Software systems have grown significantly and in *** a result of these qualities,preventing software faults is extremely *** defect prediction(SDP)can assist developers in finding potential bugs and reducing maintenan...
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Software systems have grown significantly and in *** a result of these qualities,preventing software faults is extremely *** defect prediction(SDP)can assist developers in finding potential bugs and reducing maintenance *** it comes to lowering software costs and assuring software quality,SDP plays a critical role in software *** a result,automatically forecasting the number of errors in software modules is important,and it may assist developers in allocating limited resources more *** methods for detecting and addressing such flaws at a low cost have been *** approaches,on the other hand,need to be significantly improved in terms of *** in this paper,two deep learning(DL)models Multilayer preceptor(MLP)and deep neural network(DNN)are *** proposed approaches combine the newly established Whale optimization algorithm(WOA)with the complementary Firefly algorithm(FA)to establish the emphasized metaheuristic search EMWS algorithm,which selects fewer but closely related representative *** find the best-implemented classifier in terms of prediction achievement measurement factor,classifiers were applied to five PROMISE repository *** compared to existing methods,the proposed technique for SDP outperforms,with 0.91%for the JM1 dataset,0.98%accuracy for the KC2 dataset,0.91%accuracy for the PC1 dataset,0.93%accuracy for the MC2 dataset,and 0.92%accuracy for KC3.
In today’s rapidly evolving landscape of communication technologies,ensuring the secure delivery of sensitive data has become an essential *** overcome these difficulties,different steganography and data encryption m...
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In today’s rapidly evolving landscape of communication technologies,ensuring the secure delivery of sensitive data has become an essential *** overcome these difficulties,different steganography and data encryption methods have been proposed by researchers to secure *** of the proposed steganography techniques achieve higher embedding capacities without compromising visual imperceptibility using LSB *** this work,we have an approach that utilizes a combinationofMost SignificantBit(MSB)matching andLeast Significant Bit(LSB)*** proposed algorithm divides confidential messages into pairs of bits and connects them with the MSBs of individual pixels using pair matching,enabling the storage of 6 bits in one pixel by modifying a maximum of three *** proposed technique is evaluated using embedding capacity and Peak Signal-to-Noise Ratio(PSNR)score,we compared our work with the Zakariya scheme the results showed a significant increase in data concealment *** achieved results of ourwork showthat our algorithmdemonstrates an improvement in hiding capacity from11%to 22%for different data samples while maintaining a minimumPeak Signal-to-Noise Ratio(PSNR)of 37 *** findings highlight the effectiveness and trustworthiness of the proposed algorithm in securing the communication process and maintaining visual integrity.
While moving towards a low-carbon, sustainable electricity system, distribution networks are expected to host a large share of distributed generators, such as photovoltaic units and wind turbines. These inverter-based...
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While moving towards a low-carbon, sustainable electricity system, distribution networks are expected to host a large share of distributed generators, such as photovoltaic units and wind turbines. These inverter-based resources are intermittent, but also controllable, and are expected to amplify the role of distribution networks together with other distributed energy resources, such as storage systems and controllable loads. The available control methods for these resources are typically categorized based on the available communication network into centralized, distributed, and decentralized or local. Standard local schemes are typically inefficient, whereas centralized approaches show implementation and cost concerns. This paper focuses on optimized decentralized control of distributed generators via supervised and reinforcement learning. We present existing state-of-the-art decentralized control schemes based on supervised learning, propose a new reinforcement learning scheme based on deep deterministic policy gradient, and compare the behavior of both decentralized and centralized methods in terms of computational effort, scalability, privacy awareness, ability to consider constraints, and overall optimality. We evaluate the performance of the examined schemes on a benchmark European low voltage test system. The results show that both supervised learning and reinforcement learning schemes effectively mitigate the operational issues faced by the distribution network.
Recruitment is one of the most crucial factors in shaping efficient and high-performing teams within organizations. However, the traditional recruitment processes are often plagued by biases and subjective judgments t...
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To learn and analyze graph-structured data, Graph Neural Networks (GNNs) have emerged as a powerful framework over traditional neural networks, which work well on grid-like or sequential structure data. GNNs are parti...
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Understanding and recognition of human emotions are very crucial in various fields. This paper proposes a new approach to show the different feelings that are hidden using multi-modalities like video, audio, and textu...
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This research paper based on new steganography technology with the using of fingerprint and QR code using watermarking technique. Now's a day digital technology and image steganography more useful for secure data....
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Enhancing the interconnection of devices and systems,the Internet of Things(IoT)is a paradigm-shifting *** security concerns are still a substantial concern despite its extraordinary *** paper offers an extensive revi...
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Enhancing the interconnection of devices and systems,the Internet of Things(IoT)is a paradigm-shifting *** security concerns are still a substantial concern despite its extraordinary *** paper offers an extensive review of IoT security,emphasizing the technology’s architecture,important security elements,and common *** highlights how important artificial intelligence(AI)is to bolstering IoT security,especially when it comes to addressing risks at different IoT architecture *** systematically examined current mitigation strategies and their effectiveness,highlighting contemporary challenges with practical solutions and case studies from a range of industries,such as healthcare,smart homes,and industrial *** results highlight the importance of AI methods that are lightweight and improve security without compromising the limited resources of devices and computational *** networks can ensure operational efficiency and resilience by proactively identifying and countering security risks by utilizing machine learning *** study provides a comprehensive guide for practitioners and researchers aiming to understand the intricate connection between IoT,security challenges,and AI-driven solutions.
This study investigates the challenges of permeability prediction in reservoir engineering, focusing on addressing uncertainties inherent in the data and modelling process, and leveraging Nuclear Magnetic Resonance (N...
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Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research *** fromnatural images,character images pay more attention to stroke ***,existingmodelsmainly cons...
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Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research *** fromnatural images,character images pay more attention to stroke ***,existingmodelsmainly consider pixel-level informationwhile ignoring structural information of the character,such as its edge and glyph,resulting in reconstructed images with mottled local structure and character *** solve these problems,we propose a novel generative adversarial network(GAN)framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework,i.e.,*** existing frameworks,the generator introduces the edge extractionmodule,guiding it into the denoising process through the attention mechanism,which maintains the edge detail of the restored inscription ***,a dual-domain U-Net-based discriminator is proposed to learn the global and local discrepancy between the denoised and the label images in both image and morphological domains,which is helpful to blind denoising *** proposed dual-domain discriminator and generator for adversarial training can reduce local artifacts and keep the denoised character structure *** to the lack of a real-inscription image,we built the real-inscription dataset to provide an effective benchmark for studying inscription image *** experimental results show the superiority of our method both in the synthetic and real-inscription datasets.
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