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2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020

作者： Kudva, Sowmya Norderhaug, Renat Badsha, Shahriar Sengupta, Shamik Kayes, A.S.M. La Trobe University Department of Computer Science and Information Technology Melbourne Australia

ISBN: (纸本)9781728148212

Although the current ride hailing services have revolutionized the transportation industry in today's world, they are extremely centralized. Central authority has all the control and maintains data about the users. Such platforms raise concerns about the service policies and data reliability. In case the central server faces data tampering or ransomware attacks, all the records of data is either lost or compromised. Blockchain has gained exceptional recognition while addressing aforementioned issues because of its decentralized data auditability, anonymity and immutability. Research on blockchain technology explores the possibility of implementing decentralized application in ride hailing service. Blockchain based ride hailing architecture gives us the opportunity to deviate from centralized platforms to decentralized ones. In this paper we propose our system PEBERS: Practical Ethereum Blockchain based Efficient Ride Hailing Service, in which we demonstrate how decentralized system based on consortium blockchain can be developed to keep track of ride data. In this context we explore smart contracts to build and deploy functionalities such as create ride, auto deposit transfer, cancel and complete ride methods of our decentralized ride hailing application. Our experiments show the driver smart contracts proposed in our system consume less Gas and hence prove that its an efficient system than many other existing systems with respect to the expenses of passengers and profitability of drivers. This is an effort towards providing smarter transportation for a society moving towards smart city concept. © 2020 IEEE.

关键词： Blockchain

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Towards hybrid approach based SVM and Radiomics features for COVID-19 classification and segmentation

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International Journal of Hybrid Intelligent Systems 2024年第PRE-PRESS期Pre-press卷 1-20页

作者： Azizi, Ridha Sakly, Houneida Wajdi, Abdallah Ahmed Hmida, Alaa Eddinne Ben Bouhlel, Med Salim Research Lab: Smart systems for Engineering and E-health based on Technologies of Image and Telecomunications (SETIT) ISBS Sfax University Tunisia Center for Research on Microelectronics and Nanotechnology (CRMN) Sousse Technopole Sahloul Sousse Tunisia RIADI Laboratory ENSI Manouba University Campus Universitaire de la Manouba La Manouba Tunisia Department IT Higher Institute of Computer Science and Mathematics of Monastir Tunisia Department IT Higher Institute of Computer Science and Mathematics of Monastir Tunisia

In the battle against the COVID-19 pneumonia outbreak, which is brought on by the coronavirus strain SARS-Cov-2, radiological chest exams, such as chest X-rays, are crucial. In order to understand the unique radiographic characteristics of COVID-19, this research looks into classification models to distinguish chest X-ray images based on Radiomics features. This study is performed with datasets composed of 136 segmented chest X-rays, which were used to train and test the categorization algorithms. First and second-order statistical texture characteristics were extracted from the right (R), left (L), superior, middle, and bottom lung zones for each lung side using the Pyradiomics collection. Data was divided into training (80%) and test (20%) groups for feature selection. After assessing the respective feature significance and confirmation accuracy, the most pertinent Radiomics features were chosen. A model of lung segmentation based grey level pixels was used to evaluate support vector machines (SVM) as possible classifiers (AUC = 83.7%). Our research reveals a preference for the upper lung zone and a preponderance of Radiomics feature selection in the right lung. Our future research will concentrate on COVID-19 categorization and segmentation for more precise forecast using a hybrid method based on SVM and Radiogenomics features.

关键词： COVID-19 radiomics pyradiomics classifiers segmentation SVM radiogenomics

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FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data

arXiv

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arXiv 2024年

作者： Jing, Shusen Yu, Anlan Zhang, Shuai Zhang, Songyang Department of Radiation Oncology University of California San FranciscoCA United States Department of Electrical and Computer Engineering Lehigh University BethlehemPA United States Department of Data Science New Jersey Institute of Technology NewarkNJ United States Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA United States

Recent efforts have been made to integrate self-supervised learning (SSL) with the framework of federated learning (FL). One unique challenge of federated self-supervised learning (FedSSL) is that the global objective of FedSSL usually does not equal the weighted sum of local SSL objectives. Consequently, conventional approaches, such as federated averaging (FedAvg), fail to precisely minimize the FedSSL global objective, often resulting in suboptimal performance, especially when data is non-i.i.d.. To fill this gap, we propose a provable FedSSL algorithm, named FedSC, based on the spectral contrastive objective. In FedSC, clients share correlation matrices of data representations in addition to model weights periodically, which enables inter-client contrast of data samples in addition to intra-client contrast and contraction, resulting in improved quality of data representations. Differential privacy (DP) protection is deployed to control the additional privacy leakage on local datasets when correlation matrices are shared. We also provide theoretical analysis on the convergence and extra privacy leakage. The experimental results validate the effectiveness of our proposed algorithm. © 2024, CC BY.

关键词： Supervised learning

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Tensor Levenberg-Marquardt Algorithm for Multi-Relational Traffic Prediction

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IEEE Transactions on Vehicular Technology 2023年第9期72卷 11275-11290页

作者： Chang, Shih Yu Wu, Hsiao-Chun Kuan, Yen-Cheng Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan International College of Semiconductor Technology Hsinchu30010 Taiwan Communications Research Centre Canada OttawaONK2H 8S2 Canada

Traffic prediction serves as a crucial role in intelligent transportation systems for applications including routing planning and traffic control. With the advances of sensor technologies and Internet of Things (IoTs), the exploitation of traffic data motivates us to reformulate the traffic-state prediction problem based on the big-data characteristics. In order to infer multifarious traffic information, the standard one/two-relational traffic data in the vector/matrix form needs to be expanded to multi-relational traffic data in an arbitrary tensor form. However, none of the existing approaches is capable of performing traffic prediction by characterizing and tracking the inherent multi-relational nonlinear dynamics which are often encountered in the realistic time-series analysis. Furthermore, there hardly exist convergence studies on convergence condition and convergence speed pertinent to the existing traffic-prediction methods. In order to combat the aforementioned challenges, we propose a new tensor Levenberg-Marquadt algorithm (TLMA) to conduct the multi-relational nonlinear regression for traffic prediction involving tensorized data. Meanwhile, we also derive the lower-bound of the number of iterations required to make the error-norm below a specified tolerance for global convergence. Besides, we derive the conditions for our proposed TLMA to converge to the optimal solution. The convergence rates with respect to different nonlinear cost functions have been derived theoretically. The computational- and memory-complexities of our proposed new TLMA are studied as well. Finally, numerical experiments are conducted to evaluate the traffic prediction performance of our proposed new TLMA over real traffic data in comparison with other existing multi-relational prediction methods. © 1967-2012 IEEE.

关键词： Tensors

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Group Activity Recognition using Unreliable Tracked Pose

arXiv

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arXiv 2024年

作者： Thilakarathne, Haritha Nibali, Aiden He, Zhen Morgan, Stuart Department of Computer Science & Information Technology La Trobe University Plenty Road & Kingsbury Dr BundooraVIC3086 Australia Sport Strategy & Investment Australian Institute of Sport Leverrier St BruceACT2617 Australia

Group activity recognition in video is a complex task due to the need for a model to recognise the actions of all individuals in the video and their complex interactions. Recent studies propose that optimal performance is achieved by individually tracking each person and subsequently inputting the sequence of poses or cropped images/optical flow into a model. This helps the model to recognise what actions each person is performing before they are merged to arrive at the group action class. However, all previous models are highly reliant on high quality tracking and have only been evaluated using ground truth tracking information. In practice it is almost impossible to achieve highly reliable tracking information for all individuals in a group activity video. We introduce an innovative deep learning-based group activity recognition approach called Rendered Pose based Group Activity Recognition System (RePGARS) which is designed to be tolerant of unreliable tracking and pose information. Experimental results confirm that RePGARS outperforms all existing group activity recognition algorithms tested which do not use ground truth detection and tracking information. © 2024, CC BY.

关键词： computer vision

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CNN segmentation of skin melanoma in pre-processed dermoscopy images

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Procedia computer science 2024年 235卷 2775-2782页

作者： Seifedine Kadry Elena Verdú Robertas Damasevicius laith Abualigah Vijendra Singh Venkatesan Rajinikanth Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Higher School of Engineering and Technology Universidad Internacional de La Rioja Logroño 26006 Spain Department of Applied Informatics Vytautas Magnus University 53361 Kaunas Lithuania Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu’s thresholding, (iii) Kapur’s thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database.

关键词： Watershed-algorithm skin melanoma segmentation Fuzzy-Tsallis entropy Moth-Flame algorithm evaluation

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COVID19 detection in chest x-ray using vision-transformer with different patch dimensions

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Procedia computer science 2024年 235卷 3438-3446页

作者： Seifedine Kadry laith Abualigah Rubén González Crespo Elena Verdú Robertas Damasevicius Vijendra Singh Venkatesan Rajinikanth Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de La Rioja Logroño 26006 Spain Department of Applied Informatics Vytautas Magnus University 53361 Kaunas Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays.

关键词： Covid19 X-ray Vision-transformer Patch-size Classification

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A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling

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Transportation Research Procedia 2022年 62卷 374-382页

作者： José Carlos García-García Ricardo García-Ródenas Julio Alberto López-Gómez José Ángel Martín-Baos Department of Mathematics School of Computer Science University of Castilla-La Mancha Ciudad Real Spain Institute of Applied Mathematics in Science and Engineering (IMACI) University of Castilla-La Mancha Ciudad Real Spain Department of Information Systems and Technologies School of Computer Science University of Castilla-La Mancha Ciudad Real Spain

Traditionally, Random Utility Maximization (RUM) models have been widely applied to travel mode choice modelling. Currently, Machine Learning (ML) models are being applied as an alternative to RUM models, since they provide better results in terms of prediction capability and they can manage large volumes of data. In this paper, a comprehensive comparison between classic RUM models and ML models, including single and ensemble classifiers as well as Deep Neural Networks (DNNs), is provided in order to assess systematically the performance of different models over two different datasets which have different sizes and nature of data. Numerical experiments show Random Forest (RF) is the best classifier in terms of accuracy index and the computational cost to train the model.

关键词： Random Utility Maximization (RUM) models Deep Neural Networks (DNN) Machine Learning (ML) models travel behaviour

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Automatic liver-vessel examination from CT slice using Kapur’s thresholding and watershed algorithm

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Procedia computer science 2024年 235卷 1824-1831页

computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular section for examination. A two-dimensional computed tomography (CT) slice was used for liver-vessel examination (LiVE). A simple automatic technique for supporting LiVE is being developed in this research. A CT slice is collected, a 3D to 2D conversion is done, (ii) Kapur’s tri-level thresholding and Hummingbird-Optimizer is used to enhance the CT slice, (iii) the watershed algorithm (WA) is used to extract the vessel, and (iv) the WA is compared and verified against the segmentation methods chosen. WA provides better segmentation results than other methods because it is an automatic approach. Using the chosen image database, the proposed technique achieves an overall segmentation accuracy of >97%. Other segmentation problems can be used in the future to verify the merit of this scheme.

关键词： liver CT liver-vessel Kapur’s thresholding hummingbird-optimizer watershed-algorithm

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Bike-sharing Demand Prediction based on Artificial Intelligence Algorithm Using Weather Data

Bike-sharing Demand Prediction based on Artificial Intellige...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Yebeen Lee Hyungju Son Jiin Ahn Seokheon Cho Department of Computer Science Engineering Konkuk University Seoul Republic of Korea Department of Transportation Engineering Keimyung University Daegu Republic of Korea Department of Biomedical Engineering Keimyung University Daegu Republic of Korea Qualcomm Institute University of California San Diego La Jolla USA

To alleviate traffic congestion and environmental pollution in heavily populated Seoul, South Korea, the city has actively introduced a bike-sharing service. As the demand for this service increases, efficient supply and management of shared bikes are becoming critically important. Because bike-sharing demand correlates with the weather, we performed a prediction for bike demand based on artificial intelligence algorithms utilizing weather data. The Multiple Linear Regression, Random Forest Regression, and Multi-layer Perceptron artificial intelligence algorithms were considered and applied to processed weather data collected in Seoul using various methods. The results revealed that the best performance in predicting bike-sharing demand was achieved by a model trained by the Random Forest Regression algorithm, which considers historical weather and demand data and separates itself into two sub-datasets by weekdays and weekends, but without distinguishing itself with the information of the four seasons.

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