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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Vijaya Vardan Reddy, S.P. Juliet, A. Hency Jayadurga, R. Sethu, S. Pavithra, K.N. Samuthira Pandi, V. R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha University Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Tamil Nadu Chennai India Department of Computer Science and Engineering Tamilnadu Chennai India R. M. D. Engineering College Department of Science and Humanities Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350361537

The significance of communication networks is growing in tandem with the proliferation of communication technologies. Present methods for maintaining 5G Wireless Sensor Networks are still restricted to routine maintenance and post-maintenance tasks. They lack a comprehensive function for monitoring the network's status, are unable to assess the network's health, and are difficult to maintain before the 5G-based WSNs seriously degrade. 5G Wireless Sensor Network faults can only be resolved by highly trained technicians due to low maintenance efficiency. As a result, errors cannot be detected or located promptly or accurately, leading to forced repairs that incur the expense of new network cables. First, the article lays forth the basics of network fault analysis. Then, it uses deep learning to simulate communication network problem diagnosis. Lastly, the experimental section compares various methodologies and analyses the findings of fault location. The results of the simulation demonstrate that the suggested approach mitigates the created model's flaws to a certain degree while simultaneously enhancing the network fault detection model's accuracy, universality, and robustness. A novel approach to autonomous placement, the Fault Node Recovery Protocol is described in this study. It is implemented in 5G mobile communications to detect faulty data according to wireless sensor network standards, and it is made to fix the problems with conventional techniques, such poor positioning precision and lengthy running time. The automated localization model for 5G mobile communication fault data, constructed using the suggested FNRP approach, is presented in this work. By comparing it to the standard Adhoc On-Demand Distance Vector Routing protocol, we can see how well the suggested system performs. © 2024 IEEE.

关键词： 5G mobile communication systems

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An Inconsistency-Based Hybrid Feature Selection Approach for Enhancing Medical Classification Modeling 2nd

An Inconsistency-Based Hybrid Feature Selection Approach for...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Zhao, Rong ALDharhani, Ghassan Saleh Ratnavelu, Kuru Thambiayya, Sathiyaraj Jasser, Muhammed Basheer Majeed, Anwar P. P. Abdul Luo, Yang Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Institute of Information Science and Technology Dalian University of Science and Technology Dalian China Faculty of Information Science and Technology UKM University Selangor Bangi43000 Malaysia Institute of Mathematical Sciences University of Malaya Kuala Lumpur56000 Malaysia Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur56000 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Artificial Intelligence is an essential tool for early disease recognition and supporting patient condition monitoring in the future. Timely and exact conclusions about the type of disease are significant for treatment and life extension. A combination of classification and feature selection algorithms can effectively handle complex datasets in the medical field and improve the accuracy of disease diagnosis and treatment. In this paper, we propose a new hybrid feature selection algorithm that uses inconsistency metrics as a filter method for the first step, and then feeds the resulting dataset into a wrapper method, which ultimately results in a reduced dataset. The proposed HFSIM algorithm is tested on five datasets in the medical domain from Kaggle. The obtained feature subsets are verified on three classification algorithms, KNN, LR and RF of machine learning to validate the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm obtains feature subsets with low dimensionality on most of the datasets and also has high classification accuracy after testing on the classifiers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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CLASSIFICATION OF TENNIS ACTIONS USING DEEP LEARNING

arXiv

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arXiv 2024年

作者： Hovad, Emil Hougaard-Jensen, Therese Clemmensen, Line Katrine Harder Department of AI and Data Analytics Alexandra Instituttet A/S Rued Langgaards Vej 7 S København2300 Denmark Department of Mathematics and Computer Science Technical University of Denmark Richard Petersens Plads. Building 324 Kgs. Lyngby2800 Denmark

Recent advances of deep learning makes it possible to identify specific events in videos with greater precision. This has great relevance in sports like tennis in order to e.g., automatically collect game statistics, or replay actions of specific interest for game strategy or player improvements. In this paper, we investigate the potential and the challenges of using deep learning to classify tennis actions. Three models of different size, all based on the deep learning architecture SlowFast were trained and evaluated on the academic tennis dataset THETIS. The best models achieve a generalization accuracy of 74 %, demonstrating a good performance for tennis action classification. We provide an error analysis for the best model and pinpoint directions for improvement of tennis datasets in general. We discuss the limitations of the data set, general limitations of current publicly available tennis data-sets, and future steps needed to make progress. © 2024, CC0.

关键词： Error analysis

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Trustworthy XAI and Its Applications

arXiv

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arXiv 2024年

作者： Al Nasim, M.D. Abdullah Ferdous, A.S.M. Anas Rashid, Abdur Soshi, Fatema Tuj Johura Biswas, Parag Biswas, Angona Gupta, Kishor Datta Research and Development Department Pioneer Alpha Dhaka Bangladesh Department of Biomedical Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Msc in Data Science and Analytics University of Hertfordshire Hatfield United Kingdom MSEM Department Westcliff university CA United States Department of Computer and Information Science Clark Atlanta University Georgia United States

Artificial Intelligence (AI) is an important part of our everyday lives. We use it in self-driving cars and smartphone assistants. People often call it a"black box" because its complex systems, especially deep neural networks, are hard to understand. This complexity raises concerns about accountability, bias, and fairness, even though AI can be quite accurate. Explainable Artificial Intelligence (XAI) is important for building trust. It helps ensure that AI systems work reliably and ethically. This article looks at XAI and its three main parts: transparency, explainability, and trustworthiness. We will discuss why these components matter in real-life situations. We will also review recent studies that show how XAI is used in different fields. Ultimately, gaining trust in AI systems is crucial for their successful use in society. © 2024, CC0.

关键词： Deep neural networks

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

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Text Generation to Aid Depression Detection: A Comparative Study of Conditional Sequence Generative Adversarial Networks

Text Generation to Aid Depression Detection: A Comparative S...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Tlachac, M.L. Gerych, Walter Agrawal, Kratika Litterer, Benjamin Jurovich, Nicholas Thatigotla, Saitheeraj Thadajarassiri, Jidapa Rundensteiner, Elke A. Bryant University Center for Health and Behavioral Sciences Department of Information Systems and Analytics SmithfieldRI02911 United States Data Science and Computer Science Departments WorcesterMA01609 United States University of Michigan School of Information Ann ArborMI48109 United States University of Tennessee KnoxvilleTN37996 United States

ISBN: (纸本)9781665480451

Corpuses of unstructured textual data, such as text messages between individuals, are often predictive of medical issues such as depression. The text data usually used in healthcare applications has high value and great variety, but is typically small in volume. Generating labeled unstructured text data is important to improve models by augmenting these small datasets, as well as to facilitate anonymization. While methods for labeled data generation exist, not all of them generalize well to small datasets. In this work, we thus perform a much needed systematic comparison of conditional text generation models that are promising for small datasets due to their unified architectures. We identify and implement a family of nine conditional sequence generative adversarial networks for text generation, which we collectively refer to as cSeqGAN models. These models are characterized along two orthogonal design dimensions: weighting strategies and feedback mechanisms. We conduct a comparative study evaluating the generation ability of the nine cSeqGAN models on three diverse text datasets with depression and sentiment labels. To assess the quality and realism of the generated text, we use standard machine learning metrics as well as human assessment via a user study. While the unconditioned models produced predictive text, the cSeqGAN models produced more realistic text. Our comparative study lays a solid foundation and provides important insights for further text generation research, particularly for the small datasets common within the healthcare domain. © 2022 IEEE.

关键词： Generative adversarial networks

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Partial (Neighbourhood) Singleton Arc Consistency for Constraint Satisfaction Problems

Partial (Neighbourhood) Singleton Arc Consistency for Constr...

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作者： Wallace, Richard J. Insight Centre for Data Analytics Department of Computer Science University College Cork Cork Ireland

Algorithms based on singleton arc consistency (SAC) show considerable promise for improving backtrack search algorithms for constraint satisfaction problems (CSPs). The drawback is that even the most efficient of them is still comparatively expensive. Even when limited to preprocessing, they give overall improvement only when problems are quite difficult to solve with more typical procedures such as maintained arc consistency (MAC). The present work examines a form of partial SAC and neighbourhood SAC (NSAC) in which a subset of the variables in a CSP are chosen to be made SAC-consistent or neighbourhood-SAC-consistent. Such consistencies, despite their partial character, are still well-characterized in that algorithms have unique fixpoints. Heuristic strategies for choosing an effective subset of variables are described and tested, the best being choice by highest degree and a more complex strategy of choosing by constraint weight after random probing. Experimental results justify the claim that these methods can be nearly as effective as the corresponding full version of the algorithm in terms of values discarded or problems proven unsatisfiable, while significantly reducing the effort required to achieve this. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Constraint satisfaction problems

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RIGID: Robust Linear Regression with Missing data

arXiv

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arXiv 2022年

作者： Aghasi, Alireza Feizollahi, Mohammad Javad Ghadimi, Saeed Department of Electrical Engineering and Computer Science United States Department of Data Science and Analytics Georgia State University AtlantaGA United States Department of Management Sciences University of Waterloo ON Canada

We present a robust framework to perform linear regression with missing entries in the features. By considering an elliptical data distribution, and specifically a multivariate normal model, we are able to conditionally formulate a distribution for the missing entries and present a robust framework, which minimizes the worst case error caused by the uncertainty about the missing data. We show that the proposed formulation, which naturally takes into account the dependency between different variables, ultimately reduces to a convex program, for which a customized and scalable solver can be delivered. In addition to a detailed analysis to deliver such solver, we also asymptoticly analyze the behavior of the proposed framework, and present technical discussions to estimate the required input parameters. We complement our analysis with experiments performed on synthetic, semi-synthetic, and real data, and show how the proposed formulation improves the prediction accuracy and robustness, and outperforms the competing techniques. Missing data is a common problem associated with many datasets in machine learning. With the significant increase in using robust optimization techniques to train machine learning models, this paper presents a novel robust regression framework that operates by minimizing the uncertainty associated with missing data. The proposed approach allows training models with incomplete data, while minimizing the impact of uncertainty associated with the unavailable data. The ideas developed in this paper can be generalized beyond linear models and elliptical data distributions. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Node2VecFuseClassifier: Bridging Perspectives in Modeling Transplantation Attitudes Among Dialysis Patients

Node2VecFuseClassifier: Bridging Perspectives in Modeling Tr...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Rafaa Aljurbua Avrum Gillespie Jumanah Alshehri Abdulrahman Alharbi Nouf Albarakati Zoran Obradovic Temple University Philadelphia PA USA Center for Data Analytics and Biomedical Informatics. Department of Computer Science College of Computer Qassim University Buraydah Saudi Arabia Department of Medicine Division of Nephrology Lewis Katz School of Medicine Hypertension and Kidney Transplantation Imam Abdulrahman bin Faisal University Dammam Saudi Arabia

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

The study of patient attitudes toward transplantation is crucial for dialysis clinic decision-makers. Understanding the factors influencing patient attitudes toward transplantation helps improving transplant access. While many studies attempt to predict patient attitudes toward transplantation from a patient-centric point of view by focusing on patient information, there is a limited number of studies that explore the influence of social interaction among dialysis patients on predicting their attitudes toward transplantation. In this study, we utilize machine learning-based models and social networks to classify hemodialysis patients' attitudes toward transplantation taking into account the influence of social interaction among dialysis patients. We conduct a questionnaire among 110 participants from two hemodialysis centers. We investigate the performance of machine learning algorithms, including linear, non-linear model, and graph-based machine learning models. We use developed models to classify hemodialysis clinic patients into positive and negative attitudes toward transplantation. We introduce a graph-based model named Node2VecFuseClassifier that integrates both patient inter-actions and patient features. To emphasize the significance of social interaction, we compare the benefits of using a sociodemographic patient-centric versus a social-centric problem representation that considers patient-to-patient and patient-to-staff interactions. We believe that including the social aspect, patient-to-patient, and patient-to-staff network features enhances all machine learning models' performance as compared to relying on patient-centric features alone. By combining patient experiences with staff expertise, the multilevel analysis enhances predictive capabilities by incorporating diverse roles like patients and staff. Thus, we combine patient and staff networks and observe that such a multi-network approach boosts the F1-score in predicting patient attitude toward tran

关键词： Surveys Machine learning algorithms Social networking (online) Diversity reception Focusing Machine learning Predictive models

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Exploring the Relation between Blood Tests and Covid-19 Using Machine Learning

Exploring the Relation between Blood Tests and Covid-19 Usin...

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2020 International Conference on data analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Almansoor, Mohamed Hewahi, Nabil M. University of Bahrain Big Data Science Analytics Program Bahrain University of Bahrain Department of Computer Science Bahrain

ISBN: (纸本)9781728196756

COVID-19 is a global pandemic that hit the world in 2019-2020 and caused massive losses. Every day, hundreds of thousands of tests are being done on possible infected cases. It usually takes several hours to get the results of virus test in advanced countries, whereas in other countries might take days. The aim of this study is to investigate whether normal blood medical tests help in detecting covid-19 using various machine learning approaches. If true, this would give an indication to people who should undergo the virus test. In this paper we independently use machine learning algorithms including support vector machines, adaptive boosting, random forest and k-nearest neighbors. These algorithms are then merged to form ensemble learning which leads to the classification. The results show that the ensemble learning is having the highest true positive rate of 30%. The obtained results show that normal blood tests do not help much in giving right indications about detecting COVID-19. © 2020 IEEE.

关键词： COVID-19

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