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检索条件"任意字段=Proceedings of the 7th International Conference on Algorithms, Computing and Systems"

共 3437 条记录，以下是541-550 订阅

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Benchmarking Classifiers for Loan Default Prediction using Archetypal Analysis 7

Benchmarking Classifiers for Loan Default Prediction using A...

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7th international conference on algorithms, computing and systems, ICACS 2023

作者： Antoniadis, Anastasios Georgiou, Konstantinos Charmanas, Konstantinos Mittas, Nikolaos Angelis, Lefteris School of Informatics Aristotle University Greece Department of Chemistry International Hellenic University Greece

ISBN: (纸本)9798400709098

the prediction of loan default is a critical process for the successful development of financial institutions. To effectively manage credit risk, numerous machine learning models have been employed to distinguish creditworthy from high-risk applicants. However, determining an optimal model remains a challenge. To address this, in the current study, we explore an alternative approach for model benchmarking. the main concept involves the usage of a pipeline that constructs different classifiers for loan prediction and compares their performance across several evaluation metrics. To achieve this goal, we deploy an approach based on a multivariate statistical method, known as Archetypal Analysis (AA). the proposed methodology is applied to four datasets with diverse structural characteristics. the findings demonstrate that advanced classifiers like Random Forests (RF) and Artificial Neural Networks (ANN), with oversampling, simple parameter tuning, and feature selection consistently outperform traditional classifiers across most evaluation criteria. In conclusion, the results showcase the ability of AA to intuitively identify the best and worst models for each unique scenario. © 2023 Copyright held by the owner/author(s).

关键词： Forecasting

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Statistical Bound of Genetic Solutions to Quadratic Assignment Problems 7

Statistical Bound of Genetic Solutions to Quadratic Assignme...

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7th international conference on Cloud computing and Big Data Analytics, ICCCBDA 2022

作者： Zhao, Fang Hu, Zheqian Meng, Xiangli GienTech Technology Co. Ltd. BG2-BUBA-AILAB Beijing China Information System Group Beijing Qihu Keji Co. Ltd. Beijing China China International Capital Corporation Wealth Management Securities Company Beijing China Dalarna University Sweden

ISBN: (纸本)9781665487115

Quadratic assignment problems (QAPs) are commonly solved by heuristic methods, where the optimum is sought iteratively. Heuristics are known to provide good solutions but the quality of the solutions, i.e., the confidence interval of the solution is unknown. this paper uses statistical optimum estimation techniques (SOETs) to assess the quality of Genetic algorithm solutions for QAPs. We examine the functioning of different SOETs regarding biasness, coverage rate and length of interval, and then we compare the SOET lower bound with deterministic ones. the commonly used deterministic bounds are confined to only a few algorithms. We show that, the Jackknife estimators have better performance than Weibull estimators, and when the number of heuristic solutions is as large as 100, higher order JK-estimators perform better than lower order ones. Compared with the deterministic bounds, the SOET lower bound performs significantly better than most deterministic lower bounds and is comparable with the best deterministic ones. © 2022 IEEE.

关键词： Cloud computing Estimation Big Data Genetics Genetic algorithms

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Enhancing Human Stress Detection from Optimized and Selected Features Using Feed Forward Neural Network 7th

Enhancing Human Stress Detection from Optimized and Selected...

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Habiba, Sultana Umme Mahmud, Tanjim Islam, Md. Khairul Tasnim, Farzana Nahar, Lutfun Hossain, Mohammad Shahadat Andersson, Karl Khulna University of Engineering & Technology Khulna Bangladesh Rangamati Science and Technology University Rangamati Bangladesh International Islamic University Chittagong Sonaichhari Bangladesh University of Chittagong Chittagong Bangladesh Luleå University of Technology Luleå Sweden

ISBN: (纸本)9783031733239

Stress, a psycho-physiological response to life’s challenges and changes, poses significant health risks when chronic. this study explores the link between stress and sleep quality, visualizing how stress affects the human body. Stress is categorized into five levels, and eight key physiological parameters are analyzed. Machine learning algorithms, including Naïve Bayes, Support Vector Machine, and Random Forest Classifier, achieved an impressive 100% accuracy in stress level classification. Further enhancements using Principal Component Analysis (PCA) and Chi-square tests identified optimal features, resulting in approximately 99.68% accuracy with a feed-forward neural network (Multilayer Perceptron). these findings advance stress detection and its impact on sleep quality, offering potential applications in real-world health monitoring systems. the model’s high accuracy provides valuable insights for stress management and preventive health measures, mitigating associated risks. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Multilayer neural networks

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Protein Sequence Classification through Deep Learning and Encoding Strategies 15

Protein Sequence Classification Through Deep Learning and En...

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15th international conference on Ambient systems, Networks and Technologies Networks, ANT 2024 / the 7th international conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Tasnim, Farzana Habiba, Sultana Umme Mahmud, Tanjim Nahar, Lutfun Hossain, Mohammad Shahadat Andersson, Karl International Islamic University Chittagong Bangladesh Bangladesh University of Engineering & Technology Bangladesh Rangamati Science and Technology University Rangamati4500 Bangladesh University of Chittagong Bangladesh Lulea University of Technology Skelleftea Sweden

Protein sequence classification is vital for understanding protein functionalities, aiding in the inference of novel protein functions. Machine learning and deep learning algorithms have revolutionized this field, offering insights into specific protein classes and functions. this study employs Natural Language Processing (NLP) techniques, including Integer and Blosum encoding, for efficient classification. SVM with count vectorizer achieves the highest accuracy of 92%, while Integer encoding with CNN surpasses NLP embedding techniques by 4%. the goal is to develop an automated system for predicting protein functionality based on sequence classification, contributing to advancements in proteomics and computational biology. © 2024 Elsevier B.V.. All rights reserved.

关键词： Proteins

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4th international Symposium on Devices, Circuits and systems, ISDCS 2021 - conference proceedings

4th International Symposium on Devices, Circuits and Systems...

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4th international Symposium on Devices, Circuits and systems, ISDCS 2021

ISBN: (纸本)9781665414784

the proceedings contain 32 papers. the topics discussed include: performance evaluation of unrolled cipher based glitch PUF implemented on Virtex-7;optimal sizing, placement and performance analysis of DSTATCOM with improved controller without current sensors;electrochemical impedance sensor for non-invasive living cell monitoring toward CMOS cell culture monitoring platform;SEAMBA: a semi-approximate multiplier using block-based approach and rounding;switching correction of direct torque control method in order to improve the electrical vehicle performance quality;PUF based hardware security: a review;and ROS on ARM processor embedded with FPGA for improvement of robotic computing.

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18th international conference on Interdisciplinarity in Engineering, INTER-ENG 2024

18th International Conference on Interdisciplinarity in Engi...

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18th international conference on Interdisciplinarity in Engineering, INTER-ENG 2024

ISBN: (纸本)9783031816840

the proceedings contain 43 papers. the special focus in this conference is on Interdisciplinarity in Engineering. the topics include: Molecular Feature-Based Prediction of Drug-Drug Interactions Using Graph Neural Networks;unsupervised Machine Learning Methods for Detecting Anomalies in the Structural Behavior of a University Laboratory;an Explainable Image Feature Extraction Method for Machine Learning from Chest Radiography Images;experiments with Neural Network for Optimization;a Generalization of a Result for Iterative Functions in Cn;the Contributions of Artificial Intelligence to the Optimization of the Dynamics of Supply Chains 4.0;biomedical Informatics: Considering Predictive Models for Early Detection of Heart Diseases;novel systems Based on Artificial Intelligence and Numerical algorithms for Predicting Laboratory Results: A Comparative Study of Original Automatic Prediction Model with Advances in the Field;Efficient Deep Learning-Based Fire and Smoke Detection with YOLO Architecture;application of a 2D Bin Packing Problem to a Vertical Lift Module: Case Study of a Hybrid Genetic Algorithm;reducing Complexity in Purchase and Operations Planning: A Genetic Algorithm for a 1D Cutting Stock Problem;a Biostatistical and Analytical Model for the in vitro Monitoring of Tissue Growth Correlated to Biochemical Signals;utilizing Modern Management Methods in Pathology Services: A Case Study;from Waste to Operational Excellence through Simulation Techniques in the Hospital Waste Supply Chains;motor Control Disorders (Balance and Coordination) Among Older People: A Systematic Review with the Assistance of EndNote Reference Management Software;Comparison of eHealth Interoperability Standards: HL7 FHIR, OpenEHR, and CDISC;novel Advances in Total Hip Arthroplasty;experimental Analysis of the Gait Cycle Biomechanics in Order to Optimize the Process of Rehabilitation of the Locomotor System;non-linear Analysis of Biosignals.

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18th international conference on Interdisciplinarity in Engineering, INTER-ENG 2024

18th International Conference on Interdisciplinarity in Engi...

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18th international conference on Interdisciplinarity in Engineering, INTER-ENG 2024

ISBN: (纸本)9783031813771

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Optimized Campus Navigation using Mobile App: Network Analysis for Effective Route Planning 7

Optimized Campus Navigation using Mobile App: Network Analys...

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7th international conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Mansor, Nur Suhaili Awang, Hapini Radzali, Nur Faridatul Jamalia Bluszcz, Anna Zolkipli, Amirulikhsan Yee, Pee Foo Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Sintok Malaysia School of Education and Human Sciences Albukhary International University Kedah Malaysia Faculty of Mining Safety Engineering and Industrial Automation Silesian University of Technology Poland School of Computing Universiti Utara Malaysia Sintok Malaysia

ISBN: (纸本)9798331519513

the scholarly article examines prevalent difficulties encountered by students in maneuvering through campus by proposing a solution grounded in network analysis. the investigation underscores the creation of a Route Planner mobile application, which utilizes Dijkstra's algorithm to facilitate optimized navigation and shortest-path routing. this methodology guarantees that students are provided with the most effective routes, thereby minimizing travel duration and reducing detours, thus enhancing their overall campus experience. the application was designed employing an Agile software development life cycle, which permitted adaptability and iterative enhancements informed by user feedback. Usability assessments were pivotal in refining the application's functionality and user interface, ensuring it addressed the practical requirements of students. the findings indicated that 70% of participants agreed that the Route Planner would be user-friendly. In addition to its technical execution, the Route Planner advocates for sustainable transportation practices on campus by promoting efficient route planning and minimizing resource wastage. the application also encourages inclusivity by facilitating more straightforward navigation for all students, particularly newcomers, fostering their confidence in traversing campus. this advancement exemplifies the potential of amalgamating network analysis with mobile navigation to devise practical, socially advantageous tools within academic settings. Ultimately, the research underscores the application's significance in enhancing campus exploration, augmenting usability, and contributing to the disciplines of computer networks and mobile navigation systems. © 2024 IEEE.

关键词： Mobile applications

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7th international conference on Decision Support System Technology, ICDSST 2021

7th International Conference on Decision Support System Tech...

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7th international conference on Decision Support System Technology, ICDSST 2021

ISBN: (纸本)9783030739751

the proceedings contain 10 papers. the special focus in this conference is on Decision Support System Technology. the topics include: Using FITradeoff Method for Supply Selection with Decomposition and Holistic Evaluations for Preference Modelling;DEX2Web – A Web-Based Software Implementing the Multiple-Criteria Decision-Making Method DEX;Comparison of AHP, PAPRICA, PROMEthEE, DEX and TOPSIS on an Application for Employee Selection;a Survey on Criteria for Smart Home systems with Integration into the Analytic Hierarchy Process;Modelling the Effects of Lockdown and Social Distancing in the Management of the Global Coronavirus Crisis - Why the UK Tier System Failed;a Case Study Initiating Discrete Event Simulation as a Tool for Decision Making in I4.0 Manufacturing;Methodology for Multi-aspect Ontology Development: Use Case of DSS Based on Human-Machine Collective Intelligence;investigating Oversampling Techniques for Fair Machine Learning Models.

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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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