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YOLOV9 Driven Internet of Things Enabled Sustainable Solution for Intelligent Traffic Light Management System for Emergency Vehicles in Large Scale Urban Traffic 2

YOLOV9 Driven Internet of Things Enabled Sustainable Solutio...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Vijay Ganesh, P. Sam Joshua, V. Saro Murugan, L. Mahalakshmi, S. Karpagam Academy of Higher Education Deemed to be University Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Deemed to be University Faculty of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331505745

Due to densely populated urban environment leads to huge traffic in peak hours, Intelligent traffic light management system becomes paramount for emergency vehicle transportation on leveraging the sensor technologies. However sensor data acquired from densely populated urban environment helps to process the traffic congestion based traffic density. Many researches has been carried out to enable intelligent transportation system using internet of things, artificial intelligence and communication technologies but still it requires sustainable solutions for intelligent transportation., traffic congestion management, traffic light controlling with respect to the detection of emergency vehicles like ambulance as it saves the life of the human being. In this paper, AI driven Intelligent of Things enabled sustainable solutions for intelligent traffic light management system for emergency vehicles in the large scale urban traffic. Initially sensor or camera deployed in the smart cities monitors the roads and its surroundings environments. Those acquired information is transmitted to the base station containing IoT servers. In IoT Server., video data is transformed into image frames and processed using YoloV9 based AI model. YoloV9 Model uses multiple component like backbone., neck and head for processing the image frame to recognize and tack the objects in each frame. Especially Backbone model employs convolution neural network for multi scale feature extraction and feature map generation on inclusion of the Generalized Efficient aggregation Network while neck component uses the path aggregation network for future fusion process and head component uses anchor box bounding box prediction method to detect and recognize the object of interest. On detect of the object of interest, distance and speed of the object is computed using gradient flow. Further model incorporates prediction approaches to detected emergency vehicle to estimate its speed and distance from traffic signal

关键词： Ambulances

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ResNet50-Based Deep Learning Model for Lung Disease Detection

ResNet50-Based Deep Learning Model for Lung Disease Detectio...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Hajbe, Janhavi R. Patle, Sneha K. Talvekar, Shreya V. Khonde, Tanvi W. Yesankar, Prajyot R. Faculty of Engineering and Technology Department of Computer Science & Design Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331509828

By enabling a highly accurate examination of the chest x-ray, deep learning, for example, is changing the methods of recognizing lung disorders. In order to classify lung diseases, such as bacterial pneumonia, viral pneumonia, TB, and COVID-19, this work describes the use of ResNet50, a kind of CNN and a deep learning technique. We prove the potential of deep learning to discern these diseases from the two conventional approaches and most importantly, the patterns one could implicitly observe. The article also discusses the challenges that deep learning may pose on to the healthcare industry including data privacy, availability of data and the issue of overfitting. The paper links with this part by looking at the future of this area of specialization, which includes;multimodal fusion, operational AI, real-time identification, personalized care, data issues, and concerns, and ethics. But if deep learning is successful in addressing all the above-discussed challenges then it can bring the situation to the real world where the identification of lung diseases happens in a previous phase which in turn results in better treatment procedures and higher patient outcomes. © 2025 IEEE.

关键词： Lung cancer

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

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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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Validation Without Rules: A Data Integration Case Study 28th

Validation Without Rules: A Data Integration Case Study

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28th International Symposium on Database Engineered Applications, IDEAS 2024

作者： Dzalev, Stefan Velinov, Goran Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje1000 Macedonia

ISBN: (纸本)9783031834714

Data quality assessment is one of the most fundamental operations executed during data integration. Data validity is a collection of validation rules applied to the dataset’s attributes. The validation rules provided by domain experts must be known during the data validation checks. In practice domain experts may not be available, or their number is insufficient, and the project timeline may be strict, resulting in unknown data validity rules. In this paper, we present a low code framework for outlier and anomaly detection as an alternative to traditional data validation rules. The framework comprises statistical and machine learning methods that recognize outliers and label them as invalid data. We evaluated the accuracy and scalability of four methods using the TPC-DI benchmark tests, and the results indicate a high level of correctness when the appropriate method is employed for a specific distribution. The framework is exceptionally effective for data with skewed and normal distributions for local and global outliers. Additionally, for different scaling factors, we observed that the ratio of validation time to data integration execution time is low and stable as datasets increase. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data integrity

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YOLOCSP-PEST for Crops Pest Localization and Classification

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computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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Are Fact Checkers Effective in the Post Truth World? Assessing Impact of Fact Checkers Cross Medium and Platforms 25th

Are Fact Checkers Effective in the Post Truth World? Asses...

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25th International Conference on Web Information Systems engineering, WISE 2024

作者： Masurkar, Hrishikesh Suleiman, Basem Yaqub, Waheeb Alibasa, Muhammad Johan School of Computer Science The University of Sydney Sydney Australia School of Computer Science and Engineering The University of New South Wales Sydney Australia Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia

ISBN: (纸本)9789819605668

Social media platforms help users share opinions and find new information but also spread rumors, which misinforms the public. These rumour threads often prompt users (called guardians) to respond with fact-checking articles to debunk or verify the rumour. We examine how general users’ and rumour starter respond to these fact-checking (FC) comments. This study explores these aspects by analysing users’ textual and non-textual engagements. These engagements are examined across three main factors: platforms (Twitter and Reddit), rumour topics, and the primary medium (text, image, video, and GIF) in the rumour post. We observed minimal differences across primary mediums by analyzing 147K news posts and FC comments with 20.7 million replies. Furthermore, we found that Reddit users engage with FC comments more than Twitter users in terms of likes and the rumour starter’s reply-back rates. We found that no general users engaged with the rumour post before and after the guardian intervention. The most significant differences in the emotive characteristics of replies were across topics, with rumour starters expressing more anger, disgust, and offensive language when discussing rumours about terrorism and racism. These emotions indicate the backfire effect. Our findings raise concerns about the effectiveness of fact-checking interventions in updating misinformed users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Tweets

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Thermal Model of a Soldering Iron 33rd

Thermal Model of a Soldering Iron

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33rd International Conference on Organization and Technology of Maintenance, OTO 2024

作者： Barić, Tomislav Glavaš, Hrvoje Dubravac, Anto Faculty of Electrical Engineering Computer Science and Information Technology Osijek University of Osijek Osijek Croatia

ISBN: (纸本)9783031805967

Soldering irons are a hand tool that is indispensable in the process of making small series of electronic devices. Soldering irons have evolved from very simple devices without temperature control to devices with complex temperature control. The implementation of temperature control is based on the knowledge of the thermal models of the soldering iron. Thermal models of soldering irons are based either on the theory of thermal fields or on the theory of equivalent Kirchhoff thermal circuits. The theory of thermal fields is applied for very accurate modeling. In doing so, the finite element method (FEM) is used. For the above, it is necessary to use software packages based on FEM. Unlike FEM-based modeling, thermal circuit modeling does not require specialized software tools. It is also possible to derive analytical expressions that describe the thermal process of the soldering iron. In this paper, a thermal model of a soldering iron based on equivalent Kirchhoff thermal circuits is presented. Analytical expressions were derived to describe the thermal transient. The validity of the theoretical model is supported by measurement. Measurements were carried out on two soldering irons from different manufacturers. The measurement results were analyzed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hand tools

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The TMAP Framework: Describing the Design Space of Technology-Mediated Audience Participation in Live Music

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IEEE Access 2025年 13卷 38593-38608页

作者： Hödl, Oliver Turchet, Luca University of Vienna Faculty of Computer Science Cooperative Systems Research Group Vienna1090 Austria University of Applied Sciences Upper Austria Department for Smart and Interconnected Living Hagenberg4232 Austria University of Trento Department of Information Engineering and Computer Science Trento38123 Italy

Technology-mediated audience participation (TMAP) offers a wide variety of ways to enhance the involvement of spectators during a music performance. Technological change has created rich new opportunities for such interactive performance experiences. Effective design of TMAP requires balancing knowledge from diverse human and technological perspectives and taking into account very different characteristics and requirements in live music performance. However, research in this distinctive area of interaction design is limited, and the provision of practical guidance for designers has been scarcely addressed thus far. To bridge this gap, we propose the TMAP Framework, which describes the design space of audience participation in a practice-oriented way to support design-related processes. This descriptive framework is grounded on existing literature from the last 25 years, and has been iteratively improved and peer reviewed with different experts. It was validated using actual TMAP performances not included in the framework development. The TMAP Framework contains 180 entities in a tree-like sorted structure on four levels. Alongside the contribution to knowledge of extensively describing the design space of TMAP in live music, with the validation of the framework, we demonstrate its completeness, stability and generalisability. In this regard, the framework may also serve as a common language between different perspectives and practitioners, thus addressing the highly interdisciplinary nature of the TMAP field. © 2013 IEEE.

关键词： Economic and social effects

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Enhancing Evapotranspiration Estimation: A Bibliometric and Systematic Review of Hybrid Neural Networks in Water Resource Management

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computer Modeling in engineering & sciences 2025年第2期142卷 1109-1154页

作者： Moein Tosan Mohammad Reza Gharib Nasrin Fathollahzadeh Attar Ali Maroosi Department of Water Science and Engineering Faculty of AgricultureUniversity of BirjandBirjand***Iran Department of Mechanical Engineering University of Torbat HeydariehTorbat Heydarieh***Iran Department of Water Engineering Faculty of AgricultureUrmia UniversityUrmia***Iran Department of Computer Engineering University of Torbat HeydariehTorbat Heydarieh***Iran

Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water *** study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers,selected according to PRISMA guidelines,to evaluate the performance of Hybrid Artificial Neural Networks(HANNs)in ET *** findings demonstrate that HANNs,particularly those combining Multilayer Perceptrons(MLPs),Recurrent Neural Networks(RNNs),and Convolutional Neural Networks(CNNs),are highly effective in capturing the complex nonlinear relationships and tem-poral dependencies characteristic of hydrological *** hybrid models,often integrated with optimization algorithms and fuzzy logic frameworks,significantly improve the predictive accuracy and generalization capabilities of ET *** growing adoption of advanced evaluation metrics,such as Kling-Gupta Efficiency(KGE)and Taylor Diagrams,highlights the increasing demand for more robust performance assessments beyond traditional *** the promising results,challenges remain,particularly regarding model interpretability,computational efficiency,and data *** research should prioritize the integration of interpretability techniques,such as attention mechanisms,Local Interpretable Model-Agnostic Explanations(LIME),and feature importance analysis,to enhance model transparency and foster stakeholder ***,improving HANN models’scalability and computational efficiency is crucial,especially for large-scale,real-world *** such as transfer learning,parallel processing,and hyperparameter optimization will be essential in overcoming these *** study underscores the transformative potential of HANN models for precise ET estimation,particularly in water-scarce and climate-vulnerable *** integrating CNNs for automatic feature extraction and leveraging hybr

关键词： Artificial neural networks bibliometric analysis evapotranspiration hybrid models research trends systematic literature review water resources management

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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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