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Sports Prediction Model through Cloud Computing and Big Data Based on Artificial Intelligence Method

Journal of Intelligent Learning Systems and Applications 2024年第2期16卷 53-79页

作者： Aws I. Abu Eid Achraf Ben Miled Ahlem Fatnassi Majid A. Nawaz Ashraf F. A. Mahmoud Faroug A. Abdalla Chams Jabnoun Aida Dhibi Firas M. Allan Mohammed Ahmed Elhossiny Salem Belhaj Imen Ben Mohamed Faculty of Computing Studies Arab Open University Amman Jordan Computer Science Department Science College Northern Border University Arar Kingdom of Saudi Arabia Artificial Intelligence and Data Engineering Laboratory LR21ES23 Faculty of Sciences of Bizerte University of Carthage Tunis Tunisia Applied College Northern Border University Arar Kingdom of Saudi Arabia Faculty of Specific Education Mansoura University Mansoura Egypt

This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI Model (SAIM) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIM to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics. This research underscores the synergy between these technologies and paves the way for innovative approaches to sports-related decision-making and performance enhancement.

关键词： Artificial Intelligence Machine Learning Spark Apache Big Data SAIM

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Conjugations of Unitary Operators, II

arXiv

引用

arXiv 2024年

作者： Mashreghi, Javad Ptak, Marek Ross, William T. Département dee Mathématiques et de Statistique Université Laval QuébecQCG1K 0A6 Canada Department of Applied Mathematics University of Agriculture UL. Balicka 253C Kraków30-198 Poland Department of Mathematics and Computer Science University of Richmond RichmondVA23173 United States

For a given unitary operator U on a separable complex Hilbert space H, we describe the set Cc(U) of all conjugations C (antilinear, isometric, and involutive maps) on H for which CUC = U. As this set might be empty, w... 详细信息

关键词： Hilbert spaces

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Real time analysis of Intelligent placing system for vehicles using IOT with Deep learning

Real time analysis of Intelligent placing system for vehicle...

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International Conference on Computations in Materials and applied engineering, CMAE 2021

作者： Jaiswal, Sushma Kumar Sharma, Dilip Jaiswal, Tarun Basumatary, Bhimraj Tiwari, Mohit Tiwari, Tripti Chhattisgarh Bilaspur India Department of Mathematics Jaypee University of Engineering and Technology MP Guna India Chhattisgarh Raipur492010 India Department of Math. Sciences Bodoland University Assam Kokrajhar India Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering A-4 Rohtak Road Paschim Vihar Delhi India Department of Management Studies BVIMR A-4 Rohtak Road Delhi Paschim Vihar India

There is an enormous in number of vehicles in last few years. So, it becomes important to make effective use of technology to enable inconvenience free parking at public and/or private places. In traditional placing systems, drivers face difficulty in finding available placing slots. These systems ignore the fact of placing the vehicles on roads, time management in peak hours, wrong parking of a vehicle in a placing slot. Moreover, the traditional systems require more human intervention in a placing zone. To deal with above said issues, there is an urgent requirement of developing Real Time Intelligent placing system. In this proposed system a Real Time Intelligent placing system based on IOT and Deep learning techniques to answer the real time management of placing and uncertainties. The proposed solution utilizes smart sensors, cloud computing and cyber physical system. Development of graphical user interface for administrator and end-user is a major challenge as it requires ensuring smooth monitoring, control and security of placing system. Moreover, it needs to establish effortless coordination with an end-user. The proposed system is successful in smartly addressing the challenges such as indicating status of placing slot well in advance to end-user, use of reserved and unreserved placing slots, wrong placing, unauthorized placing, real time analysis of free and occupied slots, detecting multiple objects in a placing slot such as bike in car slot, fault detection in one or more components and traffic management during peak hours. The system reduces the human work, saves time, money and energy. © 2021 Elsevier Ltd. All rights reserved.

关键词： Internet of things

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Spatial Adaptive Cooperative Graph Convolution Attention for Person Re-Identification

SSRN

引用

SSRN 2023年

作者： Xiong, Mingfu Xiong, Jiefan Wang, Zhongyuan Chen, Jia Hu, Ruimin Muhammad, Khan Xiong, Zixiang School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan430200 China Wuhan University Wuhan430072 China Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of The Department of Electrical and Computer Engineering Texas A&M University College StationTX7784 United States

Recently, person re-identification (person Re-ID) equipped with attention mechanism (AM) has gradually emerged as one of the prevalent study highlights. In fact, the existing attention-based methods still remain the challenges for recognizing the different persons with similar appearances in the unpredictable realistic environments: (1) the adaptability of the learned a single and local attention to the hostile scenes cannot be guaranteed;(2) most existing methods originated from the attention features usually try to find a simple linear transformation, which is easy to lost the function of distinguishing different pedestrians with similar appearance attributes. To tackle these issues, this paper proposed a spatial adaptive cooperative graph convolution attention (SACGCA) method to excavate the relevances of individual body local regions for each person. The proposed framework encompasses a dual-attention mechanism model (DAMM) and a spatial adaptive graph convolution model (SAGCM). In particular, the DAMM consists of the multi-region (local and global) association structure and a feature channel attention network to extract person features. Then, the SAGCM is primed for constructing the spatial nonlinear transformation relationships among the various body regions for each person by the virtue of graph optimization. Extensive experiments have conducted on the person Re-ID public datasets (Market-1501, DukeMTMC-reid and CUHK-03), which results indicate that the performance of our proposed algorithm is better than existing methods by 2%, 2%, 10% at Rank-1 or mAP respectively. © 2023, The Authors. All rights reserved.

关键词： Convolution

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Connected Coordinated Motion Planning with Bounded Stretch

arXiv

引用

arXiv 2021年

作者： Fekete, Sándor P. Keldenich, Phillip Kosfeld, Ramin Rieck, Christian Scheffer, Christian Department of Computer Science TU Braunschweig Braunschweig Germany Faculty of Electrical Engineering and Computer Science Bochum University of Applied Sciences Bochum Germany

We consider the problem of connected coordinated motion planning for a large collective of simple, identical robots: From a given start grid configuration of robots, we need to reach a desired target configuration via a sequence of parallel, collision-free robot motions, such that the set of robots induces a connected grid graph at all integer times. The objective is to minimize the makespan of the motion schedule, i.e., to reach the new configuration in a minimum amount of time. We show that this problem is NP-complete, even for deciding whether a makespan of 2 can be achieved, while it is possible to check in polynomial time whether a makespan of 1 can be achieved. On the algorithmic side, we establish simultaneous constant-factor approximation for two fundamental parameters, by achieving constant stretch for constant scale. Scaled shapes (which arise by increasing all dimensions of a given object by the same multiplicative factor) have been considered in previous seminal work on self-assembly, often with unbounded or logarithmic scale factors;we provide methods for a generalized scale factor, bounded by a constant. Moreover, our algorithm achieves a constant stretch factor: If mapping the start configuration to the target configuration requires a maximum Manhattan distance of d, then the total duration of our overall schedule is O(d), which is optimal up to constant factors. Copyright © 2021, The Authors. All rights reserved.

关键词： Motion planning

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Machine vision inspection of early failure and line-shaped defects of blue InGaN/GaN light emitting diodes soaked in liquid nitrogen for cryogenic tests

引用

Results in Physics 2023年 50卷

作者： Yang, Chun-Yen Yu, Chih-Yuan Lin, You-Li Hon, Mou-Tuong Yang, Shao-Jui Zhang, Yi-Zong Kang, Hsuan-Chia Kuo, Yaw-Wen Lin, Chia-Feng Wu, You-Lin Chou, Hsin-Hung Chen, Hsiang Li, Yung-Hui Han, Jung Department of Electrical Engineering National Chi Nan University Nantou 54561 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou 54561 Taiwan Department of Computer Science & Information Engineering National Chi Nan University Nantou 54561 Taiwan Department of Materials Science and Engineering National Chung Hsing University Taichung 40227 Taiwan AI Research Center Hon Hai Research Institute Taipei 11494 Taiwan Department of Electrical Engineering Yale University New Haven 06511 CT United States

Cryogenic tests were conducted on blue InGaN/GaN light-emitting diodes (LEDs) by immersing the device in liquid nitrogen (LN2). To study the degradation of the LEDs, a combination of optical, electrical, and material ... 详细信息

Cryogenic tests were conducted on blue InGaN/GaN light-emitting diodes (LEDs) by immersing the device in liquid nitrogen (LN₂). To study the degradation of the LEDs, a combination of optical, electrical, and material characterizations were performed on the LN₂-soaked device. The results indicate that noticeable emission spectral shifts were observed during the LED immersion due to lattice deformation. Moreover, clustering of cracks was generated in the cross-section, and line-shaped defects appeared on the surface of the GaN LED, which was different from the defects on AlInGaP LEDs after LN₂ immersion. To track the trend of device damage, computer-assisted OpenCV-Python software was used to zoom in on nuanced variations in certain areas of the emission images. Early degradation of certain areas can be detected by computer-assisted machine vision, which has the potential to provide an early warning and protective circuits for the system consisting of the initial degraded LEDs. Reliability studies on LN₂-soaked InGaN/GaN LEDs show promise in studying the reliability issues of GaN device applications in Arctic regions and space exploration. © 2023

关键词： InGaN/GaN blue LED Lattice deformation Line defects Liquid nitrogen Machine vision

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A novel transfer learning-based model for diagnosing malaria from parasitized and uninfected red blood cell images

引用

Decision Analytics Journal 2023年 9卷

作者： Qadri, Azam Mehmood Raza, Ali Eid, Fatma Abualigah, Laith Institute of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan 64200 Pakistan Technology Management Stony Brook University 11794 NY United States Department of Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Seoul 03063 South Korea Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied Science Research Center Applied science private university Amman 11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia

Malaria represents a potentially fatal communicable illness triggered by the Plasmodium parasite. This disease is transmitted to humans through the bites of Anopheles mosquitoes that carry the infection. This disease has significant and devastating consequences on the health systems of fragile countries, particularly in sub-Saharan Africa. Malaria affects red blood cells by invading and replicating within them, destroying them, and releasing toxic byproducts into the bloodstream. The parasite's ability to stick and modify the surface of red blood cells can cause them to become sticky, obstructing blood flow in vital organs such as the brain and spleen. Therefore, efficient approaches for the early detection of malaria are critical to saving patients’ lives. The main aim of this study is to develop an efficient model for early malaria diagnosis. We used malaria images based on parasitized and uninfected red blood cells for the study experiments. We applied neural network-based approaches such as Neural Search Architecture Network (NASNet) and compared its performance with machine learning techniques. Moreover, we proposed a novel NNR (NASNet Random forest) method for feature engineering. The proposed NNR approach first extracts spatial features from input malaria images, then class prediction probability features are extracted from these spatial features. The feature set obtained from the data extraction trains machine learning models. Our comprehensive experiments show that the support vector machine outperformed state-of-the-art models, achieving a high-performance score of 99% and having an inference time near 0.025 s. We validated the performance using k-fold cross-validation and optimized the hyperparameters through tuning. Our proposed research has improved the early diagnosis of malaria and can assist medical specialists in reducing the mortality rate. © 2023 The Author(s)

关键词： Deep learning Machine learning Malaria diagnosis Neural search architecture network Red blood cells Transfer learning

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INVERSE PROBLEMS FOR QUANTUM GRAPH ASSOCIATED WITH SQUARE AND HEXAGONAL LATTICES

arXiv

引用

arXiv 2024年

作者： Ando, K. Blåsten, E. Exner, P. Isozaki, H. Korotyaev, E. Lassas, M. Lu, J. Morioka, H. Department of Electrical and Electronic Engineering and Computer Science Ehime University Matsuyama790-8577 Japan School of Engineering Science LUT University Lahti Campus Lahti15210 Finland Department of Math. Analysis Saint-Petersburg State University Universitetskaya Nab. 7/9 St. Petersburg199034 Russia National Research University Higher School of Economics St. Petersburg Russia Doppler Institute for Mathematical Physics and Applied Mathematics Czech Technical University in Prague Břehová 7 Prague115 19 Czech Republic Nuclear Physics Institute Czech Academy of Science Rež 250 68 Czech Republic Graduate School of Pure and Applied Sciences University of Tsukuba Tsukuba305-8571 Japan Department of Mathematics and Statistics University of Helsinki HelsinkiFI-00014 Finland

We solve inverse problems from the D-N map for the quantum graph on a finite domain in a square lattice and that on a hexagonal lattice, as well as inverse scattering problems from the S-matrix for a locally perturbed... 详细信息

关键词： Inverse problems

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Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling

arXiv

引用

arXiv 2024年

作者： Musto, Henry Stamate, Daniel Logofatu, Doina Stahl, Daniel Data Science and Soft Compuputing Lab Goldsmiths University of London London United Kingdom School of Health Sciences The University of Manchester Manchester United Kingdom Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt Germany Department of Biostatistics Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom

The paper proposes a novel approach of survival transformers and extreme gradient boosting models in predicting cognitive deterioration in individuals with mild cognitive impairment (MCI) using metabolomics data in the ADNI cohort. By leveraging advanced machine learning and transformer-based techniques applied in survival analysis, the proposed approach highlights the potential of these techniques for more accurate early detection and intervention in Alzheimer's dementia disease. This research also underscores the importance of non-invasive biomarkers and innovative modelling tools in enhancing the accuracy of dementia risk assessments, offering new avenues for clinical practice and patient care. A comprehensive Monte Carlo simulation procedure consisting of 100 repetitions of a nested cross-validation in which models were trained and evaluated, indicates that the survival machine learning models based on Transformer and XGBoost achieved the highest mean C-index performances, namely 0.85 and 0.8, respectively, and that they are superior to the conventional survival analysis Cox Proportional Hazards model which achieved a mean C-Index of 0.77. Moreover, based on the standard deviations of the C-Index performances obtained in the Monte Carlo simulation, we established that both survival machine learning models above are more stable than the conventional statistical model. © 2024, CC BY.

关键词： Risk assessment

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Study the Improved Efficiency of the Lead-Free CsSnI3 Perovskite Solar Cells with Variation of Absorber Layer in SCAPS-1D

Study the Improved Efficiency of the Lead-Free CsSnI3 Perovs...

引用

IEEE Electron Devices Kolkata Conference (EDKCON)

作者： Arijit Kumar Barik Himadri Sekhar Das Sudipta Banerjee Heranmoy Maity Santanu Mishra Mousam Chatterjee Bidrohi Bhattachrya Asim Kumar Jana Gourisankar Roymahpatra Electronics and communication Engineering Ramakrishna Mission Vidyamandira Howrah India Electronics and communication Engineering Haldia Institute of Technology Haldia India Computer Science and Enginerring Symbiosis Institute of Technology Pune Symbiosis International (Deemed University) Pune India Department of Applied Science Haldia Institute of Technology Haldia India Electronics and Communication Engineering B. P. Poddar Institute of Management and Technology Kolkata India Electrical and Electronics Engineering Pailan college of Management and Technology Kolkata India

ISBN: (数字)9798350374643

ISBN: (纸本)9798350374650

In this research work, a novel orderly study was conducted to explore the different parameters effect of thickness of the protection tier on adeptness of the FTO/TiO 2 /CsSnI 3 /Cu 2 O/Au Perovskite solar cell by SCAPS-1D. To realize the optimized power conversion efficiency (PCE), acceptor density of the Perovskite absorption layer is (N A =2×10 19 cm -3 ) with a 40nm TiO 2 layer, 300 nm for the Perovskite absorption layer (CsSnI 3 ) and 10 nm Cu2o as HTL, with Au as electrode. After optimization process, efficiency reached from 13.60% to 17.17%. A result indicates the enhancement performance of the Perovskite solar cells (PSC).

关键词： Electrodes Performance evaluation Absorption Photovoltaic cells Instruments Lead Perovskites Power conversion Protection Optimization

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