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International Conference on Advanced computer information Technologies (ACIT)

作者： Nadia Vasylkiv Lesia Dubchak Mikolaj Karpinski Iryna Turchenko Liubomyr Flud Tetiana Nadvynychna Department of Information Computer Systems and Control West Ukrainian National University Ternopil Ukraine Department of Computer Engineering West Ukrainian National University Ternopil Ukraine Department of Cyber Security University of the National Education Commission Krakow Poland Department of Information Systems and Computer Modeling Ukrainian National Forestry University Lviv Ukraine Subdepartment of Psychology and Social Work West Ukrainian National University Ternopil Ukraine

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

A fuzzy system for setting the priority of IT project work has been developed, in which a preliminary analysis of the metrics of readiness, permission and complexity of tasks is carried out, which makes it possible to determine the priority of the project work. Based on this, the business analyst can effectively make decisions about the workload of the project team. The results of the work can be successfully applied to the effective management of the implementation of IT projects and the proactive management of project teams.

关键词： Measurement Accuracy Employment Companies Software Hardware Complexity theory information technology Fuzzy systems Business

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Selfish Users in Listen-Before-Talk Co-existence of Cellular-Wi-Fi Networks: Counteraction Methods

Selfish Users in Listen-Before-Talk Co-existence of Cellular...

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International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Saibharath S Sudeepta Mishra Gazal Arora Anamika Sharma Chittaranjan Hota Dept. of Computer Science and Information systems BITS Pilani Hyderabad Campus India Dept. of Computer Science and Engineering IIT Ropar Punjab India

5G operates in the unlicensed spectrum to expand its capacity. This spectrum provides more uplink and downlink frequency bands in 5G to satisfy ultra-dense and scalability requirements. Wi-Fi is a prominent wireless technology that operates in unlicensed bands. Due to the proximity of cellular and Wi-Fi channels, utilization of both Wi-Fi and 5G New Radio (NR) can cause interference. The co-existence of 5G NR and Wi-Fi devices lacks the means of communication for negotiation and coordination among them. Listen-Before-Talk and Duty-cycling are standard Medium Access Control (MAC) mechanisms to enable co-existence. Due to a lack of coordination, network utilization becomes unfair with the existence of selfish devices. The selfish users could maximize throughput and affect other Wi-Fi and NR users. Here, we investigate the effects of selfish nodes on this Wi-Fi Co-existence with 5G. These nodes disobey exponential backoff to boost their channel acquisition. We study the effect of misbehavior through the Order Gain metric. Through standard CSMA/CA settings with overall channel occupancy of 99.75%, we demonstrate that selfish users profoundly impact fair co-existence. We study the side effects through throughput, channel sensing, and acquisition for various backoff window access patterns. Finally, we go through counteraction methods to overcome the selfish user impact.

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Enhancing Diabetes Prediction using Hybrid Feature Selection and Ensemble Learning with AdaBoost 8

Enhancing Diabetes Prediction using Hybrid Feature Selection...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Thuraka, Bharadwaj Pasupuleti, Vikram Kodete, Chandra Shikhi Chigurupati, Ravi Swaroop Tirumanadham, N. S. Koti Mani Kumar Shariff, Vahiduddin Northwest Missouri State University School of Computer Science and Information Systems MaryvilleMO64468 United States Eastern Illinois University School of Technology CharlestonIL61920 United States Dept. of Cse Bhimavaram India Sir C R Reddy College of Engineering Dept. of Cse Eluru India

ISBN: (纸本)9798350376425

This research relies on a feature selection method termed LasNO to analyse the progress of diabetes prediction. The ANOVA integrated with Lasso regularization identifies significant predictors like Age, BMI, DiabetesPedigreeFunction, and (Glucose). An AdaBoost model with the following characteristics was developed, and the results were exceptional performance metrics: 97.02% accuracy, 87.65% precision, and 82.5% recall. The results of the primary focus on glucose levels to differentiate between events with and without diabetes have been affirmed from the visual analyses. It also calls into attention that due to the appropriate cross-validation, LasNO is very successful on improving the interpretability and the possibility of generalization of the model. Such findings make it clear that efforts towards early detection of the condition and subsequent timely implementation of measures to contain the illness can go quite a long way in enhancing the quality of the lives of people that contract the disease. Further research is likely to be devoted to the assessing of the improved and broadened validation of the dataset as a critical approach for the enhancement and expansion of the model for use in clinics. As a result, this study aims to advance diabetes theragnostic possibilities and healthcare recommendations for better action based on ML approaches enhancing the accuracy of predictions. In this study, approaches to feature selection used for improving the performance while also making the models more understandable are the focus. The idea is to ensure that medical practitioners have reliable tools to undertake early detection and management of diabetes. Thus, the main objective of dealing with patients' Health-Related Quality of Life is to simultaneously increase the quality of patients' living while decreasing the costs of their treatment. © 2024 IEEE.

关键词： Prediction models

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Gaussian process-based visual pursuit control with unknown target motion learning in three dimensions

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SICE Journal of Control, Measurement, and System Integration 2021年第1期14卷 116-127页

作者： Omainska, Marco Yamauchi, Junya Beckers, Thomas Hatanaka, Takeshi Hirche, Sandra Fujita, Masayuki Department of Information Physics and Computing The University of Tokyo Tokyo Japan Department of Electrical and Systems Engineering University of Pennsylvania PennsylvaniaPA United States Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan Department of Electrical and Computer Engineering Technical University of Munich Munich Germany

In this paper, we propose an observer-based visual pursuit control integrating three-dimensional target motion learning by Gaussian Process Regression (GPR). We consider a situation where a visual sensor equipped rigid body pursuits a target rigid body whose velocity is unknown but dependent on the target's pose. We estimate the pose from visual information and propose a Gaussian Process (GP) model to predict the target velocity from the pose estimate. We analyse stability of the proposed control by showing that estimation and control errors are ultimately bounded with high probability. Finally, simulations illustrate the performance of the proposed control schemes even if the visual measurement is corrupted by noise. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Gaussian distribution

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A Review and Analysis of Localization Techniques in Underwater Wireless Sensor Networks

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computers, Materials & Continua 2023年第6期75卷 5697-5715页

作者： Seema Rani Anju Anupma Sangwan Krishna Kumar Kashif Nisar Tariq Rahim Soomro Ag.Asri Ag.Ibrahim Manoj Gupta Laxmi Chandand Sadiq Ali Khan Department of CSE Guru Jambheshwar University of Science&TechnologyHisar125001India Department of Hydro and Renewable Energy Indian Institute of Technology RoorkeeIndia College of Computer Science and Information Systems Institute of Business ManagementKorangi Creek RoadKarachi75190SindhPakistan Faculty of Computing and Informatics Universiti Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Department of Electronics and Communication Engineering JECRC UniversityJaipurRajasthanIndia Computer Science Department UBIT Karachi UniversityKarachi75270Pakistan

In recent years,there has been a rapid growth in Underwater Wireless Sensor Networks(UWSNs).The focus of research in this area is now on solving the problems associated with large-scale *** of the major issues in such a network is the localization of underwater *** is required for tracking objects and detecting the *** is also considered tagging of data where sensed contents are not found of any use without *** is useless for application until the position of sensed content is *** article’s major goal is to review and analyze underwater node localization to solve the localization issues in *** present paper describes various existing localization schemes and broadly categorizes these schemes as Centralized and Distributed localization schemes ***,a detailed subdivision of these localization schemes is ***,these localization schemes are compared from different *** detailed analysis of these schemes in terms of certain performance metrics has been discussed in this *** the end,the paper addresses several future directions for potential research in improving localization problems of UWSN.

关键词： Underwater wireless sensor networks localization schemes node localization ranging algorithms estimation based prediction based

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Comparative Analysis of Data Dynamics Based on Wavelet Coherence Using Higher-Order Moments 2

Comparative Analysis of Data Dynamics Based on Wavelet Coher...

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2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Al-Sharo, Yasser Abu-Jassar, Amer Vasiurenko, Oleg Lyashenko, Vyacheslav Ajloun National University Faculty of Information Technology Department of Cyber Security Ajloun Jordan Ajloun National University Faculty of Information Technology Department of Computer Science Ajloun Jordan Kyiv University of Intellectual Property and Law Department of Cybersecurity It and Economics Kyiv Ukraine Kharkiv National University of Radio Electronics Department of Media Systems and Technology Kharkiv Ukraine

ISBN: (纸本)9798350373363

The article addresses the challenges of analyzing data represented as time series, depicting the evolution of a specific process, phenomenon, or object over time. Special emphasis is placed on conducting a comparative analysis of the dynamics among different data sets;so, to achieve this, a method for estimating wavelet coherence is employed. The approach includes transforming the original data through a sliding sampling window using higher-order moments to capture the memory effect. This methodology enables the assessment of dynamics in extensive data sets, thereby enhancing the efficiency of processing and analysis. The article presents results based on real data, providing an additional evaluation of their mutual dynamics. The findings affirm the reliability and effectiveness of such an analysis. © 2023 IEEE.

关键词： Dynamics

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Comparative sensitivity analysis of single and multiple modifications in multi-criteria decision analysis 28th

Comparative sensitivity analysis of single and multiple modi...

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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Wieckowski, Jakub Bialon, Pawel Salabun, Wojciech Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland

In the area of decision-making, where complexities often exceed experts' analytical capabilities, this study addresses a critical gap by introducing a novel methodology for sensitivity analysis within the Multi-Criteria Decision Analysis (MCDA) problems. Current analytical frameworks struggle to comprehensively consider potential modifications to values within decision matrices, making it challenging to understand their impact in detail. To address this challenge, decision-makers need enhanced tools, and MCDA methods combined with systematic changes in selected elements of the decision matrix stand out as a promising approach. However, existing studies primarily focus on different criteria-weight scenarios, leaving an unexplored gap in the simultaneous modification of multiple values within the decision matrix. This paper integrates sensitivity analysis within the MCDA methods, extending its role in the comprehensive assessment of decision problems. Sensitivity analysis becomes crucial in offering decision-makers a broader perspective, aiding them in navigating the complexities of decision-making in dynamic environments. Recognizing the unexplored potential in sensitivity analysis, the study proposed a novel approach of simultaneous modification of multiple values in a decision matrix, offering an extension of conventional one-at-a-time modifications. As a Proof of Concept (PoC) research work, the study investigated whether the determined approach provides divergent preference scores compared to the traditional single-modification method across ten different MCDA techniques. The results showed that modifying multiple values simultaneously produced different preference scores of alternatives than in the case of a conventional single change, showing that additional insight knowledge could be extracted from this type of sensitivity analysis. © 2024 The Authors.

关键词： Sensitivity analysis

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A Blockchain-driven Architecture for Usage Control in Solid

arXiv

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arXiv 2023年

作者： Basile, Davide Di Ciccio, Claudio Goretti, Valerio Kirrane, Sabrina Department of Computer Science Sapienza University of Rome Italy Department of Information Systems and Operations Vienna University of Economics and Business Austria

Decentralization initiatives like Solid and *** enable data owners to control who has access to their data and to stimulate innovation by creating both application and data markets. Once data owners share their data with others, though, it is no longer possible for them to control how their data are used. To address this issue, we propose a usage control architecture to monitor compliance with usage control policies. To this end, our solution relies on blockchain and trusted execution environments. We demonstrate the potential of the architecture by describing the various workflows needed to realize a motivating use case scenario for data markets. Additionally, we discuss the merits of the approach from privacy, security, integrateability, and affordability perspectives. Copyright © 2023, The Authors. All rights reserved.

关键词： Smart contract

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Evolution-Based Performance Prediction of Star Cricketers

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computers, Materials & Continua 2021年第10期69卷 1215-1232页

作者： Haseeb Ahmad Shahbaz Ahmad Muhammad Asif Mobashar Rehman Abdullah Alharbi Zahid Ullah Department of Computer Science National Textile UniversityFaisalabadPakistan Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900PerakMalaysia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Systems King Abdulaziz UniversityJeddahSaudi Arabia

Cricket databases contain rich and useful information to examine and forecasting patterns and *** paper predicts Star Cricketers(SCs)from batting and bowling domains by employing supervised machine learning *** this aim,each player’s performance evolution is retrieved by using effective features that incorporate the standard performance measures of each player and their *** is performed by applying Bayesianrule,function and decision-tree-based *** evaluations are performed to validate the applicability of the proposed *** particular,the impact of the individual features on the prediction of SCs are ***,the category and model-wise feature evaluations are also conducted.A cross-validation mechanism is applied to validate the performance of our proposed approach which further confirms that the incorporated features are statistically ***,leading SCs are extracted based on their performance evolution scores and their standings are cross-checked with those provided by the International Cricket Council.

关键词： Online social databases cricket star cricketers prediction machine learning

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Extended Forgery Detection Framework for COVID-19 Medical Data Using Convolutional Neural Network

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computers, Materials & Continua 2021年第9期68卷 3773-3787页

作者： Sajid Habib Gill Noor Ahmed Sheikh Samina Rajpar Zain ul Abidin N.Z.Jhanjhi Muneer Ahmad Mirza Abdur Razzaq Sultan S.Alshamrani Yasir Malik Fehmi Jaafar Department of Computer Science Shah Abdul Latif UniversityKhairpur66020SindhPakistan National College of Business Administration&Economics Lahore54000Pakistan School of Computer Science and Engineering SCETaylor’s UniversityMalaysia Department of Information Systems Faculty of Computer Science&Information TechnologyUniversiti MalayaMalaysia Department of Information Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Engineering&Computing SciencesNew York Institute of TechnologyVancouverCanada The Computer Research Institute of Montreal QuebecCanada

Medical data tampering has become one of the main challenges in the field of secure-aware medical data *** of normal patients’medical data to present them as COVID-19 patients is an illegitimate action that has been carried out in different ways ***,the integrity of these data can be *** detection is a method of detecting an anomaly in manipulated forged *** appropriate number of features are needed to identify an anomaly as either forged or non-forged data in order to find distortion or tampering in the original *** neural networks(CNNs)have contributed a major breakthrough in this type of *** has been much interest from both the clinicians and the AI community in the possibility of widespread usage of artificial neural networks for quick diagnosis using medical data for early COVID-19 patient *** purpose of this paper is to detect forgery in COVID-19 medical data by using CNN in the error level analysis(ELA)by verifying the noise pattern in the *** proposed improved ELA method is evaluated using a type of data splicing forgery and sigmoid and ReLU phenomenon *** proposed method is verified by manipulating COVID-19 data using different types of forgeries and then applying the proposed CNN model to the data to detect the data *** results show that the accuracy of the proposed CNN model on the test COVID-19 data is approximately 92%.

关键词： Data security data privacy medical-data forgery COVID-19 convolutional neural network machine learning deep learning

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