Machine learning (ML) is increasingly used in industry processes to advance digital technologies for Industry 4.0. This paper comprehensively reviews ML applications in manufacturing, covering supervised, unsupervised...
Machine learning (ML) is increasingly used in industry processes to advance digital technologies for Industry 4.0. This paper comprehensively reviews ML applications in manufacturing, covering supervised, unsupervised, and deep learning (DL) approaches across various industrial processes. The use of ML approaches in manufacturing process planning and control, fault identification/manufacturing/assembly, monitoring in the agricultural industry, quality control, and optimisation of logistics and robots are being investigated. Key highlights include an analysis of 70 primary studies, comparing recent trends in ML for manufacturing, and examining ML training concepts in learning factories. We also use ML techniques to assess the automotive manufacturing industry's architectures, models, and deployment challenges. Furthermore, these notions will be examined and applied to all possible approaches. The improvements in the scope of identification of the proper algorithm for the adequate set of applications will be examined further to ensure the smooth going of the process from training to the testing set.
Bengali Handwritten Text Recognition (HTR) is a challenging task due to the text's cursive disposition and significant variations in the graphemic unit, the smallest written unit in alphasyllabary languages. These...
Bengali Handwritten Text Recognition (HTR) is a challenging task due to the text's cursive disposition and significant variations in the graphemic unit, the smallest written unit in alphasyllabary languages. These graphemic units, or simply graphemes, may consist of multiple characters and diacritics, and the order of characters or diacritics between textual and visual representations is only sometimes preserved. Moreover, Bengali conjunct characters, a subset of the graphemes, often have unique visual representations vastly different from the characters they comprise. Because of these inherent characteristics, a character-based tokenizer fails to tokenize the label text appropriately for BHTR in a supervised setting, making it challenging to adapt the existing sequence-to-sequence frameworks often used for Handwritten Text Recog-nition (HTR). To address this, this paper proposes a trie- based text tokenizer (called BnGraphemizer) that allows one to tokenize text into a set of graphemes instead of characters. Experimental investigations show that the BnGraphemizer-based BHTR system performs better regarding higher Word Recognition Rate (WRR) than its character-based counterpart in two of three Bengali handwritten datasets.
RISC-V architectures are rapidly gaining in popularity in embedded systems, in which each mW counts. Since AI-related applications such as image recognition or neural networks tend to be highly energy consuming, low-p...
RISC-V architectures are rapidly gaining in popularity in embedded systems, in which each mW counts. Since AI-related applications such as image recognition or neural networks tend to be highly energy consuming, low-power techniques are required to optimise the autonomy of systems using SoCs to run such ***, the trade-off between energy consumption, application performance and resource use requires a multi-objective optimisation with a potentially very important number of optimisation parameters to be performed on the SoC. As they rely on heuristics that are likely to only return locally optimal solutions, empirical methods must be *** address this issue, a technology-agnostic mathematical model is introduced to represent how optimisations are applied to a SoC, and a workflow designed to perform an intelligent exhaustive exploration of the optimisation space has been developed to highlight a subset of optimal processor *** of the ARIANE/CV32A6 RISC-V processor, running a CNN propagation on a Xilinx Zynq 7020 FPGA, has shown very encouraging results using low degree configurations, and is likely to perform even better with higher degree configurations.
Skin cancer has emerged as a prevalent disease in recent times, notably with melanoma boasting the highest mortality rate among various skin cancer types. The early detection of melanoma significantly enhances the pro...
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Everyday, there are a growing number of people who require blood transfusions and there is no substitute for human blood. The secure blood donors are voluntary non-remunerated blood donors from risk populations. Donor...
Everyday, there are a growing number of people who require blood transfusions and there is no substitute for human blood. The secure blood donors are voluntary non-remunerated blood donors from risk populations. Donors in India and across the globe are three types namely, Voluntary donors, Professional donors and replacement donors. The deadline-driven web framework that supports Django's clean, practical design and speedy development. The existing system has a manual work restriction. This has restrictions on securely controlling the task for timely results declaration, and has a significant resource consumption. Web-based blood donation is the coordination of the supply and demand of blood through an online portal. The main goal of this research study is to give blood donors and blood searchers an online platform and to develop an engaging method of connecting the two groups. The suggested system provides a great deal of information regarding blood types, blood supply, and blood donation specifics to raise awareness among the public. Additionally, other system features like password-based security protection, report generation, chat functionality, and stock shortages can even improve the management tasks by streamlining them.
These days, breast cancer is the foremost predominant cancer among ladies around the world and one of the driving causes of cancer-related mortality. In this ponder, an existing Observation, The study of disease trans...
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In real-world scenarios, the impacts of decisions may not manifest immediately. Taking these delays into account facilitates accurate assessment and management of risk in real-world environments, thereby ensuring the ...
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Agriculture consumes a significant proportion of water reserves in irrigated areas. Improving irrigation is becoming essential to reduce this high-water consumption by adapting supplies to crop needs and avoiding loss...
Agriculture consumes a significant proportion of water reserves in irrigated areas. Improving irrigation is becoming essential to reduce this high-water consumption by adapting supplies to crop needs and avoiding losses. This global issue has prompted many scientists to think about sustainable solutions using innovative technologies. This study aims to solve the problem of equitable distribution of irrigation water to crops using the PSO (Particle Swarm Optimization) algorithm. The problem involves the allocation of water between N crops, taking into account the priorities of each crop, the minimum and maximum water thresholds required, and the total amount of water available. A precise modeling of the problem is proposed, followed by a description of the PSO algorithm adapted to this situation. The implementation of the algorithm in Python is also presented. The results and performance of the algorithm are discussed, showing its ability to find a fair and efficient solution for irrigation water allocation.
Over the years, the number of users of social media has increased drastically. People frequently share their thoughts through social platforms, and this leads to an increase in hate content. In this virtual community,...
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ISBN:
(数字)9798331508227
ISBN:
(纸本)9798331508234
Over the years, the number of users of social media has increased drastically. People frequently share their thoughts through social platforms, and this leads to an increase in hate content. In this virtual community, individuals share their views, express their feelings, and post photos, videos, blogs, and more. Social networking sites like Facebook and Twitter provide platforms to share vast amounts of content with a single click. However, these platforms do not impose restrictions on the uploaded content, which may include abusive language and explicit images unsuitable for social media. To resolve this issue, a new idea must be implemented to divide the inappropriate content. Numerous studies have been done to automate the process. In this paper, we propose a new Bi-GRU-CNN model to classify whether the text is offensive or not. The combination of the Bi-GRU and CNN models outperforms the existing models.
We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challe...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challenges posed by DP noise and local updates with streaming non-iid data, we develop a perturbed iterate analysis to control the impact of the DP noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed under a quasi-strong convexity condition. Subject to an $(\epsilon, \delta)$ DP budget, we establish a dynamic regret bound over the entire time horizon, quantifying the impact of key parameters and the intensity of changes in dynamic environments. Numerical experiments confirm the efficacy of the proposed algorithm.
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