Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data.

In recent years, the healthcare data system has expanded rapidly, allowing for the identification of important health trends and facilitating targeted preventative care. Heart disease remains a leading cause of death in developed countries, often leading to consequential outcomes such as dementia, which can be mitigated through early detection and treatment of cardiovascular issues. Continued research into preventing strokes and heart attacks is crucial.

Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detection.

Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs.

Alumina-enriched sunflower bio-oil in machining of Hastelloy C-276: a fuzzy Mamdani model-aided sustainable manufacturing paradigm.

With the increasing emphasis on sustainable manufacturing practices, eco-friendly lubricants have gained significant attention to moderate the friction coefficient at the tool-work interface. In line with this, the contemporary study aimed to examine the viability of Alumina-enriched sunflower bio-oil as a metalworking fluid. Different volume fractions of Alumina nanoparticles (varying from 0 to 1 vol%) were mixed with sunflower bio-oil, and the physical properties, for instance, contact angle and dynamic viscosity, were analyzed to determine the optimal concentration of Alumina.

MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network.

Synthetic lethality (SL) and synthetic viability (SV) are commonly studied genetic interactions in the targeted therapy approach in cancer. In SL, inhibiting either of the genes does not affect the cancer cell survival, but inhibiting both leads to a lethal phenotype. In SV, inhibiting the vulnerable gene makes the cancer cell sick; inhibiting the partner gene rescues and promotes cell viability.

Enhanced botnet detection in IoT networks using zebra optimization and dual-channel GAN classification.

The Internet of Things (IoT) permeates various sectors, including healthcare, smart cities, and agriculture, alongside critical infrastructure management. However, its susceptibility to malware due to limited processing power and security protocols poses significant challenges. Traditional antimalware solutions fall short in combating evolving threats.

Ensemble fusion model for improved lung abnormality classification: Leveraging pre-trained models.

Lung abnormalities pose significant health concerns, underscoring the need for swift and accurate diagnoses to facilitate timely medical intervention. This study introduces a novel methodology for the sub-classification of lung abnormalities within chest X-rays captured via smartphones. An accurate and timely diagnosis of lung abnormalities is essential for the successful implementation of appropriate therapy.

Hybrid deep learning model with enhanced sunflower optimization for flood and earthquake detection.

Natural catastrophes may strike anywhere at any moment and cause widespread destruction. Most people do not have the necessary catastrophe preparedness knowledge or awareness. The combination of a flood and an earthquake can cause widespread destruction.

Magnetic Lateral Ladder for Unidirectional Transport of Microrobots: Design Principles and Potential Applications of Cells-on-Chip.

Functionalized microrobots, which are directionally manipulated in a controlled and precise manner for specific tasks, face challenges. However, magnetic field-based controls constrain all microrobots to move in a coordinated manner, limiting their functions and independent behaviors. This article presents a design principle for achieving unidirectional microrobot transport using an asymmetric magnetic texture in the shape of a lateral ladder, which the authors call the "railway track.

Novel assessment tools for inland aquaculture in the western Godavari delta region of Andhra Pradesh.

The production of fisheries and shrimp has been twice every 10 years for the previous five decades, making it the most rapidly expanding food industry. This growth is due to intensive farming and the conversion of agriculture into aquaculture in many parts of South Asia. Furthermore, intensive aquaculture generates positive economic growth but leads to environmental degradation without proper monitoring.

Modelling biochemical oxygen demand in a large inland aquaculture zone of India: Implications and insights.

Water quality surveillance is tough, and a specific timely management is necessary for the inland aquaculture ponds and ecology as well. Real time quality monitoring involves the study of numerous parameters includes physical (turbidity, temperature, and specific conductivity), chemical (pH, calcium, manganese, chlorides, iron, biochemical oxygen demand), and biological (bacteria and algae). It is also crucial to recognize the inter-dependence among the parameters.

Sickle-shaped high gain and low profile based four port MIMO antenna for 5G and aeronautical mobile communication.

The construction of the four-port MIMO antenna in the form of a sickle is provided in the article. Initially, the single port element is designed and optimized. Next, a structure with two ports is created, and lastly, a design with four ports is completed.

Prediction of ammonia contaminants in the aquaculture ponds using soft computing coupled with wavelet analysis.

Intensive aquaculture practices generate highly polluted organic effluents such as biological oxygen demand (BOD), alkalinity, total ammonia, nitrates, calcium, potassium, sodium, iron, and chlorides. In recent years, Inland aquaculture ponds in the western delta region of Andhra Pradesh have been intensively expanding and are more concerned about negative environmental impact. This paper presents the water quality analysis of aquaculture waters in 64 random locations in the western delta region of Andhra Pradesh.

Isolation and Characterisation of Electrogenic Bacteria from Mud Samples.

To develop efficient microbial fuel cell systems for green energy production using different waste products, establishing characterised bacterial consortia is necessary. In this study, bacteria with electrogenic potentials were isolated from mud samples and examined to determine biofilm-formation capacities and macromolecule degradation. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry identifications have revealed that isolates represented 18 known and 4 unknown genuses.

GW- CNNDC: Gradient weighted CNN model for diagnosing COVID-19 using radiography X-ray images.

COVID-19 is one of the dangerous viruses that cause death if the patient doesn't identify it in the early stages. Firstly, this virus is identified in China, Wuhan city. This virus spreads very fast compared with other viruses.

Method for identification of 10 SSR markers from monkey genomes and its statistical inference with One & Two-way ANOVA.

DNA tracts that include simple sequence repeats (SSRs), sometimes known as genetic "stutters), are composed of a few to many tandem repetitions of a short base-pair motif. These sequences frequently mutate, changing the amount of repetitions. SSRs are frequently found in promoters, untranslated regions, and even coding sequences, therefore these alterations can significantly affect practically every aspect of gene activity.

Financial time series forecasting using optimized multistage wavelet regression approach.

This paper presents financial time series forecasting with multistage wavelet transform (WT). First, the time series data is processed through WT with different mother wavelet functions to extract high frequency and low frequency coefficients. Later, standard particle swarm optimization (PSO) algorithm is utilized to find optimal regression models in order to predict future samples.

Plant IsomiR Atlas: Large Scale Detection, Profiling, and Target Repertoire of IsomiRs in Plants.

microRNAs (miRNAs) play an important role as key regulators controlling the post-transcriptional events in plants across development, abiotic and biotic stress, tissue polarity and also in defining the evolutionary basis of the origin of the post-transcriptional machinery. Identifying patterns of regulated and co-regulated small RNAs, in particular miRNAs and their sequence variants with the availability of next generation sequencing approaches has widely demonstrated the role of miRNAs and their temporal regulation in maintaining plant development and their response to stress conditions. Although the role of canonical miRNAs has been widely explored and functional diversity is revealed, those works for isomiRs are still limited and urgent to be carried out across plants.

Diff isomiRs: Large-scale detection of differential isomiRs for understanding non-coding regulated stress omics in plants.

Plants have an amazing ability to cope with wide variety of stresses by regulating the expression of genes and thus by altering the physiological status. In the past few years, canonical microRNA variants (isomiRs) have been shown to play pivotal roles by acting as regulators of the transcriptional machinery. In the present research, we present Diff isomiRs, a web-based exploratory repository of differential isomiRs across 16 sequenced plant species representing a total of 433 datasets across 21 different stresses and 158 experimental states.

Development of an FeO@Cu silicate based sensing platform for the electrochemical sensing of dopamine.

Abnormal levels of dopamine (DA) in body fluids is an indication of serious health issues, hence development of highly sensitive platforms for the precise detection of DA is highly essential. Herein, we demonstrate an FeO@Cu silicate based electrochemical sensing platform for the detection of DA. Morphology and BET analysis shows the formation of ∼320 nm sized sea urchin-like FeO@Cu silicate core-shell nanostructures with a 174.

Evaluation of Genome-Wide Markers and Orthologous Markers in Brachypodium distachyon.

Molecular markers play an important role in identifying the species variation, characterizing the genic diversity, and also linking the identified markers to trait of interest. Genome- and transcriptome-derived molecular markers have been widely used to understand the geographical diversity and have also played a major role in the development of high-density linkage maps. In the present protocol, we present a detailed protocol on bioinformatics approaches towards the whole-genome and transcriptome-assisted simple sequence repeats (SSRs) marker mining in Brachypodium distachyon and identification of orthologus SSRs and their validation in Brachypodium ecotypes.

MONO, DI and TRI SSRs data extraction & storage from 1403 virus genomes with next generation retrieval mechanism.

Now a day׳s SSRs occupy the dominant role in different areas of bio-informatics like new virus identification, DNA finger printing, paternity & maternity identification, disease identification, future disease expectations and possibilities etc., Due to their wide applications in various fields and their significance, SSRs have been the area of interest for many researchers. In the SSRs extraction, retrieval algorithms are used; if retrieval algorithms quality is improved then automatically SSRs extraction system will achieve the most relevant results.

Data of 10 SSR markers for genomes of homo sapiens and monkeys.

In this data, we present 10 Simple Sequence Repeat(SSR) markers TAGA, TCAT, GAAT, AGAT, AGAA, GATA, TATC, CTTT, TCTG and TCTA which are extracted from the genomes of homo sapiens and monkeys using string matching mechanism [1]. All loci showed 4 Base Pair(bp) in allele size, indicating that there are some polymorphisms between individuals correlating to the number of SSR repeats that maybe useful for the detection of similarity among the genotypes. Collectively, these data show that the SSR extraction is a valuable method to illustrate genetic variation of genomes.

Facile synthesis of bovine serum albumin conjugated low-dimensional ZnS nanocrystals.

We present a facile synthesis of bovine serum albumin (BSA) conjugated low-dimensional ZnS nanocrystals. The experimental parameters such as effects of BSA concentration and precursor vol ratios of Zn:S on the formation of ZnS nanoparticles in BSA matrix were investigated. The ZnS crystalline sizes of 1.

Next generation sequencing (NGS) database for tandem repeats with multiple pattern 2°-shaft multicore string matching.

Next generation sequencing (NGS) technologies have been rapidly applied in biomedical and biological research in recent years. To provide the comprehensive NGS resource for the research, in this paper , we have considered 10 loci/codi/repeats TAGA, TCAT, GAAT, AGAT, AGAA, GATA, TATC, CTTT, TCTG and TCTA. Then we developed the NGS Tandem Repeat Database (TandemRepeatDB) for all the chromosomes of Homo sapiens, Callithrix jacchus, Chlorocebus sabaeus, Gorilla gorilla, Macaca fascicularis, Macaca mulatta, Nomascus leucogenys, Pan troglodytes, Papio anubis and Pongo abelii genome data sets for all those locis.