An innovative educational tool called the 'Auto-mated Question Generation and Answer Evaluation System' was created to make the assessment process more efficient. By automating the creation of questions, it co...
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Before a heart attack happens, treating cardiac patients effectively depends on precise heart disease prediction. A heart disease prediction system for the determination of whether the patient has a heart disease cond...
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Skin cancer diagnosis poses significant challenges in the medical field due to its varying presentations and the expertise required for accurate classification. The complexity of skin lesions and the need for speciali...
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In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)a...
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In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network *** mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using MS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.
The efficiency of transportation and road safety are critical factors for economic well-being, and they are substantially influenced by the condition of road surfaces. Unfortunately, existing practices often result in...
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Human cognition naturally excels at identifying irregular patterns in images, enabling the distinction between expected variations and anomalies. Industrial defect classification presents unique challenges, encompassi...
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ISBN:
(纸本)9798350327533
Human cognition naturally excels at identifying irregular patterns in images, enabling the distinction between expected variations and anomalies. Industrial defect classification presents unique challenges, encompassing a wide range of potential errors, from minor nuances to critical structural issues. This research work introduces PaDiM (Patch Distribution Modeling Framework) model, a novel approach for cold-start anomaly detection in industrial imagery. Prior research predominantly focuses on acquiring a model representing the standard distribution, often achieved through techniques like auto encoding, GANs, or other unsupervised adaptation methods. Even without explicit adaptation, these models exhibit robust anomaly detection capabilities, effectively localizing defects within the spatial context. PaDiM excels in industrial anomaly localization, positioning itself as a state-of-the-art solution. It maintains efficiency with swift inference times, obviating the need for specific dataset training. This characteristic renders PaDiM highly attractive for practical applications in industrial anomaly detection. In this research work, PaDiM model is implemented for real-world industry manufactured products such as Bottle, Cable, Capsule, Hazelnut and Screw using computer Vision for anomaly detection on MVTec AD dataset. And obtained an accuracy of 83%. Additional experiments highlight PaDiM high sample efficiency, matching the performance of existing anomaly detection methods while utilizing only a fraction of the nominal training data. Furthermore, PaDiMs versatility extends to its adaptability in various industrial settings. Its ability to effectively identify and classify a wide range of defects, from subtle imperfections to major structural discrepancies, showcases its potential for widespread applicability across diverse manufacturing processes. This adapta bility is a testament to Patch Core's robustness and underscores its significance as a powerful tool in mode
A comprehensive healthcare solution through a unified web application is offered Centered on cardiovascular disease prediction and broader health prognosis based on patient treatment history and recent health data, it...
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Social media platforms also function as breeding grounds for hate speech. Hate speech is offensive language targeting people based on their characteristics, including gender, race, or religion. The existing hate speec...
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The project, therefore, is to be a ResQ using deep learning capabilities for an enhanced response and coordination in cases of disaster. The best sources of real-time data on the weather services, government alerts, a...
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Current railway infrastructure faces the critical challenge of preventing looping incidents, where trains unintentionally re-enter previously traversed tracks. To address this, we propose a robust and comprehensive Io...
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