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iT and industrial Technologies (iCiT), international Conference on

作者： Fatima Faridoon Raja Hashim Ali Zain Ul Abideen Nazia Shahzadi Ali Zeeshan ijaz Usama Arshad Nisar Ali Muhammad imad Said Nabi Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

PolyCystic Ovary Syndrome (PCOS) is a hormonal disorder frequently found in women of reproductive age having a significant impact on the cause of infertility. it is an endocrine condition characterized by abnormalities in female hormone levels and aberrant synthesis of male hormones. This syndrome causes ovarian malfunction, increasing the risk of miscarriage and infertility. PCOS has a wide range of symptoms, making a diagnosis difficult. in this study, we proposed a feature selection method based on genetic algorithm with logistic regression to increase the accuracy of early diagnosis. A dataset containing the clinical and biochemical characteristics of 109 patients, including 36 with PCOS, is used. The genetic algorithm is used to extract the most important features from the dataset. Then, a logistic regression model is used for classification. The proposed model outperformed the baseline model that used all features, which had an accuracy of 86.2%, and produced a markedly improved accuracy of 95.4%. These results show that the proposed model effectively locates key characteristics for PCOS diagnosis.

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Prediction of Hypertension Disease Using Machine Learning Techniques: Case Study from Jordan

Prediction of Hypertension Disease Using Machine Learning Te...

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information Technology (ACiT)

作者： Raed Alazaidah Ghassan Samara Anwar Katrawi Wael Hadi Maram Y. Al-Safarini Farah Al-Mamoori Ali Elrashidi Muhyeeddin Alqaraleh Haneen Alzoubi Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of IT Zarqa University Zarqa Jordan Information Security Department Faculty of IT Petra University Amman Jordan Department of Pharmaceutical Faculty of Pharmacy Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Software Engineering Faculty of IT Zarqa University Zarqa Jordan Department of Computer Science Facultty of Information Technology Applied Science Private University Amman Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Hypertension is one of the most common diseases in Jordan. it is one of the main reasons of death among Jordanian adult citizens. Worldwide, nearly 13% of all deaths are due to Hypertension with nearly 8 million deaths yearly. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that Random Forest, Random Committee, and MultilayerPerceptron are the best classification models to handle the task of the early prediction of Hypertension in Jordan.

关键词： Hypertension Measurement Predictive models information technology Random forests Diseases

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Efficient Time-Delay System Optimization with Auto-Configured Metaheuristics

Efficient Time-Delay System Optimization with Auto-Configure...

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iEEE international Conference on Systems, Man and Cybernetics

作者： Roman Senkerik Aleksander Byrski Hubert Guzowski Peter Janku Tomas Kadavy Zuzana Kominkova Oplatkova Radek Matusu Michal Pluhacek Libor Pekar Maciej Smolka Adam Viktorin Department of Informatics and Artificial Intelligence A.I. Lab Faculty of Applied Informatics Tomas Bata University in Zlin Czech Republic Department of Automation and Control Engineering Faculty of Applied Informatics Tomas Bata University in Zlin Czech Republic Institute of Computer Science AGH University of Science and Technology Krakow Poland

This paper presents an experimental study that compares the performance of four selected metaheuristic algorithms for optimizing a time delay system model. Time delay system models are complex and challenging to optimize due to their inherent characteristics, such as non-linearity, multi-modality, and constraints. The study includes an explanation of the choice and core functionality of the selected algorithms, which are both baseline and state-of-the-art variants of self-organizing migrating algorithm (SOMA), state-of-the-art variant from the Success-History-based Adaptive Differential Evolution family of algorithms, with emphasis on diverse search (DiSH algorithm), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The hyperparameters of the metaheuristic algorithms were set using the iRace automatic algorithm configuration framework. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delay systems to develop more effective and efficient control strategies and precise model identifications. The experimental results highlight the effectiveness of the state-of-the-art algorithms with specific adaptive mechanisms like population organization process, diverse search and adaptation mechanisms ensuring a gradual transition from exploration to exploitation. Overall, this study contributes to understanding the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of modern metaheuristic algorithms and can help guide the selection of appropriate adaptive mechanisms of metaheuristics.

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investigating novel machine learning based intrusion detection models for NSL-KDD data sets

Investigating novel machine learning based intrusion detecti...

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iT and industrial Technologies (iCiT), international Conference on

作者： Muhammad Huzaifa Shah Muhammad Abu Bakar Raja Hashim Ali Zain Ul Abideen Usama Arshad Ali Zeeshan ijaz Nisar Ali Muhammad imad Said Nabi Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

This study investigates the application of the Mutual information (Mi) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML models, namely Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) with different kernels (1st, 2nd, and 3rd), are implemented for classification purposes. The proposed DT model in this study shows higher accuracy than the DT model proposed in the original paper by ingre et al. for intrusion Detection System (iDS). Additionally, a multi-class classification model for NSL-KDD datasets is developed, considering both normalized and non-normalized features. interestingly, it is observed that the models trained without normalized features achieve higher accuracies compared to those trained with normalized features. Moreover, the study enhances the classification performance of the DT-based iDS using the Correlation based Feature Selection (CFS) technique for feature selection. The proposed iDS is evaluated both before and after feature selection for multi-class classification (normal and various attack types) and binary classification (normal and abnormal data).

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Student Perceptions of Ai-Enhanced Adaptive Learning Systems: A Pilot Survey

Student Perceptions of AI-Enhanced Adaptive Learning Systems...

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international iEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Klara ida Katonane Gyonyoru Jozsef Katona Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

Artificial intelligence (Ai) significantly enhances adaptive learning by personalizing and tailoring instruction to individual student needs. Ai analyzes data in real-time to create personalized learning paths based on students' strengths, weaknesses, and preferences, which keeps students engaged and motivated. A major benefit of Ai in adaptive learning is the provision of real-time feedback and assessment, allowing students to correct mistakes promptly and understand concepts more thoroughly. Ai-based intelligent tutoring systems are primarily intended to simulate personalized tutoring processes that guide students in complex problem-solving and answering questions. it is convenient in teaching mathematics, sciences, and languages. Ai also supports inclusive education, dealing with diversified learning requirements and styles, such as those of learners with disabilities. For the teacher, Ai acts as a reflector of student performance so that one can intervene early and make adjustments in the method of instruction by creating effective learning environments. Ai technology is a field in constant development and harbors the potential to change the face of adaptive learning, bringing an upswing in educational outcomes. This article will summarize the advantages and features that merit improvement of the Ai-embedded adaptive learning systems with student feedback.

关键词： Surveys Adaptive learning Power engineering Data security Real-time systems Mathematics Problem-solving Artificial intelligence Faces

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Development of a Control System for a Lower Limb Exoskeleton Rehabilitation Robot for Stroke Patients with Standing instability

Development of a Control System for a Lower Limb Exoskeleton...

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Cognitive Computing and Complex Data (iCCD), international Conference on the

作者： Muxin Du Qingqing Wang Liucun Zhu Zhen Liu Lei Shi Yudong Fang Yinxi Gu School of Mechanical and Marine Engineering Beibu Gulf University Qinzhou China School of Electric and Information Engineering Beibu Gulf University Qinzhou China Sentinel Institute of Science and Technology Beibu Gulf University Qinzhou China School of Information Nagasaki Institute of Applied Science Nagasaki Japan Guangzhou Shiyuan Electronic Technology Company Limited Qinzhou China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Lower-limb exoskeleton robots can medically assist stroke patients in standing and walking, but there is little research on lower-limb exoskeleton robots that assist in standing balance. in this paper, we design the system of a lower limb exoskeleton rehabilitation robot that can assist in standing balance. We performed kinematic modeling with OpenSim and forward dynamics simulation with SCONE. Having identified the muscle groups and joints that affect standing balance more significantly. This paper focuses on the effects of the hip and iliopsoas muscles on standing balance. We have developed a control system for a lower-limb exoskeleton rehabilitation robot that realizes: when inputting a hip joint change angle signal, an opposite moment and angle are output to control the lower limb exoskeleton rehabilitation robot to assist the patient to achieve standing balance. The robotic system was simulated and analyzed using Matlab and the results showed the effectiveness of the system to keep the patient in standing balance.

关键词： Legged locomotion Limbs Exoskeletons Stroke (medical condition) Muscles Assistive robots Control systems MATLAB Robots Hip

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COViDX: computer-aided diagnosis of COViD-19 and its severity prediction with raw digital chest X-ray scans

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Quantitative Biology 2022年第2期10卷 208-220页

作者： Wajid Arshad Abbasi Syed Ali Abbas Saiqa Andleeb Maryum Bibi Fiaz Majeed Abdul Jaleel Muhammad Naveed Akhtar Computational Biology and Data Analysis Lab Department of Computer Sciences&Information TechnologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Biotechnology Lab Department of ZoologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Department of Software Engineering University of GujratGujrat 50700Pakistan Department of Computer Science (RCET)UETLahore 54000Pakistan Computational and Internet Services Division Pakistan Institute of Engineering and Applied Sciences(PIEAS)Islamabad 44000Pakistan

Background: Coronavirus disease (COViD-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the ***: in the absence or inadequate provision of therapeutic treatments of COViD-19 and the limited convenience of diagnostic techniques, there is a necessity for some alternate spontaneous screening systems that can easily be used by the physicians to rapidly recognize and isolate the infected patients to circumvent onward surge. A chest X-ray (CXR) image can effortlessly be used as a substitute modality to diagnose the ***: in this study, we present an automatic COViD-19 diagnostic and severity prediction system (COViDX) that uses deep feature maps of CXR images along with classical machine learning algorithms to identify COViD-19 and forecast its severity. The proposed system uses a three-phase classification approach (healthy vs unhealthy, COViD-19 vs pneumonia, and COViD-19 severity) using different conventional supervised classification ***: We evaluated COViDX through 10-fold cross-validation, by using an external validation dataset, and also in a real setting by involving an experienced radiologist. in all the adopted evaluation settings, COViDX showed strong generalization power and outperforms all the prevailing state-of-the-art methods designed for this ***: Our proposed method (COViDX), with vivid performance in COViD-19 diagnosis and its severity prediction, can be used as an aiding tool for clinical physicians and radiologists in the diagnosis and follow-up studies of COViD-19 infected ***: We made COViDX easily accessible through a cloud-based webserver and python code available at https://***/view/wajidarshad/software and https://***/wajidarshad/covidx, respectively.

关键词： coronavirus COViD-19 radiology machine learning chest X-ray contagious infection

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Discrimination of Quantum States Under Locality Constraints in the Many-Copy Setting

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Communications in Mathematical Physics 2023年第1期404卷 151-183页

作者： Cheng, Hao-Chung Winter, Andreas Yu, Nengkun Department of Electrical Engineering and Graduate Institute of Communication Engineering National Taiwan University Tapei 106 Taiwan Department of Mathematics National Taiwan University Tapei 106 Taiwan Center for Quantum Science and Engineering National Taiwan University Tapei 106 Taiwan Physics Division National Center for Theoretical Sciences Taipei 10617 Taiwan Hon Hai (Foxconn) Quantum Computing Center New Taipei City 236 Taiwan ICREA-Institució Catalana de Recerca i Estudis Avançats Barcelona 08010 Spain Departament de Física Física Teòrica: Informaciò i Fenòmens Quàntics Universitat Autònoma de Barcelona Barcelona 08193 Spain Computer Science Department Stony Brook University New York NY United States Centre for Quantum Software and Information and Faculty of Engineering and Information Technology University of Technology Sydney Ultimo 2007 NSW Australia

We study quantum hypothesis testing between orthogonal states under restricted local measurements in the many-copy scenario. For testing arbitrary multipartite entangled pure state against its orthogonal complement state via the local operation and classical communication (LOCC) operation, we prove that the optimal average error probability always decays exponentially in the number of copies. Second, we provide a sufficient condition for the LOCC operations to achieve the same performance as the positive-partial-transpose (PPT) operations. We further show that testing a maximally entangled state against its orthogonal complement and testing extremal Werner states both fulfill the above-mentioned condition. Hence, we determine the explicit expressions for the optimal average error probability, the optimal trade-off between the type-i and type-ii errors, and the associated Chernoff, Stein, Hoeffding, and strong converse exponents. Then, we show an infinite asymptotic separation between the separable (SEP) and PPT operations by providing a pair of states constructed from an unextendible product basis (UPB). The quantum states can be distinguished perfectly by PPT operations, while the optimal error probability, with SEP operations, admits an exponential lower bound. On the technical side, we prove this result by providing a quantitative version of the well-known statement that the tensor product of UPBs is a UPB. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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identifying COViD-19 through X-ray and CT scan images using Machine Learning

Identifying COVID-19 through X-ray and CT scan images using ...

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international Conference on Emerging Technologies, iCET

作者： Maham Shehzadi Usama Arshad Zainul Abideen Raja Hashim Ali Ammad Aslam Khan Ali Zeeshan ijaz Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Dept. of Biological Sciences Virtual University of Pakistan Rawalpindi Pakistan Dept. of Computer Science National University of Computer & Emerging Sciences Islamabad Pakistan

in the current global scenario, marked by the COViD-19 pandemic, it has become imperative for healthcare systems around the globe to swiftly and accurately diagnose the disease. This is where cutting-edge approaches such as machine learning (ML) come into play, specifically, ML-based identification of COViD-19 from chest X-ray and CT-Scan imagery. Our research collective is devotedly working towards devising a robust, accurate ML model capable of deciphering these images to identify COViD-19 effectively. This involves the application of a variety of Ai algorithms and vast data resources. Our work is grounded on a large, annotated image dataset that aids in training and refining the model. Our findings indicate that ML methodologies can discern COViD-19 from chest X-ray and CT scan images with a mean accuracy of 90%. Such a tool holds promise in aiding medical practitioners in prompt and precise patient diagnosis, thereby improving patient prognosis. The most notable contribution of our study lies in the creation of an ML model that can accurately detect COViD-19 in chest X-ray and CT-Scan images, potentially aiding in early disease detection and management.

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UX Design and Evaluation: Holism versus Reductionism Approaches - Which is Better?

UX Design and Evaluation: Holism versus Reductionism Approac...

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interdisciplinary Approaches in Technology and Management for Social innovation (iATMSi), iEEE Conference on

作者： Emmanuel O.C. Mkpojiogu Monday Jubrin Abdullahi Obianuju E. Okeke-Uzodike Universiti Utara Malaysia 06010 UUM Sintok Malaysia Department of Software Engineering Veritas University Abuja Nigeria Department of Computer Science Airforce Institute of Technology Kaduna Kaduna State Nigeria West Africa Department of Applied Management Durban University of Technology Pietermaritzburg KwaZulu-Natal South Africa

A literature content analysis strategy was used to synthesize extant literature sources to comprehend and capture the approaches to the design, evaluation and modeling of user experience. From the analysis, two main methods were identified for the UX research. These paradigms are holism and reductionism. The holist approach stresses on sense making and interpretative approaches to user experience, leading to construction and reconstruction of users’ quality perceptions about their experiences with technological artifacts. This paradigm is design based. Conversely, the reductionism approach is concerned with the deconstruction of users’ experiences. This approach conceives experiences as a model that can be broken down into aspects and dimensions that facilitates understanding of user experience. The debate among researchers on these differing viewpoints conceived and drove this study. The findings of the research show that the two paradigms are insightful, rich and unique and can be independently used in UX research, nevertheless, as both approaches a limited and address the peculiar concerns of the two respective paradigms, this paper advocates and endorses the use of a mixed paradigm especially in longitudinal studies to amalgamate the rich components of both varying approaches, thus implying the interdependent use of the two paradigms in UX research.

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