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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Soni, Mukesh Rusho, Maher Ali Byeon, Haewon Alghamdi, Azzah Hannan, Shaikh Abdul Dave, Parth Rameshchandra Dr. D. Y. Patil School of Science & Technology Tathawade Dr. D. Y. Patil Vidyapeeth Marashtra Pune India University of Colorado Department of Lockheed Martin Engineering Management BoulderCO80309 United States Inje University Department of Ai and Software & Medical Big Data Research Centre Gimhae50834 Korea Republic of Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia AlBaha University Faculty of Computing and Information AlBaha Saudi Arabia L. D. College of Engineering Department of Computer Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350350067

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare. © 2024 IEEE.

关键词： Electronic health record

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Identifying Appropriate Probability Density Function for Wind Speed Profiles of Two Coastal Cities in Pakistan for Better Wind Power Assessment 3

Identifying Appropriate Probability Density Function for Win...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Abbas, Ghulam Ali, Arshad Zubair, Muhammad Mushtaq, Zohaib Farooq, Umar Khan, Irfan A. Department of Electrical Engineering The University of Lahore Lahore Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Madinah Saudi Arabia College of Engineering and Technology University of Doha for Science and Technology Doha Qatar Department of Electrical Electronics and Computer Systems University of Sargodha Sargodha Pakistan IEECE University of the Punjab Lahore Pakistan Lab Texas A&M University GalvestonTX77553-1675 United States

ISBN: (纸本)9798331504861

To assess the wind power potential of a specific wind site, a suitable distribution function that best suits the wind data needs to be known. In this study, we employ four probability density functions (PDFs), namely Weibull, Rayleigh, Log-Normal, and Gamma, on wind speed data obtained from two coastal cities in Pakistan, Gwadar and Karachi, to check which one is the best suited. Cities along the coastline of Pakistan offer greater wind speeds and, thus, greater wind power potential. The maximum likelihood technique is used to estimate the parameters of the density distributions. Three statistical measures—Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2)—are used to assess the goodness of fit for every distribution. These measures give a complete picture of every distribution's correctness in terms of reflecting the recorded wind speed patterns. Numerical results reveal that the Weibull distribution fits well with the wind speeds of the two coastal cities compared to the other three probability distributions, providing insightful analysis for the evaluation of wind resources and possible local energy generation. The MATLAB environment is used to compare the distributions and validate the effectiveness of the proposed methodology. ©2024 IEEE.

关键词： Windmill

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things 4

A Secured and Optimized Broadcast Authentication Scheme for ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Riaz, Syed Waleed Khan, Fazlullah Ullah, Insaf Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Mardan Computer Science Department KPK Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang Ningbo315104 China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes. © 2024 IEEE.

关键词： Authentication

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Path2Vec: A Deep Representation Learning Method for Trajectory Feature Extraction and HYSPLIT Uncertainty Quantification

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Ren, Ke Jin, Chengyao Song, Yuxuan Xu, Yang Zhang, Huijie Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian223003 China School of Information Science and Technology Northeast Normal University Changchun130117 China

Accurate quantification of the uncertainty in HYSPLIT model simulations is crucial for analyzing atmospheric pollution propagation paths and assessing environmental risks. This study introduces Path2Vec, a method based on deep representation learning for extracting trajectory features and measuring uncertainty. The method is capable of mining spatiotemporal-independent trajectory motion patterns in the HYSPLIT model. We first extract spatiotemporal-invariant features of the trajectories using a sliding window technique. Subsequently, we utilize a deep representation learning model that integrates a variational autoencoder (VAE) with long short-Term memory (LSTM) networks to encode high-quality deep representations of the trajectories. By measuring the similarity and performing clustering analysis on the generated trajectory deep representations, we can identify and classify different motion patterns, and quantify the uncertainty of HYSPLIT. Experimental results indicate that the Path2Vec method surpasses traditional similarity measurement techniques, such as Euclidean distance and Edit Distance on Real sequence, in extracting spatiotemporal-independent motion patterns and quantifying uncertainty. This study provides a novel and effective approach for trajectory feature extraction and uncertainty quantification, with wide-ranging applications in fields such as meteorological simulation and air pollution propagation path analysis. © 2024 Ke Ren, Chengyao Jin, Yuxuan Song, Yang Xu and Huijie Zhang, published by Sciendo.

关键词： Risk assessment

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Developing a computer-Aided Diagnostic System for Breast Cancer Ultrasound Imaging

Developing a Computer-Aided Diagnostic System for Breast Can...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Taha, Radwa Afifi, Shereen Abd-Elghany, Mohamed A. Salem, Mohammed A.-M German University in Cairo The Faculty of Media Engineering and Technology New Cairo Egypt German University in Cairo Faculty of Media Engineering and Technology Computer Science Engineering New Cairo Egypt German University in Cairo Faculty of Information Engineering and Technology Electrical Engineering New Cairo Egypt Tu Darmstadt Integrated Electronics Systems Lab Germany German University in Cairo Faculty of Media Engineering and Technology Digital Media Engineering New Cairo Egypt

ISBN: (纸本)9798350383386

In this research we aim to develop a computer-aided diagnostic system (CAD) for breast ultrasound imaging to enhance the early detection of breast cancer. CAD tools aid physicians in the diagnostic process leading to early detection and treatment of breast cancer. The proposed CAD tool comprises three stages, the first is enhancement of breast ultrasound imaging by applying custom segmentation deep learning architectures [U-Net, SK-U-Net, RDA-U-Net] to enhance the isolation of masses in Ultrasound images, then generation of new ultrasound images by employing Generative Adversarial Networks [DAGAN, DCGAN] to overcome data limitation in available data-sets, and the last stage is the detection and classification of breast cancer masses by a CNN model. The enhancement stage shows promising results with accuracy of 98 % overall accuracy, 92 % overall precision, and 85 % overall *** Relevance: Breast cancer is one of the deadly diseases in the world. The earlier the detection the higher the chances of recovery. Thus a CAD tool that facilitates the screening process of ultrasound images will aid physicians in the early detection and diagnosis of the disease. © 2023 IEEE.

关键词： Diseases

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Privacy-Preserving information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things:A Hybrid Approach

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computers, Materials & Continua 2024年第7期80卷 1305-1323页

作者： Amal Al-Rasheed Rahim Khan Tahani Alsaed Mahwish Kundi Mohamad Hanif Md.Saad Mahidur R.Sarker Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science AbdulWali Khan University MardanMardan23200Pakistan Faculty of Computing and Informatics University Malaysia SabahKota KinabaluSabah88400Malaysia Applied College Taibah UniversityMadinah41477Saudi Arabia Maynooth International Engineering College Maynooth UniversityCo KildareW23 X021Irland Department of Mechanical Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Institute of Visual Informatics Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia University of Design Innovation and Technology(UDIT)Av.Alfonso XIII97Madrid28016Spain

Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare *** addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,***,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled *** this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT *** window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission ***,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s *** contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of ***,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the *** proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information *** and analytical results ha

关键词： Internet of things information fusion differential privacy dynamic programming Laplace function

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Improving WiFi Fingerprint Database With Irregular Distribution of Reference Points Using Interpolation Methods

Improving WiFi Fingerprint Database With Irregular Distribut...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Vu-Thanh, Trung He, Jing Duong-Bao, Ninh Lee, Seon-Woo Thi, Luong Nguyen Nguyen-Huu, Khanh College of Computer Science and Electronic Engineering Hunan University Changsha China Dalat University Faculty of Mathematics and Informatics Dalat Viet Nam Hallym University Division of Software Chuncheon Korea Republic of Dalat University Faculty of Information Technology Dalat Viet Nam Dalat University Department of Electronics and Telecommunications Dalat Viet Nam

ISBN: (纸本)9798350353983

Using WiFi Fingerprinting for indoor positioning is becoming increasingly popular due to its cost-effectiveness. However, to ensure the system can operate with high accuracy and stability, this technique requires a burden of data collection tasks from a dense distribution of reference points (RPs). To decrease the cost of data collection, as well as to improve the WiFi fingerprint database with irregular distribution of RPs, this study applies and compares different interpolation methods. Evaluating these methods in a public dataset including multiple sub-datasets with irregular distributions of RPs, the results highlight the Linear Barycentric interpolation (LB) as the most favorable method due to its low complexity and stable performance. The LB-based fingerprint database achieves an average interpolation error of less than 3 dBm compared to the original one. When applying the interpolated database to positioning tasks, it can help reduce acquisition costs by up to 50% while improving positioning accuracy by up to 11%. The study reveals the feasibility of interpolation in optimizing WiFi fingerprint data collection, especially in large and complex environments where regular distribution of RPs cannot be deployed. © 2024 IEEE.

关键词： Interpolation

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Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

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Using Pi Zero SBCs as a Low-Cost Driver Monitoring Solution

Using Pi Zero SBCs as a Low-Cost Driver Monitoring Solution

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Robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Hady A. Khalil Sherif A. Hammad Hossam E. Abdelmunim Shady A. Maged Department of Mechatronics Engineering Faculty of Engineering Ain Shams University Cairo Egypt Garraio for Software Innovations Cairo Egypt Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9798331520038

ISBN: (纸本)9798331520045

Driver monitoring systems have become a vital component of Advanced Driver Assistance systems (ADAS) for vehicle safety. According to the U.S. National Highway Traffic Safety Administration, drowsy driving contributes to nearly 100,000 accidents annually. Early driver monitoring systems relied on vehicle sensor data, and current systems often use eye-tracking technology. Recently, there has been a rising interest in applying machine vision and deep learning to driver monitoring systems. Machine vision allows for advanced driver monitoring, such as detecting driver attention states, smartphone use, and seat belts, but machine vision systems typically require high processing power, which increases the device cost. This paper introduces a cost-effective driver monitoring system utilizing Pi Zero Single Board computers (SBCs) like the Raspberry Pi Zero 2 W board, and Radxa Zero 3 W board, combined with a deep learning CNN, the system is capable of identifying various driver states, including safe driving, distraction, drowsiness, and smartphone use.

关键词： Deep learning YOLO Road transportation Performance evaluation Machine vision Lighting Artificial intelligence Monitoring Robots Vehicles

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Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait

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PeerJ computer Science 2025年 11卷 1-42页

作者： Naz, Anam Khan, Hikmat Ullah Alsahfi, Tariq Alhajlah, Mousa Alshemaimri, Bader Daud, Ali Department of Information Technology University of Sargodha Punjab Sargodha Pakistan Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Applied of Computer Science College King Saud University Riyadh Saudi Arabia Software Engineering Department College of Computing and Information Sciences King Saud University Riyadh Saudi Arabia Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates

Understanding human personality traits is significant as it helps in decision making related to consumers’ behavior, career counselling, team building and top candidates’ selection for recruitment. Among various traits, openness is essential as it shows both diverse aspects of sensitive nature or intuitive nature. The individuals having a sensing nature tends to be more practical and prefer to focus on concrete information whereas the users having intuitive trait type is characterized by a focus on abstract ideas, creative thinking and future-oriented perspectives. In this research work, we aim to explore diverse natural language processing (NLP) based features and apply state of the art deep learning algorithms for openness trait prediction. Using standard Myers-Briggs Type Indicator (MBTI) dataset, we propose the use of the latest deep features of sentence embeddings which captures contextual semantics of the content to be used with deep learning models. For comparison, we explore textual features of Frequency-Inverse Document (TF-IDF) and parts of speech (POS) tagging with machine learning models and deep features of word2vec and global vectors for word representation (GloVe) with deep learning models. The comprehensive empirical analysis reveals that TF-IDF used with gradient boosting achieves high accuracy of 90% whereas, the deep feature of sentence embeddings when used and with deep model bidirectional long short-term memory (Bi-LSTM) achieves 90.5% accuracy. The best results have been achieved using the latest Transformer-based DistilBERT, which achieves the highest accuracy of 92% outperforming the existing studies in relevant literature. Subjects Human-computer Interaction, Artificial Intelligence, Data Mining and Machine Learning, Natural Language and Speech © 2025 xxxx

关键词： Deep learning

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