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arXiv 2024年

作者： Cuong, Dinh Viet Lalić, Branislava Petrić, Mina Nguyen, Thanh Binh Roantree, Mark School of Computing Dublin City University Dublin Ireland Faculty of Agriculture University of Novi Sad Serbia Avia-GIS NV Zoersel Belgium University of Science Ho Chi Minh City Viet Nam Insight Centre for Data Analytics Dublin City University Dublin Ireland

Physics informed neural networks have been gaining popularity due to their unique ability to incorporate physics laws into data-driven models, ensuring that the predictions are not only consistent with empirical data but also align with domain-specific knowledge in the form of physics equations. The integration of physics principles enables the method to require less data while maintaining the robustness of deep learning in modelling complex dynamical systems. However, current PINN frameworks are not sufficiently mature for real-world ODE systems, especially those with extreme multi-scale behavior such as mosquito population dynamical modelling. In this research, we propose a PINN framework with several improvements for forward and inverse problems for ODE systems with a case study application in modelling the dynamics of mosquito populations. The framework tackles the gradient imbalance and stiff problems posed by mosquito ordinary differential equations. The method offers a simple but effective way to resolve the time causality issue in PINNs by gradually expanding the training time domain until it covers entire domain of interest. As part of a robust evaluation, we conduct experiments using simulated data to evaluate the effectiveness of the approach. Preliminary results indicate that physics-informed machine learning holds significant potential for advancing the study of ecological systems. © 2024, CC BY.

关键词： Population dynamics

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IoT data analytic algorithms on edge-cloud infrastructure:A review

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Digital Communications and Networks 2023年第6期9卷 1486-1515页

作者： Abel E.Edje M.S.Abd Latiff Weng Howe Chan School of Computing Faculty of EngineeringUniversiti Teknologi Malaysia81310SkudaiJohorMalaysia Department of Computer Science Delta State UniversityAbrakaP.M.B 01 AbrakaDelta StateNigeria UTM Big Data Centre Ibnu Sina Institute of Scientific and Industrial ResearchUniversiti Teknologi Malaysia81310SkudaiJohorMalaysia

The adoption of Internet of Things(IoT)sensing devices is growing rapidly due to their ability to provide realtime ***,it is constrained by limited data storage and processing *** offloads its massive data stream to edge devices and the cloud for adequate storage and *** further leads to the challenges of data outliers,data redundancies,and cloud resource load balancing that would affect the execution and outcome of data *** paper presents a review of existing analytics algorithms deployed on IoT-enabled edge cloud infrastructure that resolved the challenges of data outliers,data redundancies,and cloud resource load *** review highlights the problems solved,the results,the weaknesses of the existing algorithms,and the physical and virtual cloud storage servers for resource load *** addition,it discusses the adoption of network protocols that govern the interaction between the three-layer architecture of IoT sensing devices enabled edge cloud and its prevailing challenges.A total of 72 algorithms covering the categories of classification,regression,clustering,deep learning,and optimization have been *** classification approach has been widely adopted to solve the problem of redundant data,while clustering and optimization approaches are more used for outlier detection and cloud resource allocation.

关键词： Internet of things Cloud platform Edge Analytic algorithms Processes Network communication protocols

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Dendritic Neural Regression Model Trained by Chicken Swarm Optimization Algorithm for Bank Customer Churn Prediction 30th

Dendritic Neural Regression Model Trained by Chicken Swarm O...

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30th International Conference on Neural Information Processing, ICONIP 2023

作者： Wang, Qi Zhang, Haiyan Ji, Junkai Tang, Cheng Tang, Yajiao College of Economics Central South University of Forestry and Technology Changsha410004 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka819-0395 Japan

ISBN: (纸本)9789819981830

Recently, banks are constantly facing the problem of customers churning. Customer churn not only leads to a decline in bank funds and profits but also reduces its credit capacity and affects the bank’s operational management. As an important component of Customer Relationship Management, predicting customer churn has been increasingly urgent. Inspired by biological neurons, we build up a dendritic neural regression model (DNRM) with four layers, namely the synaptic layer, the dendritic layer, the membrane layer, and the soma layer for bank customer churn prediction. To pursue better prediction performance in this experiment, the Chicken Swarm Optimization (CSO) algorithm is defined as the training algorithm of DNRM. With the ability to balance exploration and exploitation, CSO is implemented to optimize and improve the accuracy of the DNRM. In this paper, we propose a novel dendritic neural regression model called CSO-DNRM for churn prediction, and the experimental results are based on a benchmark dataset from Kaggle. Compared with other algorithms and models, our proposed model obtains the highest accuracy of 92.27% and convergence speed in customer churn prediction. Due to the novel bionic algorithms and the pruning function of the model, it is evident that our proposed model has advantages in accuracy and computational speed in the field of customer churn prediction and can be widely applied in commercial bank customer relationship management. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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XoT (Xducation of Things): Harnessing AI and edge computing to educate all things

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IEEE Access 2024年

作者： Nurtantyana, Rio Hwang, Wu-Yuin Hariyanti, Uun Research Center for Data and Information Sciences Bandung Indonesia Telkom University School of Computing Indonesia Telkom University Center of Excellence Artificial Intelligence for Learning and Optimization Bandung Indonesia National Dong Hwa University College of Science and Engineering Department of Computer Science and Information Engineering Taiwan National Central University Graduate Institute of Network Learning Technology Taiwan Universitas Brawijaya Faculty of Computer Science Department of Information System Indonesia

Most English foreign language (EFL) studies focus solely on human beings. This research explores how edge computing can facilitate learning for all things. The XoT (Xducation of Things) framework was proposed to educate both human and all things. All things encompass two terms: AI-Agent and smartthings (covering physical and digital smart objects). At the core of this framework is Smart Question Answer Forwarding Mechanism (SQA-Forwarding), specifically designed to assist all things in building knowledge. To demonstrate this, the smartXoT environment was developed based on XoT framework, and its impacts on EFL learners was assessed. A quasi-experimental study involving 26 EFL learners, divided into an experimental group (EG) and a control group (CG), examined the differences in learning achievement of smartthings and EFL learners when using the smartXoT environment with/without SQA-Forwarding. Findings, on one hand, indicated that smartthings in the EG developed knowledge bases greater than those in the CG. On the other hand, the interaction between EFL learners and smartthings with SQA-Forwarding significantly improved learners' writing skills, with revisions playing a crucial role in enhancing writing quality. Thus, the XoT framework offers a novel and promising approach to educating both humans and all things. © 2013 IEEE.

关键词： Contrastive Learning

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RETRACTED ARTICLE: Obstacles Uncovering System for Slender Pathways Using Unmanned Aerial Vehicles with Automatic Image Localization Technique

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International Journal of Computational Intelligence Systems 2023年第1期16卷 1-14页

作者： Selvarajan, Shitharth Manoharan, Hariprasath Khadidos, Alaa O. Shankar, Achyut Khadidos, Adil O. Onyema, Edeh Michael Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University Leeds UK Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia Secure Cyber Systems Research Group (SCSRG) WMG University of Warwick Coventry UK School of Computer Science Engineering Lovely Professional University Phagwara India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Mathematics and Computer Science Coal City University Enugu Nigeria

In this study, unidentified flying machines are built with real-time monitoring in mid-course settings for obstacle avoidance in mind. The majority of the currently available methods are implemented as comprehensive monitoring systems, with significant success in monitored applications like bridges, railways, etc. So, the predicted model is developed exclusively for specific monitoring settings, as opposed to the broad conditions that are used by the current approaches. Also, in the design model, the first steps are taken by limiting the procedure to specific heights, and the input thrust that is provided for take up operation is kept to a minimum. Due to the improved altitudes, the velocity and acceleration units have been cranked up on purpose, making it possible to sidestep intact objects. In addition, Advanced Image Mapping Localization (AIML) is used to carry out the implementation process, which identifies stable sites at the correct rotation angle. Besides, Cyphal protocol integration improves the security of the data-gathering process by transmitting information gathered from sensing devices. The suggested system is put to the test across five different case studies, where the designed Unmanned aerial vehicle can able to detect 25 obstacles in the narrow paths in considered routs but existing approach can able to identify only 14 obstacle in the same routes.

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Efficient COVID-19 detection using data mining algorithms: a comparison of basic and hybrid approaches

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Soft computing 2025年第3期29卷 1437-1451页

作者： Saidi, Mohammad Gheibi, Mohammad Ghazikhani, Adel Lotfata, Aynaz Chahkandi, Benyamin Familsamavati, Sajad Behzadian, Kourosh Department of Computer Science Imam Reza International University Mashhad Iran MS of Software Engineering Information and Communication Technology Unit Mashhad Municipality Fire and Safety Services Organization Mashhad Iran Big Data Lab Imam Reza International University Mashhad Iran Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec46117 Czech Republic Tallinn10615 Estonia Department of Pathology Microbiology and Immunology School of Veterinary Medicine University of California Davis95616 United States Department of Urban and Regional Planning San Jose State University San Jose95192 United States Faculty of Civil and Environmental Engineering Gdansk University of Technology Narutowicza Street 11/12 80-233 Gdansk Poland Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Monterrey Mexico School of Computing and Engineering University of West London London United Kingdom

Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This study aims to compare the performance of basic and hybrid data mining algorithms in diagnosing COVID-19, using blood test results and clinical information to identify the most effective approach. A dataset of 200 records from suspected and infected COVID-19 patients, with 23 characteristics and one diagnostic class, was analysed. Nine data mining algorithms were tested: four basic algorithms (Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor) and five hybrid algorithms (Random Forest, AdaBoost, Majority Voting, XGBoost, Bagging). The study also integrated Response Surface Methodology (RSM) and Adaptive-Network-based Fuzzy Inference System (ANFIS) to enhance model performance. The Bagging algorithm demonstrated superior performance with an accuracy of 88%, sensitivity of 74%, and F-criterion of 78%. The integration of RSM and ANFIS further showed that a smart model could be developed for efficient pandemic crisis management, achieving up to 100% accuracy when considering key factors like AST, Albumin, and CRP. The findings suggest that Bagging and hybrid data mining algorithms can significantly improve COVID-19 detection, reducing time and errors in identifying exposed individuals. The study highlights the potential of combining machine learning techniques with RSM-ANFIS models for effective pandemic management and decision-making in medical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Support vector machines

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Implementation of Mining Frequent Patterns on big data Using new Version of Algorithm 1

Implementation of Mining Frequent Patterns on big data Using...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Prashanth, M.V. Shambhubhardwaj Raju, Ramana Pramanik, Kundan Kumar Mohanty, Swaroop Barua, Kuntal Vidyavardhaka College of Engineering Information Science and Engineering India Teer Thanker Mahaveer University Faculty of Engineering Department of Computing Sciences & IT Uttar Pradesh Moradabad India Department of Data Science & Mathematics Bangalore India ARKA Jain University Department of School of Engg. &iT Jharkhand Jamshedpur India Management Cv Raman Global University Bhubaneswar India SAGE University Madhya Pradesh Indore India

ISBN: (纸本)9781665456876

Pattern analysis is one of the most important tasks for strong data and useful data from unstructured data. The item-sets for this work must include the aforementioned homogeneity, but rather frequency in the data. Despite the fact that numerous efficient algorithms have been developed in this field, the rising demand for information has led to deterioration in the effectiveness of present pattern mining techniques. The goal of this study is to provide fresh, efficient methods for mining huge data patterns. To accomplish this, a variety of methods based on the Map reduction algorithm as well as the Apache expansive implementation have been proposed. The recommended algorithms may be divided into three main groups. The Attribute Mapper (AprioriMR) and recurrent AprioriMR techniques, which eliminate any previous objects in data without employing a pruning methodology, are the first two described. Second, two recommended approaches (space cutting AprioriMR or top AprioriMR) to improve the search space leverage certain well-generalized pro property. The last approach for mining compressed versions of frequent patterns is the maximum AprioriMR algorithm. To evaluate the efficacy of the offered strategies, up to 3 1018 interactions and more than 5 million distinct ones have been examined in a range of big data datasets. Evaluations of well-known and efficient pattern mining algorithms are part of the testing process. The results show both the benefit of employing Dremel versions when taking into consideration intricate issues and the insufficiency of this strategy when dealing with little amounts of data. © 2022 IEEE.

关键词： Deterioration

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Deep Perspective Transformation Based Vehicle Localization on Bird’s Eye View

arXiv

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arXiv 2023年

作者： Mahyar, Abtin Motamednia, Hossein Rahmati, Dara Department of Computer and Data Sciences Shahid Beheshti University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Faculty of Computer Science and engineering Shahid Beheshti University Tehran Iran

An accurate understanding of a self-driving vehicle’s surrounding environment is crucial for its navigation system. To enhance the effectiveness of existing algorithms and facilitate further research, it is essential to provide comprehensive data to the routing system. Traditional approaches rely on installing multiple sensors to simulate the environment, leading to high costs and complexity. In this paper, we propose an alternative solution by generating a top-down representation of the scene, enabling the extraction of distances and directions of other cars relative to the ego vehicle. We introduce a new synthesized dataset that offers extensive information about the ego vehicle and its environment in each frame, providing valuable resources for similar downstream tasks. Additionally, we present an architecture that transforms perspective view RGB images into bird’s-eye-view maps with segmented surrounding vehicles. This approach offers an efficient and cost-effective method for capturing crucial environmental information for self-driving cars. 1 © 2023, CC BY.

关键词： Birds

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Rapid Maize Seedling Detection Based on Receptive-Field and Cross-Dimensional Information Interaction

Rapid Maize Seedling Detection Based on Receptive-Field and ...

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

作者： Hao, Fengqi Zhu, Shulei Ma, Dexin Dong, Xiangjun Kong, Hoiio Liu, Xia Mu, Chunhua Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China City University of Macau Faculty of Data Science 999078 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China Communication College Qingdao Agricultural University Qingdao266109 China Maize Research Institute Shandong Academy of Agricultural Sciences National Engineering Laboratory of Wheat and Maize Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Jinan250100 China

ISBN: (纸本)9781665410205

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction - receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings. © 2024 IEEE.

关键词： Deep learning Maize seedling detection Object detection UAV

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RSMA-assisted SHAPTINs: secrecy performance under imperfect hardware and channel estimation errors

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Neural computing and Applications 2024年 1-18页

作者： Feng, Zhou Kefeng, Guo Jian, Cheng Sunder Ali, Khowaja Kapal, Dev Reddy, Gadekallu Thippa Hussam Al, Hamadi School of Information Technology Yancheng Institute of Technology Yancheng224051 China School of Space Information Space Engineering University Beijing101407 China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China Faculty of Computing Digital and Data Technological University Dublin DublinD07EWV4 Ireland ADAPT Centre and Department of Computer Science Munster Technological University Cork BishopstownT12 P928 Ireland Department of institute of intelligent systems University of Johannesburg Auckland Park Johannesburg2006 South Africa Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Division of Research and Development Lovely Professional University Phagwara144001 India College of Engineering and IT College of Engineering and IT University of Dubai PO Box 14143 Dubai United Arab Emirates

Satellite high aerial platform terrestrial integrated networks have become the hot topic these years, which have been regarded as the major part of the intelligence of things for future networks. During this work, we investigate the secrecy performance for rate-splitting multiple access-assisted satellite high aerial platform terrestrial integrated networks. Besides, imperfect hardware and channel estimation errors are considered in the secrecy networks. Moreover, to enhance the energy utilization efficiency, rate-splitting multiple access scheme is utilized into the considered network, which is prior to that of the non-orthogonal multiple access scheme. What's more, to enhance the satellite transmission, multiple high aerial platforms are used to forward the transmission along with multiple eavesdroppers. In addition, the direct transmission link is not considered in the secrecy networks due to the heavy fading and obstacles. Relied on the former considerations, the exact and asymptotic analysis for the secrecy performance is further obtained to confirm the rightness of the analysis. Finally, some representative Monte Carlo simulations are carried out to validate the obtained results. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Asymptotic analysis

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