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Knowledge Discovery of the Internet of Things (IoT) Using Large Language Model

International Journal of Advanced computer science and Applications 2025年第4期16卷 993-998页

作者： Alsulami, Bassma Saleh Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Internet of Things (IoT) technology quickly transformed traditional management and engagement techniques in several sectors. This work explores the trends and applications of the Internet of Things in industries, including agriculture, education, transportation, water management, air quality monitoring, underground mining, smart retail, smart home systems, and weather forecasting. The methodology involves a comprehensive review of the literature, followed by data extraction and analysis using BERT to identify key insights and patterns in IoT applications. The findings show that IoT significantly impacts the improvement of real-time monitoring, increasing efficiency, and encouraging innovative solutions in various sectors. Despite its transformative potential, cybersecurity threats, data privacy concerns, and the need for strong policy frameworks persist. The study emphasizes the necessity of multidisciplinary approaches to address these difficulties and optimize IoT implementation. Future research should focus on establishing secure IoT systems, maintaining data integrity, and encouraging collaboration between disciplines to realise the benefits of IoT technology. © (2025), (science and information Organization). All rights reserved.

关键词： Data integrity

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Cybernet Model:A New Deep Learning Model for Cyber DDoS Attacks Detection and Recognition

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computers, Materials & Continua 2024年第1期78卷 1275-1295页

作者： Azar Abid Salih Maiwan Bahjat Abdulrazaq Department of Information Technology Technical College of DuhokDuhok Polytechnic UniversityDuhokIraq Department of Computer Science Faculty of ScienceUniversity of ZakhoDuhokIraq

Cyberspace is extremely dynamic,with new attacks arising *** cybersecurity controls is vital for network *** Learning(DL)models find widespread use across various fields,with cybersecurity being one of the most crucial due to their rapid cyberattack detection capabilities on networks and *** capabilities of DL in feature learning and analyzing extensive data volumes lead to the recognition of network traffic *** study presents novel lightweight DL models,known as Cybernet models,for the detection and recognition of various cyber Distributed Denial of Service(DDoS)*** models were constructed to have a reasonable number of learnable parameters,i.e.,less than 225,000,hence the name“lightweight.”This not only helps reduce the number of computations required but also results in faster training and inference ***,these models were designed to extract features in parallel from 1D Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM),which makes them unique compared to earlier existing architectures and results in better performance *** validate their robustness and effectiveness,they were tested on the CIC-DDoS2019 dataset,which is an imbalanced and large dataset that contains different types of DDoS *** results revealed that bothmodels yielded promising results,with 99.99% for the detectionmodel and 99.76% for the recognition model in terms of accuracy,precision,recall,and F1 ***,they outperformed the existing state-of-the-art models proposed for the same ***,the proposed models can be used in cyber security research domains to successfully identify different types of attacks with a high detection and recognition rate.

关键词： Deep learning CNN LSTM Cybernet model DDoS recognition

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Modern Machine Learning Solution for Electricity Consumption Management in Smart Buildings

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IEEE Engineering Management Review 2025年第1期53卷 54-62页

作者： Gautam, Sandeep Kumar Shrivastava, Vinayak Udmale, Sandeep S. Singh, Amit Kumar Singh, Sanjay Kumar Department of Computer Science and Engineering Varanasi221005 India Department of Computer Engineering and Information Technology Mumbai400019 India National Institute of Technology Department of Computer Science and Engineering Patna800005 India

Effective management of electricity consumption (EC) in smart buildings (SBs) is crucial for optimizing operational efficiency, cost savings, and ensuring sustainable resource utilization. Accurate EC prediction enables proactive decision-making, ensuring that resources are allocated efficiently to meet actual demand levels while maintaining occupant comfort. Population growth, building expansion, and technology usage swiftly escalate electricity demand, thus necessitating economical EC management strategies and assist consumers to better understand and strategically plan their EC. To address these challenges, this article proposes a novel approach based on a hybrid prediction model combining temporal convolutional networks (TCNs) and gated recurrent units (GRU). This approach capitalizes on the strengths of both TCN and GRU. TCN is adept at efficiently identifying diverse patterns, particularly within the complex working environments of SBs, by effectively capturing high- and low-frequency information. Subsequently, GRU is leveraged to address the long-term dependencies within the data, enhancing the accuracy of EC prediction. In this article, the results demonstrate the effectiveness of the proposed hybrid model, outperforming competitive methods with an impressive mean absolute error score. This underscores the potential of this approach to improve energy management practices significantly within SB environments, ultimately enhancing both operational efficiency and occupant satisfaction. © 1973-2011 IEEE.

关键词： Forecasting

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Modified CNN Model for Classifying Gender of Thermal Images Using Cloud Computing

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Informatica (Slovenia) 2023年第10期47卷 123-128页

作者： Jalil, Alyaa J. El-Seidy, Essam Daoud, Sameh S. Reda, Naglaa M. Department of Computer Science Faculty of Computer Science and Information Technology Basrah University Basrah Iraq Department of Mathematics Faculty of Science Ain Shams University Cairo Egypt

The utilization of thermal images has become widespread in various applications, particularly for thermal examination and night surveillance systems. Although many details associated with thermal imaging are virtually obscured or unclear, thermal imaging offers numerous benefits, including the ability to determine an individual's gender even in cases where human vision is impaired. Based on thermal faces, a convolutional neural network (CNN) has been developed to distinguish between people's genders. Since cloud computing offers a suitable setting and gained attraction, its concept has been adopted. Thermal databases were used for the experiments. The suggested model provides an overall accuracy of 99% and a gender categorization precision of 97.75%. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Cloud computing

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Hybrid harris hawk-arithmetic optimization with deep learning-driven object detection and classification for surveillance video analysis

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Multimedia Tools and Applications 2024年第11期84卷 9023-9044页

作者： Saikrishnan, V. Karthikeyan, M. Department of Computer and Information Science Faculty of Science Annamalai University Tamil Nadu Chidambaram India

Video surveillance has played a pivotal role in ensuring the safety and security of the public across different sectors, including retail, transportation, and urban environments. Object detection (OD) in surveillance videos includes identifying and localizing certain objects, namely vehicles, persons, or suspicious items. Conventional approaches often struggle with variations in complex backgrounds, lighting conditions, and occlusions. Deep learning (DL)-based techniques, particularly convolutional neural network (CNN), have shown outstanding performance in attaining accurate OD and handling these challenges. Despite the success of DL-based OD in surveillance videos, various challenges exist such as dealing with variations in camera viewpoints, recognizing small objects, ensuring robustness to adverse weather conditions, and handling occlusions. This study presents a Hybrid Harris Hawk-Arithmetic Optimization with Deep Learning-Driven Object Detection and Classification (HHAODL-ODC) method for Surveillance Video Analysis. The purpose of the study is to develop a HHAODL-ODC technique for object detection and classification. To accomplish this, the HHAODL-ODC technique follows two major components namely object detector and object classifier. Primarily, the HHAODL-ODC technique employs a YOLO-v5 object detector with EfficientNet as a backbone network. Next, the classification of objects takes place using the Spatial Angular-Stacked Sparse Autoencoder (SA-SSAE) model. The HHAO algorithm has been applied for the hyperparameter tuning process to improve the object classification results of the EfficientNet model. The stimulation validation of the HHAODL-ODC method is tested using a benchmark surveillance video dataset. The experimental outcomes highlighted the superior performance of the HHAODL-ODC algorithm over other DL techniques under various measures. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Object detection

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Explainable energy consumption and speed prediction in sustainable cities using deep learning

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Neural Computing and Applications 2025年第8期37卷 6233-6249页

作者： Abd El-Latif, Eman I. El-dosuky, Mohamed Faculty of Science Benha University Benha Egypt Computer Science Department Arab East Colleges Riyadh Saudi Arabia Computer Science Department Faculty of Computers and Information Mansoura University Mansoura Egypt

The idea of sustainable cities has drawn a lot of attention due to the quick expansion of metropolitan areas as well as the growing problems brought on by resource scarcity and climate change. Cities that prioritize sustainable practices are those that minimize their negative effects on the environment, maximize resource efficiency, and improve the standard of living for their citizens. Therefore, for sustainable cities, this paper uses the vehicle energy dataset (VED) to estimate travel times and calculate vehicle energy consumption. The dataset contains 12,609,170 road elevation tracks, 12,203,044 speed limit tracks, and 12,281,719 speed limit records with direction. An open-source routing engine called Valhalla is utilized to do a variety of tasks, including finding paths, matching maps, and creating maneuvers based on paths. The three primary stages of the suggested model are data pre-processing, feature extraction, and result interpretation. In the data pre-processing stage, null values are first eliminated and data normalization is implemented. Then, three techniques known as the gated recurrent unit (GRU), recurrent neural network (RNN), and long short-term memory (LSTM) are used to optimize the model. Finally, the results are interpreted through the use of SHAP (SHapley Additive explanations) in explainable artificial intelligence (XAI) techniques. The LSTM model yields the best prediction results, achieving 15.2662 RMSE, 11.7266 MAE, and 0.6696 R2 at 8 batch size, according to the evaluation results. Additional experiments are carried out in batch sizes of 8, 16, 32, and *** lowest metrics are produced by batch sizes of 64, while the best metrics are produced by batch sizes of 8. © The Author(s) 2025.

关键词： Sustainable city

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Fuzzy vision image denoising algorithm based on Bayesian estimation

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International Journal of Reasoning-based Intelligent Systems 2025年第1期17卷 24-31页

作者： Ye, Cong Department of Computer Science and Technology Suzhou College of Information Technology Jiangsu215200 China

A fuzzy visual image denoising algorithm based on Bayesian estimation is proposed to address the problems of poor denoising performance and long denoising time in traditional image denoising algorithms. First, analyse the noise reduction requirements of fuzzy visual images, construct a visual image degradation model, and obtain fuzzy visual images. Then, analyse the spectrum of the acquired fuzzy visual image, preprocess the spectrum signal by adding a window function, and detect the edge of the fuzzy visual image according to the preprocess result to supplement the fuzzy edge. Based on the supplemented blurred visual image edges, Bayesian estimation is used for image denoising to improve the image denoising effect. The experimental results show that the visual image obtained by the proposed method is relatively clear and has high image quality, indicating that the denoising effect of the algorithm is good and the denoising time is short. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Consensus algorithm

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Predicting Age and Gender in Author Profiling: A Multi-Feature Exploration

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computers, Materials & Continua 2024年第5期79卷 3333-3353页

作者： Aiman Muhammad Arshad Bilal Khan Sadique Ahmad Muhammad Asim Department of Computer Science City University of Science and Information TechnologyPeshawar25000Pakistan EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia School of Computer Science and Technology Guangdong University of TechnologyGuangzhou510006China

Author Profiling (AP) is a subsection of digital forensics that focuses on the detection of the author’s personalinformation, such as age, gender, occupation, and education, based on various linguistic features, e.g., stylistic,semantic, and syntactic. The importance of AP lies in various fields, including forensics, security, medicine, andmarketing. In previous studies, many works have been done using different languages, e.g., English, Arabic, French,***, the research on RomanUrdu is not up to the ***, this study focuses on detecting the author’sage and gender based on Roman Urdu text messages. The dataset used in this study is Fire’18-MaponSMS. Thisstudy proposed an ensemble model based on AdaBoostM1 and Random Forest (AMBRF) for AP using multiplelinguistic features that are stylistic, character-based, word-based, and sentence-based. The proposed model iscontrasted with several of the well-known models fromthe literature, including J48-Decision Tree (J48),Na飗e Bays(NB), K Nearest Neighbor (KNN), and Composite Hypercube on Random Projection (CHIRP), NB-Updatable,RF, and AdaboostM1. The overall outcome shows the better performance of the proposed AdaboostM1 withRandom Forest (ABMRF) with an accuracy of 54.2857% for age prediction and 71.1429% for gender predictioncalculated on stylistic features. Regarding word-based features, age and gender were considered in 50.5714% and60%, respectively. On the other hand, KNN and CHIRP show the weakest performance using all the linguisticfeatures for age and gender prediction.

关键词： Digital forensics author profiling for security AdaBoostM1 random forest ensemble learning

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A Comparison Study of Prominent International University Ranking Systems: Exploring the Potential of Yemeni Universities for Rankings 1

A Comparison Study of Prominent International University Ran...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Hagree, Salah Al-Shalabi, Ahmed Abdullah Al-Sanabani, Maher Al-Majma, Nashwan Ahmed Abdulrazzak, Fuaad Hasan Al-Gaphari, Ghaleb Al-Dilami, Redwan Alawdi, Ala'a Computer Science Department Faculty of Computer and Information Technology Sana’a University Yemen Computer Science Department Faculty of Sciences Ibb University Yemen Aljazeera University Yemen Computer Science Department Faculty of Computer and Information Technology Sana’a University Sana’a Yemen Computer Science Department Faculty of Computer and Information Technology Thamar University Yemen Faculty of Computing and IT University of Science and Technology Sana’a Yemen

ISBN: (纸本)9798331533557

Specifically targeting Yemeni universities, the focus lies on their potential to enter these rankings and enhance their positions such as Quacquarelli Symonds (QS) World University Ranking (QS), Academic Ranking of World Universities (ARWU), Webometrics Ranking, Times Higher Education (THE) World University Ranking, and SCImago Institutions (SIR) Rankings. Its objective is to encourage Yemeni universities to prioritize the criteria outlined in these rankings and view them as a means to access other international rankings. The research tackles the challenge of comprehending the criteria and application process required for Yemeni universities to participate in these rankings, as well as the rankings they have achieved. The study underscores the importance of this research by providing Yemeni university administrators with valuable insights into the criteria used for international rankings and the rankings obtained by Yemeni universities. This understanding can facilitate efforts to enhance the quality of higher education within these institutions. The findings of the study reveal that there are two Yemeni universities listed in the QS. The University of science and technology, ranks 151-170, while Thamar University, ranks 171-200 for 2023. While in 2024, the University of science and technology ranked first locally and the University of Aden ranked second. This paper investigates the participation of Yemeni universities in global ranking systems such as ARWU, THE, QS, and SIR, with the aim of enhancing their international standing. It highlights the challenges and opportunities for Yemeni universities to meet ranking criteria, offering insights that can help improve the quality of higher education. © 2024 IEEE.

关键词： Quacquarelli Symonds Ranking Yemen research Yemeni Universities

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Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

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computer Modeling in Engineering & sciences 2024年第5期139卷 1167-1202页

作者： Samina Amin Muhammad Ali Zeb Hani Alshahrani Mohammed Hamdi Mohammad Alsulami Asadullah Shaikh Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information System College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their ***,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by *** paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic *** has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation *** recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM *** paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM ***,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

关键词： Social media epidemic machine learning deep learning health informatics pandemic

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