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IEEE International Symposium on Telecommunication Technologies (ISTT)

作者： Alaa Fadhil Ala'anzy Mohammed Alaa Ala'anzy Member Umar Ali Bukar Tamara Zhukabayeva Dilaram Baumuratova Nurdaulet Karabayev Department of Business Administration Al-Hikma University College Baghdad Iraq Department of Computer Science SDU University Almaty Kazakhstan Centre for Intelligent Cloud Computing (CICC) Multimedia University Melaka Malaysia Department of Information Systems L.N. Gumilyov Eurasian National University Astana Kazakhstan

ISBN: (数字)9798350392074

ISBN: (纸本)9798350392081

The rapid increase in vehicle numbers, particularly in densely populated areas, leads to significant issues such as traffic congestion, time and fuel wastage during the search for parking spots, and elevated emissions of toxic gases from idling vehicles, contributing to increased pollution. Smart parking systems, leveraging advanced Internet of Things (IoT) technologies commonly seen in Industrial Internet of Things (IIoT) applications, offer a solution to these problems by reducing traffic congestion, lowering environmental pollution, and conserving fuel. Although cloud-based parking systems have been proposed, they often suffer from high latency and bandwidth requirements due to high network usage. Fog Computing presents a more effective alternative. This paper proposes a fog computing-based smart parking system designed to address these issues for SDU University. By providing real-time information about available parking spots with minimal latency, the system optimises space usage and guides drivers efficiently, particularly during peak hours, thereby reducing the need for parking area expansion. A network usage reducing approach was developed and tested using iFogSim2 toolkit, with network usage metric for a comparative analysis between fog and cloud environments. A real case study from SDU University Park was employed to validate the system. The results demonstrate that the fog-based system outperforms the cloud-based approach in terms of efficiency and responsiveness.

关键词： Cloud computing Pollution Environmentally friendly manufacturing techniques Automated parking Real-time systems Fuels Traffic congestion Industrial Internet of Things Edge computing Vehicles

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Enhanced Consumer Healthcare Data Protection through AI-Driven TinyML and Privacy-Preserving Techniques

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IEEE Access 2025年 13卷 97428-97440页

作者： Aanjankumar, S. Muchahari, Monoj Kumar Urooj, Shabana Kaur, Ishmeet Dhanaraj, Rajesh Kumar Mengash, Hanan Abdullah Poonkuntran, S. Kaveri, Parag Ravikant VIT Bhopal University School of Computing Science and Engineering Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Institute of Engineering and Management University of Engineering and Management Department of Computer Application and Science Kolkata700091 India Princess Nourah bint Abdulrahman University Department of Electrical Engineering College of Engineering P.O. Box 84428 Riyadh11671 Saudi Arabia VIT Bhopal University School of Computing Science and Artificial Intelligence Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Pune India Princess Nourah bint Abdulrahman University Department of Information Systems College of Computer and Information Sciences P.O. Box 84428 Riyadh11671 Saudi Arabia

In the contemporary digital landscape, securing healthcare data stored on mobile devices has become imperative due to the increasing prevalence of cyberattacks. Healthcare data, often vulnerable to breaches, requires robust privacy-preserving mechanisms to ensure secure storage and sharing across various platforms. This study proposes a novel approach integrating TinyML (machine learning) with federated learning (FL) and differential privacy (DP) to enhance the security and privacy of healthcare data on resource-constrained mobile devices. The proposed TinyML model processes patient data, including ECG readings and cardiac arrhythmia diagnoses, directly on handheld devices, enabling real-time analysis with minimal resource consumption. Federated learning facilitates decentralized training of models on local devices, ensuring that sensitive data remains on user devices while model parameters are aggregated centrally. Differential privacy further strengthens security by introducing controlled noise into the data, safeguarding against malicious attacks without compromising data utility. The system demonstrates a high accuracy of 99%, significantly outperforming traditional models such as Decision Tree (76.0%), Random Forest (98.1%), and Federated Learning with Deep Q (95%). The proposed framework offers a scalable and efficient solution for real-time healthcare data processing, ensuring data privacy and security across distributed environments. Experimental results validate the model’s effectiveness in safeguarding healthcare data, with implications for broader applications in secure, privacy-preserving medical data analysis. © 2013 IEEE.

关键词： Electrocardiography

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Enhanced electroencephalography effective connectivity in frontal low-gamma band correlates of emotional regulation after mindfulness training

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Journal of Neuroscience Research 2023年第6期101卷 901-915页

作者： Ng, Hei-Yin Hydra Wu, Changwei W. Huang, Feng-Ying Huang, Chih-Mao Hsu, Chia-Fen Chao, Yi-Ping Jung, Tzyy-Ping Chuang, Chun-Hsiang Research Center for Education and Mind Sciences College of Education National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications College of Electrical Engineering and Computer Science National Tsing Hua University Hsinchu Taiwan Department of Educational Psychology and Counseling College of Education National Tsing Hua University Hsinchu Taiwan Graduate Institute of Mind Brain and Consciousness Taipei Medical University Taipei Taiwan Brain and Consciousness Research Center Taipei Medical University-Shuang Ho Hospital New Taipei Taiwan Department of Education National Taipei University of Education Taipei Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu Taiwan Graduate Institute of Behavioral Sciences Chang Gung University Taoyuan Taiwan Department of Child Psychiatry Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Otolaryngology-Head and Neck Surgery Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Institute for Neural Computation and Institute of Engineering in Medicine University of California San Diego La Jolla CA United States

Practicing mindfulness, focusing attention on the internal and external experiences occurring in the present moment with open and nonjudgement stance, can lead to the development of emotional regulation skills. Yet, the effective connectivity of brain regions during mindfulness has been largely unexplored. Studies have shown that mindfulness practice promotes functional connectivity in practitioners, potentially due to improved emotional regulation abilities and increased connectivity in the lateral prefrontal areas. To examine the changes in effective connectivity due to mindfulness training, we analyzed electroencephalogram (EEG) signals taken before and after mindfulness training, focusing on training-related effective connectivity changes in the frontal area. The mindfulness training group participated in an 8-week mindfulness-based stress reduction (MBSR) program. The control group did not take part. Regardless of the specific mindfulness practice used, low-gamma band effective connectivity increased globally after the mindfulness training. High-beta band effective connectivity increased globally only during Breathing. Moreover, relatively higher outgoing effective connectivity strength was seen during Resting and Breathing and Body-scan. By analyzing the changes in outgoing and incoming connectivity edges, both F7 and F8 exhibited strong parietal connectivity during Resting and Breathing. Multiple regression analysis revealed that the changes in effective connectivity of the right lateral prefrontal area predicted mindfulness and emotional regulation abilities. These results partially support the theory that the lateral prefrontal areas have top-down modulatory control, as these areas have high outflow effective connectivity, implying that mindfulness training cultivates better emotional regulation. © 2023 Wiley Periodicals LLC.

关键词： direct directed transfer function effective connectivity emotional regulation MBSR mindfulness

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The role of intrusion detection systems and machine in protecting industrial control systems ICS environments

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AIP Conference Proceedings 2024年第1期3207卷

作者： Amenh A. Jasim Khattab M. Ali Alheeti College of Computer and Information Technology Computer Science Department University of Anbar Ramadi Iraq Computer Networking Systems Department College of Computer Sciences and Information Technology University of Anbar Ramadi Iraq

This paper provides an overview of current cybersecurity solutions for industrial control systems (ICS) that rely on machine learning. Especially in the field of anomaly detection in the Distribution Network Protocol (DNP3). Attacks on power grid systems are a common target for cybercriminals. The energy equivalent of an entire country can be saved through these systems. Therefore, it is necessary to protect these facilities from any security breaches, as they are vital for people. For this, the paper outlines a proposal for an intrusion detection system. It introduces a classification system that uses different machine learning methods to find the most effective model for detecting flaws related to this network protocol. standard IDS datasets for (DNP3) was used to create each model (7326 observations) containing nine network attack scenarios. In terms of performance, the Random Forest algorithm outperformed the competition, SVM and KNN. Thus, it is possible to obtain a predictive accuracy of about 99%. A categorization system is thus the result of the employed method.

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Was Wuhan the early epicenter of the COVID-19 pandemic?——a critique

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National science Review 2023年第4期10卷 19-21页

作者： Yanan Cao Lingling Chen Hua Chen Yupeng Cun Xiaofeng Dai Hongli Du Feng Gao Fengbiao Guo Yalong Guo Pei Hao Shunmin He Shunping He XiongLei He Zheng Hu Boon-Peng Hoh Xin Jin Qian Jiang Qinghua Jiang Asifullah Khan Hong-Zhi Kong Jinchen Li Shuai Cheng Li Ying Li Qiang Lin Jianquan Liu Qi Liu Jian Lu Xuemei Lu Shujin Luo Qinghua Nie Zilong Qiu Tieliu Shi Xiaofeng Song Jianzhong Su Sheng-ce Tao Chaolong Wang Chuan-Chao Wang Guo-Dong Wang Jiguang Wang Qi Wu Shaoyuan Wu Shuhua Xu Yu Xue Wenjun Yang Zhaohui Yang Kai Ye Yuan-Nong Ye Li Yu Fangqing Zhao Yiqiang Zhao Weiwei Zhai Dandan Zhang Liye Zhang Houfeng Zheng Qi Zhou Tianqi Zhu Ya-ping Zhang Ruijin Hospital Shanghai Jiao Tong University College of Life Science and Technology Guangxi University Beijing Institute of Genomics Chinese Academy of Sciences Children's Hospital of Chongqing Medical University Wuxi School of Medicine Jiangnan University School of Biology and Biological Engineering South China University of Technology Department of Physics School of ScienceTianjin University School of Pharmaceutical Sciences Wuhan University Institute of Botany Chinese Academy of Sciences Institut Pasteur of Shanghai Chinese Academy of Sciences Institute of Biophysics Chinese Academy of Sciences Institute of Hydrobiology Chinese Academy of Sciences School of Life Sciences Sun Yat-sen University Institute of Synthetic Biology Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Faculty of Medicine and Health Sciences University College Sedaya International School of Medicine South China University of Technology Department of Medical Genetics Capital Institute of Pediatrics School of Life Science and Technology Harbin Institute of Technology Department of Biochemistry Abdul Wali Khan University Xiangya Hospital Central South University Department of Computer Science City University of Hong Kong College of Life Science and Technology Foshan University South China Sea Institute of Oceanology Chinese Academy of Sciences College of Ecology Lanzhou University School of Life Sciences and Technology Tongji University School of Life Sciences Peking University Kunming Institute of Zoology Chinese Academy of Sciences College of Animal Science South China Agricultural University Institute of Neuroscience Chinese Academy of Sciences School of Life Sciences East China Normal University Nanjing University of Aeronautics and Astronautics Wenzhou Institute University of Chinese Academy of Sciences Institute of Systems Biomedicine Shanghai Jiao Tong University Tongji Medical College Huazhong University of Science and Technology School of Life Sciences Xiamen University Division of Life Science and Department of

Recently, Worobey et al.(2022) published a report entitled ‘The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic' that succinctly summarizes their study [1]. A pre-print version of this study had earlier elicited a series of highprofile media coverages [2,3]. All these reports deliver a social-political message that the Huanan market is the epicenter of COVID-19.

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Pretrained Model with CNN for Lung Cancer Identification and Classification

Pretrained Model with CNN for Lung Cancer Identification and...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Shaista Kanwal Sajid Ali Tahani Jaser Alahmadi Muhammad Assam Ghulam Jillani Ansari Uzair Aslam Bhatti Department of Information Sciences University of Education Lahore Pakistan Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Software Engineering University of Science and Technology Bannu KPK Pakistan School of information and communication engineering Hainan University

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Lung cancer impacts both genders, and its early detection is key to lowering death rates. Current deep learning techniques for automated identification and classification of lung carcinoma face issues with interpretability, data variability handling, and broader applicability. In our research, we propose a novel method that merges a convolutional neural network (CNN) with a streamlined RESNET18 framework. This approach is designed for lung cancer detection and categorization from CT scans, involving image preprocessing like resizing and rescaling. It effectively differentiates between healthy and cancerous tissues, further classifying them as either Normal, Benign, or Malignant, including variants such as adenocarcinoma and squamous cell carcinoma. Our CNN+RESNET18 model boasts an impressive 99% accuracy, supporting radiologists in the intricate task of image analysis, reducing risks of incorrect or delayed diagnosis, and fostering prompt treatment for improved patient outcomes.

关键词： Accuracy Squamous cell carcinoma Computed tomography Scalability Lung cancer Mortality Streaming media Feature extraction Convolutional neural networks Artificial intelligence

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Large Language Models for Bioinformatics

arXiv

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arXiv 2025年

作者： Ruan, Wei Lyu, Yanjun Zhang, Jing Cai, Jiazhang Shu, Peng Ge, Yang Lu, Yao Gao, Shang Wang, Yue Wang, Peilong Zhao, Lin Wang, Tao Liu, Yufang Fang, Luyang Liu, Ziyu Liu, Zhengliang Li, Yiwei Wu, Zihao Chen, Junhao Jiang, Hanqi Pan, Yi Yang, Zhenyuan Chen, Jingyuan Liang, Shizhe Zhang, Wei Ma, Terry Dou, Yuan Zhang, Jianli Gong, Xinyu Gan, Qi Zou, Yusong Chen, Zebang Qian, Yuanxin Yu, Shuo Lu, Jin Song, Kenan Wang, Xianqiao Sikora, Andrea Li, Gang Li, Xiang Li, Quanzheng Wang, Yingfeng Zhang, Lu Abate, Yohannes He, Lifang Zhong, Wenxuan Liu, Rongjie Huang, Chao Liu, Wei Shen, Ye Ma, Ping Zhu, Hongtu Yan, Yajun Zhu, Dajiang Liu, Tianming School of Computing University of Georgia GA United States Department of Computer Science and Engineering University of Texas ArlingtonTX United States Department of Statistics University of Georgia GA United States Department of Epidemiology and Biostatistics University of Georgia AthensGA United States Department of Biostatistics UNC Chapel HillNC United States Department of Radiation Oncology Mayo Clinic PhoenixAZ United States Institute of Plant Breeding Genetics & Genomics University of Georgia AthensGA United States School of Computer and Cyber Sciences Augusta University GA United States School of Computer Science Carnegie Mellon University PittsburghPA United States College of Engineering University of Georgia AthensGA United States Department of Biomedical Informatics University of Colorado CO United States Department of Radiology University of North Carolina Chapel HillNC United States Department of Radiology Massachusetts General Hospital Harvard Medical School MA United States Department of Computer Science and Engineering University of Tennessee ChattanoogaTN United States Department of Computer Science Indiana University IndianapolisIN United States Department of Physics and Astronomy University of Georgia AthensGA United States Department of Computer Science and Engineering Lehigh University PA United States

With the rapid advancements in large language model (LLM) technology and the emergence of bioinformatics-specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification, and distinguishing features, alongside a detailed examination of training methodologies, datasets, and evaluation frameworks. We explore the wide-ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development, highlighting their impact and transformative potential in bioinformatics. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities. Finally, we highlight emerging trends and future directions, offering valuable insights to guide researchers and clinicians toward advancing BioLMs for increasingly sophisticated biological and clinical applications. © 2025, CC BY.

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xIDS-EnsembleGuard: An Explainable Ensemble Learning-based Intrusion Detection System 23

xIDS-EnsembleGuard: An Explainable Ensemble Learning-based I...

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23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Adil, Muhammad Jan, Mian Ahmad Bin Hakim, Safayat Song, Houbing Herbert Jin, Zhanpeng University at Buffalo Deptartment of Computer Science Buffalo United States University of Sharjah College of Computing and Informatics Sharjah United Arab Emirates University of Maryland School of Information Systems Baltimore County United States South China University of Technology Guangdong China University at Buffalo School of Future Technology Buffalo United States

In this paper, we focus on addressing the challenges of detecting malicious attacks in networks by designing an advanced Explainable Intrusion Detection System (xIDS). The existing machine learning and deep learning approaches have invisible limitations, such as potential biases in predictions, a lack of interpretability, and the risk of overfitting to training data. These issues can create doubt about their usefulness, and transparency, and decrease the trust of involved stakeholders. To overcome these challenges, we propose an ensemble learning technique called the "EnsembleGuard". This approach uses the predicted outputs of multiple models, including tree-based (LightGBM, GBM, Bagging, XGBoost, CatBoost) and deep learning models such as neural network (LSTM (long short-term memory networks) and GRU (gated recurrent unit), to maintain a balance and achieve trustworthy results. Our work is unique because it combines both tree-based and deep learning models to design an interpretable and explainable meta-model through model distillation. By considering the predictions of all individual models, our neta-model effectively addresses key challenges, and ensures both explainable and reliable results. We evaluate our model using well-known datasets, including UNSW-NB15, NSL-KDD, and CIC-IDS-2017, to assess its reliability against various types of attacks. During analysis, we found that our model outperforms both tree-based models and other comparative approaches when it comes to different kinds of attack scenarios. © 2024 IEEE.

关键词： Network intrusion

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Matching Game for Optimized Association in Quantum Communication Networks

Matching Game for Optimized Association in Quantum Communica...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Mahdi Chehimi Bernd Simon Walid Saad Anja Klein Don Towsley Mérouane Debbah Bradley Department of Electrical and Computer Engineering Wireless@VT Virginia Tech Arlington VA USA Communication Engineering Lab Technische Universität Darmstadt Darmstadt Germany Cyber Security Systems and Applied AI Research Center Lebanese American University Lebanon College of Information and Computer Sciences University of Massachusetts Amherst Amherst MA USA Electrical Engineering and Computer Science Department Khalifa University of Science and Technology Abu Dhabi UAE

Enabling quantum switches (QSs) to serve requests submitted by quantum end nodes in quantum communication networks (QCNs) is a challenging problem due to the heterogeneous fidelity requirements of the submitted requests and the limited resources of the QCN. Effectively determining which requests are served by a given QS is fundamental to foster developments in practical QCN applications, like quantum data centers. However, the state-of-the-art on QS operation has overlooked this association problem, and it mainly focused on QCNs with a single QS. In this paper, the request-QS association problem in QCNs is formulated as a matching game that captures the limited QCN resources, heterogeneous application-specific fidelity requirements, and scheduling of the different QS operations. To solve this game, a swap-stable request-QS association (RQSA) algorithm is proposed while considering partial QCN information availability. Extensive simulations are conducted to validate the effectiveness of the proposed RQSA algorithm. Simulation results show that the proposed RQSA algorithm achieves a near-optimal (within 5%) performance in terms of the percentage of served requests and overall achieved fidelity, while outperforming benchmark greedy solutions by over 13%. Moreover, the proposed RQSA algorithm is shown to be scalable and maintain its near-optimal performance even when the size of the QCN increases.

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The big-O problem for labelled Markov chains and weighted automata 31

The big-O problem for labelled Markov chains and weighted au...

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31st International Conference on Concurrency Theory, CONCUR 2020

作者： Chistikov, Dmitry Kiefer, Stefan Murawski, Andrzej S. Purser, David Department of Computer Science University of Warwick Coventry United Kingdom Department of Computer Science University of Oxford United Kingdom Max Planck Institute for Software Systems Saarland Informatics Campus Saarbrücken Germany

ISBN: (纸本)9783959771603

Given two weighted automata, we consider the problem of whether one is big-O of the other, i.e., if the weight of every finite word in the first is not greater than some constant multiple of the weight in the second. We show that the problem is undecidable, even for the instantiation of weighted automata as labelled Markov chains. Moreover, even when it is known that one weighted automaton is big-O of another, the problem of finding or approximating the associated constant is also undecidable. Our positive results show that the big-O problem is polynomial-time solvable for unambiguous automata, coNP-complete for unlabelled weighted automata (i.e., when the alphabet is a single character) and decidable, subject to Schanuel's conjecture, when the language is bounded (i.e., a subset of w1∗... wm∗ for some finite words w1,..., wm). On labelled Markov chains, the problem can be restated as a ratio total variation distance, which, instead of finding the maximum difference between the probabilities of any two events, finds the maximum ratio between the probabilities of any two events. The problem is related to Ε-differential privacy, for which the optimal constant of the big-O notation is exactly exp(Ε). © Dmitry Chistikov, Stefan Kiefer, Andrzej S. Murawski, and David Purser;licensed under Creative Commons License CC-BY 31st International Conference on Concurrency Theory (CONCUR 2020).

关键词： Polynomial approximation

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