Browsing by Author "Pandey, C."

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An Efficient AI-Based Classification of Semiconductor Wafer Defects using an Optimized CNN Model
(Institute of Electrical and Electronics Engineers Inc., 2023) Pandey, C.; Bhat, K.G.
Wafer maps used to display defect patterns in the integrated circuits industry include crucial information that quality engineers may utilize to identify the cause of a defect and increase yield. In this paper, we put forth a framework for accurately and quickly categorizing semiconductor wafer faults utilizing particularly CNN-based models. This paper seeks to provide a scalable, adaptive, and user-friendly implementation of convolutional neural networks for applications classifying semiconductor defects. In categorizing the defects found on semiconductor wafers, the suggested CNN model obtained an accuracy of 90.50% & 92.28% and losses of 0.39 & 0.29 while performing the training and validation, respectively, along with the misclassification rate of 0.0772. The suggested model also learns rapidly on the validation set at a rate of 1e-03 per second. The proposed custom CNN model architecture incorporates only two convolution layers, resulting in a greatly reduced number of parameter weights and biases. Specifically, the number of parameters is only 44000, which makes the model more compact, cost-effective, and robust against random noise. Moreover, this model can function well under low power and processing limits. Â© 2023 IEEE.
Convolutional Neural Network-Enabling Speech Command Recognition
(Springer Science and Business Media Deutschland GmbH, 2023) Patra, A.; Pandey, C.; Palaniappan, K.; Sethy, P.K.
The speech command recognition system based on deep image classification is the key that would tremendously promise to revolutionize research and development by overcoming the communication barrier between human and machine or computer. We are all aware of challenges in identifying the voice command in noise and variability in speed, pitch, and projection. This paper has developed an efficient and highly accurate speech command recognition for smart and effective speech processing applications like modern telecommunication. In particular, a novel convolutional neural network (CNN) is presented that works with a one-second audio clip consisting of one specific word including ten speech commands and other words labeled as “unknown,” and model implementations were operated in the noisy environment. The CNNs are structurally fully developed in such a way to recognize the speech commands with the utilization of deep learning (DL) for image classification concepts. Thus, this research used the concept of DL for image classification to translate the problem of speech command recognition into the image domain. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Futuristic AI convergence of megatrends: IoT and cloud computing
(wiley, 2022) Pandey, C.; Sahu, Y.K.; Kannan, N.; Rashid Mahmood, M.R.; Sethy, P.K.; Behera, S.K.
Recent years have seen increasing curiosity among users in migrating their cloud computing and internet-of-things apps. Cloud-based and internet-of-things infra-structures require specialized hardware to enable software and advanced manage-ment strategies to improve performance. Adaptability and autonomous learning capabilities are highly valuable in facilitating the configuration and complex transition of these infrastructures to customers' changing demands and designing adaptable applications. This capacity to self-adapt is increasingly essential, particularly for nonexpert managers and autonomous device applications. Cloud Networking (CN) and the Internet of Things (IoT) have arisen as modern outlets for the ICT movement of the 21st century. In this paper, we carry out a survey of nearly 183 articles on which the latest methodologies have been applied. Also, we discuss the proposed approaches and the reported advantages and limitations. The goal of this survey paper is to offer a brief idea to researchers working in this area. In order to consider the present and future challenges of such a framework, it is important to recognize critical innovations that will allow future implementa-tions. This article examines how three new paradigms (cloud computing, IoT, and artificial intelligence) can affect workspace and business. Also, we describe a range of innovations that propel these paradigms and encourage experts to address the current state and perspective directions. © 2023 Scrivener Publishing LLC.