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Cricket shot classification using Deep Learning 2

Cricket shot classification using Deep Learning

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Kallur, Shanta Thotad, Puneeth N. Yaligar, Suman Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Computer Science & Engineering Hubballi580027 India Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Master of Computer Applications Hubballi580027 India K.L.E. Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India

ISBN: (纸本)9798350388916

Cricket is a popular sport that involves various batting techniques and shot types. Accurate and real-time detection of cricket shots can provide priceless visual images for coaches, players, and analysts. This study proposes an artificial intelligence-based approach for cricket shot detection aimed at automatically recognizing and classifying different shots played by batsmen. Images are extracted and pre-processed to enhance their quality. The pre-processed images extract key features like bat trajectory, player movement, and batsman position. A training dataset is used to enable shot classification, consisting of labeled samples of different cricket shots. Neural network models, such as a convolutional neural network, VGG16, VGG19, are trained using an image set to learn the patterns and characteristics of each shot type. The model is then employed to classify shots in real time for predicting the corresponding shot type based on the selected features. The proposed approach is evaluated on a diverse dataset of cricket match images, encompassing a broad extent of shots played by professional batsmen. Execution metrics such as accuracy, precision, recall, and F1-score are used to assess the effect of the shot detection system. A VGG16 model in cricket shot prediction of the dataset yielded an impressive accuracy of 95.81%. The solution establishes the efficiency of the planned approach in accurately detecting and classifying cricket shots in real-time. The developed system has the potential to enhance cricket coaching, player performance analysis, and broadcast coverage by providing valuable insights into the shot selection and execution of batters. © 2024 IEEE.

关键词： Convolutional neural networks

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The Role of Syntactic Span Preferences in Post-Hoc Explanation Disagreement 30

The Role of Syntactic Span Preferences in Post-Hoc Explanati...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Kamp, Jonathan Beinborn, Lisa Fokkens, Antske Computational Linguistics and Text Mining Lab Vrije Universiteit Amsterdam Netherlands Institute of Computer Science Campus Institute Data Science University of Göttingen Germany Dept. of Mathematics and Computer Science Eindhoven University of Technology Netherlands

ISBN: (纸本)9782493814104

Post-hoc explanation methods are an important tool for increasing model transparency for users. Unfortunately, the currently used methods for attributing token importance often yield diverging patterns. In this work, we study potential sources of disagreement across methods from a linguistic perspective. We find that different methods systematically select different classes of words and that methods that agree most with other methods and with humans display similar linguistic preferences. Token-level differences between methods are smoothed out if we compare them on the syntactic span level. We also find higher agreement across methods by estimating the most important spans dynamically instead of relying on a fixed subset of size k. We systematically investigate the interaction between k and spans and propose an improved configuration for selecting important tokens. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Syntactics

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Integrated and Intelligent Elder-Friendly E-commerce Framework using NLP Techniques

Integrated and Intelligent Elder-Friendly E-commerce Framewo...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Sivasanjith, G. Mohan Prabhu, N. Hemaswathi, S. Rajkumar, P. Rajeshkannan, C. Dhivya, R. Kare Dept. of Information Technology Krishnankovil India Artificial Intelligence and Data Science Indra Ganesan College of Engineering Tiruchirappalli India Mount Zion College of Engg. and Tech. Dept. of Computer Science and Engg. Pudukottai India Suguna College of Engineering Dept. of Computer Science and Engg. Coimbatore India M.Kumarasamy College of Engineering Dept. of Information Technology Karur India

ISBN: (纸本)9798331529833

All over the world, the population of elderly people is increasing a lot. There needs to be special attention to the welfare of the elderly person to make them confident in their independent quality of living. In recent years, researchers have shown much interest in smart home automation for people who live alone without any human assistance. However, the purchase of medical drugs, diapers, etc., outside the house has to be fulfilled by their family members or caretakers. In most cases, it is not feasible, as the family members may stay long away from the elders. Purchasing online is easy;however, analyzing the best product requires knowledge in each e-commerce application for better quality at a low cost. This paper proposes an integrated and intelligent e-commerce framework to avoid the drawback of purchasing items online in a single window using the NLP technique. The proposed framework provides offers, discounts, star ratings, and product reviews by analyzing other customers' review data and purchase patterns. It provides interactive data analysis at a single window for the ease of elderly persons. The performance of the proposed framework was evaluated by 120 elderly persons using a simple questionnaire with a statistical analysis of a sample purchase. © 2025 IEEE.

关键词： Recommender systems

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Recognition of Handwritten Punjabi Characters Using Machine Learning Techniques 1

Recognition of Handwritten Punjabi Characters Using Machine ...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, P. Kaushik, Manas Manglik Chauhan, Yuvraj Singh Kumar, Narendra Kumar, Ashwani Rathore, Rachna Chandigarh University Dept. of CSE Chandigarh India Manipal University Dept. of Mechanical and Industrial Eng Karnataka India Chandigarh University Dept. of Computer Science Engineering Chandigarh India Usha Martin University Dept. of Computing and IT Jharkhand India Mangalayatan University Dept. of Computer Eng. and Applications Aligarh India Shrimant Madhav Rao University Dept. of Mathematics Gwalior India

ISBN: (纸本)9798350356816

Detecting handwritten Punjabi alphabets (PbAD) presents significant challenges for text detection systems due to the similarity among many characters and their complex curves and edges. Existing automatic detection systems often struggle to deliver high accuracy with real-world data. In this study, we propose an approach for PbAD that synthesizes various techniques reviewed from existing literature to optimize accuracy. Our methodology involves evaluating and combining multiple classification and feature extraction techniques, as identified through an extensive literature review. Unlike many studies that rely on manually created datasets, our approach will leverage both available online datasets and those developed in previous research. Previous research has reported accuracies ranging from 80% to 99%, and our goal is to advance these results by integrating and refining the best practices from the literature. © 2024 IEEE.

关键词： Adversarial machine learning

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Development of an AI based Priority Formation Mechanism in Criminal Law 3

Development of an AI based Priority Formation Mechanism in C...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Sharma, Kashish Amburle, Ankita Shelake, Vijay Chauhan, Ashvini Ashok Dept. of Computer Engineering Fr. Conceicao Rodrigues College of Engineering Mumbai India Dept. of Artificial Intelligence and Data Science Fr. Conceicao Rodrigues College of Engineering Mumbai India

ISBN: (纸本)9798331532420

Nowadays, there is a necessity to tackle a number of pending crime cases across different parts of the world through effective scheduling and management. AI and ML can be used in handling unfinalized criminal cases in India can be one of the possible ways of improving the functioning of the criminal justice system in this country. The approach to this project is to create an AI system that will filter through the open cases and then rank them according to complexity, time-sensitivity, prior performances, and location. Since priority in a case is explained through the use of a supervised learning based on selected set, then law enforcement as well as legal personnel are able to shift most of their attention to other significant cases. Despite feature engineering and data preprocessing being in the construction of the system from the data collected from the various sources such as court records. This means that there is than models such as;Random Forest, Apriori and Rule-Based in an effort to establish the most suitable model. Perhaps the application of this AI based mechanism to form priorities could aid India in better organization of resources and with the assistance of backlog decrease – the justice system in India. But as the right touch to measure artificial use is considered, themes of privacy, bias, and fairness must also be addressed appropriately. © 2024 IEEE.

关键词： Crime

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Localized Zeroth-Order Prompt Optimization 38

Localized Zeroth-Order Prompt Optimization

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Wenyang Shu, Yao Yu, Zongmin Wu, Zhaoxuan Lin, Xiaoqiang Dai, Zhongxiang Ng, See-Kiong Low, Bryan Kian Hsiang Dept. of Computer Science National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore China Singapore-MIT Alliance for Research and Technology Singapore School of Data Science The Chinese University of Hong Kong Shenzhen China

The efficacy of large language models (LLMs) in understanding and generating natural language has aroused a wide interest in developing prompt-based methods to harness the power of black-box LLMs. Existing methodologies usually prioritize a global optimization for finding the global optimum, which however will perform poorly in certain tasks under budget constraints. This thus motivates us to re-think the necessity of finding a global optimum in prompt optimization. To answer this, we conduct a thorough empirical study on prompt optimization and draw two major insights. Contrasting with the rarity of global optimum, local optima are usually prevalent and well-performed, which can be more worthwhile for efficient prompt optimization (Insight I). The choice of the input domain, including both the generation and the representation of prompts, affects the identification of well-performing local optima (Insight II). Inspired by these insights, we propose a novel algorithm, namely localized zeroth-order prompt optimization (ZOPO), which incorporates a Neural Tangent Kernel-based derived Gaussian process into standard zeroth-order optimization for an efficient search of well-performing local optima in prompt optimization. Remarkably, ZOPO outperforms existing baselines in terms of both the optimization performance and the query efficiency, which we demonstrate through extensive experiments. Our implementation is available at https://***/allen4747/ZOPO. © 2024 Neural information processing systems foundation. All rights reserved.

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MedPipe: End-to-End Joint Search of data Augmentation and Neural Architecture for 3D Medical Image Classification 1

MedPipe: End-to-End Joint Search of Data Augmentation and Ne...

引用

1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： He, Xin Chu, Xiaowen Hong Kong Baptist University Dept. Computer Science Hong Kong Hong Kong Data Science and Analytics Thrust Guangzhou China

ISBN: (纸本)9798350358780

data augmentation plays a crucial role in deep learning-based medical imaging analysis, but manually designing tailored data augmentation strategies for each dataset is impractical. Although automatic data augmentation (ADA) techniques have been explored, they often focus solely on data augmentation without considering the importance of neural architecture. Similarly, neural architecture search (NAS) methods mainly concentrate on optimizing the neural architecture while overlooking the impact of data augmentation. However, both data augmentation and neural architecture are interrelated and should be considered together. The joint optimization of data augmentation and neural architecture can lead to improved model performance by harnessing the complementary effects of customized data augmentation strategies and compatible neural architectures. Despite this, the seamless integration of data augmentation and neural architecture search remains under-explored. To address this research gap, we propose MedPipe, an approach that enables end-to-end joint search of data augmentation and neural architecture. We introduce a compact data augmentation search space and unify data augmentation and neural architecture into a cohesive network. This allows simultaneous exploration, optimizing their synergy for enhanced performance. Experimental evaluation on nine medical datasets highlights the necessity of the joint search for data augmentation and neural architecture, demonstrating the superior performance of our approach. Our work opens up possibilities for future applications in diverse medical domains. © 2023 IEEE.

关键词： Medical imaging

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WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking 38

WelQrate: Defining the Gold Standard in Small Molecule Drug ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Yunchao Dong, Ha Wang, Xin Moretti, Rocco Wang, Yu Su, Zhaoqian Gu, Jiawei Bodenheimer, Bobby Weaver, Charles David Meiler, Jens Derr, Tyler United States Neural Science Dept. Amherst College United States Chemistry Dept. VU United States Computer Science Dept. University of Oregon United States Data Science Institute VU United States MD Anderson Cancer Center United States Electrical and Computer Engineering Dept. VU United States Psychology Dept. VU United States Institute of Chemical Biology VU United States Center for Structural Biology VU United States Pharmacology Dept. VU United States Germany Computer Science Dept. LU Germany Chemistry Dept. LU Germany

While deep learning has revolutionized computer-aided drug discovery, the AI community has predominantly focused on model innovation and placed less emphasis on establishing best benchmarking practices. We posit that without a sound model evaluation framework, the AI community's efforts cannot reach their full potential, thereby slowing the progress and transfer of innovation into real-world drug discovery. Thus, in this paper, we seek to establish a new gold standard for small molecule drug discovery benchmarking, WelQrate. Specifically, our contributions are threefold: WelQrate dataset collection - we introduce a meticulously curated collection of 9 datasets spanning 5 therapeutic target classes. Our hierarchical curation pipelines, designed by drug discovery experts, go beyond the primary high-throughput screen by leveraging additional confirmatory and counter screens along with rigorous domain-driven preprocessing, such as Pan-Assay Interference Compounds (PAINS) filtering, to ensure the high-quality data in the datasets;WelQrate Evaluation Framework - we propose a standardized model evaluation framework considering high-quality datasets, featurization, 3D conformation generation, evaluation metrics, and data splits, which provides a reliable benchmarking for drug discovery experts conducting real-world virtual screening;Benchmarking - we evaluate model performance through various research questions using the WelQrate dataset collection, exploring the effects of different models, dataset quality, featurization methods, and data splitting strategies on the results. In summary, we recommend adopting our proposed WelQrate as the gold standard in small molecule drug discovery benchmarking. The WelQrate dataset collection, along with the curation codes, and experimental scripts are all publicly available at ***. © 2024 Neural information processing systems foundation. All rights reserved.

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DivLog: Log Parsing with Prompt Enhanced In-Context Learning 24

DivLog: Log Parsing with Prompt Enhanced In-Context Learning

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Xu, Junjielong Yang, Ruichun Huo, Yintong Zhang, Chengyu He, Pinjia The Chinese University of Hong Kong Dept. of Computer Science & Engineering Hong Kong Eth Zurich Switzerland School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data China

ISBN: (纸本)9798400702174

Log parsing, which involves log template extraction from semi-structured logs to produce structured logs, is the first and the most critical step in automated log analysis. However, current log parsers suffer from limited effectiveness for two reasons. First, traditional data-driven log parsers solely rely on heuristics or handcrafted features designed by domain experts, which may not consistently perform well on logs from diverse systems. Second, existing super-vised log parsers require model tuning, which is often limited to fixed training samples and causes sub-optimal performance across the entire log source. To address this limitation, we propose Di-vLog, an effective log parsing framework based on the in-context learning (ICL) ability of large language models (LLMs). Specifically, before log parsing, DivLog samples a small amount of offline logs as candidates by maximizing their diversity. Then, during log parsing, DivLog selects five appropriate labeled candidates as examples for each target log and constructs them into a prompt. By mining the semantics of examples in the prompt, DivLog generates a target log template in a training-free manner. In addition, we design a straightforward yet effective prompt format to extract the output and enhance the quality of the generated log templates. We conducted experiments on 16 widely-used public datasets. The results show that DivLog achieves (1) 98.1% Parsing Accuracy, (2) 92.1% Precision Template Accuracy, and (3) 92.9% Recall Template Accuracy on average, exhibiting state-of-the-art performance. © 2024 ACM.

关键词： Semantics

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Finding Optimal Routes in MaaS Framework using ParetoBFS 6

Finding Optimal Routes in MaaS Framework using ParetoBFS

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6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Mitra, Poojarini Rehena, Zeenat Mukherjee, Nandini University of Engineering & Management Dept. of Computer Applications Kolkata India Aliah University Dept. of Computer Science & Engineering Kolkata India Jadavpur University Dept. of Computer Science & Engineering Kolkata India

ISBN: (纸本)9781665410359

To offer Mobility as a Service (MaaS) to the consumers while handling time-dependent multi-modal transportation and maintaining multiple criteria is a complex task. The system needs to be intelligent enough to provide optimal routes within the least possible computation time. This paper proposes a simple method that seeks to provide a solution by using the ParetoBFS algorithm, a modified version of the traditional BFS algorithm, which uses Pareto constraints to prune the graph and find all Pareto-optimal paths in a network considering multiple criteria. Evaluations with different scenarios were performed using the proposed mechanism and its performance was compared with Weighted-Sum Dijkstra's Algorithm. It can be observed that the proposed mechanism is achieving better results than Weighted-Sum Dijkstra's Algorithm. © 2022 IEEE.

关键词： Pareto principle

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