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AI-Powered Hybrid Smart Parking: Optimizing Parking Management Across Diverse Applications in Smart Cities

Procedia computer science 2025年 258卷 1524-1535页

作者： Shalini M.K. Hanumanthappa J. K.S. Santhosh Kumar S.P. Shiva Prakash Department of Computer Science Sheshadripuram college Bangalore karnataka and 560020 India Department of Studies in Computer Science Manasagangothri Campus University of Mysore Mysuru karnataka and 570006 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering Manasagangothri Campus University of Mysore Mysuru karnataka and 570006 India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Mysuru karnataka and 570006 India

An Artificial Intelligence (AI) powered hybrid smart parking system optimizes parking allocation across various applications, including smart hospitals, colleges, offices, and shopping malls. The system uses AI and IoT technologies to enhance the user experience, streamline operations, and improve efficiency. It dynamically allocates parking spaces based on real-time demand, user preferences, and contextual factors. The system accurately predicts parking demand, optimizes space allocation and provides personalized recommendations, reducing congestion and waiting times. The hybrid smart parking algorithm combines machine learning techniques with domain-specific insights to prioritize parking allocation in diverse environments. The study emphasizes the importance of leveraging advanced technologies to address complex urban challenges, such as parking management, and aims to pave the way for sustainable, efficient, and user-centric parking solutions in smart cities. and Random Forest with an overall average score of 0.9400. Machine learning has become crucial for optimizing parking management systems, especially in densely populated cities. To address this challenge, advanced predictive models have been developed to anticipate parking duration based on slot availability, peak hours, and traffic conditions. The Random Forest model outperforms Logistic Regression, Random Forest, and K-Nearest Neighbors in predicting parking length, achieving high accuracy and performance metrics. It maintains user satisfaction and low operational costs, making it a recommended system for further implementation in parking management tasks. The classification report shows a respectable performance with 50% accuracy and a good recall for open slots. The optimization procedure was effective and did not reveal any notable areas for improvement. The optimized route (Slot2, Slot1, Slot3) provides an efficient parking sequence, with high accuracy (95%), good user satisfaction (90%) and suitabili

关键词： Artificial Intelligence Smart Parking: Smart city Machine Learning Smart Applications

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S2L-CM: Scribble-supervised nuclei segmentation in histopathology images using contrastive regularization and pixel-level multiple instance learning

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computers in Biology and Medicine 2025年第Pt B期192卷 110293-110293页

作者： Oh, Hyun-Jic Min, Seonghui Jeong, Won-Ki Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of

Deep learning-based pathology nuclei segmentation algorithms have demonstrated remarkable performance. Conventional methods mostly focus on supervised learning, which requires significant manual effort to generate ground truth labels. Recently, weakly supervised learning has been extensively explored as a method for overcoming this limitation by training models with sparse annotations. However, the performance is inferior compared to that of fully supervised learning schemes. This paper proposes S2L-CM, a scribble-supervised nuclei segmentation framework based on two ideas;first, we leverage self-generated pseudo labels from user-given sparse scribble labels to train the deep learning model without full ground-truth labels, and second, we utilize multiscale contrastive regularization and pixel-level multiple-instance learning to further refine pseudo labels to improve segmentation performance. We demonstrate the effectiveness and robustness of our method on four nuclei datasets by comparing it with existing state-of-the-art methods. Code will be available at: https://***/hvcl/S2L-CMhttps://***/hvcl/S2L-CM. © 2025

关键词： Self-supervised learning

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Continuous Arabic Sign Language Recognition Models

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Sensors 2025年第9期25卷 2916-2916页

作者： Algethami, Nahlah Farhud, Raghad Alghamdi, Manal Almutairi, Huda Sorani, Maha Aleisa, Noura Computer Science Department College of Computing and Informatics Saudi Electronic University Riyadh11673 Saudi Arabia Information Technology Department College of Computing and Informatics Saudi Electronic University Riyadh11673 Saudi Arabia

A significant communication gap persists between the deaf and hearing communities, often leaving deaf individuals isolated and marginalised. This challenge is especially pronounced for Arabic-speaking individuals, given the lack of publicly available Arabic Sign Language datasets and dedicated recognition systems. This study is the first to use the Temporal Convolutional Network (TCN) model for Arabic Sign Language (ArSL) recognition. We created a custom dataset of the 30 most common sentences in ArSL. We improved recognition performance by enhancing a Recurrent Neural Network (RNN) incorporating a Bidirectional Long Short-Term Memory (BiLSTM) model. Our approach achieved outstanding accuracy results compared to baseline RNN-BiLSTM models. This study contributes to developing recognition systems that could bridge communication barriers for the hearing-impaired community. Through a comparative analysis, we assessed the performance of the TCN and the enhanced RNN architecture in capturing the temporal dependencies and semantic nuances unique to Arabic Sign Language. The models are trained and evaluated using the created dataset of Arabic sign gestures based on recognition accuracy, processing speed, and robustness to variations in signing styles. This research provides insights into the strengths and limitations of TCNs and the enhanced RNN-BiLSTM by investigating their applicability in sign language recognition scenarios. The results indicate that the TCN model achieved an accuracy of 99.5%, while the original RNN-BiLSTM model initially achieved a 96% accuracy but improved to 99% after enhancement. While the accuracy gap between the two models was small, the TCN model demonstrated significant advantages in terms of computational efficiency, requiring fewer resources and achieving faster inference times. These factors make TCNs more practical for real-time sign language recognition applications. © 2025 by the authors.

关键词： Continuous speech recognition

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一种比较学者学术影响力的新方法（英文）

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Journal of Data and Information science 2025年第2期 40-60+187页

作者： Anand Bihari Sudhakar Tripathi Akshay Deepak P.Mohan Kumar Department of Computational Intelligence School of computer Science and Engineering Vellore Institute of Technology Department of Information Technology Rajkiya Engineering College Department of Computer Science and Engineering National Institute of Technology Patna Department of Database Systems School of computer Science and Engineering Vellore Institute of Technology

研究目的：学术影响力的比较通常依赖于学者的整体科研成果。尽管这类方法简单易行，但如何比较位于不同职业生涯阶段的学者的影响力？很明显，如果一个学者有更丰富的研究经验或开展科研的时间更长，更可能获得较高的影响力。因此，我... 详细信息

研究目的：学术影响力的比较通常依赖于学者的整体科研成果。尽管这类方法简单易行，但如何比较位于不同职业生涯阶段的学者的影响力？很明显，如果一个学者有更丰富的研究经验或开展科研的时间更长，更可能获得较高的影响力。因此，我们不能直接比较位于不同职业生涯阶段的学者的影响力。已有多种文献计量指标尝试考虑时间跨度因素，其中h指数序列和EM/EM'指数序列已经被用于评估和比较学者的科学影响力。h指数序列、EM指数序列和EM'指数序列考虑了学者的年度影响力，并通过指标值及各个年度的分量值对学者进行比较。然而当两位学者在科研年限上存在显著差距时，基于时间序列的文献计量指标难以实现对其学术影响力的有效比较。研究方法：研究提出了一种累积指数计算方法，用于评估学者在其科研生涯某一年龄节点前的科学影响力，并使用89位学者的数据进行了实证测试。研究发现：本研究提出的方法已在89位学者的发表数据上进行了测试，能够清晰地区分两类学者之间的科学影响力差异。此外，该方法还有助于根据研究影响预测未来有潜力的杰出学者。实践意义：本研究提出的方法可被各类权威机构用于比较其学者的科研影响力，资助机构亦可将其用于类似的评估目的。原创性/价值：本研究通过引入一种新的学者科研影响力比较方法，丰富了现有文献体系。研究成果对于构建更加精确且公正的科研评估框架具有重要意义，有助于实现对学术贡献更为公平的评价。

关键词： h指数 EM指数 EM’指数 h指数序列 EM指数序列 EM’指数序列科学影响力比较科研影响力

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Enhanced Diabetic Retinopathy Detection: An Explainable Semi-Supervised Approach Using Contrastive Learning

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IEEE Journal of Biomedical and Health informatics 2025年 PP卷 PP页

作者： Ali, Rashid Khan, Fiaz Gul Rehman, Zia Ur Kwak, Daehan Ali, Farman Comsats University Islamabad Department of Computer Science Abbottabad Campus KPK Abbottabad22060 Pakistan Kean University Department of Computer Science and Technology NJ07083 United States Sungkyunkwan University Department of Applied Ai School of Convergence College of Computing and Informatics Seoul03063 Korea Republic of

Diabetic retinopathy (DR) is a leading cause of blindness and represents a critical challenge to global vision health. Early detection is essential to preventing irreversible eye damage. Automated medical image analysis plays a pivotal role in enabling timely diagnosis. However, the development of robust diagnostic models is challenged by the scarcity of labeled data and the prevalence of imbalanced and unlabeled datasets. Semi-supervised learning offers a potential solution by leveraging unlabeled data to enhance model performance. However, it is often limited by challenges such as unreliable pseudo-labeling, the exclusion of low-confidence data, and biases introduced by imbalanced datasets. To address these limitations, we propose a novel semi-supervised learning framework for DR detection that combines similarity and contrastive learning. Our approach utilizes class prototypes and an ensemble of classifiers to generate reliable pseudo-labels for unlabeled data. Unlike traditional methods that discard unreliable samples, our framework integrates them into the training process using contrastive learning. This allows us to extract valuable features and improve overall performance. Furthermore, we enhance the model's transparency and interpretability by incorporating the explainable AI technique GradCAM, which provides insights into the model's predictions for specific images. We evaluated the proposed method on the publicly available Kaggle DR dataset for diabetic retinopathy classification. Experimental results demonstrate that our approach achieves improved performance compared to existing semi-supervised learning methods. It also effectively leverages unreliable samples, highlighting its potential to advance DR diagnosis. © 2013 IEEE.

关键词： Contrastive Learning

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A Practical Distortion Simulator for Screen-Shooting Resilient Image Watermarking in Consumer Electronic Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Yulin Ni, Jiangqun Malwi, Wajdan Al Asiri, Fatima Ahmad, Jawad Jiang, Donghua Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Sun Yat-sen University School of Cyber Science and Technology Shenzhen518107 China Peng Cheng Laboratory Department of New Networks Shenzhen518055 China King Khalid University College of Computer Science Department of Informatics and Computer Systems Abha61421 Saudi Arabia Prince Mohammad Bin Fahd University Cybersecurity Center Alkhobar Saudi Arabia

Nowadays screen-shooting resilient watermarking still remains as a predictive and challenging area of research for proactive data protection in consumer electronic applications. Present deep learning-based methodologies embrace end-to-end frameworks and devise specialized noise layer to simulate the distortions introduced in cross-media transmission between consumer electronics. Typically, the noise layer is designed as a deterministic image-to-image network to simulate the transformation from the clean images to the screen-captured ones, overlooking the randomness of real-world distortions, which makes it challenging to satisfy the demands of various consumer electronics and application scenarios. To address this issue, we conceptualize the screen-shooting channel as an image degradation model and accordingly develop the Resolver-Simulator framework (ReSim). The involved screen-shooting simulator is designed as a conditional image-to-image network to learn the exact degradation function. By taking advantage of the pre-trained parameter resolver, the noise components can be disentangled to compose the instance sets. Then the set sampling strategy is adopted to obtain the noise instances for realistic screen-shooting simulation for unseen images. Experimental results demonstrate that the proposed scheme outperforms the previous arts in terms of real-world robustness as well as exhibits high computational efficiency to enable real-time security solutions in consumer devices. © 1975-2011 IEEE.

关键词： Watermarking

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Enhancing Mobile Edge Computing customer reviews analysis with Ensemble Sparse Support Vector L1 Regularization Based Crossover Discrete Mycorrhized Algorithm

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Biomedical Signal Processing and Control 2025年 109卷

作者： S., Vinodh Kumar S., Bharath Babu Vellingiri, J. Ramani, D. Roja Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Tamil Nadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering New Horizon College of Engineering Karnataka Bangalore India

Mobile Edge Computing has emerged as a transformative technology, enhancing the efficiency of internet community platforms by enabling real-time data processing and analysis at the network's edge. These platforms facilitate the exchange of user opinions and ideas, providing valuable insights into user attitudes and preferences. Despite advancements in Mobile Edge Computing, challenges persist in effectively categorizing customer reviews due to latency, data scarcity, and overfitting issues in computational models. This study integrates natural language processing techniques with edge computing infrastructure to analyze reviews closer to their source, thereby minimizing latency and improving overall performance. To enhance the detection accuracy in Mobile Edge customer reviews, this paper proposes an innovative approach called the Ensemble Sparse Support Vector L1 Regularization-based Crossover Discrete Mycorrhized Algorithm. Pre-processing steps, such as tokenization, lemmatization, and stemming, are employed to improve data quality. Feature extraction is performed using a stacked autoencoder, which incorporates multiple layers to address data scarcity issues. To optimize the performance of the Sparse Support Vector L1 Regularization, a novel Crossover Discrete Mycorrhized Optimization Algorithm is introduced, mitigating overfitting and improving classification accuracy. The proposed approach is validated using datasets such as the Mobile Recommendation System Dataset, Mobile Positioning Dataset, Mobile Edge Distance Analysis Dataset, International Phone Checker API, and Financial Fraud Detection Dataset. Experimental results demonstrate superior performance, achieving 98.52% accuracy, 98.39% precision, 98.28% recall, and 98.21% F1-score in aspect category detection. The proposed method addresses critical gaps in latency reduction and data processing accuracy in MEC environments by significantly improving reliability and efficiency in customer review analysis. These

关键词： Data reduction

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PlantCare AI: An AI Based Solution for Plant Disease Diagnosis and Assistance

PlantCare AI: An AI Based Solution for Plant Disease Diagnos...

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Mobile Computing and Sustainable informatics (ICMCSI), International Conference on

作者： Snehal Ghogare Shivkanya Dhupe Abhijit Kulkarni Tanish Shingane Renuka Gound Department of Computer Science and Business Systems JSPM's Rajarshi Shahu College Of Engineering Tathawade Pune Maharashtra India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

The advancement of Artificial Intelligence (AI) and machine learning (ML) has introduced transformative solutions in plant care. This review investigates the integration of deep learning and image recognition technologies for identifying and managing plant diseases. The proposed project focuses on developing a user-centric web application designed to detect plant diseases automatically, provide treatment recommendations, suggest nearby nurseries for obtaining necessary supplies, and offer chatbot assistance for user inquiries. Leveraging advanced image recognition techniques, this system aims to make plant care more efficient and accessible. The integration of Google Maps enhances navigation to local nurseries, further simplifying access to resources and promoting better plant care practices. This study outlines current methodologies, explores challenges and opportunities, and proposes a structured framework for creating an intelligent plant care platform to meet the evolving demands of farmers and horticulture professionals.

关键词： Photography Plant diseases Image recognition Accuracy Web services Reviews Chatbots User experience Satellite images Artificial intelligence

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An Innovative Approach to Early Detection of Non-Communicable Diseases Through Machine Learning

An Innovative Approach to Early Detection of Non-Communicabl...

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International Conference on ent Computing and Control systems (ICICCS)

作者： Karthikeyan K Manoj M Jagadeesh M Sasikumar R Department of Computer Science and Business Systems K.S.Rangasamy College of Technology Namakkal Tamil Nadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Among the advanced machine learning techniques applied in this paper's comprehensive framework for heart disease prediction is the Warm and Naïve Bayes models. The methodology proposed in the paper outperformed traditional methods by 13%, achieving an astounding 88% predicted accuracy using a comprehensive dataset that included vital health variables like age, gender, blood pressure, and cholesterol levels. The work addresses critical issues for the improvement of clinical decision-making and results, like feature selection and model interpretability. Cooperation between data scientists and doctors is also required to ensure smooth integration in clinical workflows as emphasized by the framework. Future extensions of this approach could adapt the framework to other non-communicable diseases, thereby broadening its utility for early detection and preventive care. Robust data preprocessing and feature selection techniques are the reason why the models are reliable and adaptable across various patient datasets. Methodological rigor minimizes biases, increases generalizability, and highlights the importance of continuous validation and updates in predictive models for the reflection of emerging health trends and new medical data.

关键词： Heart Adaptation models Accuracy Computational modeling Medical services Predictive models Feature extraction Blood pressure Bayes methods Reliability

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Quality of Service-aware 6G-Enabled NB-IoT for Health Monitoring in Long Distance High-Speed Trains

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IEEE Transactions on Consumer Electronics 2025年

作者： Roy, Bipasha Guha Roy, Deepsubhra Guha Dutta, Piyali Khan, Surbhi Bhatia Asiri, Fatima Ayadi, Manel Institute of Engineering and Management Kolkata India Institute of Engineering and Management Kolkata India University of Salford Chitkara University Institute of Engineering and Technology Chitkara University Department of Data Science United Kingdom University Centre for Research and Development Chandigarh University India Centre for Research Impact and Outcome Punjab Rajpura140401 India King Khalid University College of computer science Informatics and computer systems department Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia

Internet of Things connectivity in home health monitoring is a high-in-demand application area. The electronics industry and procedural researchers seek high-end, secured, on-time, cost-effective ways to build reliable quality of service (QoS) proved autonomous systems using existing wireless techniques. Also, the continuous availability of a traffic-free gateway, particularly in isolated places, is necessary for large-scale data gathering and real-time data update processes to function the sensor nodes in an Internet of Things (IoT) network. Improved Narrowband IoT (NB-IoT) is one of the most desirable networks offered by 6th generation (6G) connectivity in IoT-associated remote-monitoring proceedings. In this article, we propose a QoS-aware narrow bandwidth allocation-based prototype model for healthcare monitoring in long-distance high-speed trains during the patient transfer from home to the healthcare center or vice versa. This article demonstrates a possible enhancement in social aspects of cognitive IoT applications with large data systems in industrial informatics. © 1975-2011 IEEE.

关键词： Frequency allocation

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