. Saikiran, P. Sabarivasan, N. Manoj Kumaran, S. Thangavel, Senthil Kumar Somasundaram, K. Kumar, B. Senthil Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Tamil Nadu Coimbatore India Amrita School of Physical Sciences Amrita Vishwa Vidyapeetham Department of Mathematics Tamil Nadu Coimbatore India Amrita Vishwa Vidyapeetham Amrita Darshanam-International Centre for Spiritual Studies Tamil Nadu Coimbatore India

ISBN: (纸本)9798331540364

The modern sedentary lifestyle has escalated health risks, underscoring the importance of tailored health solutions like yoga. This paper pioneers a method to determine yoga'srelevance for individuals by analyzing their lifestyle data. Using machine learning algorithms like Random Forests, Gradient Boosting, SVMs, and Neural Networks and KMeans with Convo-lution Neural Networks. We process data on activity, diet, stress, and sleep to evaluate yoga's potential benefits. We implementthe algorithms for recommending the customized yoga routines. Our experimental results show that Decision Trees give goodaccuracy in identifying candidates for yoga, offering a novel perspective for personalized health interventions and underscoring machine learning's role in enhancing public health. © 2024 IEEE.

关键词： Adversarial machine learning

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Contextual Evaluation of Segmentation Models using Spatial Reasoning 26

Contextual Evaluation of Segmentation Models using Spatial R...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Porter, Victoria Styles, Iain Curtis, Tim M. Taggart, Michael J. Albargothy, Mona J. Gault, Richard Centre for Intelligent Sustainable Computing School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom Wellcome-Wolfson Institute for Experimental Medicine Queen's University Belfast United Kingdom Biosciences Institute Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9781837242672

Image segmentation models are often evaluated using measures of overlap and boundary deviation between a ground truth and a prediction. These measures do not indicate whether a prediction is an overestimation or underestimation of the ground truth. This contextual information is critical in medical imaging applications such as tumor detection where a model's tendency to overestimate a prediction would be preferred to avoid overlooking malignant cells. Spatial reasoning provides context on a model's segmentation performance in terms of its tendency to over- or underestimate a region of interest. Such context can highlight a model's decision-making trends and can be applied to inform targeted improvements. In this work, we provide a Python module1,2 that implements a model-agnostic spatial reasoning pipeline for the contextual evaluation of segmentation methods. We apply this pipeline to the output of the Segment Anything model on 3 electron microscopy (EM) datasets and demonstrate the meaningful inferences that can be made. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Image segmentation

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