Prediction of android ransomware with deep learning model using hybrid cryptography.

In recent times, the number of malware on Android mobile phones has been growing, and a new kind of malware is Android ransomware. This research aims to address the emerging concerns about Android ransomware in the mobile sector. Previous studies highlight that the number of new Android ransomware is increasing annually, which poses a huge threat to the privacy of mobile phone users for sensitive data.

validation of hyaluronic acid - drug conjugates based targeted drug delivery for the treatment of COVID-19.

The impact of COVID-19 urges scientists to develop targeted drug delivery to manage Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) viral infections with a fast recovery rate. The aim of the study is to develop Hyaluronic Acid (HA) drug conjugates of viral drugs to target two important enzymes (Mpro and PLpro) of SARS-CoV-2. Three antiviral drugs, namely Dexamethasone (DEX), Favipiravir (FAV), and Remdesivir (REM), were chosen for HA conjugation due to their reactive functional groups.

Design of modified reference phase modulation based boost chopper fed fifteen level stepped DC link hybrid converter.

A new fifteen-level stepped DC to AC hybrid converter is proposed for Solar Photovoltaic (SPV) applications. A boost chopper circuit is designed and interfaced with the fifteen-level hybrid converters specific to Electric Vehicles' Brushless DC Motor (BLDC) drive systems. In chopper units, the output of solar panels is regulated and stepped up to obtain the nominal output voltage.

Mechanical, tribological, and surface morphological studies on the effects of hybrid ilmenite and silicon dioxide fillers on glass fibre reinforced epoxy composites.

Recently, researchers have been attempting to enhance the mechanical and tribological characteristics of thermosetting epoxy composites by incorporating inorganic nanoparticles and ensuring their uniform distribution throughout the matrix. This study characterises ball-milled ilmenite (FeTiO-size of 63 nm) and silicon dioxide (SiO-size of 67.5 nm) fillers added to epoxy in proportions of 0:0, 2.

Evaluation of the Influence of Eggshell (ES) Concentration on the Degradation Behavior of Mg-2.5Zn Biodegradable Alloy in Simulated Body Fluid.

Currently, permanent vascular stents are fabricated using titanium and stainless steel implants that are nondegradable and offer high stability, but they have certain disadvantages. For example, the prolonged exposition of aggressive ions in the physiological media and the existence of defects in the oxide film create conditions for corrosion to occur, thus triggering unwanted biological events and compromising the mechanical integrity of the implants. Moreover, when the implant does not need to be permanent, there is the need to submit the patient for a second surgery for implant removal.

Hybrid Optimization Algorithm Enabled Deep Learning Approach Brain Tumor Segmentation and Classification Using MRI.

The unnatural and uncontrolled increase of brain cells is called brain tumors, leading to human health danger. Magnetic resonance imaging (MRI) is widely applied for classifying and detecting brain tumors, due to its better resolution. In general, medical specialists require more details regarding the size, type, and changes in small lesions for effective classification.

An integrated and automated testing approach on Inception Restnet-V3 based on convolutional neural network for leukocytes image classification.

Objectives: The leukocyte is a specialized immune cell that functions as the foundation of the immune system and keeps the body healthy. The WBC classification plays a vital role in diagnosing various disorders in the medical area, including infectious diseases, immune deficiencies, leukemia, and COVID-19. A few decades ago, Machine Learning algorithms classified WBC types required for image segmentation, and the feature extraction stages, but this new approach becomes automatic while existing models can be fine-tuned for specific classifications.

Molecular insights of hyaluronic acid - ethambutol and hyaluronic acid - isoniazid drug conjugates act as promising novel drugs for the treatment of tuberculosis.

The present study examines cellular targeted drug delivery (CTDD) pattern of two novel Hyaluronic acid (HA) Tuberculosis Drug (TB) conjugates and its efficacy and strong binding affinity towards TB molecular protein targets. Two TB drugs ethambutol (EB) and isoniazid (IN) and their Hyaluronic acid conjugates (HA-EB & HA-IN) were tested for its metabolism, toxicity and excretion prediction through tools they revealed hyaluronic acid conjugate of two TB drugs exhibited good drug profile over their free form of TB drugs. Further these four molecules subjected to molecular docking study with four potential target proteins (3PD8, 4Y0L, 5DZK and 6GAU).

Lovastatin production by an oleaginous fungus, Aspergillus terreus KPR12 using sago processing wastewater (SWW).

Background: Lovastatin is one of the first statins to be extensively used for its cholesterol-lowering ability. It is commercially produced by fermentation. Species belonging to the genus Aspergillus are well-studied fungi that have been widely used for lovastatin production.

A novel approach for synthesis of silver nanoparticles using shell and its mosquito larvicidal activity.

Mosquito act as a vector for variety of deadly diseases. In this study, larvicide activity was investigated in relation to () and () of synthesised silver nanoparticles (AgNPs) of the () shell extract. The characterization techniques UV-vis spectral, Fourier transforms infrared spectroscopy (FTIR),High Resonance Scanning electron microscope (HR-SEM) analysis, X-ray diffraction studies (XRD), High Resonance-Transmission electron microscopy (HR-TEM) used to characterize biosynthesized AgNPs.

Effect of Molybdenum (Mo) Addition on Phase Composition, Microstructure, and Mechanical Properties of Pre-Alloyed Ti6Al4V Using Spark Plasma Sintering Technique.

The effect of molybdenum additions on the phases, microstructures, and mechanical properties of pre-alloyed Ti6Al4V was studied through the spark plasma sintering technique. Ti6Al4V-xMo (where x = 0, 2, 4, 6 wt.% of Mo) alloys were developed, and the sintered compacts were characterized in terms of their phase composition, microstructure, and mechanical properties.

Molecular insights of hyaluronic acid-hydroxychloroquine conjugate as a promising drug in targeting SARS-CoV-2 viral proteins.

anti-viral activity of (HCQ) and its Hyaluronic Acid-derivative (HA-HCQ) towards different SARS-CoV-2 protein molecular targets were studied. Four different SARS-CoV-2 proteins molecular target i.e.

Recombinant production of poly-(3-hydroxybutyrate) by Bacillus megaterium utilizing millet bran and rapeseed meal hydrolysates.

Fermentative poly-3-hydroxybutyrate (PHB) production is mainly limited by the cost of raw material. In this present study, low-cost feedstock viz., millet bran residue (MBRH) and rapeseed meal hydrolysates were successfully utilized for PHB production.

Segmentation and Boundary Detection of Fetal Kidney Images in Second and Third Trimesters Using Kernel-Based Fuzzy Clustering.

Organ segmentation is an important step in Ultrasound fetal images for early prediction of congenital abnormalities and to estimate delivery date. In many applications of 2D medical imaging, they face problems with speckle noise and object contours. Frequent scanning of fetal leads to clinical disturbances to the fetal growth and the quantitative interpretation of Ultrasonic images also a difficult task compared to other image modalities.

An Enhanced Symptom Clustering with Profile Based Prescription Suggestion in Biomedical application.

The application of data mining has been increasing day to day whereas the data base is also enhancing simultaneously. Hence retrieving required content from a huge data base is a critical task. This paper focus on biomedical engineering field, it concentrates on initial stage of database such as data preprocessing and cleansing to deal with noise and missing data in large biomedical data sets.

Improving the Accuracy of Feature Selection in Big Data Mining Using Accelerated Flower Pollination (AFP) Algorithm.

In recent times, the main problem associated with big data analytics is its high dimensional data over the search space. Such data gathers continuously in search space making traditional algorithms infeasible for data mining in real time environment. Hence, feature selection is an important method to lighten the load during processing while inducing a model for mining.

Incorporating EBO-HSIC with SVM for Gene Selection Associated with Cervical Cancer Classification.

Microarray technology is utilized by the biologists, in order to compute the expression levels of thousands of genes. Cervical cancer classification utilizing gene expression data depends upon conventional supervised learning methods, wherein only labeled data could be used for learning. The previous methodologies had problem with appropriate feature selection as well as accurateness of classification outcomes.

Image Retrieval Based on Local Mesh Vector Co-occurrence Pattern for Medical Diagnosis from MRI Brain Images.

In modern health-care, for evidence-based diagnosis, there is a requirement for an efficient image retrieval approach to retrieve the cases of interest that have similar characteristics from the large image databases. This paper presents a feature extraction approach that aims at extracting texture features present in the medical images using Local Pattern Descriptor (LPD) and Gray-level Co-occurrence Matrix (GLCM). As a main contribution, a novel local pattern named Local Mesh Vector Co-occurrence Pattern (LMVCoP) has been proposed by concatenating the Local Mesh Co-occurrence Pattern (LMCoP) and the Local Vector Co-occurrence Pattern (LVCoP).

A wavelet transform based feature extraction and classification of cardiac disorder.

This paper approaches an intellectual diagnosis system using hybrid approach of Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classification of Electrocardiogram (ECG) signals. This method is based on using Symlet Wavelet Transform for analyzing the ECG signals and extracting the parameters related to dangerous cardiac arrhythmias. In these particular parameters were used as input of ANFIS classifier, five most important types of ECG signals they are Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Pre-Ventricular Contraction (PVC), Ventricular Fibrillation (VF), and Ventricular Flutter (VFLU) Myocardial Ischemia.

Laser irradiation of BaMgHPO4 crystals in silica gel media at different growth environments (nucleation reduction strategy) and its characterization studies.

The Barium magnesium hydrogen phosphate (BMHP) crystals were grown in sodium meta silicate gel media. The growth processes were done by single diffusion method and double diffusion for different physical and chemical parameters, such as using different gel densities, various concentrations of phosphoric acid and supernatant solutions. The gel pH plays an important role in the formation of different HPO(4) species in the phosphoric system.