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6th International Conference on Advancements in Computing, ICAC 2024

作者： Madushan, Anjana Fernando, Priyantha Mahendra, Hesara Kottage, Nowen Kasthuriarachchi, Sanvitha Rajapaksha, Samantha Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

In cricket training, preventing injuries and developing skills are of utmost importance, especially concerning the school cricketers. This research presents a web application, 'CricBoost' which would serve to address all these vital issues through a multifaceted strategy. This study offers batsman pose accuracy assessment and correction using MediaPipe and Random Forest algorithm, Analysis of bowling and grip using MediaPipe, VGG16 and Gradient Boost models for classification and Shap for providing suggestions on the optimal methods, Suggestion of Shot selection for Batsman, where YOLOv8 and OpenCV are applied to ball pitch analysis and the player is given suggestions about the right shot to take, and cricket drill recommendation system focused on targeted skill improvement of players. CricBoost will deliver a unique combination that will increase players' technical skills, strategic intelligence, and performance while simultaneously focusing on injury prevention by teaching correct techniques. © 2024 IEEE.

关键词： Cricket (Sports)

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EMT VR - A Gamified Emergency Response Training System Using Virtual Reality and Artificial Intelligence 9

EMT VR - A Gamified Emergency Response Training System Using...

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9th IEEE Smart World Congress, SWC 2023

作者： Dib, Noor Alattas, Noor Fatima, Alneyadi Obaidat, Heba Ismail, Heba Abu Dhabi University College of Engineering Department of Computer Science and Information Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350319804

Traditional, real-life, and in-person training for emergency medical technicians (EMTs) poses many challenges for trainees, which can, in-turn, affect patients, leaving them victim to accidents in live emergency responses that can result in mortality. As an alternative, virtual reality (VR) training is a safer and more affordable method to prepare for disasters and emergencies that enhances response capacity of EMTs. In this study, we proposed an immersive gamified VR simulation, EMT VR, for trainees of the emergency response healthcare field. This system hosts 3D simulations of realistic emergency case scenarios in line with the United Arab Emirates (UAE) national EMT guidelines and protocols to help institutions and healthcare entities to train their paramedics, EMTs and other first responders. Using the proposed simulation, they can repeatedly access equipment in a fully immersive and safe environment. It has the scope to enhance EMT's preparedness for real-world crises and fine-tune their stress management and decision-making abilities. In turn, trainee self-confidence is developed because of the tolerance to errors during the simulated training process, unlike in real-life. It also develops a certification exam to be completed by trainees upon successfully passing all the training scenarios. © 2023 IEEE.

关键词： digital twin emergency response emergency response technician (EMT) game-based training Virtual reality (VR)

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A Generative Method for Finger Knuckle Print Recognition 27th

A Generative Method for Finger Knuckle Print Recognition

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wang, Yuqi Zhang, Bob Li, Shuyi Yang, Hao PAMI Research Group Department of Computer and Information Science University of Macau Macau SAR China Department of Information Beijing University of Technology Beijing100124 China

ISBN: (纸本)9783031781032

Finger knuckle print (FKP), known as a biological feature, has drawn great research attention in the field of biometrics recognition. That being said, the development of finger knuckle print recognition is still limited by the lack of data and the difficulties in the extraction of its region of interest (ROI). To resolve these issues, this paper proposes a generative method based on the simulation of the curve distribution of a finger knuckle print to generate reasonable masks of finger knuckle points. Following this, generative adversarial networks (GANs) are applied with the masks to generate the pseudo finger knuckle point images. This method can provide large amounts of training data for recognition as well as directly supplying the region of interest. Experimental results show that the generated finger knuckle print examples can effectively augment the training data for the recognition model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Structure learning of Bayesian networks using sparrow optimization algorithm

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2025年第2期9卷 14-25页

作者： Kareem, Shahab Wahhab Awla, Hoshang Qasim Mohammed, Amin Salih Department of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq Department of Computer Technical Engineering Al-Qalam University College Kirkuk Iraq Department of Computer Science Faculty of Science Soran University Soran Iraq Department of Software and Informatics Salahaddin University Erbil Iraq

Bayesian networks are powerful analytical models in machine learning, used to represent probabilistic relationships among variables and create learning structures. These networks are made up of parameters that show conditional probabilities and a structure that shows how random variables interact with each other. The structure is shown by a directed acyclic graph. Despite the NP-hard nature of learning Bayesian network structures, there has been significant progress in improving the accuracy of approximation solutions. The main focus is on score-based search strategies, which make use of functions to evaluate network models and identify structures with high scores. This study is significantly focused on structure learning Bayesian networks using the Bayesian Dirichlet equivalent uniform scoring function and metaheuristic search strategies. To this end, this paper presents the sparrow optimization algorithm (SOA), a new metaheuristic algorithm derived from the foraging behavior of sparrows. SOA performs a concurrent optimization in the solution space by simultaneously performing a local and global search that leads to the discovery of near-optimal structures. The results from our experiments on several benchmark datasets show that SOA yields overall better performance than SA and greedy search algorithms. In particular, it is claimed that by applying the proposed approach of SOA, the convergence speed is significantly higher compared with the existing ones;F1 score is 0.35 and 0.05 for the Hamming distance with better results. Given these results, signed operators prove to be very efficient in SOA’s Bayesian network structure learning as a concept, especially for real-world use. © (2024), (Society of Sytematic Innovation). All Rights Reserved.

关键词： Bayesian Network Global and Local Search Search and Score Sparrow Search Optimization Algorithm Structure Learning

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Assessment of Sentiment Analysis Using information Gain Based Feature Selection Approach

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computer Systems science & Engineering 2022年第11期43卷 849-860页

作者： R.Madhumathi A.Meena Kowshalya R.Shruthi Department of Computer Science and Engineering Sri Ramakrishna Engineering CollegeCoimbatoreIndia Government College of Technology CoimbatoreIndia

Sentiment analysis is the process of determining the intention or emotion behind an *** subjective information from the context is analyzed by the sentimental analysis of the people’s *** data that is analyzed quantifies the reactions or sentiments and reveals the information’s contextual *** social behavior,sentiment can be thought of as a latent *** and comprehending this behavior could help us to better understand the social *** sentiments are domain specific,sentimental analysis in a specific context is critical in any real-world *** sentiment analysis is done in sentence,document level and feature *** work introduces a new information Gain based Feature Selection(IGbFS)algorithm for selecting highly correlated features eliminating irrelevant and redundant *** textual sentiment analysis on sentence,document and feature levels are performed by exploiting the proposed information Gain based Feature Selection *** analysis is done based on the datasets from Cornell and Kaggle *** compared to existing baseline classifiers,the suggested information Gain based classifier resulted in an increased accuracy of 96%for document,97.4%for sentence and 98.5%for feature levels ***,the proposed method is tested with IMDB,Yelp 2013 and Yelp 2014 *** results for these high dimensional datasets give increased accuracy of 95%,96%and 98%for the proposed information Gain based classifier for document,sentence and feature levels respectively compared to existing baseline classifiers.

关键词： Sentiment analysis sentence level document level feature level information gain

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Fine tuning based Domain Adaptation for Machine Translation of Low Resource Indic Languages 15

Fine tuning based Domain Adaptation for Machine Translation ...

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15th Forum for information Retrieval Evaluation, FIRE 2023

作者： Dash, Amulya Ratna Anand, Harpreet Singh Sharma, Yashvardhan Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Rajasthan Jhunjhunu333031 India

This paper describes the proposed system for the machine translation of Indic language pairs Odia - Hindi and Hindi - Odia for the General Translation and Domain Specific Translation tasks proposed by Forum of information Retrieval Evaluation(FIRE) in 2023. For general task, the proposed system uses an ensemble of two pre-trained models and for domain specific task, the proposed system uses a pretrained model fine-tuned using domain specific training data filtered from open source datasets. © 2021 Copyright for this paper by its authors.

关键词： Search engines

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Crafting Tomorrow's Trends with Automated Precision, Efficiency, and Faultless Excellence in Apparel Industry for Medium Scale Businesses 6

Crafting Tomorrow's Trends with Automated Precision, Efficie...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Hiruni Herath, H.M. Jayaweera, J.M.M.G. Jayasinghe, J.M.Y. Randeni, K.M.T.D. Amarasena, Nelum Chathuranga Rathnayake, Samadhi Chandanathilaka, S.N. Sri Lankan Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lankan Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

In the evolving landscape of Sri Lanka's apparel industry, the predominance of manual methods in the pre-production phase necessitates innovative technological interventions to enhance efficiency. This research explores the complexities of garment production, addressing critical challenges such as machine breakdowns, maintenance, and workforce management. By employing advanced technologies such as image recognition, loT devices, and machine learning, this study aims to transform pre-production decision-making, optimize sewing machine performance, implement predictive maintenance strategies, and refine workforce optimization. These initiatives, driven by domain expertise and cutting-edge methodologies, not only aim to bridge existing gaps in the industry but also pave the way for significant advancements, ensuring a future characterized by precision, efficiency, and operational excellence. © 2024 IEEE.

关键词： Predictive maintenance

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The Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor Networks 3rd

The Effects of Weather Conditions on Data Transmission in Fr...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Kadhim, Aqeel A. Naif, Jolan R. Salman, Muna M. Hoomod, Haider K. Computer Techniques Engineering Department Imam AlKadhim University College Baghdad Iraq Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Information Technology Directorate Ministry of Science and Technology Baghdad Iraq College of Education Computer Science Department Al-Mustansiriya University Baghdad Iraq

ISBN: (纸本)9789819708918

It is necessary to collect data measured in open lands, whether for example for monitoring purposes or for border protection. The process of data transmission from active nodes and its next level to the cluster head (CH), followed by follow-up to arrive at the base station (BS), with safety and acceptability, are the main objectives of wireless sensor networks (WSNs). The key benefits of wireless systems are low-cost system installation and loss-free data transfer. Weather conditions in the spacious area affect wireless data transmission that is done between sensor nodes in free space. This study attempted to ascertain the influence of atmospheric and meteorological conditions on the loss of data when utilizing the wireless data transmission systems in a wireless sensor network. For this reason, the volume of data transfer losses that happen in free space because of three types of weather conditions is the main discussion point. The meteorological criteria that can be considered in this research are temperature, clear weather, dust, and rain as the weather elements that affect the Iraqi climate. The correlation analysis's outcomes show that the amount of precipitation and the size of raindrops have a main effect on the losses of data transmission. From a practical point of view, there is no confirmed statistical evidence of the amount of loss in the volume of transmitted data as a result of the amount of rainfall with the use of different transmission frequencies. In this research, we designed an objective routing protocol called Fuzzy Clustering with Ant Colony System FCACS, which has been utilized as a simulation program using the MATLAB 2018b application. The practical results of the proposed protocol for data transmission in free space and under different weather conditions were examined, and the simulation results were compared with those of two well-known routing protocols, which are DEEC and Z-SEP. In terms of the quantity of data received from the CH or BS a

关键词： Data transfer

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Detecting adversarial examples using constraint-based learning

Detecting adversarial examples using constraint-based learni...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Kumar, Vivek Kumar, Kamal Singh, Maheep National Institute of Technology Dept. of Computer Science -and Engineering Uttarakhand Pauri India IGDTUW Department of Information Technology New Delhi India Doon University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350394412

Deep learning models have been applied in various fields and continue producing exceptional results. However, these models are vulnerable to adversarial attacks, which are modified data samples maliciously crafted to cause misclassification by a target model. Fields like Network intrusion detection systems (NIDS) are security-critical;therefore, adversarial attacks pose a high risk in such applications. The attacker not only aims for evasion but also tries to launch an attack once the adversarial example has evaded detection by a NIDS. This paper presents a novel technique for detecting adversarial examples targeted towards a NIDS by learning the dependencies between features known as domain constraints. Our proposed method detects adversarial examples that violate these constraints learned during training. Compared with prevalent detection techniques, our method does not require generating adversarial examples for training, which reduces its computational requirement. We assume a grey-box threat model where the attacker can access the training data. We perform experiments on the CICIDS2017 dataset against five state-of-the-art adversarial attacks: FGSM, BIM, PGD, Deep fool and BIM. The results show that our method can detect up to 100% adversarial attacks, outperforming the contemporary schemes. © 2024 IEEE.

关键词： Adversarial machine learning

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Learning Age of information-Oriented Energy-Efficient Scheduling with Non-Orthogonal Multiple Access 13

Learning Age of Information-Oriented Energy-Efficient Schedu...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Li, Jinhua Cao, Yi Chan, Tse-Tin Pan, Haoyuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong

ISBN: (纸本)9798350355079

We propose a deep Q-network (DQN)-based user scheduling scheme aimed at low network-wide age of information (AoI) in a non-orthogonal multiple access (NOMA) uplink status update system. In our system, end users can harvest energy from wireless signals received on the radio-frequency band to extend the lifespan of their batteries. Given the battery replacement cost in many practical scenarios, our objective is to design a scheduling scheme to maintain a low AoI while efficiently utilizing energy harvesting and reducing the number of battery replacements. Considering the instantaneous AoI, remaining energy, and channel condition of end users, we model the user scheduling problem as a Markov decision process (MDP), which is solved by DQN. In particular, the choice of transmission power to save energy while enabling a high NOMA decoding rate is incorporated in the design of DQN. Experiments indicate that our DQN-based scheduling scheme achieves a low average AoI of the system with significantly fewer battery replacements compared to various benchmarking schemes. © 2024 IEEE.

关键词： Markov processes

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