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PosturalNet, an Advanced Architecture to Detect Shoplifting at Megastores 14

PosturalNet, an Advanced Architecture to Detect Shoplifting ...

引用

14th ieee international conference on Communication Systems and Network Technologies, CSNT 2025

作者： Ansari, Mohd Aquib Ahmad, Shahnawaz Mewada, Arvind Maurya, Sushil Kumar SCSET Bennett University Greater Noida India ITER Siksha’O’ Anusandhan University Odisha Bhubaneswar India

ISBN: (纸本)9798331531935

Human activity recognition (HAR) requires a full understanding of postural dynamics to create sophisticated surveillance systems that specifically detect shoplifting activities. PosturalNet represents a new architectural framework that uses human postural analytics to detect shoplifting incidents in large retail spaces. the proposed method uses a 2D body joint detection algorithm named OpenPose to extract postural information. Subsequently, these postures are pre-processed, and then features such as joint velocities, normalized body joints, magnitudes and angles are extracted. A combination of feature encoding techniques together with dimension reduction strategies maintains vital information while improving system computational efficiency. Testing PosturalNet on synthetic shoplifting data yielded an accurate detection rate of 96.87%. the method demonstrates robust performance in real-time applications which handle occluded environments when applied to practical store surveillance footage. © 2025 ieee.

关键词： Deep learning

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AI in Higher Education: A Survey-Based Empirical Study on applications and Student Expectations

AI in Higher Education: A Survey-Based Empirical Study on Ap...

引用

14th international conference on Educational and Information Technology, ICEIT 2025

作者： Li, Wenjie Li, Kailin Wu, Dong School of Art Design and Media Guangzhou Xinhua University Guangzhou China

ISBN: (纸本)9798331540883

Artificial Intelligence (AI) is revolutionizing higher education by improving teaching strategies, tailoring learning experiences to individual needs, and simplifying administrative processes. As AI technologies evolve and become integrated into educational settings, their potential to improve educational equity and teaching quality is increasingly recognized. However, challenges such as concerns over data privacy, technical limitations, and the need for effective implementation strategies remain significant barriers to the extensive integration of AI into education. this study explores the practical applications of AI in higher education, focusing on students' perspectives and expectations regarding AI integration. Using a survey-based empirical approach, the research examines how students perceive AI's role in personalizing learning, optimizing educational content, and improving overall learning outcomes. the findings suggest that while students are optimistic about AI's potential, particularly in enhancing personalized learning with 75.11 % expressing positive views, they also have significant concerns. More than half of the participants, 51.9%, expressed worries about data privacy and security, and 52.74% were concerned about AI's inability to understand individual emotional needs. Despite these concerns, the majority, 83.54% expressed a strong desire for greater AI integration in future courses, recognizing its potential to improve learning experiences. the study concludes by emphasizing the importance of aligning AI technologies with students' needs to optimize educational content, promote fairness, and contribute to ongoing educational reforms. this research provides valuable insights for policymakers and educators seeking to enhance AI integration in higher education and improve learning experiences for students. © 2025 ieee.

关键词： Students

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A Hybrid Reinforcement learning Framework for Optimizing Node Deployment in WSNs 14

A Hybrid Reinforcement Learning Framework for Optimizing Nod...

引用

14th ieee international conference on Communication Systems and Network Technologies, CSNT 2025

作者： Priyadarshi, Rahul Rout, Jayant Kumar Gupta, Akash Kumar Ranjan, Rakesh Verma, Amit Teja, Panduranga Ravi Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India Dept of ECE BIT Mesra Deoghar India School of Computer Science UPES Dehradun India

ISBN: (纸本)9798331531935

Efficient node deployment in Wireless Sensor Networks (WSNs) is critical for maximizing network coverage, enhancing energy efficiency, and prolonging network lifetime. However, achieving optimal node placement is challenging due to the limited energy resources and dynamic environmental conditions in WSNs. this paper presents a novel hybrid approach that combines Reinforcement learning (RL) with the Differential Evolution (DE) algorithm to optimize node deployment in WSNs. In this method, RL dynamically adapts node positions based on local network states, while DE performs global optimization to refine these placements and improve coverage. Simulation results demonstrate that the proposed RL-DE hybrid approach significantly outperforms traditional deployment methods, standalone RL, and DE in terms of coverage, energy conservation, and network longevity. the results suggest that the RL-DE approach provides an adaptive and robust solution for node deployment in WSNs, with potential applications in real-world sensor network deployments. © 2025 ieee.

关键词： Energy efficiency

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Multi-Label Satellite Image Classification using Lightweight Vision Transformers 14

Multi-Label Satellite Image Classification using Lightweight...

引用

14th ieee international conference on Communication Systems and Network Technologies, CSNT 2025

作者： Barman, Tamal Susan, Seba Department of Information Technology Delhi Technological University Delhi110042 India

ISBN: (纸本)9798331531935

To address the shortcomings of traditional deep learning applications for multi-label satellite image classification, this work shifts focus toward lightweight Vision Transformers (ViTs) that involve fewer parameters and can be deployed in resource-constrained devices. ViT offers a promising alternative to Convolutional Neural Network (CNN) due to its unique architecture which is designed to capture long-range dependencies more effectively. While CNN relies on convolutional layers to extract features, ViT divides images into patches and processes them as token sequences, similar to the manner text is handled in language processing, which is comparatively way better than CNN. this approach enables ViT to capture broader and more detailed features across the entire image, essential for dealing with multi-label satellite images that involve varying categories, sizes, and spatial arrangements. In our study, we employ six lightweight ViT models, namely, ViT-Small, ViT-Tiny, ViT-Base, Swin-Tiny, DeiT-Tiny, and DeiT-Base, to effectively capture different sets of labels associated with Amazon rainforest satellite imagery, leveraging extensive prior training to better recognize complex patterns and relationships within the data. the ViT-Tiny model achieves the best F-beta score (beta=2) of 0.9252, surpassing the performance of the baseline ViT model and other lightweight ViT architectures. © 2025 ieee.

关键词： Deep learning

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Perceptual learning Technology Based on Computer Vision and machine learning Algorithms

Perceptual Learning Technology Based on Computer Vision and ...

引用

international conference on Communication Systems and Network Technologies (CSNT)

作者： Tianjian Wang School of Computer Science and Technology Wuhan Qingchuan University Wuhan China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

To address the issue of low accuracy and poor robustness of perceptual learning in complex scenarios, a new method integrating computer vision and machine learning is adopted, that is, by applying deep neural networks, transfer learning and self-supervised learning, combined with multimodal data fusion strategy, to improve target recognition efficiency and learning ability. First, based on the preprocessing algorithm of improved Gaussian filtering and image segmentation, the quality of image features is improved. Data enhancement methods such as random rotation, flipping, and blurring are used to expand data distribution and improve model adaptability. Secondly, a convolutional neural network (CNN) is utilized in combination with an attention mechanism to extract multi-scale target features, and transfer learning is applied to transfer common features from pre-trained models to reduce dependence on large-scale labeled data. Finally, a contrastive learning framework is constructed to mine the correlation of unlabeled data. Transformer is used to realize the fusion of multimodal data of images and texts, and the model performance is optimized through multi-task learning. the mAP (Mean Average Precision) of the traditional method in dynamic occlusion scenarios is 60.5%, which is relatively weak. this may be because the traditional method cannot fully extract the effective features of the target under the occlusion of complex moving targets. the mAP of the method in this paper is 72.8%, which is higher than that of the traditional method. the perceptual learning method adopted in this paper effectively improves the accuracy and robustness in complex scenarios, and provides reliable technical support for intelligent applications. In the future, it can be combined with edge computing to further optimize the real-time processing capability and promote its application in the fields of unmanned driving and intelligent security.

关键词： Adaptation models Computer vision Image segmentation machine learning algorithms Computational modeling Transfer learning Feature extraction Data models Robustness Real-time systems

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A Bayesian Optimization Framework for Adaptive Node Deployment in Wireless Sensor Networks

A Bayesian Optimization Framework for Adaptive Node Deployme...

引用

international conference on Communication Systems and Network Technologies (CSNT)

作者： Rahul Priyadarshi Jayant Kumar Rout Anish Kumar Vishwakarma Nikhil Dhengre Rakesh Ranjan Amit Verma Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India School of Computer Science UPES Dehradun Uttarakhand India Department of ECE IIIT Nagpur Maharashtra India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Wireless Sensor Networks (WSNs) require efficient node deployment strategies to optimize network longevity, energy consumption, and coverage. this paper introduces a novel approach for strategic node deployment in WSNs using Bayesian Optimization (BO). A BO-based framework that iteratively modifies node positions to maximize network performance metrics, such as energy efficiency and coverage ratio, while minimizing execution time is proposed. through extensive simulations across diverse deployment scenarios, the proposed method is evaluated and demonstrates superior performance compared to both traditional and machine learning (ML)-based techniques. the results indicate significant improvements in coverage and energy efficiency, alongside competitive execution times. these findings highlight the potential of BO to enhance WSN deployment strategies, offering a scalable and adaptive solution for real-world applications. Future research will focus on integrating BO with other optimization techniques and extending the approach to dynamic environments and varied network configurations.

关键词： learning systems Wireless sensor networks Energy consumption Adaptive systems machine learning Energy efficiency Real-time systems Bayes methods Reliability Optimization

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A Bibliometric Analysis of Visualizing Knowledge Mapping for learning Management System

A Bibliometric Analysis of Visualizing Knowledge Mapping for...

引用

14th international conference on Educational and Information Technology, ICEIT 2025

作者： Ye, Xiquan Wang, Jiaming Liu, Danya Qin, Qinggui Guangdong Business and Technology University Zhaoqing China GXNU School of Design Guangxi Normal University Guilin China Foreign Language School Guangzhou Institute of Science and Technology Guangzhou China School of Art and Design Guangzhou Institute of Science and Technology Guangzhou China

ISBN: (纸本)9798331540883

With the advancement of technology and methodologies, learning Management System (LMS) has seen a profound and extensive application in the field of education, thereby rendering scientific and systematic research on it particularly significant. the primary objective of this study is to comprehensively explore the current application status and developmental trajectory of LMS in educational practice. To this end, the authors screened approximately 1,347 high-quality related articles from the Web of Science (WOS) and conducted a visual analysis using the CiteSpace. By analyzing countries, institutions, journals, and keywords, this study reveals the research hotspots and evolving trends of LMS over the past two decades. the results show that the research focus of LMS has gradually expanded from the initial exploration of basic theories on system design to a wide range of applications in educational practice. Furthermore, it has increasingly centered on personalized learning experiences, the improvement of learning efficiency, and the enhancement of learning outcomes. Additionally, the analysis reveals that different countries and regions exhibit diversity and variability in their research emphasis and developmental trends regarding LMS. the conclusions and findings of this study provide some references and insights for future research endeavors and practical implementations of LMS in the field of education. these contributions have the potential to promote ongoing innovation and facilitate the development of LMS in education. © 2025 ieee.

关键词： Teaching

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Lightweight Deep learning Model for Airborne Object Classification 14

Lightweight Deep Learning Model for Airborne Object Classifi...

引用

14th ieee international conference on Communication Systems and Network Technologies, CSNT 2025

作者： Puduru, Vamsi Krishna Pardhasaradhi, Bethi Koorapati, Sagar Cenkeramaddi, Linga Reddy ICT Dept. University of Agder Grimstad4879 Norway CSE Dept. Indian Institute Of Information Technology Design & Manufacturing Kurnool518007 India CPU Design Group Qualcomm Technologies 13929 Center Lake Dr Building 4.1 Suite 100 AustinTX78753 United States

ISBN: (纸本)9798331531935

Airborne object detection and classification play an important role in applications like military surveillance, border security, and public safety. the ability to accurately distinguish between airplanes, helicopters, drones, and birds is essential for monitoring and responding to potential aerial threats in real-time. this study leverages the AOD 4 dataset, which contains a comprehensive collection of 22,516 images spanning diverse environmental conditions. After organizing the dataset into four distinct classes, a custom lightweight Convolutional Neural Network (CNN) was developed to classify airborne objects. the proposed lightweight CNN achieves a remarkable accuracy of 97.12% and is able to outperform state-of-the-art pre-trained models in terms of accuracy and model size. On top of it, the model is also targeted at the various processors to unleash its potential to run in a real-time environment. the proposed model infers in 2.9 milliseconds on intel core i7 processor, which is a fourfold improvement compared to the MobileNetv3Small model. the findings from this study contribute to advancing the field of airborne object classification and pave the way for more robust real-world implementations. © 2025 ieee.

关键词： Deep learning

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the Development of a Collaborative learning Tool for AI Education: Corobolab

The Development of a Collaborative Learning Tool for AI Educ...

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14th international conference on Educational and Information Technology, ICEIT 2025

作者： Lai, Haibin Peng, Peng Lv, Yuhui Cai, Peiwei Ma, Qiongxiong Zhang, Zhun School of Optoelectronic Science and Engineering South China Normal University Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Guangzhou China

ISBN: (纸本)9798331540883

K-12 Artificial Intelligence (AI) education can potentially benefit from robots based collaborative learning tools. Currently, the scarcity of collaboration-oriented features in most educational robotics poses a challenge to the seamless implementation of collaborative learning in AI education. To address this gap, this paper presents Corobolab, a purpose-built collaborative learning tool for AI education. Its core objective is to facilitate the harmonious integration of AI education and collaborative learning for both educators and learners. Corobolab achieves this by integrating software and hardware platforms, establishing a two-tiered collaborative ecosystem. the shared programming workspace is a key feature, allowing multiple students to engage in real-time collaboration. Here, they can jointly develop and integrate functional modules, promoting teamwork and knowledge sharing. Moreover, Corobolab is highly adaptable. It can be customized according to project requirements by leveraging modular and programmable sensors and robot components. this flexibility enables educators to design courses that are tailored to different teaching objectives as well as collaborative activities. Pilot applications in primary and middle schools showed that Corobolab provides an efficient software-hardware collaborative environment for AI education classrooms. Educators now have access to a versatile set of tools that simplify the process of conducting collaborative learning classes, ultimately enhancing the quality of AI education in the K-12 context. © 2025 ieee.

关键词： Teaching

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Evaluating Text Summarization Tools for Educational applications: A Study of Copilot, ChatGPT, and Quillbot

Evaluating Text Summarization Tools for Educational Applicat...

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14th international conference on Educational and Information Technology, ICEIT 2025

作者： thi Nguyen, thuong Hong Truong, Vi Loi Huynh Chau, Ngan Tran FPT University Campus Can Tho Viet Nam

ISBN: (纸本)9798331540883

the aim of this study is to analyze and evaluate the role of text summarization software (TSumSf) in modern education, aiming to improve learning efficiency and save time for learners. the study uses the BERT algorithm to compare the summarization capabilities of tools such as Copilot, QuillBot, and Chat GPT and compare the similarity (Sim) to the original text while providing information on condensation, accuracy, and time-saving ability. the results show that Copilot is suitable for abstract natural science and computer science texts that need to condense the content, while QuillBot is suitable for natural science articles. Chat GPT provides an overview, suitable for journalistic, literary, and technical-scientific texts. the choice of text summarization tools should be based on the specific purpose of use, thereby best supporting learners in accessing and processing information. Following this study, it was found that the summary ratio ranged from Sim = 0.83 to Sim = 0.97. In addition, the study also highlighted the importance of user perception of the usefulness and effectiveness of these tools, along with reliable infrastructure and seamless integration into the educational system. © 2025 ieee.

关键词： e-Science

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