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3rd International Conference on Applied Mathematics and Computer Science, ICAMCS 2023

作者： Petriashvili, Lily Modebadze, Zurab Lominadze, Tamar Kiknadze, Mzia Otkhozoria, Nona Zhvania, Taliko Georgian Technical University Software Engineering Tbilisi Georgia Georgian Technical University Information Systems Tbilisi Georgia Georgian Technical University Information Technology Tbilisi Georgia Georgian Technical University Computer Engineering Tbilisi Georgia Georgian Technical University Microprocessors and Measurement Systems Tbilisi Georgia

ISBN: (纸本)9798350324266

Current global events, such as the COVID-19 pandemic and the Russian-Ukrainian war, have had a notable impact on the rise in the volume of freight transportation on the Georgian railway. Consequently, the efficient utilization of railway systems, along with monitoring the state of operational processes and technical equipment, plays a crucial role in ensuring smooth and uninterrupted transportation. Key factors to consider include speed, reliability, and the quality of shipments. Railway transportation is progressing worldwide, and effective management is undeniably a priority for our *** essential element for identifying and managing freight wagons is the Arabic numerals printed on them, which indicate their main characteristics and ownership. According to internationally recognized agreements, foreign wagons, as well as those owned or temporarily used by railway administrations, must be promptly returned to their country of dispatch. Within the first 24 hours of crossing the border, "inventory"freight wagons are subject to an average daily charge of 20 Swiss francs, while owned wagons incur an average daily rental fee of US$30. This circumstance, along with rolling stock management, presents one of the critical ***, hundreds of railway employees are involved in identifying the rolling stock in our country. These workers walk along the stations where the parked rolling stock is located. Once they visually recognize the freight car numbers, they relay the information to the relevant station operators via transmitters. Initially, these operators manually record the numbers in special journals and then enter the data into a unified electronic registry system. As a result, railway freight shipments require significant human and time resources, with a high probability of error. This situation considerably slows down the speed of railway service and increases its costs. © 2023 IEEE.

关键词： Radio frequency identification (RFID)

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Automated approach to predict cerebral stroke based on fuzzy inference and convolutional neural network

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Multimedia Tools and Applications 2024年 1-22页

作者： Alrowais, Fadwa Jamjoom, Arwa A. Karamti, Hanen Umer, Muhammad Alsubai, Shtwai Abate, Andrea F. Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Cerebral stroke indicates a neurological impairment caused by a localized injury to the central nervous system resulting from a diminished blood supply to the brain. Today, stroke stands as a global menace linked to the premature mortality of millions of people globally. Consequently, it is crucial to simulate how different risk factors impact the incidence of strokes and artificial intelligence is emerging as a suitable tool for achieving this goal. This study aims to construct dependable learning prediction models for stroke illness. The proposed approach can handle the inherent difficulty posed by a significant imbalance in classes, where the number of stroke patients is notably smaller compared to the healthy class. The study also provides a model based on an adaptive neuro-fuzzy inference system logic and convolutional neural networks (CNN) for accurate stroke prediction. An adaptive neuro-fuzzy inference system logic approach is adopted as it incorporates the capabilities of artificial intelligence and fuzzy inference, thereby having the potential to yield superior outcomes. The efficacy of the suggested method is extensively analyzed involving machine and deep learning classifiers and considering metrics relevant to encompass both the capacity for generalization and the accuracy in predicting. The proposed fuzzy-CNN model outperforms with the most considerable accuracy of 98.97% when using the original dataset, resampled dataset, and data imputation. K-fold cross-validation also shows superior results with an average accuracy of 99.6% for five folds. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Transitioning from motivational goal models to user stories within user-centred software design 1

Transitioning from motivational goal models to user stories ...

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1st International Interdisciplinary Workshop on Requirements Engineering for Sociotechnical systems, RESOSY 2021

作者： Oliveira, Eduardo A. Maram, Varsha Sterling, Leon Computing and Information Systems University of Melbourne Melbourne Australia Centre for Design Innovation Swinburne University of Technology Melbourne Australia

Motivational goal modelling has evolved from agent-oriented models to allow a shared understanding of a project by diverse stakeholders. Building a motivational model is in the spirit of user-centred design. Requirements artefacts such as user stories and personas should be developed consistently with the model. This paper describes a method to generate user stories from motivational models. The generated stories are checked by users and developers to ensure readabilty and clarity. The method has been partially automated within an extension to an editing tool. Copyright © 2022 for this paper by its authors.

关键词： User centered design

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Generative AI in Intrusion Detection systems for Internet of Things:A Systematic Literature Review

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4689-4717页

作者： Deng, Zhe Torim, Ants Yahia, Sadok Ben Bahsi, Hayretdin Tallinn University of Technology Department of Software Science Estonia University of Southern Denmark Center for Industrial Software Denmark Northern Arizona University School of Informatics Computing and Cyber Systems United States

The ubiquitous data streaming through the Internet of Things (IoT) creates security risks. Intrusion detection systems (IDS) based on machine learning can support user security. Generative Artificial Intelligence (GenAI) demonstrates strong capabilities in generating synthetic data based on realistic distributions and learning complex patterns from high-dimensional data. By harnessing the capabilities of generative AI, it is feasible to augment intrusion detection models, allowing for more robust and adaptive security solutions in IoT environments. This paper introduces a systematic literature review of recent GenAI applications in IoT IDS and analyzes the architectures and techniques in the models. We classify the common usages such as data augmentation and class balancing, data reconstruction, and adversarial attack generation. We outline the commonly used datasets and evaluation metrics and compare the performances of each model under these conditions. The study identifies current challenges and emerging research trends in various technologies for applying GenAI in IoT IDS. © 2020 IEEE.

关键词： Generative adversarial networks

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System to Improve the Quality of Water Resources in Sri Lanka Using Machine Learning and Image Processing 4

System to Improve the Quality of Water Resources in Sri Lank...

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4th International Conference on Advancements in computing, ICAC 2022

作者： Liyanage, Samindi M.H. Gajanayake, G.M.B.S. Wijewickrama, Oshini Fernando, S.D.S.A. Wijendra, Dinuka Gamage, Aruna Ishara Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Department Computer Systems & Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

Water covers approximately 71% of the earth's surface, but only 1.2% of it can be used for drinking. However, due to the amount of waste water released into water resources, the presence of harmful microorganisms, and natural occurrences such as eutrophication, even that water cannot be used directly for drinking purposes without purification. One method of purifying water is chlorination. However, if the chlorine level exceeds the standard, it can cause both long-term and short-term illnesses. As a result, a system is imposed to solve four problems: predicting the pH value of chlorinated drinking water, determining the quantification value of active sludge in a wastewater plant, detecting microorganisms in drinking water, and predicting the percentage of eutrophication in a water resource. © 2022 IEEE.

关键词： Potable water

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Circuit level implementation of the Reduced Quantum Genetic Algorithm using Qiskit 16

Circuit level implementation of the Reduced Quantum Genetic ...

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16th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2022

作者： Ardelean, Sebastian Mihai Udrescu, Mihai Politehnica University of Timisoara Advanced Computing Systems and Architectures Laboratory Department of Computer and Information Technology Timisoara300223 Romania

ISBN: (数字)9781665481250

ISBN: (纸本)9781665481250

Genetic Algorithm (GA) are common probabilistic optimization methods inspired by the process of natural selection. Quantum computers promise substantial speedups over conventional machines, and libraries allow the emulation of circuits on a quantum computer in different highly configurable noise models and even run on quantum computers. Therefore, we need to analyze this class of heuristic methods in the quantum context. We propose a circuit level implementation of the Reduced Quantum Genetic Algorithm (RQGA) using Python and Qiskit. Our main goal is to analyze the circuit complexity from the perspectives of the number of qubits required and the number of quantum gates used. To achieve our goal, we instantiate the framework for solving the knapsack problem, examine the results in a simulated environment, and analyze the circuit's complexity. © 2022 IEEE.

关键词： Heuristic methods

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Is Cybersecurity Winning: A CrossSectional Study in Fiji

Is Cybersecurity Winning: A CrossSectional Study in Fiji

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2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2023

作者： Reddy, Pritika Goundar, Munil Shiva Khan, Shaireen Shabnam Sharma, Bibhya Fiji National University Department of Computing Science and Information Systems Suva Fiji Health Information and Intelligence Unit Aspen Medical Lautoka Fiji School of Information Technology Engineering Mathematics and Physics The University of the South Pacific Suva Fiji

ISBN: (纸本)9798350341072

The advent of the Internet, as well as the increasing use of various web applications, social media platforms, and the Internet of Things, are posing online threats to people. Moreover, with the growing dependence on Industry 4.0 operations, cybersecurity awareness and practices have become more important to organizations and countries than ever before. The current study intends to evaluate cybersecurity practices and awareness at a local education service provider in Fiji. A quantitative methodology comprising an online survey was administered to collect responses from its students who are employees from 22 different organizations in Fiji. The results of the study showed that the employees in Fiji have an average awareness of cyber hygiene. The highly positive correlation value between employee response to cybersecurity practices and awareness indicates that some organizations follow good cyber hygiene. The results from this study indicate that organizations in Fiji need to be more vigilant to defend themselves from the raising cyber threats and vulnerabilities. © 2023 IEEE.

关键词： Cybersecurity

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ThyroidNet:A Deep Learning Network for Localization and Classification of Thyroid Nodules

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 361-382页

作者： Lu Chen Huaqiang Chen Zhikai Pan Sheng Xu Guangsheng Lai Shuwen Chen Shuihua Wang Xiaodong Gu Yudong Zhang Ultrasonic Department Zhongda Hospital Affiliated to Southeast UniversityNanjing210009China School of Physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia State Key Laboratory of MillimeterWaves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Jiangsu Second Normal UniversityNanjing211200China School of Software Engineering Quanzhou Normal UniversityQuanzhou362000China Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123China

Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques ***:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid ***,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask ***,we propose the DualLoss function,tailored to the thyroid nodule localization and classification *** balances the learning of the localization and classification tasks to help improve the model’s generalization ***,we introduce strategies for augmenting the ***,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid ***:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and *** results show that ThyroidNet outperformed these methods in localizing and classifying thyroid *** achieved improved accuracy of 3.9%and 1.5%,***:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis *** research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.

关键词： ThyroidNet deep learning TransUnet multitask learning medical image analysis

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Malware Detection Based on Stack Ensemble Using Machine Learning 2

Malware Detection Based on Stack Ensemble Using Machine Lear...

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Eke, Christopher Ifeanyi Olamide, Fawaz Ibrahim Phiri, Musa Mwenge, Mulenga Faith Fatokun, B. Shuib, Liyana Federal University of Lafia Faculty of Computing Department of Computer Science Nasarawa State Nigeria Mulungushi University Department of Computer Science and Information Technology Kabwe Zambia Malaysian Institute of Information Technology University of Kuala Lumpur Kuala Lumpur Malaysia University of Malaya Faculty of Computer Science & It Department of Information Systems Kuala Lumpur Malaysia

ISBN: (纸本)9798331542573

information security remains one of the major challenges faced by organizations and individuals in the current technological era. With the growing popularity of smart devices, the frequency of cyber-attacks targeted at these devices are increasing at a rapid pace. Among these types of attacks on information systems is malicious software also known as malware. A cyber-attack based on malware has the potential to cause loss or manipulation of confidential information. Currently, malware creators who use polymorphic techniques to evade detection can bypass traditional signature-based methods of detecting malware. Thus, various researchers have turned to Machine Learning (ML) techniques to enhance the detection of malware. However, despite the promising results, ML-based classification of malware remains a challenging task that the research community is yet to solve. Therefore, in this study, we propose an ML-based malware detection system that incorporates the AdaBoost, Random Forest, Decision Tree, and K-Nearest Neighbors Algorithm (KNN) algorithms into an ensemble stack with gradient boost as the ensemble boost for the stack meta estimator. We also use Extra Trees Classifier for feature selection. Multiple performance measures are utilized to assess the system's performance while using the Kaggle Malware Dataset. Our findings demonstrate that the proposed method successfully detects malware samples with remarkable accuracy, precision, recall, F1, and area under the curve (AUC) scores of 99.4%, 99.3%, 99.4%, 99.4%, and 99.6%, respectively. © 2024 IEEE.

关键词： Adversarial machine learning

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Image Enhancement Using Adaptive Fractional Order Filter

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Computer systems Science & Engineering 2023年第5期45卷 1409-1422页

作者： Ayesha Heena Nagashettappa Biradar Najmuddin M.Maroof Surbhi Bhatia Arwa Mashat Shakila Basheer Department of Electronics and Communication Engineering Bheemanna Khandre Institute of TechnologyBhalkiVisveswaraya Technological UniversityBelagaviIndia Department of Electronics and Communication Engineering Khaja Banda Nawaz College of EngineeringVisveswaraya Technological UniversityBelagaviIndia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl-HofufAl-AhsaSaudi Arabia Faculty of Computing&Information Technology King Abdulaziz UniversityP.O.Box 344Rabigh21911Saudi Arabia Department of Information Systems College of Computer and Information Science Princess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia

Image enhancement is an important preprocessing task as the contrast is low in most of the medical images,Therefore,enhancement becomes the mandatory process before actual image processing should *** research article proposes an enhancement of the model-based differential operator for the images in general and Echocardiographic images,the proposed operators are based on Grunwald-Letnikov(G-L),Riemann-Liouville(R-L)and Caputo(Li&Xie),which are the definitions of fractional order *** this fractional-order,differentiation is well focused on the enhancement of echocardiographic *** provoked for developing a non-linear filter mask for image *** designed filter is simple and effective in terms of improving the contrast of the input low contrast images and preserving the textural features,particularly in smooth *** novelty of the proposed method involves a procedure of partitioning the image into homogenous regions,details,and ***,a fractional differential mask is appropriately chosen adaptively for enhancing the partitioned pixels present in the *** is also incorporated into the Hessian matrix with is a second-order derivative for every pixel and the parameters such as average gradient and entropy are used for qualitative *** wide range of existing state-of-the-art techniques such as fixed order fractional differential filter for enhancement,histogram equalization,integer-order differential methods have been *** proposed algorithm resulted in the enhancement of the input images with an increased value of average gradient as well as entropy in comparison to the previous *** values obtained are very close(almost equal to 99.9%)to the original values of the average gradient and entropy of the *** results of the simulation validate the effectiveness of the proposed algorithm.

关键词： Adaptive filter differential filter enhancement mask fractional differential mask fractional-order calculus hessian matrix

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