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Automatic Anomaly Monitoring in Public Surveillance Areas

Intelligent Automation & Soft Computing 2023年第3期35卷 2655-2671页

作者： Mohammed Alarfaj Mahwish Pervaiz Yazeed Yasin Ghadi Tamara al Shloul Suliman A.Alsuhibany Ahmad Jalal Jeongmin Park Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Bahria UniversityIslamabadPakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science Air UniversityIslamabadPakistan Department of Computer Engineering Korea Polytechnic University237 Sangidaehak-ro Siheung-siGyeonggi-do15073Korea

With the dramatic increase in video surveillance applications and public safety measures,the need for an accurate and effective system for abnormal/sus-picious activity classiﬁcation also *** it has multiple applications,the problem is very *** this paper,a novel approach for detecting nor-mal/abnormal activity has been *** used the Gaussian Mixture Model(GMM)and Kalmanﬁlter to detect and track the objects,*** that,we performed shadow removal to segment an object and its *** object segmentation we performed occlusion detection method to detect occlusion between multiple human silhouettes and we implemented a novel method for region shrinking to isolate occluded *** c-mean is utilized to verify human silhouettes and motion based features including velocity and opticalﬂow are extracted for each identiﬁed *** Wolf Optimizer(GWO)is used to optimize feature set followed by abnormal event classiﬁcation that is performed using the XG-Boost classiﬁ*** system is applicable in any surveillance appli-cation used for event detection or anomaly *** of proposed system is evaluated using University of Minnesota(UMN)dataset and UBI(Uni-versity of Beira Interior)-Fight dataset,each having different type of *** mean accuracy for the UMN and UBI-Fight datasets is 90.14%and 76.9%*** results are more accurate as compared to other existing methods.

关键词： Abnormal event classiﬁcation gray wolf optimizer region shrinking xg-boost classiﬁer

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Urdu Sentiment Analysis Using Deep Learning 18

Urdu Sentiment Analysis Using Deep Learning

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Nasir, Imtisall Bazai, Sibghat Ullah Ghafoor, Muhammad Imran Marjan, Shah Balochistan University of Information Technology Engineering and Management Science Department of Computer Science Quetta Pakistan Balochistan University of Information Technology Engineering and Management Science Department of Computer Engineering Quetta Pakistan Pakistan Television Corporation Department of Engineering Lahore Pakistan Balochistan University of Information Technology Engineering and Management Science Department of Software Engineering Quetta Pakistan

ISBN: (纸本)9798331508692

Sentiment Analysis is a deep text analysis to determine the expressions from the written text, The Urdu language holds significant importance due to the usage of these social media platforms and content generated at these platform in Urdu. Effective sentiment analysis in Urdu can provide valuable information about social trends, customer feedback, and public opinion on different sets of topics in the Urdu context. This study uses an deep learning (ANN) Artificial Neural Network to predict and classify the sentiments in the Urdu language, labeling them into positive, negative, and neutral sentiments. To achieve the research goals, advanced preprocessing techniques were utilized such as removing stop words, emojis, emails, and characters, removing symbols, performing lemmatization to expand the meaning of text, tokenized each word by using Lexicon-based approach and ngram. The dataset are tweets dependent and have 1,140,021 tweets. These steps ensure that data is standardized and cleaned to be split into training and testing. The datasets were converted into sequence and categorized to be input into the ANN model. The ANN model was trained and achieved an accuracy of 90%, precision of 98%, and f1-score of 97%. These high values indicate that the model accurately predicted the sentiments in the Urdu text. However, during experimentation limitations were discovered due to the complexities of Urdu text, difficult script patterns, and linguistic problems that were not solved by the preprocessing technique. During analysis, the limitation was discovered due to the limited number of preprocessing techniques and tools for Urdu text. However, the study highlights the potential of neural networks, especially ANN in Urdu sentiment analysis. This study contributed significantly to Urdu natural language processing by providing robust techniques and identifying limitations to work on it as a research problem. © 2024 IEEE.

关键词： Natural language processing systems

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Structure learning of Bayesian networks using sparrow optimization algorithm

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2025年第2期9卷 14-25页

作者： Kareem, Shahab Wahhab Awla, Hoshang Qasim Mohammed, Amin Salih Department of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq Department of Computer Technical Engineering Al-Qalam University College Kirkuk Iraq Department of Computer Science Faculty of Science Soran University Soran Iraq Department of Software and Informatics Salahaddin University Erbil Iraq

Bayesian networks are powerful analytical models in machine learning, used to represent probabilistic relationships among variables and create learning structures. These networks are made up of parameters that show conditional probabilities and a structure that shows how random variables interact with each other. The structure is shown by a directed acyclic graph. Despite the NP-hard nature of learning Bayesian network structures, there has been significant progress in improving the accuracy of approximation solutions. The main focus is on score-based search strategies, which make use of functions to evaluate network models and identify structures with high scores. This study is significantly focused on structure learning Bayesian networks using the Bayesian Dirichlet equivalent uniform scoring function and metaheuristic search strategies. To this end, this paper presents the sparrow optimization algorithm (SOA), a new metaheuristic algorithm derived from the foraging behavior of sparrows. SOA performs a concurrent optimization in the solution space by simultaneously performing a local and global search that leads to the discovery of near-optimal structures. The results from our experiments on several benchmark datasets show that SOA yields overall better performance than SA and greedy search algorithms. In particular, it is claimed that by applying the proposed approach of SOA, the convergence speed is significantly higher compared with the existing ones;F1 score is 0.35 and 0.05 for the Hamming distance with better results. Given these results, signed operators prove to be very efficient in SOA’s Bayesian network structure learning as a concept, especially for real-world use. © (2024), (Society of Sytematic Innovation). All Rights Reserved.

关键词： Bayesian Network Global and Local Search Search and Score Sparrow Search Optimization Algorithm Structure Learning

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An improved pulse coupled neural networks model for semantic IoT

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Digital Communications and Networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation

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Lightweight and Roubust Key Agreement for Securing IIoT-Driven Flexible Manufacturing Systems

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IEEE Internet of Things Journal 2025年第11期12卷 17197-17209页

作者： Hammad, Muhammad Badshah, Akhtar Almeer, Mohammed A. Waqas, Muhammad Song, Houbing Chen, Sheng Han, Zhu GIK Institute of Engineering Sciences and Technology Faculty of Mechanical Engineering Topi23640 Pakistan University of Malakand Department of Software Engineering Dir Lower18800 Pakistan University of Bahrain Computer Engineering Department College of Information Technology 32038 Bahrain University of Greenwich Faculty of Engineering and Science Centre for Sustainable Cybersecurity United Kingdom Edith Cowan University School of Engineering PerthWA6027 Australia Department of Information Systems BaltimoreMD United States University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The ever-evolving Internet of Things (IoT) has ushered in a new era of intelligent manufacturing across multiple industries. However, the security and privacy of real-time data transmitted over the public channel of the industrial IoT (IIoT) remain formidable challenges. Existing lightweight protocols often omit one or more critical security features, such as anonymity and untraceability, and are susceptible to threats like desynchronization attacks. Additionally, they struggle to achieve an optimal balance between robust security and performance efficiency. To bridge these gaps, we introduce a new lightweight key agreement security scheme that guarantees secure access to the IIoT-enabled flexible manufacturing system (FMS). The strength of our scheme lies in its utilization of the authenticated encryption with associative data (AEAD) primitive, AEGIS, along with hash functions and physical unclonable functions, which secure the IIoT ecosystem. Additionally, our scheme offers flexibility in the form of the addition of new machines, password updates, and revocation in cases of theft or loss. A comprehensive security analysis demonstrates the efficacy of the proposed scheme in thwarting various attacks. The formal analysis, based on the Real-Or-Random (RoR) model, ensures session key indistinguishability, while the informal analysis highlights its resilience against known attacks. The comparative assessment demonstrate that the proposed scheme consistently outperforms the benchmark schemes across multiple dimensions, including security and functionality features, computational and communication overheads, and runtime efficiency. Specifically, the proposed scheme achieves peak performance enhancements of 77.55%, 44.73%, and 69.6% in computational overhead, runtime overhead, and communication overhead, respectively, underscoring its substantial performance advantages. © 2025 IEEE.

关键词： Flexible manufacturing systems

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Extended Reality and Digital Twin in the Oil and Gas Pipeline Industry: A Systematic Review on Applications, Trends, and Future Directions 23

Extended Reality and Digital Twin in the Oil and Gas Pipelin...

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23rd IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2024

作者： Sarvesh, Muskan Kang, Ryan Nam, Hyeongil Park, Simon Hugo, Ron Maurer, Frank Kim, Kangsoo University of Calgary Department of Electrical and Software Engineering Canada University of Calgary Department of Mechanical and Manufacturing Engineering Canada University of Calgary Department of Computer Science Canada

ISBN: (纸本)9798331516475

Immersive and simulation technologies, such as Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR), and Digital Twins (DTs), have proven effective in enhancing decision-making, streamlining operations, and improving safety through intuitive visualizations. Unsurprisingly, the oil and gas (O&G) pipeline industry is increasingly turning to these technologies to tackle complex construction and resource management challenges. This paper reviews the applications, benefits, and future trends of these technologies, focusing on integration and emerging trends in monitoring, training, maintenance, and testing. It identifies potential gaps, offers guidance for future research, and emphasizes adapting to technological evolution, providing insights to improve system reliability and sustainability in O&G pipeline operations using immersion and simulation methods. © 2024 IEEE.

关键词： Gas pipelines

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Adventures in data analysis: a systematic review of Deep Learning techniques for pattern recognition in cyber-physical-social systems

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Multimedia Tools and Applications 2024年第8期83卷 22909-22973页

作者： Amiri, Zahra Heidari, Arash Navimipour, Nima Jafari Unal, Mehmet Mousavi, Ali Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Software Engineering Haliç University Istanbul34060 Turkey Department of Computer Engineering Kadir Has Universitesi Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan Department of Computer Engineering Nisantasi Universitesi Istanbul Turkey Islamic Azad University Sanandaj Branch Sanandaj Iran

Machine Learning (ML) and Deep Learning (DL) have achieved high success in many textual, auditory, medical imaging, and visual recognition patterns. Concerning the importance of ML/DL in recognizing patterns due to its high accuracy, many researchers argued for many solutions for improving pattern recognition performance using ML/DL methods. Due to the importance of the required intelligent pattern recognition of machines needed in image processing and the outstanding role of big data in generating state-of-the-art modern and classical approaches to pattern recognition, we conducted a thorough Systematic Literature Review (SLR) about DL approaches for big data pattern recognition. Therefore, we have discussed different research issues and possible paths in which the abovementioned techniques might help materialize the pattern recognition notion. Similarly, we have classified 60 of the most cutting-edge articles put forward pattern recognition issues into ten categories based on the DL/ML method used: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Generative Adversarial Network (GAN), Autoencoder (AE), Ensemble Learning (EL), Reinforcement Learning (RL), Random Forest (RF), Multilayer Perception (MLP), Long-Short Term Memory (LSTM), and hybrid methods. SLR method has been used to investigate each one in terms of influential properties such as the main idea, advantages, disadvantages, strategies, simulation environment, datasets, and security issues. The results indicate most of the articles were published in 2021. Moreover, some important parameters such as accuracy, adaptability, fault tolerance, security, scalability, and flexibility were involved in these investigations. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Pattern recognition

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AutoMESC: Automatic Framework for Mining and Classifying Ethereum Smart Contract Vulnerabilities and Their Fixes 49

AutoMESC: Automatic Framework for Mining and Classifying Eth...

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49th Euromicro Conference on software engineering and Advanced Applications, SEAA 2023

作者： Soud, Majd Qasse, Ilham Liebel, Grischa Hamdaqa, Mohammad Reykjavik University Department of Computer Science Reykjavik Iceland Polytechnique Montreal Department of Computer and Software Engineering Montreal Canada

ISBN: (纸本)9798350342352

Due to the risks associated with vulnerabilities in smart contracts, their security has gained significant attention in recent years. However, there is a lack of open datasets on smart contract vulnerabilities and their fixes that allows for data-driven research. Towards this end, we propose an automated framework for mining and classifying Ethereum's smart contract vulnerabilities and their corresponding fixes from GitHub and from the Common Vulnerabilities and Exposures (CVE) records in the National Vulnerability Database. We implemented the proposed method in a fully automated framework, which we call AutoMESC. AutoMESC uses seven of the most well-known smart contract security tools to classify and label the collected vulnerabilities based on vulnerability types. Furthermore, it collects metadata that can be used in data-intensive smart contract security research (e.g., vulnerability detection, vulnerability classification, severity prediction, and automated repair). We used AutoMESC to construct a sample dataset and made it publicly available. Currently, the dataset contains 6.7K smart contract vulnerability-fix pairs written in Solidity. We assess the quality of the constructed dataset in terms of accuracy, provenance, and relevance, and compare it with existing datasets. AutoMESC is designed to collect data continuously and keep the corresponding dataset up-to-date with newly discovered smart contract vulnerabilities and their fixes from GitHub and CVE records. © 2023 IEEE.

关键词： Smart contract

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On Evaluation of Bangla Word Analogies

On Evaluation of Bangla Word Analogies

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2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023

作者： Akter, Mousumi Sarkar, Souvika Karmaker, Shubhra Kanti Lab Department of Computer Science & Software Engineering Auburn University AL United States

ISBN: (纸本)9798891760608

This paper presents a benchmark dataset of Bangla word analogies for evaluating the quality of existing Bangla word embeddings. Despite being the 7th largest spoken language in the world, Bangla is still a low-resource language and popular NLP models often struggle to perform well on Bangla data sets. Therefore, developing a robust evaluation set is crucial for benchmarking and guiding future research on improving Bangla word embeddings, which is currently missing. To address this issue, we introduce a new evaluation set of 16,678 unique word analogies in Bangla as well as a translated and curated version of the original Mikolov dataset (10,594 samples) in Bangla. Our experiments with different state-of-the-art embedding models reveal that current Bangla word embeddings struggle to achieve high accuracy on both data sets, demonstrating a significant gap in multilingual NLP research. ©2023 Association for Computational Linguistics.

关键词： Embeddings

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Study on the Role of Adaptive Power Control in Jamming Mitigation for Remote Keyless Entry Systems 29

Study on the Role of Adaptive Power Control in Jamming Mitig...

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29th IEEE Asia Pacific Conference on Communications, APCC 2024

作者： Alkofahi, Hamza Altarawneh, Amani Jordan University of Science and Technology Department of Software Engineering Irbid Jordan Tennessee Tech University Department of Computer Science CookevilleTN United States

ISBN: (纸本)9798331517816

Remote keyless entry (RKE) systems have become a standard feature in modern vehicles, offering the convenience of wireless control. However, this convenience is accompanied by significant security vulnerabilities, particularly susceptibility to jamming attacks. These attacks disrupt communication between the key fob and the vehicle, potentially leading to unauthorized access and vehicle theft. This study investigates the effectiveness of adaptive power control as a countermeasure to jamming attacks on RKE systems and examines the influence of jammer placement on data transmission success rates. We conducted experiments using a custom-built jammer, transmitter, and receiver to simulate real-world scenarios. Our findings demonstrate that jammer proximity significantly affects transmission success, and while increasing key fob power can initially mitigate jamming, its effectiveness diminishes beyond a certain threshold. © 2024 IEEE.

关键词： Jamming

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