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Divide-and-train: A new approach to improve the predictive tasks of bike-sharing systems

Mathematical Biosciences and engineering 2024年第7期21卷 6471-6492页

作者： Ali, Ahmed Salah, Ahmad Bekhit, Mahmoud Fathalla, Ahmed Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044 Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ad-Dhahirah Ibri Oman Zagazig University Department of Computer Science Faculty of Computers and Informatics Sharkeya Zagazig Egypt Sydney Australia Kaplan Business School Sydney Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt

Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS systems depends on making decisions at the proper time. Thus, there is a vital need to build predictive models on the BSS data for the sake of improving the process of decision-making. The overwhelming majority of BSS users register before utilizing the service. Thus, several BSSs have prior knowledge of the user’s data, such as age, gender, and other relevant details. Several machine learning and deep learning models, for instance, are used to predict urban flows, trip duration, and other factors. The standard practice for these models is to train on the entire dataset to build a predictive model, whereas the biking patterns of various users are intuitively distinct. For instance, the user’s age influences the duration of a trip. This endeavor was motivated by the existence of distinct user patterns. In this work, we proposed divide-and-train, a new method for training predictive models on station-based BSS datasets by dividing the original datasets on the values of a given dataset attribute. Then, the proposed method was validated on different machine learning and deep learning models. All employed models were trained on both the complete and split datasets. The enhancements made to the evaluation metric were then reported. Results demonstrated that the proposed method outperformed the conventional training approach. Specifically, the root mean squared error (RMSE) and mean absolute error (MAE) metrics have shown improvements in both trip duration and distance prediction, with an average accuracy of 85% across the divided sub-datasets for the best performing model, i.e., random forest. © 2024 the Author(s).

关键词： Forecasting

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Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

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computers, Materials & Continua 2022年第4期71卷 683-695页

作者： K.S.Bhuvaneshwari J.Uma K.Venkatachalam Mehedi Masud Mohamed Abouhawwash T.Logeswaran Department of Computer Sceince and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Electrical and Electronics Engineering M.Kumarasamy College of EngineeringKarur639113India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Electrical and Electronics Engineering Kongu Engineering CollegeErode638060India

Air pollution is one of the major concerns considering detriments to human *** type of pollution leads to several health problems for humans,such as asthma,heart issues,skin diseases,bronchitis,lung cancer,and throat and eye *** pollution also poses serious issues to the *** from the vehicle industry is the cause of greenhouse effect and CO2 ***,real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary *** monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor *** is the main issue in WSNs;if the sensor node location is unknown,then coverage and power and routing are not *** study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart *** systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants,such as PM2.5 particulate matter,PM10,nitrogen dioxide(NO2),carbon monoxide(CO),ozone(O3),and sulfur dioxide(SO2).The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding *** dataset is divided into training and testing parts based on 10 *** evaluation on predicting the air pollutant for localization is performed with the training *** error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.

关键词： Air pollution monitoring air pollutant SVM Gaussian EHO fast correlation WSN localization

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Automated Organ Segmentation in 3D Biomedical Images Using Dual-Path 3D Attention U-ResNet 4

Automated Organ Segmentation in 3D Biomedical Images Using D...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Bhavana, J. Hameed, Thushara Maruthakutti, Maheswari Sivaraman, R. Vijayanand, Sellamuthu Palanisamy Jegajothi, B. Sr University Department of Cs & Ai India College of Engineering and Technology University of Technology and Applied Sciences Nizwa Oman Dwaraka Doss Goverdhan Doss Vaishnav College Department of Mathematics Arumbakkam Tamil Nadu Chennai600 106 India Government of Engineering Department of Computer Science and Engineering Erode India Srs Tech Solutions Chennai India

ISBN: (纸本)9798331529635

Deep learning algorithms have made considerable strides in recent years, and these advancements have the potential to significantly improve multi-organ segmentation in three-dimensional biomedical images. This would result in improved diagnostic accuracy and efficiency in medical imaging. DA-TransUNet is an innovative architecture that successfully combines information on several scales and enhances feature extraction by utilizing the DA-Block encoder and the Convolutional Block Attention Module (CBAM). The assessed propsoed model using a variety of datasets, one of which being the LiTS dataset. Finally, were able to achieve a Dice coefficient of 92.56% and a Hausdorff Distance (HD95) of 28.09 mm for liver images. DA-TransUNet surpasses existing state-of-the-art segmentation models across a variety of medical imaging datasets, as demonstrated by the results of the experiments, which reveal considerable increases in accuracy and robustness. The discoveries that have obtained contribute to the advancement of automated segmentation approaches in medical imaging, which in turn facilitates increased diagnostic capabilities. © 2024 IEEE.

关键词： Image segmentation

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Data Analytic for Healthcare Cyber Physical System

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IEEE Transactions on Network Science and engineering 2023年第5期10卷 2490-2502页

作者： Gan, Wensheng Hu, Kaixia Huang, Gengsen Chien, Wei-Che Chao, Han-Chieh Meng, Weizhi Jinan University College of Cyber Security Guangzhou510632 China Pazhou Lab Guangzhou510330 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien97401 Taiwan National Dong Hwa University Department of Electrical Engineering Hualien97401 Taiwan UCSI University Institute of Computer Science and Innovation Kuala Lumpur Malaysia Department of Applied Mathematics and Computer Science Cyber Security Section Lyngby2800 Denmark

Nowadays, a large number of AI-powered healthcare cyber-physical systems (CPSs) have been used in healthcare services. In order to provide better care, AI-powered healthcare CPSs analyze the data they collect using a variety of techniques. Data analysis for artificial intelligence (AI)-driven healthcare CPS is one of these approaches. However, none of the techniques in data analysis can provide a good representation of contiguous and negative information. Therefore, we are the first to introduce the problem of contiguous negative sequential pattern mining. A novel algorithm called Contiguous Negative Sequential Pattern Miner (CNSPM) is proposed to discover and analyze contiguous negative sequential patterns (CNSPs) from the data collected by healthcare CPSs. Finally, we select some real medical and non-medical datasets to conduct numerous experiments. We further analyze the discovered patterns and show how healthcare services can use meaningful patterns for medical decision-making. The performance results on these datasets demonstrate that the proposed algorithm can discover more valuable patterns efficiently and effectively from the collected and transformed medical data. © 2013 IEEE.

关键词： Data mining

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Hummingbird-Optimizer and Otsu/ Kapur-Assisted Segmentation of Foot Ulcer 1st

Hummingbird-Optimizer and Otsu/ Kapur-Assisted Segmentation ...

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1st International Conference on Data engineering and Machine Intelligence, ICDEMI 2023

作者： Mohan, Ramya Kadry, Seifedine Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates

ISBN: (纸本)9789819776153

In addition to traditional approaches, several computerized techniques have been developed to enhance the results. The automation in the medical domain considerably reduced the burden and improved disease diagnosis, treatment planning, and implementation. With the advent of automation, the medical domain has greatly reduced the burden of diagnosing diseases, developing treatment plans, and implementing them. This research aims to develop image-processing assisted detection of Diabetic Foot Ulcer (DF) from digital photographs. Digital photographs are used to assist in diagnosing Diabetic Foot Ulcer (DF). As part of the developed tool, (i) images are collected and resized, (ii) Otsu/Kapur and Hummingbird-optimizer thresholding is supported, (iii) level-set segmentation is used to extract the DF region, and (iv) the developed scheme is compared with the binary mask and its performance is verified. A total of 300 images are examined in this work, and MATLAB software is used to conduct the experiments. Comparing Otsu + LS with Kapur + LS, the overall result achieved with Kapur’s enhanced image is better than that of Otsu + LS. Authentic clinical DF images can be used to verify the proposed scheme in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： MATLAB

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Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

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Journal of Rock Mechanics and Geotechnical engineering 2024年第10期16卷 3982-4003页

作者： Libing Yang Trung Nguyen-Thoi Trung-Tin Tran College of Civil Engineering and Architecture Hunan Institute of Science and TechnologyYueyang414006China Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityViet Nam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh CityViet Nam Department of Information Technology Greenwich VietnamFPT UniversityDa Nang550000Viet Nam

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering *** study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal *** technique is expected to improve the accuracy of friction angle prediction *** friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,***-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,***-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering *** improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.

关键词： Natural hazards Slope stability Friction angle Clay Soft computing models Geotechnical engineering

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Proof of Activity Protocol for IoMT Data Security

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computer Systems Science & engineering 2023年第1期44卷 339-350页

作者： R.Rajadevi K.Venkatachalam Mehedi Masud Mohammed A.AlZain Mohamed Abouhawwash Department of Information Technology Kongu Engineering CollegeErode638060TamilnaduIndia Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The Internet of Medical Things(IoMT)is an online device that senses and transmits medical data from users to physicians within a time ***,recent years,IoMT has rapidly grown in the medicalfield to provide healthcare services without physical *** the use of sensors,IoMT applications are used in healthcare *** such applications,one of the most important factors is data security,given that its transmission over the network may cause *** data security in IoMT systems,blockchain is used due to its numerous blocks for secure data *** this study,Blockchain-assisted secure data management framework(BSDMF)and Proof of Activity(PoA)protocol using malicious code detection algorithm is used in the proposed data security for the healthcare *** main aim is to enhance the data security over the *** PoA protocol enhances high security of data from the literature *** replacing the malicious node from the block,the PoA can provide high security for medical data in the *** with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio,precision ratio,security,and efficiency and less response time for Blockchain-enabled healthcare systems.

关键词： Blockchain IoMT malicious code detection security secure data management framework data management PoA

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Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment

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computers, Materials & Continua 2022年第1期70卷 1247-1261页

作者： J.V.Anchitaalagammai T.Jayasankar P.Selvaraj Mohamed Yacin Sikkandar M.Zakarya Mohamed Elhoseny K.Shankar Department of Computer Science and Engineering Velammal College of Engineering and TechnologyMadurai625009India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Information Technology SRMInstitute of Science and TechnologyKattankulathur603203Tamil NaduIndia Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia Department of Mathematics College of ScienceKing Khalid UniversityP.O.Box 9004Abha61413Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt College of Computer Information Technology American University in the EmiratesDubai503000United Arab Emirates Department of Computer Applications Alagappa UniversityKaraikudiIndia

Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern *** generally refers to a network of gadgets linked via wireless network and communicates via *** management,especially energy management,is a critical issue when designing IoT *** studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT *** this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e.,EECBRM in IoT *** proposed EECBRM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance *** proposed EECBRMmodel was validated through a series of experiments and the results were verified under several *** model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network *** simulated,in comparison with other methods,EECBRM model yielded excellent results in a significant ***,the efficiency of the proposed model is established.

关键词： IoT environment clustering routing resource management energy efficiency

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Data collection of wireless sensor network based on trajectory optimization of laser-charged UAV

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High-Confidence Computing 2024年第2期4卷 116-128页

作者： Chuanwen Luo Jian Zhang Jin Qian Yi Hong Zhibo Chen Yunan Hou Xiujuan Zhang Yuqing Zhu School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing 100083China College of Information Engineering Taizhou UniversityTaizhou 225300China School of Computer Science Qufu Normal UniversityRizhao 276826China Research Center of Applied Mathematics and Machine Intelligence Zhejiang LabHangzhou 311121China Department of Computer Science California State UniversityCA 90032USA

Unmanned Aerial Vehicle(UAV)can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks(WSNs),which is crucial for providing seamless services and improving the performance in the next generation wireless ***,since the UAV are powered by batteries with limited energy capacity,the UAV cannot complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring *** overcome this problem,we study the Real-time Data Collection with Lasercharging UAV(RDCL)problem,where the UAV is utilized to collect data from a specified WSN and is recharged using Laser Beam Directors(LBDs).This problem aims to collect all sensory data from the WSN and transport it to the base station by optimizing the flight trajectory of UAV such that realtime data performance is ensured It has been proven that the RDCL problem is *** address this,we initially focus on studying two sub-problems,the Trajectory Optimization of UAV for Data Collection(TODC)problem and the Charging Trajectory Optimization of UAV(CTO)problem,whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas,*** we propose an approximation algorithm to solve each of them with the constant ***,we present an approximation algorithm that utilizes the solutions obtained from TODC and CTO problems to address the RDCL ***,the proposed algorithm is verified by extensive simulations.

关键词： Wireless sensor networks Laser-charged UAV Trajectory optimization Data collection

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A New Finite-Time Stabilizing Design for a Class of High-Order Uncertain Nonlinear Systems and Its Application in Maglev Systems

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2023年第1期53卷 417-424页

作者： Yu, Le-Yuan Sun, Zong-Yao Meng, Qinghua Chen, Chih-Chiang Jining University College of Mathematics and Computer Applied Technology Qufu273155 China Institute of Automation Qufu Normal University Qufu273165 China Hangzhou Dianzi University School of Mechanical Engineering Hangzhou310018 China National Cheng Kung University Department of Systems and Naval Mechatronic Engineering Tainan70101 Taiwan

In this article, the problem of global fixed-time stabilization for a class of high-order uncertain nonlinear systems has been investigated. Quite different from traditional methods, a novel finite-time control scheme is presented for the first time based on a serial of exponential functions and fractional power integration with nested sign functions, which can guarantee that the convergent time of the states of the closed-loop systems is finite and independent of any initial conditions by the simple choice of design parameters. The remarkable contribution of this article lies in the fact that it provides an alternative to manipulate the possibility of initial states being far from the origin. As a practical application, the finite-time stabilizing design of maglev systems is provided to demonstrate the effectiveness and the superiority of the proposed strategy. © 2013 IEEE.

关键词： Lyapunov methods

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