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SSRN 2024年

作者： Afzal, Maira Shah, Jamal Hussain Rehman, Saeed ur Dahan, Fadl Yasmin, Mussarat Kadry, Seifedine Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Department of Management Information Systems College of Business Administration - Hawtat Bani Tamim Prince Sattam bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon MEU Research Unit Middle East University Amman11831 Jordan

The computer vision field has wide applications in various areas, including sports. Almost all sports events have been exploiting the best features. Sports videos are structure-based, and due to this characteristic, these videos can be categorized into interesting and non-interesting events. Identifying the view of the video and separating important events from non-interesting events is challenging. However, correct view detection can lead to correct event detection. Various researchers have proposed many strategies for detecting events in sports videos. Significant research shows some gaps while generating highlights due to limited work available in the long or short view;there is still a need for some automated methods. The main purpose of this research work is to detect key events by normalizing the data, extracting features, fusing those features, and classifying them. In this research, events are detected by classification. A new dataset is created for research purposes. The benchmark dataset is divided into two subsets, which can be used separately or as part of a larger dataset. A proposed novel approach for event highlight generation in long and short view is presented using a fusion of AlexNet and VGGNet architectures to explore the model's efficiency in the context of accurate highlight generation deep learning models fused with handcrafted. AlexNet and VGG16 pre-trained deep CNN models are applied to extract deep prior features, which are then combined with HOG to improve the results. The proposed methodology undergoes evaluation on a dataset that we created, resulting in an accuracy of 99.6%. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Internet of Things (IoT) for Remote Earthquake and Fire Detection Monitoring: Linking Safety

Internet of Things (IoT) for Remote Earthquake and Fire Dete...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Gayathri. S P. Umaeswari Muktasandhu Muktasandhu M. Vignesh Ramyamaranan Ramyamaranan Shivashish Dhondiyal R.M.K. Engineering College Department of Computer Science and Business Systems R. M. K. Engineering College Kavaraipettai Shri Vishwakarma Skill University Haryana Department of Civil Engineering Karpagam Academy of Higher Education (Deemed to Be University) Coimbatore Tamil Nadu India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to Be University Dehradun Uttarakhand India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Two examples of interconnected systems and gadgets that have evolved as a result of fast technological developments are the Internet of Things (IoT) and the Internet of Vehicles (IoV). The public safety and catastrophe management sectors are only two of many that may see revolutionary changes brought forth by this technology. An Internet of Things (IoT) and Internet of Vehicles (IoV)-based safety system for remote real-time monitoring and seismic detection is proposed in this *** monitor seismic activity and detect impending fires, the proposed system employs a network of sensors installed in various buildings and vehicles. Among the many data-gathering instruments included in these sensors are vibration sensors, which can detect earthquakes, and smoke detectors, which may detect fires. The acquired data is received by a centralised control unit using wireless *** order to accurately detect earthquakes and fires, the control unit processes the provided data using advanced data analytics and machine learning techniques. The system also makes use of the IoV concept to make the reaction measures more efficient and effective. It utilises the real-time position and trajectory data of cars to aid in rapid emergency response and optimise evacuation *** are several advantages to the proposed method over more traditional forms of monitoring and safety systems. Authorities can respond swiftly to earthquakes and fires because to its real-time monitoring capabilities, which in turn reduces the likelihood of human deaths and property damage. Improving the system's responsiveness, the integration of IoV enables intelligent decision-making based on the varying traffic circumstances and available resources.

关键词： Wireless communication Wireless sensor networks Earthquakes Real-time systems Sensor systems Public security Sensors

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Input-Driven Pushdown Automata on Well-Nested Infinite Strings 16th

Input-Driven Pushdown Automata on Well-Nested Infinite Strin...

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16th International computer science Symposium in Russia, CSR 2021

作者： Okhotin, Alexander Selivanov, Victor L. Department of Mathematics and Computer Science St. Petersburg State University 7/9 Universitetskaya Nab. Saint Petersburg199034 Russia A. P. Ershov Institute of Informatics Systems Novosibirsk Russia

ISBN: (纸本)9783030794156

Automata operating on strings of nested brackets, known as input-driven pushdown automata, and also as visibly pushdown automata, have been studied since the 1980s. They were extended to the case of infinite strings by Alur and Madhusudan ("Visibly pushdown languages", STOC 2004). This paper investigates the properties of these automata under the assumption that a given infinite string is always well-nested. This restriction enables a complete characterization of the corresponding ω -languages in terms of classical ω -regular languages and input-driven pushdown automata on finite strings. This characterization leads to a determinization construction for these automata, as well as to the first results on their topological classification. © 2021, Springer Nature Switzerland AG.

关键词： Automata theory

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Space Shift Keying with Reconfigurable Intelligent Surfaces: Phase Configuration Designs and Performance Analysis

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2021年 2卷 322-333页

作者： Li, Qiang Wen, Miaowen Wang, Shuai Alexandropoulos, George C. Wu, Yik-Chung Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou510632 China School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Sifakis Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong Hong Kong

Reconfigurable intelligent surface (RIS)-assisted transmission and space shift keying (SSK) appear as promising candidates for future energy-efficient wireless systems. In this article, two RIS-based SSK schemes are proposed to efficiently improve the error and throughput performance of conventional SSK systems, respectively. The first one, termed RIS-SSK with passive beamforming (RIS-SSK-PB), employs an RIS for beamforming and targets the maximization of the minimum squared Euclidean distance between any two decision points. The second one, termed RIS-SSK with Alamouti space-time block coding (RIS-SSK-ASTBC), employs an RIS for ASTBC and enables the RIS to transmit its own Alamouti-coded information while reflecting the incident SSK signals to the destination. A low-complexity beamformer and an efficient maximum-likelihood (ML) detector are designed for RIS-SSK-PB and RIS-SSK-ASTBC, respectively. Approximate expressions for the average bit error probabilities of the source and/or the RIS are derived in closed-form assuming ML detection. Extensive computer simulations are conducted to verify the performance analysis. Results show that RIS-SSK-PB significantly outperforms the existing RIS-free and RIS-based SSK schemes, and RIS-SSK-ASTBC enables highly reliable transmission with throughput improvement. © 2020 IEEE.

关键词： Beamforming

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Revisiting Distribution-Based Registration Methods

Revisiting Distribution-Based Registration Methods

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European Conference on Mobile Robots (ECMR)

作者： Himanshu Gupta Henrik Andreasson Martin Magnusson Simon Julier Achim J. Lilientha Centre for Applied Autonomous Sensor Systems (AASS) Orebro University Sweden Department of Computer Science University College London UK Perception for Intelligent Systems Technical University of Munich Germany and Centre for Applied Autonomous Sensor Systems (AASS) Orebro University Sweden

Normal Distribution Transformation (NDT) registration is a fast, learning-free point cloud registration algorithm that works well in diverse environments. It uses the compact NDT representation to represent point clouds or maps as a spatial probability function that models the occupancy likelihood in an environment. However, because of the grid discretization in NDT maps, the global minima of the registration cost function do not always correlate to ground truth, particularly for rotational alignment. In this study, we examined the NDT registration cost function in-depth. We evaluated three modifications (Student-t likelihood function, inflated covariance/heavily broadened likelihood curve, and overlapping grid cells) that aim to reduce the negative impact of discretization in classical NDT registration. The first NDT modification improves likelihood estimates for matching the distributions of small population sizes; the second modification reduces discretization artifacts by broadening the likelihood tails through covariance inflation; and the third modification achieves continuity by creating the NDT representations with overlapping grid cells (without increasing the total number of cells). We used the Pomerleau Dataset evaluation protocol for our experiments and found significant improvements compared to the classic NDT D2D registration approach (27.7% success rate) using the registration cost functions “heavily broadened likelihood NDT” (HBL- NDT) (34.7% success rate) and “over-lapping grid cells NDT” (OGC-NDT) (33.5% success rate). However, we could not observe a consistent improvement using the Student-t likelihood-based registration cost function (22.2% success rate) over the NDT P2D registration cost function (23.7% success rate). A comparative analysis with other state-of-art registration algorithms is also presented in this work. We found that HBL-NDT worked best for easy initial pose difficulties scenarios making it suitable for consecutive point cloud re

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Optimizing Machine Learning Algorithms for Heart Disease Prediction in Healthcare: A Comparative Study

Optimizing Machine Learning Algorithms for Heart Disease Pre...

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Electronics and Sustainable Communication systems (ICESC), 2020 International Conference on

作者： S. Subha C. Balakrishnan T.P. Anish Gokila. E Nalini. M Siva Subramanian R Department of Computer Science and Business Systems Rajalakshmi Institute of Technology Chennai Dept of CSE S.A Engineering College Dept of CSE R.M.K. College of Engineering and Technology Tiruvallur India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The healthcare business faces significant challenges in successfully maintaining and evaluating large amounts of patient data. Data analytics has emerged as a viable tool for issue resolution and decision-making as a result of technological advancements in healthcare. This automated procedure improves the resilience of healthcare systems by collecting and analyzing data to provide more effective and cost-effective treatments. One critical use of technology, primarily machine learning, involves the identification and diagnosis of diseases, notably heart disease, which contributes significantly to global mortality. This research study analyzes how machine learning methods (NB, RF, SVM, and LR) can predict the existence of heart disease. Given that nearly 90% of cardiac diseases are avoidable, using machine learning for prediction is critical. This research study assesses the performance of different algorithms using performance metrics such as accuracy, precision, area under the curve (AUC), and F1-Score. The experimental results reveal Random Forest (RF) as the most reliable predictor of heart disease with an accuracy of 83.52% compared to other supervised machine learning algorithms. The Random Forest classifier achieves F1-Score, AUC, and accuracy values of 84.21 %, 88.24%, and 88.89%, respectively. This study highlights the potential of ML, notably the Random Forest algorithm, in improving the predictive capacity of healthcare systems for more effective heart disease prevention and treatment.

关键词： Heart Measurement Accuracy Machine learning algorithms Medical services Prediction algorithms Reliability Random forests Diseases Classification tree analysis

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Current and future directions in network biology

arXiv

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arXiv 2023年

作者： Zitnik, Marinka Li, Michelle M. Wells, Aydin Glass, Kimberly Gysi, Deisy Morselli Krishnan, Arjun Murali, T.M. Radivojac, Predrag Roy, Sushmita Baudot, Anaïs Bozdag, Serdar Chen, Danny Z. Cowen, Lenore Devkota, Kapil Gitter, Anthony Gosline, Sara Gu, Pengfei Guzzi, Pietro H. Huang, Heng Jiang, Meng Kesimoglu, Ziynet Nesibe Koyuturk, Mehmet Ma, Jian Pico, Alexander R. Pržulj, Nataša Przytycka, Teresa M. Raphael, Benjamin J. Ritz, Anna Sharan, Roded Shen, Yang Singh, Mona Slonim, Donna K. Tong, Hanghang Yang, Xinan Holly Yoon, Byung-Jun Yu, Haiyuan Milenković, Tijana Department of Biomedical Informatics Harvard University United States Department of Computer Science and Engineering University of Notre Dame United States Lucy Family Institute for Data and Society University of Notre Dame United States Eck Institute for Global Health University of Notre Dame United States Channing Division of Network Medicine Brigham and Women’s Hospital Harvard Medical School United States Department of Statistics Federal University of Paraná Brazil Department of Physics Northeastern University United States Department of Biomedical Informatics University of Colorado United States Department of Computer Science Virginia Tech United States Khoury College of Computer Sciences Northeastern University United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison United States Wisconsin Institute for Discovery United States Aix Marseille Université INSERM MMG Marseille France Department of Computer Science and Engineering University of North Texas United States Department of Mathematics University of North Texas United States Department of Computer Science Tufts University United States Morgridge Institute for Research United States Pacific Northwest National Laboratory United States Department of Medical and Surgical Sciences University Magna Graecia of Catanzaro Italy Department of Computer Science University of Maryland College Park United States National Center of Biotechnology Information National Library of Medicine National Institutes of Health Department of Computer and Data Sciences Case Western Reserve University United States Computational Biology Department School of Computer Science Carnegie Mellon University United States Institute of Data Science and Biotechnology Gladstone Institutes Department of Computer Science University College London United Kingdom ICREA Catalan Institution for Research and Advanced Studies Spain Department of Computer Science Princeton University United States Departmen

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology and highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on the future directions of network biology. Additionally, we offer insights into scientific communities, educational initiatives, and the importance of fostering diversity within the field. This paper establishes a roadmap for an immediate and long-term vision for network biology. © 2023, CC BY.

关键词： Machine learning

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Impact of COVID-19 Pandemic on Medical Education: A Cross-Sectional Study in the Western Region of Saudi Arabia

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Advances in Medical Education and Practice 2022年 13卷 741-754页

作者： Samannodi, Mohammed Bulkhi, Adeeb Alwafi, Hassan Bukhari, Rahaf Salawati, Emad Hafiz, Bayan Minshawi, Faisal Dahlawi, Maryam Kamfar, Sara Halawani, Hanan Naser, Abdallah Y. Mandora, Roaa Ali, Mohamad Alshekh Almatrafi, Mohammed A. Department of Medicine College of Medicine Umm Al-Qura University Makkah Saudi Arabia Faculty of Medicine Umm Al-Qura University Mecca Saudi Arabia Department of Family Medicine Faculty of Medicine King Abdulaziz University Jeddah Saudi Arabia Department of Laboratory Medicine Faculty of Applied Medical Sciences Umm Al-Qura University Makkah Saudi Arabia Department of Medicine King Abdulaziz University Jeddah Saudi Arabia Computer Science Department College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Applied Pharmaceutical Sciences and Clinical Pharmacy Faculty of Pharmacy Al-Isra University Amman Jordan Department of Medicine Batterjee Medical College Jeddah Saudi Arabia Department of Pediatrics Umm Al-Qura University Makkah Saudi Arabia

Background: As the Coronavirus Disease 2019 (COVID-19) outbreak has made a tremendous impact on medical education and healthcare institutions, we aimed to measure effects of online classes on medical students’ comprehension in comparison with attending campus classes during the COVID-19 pandemic. Methods: A cross-sectional survey has been conducted between September 2020 and June 2021 in the western region of Saudi Arabia. The Convenience sampling technique was conducted to collect the data from medical students in their basic and clinical years, using a questionnaire that involved 45 multiple-choice and multiple-answer questions. Results: Out of 3700 questionnaires, 922 completed the questionnaires from 11 different medical schools. Umm AL-Qura University had the highest response rate with 232 responses (25.2%), followed by King Abdulaziz University with 186 responses (20.2%). The majority of institutions preferred Blackboard and Zoom as platforms for e-learning. A total of 355 (38.5%) believed that it resulted in higher academic achievement, whereas 555 (60.2%) of students believed the limitation of clinical access was one of the biggest disadvantages of e-learning. Overall, 518 (56.2%) of students did not want to continue using e-learning on its own in the future. Whereas 668 (72.5%) wished to keep using e-learning in combination with traditional learning. Conclusion: According to our findings, advantages of e-learning vary among students. Most of the students thought e-learning to be an interactive system that provides a learning opportunity. In contrast, many of the students believed that there were many disadvantages regarding online teaching methods. © 2022 Samannodi et al.

关键词： COVID-19 e-learning learning online students

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Feature Selection and Classification Using CatBoost Method for Improving the Performance of Predicting Parkinson’s Disease 1st

Feature Selection and Classification Using CatBoost Method f...

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1st International Conference of Advanced Computing and informatics, ICACIN 2020

作者： Al-Sarem, Mohammed Saeed, Faisal Boulila, Wadii Emara, Abdel Hamid Al-Mohaimeed, Muhannad Errais, Mohammed College of Computer Science and Engineering Taibah University Medina Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Computers and Systems Engineering Department Al-Azhar University Cairo Egypt Research and Computer Innovation Laboratory Hassan II University of Casablanca Casablanca Morocco

ISBN: (纸本)9789811560477

Several studies investigated the diagnosis of Parkinson’s disease (PD), which utilized machine learning methods such as support vector machine, neural network, Naïve Bayes and K-nearest neighbor. In addition, different ensemble methods were used such as bagging, random forest and boosting. On the other hand, different feature ranking methods have been used to reduce the data dimensionality by selecting the most important features. In this paper, the ensemble methods, random forest, XGBoost and CatBoost were used to find the most important features for predicting PD. The effect of these features with different thresholds was investigated in order to obtain the best performance for predicting PD. The results showed that CatBoost method obtained the best results. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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Adaptive latent fingerprint image segmentation and matching using Chan-vese technique based on EDTV model 2

Adaptive latent fingerprint image segmentation and matching ...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Hilles, Shadi M S Liban, Abdilahi Altrad, Abdullah Mahmoud Miaikil, Othman A.M. El-Ebiary, Yousef A. Baker Contreras, Jennifer Hilles, Mohanad M Istanbul OKAN University Software Engineering Department Istanbul Turkey Hargeisa University Computer Science Department Hargeisa Somalia Al-Madinah International University Computer Science Department Kuala Lumpur Malaysia Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Trengganu Malaysia FEU Institute of Technology Department of Information Technology Manila Philippines University College of Applied Science Information Technology Department Gaza Palestine

ISBN: (纸本)9781665432221

Biometrics such as face, fingerprint, iris, voice and palm prints are the most widely used, and as well the fingerprints are one of the most frequently used biometrics to identify individuals and authenticate their identity. commonly categorized into three different categories which are rolled, plain and latent fingerprints. The reliability of image segmentation for latent fingerprint which is used in criminal issues still challenges, The difficulty of latent fingerprint image segmentation mainly lies in the poor quality of fingerprint patterns and the presence of the noise in the background, This research has investigated the fingerprint segmentation and matching based on EDTV and presented Chan-vese active contour segmentation technique, in addition, presented NIST SD27 for grayscale dataset of latent fingerprint which is standard by National Institute of Standard and Technology, where is dataset have varieties of fingerprint image samples, a total about 258 of latent fingerprint, those samples collected from crime scenes and matching fingerprint and shown the performance of matching accuracy ROC and CMC curves, To evaluate the performance of the matching ROC and CMC curves has been deployed, The area under curve (AUC) of the ROC of the good images performance is 72% with CMC rank1-idnetification of 42% and rank-20 identification of 79%. the result shows that the latent fingerprint method performance is better for good latent fingerprint images compare to bad and ugly images, while there is no much difference for bad and ugly image. © 2021 IEEE.

关键词： Biometrics

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