Detection and classification of adult epilepsy using hybrid deep learning approach.

The electroencephalogram (EEG) has emerged over the past few decades as one of the key tools used by clinicians to detect seizures and other neurological abnormalities of the human brain. The proper diagnosis of epilepsy is crucial due to its distinctive nature and the subsequent negative effects of epileptic seizures on patients. The classification of minimally pre-processed, raw multichannel EEG signal recordings is the foundation of this article's unique method for identifying seizures in pre-adult patients.

Developed and Validated for the Estimation of Tapinarof in Topical Formulation and Active Pharmaceutical Ingredients.

Background: Its broad applicability and capacity to separate numerous components in a single chromatographic run led to the initial recognition of reversed-phase (RP)-HPLC as an analytical technique.

Objective: The objective of this study was to create a straightforward and reliable method for accurately and precisely measuring the amount of tapinarof in both the topical formulation and the active pharmaceutical ingredient. Additionally, a robust HPLC assay was developed specifically for analyzing the topical formulation.

An optimized approach towards bio-capture and carbon dioxide sequestration with microalgae Phormidium valderianum using response surface methodology.

As carbon dioxide emissions rise, there's need for alternative strategies, including microorganisms, to capture and mitigate them. The present study investigated on the capability and tolerance of microalgal strain, Phormidium valderianum to capture gaseous CO at varying levels (5-30 %). A biomass productivity of 0.

Breast Cancer Classification Using Synthesized Deep Learning Model with Metaheuristic Optimization Algorithm.

Breast cancer is the second leading cause of mortality among women. Early and accurate detection plays a crucial role in lowering its mortality rate. Timely detection and classification of breast cancer enable the most effective treatment.

Development of alkali activated paver blocks for medium traffic conditions using industrial wastes and prediction of compressive strength using random forest algorithm.

Geopolymer is an environment friendly construction material that could be synthesized using either the natural source or the industrial byproducts such as flyash and GGBS. The characteristics of the Geopolymer rely on the proportion of the flyash and GGBS and the concentration of the activator solution used. In this research work, the effect of partial replacement of flyash with GGBS in proportions such as 0, 10, 20, 30 and 40% is investigated.

Numerical Investigation on High-Performance Cu-Based Surface Plasmon Resonance Sensor for Biosensing Application.

This numerical research presents a simple hybrid structure comprised of TiO-Cu-BaTiO for a modified Kretschmann configuration that exhibits high sensitivity and high resolution for biosensing applications through an angular interrogation method. Recently, copper (Cu) emerged as an exceptional choice as a plasmonic metal for developing surface plasmon sensors (SPR) with high resolution as it yields finer, thinner SPR curves than Ag and Au. As copper is prone to oxidation, especially in ambient conditions, the proposed structure involves the utilization of barium titanate (BaTiO) film as a protection layer that not only preserves Cu film from oxidizing but enhances the performance of the sensor to a great extent.

An active learning machine technique based prediction of cardiovascular heart disease from UCI-repository database.

Heart disease is a significant global cause of mortality, and predicting it through clinical data analysis poses challenges. Machine learning (ML) has emerged as a valuable tool for diagnosing and predicting heart disease by analyzing healthcare data. Previous studies have extensively employed ML techniques in medical research for heart disease prediction.

Evaluation of landfill leachate biodegradability using IOT through geotracking sensor based drone surveying.

In specific this work focuses on designing and developing a trial model first of its kind which is chemically non reactive using Urea Formaldehyde (UF) and a value added flat board that is to be prepared from solid waste. The display setup is to be monitored by Arduino Controller which incorporates the assessment of numerical factors and measurable compositions in the board. The compilation of manufactured goods from organic waste is a course of development network that is centrally operated by Radio Frequency Identification (RFID) tools.

Contextual Cluster-Based Glow-Worm Swarm Optimization (GSO) Coupled Wireless Sensor Networks for Smart Cities.

The cluster technique involves the creation of clusters and the selection of a cluster head (CH), which connects sensor nodes, known as cluster members (CM), to the CH. The CH receives data from the CM and collects data from sensor nodes, removing unnecessary data to conserve energy. It compresses the data and transmits them to base stations through multi-hop to reduce network load.

Novel ANOVA-Statistic-Reduced Deep Fully Connected Neural Network for the Damage Grade Prediction of Post-Earthquake Buildings.

Earthquakes are cataclysmic events that can harm structures and human existence. The estimation of seismic damage to buildings remains a challenging task due to several environmental uncertainties. The damage grade categorization of a building takes a significant amount of time and work.

Integrated Edge Deployable Fault Diagnostic Algorithm for the Internet of Things (IoT): A Methane Sensing Application.

The Internet of Things (IoT) is seen as the most viable solution for real-time monitoring applications. But the faults occurring at the perception layer are prone to misleading the data driven system and consume higher bandwidth and power. Thus, the goal of this effort is to provide an edge deployable sensor-fault detection and identification algorithm to reduce the detection, identification, and repair time, save network bandwidth and decrease the computational stress over the Cloud.

Receptor-free phenothiazine derivative as fluorescent probe for picric acid: Investigation of the inner filter effect channel.

In this study, a highly fluorescent and receptor-free phenothiazine derivative (PDAB) was developed to detect picric acid. A combination of steady-state and time-resolved fluorescence studies was conducted to examine the excited state behavior of PDAB with picric acid in solution. The PDAB probe displayed a significant degree of selectivity and was highly sensitive to picric acid, with an extremely low detection limit of 9.

Effect of Cryo-Treated Cutting Tool End Milling on Custom 450 Stainless Steel.

Custom 450 stainless steel is the most desirable material across industries due to its widespread application in the aerospace, defense and marine industries. Stainless-steel materials are challenging to deal with and fall into the list of hard-to-process materials due to their low heat conduction coefficient and high mechanical properties. In this research work, end milling was carried out on Custom 450 stainless steel machined using TiAlN coated with tungsten carbide inserts that have been cryo-treated (CT) for 24 h (24 h) and 36 h (36 h), as well as untreated (UT) inserts.

Grammar-aware phrase dataset generated using a novel python package.

The past technique of manual dataset preparation was time-consuming and needed much effort. Another attempt to the data acquisition method was using web scraping. Such web scraping tools also produce a bunch of data errors.

A Hybrid Deep Neural Approach for Segmenting the COVID Affection Area from the Lungs X-Ray Images.

Nowadays, COVID severity prediction has attracted widely in medical research because of the disease severity. Hence, the image processing application is also utilized to analyze COVID severity identification using lungs X-ray images. Thus, several intelligent schemes were employed to detect the COVID-affected part of the lungs X-ray images.

A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques.

COVID-19, a highly infectious respiratory disease a used by SARS virus, has killed millions of people across many countries. To enhance quick and accurate diagnosis of COVID-19, chest X-ray (CXR) imaging methods were commonly utilized. Identifying the infection manually by radio imaging, on the other hand, was considered, extremely difficult due to the time commitment and significant risk of human error.

Copper transporter protein (MctB) as a therapeutic target to elicit antimycobacterial activity against tuberculosis.

Tuberculosis (TB) is a prehistoric infection and major etiologic agent of TB, , which is considered to have advanced from an early progenitor species found in Eastern Africa. By the 1800s, there were approximately 800 to 1000 fatality case reports per 100,000 people in Europe and North America. This research suggests an study to identify potential inhibitory compounds for the target Mycobacterial copper transport protein (Mctb).

Impact of the combined effect of seawater exposure with wastewater and FeO nanoparticles on Chlorella vulgaris microalgae growth, lipid content, biochar, and bio-oil production.

Microalgae is one the promising source of energy for the production of biofuel and other value-added products to replace the existing conventional fossil fuels. However, low lipid content and poor cell harvesting are the key challenges. Based on the growth conditions the lipid productivity will be affected.

Performance evaluation of deep learning techniques for lung cancer prediction.

Due to the increase in pollution, the number of deaths caused by lung disease is rising rapidly. It is essential to predict the disease in earlier stages by means of high-level knowledge and acquaintance. Deep learning-based lung cancer prediction plays a vital role in assisting the medical practioners for diagnosing lung cancer in earlier stage.

Multi-Objective Optimization of Machining Parameters for Drilling LM5/ZrO Composites Using Grey Relational Analysis.

In today's world, engineering materials have changed dramatically. Traditional materials are failing to satisfy the demands of present applications, so several composites are being used to address these issues. Drilling is the most vital manufacturing process in most applications, and the drilled holes serve as maximum stress areas that need to be treated with extreme caution.

Elucidation of infection asperity of CT scan images of COVID-19 positive cases: A Machine Learning perspective.

Owing to the profoundly irresistible nature of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, an enormous number of individuals halt in the line for Computed Tomography (CT) scan assessment, which overburdens the medical practitioners, radiologists, and adversely influences the patient's remedy, diagnosis, as well as restraint the epidemic. Medical facilities like intensive care systems and mechanical ventilators are restrained due to highly infectious diseases. It turns out to be very imperative to characterize the patients as per their asperity levels.

A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA.

We developed a framework to detect and grade knee RA using digital X-radiation images and used it to demonstrate the ability of deep learning approaches to detect knee RA using a consensus-based decision (CBD) grading system. The study aimed to evaluate the efficiency with which a deep learning approach based on artificial intelligence (AI) can find and determine the severity of knee RA in digital X-radiation images. The study comprised people over 50 years with RA symptoms, such as knee joint pain, stiffness, crepitus, and functional impairments.