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Multi-hop emergency message dissemination through optimal cooperative forwarder in grid-based 5G-VANETs

Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4461-4476页

作者： Ullah, Sami Abbas, Ghulam Waqas, Muhammad Abbas, Ziaul Haq Halim, Zahid Telecommunications and Networking Research Center GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan Department of Computer Science Shaheed Benazir Bhutto University Sheringal Upper Dir Pakistan Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan School of Engineering Edith Cowan University PerthWA6027 Australia Department of Computer Engineering College of Information Technology University of Bahrain Zallaq32038 Bahrain Faculty of Electrical Engineering GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan

Emergency message (EM) dissemination is a significant process in vehicular ad hoc networks (VANETs) and plays a vital role in road safety. Nonetheless, EMs’ dissemination while avoiding broadcast storms poses a considerable challenge. Moreover, selecting optimal cooperative forwarders is also challenging because of unreliability, high end-to-end delay, and packet loss caused by localization errors. Besides, the conventional broadcast-based schemes usually choose forwarder vehicles based on distance, neglecting the influence of links’ qualities. To that end, we propose multi-hop EM dissemination (MEMD) through optimal cooperative forwarder in grid-based fifth-generation (5G) VANET environment. To ensure reliable EMs’ dissemination, we incorporate vehicle-to-everything communication instead of the conventional short-range vehicle-to-vehicle communication. The proposed scheme obtains vehicle positions by integrating GPS measurements with a map-matching technique to avoid vehicle localization errors. Instead of a single parameter, the proposed scheme selects an optimal cooperative forwarder by using our proposed fitness function, which considers different mobility and social parameters. The proposed scheme categorizes EMs’ dissemination into single-hop and multi-hop broadcasting to avoid unnecessary rebroadcasting and communication congestion in the network. Simulation results demonstrate that for varying density, the proposed scheme achieves an average increase of 12%, 9%, and 8.9% in coverage, and 7.95%, 4%, and 3.4% in PDR, and an average reduction of 13.5%, 7.2%, and 12.7% in E2E delay, and 20.9%, 11.3%, and 4% in rebroadcast ratio, as compared to LCL, REMD, and EMDZoI, respectively. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Vehicular ad hoc networks

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An Ontology-based Three-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation 28th

An Ontology-based Three-Stage Approach to Medical Text Class...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Abdollahi, Mahdi Gao, Xiaoying Mei, Yi Ghosh, Shameek Li, Jinyan Narag, Michael School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Founder & CTO Medius Health Sydney Australia Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

The document classification (DC) task assigns predefined classes to unlabeled documents using trained models. In the medical field, DC is crucial for tasks like categorizing risk factors and classifying electronic health records. This paper addresses challenges in medical document analysis, such as the prevalence of abbreviations and acronyms. Existing classification performance in medical documents is suboptimal. The paper introduces novel feature engineering methods leveraging domain-specific knowledge to enhance classification performance. Results indicate that the Three-Stage approach surpasses related works, showcasing improved medical document classification performance. © 2024 The Authors.

关键词： Particle swarm optimization (PSO)

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Remote Sensing Data-Based Satellite Image Analysis in Water Quality Detection for Public Health Data Modelling

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 532-541页

作者： Balakrishnan, S. Preetam Raj, P Michael Somasekar, J. Kumar, Kambala Vijaya Amutha, S. Sangeetha, A. Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology (AVIT) Vinayaka Mission’s Research Foundation (Deemed to be University) Chennai India Department of Computer Science and Engineering MLR Institute of Technology Telangana Dundigal Hyderabad 500043 India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be-University) Karnataka Bengaluru 562112 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology TamilNadu Tholurpatti India

One of the most important aspects of environmental sustainability is water quality monitoring. The ecology is impacted by poor water quality in addition to aquatic life. With the volume of data being collected from satellite systems, the issue of effectively and automatically extracting water bodies is growing more pressing. One of the most important things to do for managing and safeguarding water resources is to conduct extensive monitoring of water quality parameters, or WQPs. It is still difficult to monitor optically inactive WQPs in inland waters, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP). Water quality (WQ) classification and prediction can be greatly enhanced with the use of artificial intelligence (AI). This research proposes novel method in healthcare detection by water quality analysis based on classification utilising ML model. Input is collected as healthcare dataset based on water quality as well as processed for noise removal and normalisation. The processed data features are extracted and classified utilising reinforcement Gaussian stacked layer network with variational fuzzy Markov basis neural network. In order to suggest best course of action for the water bodies, it is imperative that any study look into the spatiotemporal changes of the dominant water quality parameters (WQPs). WQP concentrations have typically been determined by extensive fieldwork. These findings could help identify and regulate non-point source pollution, offer a large-scale spatial picture of the water quality, and pinpoint vulnerable locations and times of water pollution. Experimental analysis is carried out for various water sample dataset in terms of detection accuracy, mean precision, recall, F-1 score and AUC. The proposed technique obtained 97% of detection accuracy, 95% of recall, 91% of F-1 score, 96% of AUC, and 94% of MAP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Classification Fuzzy Markov basis Healthcare detection Machine learning model Water quality analysis

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Employing Adbdconvolutional KNN, Diagnosis of Irregular Driver Behavior 11

Employing Adbdconvolutional KNN, Diagnosis of Irregular Driv...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Chandramma Manjunath, C.R. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665487320

Actual observation of unusual aggressive driving is essential for enhancing car safety. to enhance riding habits and behaviour in order to keep fatal crashes. The utilization of perception abnormal driving things that tend is becoming more and more common since it is essential to both the current level of driverless vehicles and the health of both passengers and motorists in vehicles. Recent developments in deep learning techniques, such as the impressive extension capacity of modern deep neural networks and the vast quantities of film clips necessary for fully retraining the data-driven models, may substantially help with this challenging problem. To conclude the study, new massive continuing to learn models are provided. These methods are motivated by the recently created and extensively used networked cnn model known as the Excessive Drive Control System in place (ADBD Net). © 2022 IEEE.

关键词： Deep neural networks

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An Efficient Approach Based on Attention ConvMixer Model for Lung Segmentation

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SN computer science 2024年第5期5卷 650页

作者： Oubelkas, Farah Moumoun, Lahcen Jamali, Abdellah Mathematics Computer Science and Engineering Sciences Laboratory (MISI) Faculty of Sciences and Techniques Hassan 1st University Settat Morocco

Precise segmentation of CXR lung images serves as the foundation for an automated system for analysing lung X-rays images. This segmentation process aids radiologists in identifying lung regions, subtle disease indicators, and enhancing the diagnostic procedure for patients. Nevertheless, achieving accurate semantic lung segmentation is considered a complex task because of factors like the existence of the rib cage border, diverse lung shapes, and affected by various maladies. In this study, we tackle the challenge of lung segmentation in both healthy and diseased CXR images. We proposed an approach combining ConvMixer model, a mask attention mask and a pre-processing step for enhanced performance. The ConvMixer module is introduced to capture complex patterns, dependencies, and spatial information within the feature maps, potentially leading to improved performance. Additionally, a mask attention module is implemented to improve the model’s ability to capture contextual information across various scales. To improve our segmentation results, we propose a pre-processing step to enhance the features appearance in the image. Through comprehensive experiments executed on the Shenzhen, Montgomery and JSRT datasets, our model achieves 98% PA, 98% DSC, 96% JA for the JRST and Montgomery datasets and 92% JA for the Shenzhen dataset surpassing the capabilities of current methods for lung region segmentation as well as handling challenging cases involving anomalous conditions such as the presence of tuberculosis and nodules in the lungs. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Chest X-rays ConvMixer Lung segmentation U-Net

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An Intelligent Data Analytics Approach for Crisis Management in Healthcare Systems

An Intelligent Data Analytics Approach for Crisis Management...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Madian, Fatma A. Ebrahim, Gamal A. Salah, Mostafa M. Abdelaal, Marwa A. Ain Shams University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Future University in Egypt Electrical Engineering Department Cairo Egypt Faculty of Computers and Information Sadat Academy for Management Sciences Computer Science Department Cairo Egypt

ISBN: (纸本)9798350367560

Crisis management is preparing for and managing possible crises that may impact organizations and individuals at different levels. It involves effective communication, quick decision-making, and strategic planning to minimize the negative impact of a crisis and ensure swift recovery. It plays a vital role in healthcare systems, especially in virus outbreaks. Its role is to monitor and manage the spread of viruses. Moreover, it can use Machine Learning (ML) techniques to achieve this goal. This paper discusses how to apply ML approaches to daily reports of infection cases. The adopted ML approaches are Auto Regressive Integrated Moving Average (ARIMA) model and the Long Short-Term Memory (LSTM) model. Furthermore, it uses Root Mean Square Error (RMSE) as a performance measure to evaluate the applied models. Simulation results show that ARIMA model performs better as compared to other models, which can provide a prediction accuracy of more than 99%. © 2024 IEEE.

关键词： Electronic health record

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MQTT based monitoring and control system for remote stations in automotive industry

MQTT based monitoring and control system for remote stations...

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2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Ivanovic, Denis Job, Josip Puntaric, Marko Herceg, Marijan TTTech-Auto CX Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Department of Software Engineering Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Department of Communications Osijek Croatia

ISBN: (纸本)9798350347722

In this paper is presented implementation of a lightweight supervisory control and data acquisition system for remote test stations as a part of a larger test house in the automotive industry. It is necessary to allow remote set up and monitoring of the test stations, which means defining the structure of a system with numerous test stations as endpoints. The test stations are meant to be virtual machines but can be both virtual and physical machines. A communication protocol must be used that enables this within the organization's network, regardless of actual distance and location. The parameters of the test stations to be monitored can be defined according to the needs and purpose. Even when creating your own tool, this function is not unique, i.e. on one test station parameters of operating system resources such as memory usage can be monitored, while on another test station logged-in users or running programs, reported system errors and others must be monitored. Data storage is one of the mandatory functions of this system. This process allows viewing data throughout history and more detailed analysis of events. There are numerous benefits that can be drawn and used from the data stored in the database. It is necessary to pay attention to the data stored as well as the scope and retention throughout history. The correct and secure setup of a database is one of the prerequisites for any serious system that processes data. Appropriate views should be created for users involved in the data monitoring process using all the elements mentioned so far. Creating a visually acceptable and easy-To-use interface that serves the main purpose is the final presentation of the entire system to users. A complete system leads to higher productivity, profitability and better organization of the company. It also allows project managers to better plan the use and occupancy of test stations. At any time, they have insight into the status of individual test stations, their activitie

关键词： Automotive industry

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Improved Layered Architecture: Integration of IoT with Blockchain for Increased Security and Scalability 5

Improved Layered Architecture: Integration of IoT with Block...

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5th International Conference on Artificial Intelligence and Data sciences, AiDAS 2024

作者： Jahangir, Muhammad Lee, Ming Jie Kwan, Ban-Hoe Kamal, Tariq Chai, Tong-Yuen Loh, Wing Son Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Kajang Malaysia Dhanani School of Science & Engineering Electrical and Computer Engineering Karachi Pakistan

ISBN: (纸本)9798331528553

Most of the existing Internet of Things (IoT) applications are built upon the conventional three-layered architecture that possesses several issues in terms of security, privacy and scalability. In this paper, we propose a five-layer architecture to mitigate the issues faced by the three-layered IoT applications. In particular, two layers: (i) data-link layer and (ii) IoT-Blockchaln layer were introduced. The data-link layer authenticates the legitimate device during the data transmission;while the IoT-Blockchain utilizes the distributed storage to prevent the data from being tampered. To testify the performance of the proposed method in terms of publishing time, experiments were conducted based on two setups: (i) single node setup and (ii) dual node setup. The results suggested the proposed method is highly scalable where the single node setup required an average of 9.4 seconds and dual node setup required 8.66 seconds. Future research will explore various network configurations and the integration of the Internet of Things Application (IOTA), a feeless and lightweight blockchain, to further optimize scalability and efficiency. © 2024 IEEE.

关键词： Differential privacy

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Analyzing User Satisfaction with Mobile Internet Service Providers through Technical Performance and User Experience Evaluation

Analyzing User Satisfaction with Mobile Internet Service Pro...

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2024 International Research Conference on Smart Computing and Systems engineering, SCSE 2024

作者： Pothuwilage, Prasanga Hewa Dissanayaka, Pansujee Liyanage, Uditha Prabhath University of Moratuwa Faculty of Engineering Department of Mathematics Moratuwa Sri Lanka University of Kelaniya Faculty of Science Department of Statistics and Computer Science Kelaniya Sri Lanka

ISBN: (纸本)9798350375688

In today's digital era, the Internet holds a fundamental position in daily life, particularly in Sri Lanka. This study addresses two primary objectives: first, to quantify the technical performance of internet services, and second, to assess customer satisfaction and its alignment with service provider performance. Conducted in the Narthanagala Grama Niladhari Division, specifically the 616C division in Horana, the study utilizes a structured questionnaire to gather customer experience data. Technical data are collected using the "Open Signal"and "Network Cell Info"mobile applications. Statistical techniques are employed to achieve the objectives. Principal Component Analysis reduces the initial 9 dimensions of internet service quality to a singular dimension, with the primary principal component explaining 84.40% of variance. This reduction is validated through the composite index of "Application Based Technical Performance of Mobile Data Service (AppBased_TecPerf)". Factor Analysis and simple linear regression unveil two influential latent factors: Customer-Perceived Technical Performance of Mobile Data Service (Customer_TecPerf) and Customer Service Satisfaction (Customer_Service), collectively accounting for 60.66% of variability across six dimensions. These dimensions include Video Quality Experience, Gaming Experience, Voice & Video Quality Experience on Social Media Apps, Client Rating for Internet Speed at Residence, Client Rating for Satisfaction with Price Fairness, and Client Rating for Customer Service Quality. Reliability analysis confirms the effectiveness of these factors in measuring the Application Based Technical Performance of Mobile Data Service. Regression analysis confirms that the index, AppBased_TecPerf, effectively captures Customer_TecPerf. © 2024 IEEE.

关键词： Factor analysis

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OntoMG: a unique and ontological-based intelligent framework for early identification of myasthenia gravis (MG)

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3847-3853页

作者： Mahajan, Prerna Agarwal, Trapty Vekariya, Daxa Gupta, Rajesh Malviya, Ashwini Anandaraj, S.P. Jain, Garima Anand, D. Department of Computer Science and Information Technology Jain (Deemed to Be University) Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Lucknow India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Management Sanskriti University UP Mathura India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science Engineering Presidency University Bengaluru India Department of Computer Science and Business Systems (CBS) Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Myasthenia Gravis (MG) is a neuromuscular disease causing extreme muscular fatigue, triggering problems with vision, swallowing, speech, mobility, dexterity, and breathing. However, early detection and prediction of MG is a crucial and quite challenging task for medical practitioners. So, in our proposed work, we are focused on introducing an ontology-based intelligent system for the prognosis of patients affected by this neuromuscular disease. The given paper presents a novel framework that employs the concepts of ontology, semantic web rules, and reasoner for deriving whether the patient is tested positive or negative. The suggested framework's effectiveness is assessed by comparing it to machine learning classifiers using parameters from the NIH repository for Myasthenia Gravis. The experimental results show that the ontological-based framework is able to achieve higher accuracy (80.85%), higher precision (78.37%), and higher recall (76.55%) than the existing classifiers used in the recent studies. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Machine learning (ML) Myasthenia gravis disease Ontology Reasoners Semantic web rules language (SWRL)

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