This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of $99.4\%$, $99.6\%$, $99.8\%$, $99.6\%$, and $99.4\%$ on the SARS-CoV-2 dataset, and $92.9\%$, $91.3\%$, $93.7\%$, $92.2\%$, and $92.5\%$ on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models’ predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists.

Diabetic retinopathy (DR) is a diabetes complication that affects the eye and can cause damage from mild vision problems to complete blindness. It has been observed that the eye fundus images show various kinds of color aberrations and irrelevant illuminations, which degrade the diagnostic analysis and may hinder the results. In this research, we present a methodology to eliminate these unnecessary reflectance properties of the images using a novel image processing schema and a stacked deep learning technique for the diagnosis. For the luminosity normalization of the image, the gray world color constancy algorithm is implemented which does image desaturation and improves the overall image quality. The effectiveness of the proposed image enhancement technique is evaluated based on the peak signal to noise ratio (PSNR) and mean squared error (MSE) of the normalized image. To develop a deep learning based computer-aided diagnostic system, we present a novel methodology of stacked generalization of convolution neural networks (CNN). Three custom CNN model weights are fed on the top of a single meta-learner classifier, which combines the most optimum weights of the three sub-neural networks to obtain superior metrics of evaluation and robust prediction results. The proposed stacked model reports an overall test accuracy of 97.92% (binary classification) and 87.45% (multi-class classification). Extensive experimental results in terms of accuracy, F-measure, sensitivity, specificity, recall and precision reveal that the proposed methodology of illumination normalization greatly facilitated the deep learning model and yields better results than various state-of-art techniques.

Degree attestation verification and traceability are complex one-to-one processes between the Higher Education Commission (HEC) and universities. The procedure shifted to the digitalized manner, but still, on a certain note, manual authentication is required. In the initial process, the university verified the degree and stamp seal first. Then, a physical channel of degree submission to the receiving ends is activated. After that, the degree is attested while properly examining and analyzing the tamper records related to degree credentials through e-communication with the university for verification and validation. This issue poses a serious challenge to educational information integrity and privacy. Potentially, blockchain technology could become a standardized platform to perform tasks including issuing, verifying, auditing, and tracing immutable records, which would enable the HEC, universities, and Federal Education Ministry (FEM) to quickly and easily get attested and investigate the forge proof versions of certificates. Besides, decentralized distributed data blocks in chronological order provide high security between distributed ledgers, consensus engine, digital signature, smart contracts, permissioned application, and private network node transactions that guarantee degree record validation and traceability. This paper presents an architecture (HEDU-Ledger) and detail design of blockchain-enabled hyperledger fabric applications implementation for degree attestation verification and traceable direct channel design between HEC and universities. The hyperledger fabric endorses attestation records first, and then validates (committer) the degree and maintains the secure chain of tracing between stakeholder peer nodes. Furthermore, this HEDU-Ledger architecture avoids language and administrative barriers. It also provides robustness in terms of security and privacy of records and maintains integrity with secure preservation as compared to that of the other state-of-the-art methods.

Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine’s health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.