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9th IEEE International Conference on Information, Communication and Networks, ICICN 2021

作者： Memos, Vasileios A. Psannis, Konstantinos E. Ishibashi, Yutaka University of Macedonia School of Information Sciences Dept of Applied Informatics Thessaloniki Greece Nagoya Institute of Technology School of Engineering Dept of Computer Science Nagoya Japan

ISBN: (纸本)9780738113456

In this research work, we investigate the effects of cybercrime and focus on concerns about cybercrime expansion. Since black-hat hackers are constantly one step ahead of white-hat hackers and exploit new sophisticated hack-tools to intrude to devices and networks to commit cybercrime for different reasons, the utilization of the available emerging technologies can provide better privacy and protection level, while they can be used to combat cybercriminal attacks. This shift to new technologies and methods is regarded to be an urgent necessity today. Therefore, we demonstrate a potential novel security mechanism for Police Authorities, named 'Artificial Intelligence ANTi-Attack System' or simply 'AIANTAS' that aspires to improve the detection rate of cybercriminals and lead to their faster arrest. This model can be integrated into the other National Security defense mechanisms both in Greece and worldwide. © 2021 IEEE.

关键词： Data Analytics

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Cutting-Edge Predictive Methods for Enhancing Short-Term Load Prediction

Cutting-Edge Predictive Methods for Enhancing Short-Term Loa...

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2024 International Conference on Electrical and computer engineering Researches, ICECER 2024

作者： Ghazal, Taher M. Janjua, Jamshaid Iqbal Abushiba, Walid Ahmad, Munir Abbas, Tahir Ateeq, Karamath College of Arts & Science Applied Science University P.O.Box 5055 Manama Bahrain Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Selangor Malaysia Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul02841 Korea Republic of TIMES Institute Department of Computer Science Multan Pakistan School of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798331539733

Efficient management and cost-related factors in the power sector call for accurate short-term load forecasting as it enables better planning with the electric grid and achieving stability within it. This paper looks into the state-of-the-art forecasting strategies that enhance load forecasts made in shorter time frames. Such techniques include but are not limited to machine learning applications, effective data models, and multi-dimensional modeling leveraging historical, meteorological, and economic data. Their review is carried out in detail with their advantages and disadvantages and their application in practice. Furthermore, this study strives to understand the barriers that need to be overcome in the quest to increase the accuracy of the forecasting models especially concerning renewable energy sources. We illustrate how new trends relate to future developments of predictive models about supporting the decision-making in operating power systems and improving the reliability and sustainability of energy systems. © 2024 IEEE.

关键词： Prediction models

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Fuzzy Attack Tree: Assessing Cyberattack Risk Using Attack Tree and Fuzzy Logic 3

Fuzzy Attack Tree: Assessing Cyberattack Risk Using Attack T...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Naik, Nitin Jenkins, Paul Grace, Paul Naik, Dishita Song, Jingping Prajapat, Shaligram Mishra, Durgesh Yang, Longzhi Boongoen, Tossapon Iam-On, Natthakan School of Informatics and Digital Engineering Aston University United Kingdom Cardiff School of Technologies Cardiff Metropolitan University United Kingdom Birmingham City University United Kingdom Software College Northeastern University China International Institute of Professional Studies Devi Ahilya University India School of Computer Science & Information Technology Symbiosis University of Applied Sciences India Northumbria University Department of Computer and Information Sciences United Kingdom Aberystwyth University Department of Computer Science United Kingdom

ISBN: (纸本)9798350343274

Organisations and users have been experiencing significant rises in cyberattacks and their severity, which means that they require a greater awareness and understanding of the anatomy of cyberattacks, to prevent and mitigate their effects. In analysing cyberattacks, there are a number of different approaches that may be used to assess their potential risks and effects. However, these are utilised in specific types of cyberattacks and their analysis, which means they cannot be applied in every situation or cyberattack. Moreover, several other factors may influence the decision to use these approaches, such as cost, complexity, skills and adaptability. As a result, continuous research to design and enhance these approaches is undertaken to produce a generic, cost-effective, easy and adaptable approach. This paper presents one such approach to assess the risk of cyberattacks utilising an attack tree and fuzzy logic. An attack tree is a systematic and illustrative method for describing an attack on a system and analysing its taxonomy and other aspects. Subsequently, the probability and risk of each leaf node in the attack tree are calculated using the proposed formulas. Finally, fuzzy logic enables decision making based on imprecise data and heuristics to obtain the overall risk of attack. This proposed approach comprises systematic steps to accomplish an assessment of any cyberattack and its associated risks in an uncomplicated and effective manner, enabling its prevention and mitigation to be determined. The paper illustrates an application of the proposed approach to assess the risk of an information theft attack on an organisation, which can then be utilised to assess the risk of other cyberattacks. © 2023 IEEE.

关键词： Decision making

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Quantum-noise-limited optical neural networks using few photons per neuron activation

Quantum-noise-limited optical neural networks using few phot...

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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Ma, Shi-Yuan Wang, Tianyu Laydevant, Jérémie Wright, Logan G. McMahon, Peter L. School of Applied and Engineering Physics Cornell University IthacaNY14853 United States USRA Research Institute for Advanced Computer Science Mountain ViewCA94035 United States NTT Physics and Informatics Laboratories NTT Research Inc. SunnyvaleCA94085 United States Kavli Institute Cornell for Nanoscale Science Cornell University IthacaNY14853 United States

ISBN: (纸本)9781957171296

We pushed the optical energy consumption of optical neural networks to a new regime. Despite dominant quantum noise, we experimentally achieved accurate image classification using 0.008 photons/MAC, demonstrating deterministic machine-learning tasks with ultra-low-power stochastic systems. © Optica Publishing Group 2023, © 2023 The Author(s)

关键词： Biomedical imaging Laser systems Neural networks Optical neural systems Quantum noise Stochastic processes

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Control of Type 1 Diabetes Mellitus using direct reinforcement learning based controller

Control of Type 1 Diabetes Mellitus using direct reinforceme...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Denes-Fazakas, Lehel Siket, Mate Kertesz, Gabor Szilagyi, Laszlo Kovacs, Levente Eigner, Gyorgy Óbuda University Physiological Controls Research Center Bécsi street 96/B BudapestH-1034 Hungary Óbuda University Biomatics and Applied Artificial Intelligence Institute John von Neumann Faculty of Informatics Bécsi street 96/B BudapestH-1034 Hungary Software Engineering Institute John von Neumann Faculty of Informatics Óbuda University Hungary Óbuda University Applied Informatics and Applied Mathematics Doctoral School Bécsi street 96/B BudapestH-1034 Hungary Institute for Computer Science and Control Kende utca 13-17 BudapestH-1111 Hungary

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

One of the most challenging area of diabetes research is to provide such automated insulin delivery systems - so called artificial pancreas systems - that have robust and adaptive capabilities in a highly sophysticated way. I.e. they are able to provide robust insulin delivery actions at the beginning of the therapy to satisfy the requirements of the patients without knowing the users daily lifestyle and preferences however adaptive on the short-term to learn these patient specifics to increase the quality of the therapy. One possible solution is the closed-loop systems that have self-learning features. In the present study, we have examined a glucose regulatory problem using direct reinforcement learning based controller. The approach represents the fully automatic insulin administration as the timepoint and the carbohydrate content of the meals were unknown and randomized. We constructed a virtual environment of the patient with type 1 diabetes by applying a mathematical model. Proximal policy optimization learning model with continuous action space was used. Furthermore, we evaluated the effect of different training lengths on the test scenario. © 2022 IEEE.

关键词： Reinforcement learning

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Quantum Meets the Minimum Circuit Size Problem 13

Quantum Meets the Minimum Circuit Size Problem

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13th Innovations in Theoretical computer Science Conference, ITCS 2022

作者： Chia, Nai-Hui Chou, Chi-Ning Zhang, Jiayu Zhang, Ruizhe Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States School of Engineering and Applied Sciences Harvard University BostonMA United States Department of Computer Science Boston University MA United States California Institute and Technology PasadenaCA United States Department of Computer Science The University of Texas AustinTX United States

ISBN: (纸本)9783959772174

In this work, we initiate the study of the Minimum Circuit Size Problem (MCSP) in the quantum setting. MCSP is a problem to compute the circuit complexity of Boolean functions. It is a fascinating problem in complexity theory - its hardness is mysterious, and a better understanding of its hardness can have surprising implications to many fields in computer science. We first define and investigate the basic complexity-theoretic properties of minimum quantum circuit size problems for three natural objects: Boolean functions, unitaries, and quantum states. We show that these problems are not trivially in NP but in QCMA (or have QCMA protocols). Next, we explore the relations between the three quantum MCSPs and their variants. We discover that some reductions that are not known for classical MCSP exist for quantum MCSPs for unitaries and states, e.g., search-to-decision reductions and self-reductions. Finally, we systematically generalize results known for classical MCSP to the quantum setting (including quantum cryptography, quantum learning theory, quantum circuit lower bounds, and quantum fine-grained complexity) and also find new connections to tomography and quantum gravity. Due to the fundamental differences between classical and quantum circuits, most of our results require extra care and reveal properties and phenomena unique to the quantum setting. Our findings could be of interest for future studies, and we post several open problems for further exploration along this direction. © Nai-Hui Chia, Chi-Ning Chou, Jiayu Zhang, and Ruizhe Zhang;licensed under Creative Commons License CC-BY 4.0

关键词： Boolean functions

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 7

Convolutional Neural Network Based Deep Neural Network Model...

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7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Khudhur, Ahmed Mahmood Alwan, Mohammed Hasan Al Omairi, Qayssar Mahmood, Omar Abdulkareem Hammadi, Yousif I Muthanna, Mohammed Saleh Ali Aziz, Ahmed Muthanna, Ammar Information Technology Department College Of Computer Science And Information Technology Kirkuk University Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Computer Engineering College Of Engineering University Of Diyala Iraq Bilad Alrafidain University College Diyala32001 Iraq Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan Department Of Telecommunication Networks And Data Transmission The Bonch Bruevich Saint Petersburg State University Of Telecommunications Russia Department Of Applied Probability And Informatics Peoples Friendship University Of Russia Russia

ISBN: (纸本)9798400709036

Cardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant depletion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly deplete resources. © 2023 ACM.

关键词： Electrocardiograms

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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

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SN computer Science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

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Parametric activation functions modelling fuzzy connectives for better explainability of neural models 20

Parametric activation functions modelling fuzzy connectives ...

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20th IEEE Jubilee International Symposium on Intelligent Systems and informatics, SISY 2022

作者： Pusztahazi, Luca Sara Csiszar, Gabor Gashler, Michael S. Csiszar, Orsolya Physiological Control Research Center Obuda University Budapest Hungary Applied Informatics and Applied Mathematics Doctoral School Obuda University Budapest Hungary Institute of Materials Physics University of Stuttgart Stuttgart Germany SupplyPike Fayetteville Arkansas72703 United States Faculty of Electrical Engineering and Computer Science Aalen University Aalen Germany

ISBN: (纸本)9781665489881

In this work, a neuro-fuzzy hybrid deep learning model is presented for finding human-readable relationships between input features with the help of nilpotent fuzzy logic and multi-criteria decision making (MCDM). In the neural network a parameterized, differentiable activation function is used, where the parameter is determined by gradient descent. The goal is to find the optimal regularization value by applying the deep learning model to classification problems from the UCI Machine Learning Repository. © 2022 IEEE.

关键词： Fuzzy inference

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Mobile Game Development Using Unity Engine 13th

Mobile Game Development Using Unity Engine

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13th International Conference on Methodologies and Intelligent Systems for Technology Enhanced Learning, mis4TEL 2023

作者： Sapundzhi, Fatima Kitanov, Anton Lazarova, Meglena Georgiev, Slavi Department of Communication and Computer Engineering Faculty of Engineering South-West University "Neofit Rilski" 66 Ivan Mihailov Street Blagoevgrad2700 Bulgaria Faculty of Applied Mathematics and Informatics Technical University of Sofia 8 Kliment Ohridski Blvd. Sofia1000 Bulgaria Institute of Mathematics and Infromatics Bulgarian Academy of Sciences 8 Acad. Georgi Bonchev Street Sofia1113 Bulgaria Department of Applied Mathematics and Statistics University of Ruse "Angel Kanchev" 8 Studentska Street Ruse7004 Bulgaria

ISBN: (纸本)9783031421334

Unity is a popular game development platform. Various industries are inspired by it and this can be a positive impact on the learning motivation, career growth and job opportunities. The aim of this paper is to develop and implement a 3D game application for education promoting through innovation and digital skills. The game consists of two parts. The first part of the developed application is a maze that the player must overcome. In this labyrinth the player has to collect a certain number of specific bags that contain pieces of the needed puzzle. The second part is a puzzle that the player begins to arrange. If the puzzle is arranged correctly, then the player can read an interesting fact about what is seen on the picture. The theme given on the picture is an educational theme and it main aim is to develop the educational and mental abilities of the playersp who may also be students. The game is suitable for a wide range of potential players as it can be interesting and educational for children, teenagers and even adults. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Image processing

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