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Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks

SN computer science 2024年第1期5卷 190页

作者： Almahadin, Ghayth Subburaj, Maheswari Hiari, Mohammad Sathasivam Singaram, Saranya Kolla, Bhanu Prakash Dadheech, Pankaj Vibhute, Amol D. Sengan, Sudhakar Department of Networks and Cybersecurity Faculty of Information Technology Al Ahliyya Amman University Country Amman Jordan School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai 600127 India Department of Networks and Cybersecurity Information Technology Al Ahliyya Amman University Amman Jordan Department Computing Technology School of Computing SRM Institute of Science and Technology Kattankulathur Campus Tamil Nadu Chennai 603203 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Management and Gramothan (SKIT) Rajasthan Jaipur 302017 India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) MH Pune 411016 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Identifying suspicious activities or behaviors is essential in the domain of Anomaly Detection (AD). In crowded scenes, the presence of inter-object occlusions often complicates the detection of such behaviors. Therefore, developing a robust method capable of accurately detecting and locating anomalous activities within video sequences becomes crucial, especially in densely populated environments. This research initiative aims to address this challenge by proposing a novel approach focusing on AD behaviors in crowded settings. By leveraging a spatio-temporal method, the proposed approach harnesses the power of both spatial and temporal dimensions. This enables the method to effectively capture and analyze the intricate motion patterns and spatial information embedded within the continuous frames of video data. The objective is to create a comprehensive model that can efficiently detect and precisely locate anomalies within complex video sequences, specifically those featuring human crowds. The efficacy of the proposed model will be rigorously evaluated using a benchmark dataset encompassing diverse scenarios involving crowded environments. The dataset is designed to simulate real-world conditions where millions of video footage need to be continuously monitored in real time. The focus is on identifying anomalies, which might occur within short time frames, sometimes as brief as five minutes or even less. Given the challenges posed by the massive volume of data and the requirement for rapid AD, the research emphasizes the limitations of traditional Supervised Learning (SL) methods in this context. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection Autoencoders LSTM Machine learning Spatio-temporal features Suspicious activities

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Enhancing Data Aggregation in Cluster-Based Wireless Sensor Networks with LCSS: Longest Common Subsequence Empowerment 8th

Enhancing Data Aggregation in Cluster-Based Wireless Sensor ...

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8th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2024

作者： Jamil, Muhammad Farooq, Umer Tariq, Noshina Alwakid, Ghadah Humayun, Mamoona Khan, Nadia Department of Computer Science Abdul Wali Khan University Mardan Pakistan Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Department of Avionics Engineering Air University Islamabad Pakistan Department of Computer Science College of Computer and Information Sciences Jouf University Sakaka Al-Jouf Saudi Arabia Department of Information Systems College of Computer and Information Sciences Jouf University Sakaka Al-Jouf Saudi Arabia Institute of Computer Sciences and Information Technology Faculty of Management and Computer Sciences The University of Agriculture Peshawar Pakistan

ISBN: (纸本)9789819793266

A wireless sensor network (WSN) is a system of interconnected sensors that can gather environmental information. On the other hand, data redundancy is a common source of problems with WSNs. The literature presents a plethora of approaches. Despite these mechanisms’ best efforts, most current systems still need help with difficulties like noise, redundancy, or application specificity. Thus, we proposed an algorithm to overcome all the problems with redundant and highly correlated data. The longest common subsequence (LCSS)-enabled sliding window algorithm can resolve issues in constrained networks related to energy conservation and congestion control. The proposed method exhibits remarkable performance on multiple benchmark datasets. According to the result analysis, the suggested technique can outperform its equivalents for a range of data sets by about 32%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Congestion control (communication)

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Modeling and Verification of a Sentiment Analysis System Using Aspect-Oriented Petri Nets

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Journal of Electronic science and technology 2022年第2期20卷 209-223页

作者： Shu-Hung Yang Yi-Nan Lin Cheng-Ying Yang Ming-Kuen Chen Victor R.L.Shen Yu-Wei Lin the Department of Business Management National Taipei University of TechnologyTaipei 10608 the Department of Computer Science University of TaipeiTaipei 100 the Department of Information Management Chaoyang University of TechnologyTaichung 413 the Department of Computer Science and Information Engineering National Taipei UniversityNew Taipei 23741

An increasing number of social media and networking platforms have been widely used. People usually post the online comments to share their own opinions on the networking platforms with social media. Business companies are increasingly seeking effective ways to mine what people think and feel regarding their products and services. How to correctly understand the online customers’ reviews becomes an important issue. This study aims to propose a method with the aspect-oriented Petri nets(AOPN) to improve the examination correctness without changing any process and program. We collect those comments from the online reviews with Scrapy tools, perform sentiment analysis using SnowNLP, and examine the analysis results to improve the correctness. In this paper, we apply our method for a case of the online movie comments. The experimental results have shown that AOPN is helpful for the sentiment analysis and verifying its correctness.

关键词： own correctness companies

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Handling Imbalanced Datasets in Researcher Scientific Classification Using Oversampling and Undersampling Approaches 11

Handling Imbalanced Datasets in Researcher Scientific Classi...

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11th International Conference on Electrical engineering, computer science and Informatics, EECSI 2024

作者： Zulius, Antoni Defit, Sarjon Stiawan, Deris Abdiansah, Abdiansah Samsuryadi, Samsuryadi Rahmi, Nadya Alinda Universitas Bina Insan Lubuklinggau Universitas Sriwijaya Faculty of Engineering Sciences Faculty of Engineering Palembang Indonesia Universitas Putra Indonesia YPTK Department of Information Technology Faculty of Computer Science Padang Indonesia Universitas Sriwijaya Department of Computer Engineering Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Department of Informatic Engineering Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9798350355314

The challenge of class imbalance in datasets is a prevalent issue in machine learning, often resulting in biased and inaccurate predictions. This study investigates the effectiveness of different data balancing techniques in enhancing classifier accuracy when applied to an imbalanced Indonesian National Scientific Manuscript dataset. Specifically, we examine how various classifiers respond to three widely used data balancing methods: Synthetic Minority Over-sampling Technique (SMOTE), Random Under-sampling, and Random Over-sampling. We assess the performance of six classifiers-Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Naïve Bayes (NB), and Gradient Boosting (GB)-when these techniques are applied. The analysis reveals diverse outcomes, with some classifiers showing substantial accuracy gains, while others experience moderate or even adverse effects. The findings underscore the importance of selecting an appropriate balancing method based on the dataset's characteristics and the specific classification algorithm employed in the knowledge classification process. © 2024 IEEE.

关键词： Adversarial machine learning

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (science and information Organization). All rights reserved.

关键词： Quantum cryptography

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Compound attention embedded dual channel encoder-decoder for ms lesion segmentation from brain MRI

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Multimedia Tools and Applications 2024年 1-33页

作者： Ghosal, Palash Roy, Abhijit Agarwal, Rohit Purkayastha, Kunal Sharma, Aaditya Lochan Kumar, Amish Department of Information Technology Sikkim Manipal Institute of Technology Sikkim Manipal University Majhitar Sikkim Rangpo737136 India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Majhitar Sikkim Rangpo737136 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute West Bengal Kolkata700108 India

Multiple Sclerosis (MS) lesions’ segmentation is difficult due to their variegated sizes, shapes, and intensity levels. Besides this, the class imbalance problem and the availability of limited annotated data samples obstruct the building of highly efficient deep learning-based models. Though researchers have made many attempts to design efficient deep learning-based models, the maximum Dice Coefficient achieved by their models is fairly below the acceptable level of 0.70. The possible reason may be due to the inability to capture sufficient local and global features of the lesions required for accurate segmentation. In this paper, we present a new deep-learning architecture based on compound attention for MS lesion segmentation from magnetic resonance images that handles the challenges of capturing the local and global variable features of the MS lesions. The proposed model is equipped with a dual-channel CNN encoder-decoder structure employing residual connections in one channel and residual channel and spatial attention in the other. The residual connections alleviate the vanishing gradient problem and pass the fine-grained information through the channels, which is crucial for pixel-wise prediction. The attention mechanism used in a channel helps to capture long-range dependencies. Thus, the complete model leverages rich global and local information through the two channels for lesion segmentation. The problem of data imbalance is handled by using the Focal Tversky loss function. Through rigorous evaluation using 3-fold cross-validation on the MICCAI 2016 challenge dataset, our model demonstrates superior performance, achieving a Dice Coefficient of 0.73, surpassing state-of-the-art models in both qualitative and quantitative assessments. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

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Systematic Review on Recommendation Systems to select Trustworthy Cloud Services and ensure Data Integrity 2

Systematic Review on Recommendation Systems to select Trustw...

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2nd International Conference on Technological Advancements in Computational sciences, ICTACS 2022

作者： Saxena, Urvashi Rahul Alam, Taj Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (数字)9781665476577

ISBN: (纸本)9781665476577

With unprecedented growth and popularity of cloud computing in due course of time, there has been a constant urge and continuous demand for cloud service providers to deliver cloud services. When it comes to opting for a cloud service, several crucial factors like availability, reliability, performance, scalability, etc., are taken into consideration. Yet, identifying the service that best meets the consumer requirements is still a challenge. Recommendations and feedbacks facilitate the users in making appropriate decisions in selection for the correct type of service. In recent times, various approaches like content-based filtering models, collaborative filtering, fuzzy logic-based models, k-means clustering technique have been used by different authors to predict cloud services. This paper presents a comparative study of different methods adopted to suggest suitable cloud services to cloud consumers. This work aims to unfold the strengths, limitations, handle issues and analyze the solutions proposed by the authors in making suitable recommendations for cloud service. © 2022 IEEE.

关键词： Collaborative filtering

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Internet of Everything: Application and Various Challenges Analysis a Survey 1

Internet of Everything: Application and Various Challenges A...

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1st International Conference on Informatics, ICI 2022

作者： Murtuza, Shariq Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9781665437219

The Internet of Everything (IoE) is a fast-growing field with capabilities to revolutionize the whole industry. As per market trends, more than 20 billion smart devices will be deployed in the next 5 years. These interconnected devices will be generating sensitive data which needs to be protected. The field of IoE is making leaps and bounds technologically. In this survey we throw light on new domains that can benefit from IoE, along with presenting a comprehensive study of available hardware, software, infrastructure, communication protocols, attack classification, attack surfaces and individual layer security. © 2022 IEEE.

关键词： Internet of Everything

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Brain Tumor Classification using Machine Learning and Deep Learning Algorithms: A Comparison: Classifying brain MRI images on thebasis of location of tumor and comparingthe various Machine Learning and Deep LEARNING models used to predict best performance 2022

Brain Tumor Classification using Machine Learning and Deep L...

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14th International Conference on Contemporary Computing, IC3 2022

作者： Joshi, Ananya Rana, Vipasha Sharma, Aman Computer Science and Engineering Jaypee University of Information Technology India

ISBN: (纸本)9781450396752

In both children and middle aged adults, brain tumors are considered as one of the most dangerous diseases. Brain Tumors are classified as: Malignant Tumor, Benign Tumor, Pituitary Tumor, etc. During the MRI scans, massive volumes of data pictures are created. The radiologist inspects these photographs but due to the intricacy of tumors, inspecting the MRIs manually might be susceptible to errors. Machine Learning (ML) and Artificial Intelligence (AI) based automated classification systems have consistently surpassed manual categorization. As a result, we suggest comparing and achieving improved accuracy by detecting and classifying various ML and Deep Learning (DL) Algorithms such as Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Convolution Neural Network (CNN), and VGG-16. The project's goal is to use ML and DL to improve the quality and reliability of real-world MRI data. Furthermore, to assess the accuracies of various models, to choose the best model for tumor classification, and to detect the category of brain tumor in MRI images. © 2022 ACM.

关键词： Magnetic resonance imaging

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On Construction of Precise Positioning System via IEEE 802.11ax

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IEEE Internet of Things Journal 2023年第14期10卷 12951-12960页

作者： Tsung, Chen-Kun Yang, Chao-Tung Liu, Jung-Chun Hsiung, Chun Chang, Shih-Kuang Hsu, Ming-Shang National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung41170 Taiwan Tunghai University Department of Computer Science and Research Center for Smart Sustainable Circular Economy Taichung407224 Taiwan Tunghai University Department of Computer Science Taichung40704 Taiwan Krystal Technology Company Ltd. Network Department Taichung42756 Taiwan

Wireless network stability is critical for organizations. Most companies rely on a solid Internet connection for at least part of their day-to-day activities. It is essential to show the fast and high capacity of Wi-Fi. Wireless network management also significantly supplies the user with quality service and helps the system administrator manage and track the network infrastructure. The user must be able to stay connected. The Wi-Fi connection engine was applied in this article using the analysis and locations engine (ALE). ALE provides the information, including MAC address, location, floor information, and the building where the device belongs. From this data, we presented the Web visualization of the Wi-Fi network monitoring system on the map using Cesium. Except for the common information about Wi-Fi users, this work presents the vertical position visualization in the buildings to provide more details about user usage. This work analyzes the user experience of using the proposed system, and latency is the appropriate metric to measure the user experience. The experiment results show that the optimized number of cluster partitions is four, and the latency is less than 1.3 s for 4-MB data. Therefore, the system provides a visualization platform with acceptable system response, and Internet managers would consider the proposed system as the management tool. © 2014 IEEE.

关键词： Engines

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