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International Conference on Medical Imaging and computer-Aided Diagnosis, MICAD 2022

作者： Sqalli, Mohammed Tahri Al-Thani, Dena Elshazly, Mohamed B. Al-Hijji, Mohammed Alahmadi, Alaa Houssaini, Yahya Sqalli Division of Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates Department of Economics School of Foreign Services Georgetown University in Qatar Doha Qatar Information and Computing Technology Division College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Department of Cardiology and Electrophysiology Medical University of South Carolina North CharlestonSC United States Interventional & Structural Cardiology Division Heart Hospital at Hamad Medical Corporation Doha Qatar College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Department of Medicine Mohammed V University Faculy of Medecine and Pharmacy Rabat Morocco

ISBN: (纸本)9789811667749

The electrocardiogram, known as the ECG or EKG, is considered among the mostly used medical diagnostic tests worldwide. Despite the test’s prevalence in the healthcare sector, there still exist gaps into training medical practitioners become skilled and efficient ECG interpreters. Moreover, this also brings the challenge of assessing the expertise of those practitioners. This is primarily due to the difficulty of assessing visual expertise. Visual expertise is the skill of interpreting images relating to a certain technical field. Due to the limited quantitative research methodologies that could not capture this subtle skill during the previous two decades, a limited number of models are being conceptualized and assessed. In addition, automated ECG interpretation models based on artificial intelligence are still not accurate enough to be fully deployed in the medical field. This therefore leaves only one choice, which is to focus on improving on methodologies to train and assess medical practitioners’ visual expertise. This approach will contribute towards increasing the accuracy of ECG interpretations within medical institutions by forming competent medical staff. In this paper, we present a road map for the development of an eye-tracking based machine learning model that leads towards the prediction of visual expertise within medical practitioners. To develop the model, we built on top of our previously conducted research that aimed at understanding the differences in visual patterns within medical practitioners with different expertise levels. The developed model could predict the expertise level of the ECG interpreter with an accuracy of 94.08%. This is thanks to the eye movement patterns of the participant. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electrocardiograms

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Cloud-Enabled Predictive Vehicle Health Monitoring and Maintenance with LSTM Networks for Time-Series Analysis

Cloud-Enabled Predictive Vehicle Health Monitoring and Maint...

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Innovations in Communications, Electrical and computer engineering (ICICEC), International Conference on

作者： Viyyapu Lokeshwari Vinya K. K. Manivannan S. K. Manivannan C. Sathiyamoorthy M. Jagadeeswaran B. Meenakshi Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Physics School of Advanced Sciences and Languages VIT Bhopal University Bhopal Madhya Pradesh India Department of Information Technology J.J College of Engineering and Technology Tiruchirappalli Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Maximizing operational efficiency and customer satisfaction in the automobile sector depends on maintaining vehicle health and avoiding maintenance downtime. Vehicle Internet of Things (IoT) devices capture massive volumes of sensor data, enabling predictive maintenance. This research offers cloud-based predictive vehicle health monitoring and maintenance systems using Long Short-Term Memory (LSTM) networks for time-series analysis. System architecture uses IoT sensors in automobiles to gather real-time data on engine temperature, oil pressure, and vibration. This data is stored, analyzed, and predicted on clouds. To assess time-series sensor data and forecast failures or maintenance requirements, LSTM networks, a form of recurrent neural network, are used. The proposed system uses data preprocessing to manage missing values and outliers, feature extraction to find significant sensor data patterns, and LSTM-based predictive modeling to predict vehicle health conditions. Technology may predict future faults by continually monitoring and evaluating the vehicle's state, allowing proactive maintenance to avoid expensive breakdowns and downtime. The cloud-based architecture is scalable and flexible, providing smooth connection with fleet management systems and remote vehicle health monitoring across scattered sites. The suggested method optimizes maintenance schedules and resource allocation using predictive insights from real-time sensor data to improve operational efficiency and save costs. It may improve predictive vehicle health monitoring and maintenance in the automobile sector, improving dependability, safety, and cost.

关键词： Cloud computing Costs Time series analysis Real-time systems Maintenance Safety Automobiles Internet of Things Monitoring Long short term memory

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AirLine: Efficient Learnable Line Detection with Local Edge Voting

AirLine: Efficient Learnable Line Detection with Local Edge ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Xiao Lin Chen Wang College of Computing Georgia Institute of Technology Atlanta GA USA Department of Computer Science and Engineering Spatial AI & Robotics Lab State University of New York at Buffalo NY USA

Line detection is widely used in many robotic tasks such as scene recognition, 3D reconstruction, and simultaneous localization and mapping (SLAM). Compared to points, lines can provide both low-level and high-level geometrical information for downstream tasks. In this paper, we propose a novel learnable edge-based line detection algorithm, AirLine, which can be applied to various tasks. In contrast to existing learnable endpoint-based methods, which are sensitive to the geometrical condition of environments, AirLine can extract line segments directly from edges, resulting in a better generalization ability for unseen environments. To balance efficiency and accuracy, we introduce a region-grow algorithm and a local edge voting scheme for line parameterization. To the best of our knowledge, AirLine is one of the first learnable edge-based line detection methods. Our extensive experiments have shown that it retains state-of-the-art-Ievel precision, yet with a $3-80\times$ runtime acceleration compared to other learning-based methods, which is critical for low-power robots.

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Coronavirus: A “Mild” Virus Turned Deadly Infection

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computers, Materials & Continua 2021年第5期67卷 2631-2646页

作者： Rizwan Ali Naqvi Muhammad Faheem Mushtaq Natash Ali Mian Muhammad Adnan Khan Atta-ur-Rahman Muhammad Ali Yousaf Muhammad Umair Rizwan Majeed Department of Unmanned Vehicle Engineering Sejong UniversitySeoul05006Korea Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan School of Computer and Information Technology Beaconhouse National UniversityTarogilLahore53700Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Faculty of Information Technology University of Central PunjabLahore54782Pakistan Directorate of IT The Islamia University of BahawalpurBahawalpur63100Pakistan

Coronaviruses are a family of viruses that can be transmitted from one person to *** strains have only been mild viruses,but the current form,known as coronavirus disease 2019(COVID-19),has become a deadly *** outbreak originated in Wuhan,China,and has since spread *** symptoms of COVID-19 include a dry cough,sore throat,fever,and nasal *** drugs,pathogen–host interaction,and 2 weeks of isolation have been recommended for the treatment of the *** operating procedures,such as the use of face masks,hand sanitizer,handwashing with soap,and social distancing,are also ***,travel bans for cities,states,and countries have been put in place,along with lockdowns to control the *** restrictions,mask use,sanitizer or soap use,and avoidance of touching the face and nose have produced encouraging results,whereas the effectiveness of antibiotics has not been *** results of isolation for the recovery of infected people have also been *** bans and lockdowns have caused a slump in economies,and unemployment has risen sharply,resulting in an increase in mental health cases *** date,vaccines have been developed and are in use in certain countries,but following standard operating procedures remain *** countries following the guidelines can eradicate this *** Zealand was the rst country to eliminate the virus from their territory.

关键词： Coronavirus severe acute respiratory syndrome middle east respiratory syndrome world health organization

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Robustness modularity in complex networks

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Physical Review E 2022年第5期105卷 054308-054308页

作者： Filipi N. Silva Aiiad Albeshri Vijey Thayananthan Wadee Alhalabi Santo Fortunato Indiana University Network Science Institute (IUNI) Bloomington Indiana 47408 USA Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Kingdom of Saudi Arabia Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana 47408 USA

A basic question in network community detection is how modular a given network is. This is usually addressed by evaluating the quality of partitions detected in the network. The Girvan-Newman (GN) modularity function is the standard way to make this assessment, but it has a number of drawbacks. Most importantly, it is not clearly interpretable, given that the measure can take relatively large values on partitions of random networks without communities. Here we propose a measure based on the concept of robustness: modularity is the probability to find trivial partitions when the structure of the network is randomly perturbed. This concept can be implemented for any clustering algorithm capable of telling when a group structure is absent. Tests on artificial and real graphs reveal that robustness modularity can be used to assess and compare the strength of the community structure of different networks. We also introduce two other quality functions: modularity difference, a suitably normalized version of the GN modularity, and information modularity, a measure of distance based on information compression. Both measures are strongly correlated with robustness modularity, but have lower time complexity, so they could be used on networks whose size makes the calculation of robustness modularity too costly.

关键词： Community structure Complex systems Robustness

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When Metaverses Meet Vehicle Road Cooperation: Multi-Agent DRL-Based Stackelberg Game for Vehicular Twins Migration

arXiv

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arXiv 2023年

作者： Kang, Jiawen Zhang, Junhong Yang, Helin Ye, Dongdong Hossain, M. Shamim The Guangdong University of Technology China The School of Informatics Xiamen University China The Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia

Vehicular Metaverses represent emerging paradigms arising from the convergence of vehicle road cooperation, Metaverse, and augmented intelligence of things. Users engaging with Vehicular Metaverses (VMUs) gain entry by consistently updating their Vehicular Twins (VTs), which are deployed on RoadSide Units (RSUs) in proximity. The constrained RSU coverage and the consistently moving vehicles necessitate the continuous migration of VTs between RSUs through vehicle road cooperation, ensuring uninterrupted immersion services for VMUs. Nevertheless, the VT migration process faces challenges in obtaining adequate bandwidth resources from RSUs for timely migration, posing a resource trading problem among RSUs. In this paper, we tackle this challenge by formulating a game-theoretic incentive mechanism with multi-leader multi-follower, incorporating insights from social-awareness and queueing theory to optimize VT migration. To validate the existence and uniqueness of the Stackelberg Equilibrium, we apply the backward induction method. Theoretical solutions for this equilibrium are then obtained through the Alternating Direction Method of Multipliers (ADMM) algorithm. Moreover, owing to incomplete information caused by the requirements for privacy protection, we proposed a multi-agent deep reinforcement learning algorithm named MALPPO. MALPPO facilitates learning the Stackelberg Equilibrium without requiring private information from others, relying solely on past experiences. Comprehensive experimental results demonstrate that our MALPPO-based incentive mechanism outperforms baseline approaches significantly, showcasing rapid convergence and achieving the highest reward. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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The smart Cross-Layer Design for Congestion Control in Wireless Networks

The smart Cross-Layer Design for Congestion Control in Wirel...

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International Conference on computing and Networking Technology (ICCNT)

作者： Rakesh Kumar Giri S.K. Manju Bargavi Kriti Kusum Abhinav Mishra Dharmesh Dhabliya B. Jaison Department of Computer Science & Engineering Sunrise University Alwar Department of Computing Sciences And Information Technology Jain (deemed to be) University Bangalore Karnataka India Department of Communication Design Symbiosis International University Pune Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Information Technology Vishwakarma Institute of Technology Pune Maharashtra India Department of Computer Science and Engineering R.M.K.Engineering College Kavarapettai

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The clever pass-layer layout for congestion management in wireless networks is a complete approach that leverages the interaction between community layers to develop efficient, dependable, and cozy Wi-Fi networks. This design combines the transport and MAC (media access control) layer protocols, which include TCP (transmission manage protocol) and IEEE (Institute of Electrical and Electronics Engineers) 802.11, respectively, so that it will manage the flow of records and decrease congestion. It considers both packet-stage information, including packet arrival, inter-packet arrival, and packet size, as well as bodily layer parameters, including channel country information, multi-user variety, and interference. Also, it addresses the ISPs’ necessity of changing transmission charges and granting the proper stability among throughput and honest distribution of assets to defend different programs. This layout should respond to modifications within the network quickly and efficiently to obtain the favored quality of service (satisfactory service). Additionally, the cross-layer layout needs to be flexible and sufficient to be extended to modern-day and future communique networks while configurable to specific varieties of networks for better deployment.

关键词： Cross layer design Wireless sensor networks Protocols Sensitivity Wireless networks Layout Media Access Control Quality of service Throughput Wireless fidelity

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An Improved Cuttlefish Optimization Algorithm Based Faster R-CNN for Brain Tumour Detection

An Improved Cuttlefish Optimization Algorithm Based Faster R...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： AR Arunarani Vedant Bhenia T Abirami N Aparna Md. Abul Ala Walid Sharada K A Department of Computational and Intelligence School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Campus Chennai Tamilnadu India Department of Computer Science and Engineering IPS Academy Institute of Engineering and Science Indore Madhya Pradesh India Information Technology Kongu Engineering College Erode Tamilnadu India Department of Biomedical Engineering Dhaanish Ahmed Institute of Technology Coimbatore Tamilnadu India Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Bangladesh Department of CSE HKBK College of Engineering VTU Bengaluru Karnataka India

Brain tumor is caused due to the anomalous development of tissues in the brain. Precise identification of the tumor in premature stage is essential to provide effective medication and curtail further impact of the disease in affected individuals. This present research includes four main stages for the diagnosis of brain tumor including segmentation and classification based on deep learning approaches. The preprocessing stage makes use of filters to reduce the noise impact in the brain imagery. The segmentation stage is responsible to segment the tumor portions from the images using Enhanced Region Growth algorithm. Feature selection stage utilizes Improved Cuttlefish Optimization (ICO) algorithm to extricate the optimal features from the segmented images. The final stage is classification in which Faster R-CNN (FRCNN) algorithm is employed to categorize the brain images as tumor affected or not. BRA TS-2020 dataset is used for the experimental investigation of the proposed FRCNN-ICO method. Performance of this method is assessed using parameters such as Accurate Prediction Rate (APR), Imprecise Prediction Rate (IPR) and Imprecise Denial Rate (IDR). The proposed FRCNN-ICO produces the highest APR value of 99.2% compared to the existing researches on brain tumor detection and proves to perform superior than the methods considered for comparison purposes.

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Efficient Resource Management in IoTs Using Evolutionary and Swarm Intelligence Algorithms

Efficient Resource Management in IoTs Using Evolutionary and...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Keshav Dev Gupta Korhan Cengiz Charu Awasthi Siva Akash Ramini Shyamapriya Chatterjee M Pavithra Department of Computer Science Engineering Poornima College of Engineering jaipur India College of Information Technology University of Fujairah UAE Department of Computer Engineering Istinye University Istanbul Turkey Department of Information Technologies Faculty of Informatics and Management University of Hradec Kralove Kralove Czech Republic Department of information technology JSS academy of Technical Education Noida India Department of School of Computer Science and Engineering (SCOPE) VIT-AP University Amaravati Andhra Pradesh Department of Information Technology Narula Institute of Technology Kolkata West Bengal Department of Computer Science and Engineering Jansons Institute of Technology Karumathampatti Coimbatore

The proliferation of the Internet of Things (IoT) has ushered in a transformative era of connected devices, emphasizing the critical need for effective resource management. This study introduces an innovative approach that harnesses Evolutionary and Swarm Intelligence algorithms for IoT Task Scheduling, addressing resource optimization challenges. The approach offers superior resource utilization through advanced optimization techniques, reducing energy consumption and enhancing efficiency. Furthermore, it significantly reduces task scheduling execution time, enabling prompt decision-making in dynamic IoT environments. This results in improved task allocation quality, meeting application-specific requirements, and prioritizing critical tasks. Notably, the approach excels in minimizing completion time, making it particularly advantageous for real-time IoT applications. This research contributes to the advancement of IoT resource management, offering an adaptive, efficient, and intelligent solution with broad applicability. In the evolving landscape of IoT, the findings presented here lay a solid foundation for future research and practical implementations, ultimately promoting more responsive, sustainable, and intelligent IoT ecosystems.

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Mathematical fusion of efficient transfer learning with fine-tuning model for laryngeal cancer detection and classification in the throat region using biomedical images

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Alexandria engineering Journal 2025年 129卷 658-671页

作者： Amer Mahdi F.Al Khulayf Mohammad Alamgeer Marwa Obayya Mohammed Alqahtani Shouki A. Ebad Nojood O. Aljehane Faisal Mohammed Nafie Department of Mathematics College of Science and Humanities in Al-Kharj Prince Sattam Bin Abdul Aziz University Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Information Systems and Cyber Security College of Computing and Information Technology University of Bisha Bisha 61922 Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Engineering and Technology Unit Applied College Majmaah University Al Majmaah 11952 Saudi Arabia

In recent times, the number of laryngeal cancer patients has increased significantly around the world. Proper care for laryngeal cancer is complex, particularly in the last phase. This cancer category is a complicated tumour in the head and neck regions of the individual. Recently, investigators have established different diagnosis methods and devices to assist medical experts in recognizing laryngeal cancer efficiently. Endoscopic schemes with narrow-band imaging (NBI), which improves the visualization of subepithelial and epithelial microvascular patterns, attain a high specificity or sensitivity in the initial recognition of laryngeal cancer. The computer-aided system uses artificial intelligence (AI) over machine learning (ML) and deep learning (DL), containing a convolutional neural network (CNN), for automatic disease detection and diagnosis. Therefore, this study presents a Fusion of Efficient Transfer Learning Models with Pelican Optimization for Accurate Laryngeal Cancer Detection and Classification (FETLM-POALCDC) method. The main intention of the FETLM-POALCDC method is to facilitate the classification and recognition of laryngeal cancer in throat region images. Initially, the FETLM-POALCDC method employs the Wiener filter (WF) model in the image preprocessing to eliminate noise while preserving critical image details. Furthermore, the fusion of transfer learning comprising three models, EfficientNetV2, NASNetMobile, and ResNet152, is employed to capture multiscale and discriminative features. Integrating convolutional neural networks with bidirectional gated recurrent units (CNN+BiGRU) classifier is utilized to detect laryngeal cancer. To enhance the hyperparameters of the CNN+BiGRU method, the pelican optimization algorithm (POA) is employed to ensure improved performance by fine-tuning essential parameters. A series of empirical studies of the FETLM-POALCDC approach is performed under the Laryngeal dataset. The experimental validation of the FETLM-POAL

关键词： Laryngeal cancer Transfer learning Throat region images Pelican optimization algorithm Weiner filter Deep learning Mathematical models

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