With rapidly expanding cloud-enabled big data environments, there is an imperative need for efficient data-sharing mechanisms that are multidimensional and balance both speed and security. In this connection, high-spe...
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Roadside and outside environmental elements contribute to the road traffic setting's highly dynamic and turbulent nature. The human factor, primarily disregarded in the present research, is an essential element th...
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Roadside and outside environmental elements contribute to the road traffic setting's highly dynamic and turbulent nature. The human factor, primarily disregarded in the present research, is an essential element that contributes to the traffic context in addition to infrastructure-related elements like traffic signals, road infrastructure, and other road networks. Timing the green light and tracing the object that makes the incorrect turn using real-time visual information for traffic monitoring are still challenging tasks for the conventional traffic control system. We describe a self-adaptive real-time algorithm based on real-time traffic flow and monitoring. Combining image processing with AI-powered, self-adaptive machine learning for controlling traffic clearance at intersections is a forward-thinking approach with great potential. The suggested system uses the You Only Look Once v3 (YOLOv3) model and single image processing using a neural network to determine traffic clearance at the signal. YOLOv3 method to recognize objects from video frames. Subsequently, the centroid object tracking technique is used to monitor the movement of each vehicle within a proposed framework. We implemented algorithms to identify vehicles traveling in the incorrect direction based on their trajectories. This integrated approach enhances accurate object recognition, real-time vehicle tracking, and the detection of traffic violations, enhancing overall road safety measures. The experimental findings are quite promising, achieving an exclusive comparison between expected and actual vehicle numbers is crucial for any traffic monitoring system. The average object detection accuracy of 88.43% is impressive, and the exceptional 90.45% accuracy in tracking vehicles engaging in wrong turns or reckless driving behaviors is particularly noteworthy—it provides the system's ability to address safety concerns effectively. Integrating a Convolutional Neural Network (CNN) into the algorithm to all
Human activity recognition (HAR) techniques pick out and interpret human behaviors and actions by analyzing data gathered from various sensor devices. HAR aims to recognize and automatically categorize human activitie...
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The naive Bayesian classifier(NBC) is a supervised machine learning algorithm having a simple model structure and good theoretical interpretability. However, the generalization performance of NBC is limited to a large...
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The naive Bayesian classifier(NBC) is a supervised machine learning algorithm having a simple model structure and good theoretical interpretability. However, the generalization performance of NBC is limited to a large extent by the assumption of attribute independence. To address this issue, this paper proposes a novel attribute grouping-based NBC(AG-NBC), which is a variant of the classical NBC trained with different attribute groups. AG-NBC first applies a novel effective objective function to automatically identify optimal dependent attribute groups(DAGs). Condition attributes in the same DAG are strongly dependent on the class attribute, whereas attributes in different DAGs are independent of one another. Then,for each DAG, a random vector functional link network with a SoftMax layer is trained to output posterior probabilities in the form of joint probability density estimation. The NBC is trained using the grouping attributes that correspond to the original condition attributes. Extensive experiments were conducted to validate the rationality, feasibility, and effectiveness of AG-NBC. Our findings showed that the attribute groups chosen for NBC can accurately represent attribute dependencies and reduce overlaps between different posterior probability densities. In addition, the comparative results with NBC, flexible NBC(FNBC), tree augmented Bayes network(TAN), gain ratio-based attribute weighted naive Bayes(GRAWNB), averaged one-dependence estimators(AODE), weighted AODE(WAODE), independent component analysis-based NBC(ICA-NBC), hidden naive Bayesian(HNB) classifier, and correlation-based feature weighting filter for naive Bayes(CFW) show that AG-NBC obtains statistically better testing accuracies, higher area under the receiver operating characteristic curves(AUCs), and fewer probability mean square errors(PMSEs) than other Bayesian classifiers. The experimental results demonstrate that AG-NBC is a valid and efficient approach for alleviating the attribute i
State-of-the-art recommender systems are increasingly focused on optimizing implementation efficiency, such as enabling on-device recommendations under memory constraints. Current methods commonly use lightweight embe...
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State-of-the-art recommender systems are increasingly focused on optimizing implementation efficiency, such as enabling on-device recommendations under memory constraints. Current methods commonly use lightweight embeddings for users and items or employ compact embeddings to enhance reusability and reduce memory usage. However, these approaches consider only the coarse-grained aspects of embeddings, overlooking subtle semantic nuances. This limitation results in an adversarial degradation of meta-embedding performance, impeding the system's ability to capture intricate relationships between users and items, leading to suboptimal recommendations. To address this, we propose a novel approach to efficiently learn meta-embeddings with varying grained and apply fine-grained meta-embeddings to strengthen the representation of their coarse-grained counterparts. Specifically, we introduce a recommender system based on a graph neural network, where each user and item is represented as a node. These nodes are directly connected to coarse-grained virtual nodes and indirectly linked to fine-grained virtual nodes, facilitating learning of multi-grained semantics. Fine-grained semantics are captured through sparse meta-embeddings, which dynamically balance embedding uniqueness and memory constraints. To ensure their sparseness, we rely on initialization methods such as sparse principal component analysis combined with a soft thresholding activation function. Moreover, we propose a weight-bridging update strategy that aligns coarse-grained meta-embedding with several fine-grained meta-embeddings based on the underlying semantic properties of users and items. Comprehensive experiments demonstrate that our method outperforms existing baselines. The code of our proposal is available at https://***/htyjers/C2F-MetaEmbed.
Water resource management and disaster response have become some of the most challenging tasks, especially when disasters pose a threat, as delays could lead to more impacts. The centralized system used for water dyna...
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Water resource management and disaster response have become some of the most challenging tasks, especially when disasters pose a threat, as delays could lead to more impacts. The centralized system used for water dynamics and disaster control usually presents itself as a scalability problem since more clients present a problem, the system's latency is high, and the system is always prone to a single-point failure. The previous approach lacks flexibility and does not synchronously guarantee the integration of several subjects in real time, especially during unpredictable disaster conditions. The proposed FL-MAPPO model surpasses current methods by facilitating decentralized, privacy-protecting decision-making minimizing latency and single-point failures. In contrast to LSTM, Bi-LSTM, and DRNN, which are based on centralized data processing, FL-MAPPO provides real-time adaptability and effective resource management. Experimental results validate that it has lower MSE, higher R² scores, and quicker response times, making it better suited for flood prediction and disaster response. To this end, this study advances a solution through a Decentralized Learning-Driven Multi-Agent Autonomous System (DL-MAAS). The new feature is a Decentralized Cooperation environment in which intelligent and self-managing agents learn utilizing Reinforcement Learning (RL) and Federated Learning (FL) algorithms for enhancing smart water management and real-time disaster relief. IoT devices are adopted for sensing and data acquisition, adaptive learning for decision-making, and optimization of energy use among the agents in the system through metaheuristic algorithms. The research methodology for implementing the proposed solution involves the design of a multi-layered architecture, including data acquisition, decentralized learning, and real-time execution. With a Mean Squared Error (MSE) of 0.112, R-squared (R²) of 0.953, and Mean Absolute Error (MAE) of 0.207, the proposed method is better
Hearing and Speech impairment can be congenital or *** and speech-impaired students often hesitate to pursue higher education in reputable institutions due to their ***,the development of automated assistive learning ...
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Hearing and Speech impairment can be congenital or *** and speech-impaired students often hesitate to pursue higher education in reputable institutions due to their ***,the development of automated assistive learning tools within the educational field has empowered disabled students to pursue higher education in any field of *** learning devices enable students to access institutional resources and facilities *** proposed assistive learning and communication tool allows hearing and speech-impaired students to interact productively with their teachers and *** tool converts the audio signals into sign language videos for the speech and hearing-impaired to follow and converts the sign language to text format for the teachers to *** educational tool for the speech and hearing-impaired is implemented by customized deep learning models such as Convolution neural networks(CNN),Residual neural Networks(ResNet),and stacked Long short-term memory(LSTM)network *** assistive learning tool is a novel framework that interprets the static and dynamic gesture actions in American Sign Language(ASL).Such communicative tools empower the speech and hearing impaired to communicate effectively in a classroom environment and foster *** deep learning models were developed and experimentally evaluated with the standard performance *** model exhibits an accuracy of 99.7% for all static gesture classification and 99% for specific vocabulary of gesture action *** two-way communicative and educational tool encourages social inclusion and a promising career for disabled students.
A multi-secret image sharing (MSIS) scheme facilitates the secure distribution of multiple images among a group of participants. Several MSIS schemes have been proposed with a (n, n) structure that encodes secret...
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Autonomous aerial vehicles (AAVs) with wireless power transfer (WPT) technology offer a promising solution to extend the lifetime of wireless rechargeable sensor networks (WRSNs) by swiftly recharging multiple sensor ...
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Human action recognition is applicable in different domains. Previously proposed methods cannot appropriately consider the sequence of sub-actions. Herein, we propose a semantical action model based on the sequence of...
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