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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Rinku Sharma Dixit Shailee Lohmor Choudhary Mahaveerakannan R KAVEYA E Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Forest fires are extremely dangerous to ecosystems, animals, and humans; thus, it is crucial to detect them early on to limit the harm they cause. Presenting a novel framework for the detection of forest fires is the objective of this study. To increase detection speed and accuracy, this framework mixes colour, motion, and form information with machine learning algorithms. To capture the dynamic nature of fire—its ever-changing hues, distorted forms, and unending motion—an iterative method is employed. Colour probabilities are determined during the segmentation stage before going to the verification step. The categorisation stage and subsequent picture processing both make use of colour histograms. These characteristics allow the system to distinguish between flames and other things more effectively. Evaluating the framework's efficacy involved using intersection over union (IoU) ratios and frame-based evaluation. While IoU is used to test the system's ability to localise the fire inside an image, frame-based assessment is used to test the accuracy of fire detection. Tests on the VisiFire dataset proved the framework's dependability with an 89.97% true-positive rate and a 10.03% false-negative rate. And with an average frame rate of 21.70 FPS, the system is fast enough for real-time processing workloads. Considering these results, it seems like the suggested approach could greatly enhance forest fire early warning capabilities. It can also maintain a high degree of accuracy while providing quicker response times. Protecting forest ecosystems, reducing human life danger, and improving disaster management are all possible outcomes of integrating this technology into early warning systems. This framework is a huge step forward in the world of modern fire detection systems.

关键词： Support vector machines Accuracy Image color analysis Ecosystems Fires Forestry Alarm systems Real-time systems Random forests Monitoring

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Probabilistic Mission Design in Neuro-Symbolic Systems

arXiv

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arXiv 2024年

作者： Kohaut, Simon Flade, Benedict Ochs, Daniel Dhami, Devendra Singh Eggert, Julian Kersting, Kristian Artificial Intelligence and Machine Learning Lab Department of Computer Science TU Darmstadt Darmstadt64283 Germany Honda Research Institute Europe GmbH Carl-Legien-Str. 30 Offenbach63073 Germany Uncertainty in Artificial Intelligence Group Department of Mathematics and Computer Science TU Eindhoven Eindhoven5600 MB Netherlands Hessian AI Centre for Cognitive Science Germany

Advanced Air Mobility (AAM) is a growing field that demands accurate modeling of legal concepts and restrictions in navigating intelligent vehicles. In addition, any implementation of AAM needs to face the challenges posed by inherently dynamic and uncertain human-inhabited spaces robustly. Nevertheless, the employment of Unmanned Aircraft Systems (UAS) beyond visual line of sight (BVLOS) is an endearing task that promises to enhance significantly today's logistics and emergency response capabilities. To tackle these challenges, we present a probabilistic and neuro-symbolic architecture to encode legal frameworks and expert knowledge over uncertain spatial relations and noisy perception in an interpretable and adaptable fashion. More specifically, we demonstrate Probabilistic Mission Design (ProMis), a system architecture that links geospatial and sensory data with declarative, Hybrid Probabilistic Logic Programs (HPLP) to reason over the agent's state space and its legality. As a result, ProMis generates Probabilistic Mission Landscapes (PML), which quantify the agent's belief that a set of mission conditions is satisfied across its navigation space. Extending prior work on ProMis' reasoning capabilities and computational characteristics, we show its integration with potent machine learning models such as Large Language Models (LLM) and Transformer-based vision models. Hence, our experiments underpin the application of ProMis with multi-modal input data and how our method applies to many important AAM scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Air mobility

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Asymmetric Visual Semantic Embedding Framework for Efficient Vision-Language Alignment

arXiv

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arXiv 2025年

作者： Liu, Yang Liu, Mengyuan Huang, Shudong Lv, Jiancheng College of Computer Science Sichuan University Chengdu610065 China Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education Chengdu China State Key Laboratory of General Artificial Intelligence Peking University Shenzhen Graduate School Shenzhen China

learning visual semantic similarity is a critical challenge in bridging the gap between images and texts. However, there exist inherent variations between vision and language data, such as information density, i.e., images can contain textual information from multiple different views, which makes it difficult to compute the similarity between these two modalities accurately and efficiently. In this paper, we propose a novel framework called Asymmetric Visual Semantic Embedding (AVSE) to dynamically select features from various regions of images tailored to different textual inputs for similarity calculation. To capture information from different views in the image, we design a radial bias sampling module to sample image patches and obtain image features from various views, Furthermore, AVSE introduces a novel module for efficient computation of visual semantic similarity between asymmetric image and text embeddings. Central to this module is the presumption of foundational semantic units within the embeddings, denoted as "meta-semantic embeddings." It segments all embeddings into meta-semantic embeddings with the same dimension and calculates visual semantic similarity by finding the optimal match of meta-semantic embeddings of two modalities. Our proposed AVSE model is extensively evaluated on the large-scale MS-COCO and Flickr30K datasets, demonstrating its superiority over recent state-of-the-art methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Embeddings

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The Constitutional Filter: Bayesian Estimation of Compliant Agents

arXiv

引用

arXiv 2024年

作者： Kohaut, Simon Divo, Felix Flade, Benedict Dhami, Devendra Singh Eggert, Julian Kersting, Kristian Artificial Intelligence and Machine Learning Group Department of Computer Science TU Darmstadt Darmstadt64283 Germany Honda Research Institute Europe GmbH Carl-Legien-Str. 30 Offenbach63073 Germany Uncertainty in Artificial Intelligence Group Department of Mathematics and Computer Science TU Eindhoven Eindhoven5600 MB Netherlands Hessian AI Centre for Cognitive Science Germany

Predicting agents impacted by legal policies, physical limitations, and operational preferences is inherently difficult. In recent years, neuro-symbolic methods have emerged, integrating machine learning and symbolic reasoning models into end-to-end learnable systems. Hereby, a promising avenue for expressing high-level constraints over multi-modal input data in robotics has opened up. This work introduces an approach for Bayesian estimation of agents expected to comply with a human-interpretable neuro-symbolic model we call its Constitution. Hence, we present the Constitutional Filter (CoFi), leading to improved tracking of agents by leveraging expert knowledge, incorporating deep learning architectures, and accounting for environmental uncertainties. CoFi extends the general, recursive Bayesian estimation setting, ensuring compatibility with a vast landscape of established techniques such as Particle Filters. To underpin the advantages of CoFi, we evaluate its performance on real-world marine traffic data. Beyond improved performance, we show how CoFi can learn to trust and adapt to the level of compliance of an agent, recovering baseline performance even if the assumed Constitution clashes with reality. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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GSLB: The Graph Structure learning Benchmark 37

GSLB: The Graph Structure Learning Benchmark

引用

37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9781713899921

Graph Structure learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Graphic methods

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machine learning for Blood Donors Classification Model Using Ensemble learning 3rd

Machine Learning for Blood Donors Classification Model Using...

引用

3rd World Conference on Internet of Things: Applications and Future, ITAF 2023

作者： El-rashidy, Nora El-Ghamry, Amir ElSayed, Nesma E. Machine Learning and Information Retrieval Department Faculty of Artificial Intelligence Kafrelsheiksh University Kafrelsheiksh Egypt Faculty of Computers and Information Mansoura University Mansoura Egypt School of Engineering and Computer Science Hosted By Global Academic Foundation University of Hertfordshire Garden City Egypt Faculty of Computer Science and Engineering New Mansoura University Mansoura Egypt Delta Higher Institute for Management and Accounting Information Systems Mansoura Egypt

ISBN: (纸本)9789819947638

Blood transfusion is in constant demand, as it is required for several medical procedures and life-or-death operations. This study is motivated by the fact that the need for blood transfusions is steadily on the rise because of accidents, operations, diseases, etc. such that the ability to accurately predict the number of blood donors allows medical personnel to predict the future blood supply and plan accordingly to recruit enough volunteers to meet demand. We attempt to handle this supply–demand gap by using a predictive model that identifies potential donors. We model the probability of a person donating blood, depending on five specific features, using machine learning techniques. This paper implements a machine learning pipeline that includes data preprocessing and donor classification. Several ensemble models have been developed. The results show that the random forest model led to the best test set accuracy (96.3%), which beat other methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： machine learning

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Challenges in Cyber Physical Social Systems and Internet of Things 5

Challenges in Cyber Physical Social Systems and Internet of ...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Revathi, S. Raja, J. Mohanraj, M. Malathi, K. Mallala, Balasubbareddy Vidhya, R.G. B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Sri Eshwar college of engineering Department of Computer Science and Business Systems Coimbatore India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Telangana Hyderabad India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798331504403

This research analyses the complex dynamics of Cyber-Physical-Social Systems (CPSS), encompassing cyber-physical systems, cybersecurity, the Internet of Things (IoT), and social media. By exploring the interactions among these elements, this study analyses the vulnerabilities, risks, and potential benefits within the CPSS framework. This research study analyses the evolving landscape of cybersecurity by considering various aspects such as data integrity, privacy in the IoT era, and the influence of social media on user behaviour and influence dynamics. The study also introduces innovative strategies to enhance CPSS resilience, including methods to mitigate cyber threats and enhance overall system integrity. Additionally, we investigate how social media shapes CPSS behaviours, offering valuable insights into influence dynamics. This research advances the comprehensive understanding of CPSS to support future studies and advancements in the protection and improvement of interconnected systems. © 2024 IEEE.

关键词： Cyber-Physical Systems Cyber-Physical-Social Systems (CPSS) Cybersecurity Internet of Things (IoT) Social Media

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Optimized high order product quantization for approximate nearest neighbors search

引用

Frontiers of computer science 2020年第2期14卷 259-272页

作者： Linhao LI Qinghua HU School of Artificial Intelligence Hebei University of TechnologyTianjin 300401China Tianjin Key Lab of Machine Learning School of Computer Science and TechnologyTianjin UniversityTianjin 300072China

Product quantization is now considered as an effective approach to solve the approximate nearest neighbor(ANN)search.A collection of derivative algorithms have been ***,the current techniques ignore the intrinsic high order structures of data,which usually contain helpful information for improving the computational *** this paper,aiming at the complex structure of high order data,we design an optimized technique,called optimized high order product quantization(O-HOPQ)for ANN *** O-HOPQ,we incorporate the high order structures of the data into the process of designing a more effective subspace decomposition *** a result,spatial adjacent elements in the high order data space are grouped into the same ***,O-HOPQ generates its spatial structured codebook,by optimizing the quantization *** from the structured codebook,the global optimum quantizers can be obtained effectively and *** results show that appropriate utilization of the potential information that exists in the complex structure of high order data will result in significant improvements to the performance of the product ***,the high order structure based approaches are effective to the scenario where the data have intrinsic complex structures.

关键词： product quantization high order structured data tensor theory approximate nearest neighbor search

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A Hybrid Deep learning Model for Optimizing Electric Vehicle Battery and Navigation Systems 2

A Hybrid Deep Learning Model for Optimizing Electric Vehicle...

引用

2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Arul Jose, R. Ebby Darney, P. Santhana Krishnan, R. Relin Francis Raj, J. Jansi Rani, D. Sundaravadivel, P. Rajarajeswari College of Engineering Department of Electrical and Electronics Engineering Bengaluru India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331505745

This paper proposes a deep learning-based optimization framework for Electic Vehicle that leverages LSTM, CNN, and DRL to enhance battery management, autonomous navigation, and energy efficiency. The LSTM model predicts the State of Charge (SoC) and State of Health (SoH) of the EV battery, utilizing temporal patterns in sensor data to improve predictive maintenance. The CNN-based model focuses on real-time obstacle detection and fault recognition in autonomous driving systems, processing visual data from onboard cameras, LiDAR, and radar sensors. Finally, the DRL model optimizes energy consumption and route planning by training an agent to navigate traffic and road conditions efficiently while minimizing power usage. Data for the system is collected from various sensors embedded in the vehicle, including battery data, traffic information, GPS, and driver behavior. The data is preprocessed and augmented to ensure the models are robust and generalizable. Training involves GPU-accelerated computing platforms to enhance model efficiency. The performance of the models is evaluated through metrics such as R2 score, accuracy, and energy efficiency improvements, with results showing a 20-30% enhancement in energy consumption and battery life. The proposed system has practical applications in battery management, autonomous driving, and real-time energy optimization, contributing to the advancement of intelligent EV technologies. The work presents a step toward integrating deep learning techniques into EVs for improved performance and operational efficiency. © 2025 IEEE.

关键词： State of charge

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Comprehensive Analysis of Pose Estimation and machine learning Classifiers for Precise Yoga Pose Detection and Classification

引用

Procedia computer science 2025年 258卷 3345-3356页

作者： Meghana J.H. Chethan H.K. K.S. Santhosh Kumar S.P. Shiva Prakash Department of Studies in Computer Science University of Mysore Karnataka Mysuru-570006 India Department of Computer Science and Engineering Maharaja Institute of Technology Thandavapura Mysuru-571302 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering University of Mysore Mysuru-570006 Karnataka India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Mysuru-570006 Karnataka India

Yoga contributes to mental and physical well-being by improving flexibility, strength, balance, and emotional stability when integrated into daily routines. This ancient practice can become more accessible and adaptable to a wider audience when combined with modern artificial intelligence (AI). This study introduces a comprehensive system for detecting and classifying yoga poses using computer vision and machine learning techniques. Central to this work is the application of posture estimation algorithms, such as MediaPipe, PoseNet, and OpenPose, to identify key points on the human body within a single image or video frame. These key points are analyzed in both two-dimensional (2D) and three-dimensional (3D) spaces to construct a skeletal representation of the body, enabling accurate classification of yoga poses. The study focuses on five distinct yoga poses: Downdog, Goddess, Plank, Tree, and Warrior II. To categorize these poses, machine learning classifiers including Support Vector machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes utilize the key points extracted from the pose estimation models. This research is distinctive in its thorough evaluation of various conventional classifiers across multiple yoga positions. A comprehensive comparative analysis is essential for identifying the most effective classifiers for posture detection and classification. The dataset used in this study has been carefully curated to encompass a wide array of yoga poses and is divided into training and testing sets at various ratios (90:10, 80:20, 70:30, and 60:40) to ensure robust validation. Results indicate the system’s effectiveness, with SVM and KNN consistently achieving high values for Accuracy PE , Precision PE , Recall PE , and F1-S core PE across all yoga poses. Notably, Random Forest attains up to 100% Accuracy PE in detecting and classifying certain poses, demonstrating its robustness and reliability. This research highlights the potential of integ

关键词： Yoga Pose Estimation machine learning Deep learning

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