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2nd International Symposium on information Technology and Digital Innovation, ISITDI 2024

作者： Indra, Zul Jusman, Yessi Elfizar Mahdiyah, Evfi Salambue, Roni Winarso, Doni Universitas Riau Departement of Computer Science Pekanbaru Indonesia Universitas Muhammadiyah Yogyakarta Departement of Electrical Engineering Bantul Indonesia Universitas Muhammadiyah Riau Departement of Information System Pekanbaru Indonesia

ISBN: (纸本)9798350389241

Malaria has spread worldwide since the early 20th century and causes nearly half a million deaths each year. Malaria is actually not a dangerous disease and can be easily cured if the treatment can be carried out effectively. Therefore, efforts to identify the presence of the plasmodium parasite early will greatly help reduce the death rate caused by this disease. However, the fact is that this disease often does not get serious attention. Generally, this disease is only treated in the critical phase because it is considered just a common flu. This research is intended to produce a computer-aided disease diagnosis (CAD) system enriched with CNN algorithms to assist in early malaria diagnosis. This CAD system is expected to provide an effective and reliable malaria diagnosis, and avoid the limitations of manual diagnosis. In order to develop the CAD applications, this study proposed a novel CNN architecture called as Mala-Net by utilizing red thick blood dataset. In conclusion, this study successfully surpassed previous studies used as benchmarks with an accuracy value above 97%. © 2024 IEEE.

关键词： Diseases

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Secure-by-Construction Synthesis for Control systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 4170-4177页

作者： Zhong, Bingzhuo Liu, Siyuan Caccamo, Marco Zamani, Majid Thrust of Artificial Intelligence Information Hub The Thrust of Intelligent Transportation System Hub China KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden Technical University of Munich TUM School of Engineering and Design Germany University of Colorado Boulder Department of Computer Science United States Ludwig Maximilian University Department of Computer Science Munich Germany

In this note, we present the synthesis of secure-by-construction controllers that address safety and security properties simultaneously in cyber-physical systems. Our focus is on studying a specific security property called opacity, which characterizes the system's ability to maintain plausible deniability of its secret behavior in the presence of an intruder. These controllers are synthesized based on a concept of so-called (augmented) control barrier functions. We propose conditions that facilitate the construction of the desired (augmented) control barrier functions and their corresponding secure-by-construction controllers. To compute these functions, we propose an iterative scheme that leverages iterative sum-of-square programming techniques. This approach enables efficient computation of these functions, particularly for polynomial systems. Finally, we validate the effectiveness of our results through a case study of a vehicle. © 1963-2012 IEEE.

关键词： Iterative methods

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Insights into the Future: XAI Integration in O-RAN and Space Communication systems

Insights into the Future: XAI Integration in O-RAN and Space...

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2024 Multimedia University engineering Conference, MECON 2024

作者： Tahir, H. Ahmed Nabi, Fahad Tariq, Muhammad Ziad Khan, Ahmad Fasih Mahmud, Azwan Multimedia University Faculty of Engineering Cyberjaya Malaysia University of Kiel Department of Electrical Engineering & Information Technology Kiel Germany Department of Computer Science SZABIST University Islamabad Pakistan Roots School System Islamabad Pakistan

ISBN: (纸本)9798331530747

This paper outlines the merging of Explainable Artificial Intelligence (XAI) with Open Radio Access Network (O-RAN) and space communication systems. The usage of AI drives decision-making in critical domains of telecommunications and space explorations requires transparency and interpretability. However, traditional AI models often operate as 'black boxes,' making it difficult to understand their decision-making processes. This lack of explainability poses significant risks, including misinterpretation of signals, undetected anomalies, and erroneous decision-making, which can compromise the integrity of communication systems. Specifically, the challenges include accurately distinguishing between legitimate signals and attacks in anti-jamming scenarios, understanding the behaviour of complex models like LSTM in traffic prediction, and ensuring the reliability of telemetry data despite errors and noise. Integration of XAI techniques within O-RAN architecture and space communication protocols can ensure trust, reliability and safety in AI-enabled systems. This paper investigates the opportunities and challenges of incorporating XAI in O-RAN and space communications. Additionally, it presents the implications of XAI, the need for interpretability in autonomous spacecraft operations, anomaly detection and decision support for mission-critical tasks. By bridging the gap in AI transparency and advanced communication technology, this paper aims to present a detailed analysis of implemented AI and Machine Learning (ML) in the telecommunication domain. It also presents the challenges currently faced in the intersection of XAI and communication. Use cases of XAI in the domain of Open Radio Access Network and Space Communications in light of LIME and SHAP techniques were presented to give a hypothetical modelling for future experimentation to provide local and global interpretation and explanations for the currently employed AI/ML models in the respective domains. Overall, this

关键词： Radio access networks

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A Comprehensive Review on Cybersecurity Issues and Their Mitigation Measures in FinTech

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer Science and Mathematics 2024年第3期5卷 45-91页

作者： Ali, Guma Mijwil, Maad M. Buruga, Bosco Apparatus Abotaleb, Mostafa Muni University Arua00256 Uganda Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Library and Information Services Muni University Arua00256 Uganda Department of System Programming South Ural State University Chelyabinsk Russia

The fourth industrial revolution has seen the evolution and wide adoption of game-changing and disruptive innovation "financial technologies (FinTech)" around the globe. However, the security of FinTech systems and networks remains critical. This paper comprehensively reviews the evolving landscape of cybersecurity issues within the FinTech sector and explores effective mitigation measures. Four independent researchers reviewed relevant literature from IEEE Xplore Digital Library, ScienceDirect, Taylor & Francis, Emerald Insight, Springer, SAGE, Wiley Online Library, Hindawi, MDPI, ACM Digital Library, IGI Global, and Google Scholar. The research paper begins by examining the history and evolution of FinTech, drivers for the growth of FinTech, segments of the FinTech industry, FinTech ecosystem, FinTech business model, and FinTech application. Subsequently, it then delves into an analysis of the most pressing cybersecurity issues confronting FinTech firms, such as privacy concerns, data breaches, malware attacks, hacking, insider threats, identity theft, social engineering attacks, distributed denial-of-service attacks, and others. In response to these cybersecurity issues, the paper evaluates various mitigation strategies and best practices adopted by FinTech firms and regulatory bodies globally. These measures include technological solutions such as authentication and access control mechanisms, cryptography, big data analytics, intrusion detection/prevention systems, regular data backup, artificial intelligence and machine learning, cloud computing technologies, blockchain technologies, and fraud detection and prevention systems. The paper also emphasizes the importance of FinTech regulatory sandboxes, regulatory compliance, basic security training, continuous monitoring of threats, zero-trust policy, robust cybersecurity culture, regular testing, and stringent security policies to strengthen the FinTech ecosystem's cyber resilience. Based on empirical research, i

关键词： Denial-of-service attack

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Leaching Fraction (LF) of Irrigation Water for Saline Soils Using Machine Learning

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Intelligent Automation & Soft Computing 2023年第5期36卷 1915-1930页

作者： Rab Nawaz Bashir Imran Sarwar Bajwa Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel ABahaddad Khalid Ali Almarhabi Department of Computer Science COMSATS University IslamabadVehari CampusPakistan Department of Computer Science and IT Islamia University BahawalpurPakistan Department of Software Engineering Superior University Lahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Soil salinity is a serious land degradation issue in *** is a major threat to agriculture *** irrigation water is applied to leach down the salts from the root zone of the plants in the form of a Leaching fraction(LF)of irrigation *** the leaching process to be effective,the LF of irriga-tion water needs to be adjusted according to the environmental conditions and soil salinity level in the form of Evapotranspiration(ET)*** relationship between environmental conditions and ET rate is hard to be deﬁned by a linear relationship and data-driven Machine learning(ML)based decisions are required to determine the calibrated Evapotranspiration(ETc)***-assisted ETc is pro-posed to adjust the LF according to the ETc and soil salinity level.A regression model is proposed to determine the ETc rate according to the prevailing tempera-ture,humidity,and sunshine,which would be used to determine the smart LF according to the ETc and soil salinity *** proposed model is trained and tested against the Blaney Criddle method of Reference evapotranspiration(ETo)*** validation of the model from the test dataset reveals the accu-racy of the ML model in terms of Root mean squared errors(RMSE)are 0.41,Mean absolute errors(MAE)are 0.34,and Mean squared errors(MSE)are 0.28 mm *** applications of the proposed solution in a real-time environ-ment show that the LF by the proposed solution is more effective in reducing the soil salinity as compared to the traditional process of leaching.

关键词： Leaching fraction saline soil evapotranspiration machine learning calibrated evapotranspiration artiﬁcial intelligence blaney criddle method

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Distributed Hierarchical AI Architecture for Meta-Described Local Dynamic Maps in Autonomous Driving 16

Distributed Hierarchical AI Architecture for Meta-Described ...

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16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Miyata, Hiroshi Arimoto, Kazutami Saitoh, Masatoshi Hayami, Atsushi Mizuno, Hisayoshi Shimazu, Yukihiko Yokogawa, Tomoyuki Technoaccel Networks Corp. Tokyo Office Tokyo Japan Okayama Prefectural University Computer Science and System Engineering Sousya Okayama Japan Toyota Motor Corporation Information and Communication Planning Division Tokyo Japan Okayama Prefectural University Computer Science and System Engneering Sousya Okayama Japan

ISBN: (纸本)9798350377903

In autonomous driving, sharing information among vehicles to enhance safety is an important issue. However, it is not yet clear how each terminal car processes information and how to share it with other cars. Therefore, this paper proposes a distributed hierarchical AI architecture in which each edge distributed terminal of a car or intermediate cloud is equipped with a processing AI that generates metadata and builds them hierarchically. The hierarchical AI consists of multiple AI information processing layers and generates metadata links by concatenating the output of each layer. The hierarchical and spatially distributed nature of information processing allows for a compact digital twin that enables efficient spatiotemporal processing by receiving only the data necessary for that terminal and facilitating the processing necessary for that terminal. As a concrete example, we created software for vehicle front camera image processing for local dynamic maps for autonomous driving, and we evaluated the intensity and processing load of the proposed architecture. In the future, we will continue to study the software and hardware necessary to increase the hierarchy and speed of AI processing. © 2024 IEEE.

关键词： Spatio-temporal data

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

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Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

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computers, Materials & Continua 2023年第1期74卷 607-621页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohamed K.Nour Mashael M.Asiri Ali M.Al-Sharafi Mahmoud Othman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University Malaysia 53100 Kuala LumpurMalaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital *** Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their *** may result in compromised security of the systems,scams,and other such *** attacks hijack huge quantities of the available data,incurring heavy financial *** the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify *** this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)*** presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine *** attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding *** addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious ***,AFSA is applied to the proposed model to enhance the efficiency of GRU *** proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle *** simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

关键词： Malicious URL cybersecurity deep learning machine learning metaheuristics gated recurrent unit

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Identification of Key Signaling Pathways and Novel Computational Drug Target for Depression and Coronary Artery Disease

Identification of Key Signaling Pathways and Novel Computati...

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2024 IEEE International Conference on Computing, Applications and systems, COMPAS 2024

作者： Hosen, Md. Faruk Basar, Md. Abul Yasmin, Mst. Farjana Morshed, Monir Uddin, Muhammad Shahin Daffodil International University Department of Computing and Information System Dhaka1216 Bangladesh Department of Computer Science and Engineering Dhaka1230 Bangladesh Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail1902 Bangladesh

ISBN: (纸本)9798331529765

Psychological disorders, such as anxiety, bipolar disorder, panic disorder, stress, depression, and schizophrenia, are increasingly prevalent worldwide. Among these conditions, depression is particularly notable as one of the most common and debilitating neuropsychiatric disorders. Individuals with depression may be at a higher risk of developing oronary Artery Disease (CAD). Depression can contribute to poor lifestyle choices, such as unhealthy eating, lack of exercise, and smoking, which are risk factors for CAD. The emotional stress and anxiety associated with depression can strain the heart and exacerbate CAD symptoms. The relationship is not one-sided. CAD itself can be a significant source of emotional distress, leading to symptoms of depression and anxiety in affected individuals. Managing both conditions in tandem can be complicated. Treating CAD may involve medications, lifestyle modifications, and potentially surgical interventions. Meanwhile, depression often requires therapy, counseling, and medication. Coordinating care and addressing both conditions simultaneously is crucial. In our study, we investigated the molecular connections between CAD and Depression using GSE98793 and GSE20681 microarray datasets. After preprocessing, we identified key hub genes, including CCT2, SVIL, REPS2, ASPH, and UBC, in the shared ProteinProtein Interaction network. KEGG pathways linked these DEGs to colorectal and cancer pathways. Our next steps involve exploring microRNAs, TFs, and GO analysis. These findings offer promising leads for potential therapies, uniting CAD and Depression under a common molecular framework, advancing our understanding of these conditions. © 2024 IEEE.

关键词： Blood vessels

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Blue and Ultraviolet Light Beams with Nonlinear TiO2 Metasurfaces

Blue and Ultraviolet Light Beams with Nonlinear TiO2 Metasur...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Sedeh, Hooman Barati Zheng, Yuruo Gao, Jiannan Lai, Fangxing Li, Hao Xiao, Shumin Litchinitser, Natalia M. Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen Shenzhen518055 China

We design and experimentally demonstrate the generation of blue and ultraviolet third-harmonic generation from a TiO2 metasurface supporting quasi-bound state in the continuum with 0.2% conversion efficiency. © O... 详细信息

ISBN: (纸本)9781957171395

关键词： Laser beams

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