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2023 International Conference on ENTERprise information Systems, CENTERIS 2023 - International Conference on Project MANagement, ProjMAN 2023 - International Conference on Health and Social Care information Systems and Technologies, HCist 2023

作者： Elgendy, Nada Paivarinta, Tero Elragal, Ahmed Hannula, Karoliina Puolitaival, Kaisa M3S Faculty of Information Technology and Electrical Engineering University of Oulu Finland Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Antell Oulu Finland

Human-machine collaboration has potentially led to higher quality and more informed data-driven decisions. However, evaluating these decisions is necessary to measure the benefits, as well as enable experiential learning and posterior rationalization of the results and consequences. Nevertheless, the multiplicity of human-machine collaboration modes, as well as the multi-faceted nature of data-driven decisions complicates evaluation, and evaluation solutions are lacking both in research and in practice. This is further reflected in the complexity of incorporating evaluation in the design of such data-driven decision making systems, since developers are left without theoretically grounded and practically feasible principles to guide implementation. In this paper, we propose a set of five design principles, explicated from theory and practice, for systems implementing data-driven decision evaluation as the output of design science research cycles. The design principles are: 1) multi-faceted evaluation criteria, 2) unified viewpoint, 3) collaborative rationality, 4) processual ex-post evaluation, and 5) adaptive feedback and learning loops. They are further contextualized in the case of AI-enabled menu design at Antell, an innovative pioneer in the restaurant business in Finland, and consequently evaluated by the development managers of the project. Accordingly, the design principles contribute to the knowledge base on metahuman systems and data-driven decision evaluation, by concretizing existing normative concepts into prescriptive knowledge, also guiding future research and generalizing towards a design theory. Furthermore, they provide implementable statements for designing and developing such systems in practice and can be used as a checklist to compare and evaluate existing systems. © 2024 The Author(s).

关键词： Man machine systems

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Numerical solution of the problem of turbulent currents that occur in the case of edge hardening 15

Numerical solution of the problem of turbulent currents that...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Codrean, Marius Novac, Mihaela Cornelia Codrean, Mihaela Gordan, Cornelia Emilia Novac, Ovidiu Constantin Bujdoso, Gyongyi Slovac, Francisc University of Oradea Department of Electrical Engineering Electrical Engineering and Information Technology Faculty Oradea Romania National Geothermal Research Center Technology Transfer Center NGRC-TTC University of Oradea Oradea Romania University of Oradea Department of Electronics and Telecommunications Electrical Engineering and Information Technology Faculty Oradea Romania University of Oradea Department of Computers and Information Technology Electrical Engineering and Information Technology Faculty Oradea Romania University of Debrecen Faculty of Informatics Department of Computer Science and Library and Information Science Debrecen Hungary

ISBN: (纸本)9798350321388

The aim of this work is to develop numerical procedures for analyzing edge hardening using induction heating by turbulent currents. The section through the steel bar, whose lower edge needs to be hardened, is presented. For the inductor adapted to the piece configuration, the 2D model is assumed. The surface treatment of the piece must be differentiated: in the tip area, the heating must be greater than in the rest of the surface. Only the tip area needs to be subjected to hardening, while the rest of the piece must remain elastic to ensure the desired mechanical strength. The main purpose of this work is to propose inductor models for achieving differentiated heating of the blade. © 2023 IEEE.

关键词： Induction heating

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Comparison of different acceleration sensors for low power vibrations and shock detection in supply chain 45

Comparison of different acceleration sensors for low power v...

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45th Jubilee International Convention on information, Communication and Electronic technology, MIPRO 2022

作者： Zidar, Josip Aleksi, Ivan Matic, Tomislav Zagar, Drago Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

ISBN: (纸本)9789532331035

The supply chain is a large process that is responsible for the delivery of products all around the world. Different hazards can affect the products and cause damage during product delivery, e.g. temperature, moisture, vibrations, etc. Shock and vibrations are common during transporting and handling goods in the supply chain and can cause product failures like breakage, deformation, etc. This paper analyzes the properties of several commercially available acceleration sensors for shock and vibrations detection. A Custom PCB board is developed, and sensors are tested to detect product vibrations and shock in the laboratory environment. Packages can travel long journeys before reaching the set destination. Therefore, power consumption is analyzed for the tested sensors. Presented results show that different acceleration sensors need to be used for vibrations and shock detection in the supply chain. © 2022 Croatian Society MIPRO.

关键词： Supply chains

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Decision Level Fusion Using Hybrid Classifier for Mental Disease Classification

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computers, Materials & Continua 2022年第9期72卷 5041-5058页

作者： Maqsood Ahmad Noorhaniza Wahid Rahayu A Hamid Saima Sadiq Arif Mehmood Gyu Sang Choi Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia86400 Parit RajaBatu PahatJohorMalaysia Department of Computer Science Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Information Technology The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan 38542Korea

Mental health signifies the emotional,social,and psychological well-being of a *** also affects the way of thinking,feeling,and situation handling of a *** mental health helps in working with full potential in all stages of life from childhood to adulthood therefore it is of significant importance to find out the onset of the mental disease in order to maintain balance in *** health problems are rising globally and constituting a burden on healthcare *** diagnosis can help the professionals in the treatment that may lead to complications if they remain *** machine learning models are highly prevalent for medical data analysis,disease diagnosis,and psychiatric *** research addresses the challenge of detecting six major psychological disorders,namely,Anxiety,Bipolar Disorder,Conversion Disorder,Depression,Mental Retardation and *** challenges are mined by applying decision level fusion of supervised machine learning algorithms.A dataset was collected from a clinical psychologist consisting of 1771 observations that we used for training and testing the ***,to reduce the impact of a conflicting decision,a voting scheme Shrewd Probing Prediction Model(SPPM)is introduced to get output from ensemble model of Random Forest and Gradient Boosting Machine(RF+GBM).This research provides an intuitive solution for mental disorder analysis among different target class labels or groups.A framework is proposed for determining the mental health problem of patients using observations of medical *** framework consists of an ensemble model based on RF and GBM with a novel SPPM *** proposed decision level fusion approach by combining RF+GBM with SPPM-MIN significantly improves the performance in terms of Accuracy,Precision,Recall,and F1-score with 71\%,73\%,71\%and 71\%*** framework seems suitable in the case of huge and more diverse multiclass ***,three

关键词： Mental health diagnosis machine learning depression shrewd probing diagnostic approach

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Improving performance of multi-label classification using ensemble of feature selection and outlier detection 12

Improving performance of multi-label classification using en...

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12th International Conference on computer and Knowledge engineering, ICCKE 2022

作者： Zarif, Mohammad Ali Hamidzadeh, Javad Sadjad University Faculty of Computer Engineering and Information Technology Mashhad Iran

ISBN: (数字)9781665476133

ISBN: (纸本)9781665476133

Nowadays classification has become one of the most common techniques in machine learning. In classification, there are two types of data;single-label and multi-label. In multilabel datasets, one sample can have multiple labels at the same time. In recent years, classification of multi-label data has gained a lot of attention. Multi-label classification algorithms can be divided into 3 main parts: problem transformation methods, algorithm adaptation methods and ensemble methods. In problem transformation methods, classification of multi-label data is transformed to other fields. In algorithm adaptation methods, common single-label classification algorithms are changed so that they can deal with multi-label data. In third category, algorithms of two previous categories are combined together. Despite of many different proposed algorithms in this field, improvement of methods in terms of evaluation metrics has always been a challenge. Also, there is a lack of systems which can self-improve the base classifier. Thus, in this paper we try to present a novel ensemble system which can improve any classifier. The presented system has a novel structure which consists of two tree ensembles and each one has its own specific function. One of them has the task of removing noisy and outlier data with a novel approach and the other one has the task of removing noisy and redundant features. In one group some random samples are selected and in the other one, some random features are selected. If the evaluation metrics of the created child are improved, the algorithm can go to the next step and create its own child and if not, the parents create another child. Lastly, the results of these groups are combined together. The conducted experiments on 10 various datasets and 5 evaluation metrics show the superiority of the proposed method. © 2022 IEEE.

关键词： Anomaly detection

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OntoMG: a unique and ontological-based intelligent framework for early identification of myasthenia gravis (MG)

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International Journal of information technology (Singapore) 2024年第6期16卷 3847-3853页

作者： Mahajan, Prerna Agarwal, Trapty Vekariya, Daxa Gupta, Rajesh Malviya, Ashwini Anandaraj, S.P. Jain, Garima Anand, D. Department of Computer Science and Information Technology Jain (Deemed to Be University) Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Lucknow India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Management Sanskriti University UP Mathura India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science Engineering Presidency University Bengaluru India Department of Computer Science and Business Systems (CBS) Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Myasthenia Gravis (MG) is a neuromuscular disease causing extreme muscular fatigue, triggering problems with vision, swallowing, speech, mobility, dexterity, and breathing. However, early detection and prediction of MG is a crucial and quite challenging task for medical practitioners. So, in our proposed work, we are focused on introducing an ontology-based intelligent system for the prognosis of patients affected by this neuromuscular disease. The given paper presents a novel framework that employs the concepts of ontology, semantic web rules, and reasoner for deriving whether the patient is tested positive or negative. The suggested framework's effectiveness is assessed by comparing it to machine learning classifiers using parameters from the NIH repository for Myasthenia Gravis. The experimental results show that the ontological-based framework is able to achieve higher accuracy (80.85%), higher precision (78.37%), and higher recall (76.55%) than the existing classifiers used in the recent studies. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Machine learning (ML) Myasthenia gravis disease Ontology Reasoners Semantic web rules language (SWRL)

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Defending Against Digital Threats: Machine Learning Techniques for Cyber Persecution Detection 5

Defending Against Digital Threats: Machine Learning Techniqu...

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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Krishnan, Kesavan Kumar, Vijay Jalil, Norazira Binti A Kiran, Ajmeera School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Graphic Era Hill University Department of Physics Dehardun India Graphic Era Deemed to be University Dehradun India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331528553

Cyber persecution has become a widespread problem on the social media. It has resulted in omissions such as suicide and sadness. Content regulation on social media sites is becoming increasingly important. The following work employs natural language processing and Machine Learning techniques to construct a cyber harassment detection model utilizing data from cyberbullying, hate speech tweets from Twitter, and personal assaults from Wikipedia. The optimal approach is investigated by two feature extraction methods and six classifiers (SVM, Random Forest, XGBoost, MLP, and Logistic Regression). This study summarizes the general procedure for detecting cyberbullying and, more crucially, the technique. During experimentation, SVM, Random Forest, and Logistic Regression performed well with an average of 90%. Our model has been created to detect offensive comments on social media websites. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimized Weighted Ensemble Using Dipper Throated Optimization Algorithm in Metamaterial Antenna

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computers, Materials & Continua 2022年第12期73卷 5771-5788页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz AAbdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

Metamaterial Antennas are a type of antenna that uses metamaterial to enhance *** bandwidth restriction associated with small antennas can be solved using metamaterial *** learning is gaining popularity as a way to improve solutions in a range of *** learning approaches are currently a big part of current research,and they’re likely to be huge in the *** model utilized determines the accuracy of the prediction in large *** goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and *** basic models employed in the developed ensemble are Support Vector Regression(SVR),K-NearestRegression(KNR),Multi-Layer Perceptron(MLP),Decision Trees(DT),and Random Forest(RF).The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization(DTO)*** choose the best features from the dataset,the binary(bDTO)algorithm is *** proposed ensemble model is compared to the base models and results are recorded and analyzed *** addition,two other ensembles are incorporated in the conducted experiments for *** ensembles are average ensemble and K-nearest neighbors(KNN)-based *** comparison is performed in terms of eleven evaluation *** evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.

关键词： Metamaterial antenna dipper throated optimization feature selection parameters prediction

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Breast Cancer Detection Using Breastnet-18 Augmentation with Fine Tuned Vgg-16

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Intelligent Automation & Soft Computing 2023年第5期36卷 2363-2378页

作者： S.J.K.Jagadeesh Kumar P.Parthasarathi Mofreh A.Hogo Mehedi Masud Jehad F.Al-Amri Mohamed Abouhawwash Department of Computer Science and Engineering Kathir College of EngineeringCoimbatore641062India Department of Computer Science and Engineering Bannari Amman Institute of TechnologySathyamangalam638401TamilnaduIndia Electrical Engineering Department Faculty of EngineeringBenha UniversityBenha13518Egypt Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Women from middle age to old age are mostly screened positive for Breast cancer which leads to *** over the past decades,the overall sur-vival rate in breast cancer has improved due to advancements in early-stage diag-nosis and tailored *** all hospital brings high awareness and early detection technologies for breast *** increases the survival rate of *** traditional breast cancer treatment takes so long,early cancer techniques require an automation *** research provides a new methodol-ogy for classifying breast cancer using ultrasound pictures that use deep learning and the combination of the best ***,after successful learning of Convolutional Neural Network(CNN)algorithms,data augmentation is used to enhance the representation of the feature *** it uses BreastNet18 withﬁne-tuned VGG-16 model for pre-training the augmented *** feature classiﬁcation,Entropy controlled Whale Optimization Algorithm(EWOA)is *** features that have been optimized using the EWOA were utilized to fuse and optimize the *** identify the breast cancer pictures,training classiﬁers are *** using the novel probability-based serial technique,the best-chosen characteristics are fused and categorized by machine learning *** main objective behind the research is to increase tumor prediction accuracy for saving human *** testing was performed using a dataset of enhanced Breast Ultrasound Images(BUSI).The proposed method improves the accuracy com-pared with the existing methods.

关键词： Deep learning classiﬁcation data augmentation feature extraction the fusion of features breast cancer optimization classiﬁcation

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An Architecture Supporting Intelligent Mobile Healthcare Using Human-computer Interaction HCI Principles

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computer Systems Science & engineering 2022年第2期40卷 557-569页

作者： Mesfer Alrizq Shauban Ali Solangi Abdullah Alghamdi Muhammad Ali Nizamani Muhammad Ali Memon Mohammed Hamdi College of Computer Science and Information Systems Najran UniversityNajranSaudi Arabia Faculty of Engineering&Technology University of SindhJamshoro78060Pakistan

Recent advancements in the Internet of Things IoT and cloud computing have paved the way for mobile Healthcare(mHealthcare)services.A patient within the hospital is monitored by several ***,upon leaving the hospital,the patient can be remotely monitored whether directly using body wearable sensors or using a smartphone equipped with sensors to monitor different user-health *** raises potential challenges for intelligent monitoring of patient's *** this paper,an improved architecture for smart mHealthcare is proposed that is supported by HCI design *** HCI also provides the support for the User-Centric Design(UCD)for smart mHealthcare ***,the HCI along with IoT's(Internet of Things)5-layered architecture has the potential of improving User Experience(UX)in mHealthcare design and help saving *** intelligent mHealthcare system is supported by the IoT sensing and communication layers and health care providers are supported by the application layer for the medical,behavioral,and health-related *** care providers and users are further supported by an intelligent layer performing critical situation assessment and performing a multi-modal communication using an intelligent *** HCI design focuses on the ease-of-use,including user experience and safety,alarms,and error-resistant displays of the end-user,and improves user's experience and user satisfaction.

关键词： Human computer interaction mhealthcare user-centric design sensor network nternet-of-things

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