Hybrid Mexican axolotl and bitterling fish optimization algorithm-based spectrum sensing multi‑hop clustering routing protocol for cognitive sensor networks.

In clustered cognitive radio sensor networks (CRSNs), availability of free channels, spectrum sensing and energy utilization during clustering and cluster head (CH) selection is essential for fairness of time and event-driven data traffic. The existing multi-hop routing protocols in CRSNs generally adopt a perfect spectrum sensing which is not same in the practical spectrum sensing of nodes in real networks. High imbalance in residual energy between the selected CHs negatively impacts the delivery of data packets.

Investigating the neuroprotective properties of extract against MPP-induced apoptosis in SH-SY5Y human neuroblastoma cells.

This study aims to assess the neuroprotective effects of the methanolic extract of against oxidative stress and cell death induced by neurotoxins MPP  in SH-SY5Y cells. Briefly, the methanolic extract of decreased the cytotoxicity of MPP  in SH-SY5Y cells. Treatment with extract at a concentration of 400 µg/ml resulted in a notable decrease in cell death, particularly in MPP  -induced cells.

Multiagent DDOS attack detection model: Optimal trained hybrid classifier and entropy-based mitigation process.

This study proposes a novel multi-agent system designed to detect Distributed Denial of Service (DDoS) attacks, addressing the increasing need for robust cybersecurity measures. The hypothesis posits that a structured multi-agent approach can enhance detection accuracy and response efficiency in DDoS attack scenarios. The methodology involves a five-stage detection model: (1) Preprocessing using a modified double sigmoid normalization technique to eliminate duplicate data; (2) Feature Extraction where raw data and improved correlation-based features, mutual information, and statistical features are identified; (3) Dimensionality Reduction conducted by a reducer agent to streamline the feature set; (4) Classification utilizing Deep Belief Networks (DBN), Bi-LSTM, and Deep Maxout models, with their weights optimally tuned using the hybrid optimization algorithm, WUJSO; and (5) Decision Making by the decision agent to ascertain the presence of attacks, followed by mitigation through modified entropy-based techniques.

An extensive analysis of artificial intelligence and segmentation methods transforming cancer recognition in medical imaging.

Recent advancements in computational intelligence, deep learning, and computer-aided detection have had a significant impact on the field of medical imaging. The task of image segmentation, which involves accurately interpreting and identifying the content of an image, has garnered much attention. The main objective of this task is to separate objects from the background, thereby simplifying and enhancing the significance of the image.

Utilizing adaptive deformable convolution and position embedding for colon polyp segmentation with a visual transformer.

Polyp detection is a challenging task in the diagnosis of Colorectal Cancer (CRC), and it demands clinical expertise due to the diverse nature of polyps. The recent years have witnessed the development of automated polyp detection systems to assist the experts in early diagnosis, considerably reducing the time consumption and diagnostic errors. In automated CRC diagnosis, polyp segmentation is an important step which is carried out with deep learning segmentation models.

Unsupervised local center of mass based scoliosis spinal segmentation and Cobb angle measurement.

Scoliosis is a medical condition in which a person's spine has an abnormal curvature and Cobb angle is a measurement used to evaluate the severity of a spinal curvature. Presently, automatic Existing Cobb angle measurement techniques require huge dataset, time-consuming, and needs significant effort. So, it is important to develop an unsupervised method for the measurement of Cobb angle with good accuracy.

Internet of Things enabled open source assisted real-time blood glucose monitoring framework.

Regular monitoring of blood glucose levels is essential for the management of diabetes and the development of appropriate treatment protocols. The conventional blood glucose (BG) testing have an intrusive technique to prick the finger and it can be uncomfortable when it is a regular practice. Intrusive procedures, such as fingerstick testing has negatively influencing patient adherence.

Time-dependent biomechanical evaluation for corrective planning of scoliosis using finite element analysis - A comprehensive approach.

Scoliosis is a medical condition marked by an abnormal lateral curvature of the spine, typically forming a sideways "S" or "C" shape. Mechanically, it manifests as a three-dimensional deformation of the spine, potentially leading to diverse clinical issues such as pain, diminished lung capacity, and postural abnormalities. This research specifically concentrates on the Adolescent Idiopathic Scoliosis (AIS) population, as existing literature indicates a tendency for this type of scoliosis to deteriorate over time.

Unveiling the impact of aging on BBB and Alzheimer's disease: Factors and therapeutic implications.

Alzheimer's disease (AD) is a highly prevalent neurodegenerative condition that has devastating effects on individuals, often resulting in dementia. AD is primarily defined by the presence of extracellular plaques containing insoluble β-amyloid peptide (Aβ) and neurofibrillary tangles (NFTs) composed of hyperphosphorylated tau protein (P-tau). In addition, individuals afflicted by these age-related illnesses experience a diminished state of health, which places significant financial strain on their loved ones.

Structural, surface morphological and dielectric studies of guanidinium salt incorporated poly (ethylene oxide)/poly (vinyl pyrrolidone) solid polymer electrolytes.

The research article investigates the effect of incorporating the guanidinium carbonate (GuC) salt into the poly vinylpyrrolidone (PVP) and polyethylene oxide (PEO) polymer matrix. Various weight percentages of GuC enriched PVP/PEO solid polymer electrolytes (SPEs) have been prepared by the simplest solution casting process. XRD analysis revealed that the incorporation of the GuC salt led to changes in the crystalline structure of the PVP/PEO.

A practical green synthesis method of Ag NPs using rosy periwinkle plant leaves for solar panel coating.

Coated silver nanoparticles (Ag NPs) are currently receiving interest because of their numerous uses in various fields of electronics, antimicrobials, manufacturing sectors, optical science, and pharmaceuticals. Among others, it gained significant attention in the power electronic system. The goal of the proposed study is to use a cost-effective coating material for solar panels; to accomplish this, silver nanoparticles were synthesized from the leaves of the plants.

Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers.

Mild cognitive impairment (MCI) is the precursor to the advanced stage of Alzheimer's disease (AD), and it is important to detect the transition to the MCI condition as early as possible. Trends in daily routines/activities provide a measurement of cognitive/functional status, particularly in older adults. In this study, activity data from longitudinal monitoring through in-home ambient sensors are leveraged in predicting the transition to the MCI stage at a future time point.

Computation of Vascular Parameters: Implementing Methodology and Performance Analysis.

This paper presents the feasibility of automated and accurate in vivo measurements of vascular parameters using an ultrasound sensor. The continuous and non-invasive monitoring of certain parameters, such as pulse wave velocity (PWV), blood pressure (BP), arterial compliance (AC), and stiffness index (SI), is crucial for assessing cardiovascular disorders during surgeries and follow-up procedures. Traditional methods, including cuff-based or invasive catheter techniques, serve as the gold standard for measuring BP, which is then manually used to calculate AC and SI through imaging algorithms.

Moth-Flame Optimization for Early Prediction of Heart Diseases.

Heart disease is among the leading causes of mortality globally. Predicting cardiovascular disease is a major difficulty in clinical data analysis. AI has been demonstrated to be powerful in deciding and anticipating an enormous measure of information created by the health domain.

ITFDS: Channel-Aware Integrated Time and Frequency-Based Downlink LTE Scheduling in MANET.

One of the crucial problems in Industry 4.0 is how to strengthen the performance of mobile communication within mobile ad-hoc networks (MANETs) and mobile computational grids (MCGs). In communication, Industry 4.

Development of Big Data Predictive Analytics Model for Disease Prediction using Machine learning Technique.

Now days, health prediction in modern life becomesvery much essential. Big data analysis plays a crucial role to predict future status of healthand offerspreeminenthealth outcome to people. Heart disease is a prevalent disease cause's death around the world.

Design, graph theoretical analysis, density functionality theories, Insilico modeling, synthesis, characterization and biological activities of novel thiazole fused quinazolinone derivatives.

Hit, Lead & Candidate Discovery A series of 2-(2-substituted benzylidenehydrazinyl-2-oxopropyl)-3-(4-[4-oxo-2-phenylthiazolo din-3-yl]phenyl)quinazolin-4(3H)-one 7a-7l were synthesized and characterized by IR, H-NMR, C-NMR, mass spectroscopy and elemental analyses. In this present study, the density functionality theory was performed to identify drug stability. Further we introduced graph theoretical analysis by utilised Kyoto Encyclopedia of Genes and Genomes (KEGG) database and Cytoscape software to identify drug target.

Graph Theoretical Analysis, Insilico Modeling and Formulation of Pyrimidine Nanoparticles as p38α MAP Kinases inhibitors: A Quantitative Proteomics Approach.

In this study, the optimized 4-(4-hydroxybenzyl)-2-amino-6-hydroxypyrimidine-5-carboxamide derivative was formulated as nanoparticles to evaluate for their anticancer activity. The response surface methodology (RSM) was performed with utilization of Box-Behnken statistical design (BBSD) to optimize the experimental conditions for identification of significant synthetic methodology. To explore the stability of the derivative was done by density functional theory (DFT).

Graph Theoretical Analysis, In Silico Modeling, Synthesis, Anti-Microbial and Anti-TB Evaluation of Novel Quinoxaline Derivatives.

Background: We designed to synthesize a number of 2-(2-(substituted benzylidene) hydrazinyl)-N-(4-((3-(phenyl imino)-3,4-dihydro quinoxalin-2(1 H)-ylidene)amino) phenyl) acetamide S1-S13: with the hope to obtain more active and less toxic anti-microbial and anti-TB agents.

Methods: A series of novel quinoxaline Schiff bases S1-S13: were synthesized from o-phenylenediamine and oxalic acid by a multistep synthesis. In present work, we are introducing graph theoretical analysis to identify drug target.

Spatial Fuzzy C Means and Expectation Maximization Algorithms with Bias Correction for Segmentation of MR Brain Images.

The Fuzzy C Means (FCM) and Expectation Maximization (EM) algorithms are the most prevalent methods for automatic segmentation of MR brain images into three classes Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). The major difficulties associated with these conventional methods for MR brain image segmentation are the Intensity Non-uniformity (INU) and noise. In this paper, EM and FCM with spatial information and bias correction are proposed to overcome these effects.

Lossless Compression on MRI Images Using SWT.

Medical image compression is one of the growing research fields in biomedical applications. Most medical images need to be compressed using lossless compression as each pixel information is valuable. With the wide pervasiveness of medical imaging applications in health-care settings and the increased interest in telemedicine technologies, it has become essential to reduce both storage and transmission bandwidth requirements needed for archival and communication of related data, preferably by employing lossless compression methods.