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Optimal Logistics Activities Based Deep Learning Enabled Traffic Flow Prediction Model

computers, Materials & Continua 2022年第12期73卷 5269-5282页

作者： Basim Aljabhan Mahmoud Ragab Sultanah M.Alshammari Abdullah S.Al-Malaise Al-Ghamdi Ports and Maritime Transportation Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia S 7Information Systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia

Traffic flow prediction becomes an essential process for intelligent transportation systems(ITS).Though traffic sensor devices are manually controllable,traffic flow data with distinct length,uneven sampling,and missing data finds challenging for effective *** traffic data has been considerably increased in recent times which cannot be handled by traditional mathematical *** recent developments of statistic and deep learning(DL)models pave a way for the effectual design of traffic flow prediction(TFP)*** this view,this study designs optimal attentionbased deep learning with statistical analysis for TFP(OADLSA-TFP)*** presentedOADLSA-TFP model intends to effectually forecast the level of traffic in the *** attain this,the OADLSA-TFP model employs attention-based bidirectional long short-term memory(ABLSTM)model for predicting traffic *** order to enhance the performance of the ABLSTM model,the hyperparameter optimization process is performed using artificial fish swarm algorithm(AFSA).A wide-ranging experimental analysis is carried out on benchmark dataset and the obtained values reported the enhancements of the OADLSA-TFP model over the recent approaches mean square error(MSE),root mean square error(RMSE),and mean absolute percentage error(MAPE)of 120.342%,10.970%,and 8.146%respectively.

关键词： Traffic flow prediction deep learning artificial fish swarm algorithm mass gatherings statistical analysis logistics

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Weather Forecasting Prediction Using Ensemble Machine Learning for Big Data Applications

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computers, Materials & Continua 2022年第11期73卷 3367-3382页

作者： Hadil Shaiba Radwa Marzouk Mohamed K Nour Noha Negm Anwer Mustafa Hilal Abdullah Mohamed Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

The agricultural sector’s day-to-day operations,such as irrigation and sowing,are impacted by the ***,weather constitutes a key role in all regular human *** forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from ***,wind speed,humidity,wind direction,cloud,temperature,and other weather forecasting variables are used in this work for weather *** research works have been conducted on weather *** drawbacks of existing approaches are that they are less effective,inaccurate,and *** overcome these issues,this paper proposes an enhanced and reliable weather forecasting *** well as developing weather forecasting in remote *** data analysis and machine learning techniques,such as Gradient Boosting Decision Tree,Random Forest,Naive Bayes Bernoulli,and KNN Algorithm are deployed to anticipate weather conditions.A comparative analysis of result outcome said in determining the number of ensemble methods that may be utilized to improve the accuracy of prediction in weather *** aim of this study is to demonstrate its ability to predict weather forecasts as soon as *** evaluation shows our ensemble technique achieves 95%prediction ***,for 1000 nodes it is less than 10 s for prediction,and for 5000 nodes it takes less than 40 s for prediction.

关键词： Weather forecasting KNN random forest gradient boosting decision tree naive bayes bernoulli

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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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IAMS-Net: An Illumination-Adaptive Multi-Scale Lesion Segmentation Network

IAMS-Net: An Illumination-Adaptive Multi-Scale Lesion Segmen...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Zheng, Yisen Huang, Guoheng Zhang, Feng Cheng, Lianglun Yuan, Xiaochen Zhong, Guo Luo, Shenghong School of Computer Science and Technology Guangdong University of Technology Guangzhou China The First Affiliated Hospital of Sun Yat-Sen University Department of Otorhinolaryngology Guangzhou China Macao Polytechnic University Faculty of Applied Sciences China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China University of Macau Faculty of Science and Technology China

ISBN: (纸本)9781665410205

In recent years, many Lesion segmentation (LS) models based on UNet have been proposed. However, existing researches rarely consider the influence of illumination change leads to the weak boundary area. Such as melanomas and polyps, the demarcation of the boundary between the diseased area and the surrounding tissue remains particularly challenging. To overcome these challenges, we propose an IlluminationAdaptive Multi-scale Lesion Segmentation Network (IAMS-Net). In IAMS-Net, we integrate Illumination-Adaptive MultiStream Attention (IAMA) and Contour Perception Module (CPM). In the decoding stage, the IAMA is used as a bridge between the encoder and the decoder to solve the adverse effects of illumination changes on the segmentation of weak boundary lesions. In order to further enhance the boundary features lost due to illumination change in the low-contrast lesion area, we introduce the CPM to improve the perception of the integrity of the lesion area. Subsequently, we performed comparison and ablation experiments using the publicly available ISIC2018 dataset and the individually collected data set BoreIllumination(BI). © 2024 IEEE.

关键词： Signal encoding

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GA-Net: Gated Attention Mechanism Based Global Refinement Network for Image Inpainting 29

GA-Net: Gated Attention Mechanism Based Global Refinement Ne...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Qiu, Ran Zhao, Shengrong Liang, Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Underwater images are often affected by problems such as light attenuation, color distortion, noise and scattering, resulting in image defects. A novel image inpainting method is proposed to intelligently predict and fill damaged areas for complete and continuous visualization of the image. First, in order to effectively solve the problem of color distortion caused by light refraction in underwater environments, the improved gated attention mechanism is used. This mechanism improves the local details by learning and weighting the important features of the image. Second, gated convolution automatically determines the degree of restoration for each pixel based on local features of the original image. It eliminates distractions such as low contrast and scattering, retaining more original detailed information. By doing so, image inpainting techniques improve the quality and visualization of underwater images. © 2023 IEEE.

关键词： Gated convolution Image inpainting Underwater images

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd International Conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference 29

Multilingual Knowledge Graph Completion via Multimodal Decou...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Liu, Xiaojing Jiang, Xuesong Yi, Fengge Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

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Evaluating and Addressing Interoperability Constraints in Health information systems for Enhanced Healthcare Delivery in Zimbabwe

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Journal of computer and Communications 2024年第7期12卷 133-154页

作者： Prosper Tafadzwa Denhere Nelson Matanana Munyaradzi Zhou Caleb Manjeese Talent Mawere Department of Marketing and Information Sciences Midlands State University Gweru Zimbabwe Department of Computer Science and Information Systems Manicaland State University of Applied Sciences Mutare Zimbabwe Department of Mathematics and Computer Science Great Zimbabwe University Masvingo Zimbabwe

Interoperability constraints in health information systems pose significant challenges to the seamless exchange and utilization of health data, hindering effective healthcare delivery. This paper aims to evaluate and address these constraints to enhance healthcare delivery. The study examines the current state of interoperability in health information systems, identifies the key constraints, and explores their impact on healthcare outcomes. Various approaches and strategies for addressing interoperability constraints are discussed, including the adoption of standardized data formats, implementation of interoperability frameworks, and establishment of robust data governance mechanisms. Furthermore, the study highlights the importance of stakeholder collaboration, policy development, and technical advancements in achieving enhanced interoperability. The findings emphasize the need for a comprehensive evaluation of interoperability constraints and the implementation of targeted interventions to promote seamless data exchange, improve care coordination, and enhance patient outcomes in healthcare settings.

关键词： Health information System Interoperability Interoperability Constraints

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Towards Data-Driven Network Intrusion Detection systems: Features Dimensionality Reduction and Machine Learning

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第14期16卷 123-135页

作者： Maabreh, Majdi Obeidat, Ibrahim Elsoud, Esraa Abu Alnajjai, Asma Alzyoud, Rahaf Darwish, Omar Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University P.O. Box 330127 Zarqa13133 Jordan Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Information Security and Applied Computing Department Eastern Michigan University MI United States

Cyber attacks have increased in tandem with the exponential expansion of computer networks and network applications throughout the world. Fortunately, various machine/deep learning models have demonstrated excellent accuracy in predicting network attacks in the literature;nonetheless, having simple and understandable models might be a big benefit in network monitoring systems. In this study, we evaluate four feature selection algorithms to find the minimal set of predictive features of network attacks, seven classical machine learning algorithms, and the deep learning algorithm on one million random instances of the CSE-CIC-IDS2018 big data set for network intrusions. The feature selection algorithms highlighted the importance of features related to forwarding direction (FWD) and two flow measures (FLOW) in predicting the binary traffic type;benign or attack. Furthermore, the results revealed that not all features are required to build efficient ML/DL in detecting network attacks, four features unanimously selected by the feature selection algorithms were enough to build comparable ML models to those trained on all features. This might lead to models that are more suitable for deployment in terms of complexity, explainability, and scalability. Moreover, by selecting four unanimity features instead of all traffic features, the training time may be decreased by 10% to 50%. © 2022. All Rights Reserved.

关键词： Network security

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Heart Disease Prediction Using Machine Learning 2

Heart Disease Prediction Using Machine Learning

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ghazal, Taher M. Ibrahim, Amer Akram, Ali Sheraz Qaisar, Zahid Hussain Munir, Sundus Islam, Shanza Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia Skyline University College University City School of Information Technology Sharjah1797 United Arab Emirates American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan NFC Institute of Engineering and Technology Department of Computer Science Multan Pakistan Lahore Garrison University Lahore Department of Computer Science Lahore Pakistan Lahore Garrion University Lahore Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350335644

The heart disease cases are rising day by day and it is very Important to predict such diseases before it causes more harm to human lives. The diagnosis of heart disease is such a complex task i.e., it should be performed very carefully. The work done in this research paper mainly focuses on which patients has more chance to suffer from this based on their various medical feature such as chest pain etc. We proposed a system of heart disease prediction that is used to diagnose whether the patient is a victim or not by using the previous medical features of the patient. Support vector machine and k-nearest neighbor algorithms of machine learning are used to predict and classify the patient with heart disease. The models gave satisfactory results and were capable for predicting a heart disease by using k-nearest neighbor and support vector machine which gave a good accuracy in contrast to the algorithms that were used in the previous research such as naive bayes etc. © 2023 IEEE.

关键词： Support vector machines

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