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Biomedical Materials and Devices 2025年 1-16页

作者： Menagadevi, M. Nirmala, M. Thiyagarajan, D. Somasundram, D. Department of Computer Science and Engineering School of Engineering Malla Reddy University Hyderabad India Department of Biomedical Engineering Dr. N G P Institute of Technology Coimbatore India Department of Artificial Intelligence and Machine Learning School of Engineering Malla Reddy University Hyderabad India Department of Micro & Nanoelectronics School of Electronics Engineering Vellore Institute of Technology Vellore India

A biomaterial is a biocompatible substance engineered to interact with biological systems for therapeutic and diagnostic, such as pacemakers, dental implants, vascular stents, artificial joints, drug delivery systems, tissue engineering, biosensors, image contrast agents in computer Tomography, Magnetic Resonance Imaging, optical imaging, and ultrasound. Biomaterials are also used as assays in point-of-care testing devices. Based on the organs’ biological properties, the biomaterials are mainly classified into metals, polymers, ceramics, and composite biomaterials. The success rate of the implant depends on the physical, chemical, and mechanical properties of the selected material. Each biomaterials have unique properties, so it is used in both soft and hard tissue replacements. In this review, various properties and applications of biomaterials are discussed. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Applications Biocompatibility Biomaterials Properties Types

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Optimal Kernel Choice for Score Function-based Causal Discovery 41

Optimal Kernel Choice for Score Function-based Causal Discov...

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41st International Conference on machine learning, ICML 2024

作者： Wang, Wenjie Huang, Biwei Liu, Feng You, Xinge Liu, Tongliang Zhang, Kun Gong, Mingming School of Mathematics and Statistics The University of Melbourne Australia Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates University of California San Diego United States School of Computing and Information Systems The University of Melbourne Australia Huazhong University of Science and Technology China School of Computer Science Faculty of Engineering The University of Sydney Australia Department of Philosophy Carnegie Mellon University United States

Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Recently, Huang et al. (2018) proposed a generalized score function that can handle general data distributions and causal relationships by modeling the relations in reproducing kernel Hilbert space (RKHS). The selection of an appropriate kernel within this score function is crucial for accurately characterizing causal relationships and ensuring precise causal discovery. However, the current method involves manual heuristic selection of kernel parameters, making the process tedious and less likely to ensure optimality. In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data. Specifically, we model the generative process of the variables involved in each step of the causal graph search procedure as a mixture of independent noise variables. Based on this model, we derive an automatic kernel selection method by maximizing the marginal likelihood of the variables involved in each search step. We conduct experiments on both synthetic data and real-world benchmarks, and the results demonstrate that our proposed method outperforms heuristic kernel selection methods. Copyright 2024 by the author(s)

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Intrusion Detection in IoT Environment Using Stochastic Gradient Descent with Warm Restarts and Gated Recurrent Unit 2

Intrusion Detection in IoT Environment Using Stochastic Grad...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Krishnamurthy, Revatthy Alabdeli, Haider Rajani, N. Ramesh, Vankudoth Kumar, Bura Vijay Rajalakshmi Engineering College Department of Artificial Intelligence & Machine Learning Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Dept. of Ece Bengaluru India Cvr College of Engineering Department of CSE-DS Mangalpalle India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350373110

The rapid growth of Internet of Things (IoT) has attracted the cybercriminals which resulted in increase of cyber-attacks on IoT devices. If the cyber-attacks are undetected for a longer time, it causes serious service interruptions, identity protection threats. To handle these issues, this research proposes a novel intrusion detection system by employing Stochastic Gradient Descent with warm Restarts and Gated Recurrent Unit (SGDR-GRU) method. The data is normalized to obtain common scaled data by using z-score normalization technique and the feature selection is performed by Stochastic Gradient Descent with warm Restarts (SGDR). The SGDR is an optimization technique that escapes local minima, reduces overfitting and improves the performance by adjusting learning rate. Finally, classification of normal and anomaly data is carried out by Gated Recurrent Unit (GRU) that deals with gradient descent problem and captures long-range dependencies. The performance of the proposed model is evaluated on NSL-KDD dataset and achieved high accuracy 99.12%, precision 99.45%, recall 99.23% in identifying intrusion over IoT devices. The proposed method surpassed existing intrusion detection models such as stacked-deep polynomial network (SDPN), Hybrid Chicken Swarm Genetic Algorithm (HCSGA). © 2024 IEEE.

关键词： Genetic algorithms

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Escaping Strategy with Golden Jackal Optimization Based Clustering and Routing in Mobile Ad Hoc Network 2

Escaping Strategy with Golden Jackal Optimization Based Clus...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Alkhayyat, Ahmad Kumar, Komuravelly Sudheer Hiremath, Murigendrayya M. Das, Abhijeet Madhura, G.K. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq School of Computer Science & Artificial Intelligence Sr University Warangal India Dayananda Sagar College of Engineering Department of Medical Electronics Engineering Bengaluru India Department of Civil Engineering Odisha Bhubaneswar India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350373110

The Mobile Ad Hoc Network (MANET) is organized with various nodes which communicates wirelessly. Therefore, the communication in MANET suffered from different problems like low stability, high energy consumption and link failure. Additionally, the path failure happens in network because of unrestricted movement of node, that maximized energy usage, delay and minimized nodes lifetime. In this research, the Escaping Strategy with Golden Jackal Optimization (ES-GJO) algorithm is developed for cluster head selection and routing in the MANET network. The appropriate Cluster Head (CH) and routes are chosen using ES-GJO algorithm that boosts network lifetime and predicts link failure with less energy consumption. The ES-GJO algorithm is estimated with metrics of Packet Delivery Ratio (PDR), Delay and Energy consumption for 100 to 500 nodes. The proposed ES-GJO algorithm attained highest PDR of 99.87%, 99.83%, 99.65%, 98.76% and 97.38% for nodes 100, 200, 300, 400 and 500 which is efficient than Eagle Based Density Clustering (EBDC) and Optimal Route Selection Mobile Ad hoc Networks (ORSMAN) algorithms. © 2024 IEEE.

关键词： Mobile ad hoc networks

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AI-Powered Hybrid Smart Parking: Optimizing Parking Management Across Diverse Applications in Smart Cities 3

AI-Powered Hybrid Smart Parking: Optimizing Parking Manageme...

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3rd International Conference on machine learning and Data Engineering, ICMLDE 2024

作者： Shalini, M.K. Hanumanthappa, J. Santhosh Kumar, K.S. Shiva Prakash, S.P. Department of Computer Science Sheshadripuram College karnataka Bangalore560020 India Department of Studies in Computer Science Manasagangothri Campus University of Mysore karnataka Mysuru570006 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering Manasagangothri Campus University of Mysore karnataka Mysuru570006 India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University karnataka Mysuru570006 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： Logistic regression

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Hybrid CNN Classifier for Accurate Detection of Oral Cysts in Panoramic Dental Radiograph 9

Hybrid CNN Classifier for Accurate Detection of Oral Cysts i...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Sivagami, S. Senthilvadivu, S. Geetha, P. Yukthavarshika, J. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377972

In dentistry, machine learning approaches play a significant role in the early detection and diagnosis of oral cysts. When planning accurate medical treatment, oral and maxillofacial surgeons can benefit from preoperative detection of these cysts. Surgical planning is directly affected by classifying oral cysts as dentigerous, radicular, ameloblastoma, odontogenic carcinoma, and odontogenic keratocyte. In dentistry, the outcomes of differential diagnosis based solely on imaging are unacceptable. This research proposes a three-step framework for autonomous cyst classification, segmentation, and detection based on a Convolutional Neural Network (CNN). To begin, DENT-CNN, a modified CNN architecture is proposed to classify the oral cyst as normal or abnormal. Second, UNet is a CNN variant used to segment the categorized cyst regions on panoramic dental images. Third, the Gray Level Co-occurrence Matrix (GLCM) is combined with CNN to identify segmented cysts as benign or malignant. The proposed work is extensively evaluated using 1500 real-time dental panoramic radiographic images of the oral cyst collected from a dental hospital. To alleviate overfitting, the data augmentation approach is used. The performance of the proposed work is analyzed in terms of a cyst classification rate of 96.8 %, a Dice Score of 96.3 % for cyst segmentation, and an accuracy for the Identification phase of 96.5%. © 2024 IEEE.

关键词： Dentistry

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Synthetic Gradient Optimization-Based Implicit Amortized Bayesian Meta-learning for Few-Shot Pumi Spectrographic Image Recognition

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Fu, Meijun Wang, Xiaomin Wang, Jun Yi, Zhang Southwest Jiaotong University School of Computing and Artificial Intelligence The School of Information Science and Technology Chengdu611756 China Yunnan Minzu University School of Mathematics and Computer Science Kunming650500 China Sichuan University Machine Intelligence Laboratory College of Computer Science Chengdu610065 China

Meta-learning provides a promising solution to the issue of insufficient training samples in Pumi spectrogram recognition. However, capturing model uncertainty remains a critical challenge, particularly for tasks influenced by lexical ambiguities. To overcome this problem, we propose a novel method, Synthetic Gradient Optimization-Based Implicit Amortized Bayesian Meta-learning (SGO-IABML), which captures model uncertainty by evaluating posterior distributions within a hierarchical Bayesian framework, thereby facilitating few-shot Pumi spectrogram recognition. Specifically, SGO-IABML reformulates meta-learning as a bi-level variational inference problem, leveraging information bottleneck principles. At the lower level, a generative inference module is developed to implicitly model task-specific variational posteriors, thereby enhancing the model's expressiveness. Given the lack of analytical forms for implicit distributions, we derive the Fenchel-Bayesian Bound Theorem to measure the divergence between arbitrary distributions. For the meta-learning of variational parameters, SGO-IABML constructs a synthetic gradient optimizer, integrating prior gradient information to facilitate rapid adaptation to new tasks. At the upper level, the model is calibrated by estimating the local geometry of the posterior distribution, utilizing the Generalized Gauss-Newton Matrix to capture the directional sensitivity of the loss function. Comprehensive experimental results on Pumi spectrograms demonstrate that SGO-IABML achieves state-of-the-art performance in generalization, calibration, expressiveness, versatility, and cross-domain adaptability. Furthermore, ablation studies confirm the contribution of each component to the overall performance improvement. © 1991-2012 IEEE.

关键词： Variational techniques

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Localization for V2X communication with noisy distance measurement

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International Journal of Intelligent Networks 2023年第1期4卷 355-360页

作者： Javed, Iram Tang, Xianlun Saleem, Muhammad Asim Javed, Ashir Zia, Muhammad Azam Shoukat, Ijaz Ali School of Computer Science and Technology Chongqing University of Posts and Telecommunications China Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Aging Research Center Karolinska Institutet Stockholm171 65 Sweden Department of Computer Science University of Agriculture Faisalabad Pakistan Riphah College of Computing Riphah International University Faisalabad Campus Pakistan

Mobile sensor network localization is a growing research topic after IEEE 802.15.4 specified the procedure of low-rate wireless personal area networks (LR-WPANs), which further helps localize vehicles in the automobile industry. This paper presents a new localization scheme based on flying anchors deployed in vehicular infrastructure. The mobile anchor nodes follow a random C-shaped trajectory. A global positioning system (GPS) is attached to each anchor node, transmitting beacons with ID and location to all other vehicles in a network. Distance calculation is facilitated through link quality induction, employing the centroid method to compute localization error. Mobile anchor localization, particularly when employing a C-shaped trajectory commonly adopted by various topologies, consistently yields optimal positioning outcomes. However, this approach can be susceptible to the impact of noisy measurements, potentially reducing overall localization performance. To overcome this problem, we proposed a framework based on extended Kalman filtering (EKF), which is used to refine the coordinates of the vehicles. To compute the lower bounding of the vehicular node, an analytical framework is also proposed to enhance the localization error accuracy. Simulation results show that the EKF framework provides better positioning accuracy compared to the existing C-shaped solution, irrespective of noise statistics, topology selection, and anchor node density. With the help of the Extended Kalman Filter (EKF) framework, we achieved a comprehensive localization error of 0.99 m, accompanied by a standard deviation of 0.47. © 2023 The Authors

关键词： Extended Kalman filters

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IoT-based Efficient Waste Management on University Campus: Enhancing Monitoring and Optimization through machine learning 3

IoT-based Efficient Waste Management on University Campus: E...

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3rd International Conference on machine learning and Data Engineering, ICMLDE 2024

作者： Sumathi, M. Hanumanthappa, J. Santhosh Kumar, K.S. Shiva Prakash, S.P. Meghana, J. Department of Studies in Computer Science University of Mysore Manasagangothri Campus Mysuru Karnataka570006 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering University of Mysore Manasagangothri Campus Mysuru Karnataka570006 India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Mysuru Karnataka570006 India Department of Computer Science Mahajana Post Graduate Center Mysuru Karnataka570016 India

The introduction of an IoT-based smart waste management system is proposed to optimize various aspects of university campus waste recycle management processes, such as garbage collection scheduling, waste type identification, and collection routes. This system utilizes advanced data analytics algorithms, wireless communication protocols, and sensors to measure and document variations in trash levels, enabling real-time monitoring of waste accumulation. Through this IoT-based infrastructure, university administrators and waste management staff gain fine-grained visibility and control over garbage collection processes. The implications for sustainability advocacy and environmental stewardship in academic environments are significant. By fostering a culture of environmental awareness and accountability, universities can instill ideals of conservation and resourcefulness in their student populations and staff. The proposed IoT-enabled waste bin system exemplifies the synergy between technological innovation and environmental responsibility, serving as a model for institutions worldwide. Collaboration among universities can leverage IoT technology to drive positive change, mitigate environmental degradation, and promote sustainable practices beyond campus boundaries. Efficient waste management is crucial for nurturing sustainability on college and university campuses, as evidenced by the urgent need for the collection and recycling of compost, general waste, and recyclables. Our models consistently achieve high performance, with perfect scores (1.000) across all split ratios, indicating flawless classification of waste types. Although the artificial Neural Network (ANN) demonstrates slightly lower accuracy, ranging from 0.973 to 0.997 depending on data splits, it still performs well. As a result, Naive Bayes produces less accurate results, typically surpassing 0.93 but falling short of Random Forest, K-Nearest Neighbors (KNN), and Support Vector machine (SVM)'s flawless

关键词： Electronic Waste

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Deep learning-Based Dynamic Scheduling for Semiconductor Manufacturing with High Uncertainty of Automated Material Handling System Capability 2

Deep Learning-Based Dynamic Scheduling for Semiconductor Man...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Dineshkumar, R. Al-Attabi, Kassem Kumar, Komuravelly Sudheer Madhura, G.K. Sharma, Jaya Saveetha Institute Of Medical And Technical Sciences Saveetha University Saveetha School Of Engineering Department Of Ece Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Sr University School Of Computer Science & Artificial Intelligence Telangana Warangal India Nitte Meenakshi Institute Of Technology Department Of Artificial Intelligence And Machine Learning Bengaluru India Gitam University Department Of Cse Bengaluru India

ISBN: (纸本)9798350381665

To improve the dynamic responsiveness and production efficiency of manufacturing workshops to personalized orders, a multi-agent manufacturing system. However, the previous methods cannot work well in a changeable workshop environment when encountering a large number of stochastic disturbances of orders and resources. Develop a robust scheduling approach for dynamic workshop environments, effectively managing stochastic disturbances in orders and resources to optimize production efficiency and minimize disruptions. Dynamic scheduling approach for semiconductor manufacturing using Proximal Policy Optimization (PPO) and Autoencoder (AE) for feature extraction, defining state space to capture task and production attributes. Action space design employs dispatching rules for operation selection, while the reward function adapts production to demand, trained in action and state phases. Autoencoders compress high-dimensional input data into a lower-dimensional latent space while preserving important features. The obtained results demonstrate that the proposed method achieves better performance Delivery Data Rate (DDR) of 41.20 %, Mean Cycle Time (MCT) of 51.66%, Mean of 12.85%, and Execution time of 5.07s. These results ensure superior dynamic scheduling performance compared to other existing methods, such as Dynamic Scheduling Using Flexible Job Shop Scheduling problem (FJSP), and Multiple-DynamicScheduling-Rule (MDSR). © 2024 IEEE.

关键词： Production efficiency

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