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Artificial intelligence algorithms for object detection and recognition in video and images

Multimedia Tools and Applications 2025年 1-18页

作者： Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Computer Science Engineering Saveetha School Of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai6021055 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India

The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional Neural Networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Offline behavior distillation 24

Offline behavior distillation

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Shiye Lei Sen Zhang Dacheng Tao School of Computer Science The University of Sydney College of Computing & Data Science Nanyang Technological University

ISBN: (纸本)9798331314385

Massive reinforcement learning (RL) data are typically collected to train policies offline without the need for interactions, but the large data volume can cause training inefficiencies. To tackle this issue, we formulate offline behavior distillation (OBD), which synthesizes limited expert behavioral data from sub-optimal RL data, enabling rapid policy learning. We propose two naive OBD objectives, DBC and PBC, which measure distillation performance via the decision difference between policies trained on distilled data and either offline data or a near-expert policy. Due to intractable bi-level optimization, the OBD objective is difficult to minimize to small values, which deteriorates PBC by its distillation performance guarantee with quadratic discount complexity O(1/(1 - γ)2). We theoretically establish the equivalence between the policy performance and action-value weighted decision difference, and introduce action-value weighted PBC (Av-PBC) as a more effective OBD objective. By optimizing the weighted decision difference, Av-PBC achieves a superior distillation guarantee with linear discount complexity O(1/(1 - γ)). Extensive experiments on multiple D4RL datasets reveal that Av-PBC offers significant improvements in OBD performance, fast distillation convergence speed, and robust cross-architecture/optimizer generalization. The code is available at https://***/LeavesLei/OBD.

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Finding Top-k Important Edges on Bipartite Graphs: Ego-betweenness Centrality-based Approaches 39

Finding Top-k Important Edges on Bipartite Graphs: Ego-betwe...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Weng, Tongfeng Zhou, Xu Fang, Yixiang Tan, Kian-Lee Li, Kenli Hunan University College of Computer Science and Electronic Engineering China The Chinese University of HongKong School of Data Science Shenzhen China National University of Singapore School of Computing Singapore

ISBN: (纸本)9798350322279

Bipartite graph is an important data structure that widely exists in disease prevention and control, community detection, and other real-life applications. In a bipartite graph, edges not only connect entries of different types but also are bridges of different communities in the above applications. However, research to date has not yet focused on edge importance in bipartite graphs. Inspired by this, we study a new problem of top-k edge search in bipartite graphs with the goal of finding k most important edges for a given bipartite graph;these edges are crucial bridges among communities. In particular, we introduce the measure of ego-betweenness for evaluating the importance of edges. To handle this problem effectively, a lazy bound-based algorithm is first proposed by integrating an upper bound pruning strategy. After that, to further get better efficiency, a greedy bound-based heuristic algorithm is explored on the basis of a tighter upper bound which contributes to reducing redundant computation for calculating ego-betweenness. Last but not least, two parallel techniques with different levels of granularity, called P -src and P -task, are respectively introduced to further improve the search efficiency. The experimental results on both real-world and synthetic graphs demonstrate the efficiency and scalability of the proposed algorithms. © 2023 IEEE.

关键词： Heuristic algorithms

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Adaptive Decision Boundary for Few-Shot Class-Incremental Learning 39

Adaptive Decision Boundary for Few-Shot Class-Incremental Le...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Linhao Tan, Yongzhang Yang, Siyuan Cheng, Hao Dong, Yongfeng Yang, Liang School of Artificial Intelligence and Data Science Hebei University of Technology China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)157735897X

Few-Shot Class-Incremental Learning (FSCIL) aims to continuously learn new classes from a limited set of training samples without forgetting knowledge of previously learned classes. Conventional FSCIL methods typically build a robust feature extractor during the base training session with abundant training samples and subsequently freeze this extractor, only fine-tuning the classifier in subsequent incremental phases. However, current strategies primarily focus on preventing catastrophic forgetting, considering only the relationship between novel and base classes, without paying attention to the specific decision spaces of each class. To address this challenge, we propose a plug-and-play Adaptive Decision Boundary Strategy (ADBS), which is compatible with most FSCIL methods. Specifically, we assign a specific decision boundary to each class and adaptively adjust these boundaries during training to optimally refine the decision spaces for the classes in each session. Furthermore, to amplify the distinctiveness between classes, we employ a novel inter-class constraint loss that optimizes the decision boundaries and prototypes for each class. Extensive experiments on three benchmarks, namely CIFAR100, miniImageNet, and CUB200, demonstrate that incorporating our ADBS method with existing FSCIL techniques significantly improves performance, achieving overall state-of-the-art results. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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PosturaSenseAI: AI-Powered Posture Assessment

PosturaSenseAI: AI-Powered Posture Assessment

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data science and Business Systems (ICDSBS), International Conference on

作者： Anushka Srivastava Varun P Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

Correct posture is essential to maintain musculoskeletal health, but people tend to fall prey to poor postural practices that culminate in long-term complications. This work proposes PosturaSenseAI, a sophisticated posture detection and correction system, which not only detects postural aberrations but also offers real-time corrective feedback and long-term monitoring. OpenCV is utilised for preprocessing and a Convolutional Neural Network (CNN) for joint detection, and the system computes important angles through trigonometry (cosine function) to evaluate posture. Real-time suggestions are made through RULA/REBA scoring, providing accurate corrective suggestions like “straighten your left shoulder.” Furthermore, a repository of posture allows users to monitor their improvement over time, representing improvement through graphed RULA/REBA scores. Experimental tests emphasise the precision and efficiency of the system in practical applications, rendering a useful tool for posture correction. The present work presents a detailed examination of methodologies applied, experimental findings, and the possible influence of our model on ergonomics and health monitoring.

关键词： Musculoskeletal system Ergonomics Shoulder Psychology Rendering (computer graphics) Real-time systems Maintenance Convolutional neural networks Monitoring Wearable sensors

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Enhancing Credit Card Fraud Detection with Explainable AI and Model Comparison

Enhancing Credit Card Fraud Detection with Explainable AI an...

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data science and Business Systems (ICDSBS), International Conference on

作者： Anushka Srivastava Soumya Singh Akshay Kumar T Ragupathi Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

With the rapid growth of digital financial transactions, detecting fraudulent activities has become a major challenge for financial institutions. Traditional machine learning models often struggle with imbalanced datasets and the complexity of fraud patterns. In this paper, we suggest a fraud detection model using XG Boost that effectively captures the transactional relationships between users, merchants, and transactions. Additionally, we incorporate Explainable AI (XAI) techniques using Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) to improve transparency, making fraud predictions more interpretable. Our results demonstrate that the XG Boost-based approach outperforms traditional classifiers such as Random Forest, SVM, and Logistic Regression in identifying fraudulent transactions. The integration of SHAP and LIME-based feature attribution further enhances model credibility, ensuring regulatory compliance and fostering trust in financial applications.

关键词： Support vector machines Deep learning Logistic regression Explainable AI Predictive models data science Real-time systems Fraud Security Random forests

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Mitigating Over-Unlearning in Machine Unlearning with Synthetic data Augmentation 24th

Mitigating Over-Unlearning in Machine Unlearning with Synt...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wang, Baohai Qu, Youyang Gao, Longxiang Li, Conggai Li, Lin Smith, David Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Data61 Commonwealth Science and Industry Research Organization CanberraVIC Australia College of Business and Law Royal Melbourne Institute of Technology MelbourneVIC Australia

ISBN: (纸本)9789819615445

In machine learning, data privacy and security has become an increasingly growing concern. The introduction of machine unlearning offers the ability to address this issue through the removal of personal and sensitive data from trained models to comply with laws, regulations, and user privacy requirements. However, despite the significant benefits of this technique, performing unlearning operations is only sometimes smooth in practice. When we attempt to remove specific data, the model may over-adjust, resulting in its performance on unlearned data. This phenomenon not only reduces the accuracy of the model on known data but also affects its ability to generalize new data, thus weakening the model’s overall performance. Therefore, in this paper, we analyze the phenomenon of over-unlearning;firstly, we explore how to mitigate the effects of over-unlearning by generating synthetic data to fill in the forgotten parts, using data synthesis-based techniques. Secondly, we combine the data synthesis-based compensation strategy with model fine-tuning to further improve the model’s adaptability and generalization capability by further training the model to accommodate synthetic data. Through comprehensive experimentation, we verify the effectiveness of the proposed data synthesis-based compensation strategy. The experimental results show that the data synthesis-based compensation strategy can effectively mitigate the effects of the over-unlearning phenomenon, as well as maintaining the stability and accuracy of the model after removing specific data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data assimilation

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Evolution of the Participation of Women in University Computer science Studies in Spain and in Europe 53

Evolution of the Participation of Women in University Comput...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Nebot, Àngela Mugica, Francisco Soft Computing Research Group Intelligent Data Science and Artificial Intelligence Research Center Universitat Politécnica de Catalunya Barcelona Spain

ISBN: (纸本)9798350336429

This paper presents a comprehensive examination of the current state of underrepresentation of women in computer science (CS) degrees in Spain. It offers an overview of the situation across autonomous communities in Spain, highlighting the evolving trends within the country, regional variations, and the distinctions between public and private universities. The research employs descriptive statistical methods to analyze data obtained from the Spanish Ministry of Universities, Eurostat, as well as recent literature reviews. Furthermore, the study contextualizes the evolution of female enrollments in Spain by comparing it with data from other European countries. The primary objective is to gain a thorough understanding of the broader Spanish landscape and suggest future research directions aimed at reducing the gender gap in CS studies. © 2023 IEEE.

关键词： computer science degree descriptive statistical methods gender imbalance

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Fire Detection System using Deep Learning Techniques 4

Fire Detection System using Deep Learning Techniques

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4th International Conference on Inventive Research in computing Applications, ICIRCA 2022

作者： Srividhya, S. Sindhu, S. Sornalakshmi, K. Hemavathi, D. Rajaram, V. School of Computing Srm Institute of Science and Technology Data Science and Business Systems Chennai India School of Computing Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

An image-based fire detection system is designed using an image dataset to train the model, and further, any image capturing device will be used for later prediction purposes. The training and prediction are done using deep learning techniques. Convolution Neural Network is used for implementation. As another fire identification innovation, picture fire location has as of late assumed a pivotal part in diminishing property misfortune and harm during a fire by disturbing clients at a beginning phase through early fire discovery. Picture fire location depends on an algorithmic investigation of pictures. Nonetheless, there is a lower precision, postponed recognition, and much calculation in like manner identification calculations, including physically and machine naturally separating picture highlights. The model is designed to alert the user via SMS and email along with a captured image of fire when a fire occurs. The proposed system in development aims at providing a detection system that can alert a user instantly at the time of a hazardous fire. The aim is to create an accurate model that does not result in false alerts. The model is trained using accurate image datasets to differentiate between a fire and a non-fire scenario. Existing models used the RGB-HIS colour space for their model. Particularly, it is used to obtain pre-detected areas rather than the traditional motion differential method. The centroid variety of candidates is used to measure the flame colour dispersions in subsequent frames. Multi-expert systems have the following;dispersion of colour and centroid movement are entrenched to classify fire based on testing various clips acquired by a surveillance camera in private confinement. © 2022 IEEE.

关键词： Fires

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Emotional Analysis Based on LSTM-CNN Hybrid Neural Network Model 8

Emotional Analysis Based on LSTM-CNN Hybrid Neural Network M...

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8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023

作者： Wu, You Al-Daweri, Muataz Salam Kumar, Venkata Durga School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia University of Nizwa College of Economics Mangement & Information System Nizwa Oman

ISBN: (纸本)9798350310931

In the realm of natural language processing (NLP), text classification is an important task. Current techniques for text classification tasks still need development, however, because of the complex abstraction of text semantic information and the considerable impact of context. This research synthesizes the strengths of the Bidirectional Long Short-Term Memory (Bi-LSTM) and the Convolutional Neural Network (CNN) models of neural networks. The LSTM-CNN hybrid model is a combination of the two models: the LSTM-generated text feature vector is then extracted using the CNN structure. The Bi-LSTM can retain long text sequences while taking into account the importance of the whole text, and it can subsequently use the organizational principles of the CNN to extract local text features. The research's experiment compares the effectiveness of the LSTM-CNN, the Bi-LSTM, and the Random Forest (RF) in a sentiment analysis project based on the ternary classification (Neutral, Positive, and Negative) of the metaverse, which can be described as a virtual reality space where individuals can interact with a computer-generated environment and other users in real-time, related topics. From what can be gathered from this research experiments, the LSTM-CNN outperforms other models when it comes to boosting text classification accuracy and achieving lower loss. © 2023 IEEE.

关键词： Classification (of information)

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