Due to the augmented rise of COVID-19, clinical specialists are looking for fast faultless diagnosis strategies to restrict COVID spread while attempting to lessen the computational complexity. In this way, swift diagnosis techniques for COVID-19 with high precision can offer valuable aid to clinical specialists. RT-PCR test is an expensive and tedious COVID diagnosis technique in practice. Medical imaging is feasible to diagnose COVID-19 by X-ray chest radiography to get around the shortcomings of RT-PCR. Through a variety of deep transfer-learning models, this research investigates the potential of AI-based early diagnosis of COVID-19 via X-ray chest radiographs. With 13000 normal and 3000 COVID X-ray chest radiographs, the deep transfer-learning models are optimized to further the accurate diagnosis. By using contrast variation and picture scaling techniques during the image pre-processing phase, the quality of the input x-ray chest radiographs is altered to improve the diagnosis accuracy. The altered x-ray chest radiographs undergo a data augmentation phase before developing a modified dataset to train the deep transfer-learning models (Khang et al., 2023a). The deep transfer-learning architectures are trained using a feature extraction/edge detection-based decision boundary. During training, the classification of X-ray chest radiographs based on feature extraction algorithm values is converted into a feature label set containing the classified image data with a feature string value representing the number of edges detected after edge detection (Morris et al., 2023). The feature label set is further tested with the SVM classifier and KNN classifier to audit the quality metrics of the proposed model. The quality metrics include confusion matrix, accuracy, precision, F1 score, recall, and ROC-AUC. The Inception-V3 dominates the six deep transfer-learning models, according to the assessment results, with a training accuracy of 84.79 % and a loss function of 2.4%. The performance of Cubic SVM was superior to that of the other SVM classifiers, with an AUC score of 0.99, precision of 0.983, recall of 0.8977, accuracy of 95.8%, and F1 score of 0.9384. Cosine KNN fared better than the other KNN classifiers with an AUC score of 0.95, precision of 0.974, recall of 0.777, accuracy of 90.8%, and an F1 score of 0.864. The performance assessment metrics uncover that the proposed methodology can aid in preliminary COVID diagnosis.

Printed circuit boards (PCBs) are the first stage in manufacturing any electronic product. The reliability of the electronic product depends on the PCB. The presence of manufacturing defects in PCBs might affect the performance of the PCB and thereby the reliability of the electronic products. In this paper, the various challenges faced in identifying manufacturing defects along with a review of various learning methods employed for defect detection are presented. We compare the various techniques available in the literature for further understanding of the accuracy of these techniques in defect detection.

Printed Circuit Boards (PCBs) are the building blocks for all electronic products. Fabrication of a PCB involves various mechanical and chemical processes. As obtaining accuracy in the mechanical and chemical processes is very difficult, various defects/faults are formed during PCBs fabrication. These fabrication defects lead to performance degradation of electronic products. In this review, we describe various defects present in PCBs under the Through hole and SMD categories. To understand the frequency of occurrence and reason for the occurrence of defects in both manual and machine, PCB fabrication data was collected and analysed from April 2017 to July 2020 as a part of industry collaboration.

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Citation is a measure that quantifies the impact of the researcher, research article and journal’s quality. Investigating the citation of articles and/or researchers is one of the important tasks in the research community. So, understanding and predicting citation patterns of research articles has become popular in scientific research fields. In this work, we give a machine learning approach to predict the citations of research articles using the keywords. We study the citation impact based on keywords motioned in the articles using the data set of publications which are published in the various physical review journals from 1985-2012. In this dataset, for each publication is allocated some PACS codes (keywords) by their authors which represent a sub-field of Physics. In this work, we are investigating the impact of PACS codes of article on article’s citation. We are performing our analysis on the first (sub-field of physics), second (sub area of sub-field of physics) and third level of PACS codes. We observed that compared to the first level, every pair of citation patterns of the second level is highly correlated. We also obtained a universal approximation curve for the third level that matches with the average value of the first level. This curve looks like a shifted and scaled version of the Gaussian function and is right skewed. We can also predict the citations based on the keywords by using this universal curve.

Infrastructure plays an important role in the development of any nation. Roads are very important for the movement of humans and goods. Good road infrastructure reduces travel time and cost. Poor road infrastructure can cause an increase in travel time, more consumption of fuel and lead to accidents due to difficulty in driving. All these factors will have a cascading effect on the economy as the prices of essential commodities are directly related to the easy of transportation. Hence, it is necessary to detect the distresses on roads like potholes and do regular repairs. Initially, manual inspection was done by the concerned employers, who go check the roads and take note of the areas where there is a requirement of repair. This method involves a lot of time and labor. With the development in technology, the focus on implementing a smart method of inspection increased. To build an accurate and quick detection system, an Earth Mover's Distance (EMD) based model is proposed in this paper. This algorithm finds the presence of road distress by comparing the image of the current testing road with that of the reference image. The experiment also considers the chance of occurrence of Gaussian noise and Salt and Pepper noise while capturing the images, to fit the real-world situations.

Printed Circuit Boards (PCBs) have become an inevitable component in all electronic devices, including the gadgets that we use in our day-to-day life. To maintain the quality of a product it is necessary to make sure the PCB is void of defects. To identify whether PCB has defects or not, it has to undergo tests in various stages. In this paper, we propose a reference-based model using Wasserstein distance metric to detect short faults in PCBs. The study includes the effect of Gaussian and Salt-Pepper Noise have on fault detection, which usually occurs in the images procured by digital cameras.