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2023 International Conference on computer, Electronics and Electrical engineering and their Applications, IC2E3 2023

作者： Jayanthi, R. Christinal, A. Hepzibah Hephzibah, R. Shekinah, T. Bajaj, Chandrajit Chandy, D. Abraham Karunya University Department of Mathematics Coimbatore India Loyola - Icam College of Engineering and Technology Department of Computer Science and Engineering Chennai India University of Texas Computational Applied Mathematics Chair in Visualization Austin United States Karunya University Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350338003

Brain tumor classification is a critical task in medical imaging, where the early and accurate detection of brain tumors is crucial for patient treatment and survival. In recent years, Machine Learning techniques have emerged as a promising approach for automated brain tumor classification. In this paper, we investigate the performance of three ML classifiers - Support Vector Machine, Naïve Bayes, and an ensemble of SVM and Naïve Bayes - for the two-class classification of brain tumor data into a tumor and no tumor classes. Our experiments demonstrate that the ensemble of SVM and Naïve Bayes classifier achieved an accuracy of 97% outperforming the other classifiers. These results indicate that our ensemble classifier can be a powerful tool for the accurate and efficient classification of brain tumor data, potentially leading to improved patient outcomes. © 2023 IEEE.

关键词： Tumors

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Data-driven modeling of dislocation mobility from atomistics using physicsinformed machine learning

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npj computational Materials 2024年第1期10卷 937-948页

作者： Yifeng Tian Soumendu Bagchi Liam Myhill GiacomoPo Enrique Martinez Yen Ting Lin Nithin Mathew Danny Perez Information Sciences Group ComputerComputational and Statistical Sciences Division(CCS-3)Los Alamos National LaboratoryLos Alamos87545 NMUSA Physics and Chemistry of Materials Theoretical Division(T-1)Los Alamos National LaboratoryLos Alamos87545 NMUSA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeUSA Department of Materials Science and Engineering Clemson UniversityClemson29623 SCUSA Department of Mechanical and Aerospace Engineering University of MiamiMiami33146 FLUSA

Dislocationmobility,which dictates the response of dislocations to an applied stress,is a fundamental property of crystalline materials that governs the evolution of plastic *** approaches for deriving mobility laws rely on phenomenological models of the underlying physics,whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and *** tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress,temperature,and local environment,such as body-centered cubic crystals(BCC)metals and *** this paper,we present a novel,uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations,using Graph Neural Networks(GNN)with a physics-informed *** demonstrate that this Physics-informed Graph Neural Network(PI-GNN)framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

关键词： stress dislocation consuming

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MLRegTest: a benchmark for the machine learning of regular languages

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 13641-13685页

作者： Sam Van der Poel Dakotah Lambert Kalina Kostyszyn Tiantian Gao Rahul Verma Derek Andersen Joanne Chau Emily Peterson Cody St. Clair Paul Fodor Chihiro Shibata Jeffrey Heinz School of Mathematics Georgia Institute of Technology Department of Computer Science Haverford College Department of Linguistics & Institute of Advanced Computational Science Stony Brook University Department of Computer Science Stony Brook University Department of Computer Science Stony Brook University Department of Linguistics Stony Brook University Department of Advanced Sciences Graduate School of Science and Engineering Hosei University

Synthetic datasets constructed from formal languages allow fine-grained examination of the learning and generalization capabilities of machine learning systems for sequence classification. This article presents a new benchmark for machine learning systems on sequence classification called MLRegTest, which contains training, development, and test sets from 1,800 regular *** kinds of formal languages represent different kinds of long-distance dependencies, and correctly identifying long-distance dependencies in sequences is a known challenge for ML systems to generalize successfully. MLRegTest organizes its languages according to their logical complexity (monadic second order, first order, propositional, or restricted propositional) and the kind of logical literals (string, tier-string, subsequence, or combinations thereof). The logical complexity and choice of literal provides a systematic way to understand different kinds of long-distance dependencies in regular languages, and therefore to understand the capacities of different ML systems to learn such long-distance ***, the performance of different neural networks (simple RNN, LSTM, GRU, transformer) on MLRegTest is examined. The main conclusion is that performance depends significantly on the kind of test set, the class of language, and the neural network architecture.

关键词： formal languages regular languages subregular languages sequence classification neural networks long-distance dependencies

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The Prediction of Micro Plasma Impacts of Farm Fresh Vegetables Using Machine Learning

The Prediction of Micro Plasma Impacts of Farm Fresh Vegetab...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Maragatham, G. Sundaramoorthy, K. Maheswari, K. Sajana, T. Shiba, C. Mary Kumar, Ashok Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Tamil Nadu Chennai600100 India Department of Computer Science and Engineering Cmr Technical Campus Kandlakoya Telangana Hyderabad501401 India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Business Systems R. M. K Engineering College Tamil Nadu Chennai601206 India Department of Computer Science Banasthali Vidyapith Rajasthan304022 India

ISBN: (纸本)9798350334357

In general, the innovative foods produced on fruit and vegetable based farms are always high quality and healthy. Indicators of fruit and vegetable consumption include plasma vitamin C and arytenoids, which are plant pigments discovered in blood samples. The researchers decided to utilize blood samples rather than the more common food frequency questionnaire in their investigation. to assess the amount of food consumed in order to forestall measuring errors and to establish dependencies. Because vitamin C and arytenoids may be found in a wide variety of fruits and vegetables, we can use them as objective measures of our consumption of these food groups. The fact that individuals who do not consume a diet that is abundant in fruits and vegetables do not consume significant quantities of vitamin C and arytenoids is reflected in the plasma levels of these individuals. In this paper a smart machine learning algorithm was proposed to predict the micro plasma impacts. This monitors the regular shape and harvesting of different farm fresh products and predicts the impacts of it. This will helpful for farmers to enhance the harvesting. © 2023 IEEE.

关键词： Machine learning

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Hydrodynamic parameter estimation using statistical machine learning for dynamic radiography

Hydrodynamic parameter estimation using statistical machine ...

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2023 computational Optical Sensing and Imaging, COSI 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

作者： De, Soumi Klasky, Marc Korobkin, Oleg Sidharth, G.S. Xu, Xiaojian Computer Computational and Statistical Sciences Division Los Alamos National Laboratory United States Theoretical Division Los Alamos National Laboratory United States Department of Aerospace Engineering Iowa State University United States Department of Electrical Engineering & Computer Science University of Michigan United States

Characterization of material properties of objects undergoing strong deformations is an important task in material science. Using neural networks with dynamic features extracted from radiographic projections we obtain... 详细信息

ISBN: (纸本)9781957171289

关键词： Parameter estimation

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Predicting Breast Cancer by Analysing Wisconsin Dataset Through Machine Learning Algorithms Using Orange Tool

Predicting Breast Cancer by Analysing Wisconsin Dataset Thro...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： Anamika Saroha Aashish S Kamath Monesh Kumar T R Saravanan N. Kanimozhi Computer Science and Engineering SRM Institute of Science and Technology Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

Breast cancer is still a major global health issue, and early detection is important for better patient outcomes. This research discusses the use of machine learning algorithms for predicting breast cancer through the Orange data mining software. We utilized a comprehensive dataset containing various breast cancer features to develop and evaluate multiple machine learning models. The Orange tool facilitated data preprocessing, feature selection, model training, and performance evaluation. Our analysis compared the efficacy of several algorithms, in-cluding random forests, support vector machines, KNN, Gradient Boosting and neural networks. The study highlights the potential of orange tool for machine learning in enhancing breast cancer detection and diagnosis, potentially aiding healthcare profession-als in making more informed decisions. Furthermore, the user-friendly interface of the Orange tool showcases its utility in medical research and potential for wider adoption in clinical settings. For this paper, we consulted the Wisconsin dataset. The findings reveal that SVM models, particularly those utilizing ensemble methods like bagging and boosting with RBF kernels, significantly outperform traditional classifiers in both small and large datasets. In contrast, while Neural Networks demonstrated competitive accuracy, they often faced challenges related to overfitting and computational demands. Feature selection techniques were identified as crucial for enhancing model performance, allowing for more efficient training processes.

关键词： Support vector machines Training Machine learning algorithms Accuracy Neural networks Nearest neighbor methods Feature extraction Breast cancer Random forests Overfitting

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Unleashing the Potential of IoT in Tackling Ocean Pollution: A Data-Driven Approach for Marine Ecosystem Conservation 2

Unleashing the Potential of IoT in Tackling Ocean Pollution:...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Srinivasan, S. Vimala Ithayan, J. Devi, A. Sangeerani Saravanan, T.R. Ganesh, E.N. Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India Avadi Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

This study investigates the potentially revolutionary role that the Internet of Things (IoT) may play in combating the urgent ocean pollution problem and promoting the growth of ecologically sound marine ecosystems. An approach that is data-driven has become essential in light of the fact that pollution poses a risk to the delicate balance of marine life and puts the future of the seas in jeopardy. The user can get real-time data on water quality, pollutant levels, and plastic trash dispersion if the user installs sensors and devices that the IoT enables. This will allow us to gain a greater knowledge of the patterns and causes of pollution. Using the plethora of information at people’s disposal, the system can develop innovative intervention tactics that successfully and efficiently combat ocean pollution. In addition, early warning systems powered by the Internet of Things might make it easier to respond quickly to oil spills, reducing the negative effects on the environment. This research highlights the importance of cooperation among stakeholders and underlines the need for public awareness and education to create changes in behavioral patterns. The adoption of technologies related to the Internet of Things is the most important step that can be taken to protect marine biodiversity and to create a brighter, more sustainable future for seas. © 2023 IEEE.

关键词： Critical Ocean Pollution Internet of Things (IoT) Marine Conservation Initiative Marine Ecosystems Real-Time Data

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Optimization of Cognitive Radio System Using Self-Learning Salp Swarm Algorithm

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computers, Materials & Continua 2022年第2期70卷 3821-3835页

作者： Nitin Mittal Harbinder Singh Vikas Mittal Shubham Mahajan Amit Kant Pandit Mehedi Masud Mohammed Baz Mohamed Abouhawwash Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India School of VLSI Design and Embedded Systems National Institute of TechnologyKurukshetra136119India School of Electronics&Communication Engineering Shri Mata Vaishno Devi UniversityKatra182320India Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21994Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral *** improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the *** Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter *** meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user ***-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of *** this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary *** results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.

关键词： Cognitive radio meta-heuristic algorithm cognitive decision engine salp swarm algorithm

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Software Design Specification Proposal of a Diagnostic Decision Support System for Clinical Low Back Pain

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Procedia computer science 2024年 231卷 243-250页

作者： Ishaya Gambo Christopher Agbonkhese Department of Computer Science and Engineering Obafemi Awolowo University Ile-Ife Nigeria Department of Digital and Computational Studies Bates College Lewiston ME 04240 USA

Low Back Pain (LBP) is a prevalent health issue that affects many people's daily functioning and well-being. However, diagnosing LBP is challenging because it requires expert knowledge of the spine's structure and function. Therapists usually rely on asking patients questions to guide their tests, but this method is not very efficient. Moreover, existing systems for LBP diagnosis and information exchange are narrow in their coverage. To address these limitations, this paper proposes a design specification for a web-based decision support system that offers a preliminary diagnosis and suggests a comprehensive diagnosis that may have multiple components. The system design incorporates an intuitive and user-friendly framework that can handle the complex features of LBP diagnosis and uses verbal probability estimation to cope with the uncertainty involved. The design specification can facilitate communication among the relevant stakeholders.

关键词： design specification low back pain software design decision support system diagnosis

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Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

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computers, Materials & Continua 2024年第6期79卷 3549-3568页

作者： Reem Ibrahim Alkanhel Doaa Sami Khafaga Ahmed Mohamed Zaki Marwa M.Eid Abdyalaziz A.Al-Mooneam Abdelhameed Ibrahim S.K.Towfek Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science and Intelligent Systems Research Center BlacksburgVirginia24060USA Delta Higher Institute for Engineering and Technology Mansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Artificial Intelligence and Computational Intelligence Research Center MeridenConnecticut06450USA School of ICT Faculty of EngineeringDesign and Information&Communications Technology(EDICT)Bahrain PolytechnicP.O.Box 33349Isa TownBahrain

The networks of wireless sensors provide the ground for a range of applications,including environmental moni-toring and industrial *** the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network *** infrastructure planning should be focused on increasing performance,and it should be affected by the detailed data about node *** work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location,which will contribute to better network planning and *** using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization,our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and *** implementing adaptive protocols according to varying environments and sensor constraints,our study aspires to improve overall network *** compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization,Particle Swarm Optimization,Genetic Algorithms,and Whale Optimization about performance on real-world *** the most efficient in the optimization process,Biruni displays the lowest error rate at *** two other statistical techniques,like ANOVA,are also useful in discovering the factors influencing the nature of sensor data and network-specific *** to the multi-faceted support the comprehensive approach promotes,there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be *** delivering better performance and reliability for various in-situ applications,this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

关键词： Wireless sensor networks optimization ARIMA model BER algorithm metaheuristic algorithms

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