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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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Time-Based Steganography Image with Dynamic Encryption Key Generation

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Procedia computer science 2023年 227卷 233-242页

作者： Kevin Wijaya Bryan Lansky Cecilia Ariani Dewi Rojali Ghinaa Zain Nabiilah Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

The rapid advancement of technology has necessitated stronger protection measures for data exchange. Encryption, coupled with robust keys, is an effective method to safeguard sensitive information. This study aims to improve the security of key levels by implementing a combination of steganography and encryption. The approach involves using two keys, one in the form of a normal string and the other based on the insertion time. These keys are combined to create a new, stronger encryption key. The secret messages are then encrypted using this new key and inserted using the Least Significant Bit method in steganography. This integrated approach aims to provide a higher level of protection for data during transmission and storage.

关键词： Type your keywords here separated by semicolons

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Dynamic-Feature-Based Object Tracking Using Real-Time Image Processing 5th

Dynamic-Feature-Based Object Tracking Using Real-Time Image ...

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5th International Conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Mishra, Awakash Rengarajan Bhatt, Rahul Prince, P. Blessed Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This research explores the utility of today's real-time picture processing for dynamic-characteristic-primarily based object monitoring. Notably, this painting proposes a novel tracking method that combines an actual-time, place-primarily based convolutional neural community (R-CNN) with monitoring with the detection (TbD) technique. The proposed approach is for contemporary R-CNNs to constantly come across transferring objects in video frames and then modern-day the TbD technique to generate accurate item tracking information. The proposed system is evaluated on two benchmark datasets and compared to numerous tracking methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Blockchain applications in drug data records

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Procedia computer science 2023年 216卷 739-748页

作者： Robert Muliawan Jaya Valentino Dhamma Rakkhitta Pranata Sembiring Ivan Sebastian Edbert Derwin Suhartono Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Blockchain is a data storage technique in the form of blocks where the hash system and blocks that cannot be manipulated make blockchain suitable for storing important data. One of them is drug data, where currently in drug data, data manipulation often occurs which leads to drug counterfeiting. From previous research, blockchain in the medical world has been applied in storing patient history by utilizing smart contracts. Goals of blockchain application, drug data from manufacturers can be directly viewed and purchased by buyers. The research more towards innovation, blockchain as a database for storing drug data. Ethereum blockchain can be implemented and stored data can be well integrated with Smart Contract. The existence of smart contracts that support and facilitate transactions between producers and buyers. To maintain data security, system will be implementing access permissions in smart contracts to maintain data integrity and security. Eventually maintaining privacy, decentralization, transparency, and authentication in drug data and every drug transaction can be implemented properly.

关键词： Blockchain Ethereum Decentralization Drug data Smart Contract

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Parallel Segment Entanglement Swapping 1

Parallel Segment Entanglement Swapping

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1st International Conference on Quantum Communications, Networking, and Computing, QCNC 2024

作者： He, Binjie Loke, Seng W. Zhang, Dong College of Computer and Data Science Fuzhou University Fuzhou China School of Information Technology Deakin University Melbourne Australia Zhicheng College Fuzhou University Fuzhou China

ISBN: (纸本)9798350366778

In the noisy intermediate-scale quantum era, scientists are trying to improve the entanglement swapping success rate by researching anti-noise technology on the physical level, thereby obtaining a higher generation rate of long-distance entanglement. However, we may improve the generation rate from another perspective, which is studying an efficient entanglement swapping strategy. This paper analyzes the challenges faced by existing entanglement swapping strategies, including the node allocation principle, time synchronization, and processing of entanglement swapping failure. We present Parallel Segment Entanglement Swapping (PSES) to solve these problems. The core idea of PSES is to segment the path and perform parallel entanglement swapping between segments to improve the generation rate of long-distance entanglement. We construct a tree-like model as the carrier of PSES and propose heuristic algorithms called Layer Greedy and Segment Greedy to transform the path into a tree-like model. Moreover, we realize the time synchronization and design the on-demand retransmission mechanism to process entanglement swapping failure. The experiments show that PSES performs superiorly to other entanglement swapping strategies, and the on-demand retransmission mechanism can reduce the average entanglement swapping time by 80% and the average entanglement consumption by 80%. © 2024 IEEE.

关键词： Quantum communication

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The Practicality of Machine Learning for Airline Forward Sales Forecast

The Practicality of Machine Learning for Airline Forward Sal...

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2022 IEEE Asia-Pacific Conference on computer science and data Engineering, CSDE 2022

作者： Gounder, Muni Prashneel Kumar, Ravnesh Kumar, Kunal University of Fiji School of Science and Technology Department of Computer Science and Mathematics Lautoka Fiji

ISBN: (纸本)9781665453059

Forecasting is used in the aviation industry, among other things, to estimate the number of passengers for each trip. This is advantageous for the practice of revenue management because the forecast serves as the foundation for determining the cost of each flight. In order to maximize profit, it is therefore important to know whether these forecasts are accurate. Today, the price of an airline ticket can vary significantly and quickly during the same journey, even for neighboring seats in the same cabin. Customers search for the greatest offer while airlines seek to maintain a high overall revenue and maximize their profit. Airlines use a range of computational techniques, including as pricing discrimination and demand forecasting, to increase their revenue. Demand forecasts have a crucial role in how well airline pricing and revenue management systems perform. The demand for different fare classes is assumed independent by traditional airline forecasting models, which ignore the demand's sensitivity to the airline's inventory control procedures, price variations, and the range of available alternatives. This study looks at several forecasting algorithm implementations in the context of sales booking prediction for the aviation sector, along with its benefits and drawbacks. The research is accompanied with use cases in which Time Series forecasting algorithms ARIMA and Random Forest are tested against a passenger data for American Airlines against performance indicators: confidence interval, usability, and performance. Academics, business intelligence and revenue analysts, airline operators, the revenue management team, and functional silos across the organization are the main beneficiaries of this article as sales forecast will be utilized to plan and budget flights (capacity) and to make strategic decision. © 2022 IEEE.

关键词： Sensitivity analysis

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Neutrosophic Sets and Systems: A Decade of Scientific Contribution and Growth

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Neutrosophic Sets and Systems 2025年 81卷 438-465页

作者： Saqlain, Muhammad Merigó, José M. Kumam, Poom Department of Mathematics King Mongkut’s University of Technology Thonburi Bangkok10140 Thailand School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway UltimoNSW2007 Australia

Neutrosophic Sets and Systems (NSS) has become an important Journal for neutrosophic theory and its applications in uncertainty modeling and decision sciences. In 2023, NSS celebrated its 10th anniversary, marking a decade of contributions to scientific literature. Motivated by this event, this paper provides a critical bibliometric analysis of NSS’s evolution as a leading academic journal in neutrosophy. This study explores the publication trends, citation impact, most productive authors, institutions, and global collaboration networks of NSS. The study maps bibliographic coupling, keyword co-occurrence and co-citation analysis using data from the Scopus database, secondary search using NSS webpage, and visualization tools like the VOSviewer. The findings identify Neutrosophic Sets, Decision-Making, and Fuzzy Sets as the most frequently studied keywords that highlight the increasing integration of neutrosophic approaches into multi-criteria decision-making (MCDM) and soft computing. The results indicate the most significant areas of research that are topology, soft set theory, hypersoft sets, and uncertainty modeling. The findings highlight the NSS increasing international collaboration, with contributions from America, Middle East, and Asia. Additionally, the analysis explores NSS’s alignment with sustainable development goals (SDGs), revealing notable contributions to good health and well-being (SDG 3), quality education (SDG 4), and peace, justice, and strong institutions (SDG 16). These contributions highlight how neutrosophic sets and systems is valuable across many researchers, from public health to governance to education. Overall, this paper serves as a fundamental study for researchers looking to explore emerging trends and novel applications of neutrosophic theories across diverse scientific and practical domains. © 2025, University of New Mexico. All rights reserved.

关键词： Bibliographies

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Individual Performance of Students in Online Learning Environments: Study of User Satisfaction 10

Individual Performance of Students in Online Learning Enviro...

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10th International Conference on Information Technology, computer, and Electrical Engineering, ICITACEE 2023

作者： Alamsyah, Doni Purnama Indriana Utomo, Satrio Matin Siswanto, Boby Susanti, Leni Morika, Doni Bina Nusantara University BINUS Business School Entrepreneurship Department Jakarta11480 Indonesia Bina Nusantara University Management Program BINUS Business School Jakarta11480 Indonesia Bina Nusantara University Computer Science Study Program School of Computer Science Jakarta11480 Indonesia Bina Nusantara University Interior Design Study Program School of Design Jakarta11480 Indonesia

ISBN: (纸本)9798350322729

Investigation on e-learning is important, especially user satisfaction in support of student academic performance. Evaluation of user satisfaction can be measured by evaluating perceived quality on the user's side, so this research aims to investigate the role of perceived quality over online learning on User satisfaction. Users of online learning targeted are students because students have experience and knowledge of the implementation of online learning at the university. Collected data and information from students related to satisfaction with online learning through online questionnaires. 615 students filled out questionnaires, then questionnaire data was processed quantitatively through SPSS with statistical testing based on research hypotheses. Three factors essentially increase user satisfaction in online learning, regarding the system, service, and information. Student satisfaction can be improved when supported by information on the platform. The role of a platform regarding information in the online learning context is important, a measure that explains the quality of information among other things useful, understandable, interesting, and reliable. Research findings provide important information for universities as organizers of education, aimed at attaining information quality related to online learning. © 2023 IEEE.

关键词： Students

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Implementation of Handwriting Recognition and Answer Evaluation with Recurrent Neural Network

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Procedia computer science 2023年 227卷 779-784页

作者： John Suryajaya Pangestu Kelvin Kho Dewi Suryani Leo Andika Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Many teachers “still utilize exams to gauge their pupils' knowledge. However, as the teacher must comprehend each answer provided by each student individually, analyzing the exam response can take a long time. Additionally, different handwriting styles may lead to errors in scoring the response, resulting in an erroneous mark. This study suggests a web-based program that can read students' handwriting to assist teachers in assessing students’ responses. The objective of this study was to develop a web-based application that can accept student responses as input and output correctness.

关键词： Handwriting Recognition Exam Recurrent Neural Network

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Urban Slum Mapping Using Homogeneous Urban Patches 3

Urban Slum Mapping Using Homogeneous Urban Patches

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Prabhu, R. Priya, M. Sangeetha Dayana, R. Suresh, M. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Campus Chennai India Saveetha Engineering College Department of Computer Science and Engineering Thandalam Chennai India Jeppiaar Institute of Technology Department of Computer Science and Engineering Sriperumbudur Chennai India R.M.D. Engineering College Kavaraipettai Department of Mathematics Thiruvallur India

ISBN: (纸本)9798350361186

Slums (Informal Settlements) are the most dynamic areas whose geographical locations are inaccurate according to official statistics and maps because of the exponential expansion of urbanization strategies. As a result, a method has to be developed to distinguish between informal settlements and track their spatial patterns. This paper presents an integrated method for detecting slums from satellite images that employs homogeneous urban patches (HUP) and grey level co-occurrence matrix (GLCM)-based image segmentation. In order to extract meaningful spatial features, urban patches are segmented according to their homogeneity and GLCM analysis provides useful texture information about slum areas. The combination of both these GLCM and HUP features allows for the creation of a classification map that accurately identifies and maps slum areas. We utilized WorldView-2 (1.84m) satellite imagery of Madurai to test the effectiveness of proposed HUP approach. The validation process includes rigorous testing against ground truth data, with careful consideration given to the selection of training and testing datasets. The effectiveness of the proposed method is evaluated using metrics including overall accuracy, user accuracy, and producer accuracy Furthermore, the proposed algorithm outperformed existing GLCM-based approaches in terms of accuracy. © 2024 IEEE.

关键词： Image segmentation

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