Enhanced IoMT security framework using group teaching optimized auto-encoder for intrusion detection.

Providing security to Internet of Medical Things (IoMT) is significant worldwide problem for future generations its implementation to be successful. The traditional security methodologies developed for IoMT struggles with the specific issues of high false positives and lower detection rate. Therefore, the proposed work aims to develop a ground-breaking intrusion detection model, named as, Group Teaching Optimized Probabilistic Deep Auto-Encoder (GTPDA) for increasing the security of IoMT networks.

Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition.

Early detection of colorectal carcinoma (CRC), one of the most prevalent forms of cancer worldwide, significantly enhances the prognosis of patients. This research presents a new method for improving CRC detection using a deep learning ensemble with the Computer Aided Diagnosis (CADx). The method involves combining pre-trained convolutional neural network (CNN) models, such as ADaRDEV2I-22, DaRD-22, and ADaDR-22, using Vision Transformers (ViT) and XGBoost.

LC-MS/MS characterization of pirtobrutinib impurities and product degradation: stability studies.

This study examined the fragmentation, degradation pathways and DPs of pirtobrutinib, which have not been previously reported in the literature. The main goal of the current work is to develop, validate, and characterize forced degradation products using LC-MS/MS. An isocratic HPLC methodology was developed for the quantitative measurement of pirtobrutinib at a of 219 nm.

Mechanical, fatigue and super plasticity properties of Cu-Al-Mn, Cu-Al-Be-Mn shape memory alloy and their metal matrix composites.

This study explores the characteristics and potential engineering applications of Cu-Al-Mn and Cu-Al-Be-Mn shape memory alloys (SMAs). The research investigates the chemical composition, transformation temperatures, and mechanical properties of these SMAs when incorporated into Al metal matrix composites (MMCs). It was found that the addition of Mn and Be, has a significant impact on the performance of Cu-Al alloys.

Hybrid raven roosting intelligence framework for enhancing efficiency in data clustering.

The field of data exploration relies heavily on clustering techniques to organize vast datasets into meaningful subgroups, offering valuable insights across various domains. Traditional clustering algorithms face limitations in terms of performance, often getting stuck in local minima and struggling with complex datasets of varying shapes and densities. They also require prior knowledge of the number of clusters, which can be a drawback in real-world scenarios.

A practical approach to demonstrate the circular economy in remediation of textile dyes using nutraceutical industrial spent.

We used Nutraceutical Industrial Coriander Seed Spent (NICSS), a readily available, cheap, eco-friendly, and ready-to-use material, as an innovative adsorbent for the bioremediation of a bisazo Acid Red 119 (AR 119) dye, which is likely a mutagen from textile industrial effluents (TIE). A laboratory-scale experiment was tailored to demonstrate the framework of the circular economy (CE) in the remediation of textile dyes using Nutraceutical Industrial Spent to align with the principles of sustainability and valorization. An experimental value of 97.

Forecasting of coronavirus active cases by utilizing logistic growth model and fuzzy time series techniques.

Coronavirus has long been considered a global epidemic. It caused the deaths of nearly 7.01 million individuals and caused an economic downturn.

Investigating topic modeling techniques through evaluation of topics discovered in short texts data across diverse domains.

The online channel has affected many facets of an individual's identity, commercial, social policy, and culture, among others. It implies that discovering the topics on which these brief writings are focused, as well as examining the qualities of these short texts is critical. Another key issue that has been identified is the evaluation of newly discovered topics in terms of topic quality, which includes topic separation and coherence.

Studies on the Removal of Malachite Green from Its Aqueous Solution Using Water-Insoluble β-Cyclodextrin Polymers.

The rising global pollution of natural waters by dyes has brought to light the need for adaptable and efficient removal techniques. To create water-insoluble β-cyclodextrin (β-CD) polymers like CA/-CD, TA/-CD, and MA/-CD, several organic acids including citric acid (CA), tartaric acid (TA), and malic acid (MA) were cross-linked with β-cyclodextrin in this study. The obtained polymers were characterized by different advanced analytical techniques such as FTIR, SEM, and UV-vis spectrophotometry.

A vehicular network based intelligent transport system for smart cities using machine learning algorithms.

Smart cities and the Internet of Things have enabled the integration of communicating devices for efficient decision-making. Notably, traffic congestion is one major problem faced by daily commuters in urban cities. In developed countries, specialized sensors are deployed to gather traffic information to predict traffic patterns.

Seasonal variations in the phenolic profile, antioxidant activity, and mineral content of south Indian black tea (Camellia sinensis (L.) O. Kuntze).

In the Anamallais region of south India, crop shoots from the UPASI-3, UPASI-9, UPASI-17, Assam seedlings, and TRI-2043 cultivars were examined for seasonal variations in total phenolics, antioxidant activity, and minerals during four harvest seasons: summer (January to March), premonsoon (April and May), monsoon (June to September), and winter (October to December) of two consecutive years. The total phenolics of all cultivars were lower in monsoon period and grew over rest of the seasons and it was greater during summer. Crop shoot antioxidant activity as measured by the DPPH radical scavenging experiment exhibited a similar pattern to total phenolics.

Influence of Alloying Materials Al, Cu, and Ca on Microstructures, Mechanical Properties, And Corrosion Resistance of Mg Alloys for Industrial Applications: A Review.

Magnesium is renowned for its favorable low-density attributes, rendering it a viable choice for commercial engineering applications in which weight has substantial design implications. Magnesium (Mg) stands as a readily obtainable metallic element, exhibiting robustness, efficient heat dissipation, and excellent damping properties. The utilization of pure magnesium remains infrequent due to its susceptibility to instability under high temperatures and pronounced vulnerability to corrosion within humid environments.

Effect of silicon carbide on kerf convergence and irregularity of the surface during abrasive water jet machining of fiber-metal hybrid composites.

The traditional way to machine hybrid composites is hard because they tend to break, have a high retraction, have a high service temperature, and have an uneven surface irregularity. For high-strength fiber/metal composite constructions, alternative machining methods have drawn interest as a solution to these problems. Current research focuses on enhancing the Abrasive Water Jet Machining process by optimizing its variables using a composite material of epoxy reinforced with silicon carbide, stainless steel wire mesh, and Kevlar.

Experimental Investigation and Characterization of Friction Stir Spot-Welded Dissimilar Aluminum Copper Metallic Lap Joints.

Due to their contrasting physical properties, joining materials like copper and aluminum has always proven difficult. The disadvantages of traditional joining methods include additional weight, solidification problems, and energy waste. Friction stir spot-welding (FSSW) was utilized for joining copper and aluminum in order to get around these difficulties.

Elastoplastic Analysis of Metallic Parts Employing a Meshless Method.

The use of finite element method-based approaches has been popular in studying the elastoplastic behavior of metal parts. However, there has been a growing demand for meshless methods. In response, researchers have developed a meshless solution for 2D elastoplastic evaluation of metal components.

Influence of Graphite Particles on the Mechanical and Wear Characterization of Al6082 Alloy Composites.

In the current study, a two-stage stir cast process was used to produce Al6082 reinforced with sized graphite particulates, and the material's mechanical and tribological properties were analyzed. The graphite content in the Al6082 alloy was increased from 2 to 6% in steps of 2 wt %. The impact of graphite addition to Al6082 was evaluated using microstructural micrographs, hardness test, tensile test, and wear test outcomes.

Impact of Bonding Temperature on Microstructure, Mechanical, and Fracture Behaviors of TLP Bonded Joints of Al2219 with a Cu Interlayer.

The present study aims at producing transient liquid phase (TLP) bonded Al2219 joints with pure Cu (copper) as an interlayer. The TLP bonding is carried out at the bonding temperatures in the range of 480 to 520 °C while keeping the bonding pressure (2 MPa) and time (30 min.) constant.

Glass Fiber-Epoxy Composites with Carbon Nanotube Fillers for Enhancing Properties in Structure Modeling and Analysis Using Artificial Intelligence Technique.

Hybrid composite materials are a form of material that incorporates more than one type of reinforcement into a matrix to attain enhanced qualities. This usually includes the use of nanoparticle fillers in classic advanced composites with fiber reinforcements such as carbon or glass. In the current investigation, the impact of carbon nanopowder filler on the wear and thermal performance of the chopped strand mat E-glass fiber-reinforced epoxy composite (GFREC) were analyzed.

Impact of Boron Carbide Particles and Weight Percentage on the Mechanical and Wear Characterization of Al2011 Alloy Metal Composites.

Micron-sized BC addition to the Al2011 alloy was investigated for its impact on mechanical and wear performance. The stir-casting method was used to manufacture the Al2011 alloy metal matrix composites reinforced with varying percentages of BC particulates (2, 4, and 6). The microstructural, mechanical, and wear properties of the synthesized composites were tested.

Design of optimal Elman Recurrent Neural Network based prediction approach for biofuel production.

Renewable sources like biofuels have gained significant attention to meet the rising demands of energy supply. Biofuels find useful in several domains of energy generation such as electricity, power, or transportation. Due to the environmental benefits of biofuel, it has gained significant attention in the automotive fuel market.

Al2014-Alumina Aerospace Composites: Particle Size Impacts on Microstructure, Mechanical, Fractography, and Wear Characteristics.

An Al2014-alumina (AlO) composite's characteristics are significantly influenced by the reinforcement particle size variation. Therefore, this study examines the microstructure, mechanical, fractography, and wear performance of an Al2014-AlO composite made using a unique two-stage stir casting method and various alumina weight fractions (9, 12, and 15 wt %). Three categories of alumina particle size are used, i.

Comparative Study of Soft Computing and Metaheuristic Models in Developing Reduced Exhaust Emission Characteristics for Diesel Engine Fueled with Various Blends of Biodiesel and Metallic Nanoadditive Mixtures: An ANFIS-GA-HSA Approach.

Since the discovery of petrol-based products, a surge in energy-requiring equipment has been established across the world. Recent depletion of the existing crude oil resources has motivated researchers to opt for and analyze potential fuels that could potentially provide a cost-effective and sustainable solution. The current study selects a waste plant known as through which biodiesel is generated, and its blends are tested in diesel engines for feasibility.