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检索条件"机构=Computer Science with Artificial Intelligence and Machine Learning"

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Smart Education Case: A detection method of the driver's smoking behavior 2

Smart Education Case: A detection method of the driver's smo...

引用

2nd International Conference on Informatics, Networking, and Computing, ICINC 2023

作者： Xu, Yujia Shi, Rui Pan, Deyu Yang, Li He, Tingting Ding, Ling School of Computer Science Hubei University of Education Wuhan China Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan China

ISBN: (纸本)9781510674745

This article presents a case study of our process in Smart Education. This is a case of detecting driver smoking behavior, in which intelligent image processing technology was used. Let students know how to conduct academic research through case analysis. Train students how to do image preprocessing, how to do detection algorithm, how to deal with experimental results, how to do result analysis and so on. Drivers have a kinds of common abnormal behaviors, including smoking, making phone calls, eating, and leaving the steering wheel. Among them, smoking is closely related to the fatigue state of the driver;when drivers make other abnormal behaviors, their hands will leave the steering wheel, which is easy to cause the car to get out of control. Therefore, this paper mainly focuses on the abnormal driving behaviors of smoking and the hands leaving the steering wheel. © 2024 SPIE.

关键词： Students

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Safe Steps: A Smart Security System for Elderly

Safe Steps: A Smart Security System for Elderly

引用

Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC), International Conference on

作者： Sanjana Karunya KV Ushashree Veja J Shetty Gurusiddayya Hiremath Department of Computer Science & Engineering(Artificial Intelligence & Machine Learning) Sahyadri College of Engineering and Management Mangalore Karnataka India

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517908

Falls are one of the primary causes of injuries among older adults, which lowers their quality of life and has detrimental effects on their health. As the global population ages, it's crucial that they maintain their independence and safety. The proposed model aims to provide a comprehensive solution to enhance senior citizen protection. Promptly identifying and avoiding falls can improve the well-being of elderly individuals and greatly reduce risks. Traditional methods of fall detection are prone to errors. Thus, the proposed model is designed to overcome such errors and accurately predict the falls. Tiny ML simplifies the deployment of machine learning models on low-power microcontrollers and edge devices, making it a great choice for lightweight and energy efficient applications by designing a small wearable device that can process data locally for quick detection and alert generation which helps to improve the safety and independence of elderly people. The proposed model also includes camera-based fall detection for indoor navigation.

关键词： Tiny machine learning Virtual assistants Smart homes Real-time systems Energy efficiency Older adults Fall detection Wearable devices Usability Monitoring

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PROCEDURAL FAIRNESS THROUGH DECOUPLING OBJECTIONABLE DATA GENERATING COMPONENTS 12

PROCEDURAL FAIRNESS THROUGH DECOUPLING OBJECTIONABLE DATA GE...

引用

12th International Conference on learning Representations, ICLR 2024

作者： Tang, Zeyu Wang, Jialu Liu, Yang Spirtes, Peter Zhang, Kun Department of Philosophy Carnegie Mellon University United States Computer Science and Engineering Department University of California Santa Cruz United States Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates ByteDance Research China

We reveal and address the frequently overlooked yet important issue of disguised procedural unfairness, namely, the potentially inadvertent alterations on the behavior of neutral (i.e., not problematic) aspects of data generating process, and/or the lack of procedural assurance of the greatest benefit of the least advantaged individuals. Inspired by John Rawls's advocacy for pure procedural justice (Rawls, 1971;2001), we view automated decision-making as a microcosm of social institutions, and consider how the data generating process itself can satisfy the requirements of procedural fairness. We propose a framework that decouples the objectionable data generating components from the neutral ones by utilizing reference points and the associated value instantiation rule. Our findings highlight the necessity of preventing disguised procedural unfairness, drawing attention not only to the objectionable data generating components that we aim to mitigate, but also more importantly, to the neutral components that we intend to keep unaffected. © 2024 12th International Conference on learning Representations, ICLR 2024. All rights reserved.

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Evaluation of machine learning Models for Kidney Stone Classification Using HOG Features on CT Images

Evaluation of Machine Learning Models for Kidney Stone Class...

引用

2024 International Conference on Intelligent and Innovative Practices in Engineering and Management, IIPEM 2024

作者： Ravikumar, S. Chandralekha, E. Vijay, K. Sundar, Koushik Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Citibank NA Jersey CityNJ United States

ISBN: (纸本)9798350390049

Kidney stones, or renal calculi, represent hard mineral and salt deposits which are formed in the kidneys and may cause intense pain, infections of the urinary tracts, and other complications. machine learning techniques are commonly applied to the problem of kidney stone classification, as this task is critical in medical imaging and contributes to correct diagnosis and treatment. This work aims to evaluate the performance of Gradient Boost, XGBoost, AdaBoost, and CatBoost models when processing a set of kidney CT images organized in Cyst, Normal, Stone, and Tumor classes. The initial dataset, available on Kaggle, is placed in the public domain and includes grayscale images resized to 128×128 pixels: these images are further processed using Histograms of Oriented Gradients. It was found that although AdaBoost achieved high precision for Cysts, lower overall accuracy (0.71) and variable performance, particularly in Stone classification, were observed. Conversely, XGBoost, Gradient Boosting, and CatBoost demonstrated near-perfect accuracy (0.99) and ROC values of 1.00 across all categories, with CatBoost showing especially consistent performance. The superior classification capabilities of XGBoost, Gradient Boosting, and CatBoost for kidney stone identification were highlighted, emphasizing the importance of selecting ML models based on detailed performance metrics. Future research may focus on optimizing these models or enhancing feature extraction methods. © 2024 IEEE.

关键词： Medical imaging

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Optimizing Semantic Segmentation for Enhanced Football Analytics: A Pixel-level Approach 2

Optimizing Semantic Segmentation for Enhanced Football Analy...

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2nd International Conference on machine learning and Data Engineering, ICMLDE 2023

作者： Malakreddy, Bharathi Venkataraman, Sadanand Khan, Mohammed Sinan Nidhi Padmanabhuni, Srinivas Natarajan, Santhi Research & Consultancy Cell BMS Institute of Technology & Management Bengaluru India Department of Computer Science and Engineering Research Shiv Nadar University Chennai India Department of Artificial Intelligence & Machine Learning BMS Institute of Technology & Management Bengaluru India *** Bengaluru India

Semantic Segmentation, a pivotal technique in image analysis, is adeptly leveraged in this research to bolster sports analytics, with a concentrated focus on football. A comprehensive pipeline is unveiled for an in-depth analysis of a select portion of the IAUFD 100k dataset, encompassing 2030 manually annotated football images. The methodology entails a thorough evaluation and comparison of diverse semantic segmentation models, supplemented by the integration of advanced pre-processing strategies and optimal training techniques. Such a holistic approach culminates in a marked enhancement in model performance, as evidenced by a significant uptick in the mean Intersection over Union (mIoU). This research offers granular, object-oriented insights that substantially augment player tracking, action recognition, and event detection in football. The conclusive remarks of the study highlight prospective avenues for further research, emphasizing the potential incorporation of Explainable AI and advanced Metamorphic and Security Testing to fortify sports analytics. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sports

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Improving Accuracy in Network Intrusion Detection via machine learning Algorithms: An In-Depth Examination

Improving Accuracy in Network Intrusion Detection via Machin...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Suriya Prakash, J. Trishlaa, S. Soniya, R. Lakshmanan, V. Kiran, S. Department of Cse Ramnagara India Rajarajeshwari College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Nitte Meenakshi Institute of Technology Department of Mathematics Bengaluru India

ISBN: (纸本)9798350375442

Intrusion detection is essential for securing computer networks by monitoring and analyzing activity to detect and respond to unauthorized access and malicious behavior. By examining network traffic and system logs, intrusion detection systems (IDS) help protect data integrity and availability, enabling swift responses to potential security incidents. In an ever-evolving cyber threat landscape, IDS plays a critical role in maintaining network security. This research investigates the performance of various machine learning algorithms for classification tasks using a dataset from Kyoto collected on December 31st, 2015. The algorithms explored include Naive Bayes, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), AdaBoost, and CatBoost. Training and testing the model with different data splits (80/20, 70/30, 60/40) revealed that AdaBoost, LGBM, XGBoost, Decision Tree, Gradient Boosting, and Random Forest consistently delivered the highest accuracy. The study concludes that selecting the appropriate algorithms and data splits is crucial for enhancing IDS effectiveness, providing valuable insights for future research. Explore the code behind this study at https://***/Trishlaa07/Kyoto_2015_Dec_day31 © 2024 IEEE.

关键词： Network intrusion

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Rotation-Invariant Self-Supervised Representation learning for Object Detection in Autonomous Driving 2

Rotation-Invariant Self-Supervised Representation Learning f...

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2nd International Conference on Computational intelligence and Network Systems, CINS 2024

作者： Marathe, Aboli Roy, Sidharth Singh, Devansh Walambe, Rahee Kotecha, Ketan Machine Learning Dept. Carnegie Mellon University Pittsburgh United States Dept. of Computer and Information Science University of Pennsylvania Philadelphia United States Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International University Pune India

ISBN: (纸本)9798331504106

This paper describes a self supervised representation learning approach that can perform robust object detection in out-of-distribution rotated images for autonomous driving task. Keeping in mind the limitations of vehicle sensors we incorporate self-supervised rotation invariance for correcting rotational corruptions. We present a comprehensive analysis of observed label shifts, validate the results using holdout sets and provide explainability for the performance uisng GradCAM appaorach. We demonstrate results on real-world adversarial examples: (Autonomous Scenes - DAWN dataset) that significantly outperform common methods. The proposed framework outperforms the rotation-affected benchmark by 14.73 points of mAP for the OD task in autonomous driving applications. ©2024 IEEE.

关键词： Self-supervised learning

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Sign Language Detection in Real-Time Applications

Sign Language Detection in Real-Time Applications

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Milinda, Renata Rachael Sara Ramsiej, Barrister Yasmin, Reshma Ambeth Kumar, V.D. Sekar, K. Anna University India Dept. of Artificial Intelligence and Machine Learning Anna University India Dept. of Computer Engineering Mizoram University Aizawl India Department of AIML Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798331510299

Individuals with hearing and speech impairments often rely on gesture language for communication, but a lack of widespread understanding of this language among others creates significant barriers. A machine learning model that recognizes hand signals and converts them into English can help bridge this gap, facilitating communication between hearing and deaf individuals. Existing non-verbal communication identification systems, while leveraging computational learning and AI models for both single- and double-handed gestures, generally lack real-time capabilities. This research proposes a live symbolic language identification platform that uses a webcam to build a regional gesture communication dataset and TensorFlow for cross-domain learning. Despite a smaller dataset, the system targets high accuracy in recognition. Advanced technologies, including computer vision and deep learning, are applied to improve communication for deaf individuals by developing accessible technical applications and platforms. The proposed model, utilizing TensorFlow and OpenCV, aims to identify commonly used American Sign Language gestures in real time with accuracy and efficiency, including signs such as "hello, " "thanks, " "bye, " "yes, " "no, " "dad, " and "mom". © 2024 IEEE.

关键词： Adversarial machine learning

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The Smart Factory of Tomorrow: artificial intelligence and machine learning Reshaping Manufacturing Processes 2

The Smart Factory of Tomorrow: Artificial Intelligence and M...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Priyanga, P. Ashwini, K. Deepa, S.R. Sridevi, S. Department of Artificial Intelligence and Machine Learning RNS Institute of Technology Karnataka Bangalore India Department of Computer Science and Design K.S. Institute of Technology Karnataka Bangalore India Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

The smart factory of the future would not be possible without the development of AI and ML technologies, which have ushered in a new era of production. Traditional industrial processes are being revolutionized by AI and ML due to their capacity to evaluate large quantities of data and make autonomous choices, which is leading to greater efficiency, productivity, and profitability. Predictive maintenance is one area where AI and ML are making important contributions. These systems may prevent unexpected and expensive failures by constantly monitoring equipment performance and analyzing real-time data. Taking preventative measures like these results in less downtime, lower maintenance expenses, and more efficient machinery. Synergies between AI and ML are improving factory quality assurance. These technologies can identify even the smallest flaws or deviations from product standards using sophisticated vision systems and pattern recognition algorithms. Manufacturing companies may reduce waste and customer complaints by maintaining a constant quality standard via the use of automated inspection methods. Optimization of production planning and scheduling is another important use of AI and ML in manufacturing. © 2023 IEEE.

关键词： artificial intelligence Collaborative Robots (Cobots) Industrial Internet of Things (IIoT) Lean Manufacturing machine learning Manufacturing Processes Predictive Maintenance Production Planning Quality Control Smart Factory

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MULTI-TASK REINFORCEMENT learning WITH MIXTURE OF ORTHOGONAL EXPERTS 12

MULTI-TASK REINFORCEMENT LEARNING WITH MIXTURE OF ORTHOGONAL...

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12th International Conference on learning Representations, ICLR 2024

作者： Hendawy, Ahmed Peters, Jan D'Eramo, Carlo Department of Computer Science TU Darmstadt Germany Germany Center for Cognitive Science TU Darmstadt Germany Systems AI for Robot Learning Germany Center for Artificial Intelligence and Data Science University of Würzburg Germany

Multi-Task Reinforcement learning (MTRL) tackles the long-standing problem of endowing agents with skills that generalize across a variety of problems. To this end, sharing representations plays a fundamental role in capturing both unique and common characteristics of the tasks. Tasks may exhibit similarities in terms of skills, objects, or physical properties while leveraging their representations eases the achievement of a universal policy. Nevertheless, the pursuit of learning a shared set of diverse representations is still an open challenge. In this paper, we introduce a novel approach for representation learning in MTRL that encapsulates common structures among the tasks using orthogonal representations to promote diversity. Our method, named Mixture Of Orthogonal Experts (MOORE), leverages a Gram-Schmidt process to shape a shared subspace of representations generated by a mixture of experts. When task-specific information is provided, MOORE generates relevant representations from this shared subspace. We assess the effectiveness of our approach on two MTRL benchmarks, namely MiniGrid and MetaWorld, showing that MOORE surpasses related baselines and establishes a new state-of-the-art result on MetaWorld. © 2024 12th International Conference on learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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