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Machine learning for Internet of Things (IoT) device identification: a comparative study

PeerJ computer science 2025年 11卷 1-34页

作者： Tahaei, Hamid Liu, Anqi Forooghikian, Hamid Gheisari, Mehdi Zaki, Faiz Anuar, Nor Badrul Fang, Zhaoxi Huang, Longjun Institute of Artificial Intelligence Shaoxing University Shaoxing China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia Department of Management Islamic Azad University of Kish Kish Iran Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Selangor Kuala Lumpur Malaysia Department of Computer Science and Engineering Shaoxing University Shaoxing China

The rapid deployment of millions of connected devices brings significant security challenges to the Internet of Things (IoT). IoT devices are typically resource-constrained and designed for specific tasks, from which new security challenges are introduced. As such, IoT device identification has garnered substantial attention and is regarded as an initial layer of cybersecurity. One of the major steps in distinguishing IoT devices involves leveraging machine learning (ML) techniques on device network flows known as device fingerprinting. Numerous studies have proposed various solutions that incorporate ML and feature selection (FS) algorithms with different degrees of accuracy. Yet, the domain needs a comparative analysis of the accuracy of different classifiers and FS algorithms to comprehend their true capabilities in various datasets. This article provides a comprehensive performance evaluation of several reputable classifiers being used in the literature. The study evaluates the efficacy of filter-and wrapper-based FS methods across various ML classifiers. Additionally, we implemented a Binary Green Wolf Optimizer (BGWO) and compared its performance with that of traditional ML classifiers to assess the potential of this binary meta-heuristic algorithm. To ensure the robustness of our findings, we evaluated the effectiveness of each classifier and FS method using two widely utilized datasets. Our experiments demonstrated that BGWO effectively reduced the feature set by 85.11% and 73.33% for datasets 1 and 2, respectively, while achieving classification accuracies of 98.51% and 99.8%, respectively. The findings of this study highlight the strong capabilities of BGWO in reducing both the feature dimensionality and accuracy gained through classification. Furthermore, it demonstrates the effectiveness of wrapper methods in the reduction of feature sets. 2025 Tahaei et al.

关键词： Cyber attacks

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Segmentation of CAD models using hybrid representation

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虚拟现实与智能硬件(中英文) 2025年第2期7卷 188-202页

作者： Claude UWIMANA Shengdi ZHOU Limei YANG Zhuqing LI Norbelt MUTAGISHA Edouard NIYONGABO Bin ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peace-Bay Academy of Virtual Reality Shenyang 110004China China Aerospace Systems Engineering Corporation Beijing 100070China Department of Signal and Information Processing Beihang UniversityBeijing 100191China Department of Computer Science University of SherbrookeSherbrooke J1C0A1Canada

In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD *** previous CAD segmentation methods have achieved impressive performance using single representations,such as meshes,CAD,and point ***,existing methods cannot effectively combine different three-dimensional model types for the direct conversion,alignment,and integrity maintenance of geometric and topological ***,we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations,as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation *** combine these two model types,our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh *** complex CAD models,model segmentation is crucial for model retrieval and *** partial retrieval,it aims to segment a complex CAD model into several simple *** first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh *** second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD *** combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object *** study uses the Fusion 360 Gallery *** results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.

关键词： B-RepNet hybrid segmentation CAD models classification MeshCNN MeshCAD-Net

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets

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Neural Computing and Applications 2025年第8期37卷 6217-6232页

作者： Nematzadeh, Hossein Mani, Joseph Nematzadeh, Zahra Akbari, Ebrahim Mohamad, Radziah Modern College of Business and Science Muscat Oman Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL32901 United States Otolaryngology Department School of Medicine Emory University AtlantaGA30322 United States Department of Computer Engineering Sari Branch Islamic Azad University Sari Iran Faculty of Computing Universiti Teknologi Malaysia Skudai81310 Malaysia

Abstract: Feature selection poses a challenge in high-dimensional datasets, where the number of features exceeds the number of observations, as seen in microarray, gene expression, and medical datasets. There is not a universally optimal feature selection method applicable to any data distribution, and as a result, the literature consistently endeavors to address this issue. One recent approach in feature selection is termed frequency-based feature selection. However, existing methods in this domain tend to overlook feature values, focusing solely on the distribution in the response variable. In response,this paper introduces the Distance-based Mutual Congestion (DMC) as a filter method that considers both the feature values and the distribution of observations in the response variable. DMC sorts the features of datasets, and the top 5% are retained and clustered by KMeans to mitigate multicollinearity. This is achieved by randomly selecting one feature from each cluster. The selected features form the feature space, and the search space for the Genetic Algorithm with Adaptive Rates (GAwAR) will be approximated using this feature space. GAwAR approximates the combination of the top 10 features that maximizes prediction accuracy within a wrapper scheme. To prevent premature convergence, GAwAR adaptively updates the crossover and mutation rates. The hybrid DMC-GAwAR is applicable to binary classification datasets, and experimental results demonstrate its superiority over some recent works. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature Selection

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Colorectal cancer classification using weakly annotated whole slide images: Multiple instance learning optimization study

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computers in Biology and Medicine 2025年 186卷 109649-109649页

作者： Saeed, Ahmed Ismail, Mohamed A. Ghanem, Nagia M. Computer and Systems Engineering Department Faculty of Engineering Alexandria University Alexandria Egypt

Colorectal cancer (CRC) is considered one of the most deadly cancer types nowadays. It is rapidly increasing due to many factors, such as unhealthy lifestyles, water and food pollution, aging, and medical diagnosis development. Detecting CRC in its early stages can help stop its growth by providing the necessary treatments, thereby saving many people's lives. There are various tests that doctors can perform to diagnose CRC;however, biopsy using histopathological images is considered the "gold standard" for CRC diagnosis. Deep learning techniques can now be leveraged to build computer-aided diagnosis (CAD) systems that can affirm if an input sample shows any symptoms of cancer and determine its stage and location with an acceptable degree of confidence. In this research, we utilize deep learning to study the CRC classification problem using weakly annotated histopathological whole slide images (WSIs). We relax the constraints of the multiple instance learning (MIL) algorithm and primarily propose WSI-label prediction functions to be integrated with MIL, which significantly enhances the performance of WSI-level classification. We also applied efficient preprocessing techniques that output a computationally power-efficient dataset representation and performed multiple experiments to compose the most efficient CAD system. Our study introduces a notable improvement over the results obtained by the baseline research where we achieved an accuracy of 93.05% compared to 84.17%. Furthermore, our results using only the weakly annotated WSIs outperformed the baseline results that are based on performing initial pre-training using a strongly annotated part of the dataset. © 2025 Elsevier Ltd

关键词： Diseases

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Intrusion Detection in NSL-KDD Dataset Using Hybrid Self-Organizing Map Model

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computer Modeling in engineering & sciences 2025年第4期143卷 639-671页

作者： Noveela Iftikhar Mujeeb Ur Rehman Mumtaz Ali Shah Mohammed J.F.Alenazi Jehad Ali Knowledge Unit of Systems and Technology University of Management and TechnologySialkot51310Pakistan Department of Computer Science University ofWahWah Cantt47010Pakistan Department of Computer Engineering College of Computer and Information Sciences(CCIS)King Saud UniversityRiyadh11451Saudi Arabia Department of AI Convergence Network Ajou UniversitySuwon16499Republic of Korea

Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few *** devices are now easy targets for hackers because of their built-in security *** a Self-Organizing Map(SOM)hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting(XGBoost)for multi-class classification can improve network traffic intrusion *** proposed model is evaluated on the NSL-KDD *** hybrid approach outperforms the baseline line models,Multilayer perceptron model,and SOM-KNN(k-nearest neighbors)model in precision,recall,and F1-score,highlighting the proposed approach’s scalability,potential,adaptability,and real-world ***,this paper proposes a highly efficient deployment strategy for resource-constrained network *** results reveal that Precision,Recall,and F1-scores rise 10%-30% for the benign,probing,and Denial of Service(DoS)*** particular,the DoS,probe,and benign classes improved their F1-scores by 7.91%,32.62%,and 12.45%,respectively.

关键词： Intrusion detection self-organizing map Internet of Things dimensionality reduction

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A Novel Deep Learning Framework for Contraband Items Recognition in Smart City Applications 2nd

A Novel Deep Learning Framework for Contraband Items Recogni...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Khalid, Shehzad Ahmed, Zubair Noor, Ayesha Sedik, Ahmed Asif, Waqar Department of Computer Engineering Bahria University Islamabad Pakistan Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh Saudi Arabia Computer Science and Cyber Security University of West London London United Kingdom

ISBN: (纸本)9789819783441

Ensuring safety and security is paramount in today’s complex environment, and the effective detection of contraband items plays a pivotal role in achieving this objective. Contraband items, ranging from illegal substances to unauthorized goods, pose a threat to public safety, security, and the overall well-being of smart city inhabitants. Such items are currently detected by human operator reviewing the images from X-ray baggage scanners. However, manual detection of contraband items is inherently challenging and time-consuming resulting in significant delays at crowded places such as airports, train-stations, shopping malls etc. Moreover, there is a significant risk of overlooking certain items that could pose potential harm. To address these challenges, there is a growing demand for intelligent systems for contraband items detection that can efficiently and accurately detect items whilst minimizing false negatives. Automated deep learning solutions offer a sophisticated and technologically advanced approach to enhance the accuracy and speed of the detection process. In our pursuit to address this challenge comprehensively, we have obtained an X-ray Imaging Dataset specifically curated for this purpose. The dataset includes five types of objects including guns, knives, pliers, scissors, and wrenches that are typically banned to carry along. In this paper, we have proposed a deep learning-based approach to efficiently and accurately detect contraband items from X-ray images. The proposed approach is based on YOLO architectures that has been shown to perform better for object detection in variety of domains both in terms of accuracy and real-time performance. We have evaluated different versions of YOLO to select the version that works best for contraband item detection from X-ray images. Yolo-v8 has shown superior performance followed by Yolo-v5 in terms of accuracy. Challenges regarding class imbalance have been addressed using data augmentation especially for clas

关键词： Deep learning

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory Data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid Data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks 3

Agricultural Water Conservation through AI-Enhanced Solar Io...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Maniraj, S.P. Monisha, J. Amutha, R. Kavitha, R. Shriidhar, P.J. Srinivasan, C. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur Tamilnadu Chennai India Tamil Nadu Chennai India Ultra College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Madurai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha Universityi Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future. © 2025 IEEE.

关键词： Smart agriculture

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Exploring Large Language Models’ Ability to Imitate Coping’s Influence on Beliefs and Goals 17th

Exploring Large Language Models’ Ability to Imitate Coping...

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17th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Yongsatianchot, Nutchanon Marsella, Stacy Faculty of Engineering Thammasat School of Engineering Thammasat University Bangkok Thailand Khoury College of Computer Science and Department of Psychology Northeastern University Boston United States

ISBN: (纸本)9789819606948

Large Language Models (LLMs) have shown impressive capabilities across various fields, including across social sciences. While research has explored LLMs’ emotion capabilities, studies on coping mechanisms are limited. This work explored LLMs’ ability to represent emotion-focused coping, specifically updating beliefs and goals in response to emotional events. Coping has broad influences on physiological, cognitive, and social processes. We conducted two experiments on leading LLMs: a free-choice paradigm and a hurricane scenario. In the first experiment, LLMs did not exhibit preference changes between easy and hard choices, unlike humans. In the second experiment, LLMs showed no difference in hurricane perceptions based on evacuation decisions. Our findings suggest that current LLMs do not change or predict changes in beliefs and goals similarly to humans when coping with emotions, highlighting their limitations in modeling and predicting coping behaviors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Coping Emotion Large Language Models

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