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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Abdelrahman Osheba Ahmed Abou-El-Ela Osama Adel Youssef Maaod Tamer Abuhmed Shaker El-Sappagh Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University South Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications.

关键词： Drugs Electric potential Hospitals Large language models Mortality Medical services Chatbots Real-time systems Safety Electronic medical records

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EPM: Evolutionary Perception Method for Anomaly Detection in Noisy Dynamic Graphs

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IEEE Transactions on Knowledge and Data engineering 2025年

作者： Wang, Huan Chen, Junyang Wu, Yirui Leung, Victor C. M. Wang, Di King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Saudi Arabia Huazhong Agricultural University College of Informatics China Shenzhen University College of Computer Science and Software Engineering China Hohai University College of Computer and Information China

With the rapid expansion of interactions across various domains such as knowledge graphs and social networks, anomaly detection in dynamic graphs has become increasingly critical for mitigating potential risks. However, existing anomaly detection methods often assume noise-free dynamic graphs, overlooking the prevalence of noisy dynamic graphs in real-world applications. Specifically, noisy dynamic graphs affected by structural noises-such as spurious and missing nodes and edges-struggle to consistently provide reliable structural evidence for anomaly detection. To tackle this challenge, we propose an Evolutionary Perception Method (EPM) for identifying anomalous nodes in noisy dynamic graphs by resisting the interference of structural noises. EPM primarily consists of two components: a dynamic fitter and a filtering reviser. The dynamic fitter characterizes the interaction dynamics of nodes that removes and generates links at each period as a multiple superposition state, utilizing various link prediction algorithms to fit evolutionary mechanisms. Additionally, the filtering reviser designs evolutional entropies to quantify the evolutional uncertainty in multiple superposition states, further designing the Kalman filter to optimize these entropies. Extensive experiments show that the proposed EPM method surpasses state-of-the-art approaches in detecting anomalous nodes in noisy dynamic graphs. © 1989-2012 IEEE.

关键词： Kalman filters

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Deep Reinforcement Learning-Based Framework for Enhancing Cybersecurity

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第3期19卷 170-190页

作者： Al-Nawashi, Malek M. Al-Hazaimeh, Obaida M. Tahat, Nedal M. Gharaibeh, Nasr Abu-Ain, Waleed A. Abu-Ain, Tarik Al-Huson University College Al-Balqa Applied University Irbid Jordan Faculty of Information Technology Department of Information Security and Cybersecurity Philadelphia University Amman Jordan Faculty of Science The Hashemite University Zarqa Jordan College of Science and Computer Engineering Taibah University Yanbu Saudi Arabia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The detection of cyberattacks has been increasingly emphasized in recent years, focusing on both infrastructure and people. Conventional security measures such as intrusion detection, firewalls, and encryption are insufficient in protecting cyber systems against growing and changing threats. In order to address this problem, scholars have explored reinforcement learning (i.e., RL) as a potential solution for intricate cybersecurity decision-making difficulties. Nevertheless, the use of RL faces several obstacles, including dynamic attack scenarios, insufficient training data, and the challenge of replicating real-world complexities. This study presents a novel framework that uses deep reinforcement learning (i.e., DRL) to simulate harmful cyberattacks and improve cybersecurity. This study presents an agent-based framework that is capable of ongoing learning and adaptation in a dynamic network security environment. The agent determines the optimal course of action by considering the current state of the network and the rewards it receives for its decisions. The CIC-IDS-2018 database, constructed using Python 3.7 programming, was used. The conducted studies yielded outstanding results, with a detection accuracy of 98.82% achieved for the CIC-IDS-2018 database in cyber-attack classification. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cybersecurity

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Recursive Broadcasting Approach 33

Recursive Broadcasting Approach

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33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Harutyunyan, Hovhannes A. Hovhannisyan, Narek Concordia University Department of Computer Science and Software Engineering MontrealQCH3G 1M8 Canada Institute for Informatics and Automation Problems NAS RA Yerevan0014 Armenia

ISBN: (纸本)9798331524937

Broadcasting is one of the fundamental information dissemination primitives in interconnection networks, where a message is passed from one node (called originator) to all other nodes in the network. Following the increasing interest in interconnection networks, extensive research was dedicated to broadcasting. Two main research goals of this area are finding inexpensive network structures that maintain efficient broadcasting and finding the broadcast time for well-known and widely used network topologies. In the scope of this study, we will mainly focus on determining the broadcast time and nearoptimal broadcasting schemes in networks. Determination of the broadcast time of any node in an arbitrary network is known to be NP-hard. Polynomial time solutions are known only for a few network topologies. There also exist various heuristic and approximation algorithms for different network topologies. In this study, we consider the broadcast time problem on graphs that comprise some recursive structures. We initiate a novel direction to designing broadcasting algorithms on recursively defined graphs. We provide a theoretical foundation for future broadcasting studies, as well as discuss several practical applications of the approach we introduce. © 2025 IEEE.

关键词： Interconnection networks (circuit switching)

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ZKCross: An Efficient and Reliable Cross-Chain Authentication Scheme Based on Lightweight Attribute-Based Zero-Knowledge Proof 24th

ZKCross: An Efficient and Reliable Cross-Chain Authenticati...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xu, Yuwei Cai, Hailang Chen, Jialuo Xiang, Qiao Xu, Jingdong Cheng, Guang School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China School of Informatics Xiamen University Xiamen China College of Computer Science Nankai University Tianjin China

ISBN: (纸本)9789819615506

The consortium blockchain finds wide application in finance and logistics. However, these independent networks create information silos. Cross-chain authentication aims to bridge these gaps. It verifies qualifications across different blockchains, yet it can risk leaking sensitive data. The main methods include Attribute-Based Signatures (ABS) and Attribute-Based Zero-Knowledge Proof (ABZKP). ABZKP better handles key management, general applicability, and attribute verification than ABS. However, the current ABZKP suffers from large proof files and slow verification. Additionally, many schemes can’t handle sets of multiple attributes and may introduce centralization. Overall, the current schemes based on zero-knowledge proof still struggle to strike a balance between security and efficiency. We propose a reliable cross-chain authentication scheme. Our lightweight zero-knowledge proof algorithm enhances verification speed and reduces file size. We also introduce parallel verification to address security concerns. Security analysis and experiments show that ZKCross when properly configured, achieves near-perfect accuracy and outperforms others in size and speed. Thus, ZKCross stands as an efficient and dependable cross-chain authentication scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication Protocol

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Leveraging LLM in Genetic Programming Hyper-heuristics for Dynamic Microservice Deployment 37th

Leveraging LLM in Genetic Programming Hyper-heuristics for...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Fang, Zhengxin Ma, Hui Chen, Gang Hartmann, Sven Wang, Chen Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Informatics Clausthal University of Technology Clausthal-Zellerfeld Germany National Institute of Water and Atmospheric Research Wellington New Zealand

ISBN: (纸本)9789819603473

Microservice deployment in cloud computing is a challenging combinatorial optimization problem due to the complex dependencies among microservices and the intricate trade-offs among different QoS requirements, e.g., minimizing Energy Consumption (EC) vs. minimizing Communication Overhead (CO). Recently, some hyper-heuristics methods, particularly Genetic Programming Hyper-Heuristics (GPHH), have been proposed to automatically generate heuristics for solving dynamic microservice deployment problems. Meanwhile, Large Language Models (LLMs) are becoming popular for solving various domain-specific problems thanks to their strong ability to learn problem-related knowledge. However, hybridizing GPHH with LLM by combining their abilities in solving complex optimization problems remains unexplored. In this paper, we propose an LLM-enhanced Genetic Programming Hyper-Heuristic (LLM-GPHH) algorithm to evolve heuristics for the dynamic deployment of applications composed of microservices, to jointly optimize EC and CO. Our experiments on real-world datasets demonstrate the effectiveness of the newly proposed LLM-GPHH. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic programming

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Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions

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Discover Internet of Things 2025年第1期5卷 1-23页

作者： Mazhar, Tehseen khan, Sunawar Shahzad, Tariq khan, Muhammad Amir Saeed, Mamoon M. Awotunde, Joseph Bamidele Hamam, Habib School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science International Islamic University Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Science and Information Technology School Education Department Government of Punjab 31200 Layyah Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized. © The Author(s) 2025.

关键词： Electronic health record

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A DNN Framework on Waveform Design for Over-the-Air Computation

A DNN Framework on Waveform Design for Over-the-Air Computat...

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IEEE Conference on Wireless Communications and Networking

作者： Nikos G. Evgenidis Nikos A. Mitsiou Sotiris A. Tegos Panagiotis D. Diamantoulakis Panagiotis Sarigiannidis George K. Karagiannidis Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Informatics and Telecommunication Engineering University of Western Macedonia Kozani Greece

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

One of the main applications expected to be enabled by next-generation networks is computing. The goal-oriented nature of computing allows the use of different implementation techniques, with over-the-air (OTA) computation being one of the main proposed schemes due to its effective resource management and computational efficiency. In this work, we aim at optimizing the waveform of the system in the presence of intersymbol interference (ISI) and sampling error. To this end, we propose a deep neural network (DNN) framework that generates an optimal waveform that minimizes the mean square error (MSE) of the OTA computation system. To ensure that the generated waveform exhibits the same behavior as other common waveforms, weighted energy and spectrum constraints are included in the loss function of the training phase. To better mitigate ISI, the spectrum constraint integrates the roll-off factor of the waveform, allowing for the generation of different waveforms. Simulation results verify that the desired constraints are met and show a significant performance gain over state-of-the-art waveforms.

关键词： Training Wireless networks Simulation Intersymbol interference Artificial neural networks Mean square error methods Performance gain Computational efficiency Resource management Next generation networking

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Design of mmWave Broadband Rotary Joint and 360° Beam-Steering Rotenna Based on Gap Waveguide Technology

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IEEE Transactions on Antennas and Propagation 2025年

作者： Farahbakhsh, Ali Zarifi, Davood Mrozowski, Michal Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Microwave and Antenna Engineering Gdansk80-233 Poland Graduate University of Advanced Technology Department of Electrical and Computer Engineering Kerman Iran University of Kashan School of Electrical and Computer Engineering Kashan Iran

This work presents the design and fabrication of a groove gap waveguide-based rotary joint and a rotenna (rotary antenna) with 360° mechanical beam-steering for wideband mmWave applications. The proposed rotary joint incorporates two WR-22 to groove gap waveguide transitions, connected back-to-back with an innovative transformer. A key advantage of gap waveguide technology is its ability to operate without physical contact between the rotary joint's components, allowing the rotor to rotate freely near the stator with an air gap, while wave leakage is suppressed. By replacing common coaxial connectors with standard waveguide flanges and introducing a novel transformer between the rotor and stator, we have successfully developed a wideband rotary joint suitable for mmWaves. To demonstrate the practical applicability of this concept, we designed a rotenna with 360° mechanical beam-steering. This design addresses the common limitations of electronically steerable antennas, such as limited steering range, gain degradation, and high cost and complexity. To validate the concept, prototypes of the proposed rotary joint and rotenna were fabricated and measured. The measured and simulated results show good agreement. The rotary joint exhibits a 44.4% fractional bandwidth over the frequency range of 35 to 55 GHz with an insertion loss below 0.68 dB. Additionally, the engineered rotenna demonstrates a peak realized gain of 13 dBi with no scan loss over the full 360° steering range. © 1963-2012 IEEE.

关键词： Steerable antennas

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Curious Administrator’s Misbehaviors in Message Layer Security (MLS)

Curious Administrator’s Misbehaviors in Message Layer Secur...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Hyoung-Kee Choi Songhui Kwon College of Computing and Informatics Sungkyunkwan University Suwon Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

A key factor of the MLS group protocol is that it provides asynchronously efficient group key establishment with forward secrecy and post-compromise security for many groups in a standardized way. End-to-end encryption (E2EE) is essential to fulfilling the correctness of security in a messenger application. The correctness heavily depends on the trustworthiness of a middleman server in the center where a curious administrator may break E2EE. If this happened the administrator as a nonmember could read messages and impersonate a member by intervening with group-key management. This paper presents an in-depth explanation of the MLS protocol and demonstrates a proof-of-concept of curious administrator’s misbehaviors by analyzing the protocol. Our research outputs suggest that unconditionally trustworthy relationships on the central server in designing network protocols for security needs to prepare for any incidents caused by misbehaving administrator of the central server.

关键词： Protocols Oral communication Message services Encryption Security Servers

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