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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Ahmad, Salman Khursheed Ikra, K.M. Sharma, Preeti Kaushik, Yogita Choudhary, Amar Tripathi, Pradeep Kumar Gl Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Mangalmay Institute of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science -DS Ghaziabad India Sunder Deep Group of Institutions Department Mca Ghaziabad India Alliance University Department of Electronics and Communication Engineering Bengaluru India Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India

ISBN: (纸本)9798350360165

This study presents a novel IoT-enabled fitness tracking device intended to improve patient care through the capture of real-time physiological records and predictive analytics. The system uses a variety of sensors, including an Arduino UNO, an LM35, a MAX30100 pulse charge and SPo2 sensor, and a stress sensor, to continuously gather health data from ten different patients. Proactive health assessments and early intervention are made possible by the combination of cloud-based analysis and advanced system learning models, such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbours (KNN), and Recurrent Neural Networks (RNN). Tested on real-world datasets, the experimental results show how effective the device is at making accurate and timely predictions. Notably, SVM is the most accurate variant, closely followed by ANN, KNN, and RNN. The confusion matrices ensure accurate and thorough examination of each version's performance, aiding in selecting the most relevant set of rules for real-time health monitoring to be used by clinicians. The present study, as groundbreaking work, demonstrates a major leap in merging IoT with device expertise in the healthcare sector that usages new possibilities in proactive and personalized treatment strategies. The recommended tool's innovative and reliable predictive analytics can potentially revolutionize patient transportation in the healthcare system. © 2024 IEEE.

关键词： Electronic health record

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Comparative Analysis of Deep Learning Models for Telugu News Text Classification 21

Comparative Analysis of Deep Learning Models for Telugu News...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Settineni, Pooja Veerendra, B. Reddy, Mure Sai Jaideep Samuel, Prithi Sumathi, D. VIT-AP University School of Computer Science and Engineering Andhra Pradesh Amaravati India SRM Institute of Science Kattankulanthur Campus Department of Computational Intelligence India

ISBN: (纸本)9798350358230

Text classification is the process of labeling or classifying the text to certain categories based on the content into various text classes. There are many use cases like Natural language processing, sentimental analysis, topic detection, Spam filtering news categorization have been solved using this mechanism. We documented a comparative study of four Deep Learning (DL) algorithms to classify and categories the text for two different data sets. These datasets contain the content of Telugu news articles. The four DL models that are used in this works are Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), bidirectional Gated Recurrent Unit (Bi-GRU) and bidirectional Long Short-Term Memory (Bi-LSTM) which are the variants of the recurrent neural networks (RNN) which can process sequential data such as text. The model performance will be compared using the accuracy of it as a metrics. We also have documented pros and cons of each model using the results that they admit. This paper analyzes the results and discuss the advantages and disadvantages of each model. In this paper we first start with the preprocessing techniques used for process the data and further we move to the architecture's explanation for each model. The experimental results are presented using graphs and pie charts. The findings and implications of the study are discussed. Some limitations and future directions of the work are mentioned. © 2023 IEEE.

关键词： Long short-term memory

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Parallel Repetition of k-Player Projection Games 27

Parallel Repetition of k-Player Projection Games

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27th International Conference on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2024 and the 28th International Conference on Randomization and Computation, RANDOM 2024

作者： Bhangale, Amey Braverman, Mark Khot, Subhash Liu, Yang P. Minzer, Dor Department of Computer Science and Engineering University of California RiversideCA United States Department of Computer Science Princeton University NJ United States Department of Computer Science Courant Institute of Mathematical Sciences New York University NY United States School of Mathematics Institute for Advanced Study PrincetonNJ United States Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States

We study parallel repetition of k-player games where the constraints satisfy the projection property. We prove exponential decay in the value of a parallel repetition of projection games with a value less than 1. ... 详细信息

ISBN: (纸本)9783959773485

关键词： Multiplayer games Parallel Repetition Projection games

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A unique computational investigation of the exact traveling wave solutions for the fractional-order Kaup-Boussinesq and generalized Hirota Satsuma coupled KdV systems arising from water waves and interaction of long waves

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Journal of Ocean engineering and science 2024年第5期9卷 437-453页

作者： Xiaofeng Wang Xiao-Guang Yue Mohammed K.A.Kaabar Arzu Akbulut Melike Kaplan Shenzhen University ShenzhenChina Department of Computer Science and Engineering European University CyprusNicosiaCyprus Institute of Mathematical Sciences Faculty of ScienceUniversity of MalayaKuala Lumpur 50603Malaysia Department of Mathematics and Statistics Washington State UniversityPullmanWA 99163USA Jabalia Camp United Nations Relief and Works Agency(UNRWA)Palestinian Refugee CampGaza Strip JabalyaPalestine Mathematics-Computer Department Art-Science FacultyEski¸s ehir Osmangazi UniversityEski¸s ehirTurkey Department of Computer Engineering Faculty of Engineering and ArchitectureKastamonu UniversityKastamonuTurkey

A novel technique,named auxiliary equation method,is applied in this research work for obtaining new traveling wave solutions for two interesting proposed systems:the Kaup-Boussinesq system and generalized Hirota-Satsuma coupled KdV system with beta time fractional *** solutions were obtained using MAPLE *** technique shows a great potential to be applied in solving various nonlinear fractional differential equations arising from mathematical physics and ocean *** a standard equation has not been used as an auxiliary equation for this technique,different and novel solutions are obtained via this technique.

关键词： Symbolic computation Fractional differential equations Beta derivative Auxiliary equation method Solitary solutions Nonlinear equations

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Stock Price Prediction Using Gradient Boosting Machine with Technical Indicators 3

Stock Price Prediction Using Gradient Boosting Machine with ...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Vinston Raja, R. Jayanthi, G. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India Saveetha Nagar Tamil Nadu Chennai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

This research endeavors to design a predictive model to forecast stock prices using a Gradient Boosting Machine (GBM) regressor, specifically emphasizing the adjusted closing price to mitigate the autocorrelation issues intrinsic to time series data. The investigation encompasses a 12-month duration of historical stock data, integrating two technical indicators, namely Exponential Moving Averages (EMA-5 and EMA-10), to augment predictive precision. Gradient Boosting, recognized for its proficiency in managing intricate and non-linear data patterns, was employed to forecast forthcoming stock prices. Critical performance indicators, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2), played a role in evaluating model accuracy, attaining an R2 score of 0.99, thereby signifying an almost perfect correlation between actual and predicted prices. Beyond numerical assessments, visual examinations through scatter plots, residual plots, and prediction error distribution further elucidated the model's efficiency. The findings reveal that the amalgamation of Gradient Boosting with Technical Indicators such as EMA-5 and EMA-10, coupled with an emphasis on the adjusted closing price, proves exceptionally effective for stock price forecasting. This methodology achieved an accuracy rate nearing 99%, illustrating its substantial promise for stock market prediction and its proficiency in alleviating autocorrelation challenges within time series datasets. © 2025 IEEE.

关键词： Autocorrelation

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Rosacea Classification Using Deep Stacking Multi-Layer Model (DSMLM) 2

Rosacea Classification Using Deep Stacking Multi-Layer Model...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Antony Sophia, N. Saravanan, T.R. Vigneshwari, M. Deepika, N. Uma, K. Madhavan, P. Srm Institute of Science & Technology Department of Computational Intelligence Chennai Kattankulathur India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Chennai India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Katpadi632014 India

ISBN: (纸本)9798350328202

Rosacea, a widespread and complex skin disorder affecting most of the world population, is divided into various subtypes with unique clinical features the complexity of these subtypes makes accurate classification difficult. A unique technique called the Deep Stacking Multi-Layer Model (DSMLM) has been proposed as a solution for this complex classification. To extract microelements from clinical images and patient history data, DSMLM uses a multilayered neural network architecture. Using the stacking ensemble strategy to combine predictions from different base models makes it possible to improve the classification performance. The effectiveness of the DSMLM is evaluated using a large dataset of different rose cases, and it shows remarkable accuracy in distinguishing between different rose subtypes © 2024 IEEE.

关键词： Large datasets

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Autism Spectrum Disorder Prediction by an Explainable Deep Learning Approach

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computers, Materials & Continua 2022年第4期71卷 1459-1471页

作者： Anupam Garg Anshu Parashar Dipto Barman Sahil Jain Divya Singhal Mehedi Masud Mohamed Abouhawwash Computer Science and Engineering Department Thapar Institute of Engineering and TechnologyPatialaIndia School of Computer Science and Statistics Trinity College DublinDublinIreland University Institute of Biotechnology Chandigarh UniversityMohaliIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Autism Spectrum Disorder (ASD) is a developmental disorderwhose symptoms become noticeable in early years of the age though it canbe present in any age group. ASD is a mental disorder which affects the communicational, social and non-verbal behaviors. It cannot be cured completelybut can be reduced if detected early. An early diagnosis is hampered by thevariation and severity of ASD symptoms as well as having symptoms commonly seen in other mental disorders as well. Nowadays, with the emergenceof deep learning approaches in various fields, medical experts can be assistedin early diagnosis of ASD. It is very difficult for a practitioner to identifyand concentrate on the major feature’s leading to the accurate prediction ofthe ASD and this arises the need for having an automated approach. Also,presence of different symptoms of ASD traits amongst toddlers directs tothe creation of a large feature dataset. In this study, we propose a hybridapproach comprising of both, deep learning and Explainable Artificial Intelligence (XAI) to find the most contributing features for the early and preciseprediction of ASD. The proposed framework gives more accurate predictionalong with the recommendations of predicted results which will be a vital aidclinically for better and early prediction of ASD traits amongst toddlers.

关键词： Deep learning explainable artificial intelligence autism spectrum disorder machine learning

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Artificial Intelligence in Health Care Computing and Assessments in Oman: an Effectual Study 2

Artificial Intelligence in Health Care Computing and Assessm...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Jabri, Jaber Saleh Salim Al Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Aruthenganvilai India

ISBN: (纸本)9798331505745

Health care plays a vital responsibility in a human life span. World countries targets to provide better care for their citizens. Digital transformations and innovations in Artificial Intelligence (AI) technologies helps in deep for the novel solutions to speed-up the decision making process in health management and patient consulting. AI branches including Machine Learning (ML), Deep Learning (DL), Neural Networks (NN), Natural Language Processing (NLP) and so forth can support innovations in heal care domain. This paper presents the comprehensive and noble review on recent existing tools and techniques and also recommends advanced techniques for health care industry that are to be used in Sultanate of Oman. Furthermore, overcoming AI challenges are resulted and presented through the study. This work mainly aimed to produce the multiple dimensions and challenges that helps to develop AI powered real-life applications in health care support system. © 2025 IEEE.

关键词： Nutrition

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On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

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应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

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Revolutionizing Skin Cancer Detection with Raspberry Pi-Embedded ANN Technology in an Automated Screening Booth 4

Revolutionizing Skin Cancer Detection with Raspberry Pi-Embe...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Srinivasan, S. Indra, G. Saravanan, T.R. Murugan, S. Srinivasan, C. Muthulekshmi, M. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350307757

In this ground-breaking endeavor, we describe an automated skin cancer screening booth that can transform current approaches to early detection. The merging of Raspberry Pi-controlled technology with cutting-edge Artificial Neural Network (ANN) models forms the basis of our groundbreaking breakthrough. The booth uses high-resolution imaging sensors to acquire detailed photographs of the user's skin, which are then analyzed in real-time by an ANN. The ability to identify potentially malignant anomalies in a timely and precise manner is made possible by this combination. In public places, user contact is facilitated by the use of an intuitive touchscreen interface, which ensures both accessibility and convenience of use. The ANN model, trained on a wide variety of data, is very good at differentiating between normal and malignant skin diseases, with the findings being instantly shown on the interface. Beyond its technological capabilities, our technology bears the prospect of broad application, which will turn screening for skin cancer into a preventative healthcare tool available to everyone. This all-encompassing strategy highlights the significance of early detection and argues for a solution that is both scalable and effective at the crossroads of technology and public health. © 2024 IEEE.

关键词： Neural networks

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