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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering systems

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Computer Modeling in engineering & Sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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CNN-based segmentation frameworks for structural component and earthquake damage determinations using UAV images

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Earthquake engineering and engineering Vibration 2023年第2期22卷 359-369页

作者： Taisei Saida Muhammad Rashid Yudai Nemoto Shota Tsukamoto Takehiko Asai Mayuko Nishio Graduate School of Science and Technology University of TsukubaTsukubaJapan Faculty of Engineering Information and SystemsUniversity of TsukubaTsukubaJapan

Buildings undergo various kinds of structural damage during earthquakes,and damage detection and functional assessment of these structures in the aftermath of the events have been challenging *** these circumstances,computer vision techniques offer a promising solution by automating the inspection *** study presents an effective methodology for automatic structural components and damage detection using unmanned aerial vehicle(UAV)images of damaged *** types of neural network architectures are considered for appropriate feature extractions in different task *** feature pyramid network(FPN)is employed for crack,spall,rebar,and component damage segmentation,while the UNet++network is utilized for the damage *** network training and validation,a total of 3805 original images of size 1920×1080 pixels are processed by the proposed method and reduced the image *** the FPN,the achieved highest intersection over unions(IoUs)were 0.59,0.93,0.42,and 0.99 for crack,spall,rebar,and components,*** predicted labels were found in close agreement with the ***,the UNet++recognized the semantic information and damage state with an IoU of *** demonstrated the applicability of the proposed method for automated post-earthquake building inspection process accurately without information loss from the original images.

关键词： computer vision semantic segmentation component segmentation damage evaluation FPN UNet++

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Prediction of solid particle erosion behavior in PMMA using artificial neural network and metaheuristic algorithms

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Neural Computing and Applications 2024年第4期37卷 2825-2847页

作者： Fidan, Sinan Ürgün, Satılmış Yiğit, Halil Faculty of Aeronautics and Astronautics Department of Airframe and Powerplant Maintenance Kocaeli University Kocaeli Turkey Faculty of Aeronautics and Astronautics Department of Aviation Electrics and Electronics Kocaeli University Kocaeli Turkey Faculty of Technology Information Systems Engineering Department of Information Systems Engineering Kocaeli University Kocaeli Turkey

This paper aims to predict and optimize the behavior of solid particle erosion in polymethyl methacrylate material through artificial neural networks assisted with the implementation of metaheuristic algorithms. For the study, artificial neural networks (ANNs) and the corresponding metaheuristic algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA), gray wolf optimizer (GWO), student psychology-based optimization algorithm (SPBO), ant lion optimizer (ALO), teaching learning-based optimization (TLBO), equilibrium optimizer (EO), flow direction algorithm (FDA), and covariance matrix adaptation evolution strategy (CMA-ES) have been implemented along with jellyfish search optimizer (JSO) to predict the erosion rate and erosion velocity of poly methyl methacrylate (PMMA) eroded by white aluminum oxide at different impact angles and particle sizes. It was indicated that ANN and metaheuristic prediction models were suited to the experimental results, affirming that the approach used is a successful one in predicting solid particle erosion of polymeric materials. JSO exhibited the most robust results with the lowest standard deviation of the erosion rate. In the present study, it is observed that the experimental data are in good agreement with what is obtained from the ANN model fed with input parameters optimized using various metaheuristic algorithms. In the experiment, characterization with polynomials was done regarding the aluminum oxide (Al2O3) abrasive particles' erosion behavior on PMMA at three different mesh sizes and three different blasting pressures. These polynomials were used as the fitness functions for the metaheuristic algorithms applied. The results show that the combined use of ANNs and metaheuristics yields even higher prediction accuracy than those provided by each alone. The JSO algorithm has demonstrated excellence in erosion rate and amount and in its ability to optimize over other approac

关键词： Particle swarm optimization (PSO)

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A Reputation-Based AODV Protocol for Blackhole and Malfunction Nodes Detection and Avoidance

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Computers, Materials & Continua 2024年第8期80卷 1867-1888页

作者： Qussai M.Yaseen Monther Aldwairi Ahmad Manasrah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjman346United Arab Emirates Department of Computer Information Systems Faculty of Computer and Information TechnologyJordan University of Science and TechnologyIrbid22110Jordan College of Technological Innovation Zayed UniversityAbu Dhabi144534United Arab Emirates Faculty of Information Technology and Computer Science Yarmouk UniversityIrbid21163Jordan Computer Information Systems Higher Colleges of TechnologySharjah341214United Arab Emirates

Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their ***,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN *** paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering *** proposed version employs a network-based reputation system to select the best and most secure path to a *** achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation *** minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation ***,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast *** proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other *** the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing *** paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different ***,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.

关键词： AODV blackhole malfunction nodes pathrater reputation system wireless sensor networks(WSNs) watchdogs

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Offshore Software Maintenance Outsourcing Process Model Validation:A Case Study Approach

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Computers, Materials & Continua 2023年第3期74卷 5035-5048页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Adel Sulaiman Muhammad Akram Ahmad Salman Khan Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan

The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO *** a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development *** frameworks have been developed focusing on guiding software systemmanagers concerning offshore software ***,none of these studies delivered comprehensive guidelines for managing the whole process of *** is a considerable lack of research working on managing OSMO from a vendor’s ***,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s *** study validated the preliminary OSMO process model via a case study research *** results showed that the OSMO process model is applicable in an industrial setting with few *** industrial data collected during the case study enabled this paper to extend the preliminary OSMO process *** refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk.

关键词： Offshore outsourcing process model model validation vendor challenges case study

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UAV-based Intelligent information systems on Winter Road Safety for Autonomous Vehicles 10

UAV-based Intelligent Information Systems on Winter Road Saf...

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10th IEEE Smart World Congress, SWC 2024

作者： Ariram, Siva Pekkala, Veikko Maenpaa, Timo Tikanmaki, Antti Roning, Juha Faculty of Information Technology and Electrical Engineering Biomimetics and Intelligent Systems Group Finland University of Oulu Civil Engineering Faculty of Technology Finland

ISBN: (纸本)9798331520861

As autonomous vehicles continue to revolutionize transportation, addressing challenges posed by adverse weather conditions, particularly during winter, becomes paramount for ensuring safe and efficient operations. One of the most important aspects of a road safety inspection during adverse weather is when a limited lane width can reduce the capacity of the road and raise the risk of serious accidents involving autonomous vehicles. In this research, a method for improving driving challenges on roads in winter conditions, with a model that segments and estimates the width of the road from the perspectives of Uncrewed aerial vehicles and autonomous vehicles. The proposed approach in this article is needed to empower selfdriving cars with up-to-date and accurate insights, enhancing their adaptability and decision-making capabilities in winter landscapes. © 2024 IEEE.

关键词： Autonomous vehicles

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Design and Implementation of Safety Functions for the CNC Milling Machine 15

Design and Implementation of Safety Functions for the CNC Mi...

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15th International Conference Elektro, ELEKTRO 2024

作者： Brtis, Jozef Zdansky, Juraj Pirnik, Rastislav University of Žilina Faculty of Electrical Engineering and Information Technology Department of Control and Information Systems Žilina Slovakia

ISBN: (纸本)9798350372359

The paper deals with the design and implementation of safety functions for a CNC milling machine. It is a relatively small CNC milling machine that does not pose a great danger to trained persons. Because it is used in areas where even untrained persons can come into contact with it, it was necessary to implement a safety system that implements safety functions preventing accidents when entering the dangerous zone of the CNC milling machine. The safety system is implemented based on a safety PLC and the sensors are designed using a two-channel architecture to meet the required SIL (safety integrity level). © 2024 IEEE.

关键词： Security systems

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Bidirectional attentional mechanism for Arabic chatbot

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International Journal of information technology (Singapore) 2024年第5期16卷 3109-3120页

作者： Youness, Farida Elshenawy, Ayman Makour, Mohamed Ashraf Systems and Computer Engineering Department Faculty of Engineering Al-Azhar University Cairo Egypt Software Engineering and Information Technology Department Faculty of Engineering and Technology Egyptian Chinees University Cairo Egypt

A chatbot is an intelligent agent that developed based on Natural language processing (NLP) to interact with people in a natural language. The development of multiple deep NLP models has allowed for the creation of a vector representation of words and sentences that captures meaning in context. In this paper, a generative Arabic chatbot is trained with the Sequence to Sequence (Seq2Seq) model based on encoder-decoder architecture. It utilizes both the bi-directional encoder types of Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM). The chatbot was trained using preprocessed dataset of 12 k conversations that were translated from English to Arabic. An attention layer was provided to the RNNs to focus on the important data between the input and output sequences. The obtained results showed that the proposed chatbot performed well with long sentences and elicited positive responses. In addition to that BiLSTM with attention performs better than BiGRU without attention. To assess the model, the Bilingual Evaluation Understudy (BLEU) Score is employed. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Arabic chatbot Attention mechanism Deep learning RNN

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