Hybrid optimization enabled DenseNet for autism spectrum disorders using MRI image.

Autism spectrum disorder (ASD) is the neuro-developmental disorder caused by various changes in the brain. It affects the life conditions with social interaction and communication. Most of the previous researches used the various techniques for the early detection to reduce the ASD, but it had been occurred several complications such as, time expenses, and low accessibility for diagnosis.

A multi-modal geospatial-temporal LSTM based deep learning framework for predictive modeling of urban mobility patterns.

Urban mobility prediction is crucial for optimizing resource allocation, managing transportation systems, and planning urban development. We propose a novel framework, GeoTemporal LSTM (GT-LSTM), designed to address the intricate spatiotemporal dynamics of urban environments. GT-LSTM integrates temporal dependencies with geographic information through a multi-modal approach that combines attention mechanisms and Recurrent Neural Networks (RNNs).

Energy efficient group priority MAC protocol using hybrid Q-learning honey Badger Algorithm (QL-HBA) for IoT Networks.

In Internet of Things (IoT) networks, identifying the primary Medium Access Control (MAC) layer protocol which is suited for a service characteristic is necessary based on the requirements of the application. In this paper, we propose Energy Efficient and Group Priority MAC (EEGP-MAC) protocol using Hybrid Q-Learning Honey Badger Algorithm (QL-HBA) for IoT Networks. This algorithm employs reinforcement agents to select an environment based on predefined actions and tasks.

Inclusive AI for radiology: Optimising ChatGPT-4 with advanced prompt engineering.

This letter responds to the article "Encouragement vs. liability: How prompt engineering influences ChatGPT-4's radiology exam performance," offering additional perspectives on optimising ChatGPT-4 for Radiology applications. While the study highlights the significance of prompt engineering, we suggest that addressing additional key challenges such as age-related diagnostic needs, socio-economic diversity, data security, and liability concerns is essential for responsible AI integration.

Transforming Cancer Classification: The Role of Advanced Gene Selection.

Accurate classification in cancer research is vital for devising effective treatment strategies. Precise cancer classification depends significantly on selecting the most informative genes from high-dimensional datasets, a task made complex by the extensive data involved. This study introduces the Two-stage MI-PSA Gene Selection algorithm, a novel approach designed to enhance cancer classification accuracy through robust gene selection methods.

Predicting the biomechanical behavior of lumbar intervertebral Discs: A comparative finite element analysis of a novel artificial disc design.

Osseointegration along with better mimicry of natural bone behaviour addresses the long-term performance of artificial intervertebral disc prosthesis. Here the effect of a novel artificial intervertebral disc geometry on stress, deformation and strain on lumbar segments to restore movement of the spine was investigated. The process involved, using CT image data, and solid modelling, simulation-driven design and finite element (FE) analysis, hexahedral mesh sensitivity analysis, implant placements.

Integrating ChatGPT: Enhancing postpartum mental healthcare with artificial intelligence (AI) support.

We are writing to extend the discourse on the innovative work published in digital health on the feasibility of videoconferencing-based therapy groups for postpartum depression and anxiety. The pragmatic evaluation demonstrated promising outcomes in terms of acceptability, appropriateness, and group process, suggesting that this modality is a viable alternative to traditional in-person therapy, especially in addressing the challenges faced by new mothers. Building on this study, we propose considering the integration of artificial intelligence (AI)-driven tools such as ChatGPT2 into such group therapy settings.

Metaverse forensics framework: A NIST based investigation framework for metaverse.

The Metaverse is an intersection of virtual, augmented, and mixed reality that offers users an interactive virtual environment. This new technology has enormous possibilities for both people and companies. However, owing to the existence of inherent vulnerabilities associated with Metaverse, misconduct is a major concern.

A high-performance ultra-compact plasmonic metamaterial structure for optical THz absorption.

This study presents the design and analysis of a high-performance metamaterial absorber for the optical terahertz (THz) regime. The proposed absorber utilizes a unique nested flower-shaped structure composed of nickel (Ni) and silicon dioxide (SiO), achieving an average absorption exceeding 97.91% with a broad bandwidth of 1320 THz (180 THz - 1500 THz).

A comprehensive analysis of phytochemicals, antioxidant, anti-inflammatory, antibacterial, antifungal and phytoestrogenic properties of different parts of .

L., a medicinal plant rich in secondary metabolites, was studied for optimising bioactive compound extraction from various parts of the plant using ethanol-water (50:50), ethanol, and methanol solvents. Analysis of extracts for key phytochemicals like polyphenols, flavonoids, saponins, and alkaloids was performed using HPTLC, HPLC and gas chromatography.

Corrolato(oxo)antimony(V) Dimer with Hydrogen-Bond Donor Groups in Secondary Coordination Sphere as a Catalyst for Hydrogen Evolution Reaction.

The focus is on developing alternative molecular catalysts using main-group elements and implementing strategic improvements for sustainable hydrogen production. For this purpose, a FB corrole with a (2-(2-((4-methylphenyl)sulfonamido)ethoxy)phenyl) group inserted into the position (C-10) of the corrole, along with its high-valent (corrolato)(oxo)antimony(V) dimer, was synthesized. In the crystal structure analysis of the FB corrole and the (corrolato)(oxo)antimony(V) dimer complex, it was noted that the sulfonamido group in the ligand periphery sits atop the corrole ring.

SLB - SMOTE logistic blending hybrid machine learning model for chronic polycystic ovary syndrome prediction with correlated feature selection.

Objective: In this study, we aimed to develop a machine learning (ML) model for predicting Polycystic Ovary Syndrome (PCOS) based on demographic, clinical, and biochemical parameters.

Methodology: We collected data from Kaggle, which included information on age, body mass index, menstrual cycle length, follicle-stimulating hormone, hair growth, and more. Using this data, we trained several traditional ML and ensemble algorithms to predict PCOS.