MSCNet-FS: development of intelligent epileptic seizure anticipation model by multi serial cascaded network with feature Specific using scalogram images of EEG signal.

The early stage of the Epileptic Seizure Anticipation (ESA) model plays a significant part in supplying accurate medical care. In this research work, a novel Multi Serial Cascaded Network with Feature Specific model is developed. The scalogram images are given as input to a developed model.

Development of lateral acceleration prediction models and consistency evaluation criteria for horizontal curves with gradient on two lane rural highways.

Objectives: The challenge of combined horizontal and vertical highway alignments continues to be a concern in transportation design, particularly regarding truck stability. This study analyses the stability of trucks on horizontal curves combined with ascending or descending gradients using center of gravity (CoG) height and curve geometry as parameters. It also develops models that predict lateral stability of trucks and criteria for evaluating geometric design consistency based on vehicle stability.

An integrative analysis to identify pancancer epigenetic biomarkers.

Integrating and analyzing the pancancer data collected from different experiments is crucial for gaining insights into the common mechanisms in the molecular level underlying the development and progression of cancers. Epigenetic study of the pancancer data can provide promising results in biomarker discovery. The genes that are epigenetically dysregulated in different cancers are powerful biomarkers for drug-related studies.

Analysis of quality parameters and preservative concentrations in Sahacharadi Kwatha: A comparative study of three commercial brands.

Background: Sahacharadi Kwatha is traditionally employed in Ayurvedic therapy for "vata" related conditions such as back pain, herniated disc, palsy, sciatica, and paralysis. Classical Ayurvedic texts recommend the use of freshly prepared Kwatha for optimal patient benefits. However, in response to the commercialization of Ayurveda and the demand for convenient over-the-counter (OTC) formulations, various commercial preservatives have been incorporated by Ayurvedic manufacturers to facilitate OTC preparation and prolong shelf life.

A deep drug prediction framework for viral infectious diseases using an optimizer-based ensemble of convolutional neural network: COVID-19 as a case study.

The SARS-CoV-2 outbreak highlights the persistent vulnerability of humanity to epidemics and emerging microbial threats, emphasizing the lack of time to develop disease-specific treatments. Therefore, it appears beneficial to utilize existing resources and therapies. Computational drug repositioning is an effective strategy that redirects authorized drugs to new therapeutic purposes.

Synthesis and characterization of piperine-modified mesoporous silica nanoparticles for biomedical applications.

Mesoporous silica nanoparticles (MSNs) have displayed high-potential prospects in biomedical use, especially for drug delivery due to large surface area, tunable pore size and simple surface functionalization. The objective behind the present research is to synthesize and profile piperine-modified MSNs for their preparation due to antioxidative anticarcinogenic, anti-inflammatory properties of the alkaloid chosen as a modifier. In the study, silica piperine nanoparticles (SPN) were fabricated based on a modified Stöber method.

Augmenting interpretation of vaginoscopy observations in cycling bitches with deep learning model.

Successful identification of estrum or other stages in a cycling bitch often requires a combination of methods, including assessment of its behavior, exfoliative vaginal cytology, vaginoscopy, and hormonal assays. Vaginoscopy is a handy and inexpensive tool for the assessment of the breeding period. The present study introduces an innovative method for identifying the stages in the estrous cycle of female canines.

Semantic segmentation in skin surface microscopic images with artifacts removal.

Skin surface imaging has been used to examine skin lesions with a microscope for over a century and is commonly known as epiluminescence microscopy, dermatoscopy, or dermoscopy. Skin surface microscopy has been recommended to reduce the necessity of biopsy. This imaging technique could improve the clinical diagnostic performance of pigmented skin lesions.

Analysing the role of Fe (II) on flocculation of sand-clay mixtures under estuarine mixing.

Estuaries are fragile environment that are most affected by climate change. One of the major consequences of climate change on estuarine processes is the enhancement in salt intrusion leading to higher salinity values. This has several implications on the estuarine sediment dynamics.

HBNET: A blended ensemble model for the detection of cardiovascular anomalies using phonocardiogram.

Background: Cardiac diseases are highly detrimental illnesses, responsible for approximately 32% of global mortality [1]. Early diagnosis and prompt treatment can reduce deaths caused by cardiac diseases. In paediatric patients, it is challenging for paediatricians to identify functional murmurs and pathological murmurs from heart sounds.

Identification of Novel EGFR Inhibitors for the Targeted Therapy of Colorectal Cancer Using Pharmacophore Modelling, Docking, Molecular Dynamic Simulation and Biological Activity Prediction.

Background: Colorectal cancer (CRC) is considered the second deadliest cancer in the world. One of the reasons for the occurrence of this cancer is the deregulation of the Epidermal Growth Factor Receptor (EGFR), which plays a critical role in regulating cell division, persistence, differentiation, and migration. The overexpression of the EGFR protein leads to its dysregulation and causes CRC.

A comparative evaluation of streamflow prediction using the SWAT and NNAR models in the Meenachil River Basin of Central Kerala, India.

Reliable and accurate modelling of streamflow is still a challenging task due to their complex behaviour, need for extensive parameter for development as well as lack of complete or accurate data. In this study, the applicability of an emerging data-driven model, specifically a neural network autoregression (NNAR) model, was evaluated for the first time as a substitute to the physically based hydrological model Soil and Water Assessment Tool (SWAT) for predicting streamflow under data-scarce conditions and for immediate high-quality modelling results. The inputs to the NNAR model were the lagged values of the daily streamflow time series data, and the output was the predicted value for the next day.

Mechanical superiority of Pseudoxytenanthera bamboo for sustainable engineering solutions.

The advancement in natural fibre composites has replaced synthetic fibres in various commercial sectors. Bamboo species possess high mechanical properties due to their lignocellulosic fibre content, which makes them suitable for engineering applications and potential alternatives to solid wood. However, despite Bamboo being composed of 130 genera and 1700 different species, out of which many still remains underexplored.

discovery of novel calcineurin inhibitors using ligand-based 3-D pharmacophore modelling and molecular dynamics simulation.

Calcineurin is a serine-threonine protein phosphatase that is activated with the binding of calmodulin in the presence of increased calcium concentration and has a major role in various signaling pathways. Its role in regulating homeostasis, developmental processes, and different disease progression has already been reported. The dysregulated Ca/calcineurin/NFAT1-4 pathway is observed in Autoimmune disorders and hence the use of Calcineurin inhibitors like Cyclosporin A (CsA) and Tacrolimus (FK506) is widely done in such cases.

Machine Learning based tumor diagnosis using compressive sensing in MRI images.

Despite the widespread use of Magnetic Resonance Imaging (MRI) analysis for disease diagnosis, processing and analyzing the substantial amount of acquired data may be challenging. Compressive Sensing (CS) offers a promising solution to this problem. MRI diagnosis can be performed faster and more accurately using CS since it requires fewer data for image analysis.

Fourier ptychographic and deep learning using breast cancer histopathological image classification.

Automated, as well as accurate classification with breast cancer histological images, was crucial for medical applications because of detecting malignant tumors via histopathological images. In this work create a Fourier ptychographic (FP) and deep learning using breast cancer histopathological image classification. Here the FP method used in the process begins with such a random guess that builds a high-resolution complex hologram, subsequently uses iterative retrieval using FP constraints to stitch around each other low-resolution multi-view means of production owned from either the hologram's high-resolution hologram's elemental images captured via integral imaging.

Digital holographic technique based breast cancer detection using transfer learning method.

The digital holographic technique is an interferometric method that provides comprehensive information on morphological traits such as cell layer thickness and shape as well as access to biophysical attributes of cells like refractive index, dry mass, and volume. This method helps characterize sample structures in three dimensions both statically and dynamically, even for transparent objects like living biological cells. This research work captures the digital holograms of breast tissues and analyzes the malignancy of the tissue using a deep learning technique.

Generation and analysis of synthetic surface electromyography signals under varied muscle fiber type proportions and validation using recorded signals.

The magnitude and duration of muscle force production are influenced by the fiber type proportion. In this work, surface electromyography (sEMG) signals of muscles with varied fiber type proportions, are generated. For this, relevant components of existing models reported in various literature have been adopted.

Facile encapsulation of nano zero-valent iron with calcium carbonate: synthesis, characterization and application for iron remediation.

In this study, CaCO was used as a modifier for nano zero-valent iron (nZVI) surface to prevent rapid aggregation and effectively utilized for iron remediation from aqueous solution. Surface chemistry and morphology of CaCO encapsulated nZVI (CaCO-nZVI) before and after treatment of contaminant iron solution were characterized by scanning electron microscopy-energy dispersive X-ray (SEM-EDX), X-ray diffraction (XRD), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). The mechanisms of surface modification as well as iron remediation were well depicted with the help of these characterisation tools.

An efficient Alzheimer's disease prediction based on MEPC-SSC segmentation and momentum geo-transient MLPs.

A decline in cognitive functioning of the brain termed Alzheimer's Disease (AD) is an irremediable progressive brain disorder, which has no corroborated disease-modifying treatment. Therefore, to slow or avoid disease progression, a greater endeavour has been made to develop techniques for earlier detection, particularly at pre-symptomatic stages. To predict AD, several strategies have been developed.

Efficacious slosh suppression technique in road tankers to enhance the effectiveness of drinking water distribution.

During summer months, many parts of the world experience the water crisis and water tankers transport fresh drinking water from rivers and other reservoirs and distribute to communities often located at far off places. Effectiveness of this distribution is adversely affected by the instability of tankers due to the phenomenon called liquid sloshing, the oscillation of free liquid surface subjected to external excitation. The liquid within the tank moves along the side walls back and forth creating an oscillation which becomes dangerously worse at certain situations.

Performance evaluation of deep neural network on malware detection: visual feature approach.

Nowadays, several malicious applications target computers and mobile users. So, malware detection plays a vital role on the internet so that the device is secure without any malicious activity affecting or gathering the useful content of the user. Researches indicate that the vulnerability of adversarial attacks is more in deep neural networks.

Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force.

An intelligent sensing framework using Machine Learning (ML) and Deep Learning (DL) architectures to precisely quantify dielectrophoretic force invoked on microparticles in a textile electrode-based DEP sensing device is reported. The prediction accuracy and generalization ability of the framework was validated using experimental results. Images of pearl chain alignment at varying input voltages were used to build deep regression models using modified ML and CNN architectures that can correlate pearl chain alignment patterns of Saccharomyces cerevisiae(yeast) cells and polystyrene microbeads to DEP force.

Automated grading of diabetic retinopathy using CNN with hierarchical clustering of image patches by siamese network.

Diabetic retinopathy (DR) is a progressive vascular complication that affects people who have diabetes. This retinal abnormality can cause irreversible vision loss or permanent blindness; therefore, it is crucial to undergo frequent eye screening for early recognition and treatment. This paper proposes a feature extraction algorithm using discriminative multi-sized patches, based on deep learning convolutional neural network (CNN) for DR grading.