Development and Validation of Embedded Device for Electrocardiogram Arrhythmia Empowered with Transfer Learning.

With the emergence of the Internet of Things (IoT), investigation of different diseases in healthcare improved, and cloud computing helped to centralize the data and to access patient records throughout the world. In this way, the electrocardiogram (ECG) is used to diagnose heart diseases or abnormalities. The machine learning techniques have been used previously but are feature-based and not as accurate as transfer learning; the proposed development and validation of embedded device prove ECG arrhythmia by using the transfer learning (DVEEA-TL) model. This model is the combination of hardware, software, and two datasets that are augmented and fused and further finds the accuracy results in high proportion as compared to the previous work and research. In the proposed model, a new dataset is made by the combination of the Kaggle dataset and the other, which is made by taking the real-time healthy and unhealthy datasets, and later, the AlexNet transfer learning approach is applied to get a more accurate reading in terms of ECG signals. In this proposed research, the DVEEA-TL model diagnoses the heart abnormality in respect of accuracy during the training and validation stages as 99.9% and 99.8%, respectively, which is the best and more reliable approach as compared to the previous research in this field.

Frequency based feature selection method using whale algorithm.

Feature selection is the problem of finding the best subset of features which have the most impact in predicting class labels. It is noteworthy that application of feature selection is more valuable in high dimensional datasets. In this paper, a filter feature selection method has been proposed on high dimensional binary medical datasets - Colon, Central Nervous System (CNS), GLI_85, SMK_CAN_187. The proposed method incorporates three sections. First, whale algorithm has been used to discard irrelevant features. Second, the rest of features are ranked based on a frequency based heuristic approach called Mutual Congestion. Third, majority voting has been applied on best feature subsets constructed using forward feature selection with threshold τ = 10. This work provides evidence that Mutual Congestion is solely powerful to predict class labels. Furthermore, applying whale algorithm increases the overall accuracy of Mutual Congestion in most of the cases. The findings also show that the proposed method improves the prediction with selecting the less possible features in comparison with state of the arts. https://github.com/hnematzadeh.

Reversible and Fragile Watermarking for Medical Images.

A novel reversible digital watermarking technique for medical images to achieve high level of secrecy, tamper detection, and blind recovery of the original image is proposed. The technique selects some of the pixels from the host image using chaotic key for embedding a chaotically generated watermark. The rest of the pixels are converted to residues by using the Residue Number System (RNS). The chaotically selected pixels are represented by the polynomial. A primitive polynomial of degree four is chosen that divides the message polynomial and consequently the remainder is obtained. The obtained remainder is XORed with the watermark and appended along with the message. The decoder receives the appended message and divides it by the same primitive polynomial and calculates the remainder. The authenticity of watermark is done based on the remainder that is valid, if it is zero and invalid otherwise. On the other hand, residue is divided with a primitive polynomial of degree 3 and the obtained remainder is appended with residue. The secrecy of proposed system is considerably high. It will be almost impossible for the intruder to find out which pixels are watermarked and which are just residue. Moreover, the proposed system also ensures high security due to four keys used in chaotic map. Effectiveness of the scheme is validated through MATLAB simulations and comparison with a similar technique.

Robust and Fragile Medical Image Watermarking: A Joint Venture of Coding and Chaos Theories.

A secure spatial domain, hybrid watermarking technique for obtaining watermark (authentication information) robustness and fragility of the host medical image (content integrity) using product codes, chaos theory, and residue number system (RNS) is proposed. The proposed scheme is highly fragile and unrecoverable in terms of the host image, but it is significantly robust and recoverable in terms of the watermark. Altering the medical image may result in misdiagnosis, hence the watermark that may contain patient information and organization logo must be protected against certain attacks. The host medical image is separated into two parts, namely, the region of interest (ROI) and region of noninterest (RONI) using a rectangular region. The RONI part is used to embed the watermark information. Moreover, two watermarks are used: one to achieve authenticity of image and the other to achieve the robustness against both incidental and malicious attacks. Effectiveness in terms of security, robustness, and fragility of the proposed scheme is demonstrated by the simulations and comparison with the other state-of-the-art techniques.

Biotransformation of finasteride by Ocimum sanctum L., and tyrosinase inhibitory activity of transformed metabolites: experimental and computational insights.

Transformation of Finasteride (I) by cell suspension cultures of Ocimum sanctum L. was investigated. Fermentation of compound (I) with O. sanctum afforded three oxidized derivatives, 16ß-hydroxyfinasteride (II), 11α-hydroxyfinasteride (III) and 15ß-hydroxyfinasteride (IV). Among these metabolites, compound (II) was a new metabolite. Compound (I) and its derivatives were studied for their tyrosinase inhibition assay. All test compounds exhibited significant activity compared to standard drug kojic acid, with compound IV being the most potent member with an IC50 of 1.87µM. Molecular docking revealed significant molecular interactions behind the potent tyrosinase inhibitory activity of the tested compounds.