At present, the open stopping with subsequent filling mining of the 'two step' mode has received significant attention. The recovery method entails dividing the ore block into the first step ore room and the s...
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Biometric cryptosystems are security tools that use an individual’s distinctive physical or behavioral characteristics to secure sensitive data. Any biometric cryptosystem’s effectiveness is dependent on how well th...
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As multimedia technologies advance, the education system is increasingly adopting e-Learning methods, such as e-classes, virtual classrooms, and online video lectures. This shift requires significant memory for storag...
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Machine learning techniques can potentially revolutionise healthcare;training such models demands significant expertise. We aim to assess the effectiveness of automated machine learning modules in empowering healthcar...
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The identification of fraudulent movies or images created using deep learning algorithms is the subject of this research and attempts an in-depth investigation of Deepfake Detection. Deepfakes are created by manipulat...
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ISBN:
(数字)9798350304053
ISBN:
(纸本)9798350304060
The identification of fraudulent movies or images created using deep learning algorithms is the subject of this research and attempts an in-depth investigation of Deepfake Detection. Deepfakes are created by manipulating or replacing certain parts of an original video or image using machine learning algorithms, usually concentrating on face features. Deepfake detection's main goal is to precisely recognize and distinguish these altered media from real movies and photos. This study looks at a number of deepfake detection techniques, including forensic methods, machine learning algorithms, and picture analysis. These approaches' efficiency and performance are assessed based on their capacity to accurately identify and categories deep-fakes. The paper also examines the difficulties and restrictions of deepfake detection, such as the development of more complex and convincing deepfakes. Further, prospective uses and future possibilities for deepfake detection research are examined, with an emphasis on improving detection skills and creating effective countermeasures. Overall, this research offers insightful information about cutting-edge methods and developments in Deepfake Detection, giving a greater comprehension of its importance in resolving the issues brought on by manipulated media in the current digital era.
computer!! The one everyone uses almost every day in this busy world, a device capable of handling tasks from simple addition to complex tasks like launching rockets, but to sustain these tasks, and be a part of the h...
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In medical image segmentation, obtaining large volume of high quality labeled data is a persistent challenge, especially for intricate tasks like brain lesion segmentation, where annotations are time-consuming, costly...
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The breast cancer detection performs a key function in the health care network. The precise and early detection of cancer in the breast could aid to save life of the sufferer. The traditional machine learning methods ...
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In Intelligent Manufacturing,Big Data and industrial information enable enterprises to closely monitor and respond to precise changes in both internal processes and external environmental factors,ensuring more informe...
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In Intelligent Manufacturing,Big Data and industrial information enable enterprises to closely monitor and respond to precise changes in both internal processes and external environmental factors,ensuring more informed decision-making and adaptive system *** also promotes decision making and provides scientific analysis to enhance the efficiency of the operation,cost reduction,maximizing the process of production and so *** methods are employed to enhance productivity,yet achieving sustainable manufacturing remains a complex challenge that requires careful *** study aims to develop a methodology for effective manufacturing sustainability by proposing a novel Hybrid Weighted Support Vector-based Lévy flight(HWS-LF)*** objective of the HWS-LF method is to improve the environmental,economic,and social aspects of manufacturing *** this approach,Support Vector Machines(SVM)are used to classify data points by identifying the optimal hyperplane to separate different classes,thereby supporting predictive maintenance and quality control in *** Forest is applied to boost efficiency,resource allocation,and production optimization.A Weighted Average Ensemble technique is employed to combine predictions from multiple models,assigning different weights to ensure an accurate system for evaluating manufacturing ***,Lévy flight Optimization is incorporated to enhance the performance of the HWS-LF method *** method’s effectiveness is assessed using various evaluation metrics,including accuracy,precision,recall,F1-score,and *** show that the proposed HWS-LF method outperforms other state-of-the-art techniques,demonstrating superior productivity and system performance.
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