Efficient brain tumor grade classification using ensemble deep learning models.

Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental to the diagnosis because it contains detailed structural views of the brain, which is vital in identifying any of its abnormalities. The other option of performing an invasive biopsy is very painful and uncomfortable, which is not the case with MRI as it is free from surgically invasive margins and pieces of equipment.

Unraveling the Ferroptosis-inducing Potential of Methanol Leaves Extract of Prosopis Juliflora Via Downregulation of SLC7A11 and GPX4 mRNA Expression in A549 Lung Cancer Cells.

Introduction: Prosopis juliflora has been employed in many traditional treatments. As evidenced by our earlier research, Prosopis juliflora leaf methanol extract (PJME) has a promising future in the fight against lung cancer. It may also be used in conjunction with other treatments to effectively manage lung cancer.

Epigenetic frontiers: miRNAs, long non-coding RNAs and nanomaterials are pioneering to cancer therapy.

Cancer has arisen from both genetic mutations and epigenetic changes, making epigenetics a crucial area of research for innovative cancer prevention and treatment strategies. This dual perspective has propelled epigenetics into the forefront of cancer research. This review highlights the important roles of DNA methylation, histone modifications and non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long non-coding RNAs, which are key regulators of cancer-related gene expression.

IoT-based framework for optimizing energy efficiency and reliability in acoustic sensor networks using mobile sinks.

In today's world, there is an increasing demand for environmental monitoring, surveillance, and oceanographic research, which poses challenges in improving energy efficiency and data transfer reliability in Acoustic Sensor Networks. Existing methods face hurdles due to limited energy resources and unreliable data transmission. We propose a Reliable and Energy-Efficient Framework with Sink Mobility (REEFSM) to address these issues.

An Updated Review Summarizing the Pharmaceutical Efficacy of Genistein and its Nanoformulations in Ovarian Carcinoma.

Implementing lifestyle interventions as a primary prevention strategy is a cost-effective approach to reducing the occurrence of cancer, which is a significant contributor to illness and death globally. Recent advanced studies have uncovered the crucial role of nutrients in safeguarding women's health and preventing disorders. Genistein is an abundant isoflavonoid found in soybeans.

A comprehensive health assessment approach using ensemble deep learning model for remote patient monitoring with IoT.

The goal of this research is to create an ensemble deep learning model for Internet of Things (IoT) applications that specifically target remote patient monitoring (RPM) by integrating long short-term memory (LSTM) networks and convolutional neural networks (CNN). The work tackles important RPM concerns such early health issue diagnosis and accurate real-time physiological data collection and analysis using wearable IoT devices. By assessing important health factors like heart rate, blood pressure, pulse, temperature, activity level, weight management, respiration rate, medication adherence, sleep patterns, and oxygen levels, the suggested Remote Patient Monitor Model (RPMM) attains a noteworthy accuracy of 97.

Robust brain tumor classification by fusion of deep learning and channel-wise attention mode approach.

Diagnosing brain tumors is a complex and time-consuming process that relies heavily on radiologists' expertise and interpretive skills. However, the advent of deep learning methodologies has revolutionized the field, offering more accurate and efficient assessments. Attention-based models have emerged as promising tools, focusing on salient features within complex medical imaging data.

A multi-objective optimisation approach with improved pareto-optimal solutions to enhance economic and environmental dispatch in power systems.

This work implements the recently developed nth state Markovian jumping particle swarm optimisation (PSO) algorithm with local search (NS-MJPSOloc) awareness method to address the economic/environmental dispatch (EED) problem. The proposed approach, known as the Non-dominated Sorting Multi-objective PSO with Local Best (NS-MJPSOloc), aims to enhance the performance of the PSO algorithm in multi-objective optimisation problems. This is achieved by redefining the concept of best local candidates within the search space of multi-objective optimisation.

A robust self-supervised approach for fine-grained crack detection in concrete structures.

This work addresses a critical issue: the deterioration of concrete structures due to fine-grained cracks, which compromises their strength and longevity. To tackle this problem, experts have turned to computer vision (CV) based automated strategies, incorporating object detection and image segmentation techniques. Recent efforts have integrated complex techniques such as deep convolutional neural networks (DCNNs) and transformers for this task.

Effect of TiO Nanofillers on the Mechanical and Abrasive Wear Properties of Epoxy Reinforced with Carbon Fabric Hybrid Composites.

Recent studies show that nanofillers greatly contribute to the increase in the mechanical and abrasive behaviors of the polymer composite. In the current study, epoxy composites were made by hand lay-up with the reinforcement of carbon fabric and titanium dioxide (TiO) nanoparticles as secondary reinforcement in weight percentages of 0.5, 1.

The connectivity indices concept of neutrosophic graph and their application of computer network, highway system and transport network flow.

To address information ambiguities, this study suggests using neutrosophic sets as a tactical tool. Three membership functions (called and ) that indicate an object's degree of truth, indeterminacy, and false membership constitute the neutrosophic set. It becomes clear that the neutrosophic connectivity index (CIN) is an essential tool for solving practical problems, especially those involving traffic network flow.

Deep Learning-Based Ensembling Technique to Classify Alzheimer's Disease Stages Using Functional MRI.

The major issue faced by elderly people in society is the loss of memory, difficulty learning new things, and poor judgment. This is due to damage to brain tissues, which may lead to cognitive impairment and eventually Alzheimer's. Therefore, the detection of such mild cognitive impairment (MCI) becomes important.

A Huffman code LSB based image steganography technique using multi-level encryption and achromatic component of an image.

In the recent couple of years, due to the accelerated popularity of the internet, various organizations such as government offices, military, private companies, etc. use different transferring methods for exchanging their information. The Internet has various benefits and some demerits, but the primary bad mark is security of information transmission over an unreliable network, and widely uses of images.

Detection of autism spectrum disorder (ASD) in children and adults using machine learning.

Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects.

Social, economic and environmental implications of the COVID-19 pandemic.

The COVID-19 pandemic led to global lockdowns that severely curtailed economic activity. In this study, we set out to examine the social, economic, and environmental ramifications of the COVID-19 pandemic. This is a rare project that will have far-reaching consequences for the field.

Power mean based image segmentation in the presence of noise.

In image segmentation and in general in image processing, noise and outliers distort contained information posing in this way a great challenge for accurate image segmentation results. To ensure a correct image segmentation in presence of noise and outliers, it is necessary to identify the outliers and isolate them during a denoising pre-processing or impose suitable constraints into a segmentation framework. In this paper, we impose suitable removing outliers constraints supported by a well-designed theory in a variational framework for accurate image segmentation.

The relationship among idiosyncratic deals, psychological empowerment, and internal locus of control: A moderated mediation model.

Building upon the job demands-resources (JD-R) theory, this research offers an in-depth exploration of the mechanisms by which idiosyncratic deals (I-deals), such as personalized work arrangements, can enhance academics' psychological empowerment (PE) and hence affect their work engagement. This study's purpose was to investigate whether PE mediates the relationships between task and work responsibilities I-deals, flexibility I-deals, and work engagement among academics in higher education and whether the mediating effects are moderated by academics' internal locus of control. Using an online platform, the survey questionnaire was sent to 650 academics working in higher education.

Deep Learning Model for the Image Fusion and Accurate Classification of Remote Sensing Images.

Deep learning is widely used for the classification of images that have various attributes. Image data are used to extract colour, texture, form, and local features. These features are combined in feature-level image fusion to create a merged remote sensing image.

An integrated model of UTAUT2 to understand consumers' 5G technology acceptance using SEM-ANN approach.

It has been a decade since the first extensive study on the internet's adoption and use was conducted. Circumstances have changed in the last decade internet has become an essential need for every human being. Socio-psychological, economic, and personal factors play a significant role in shaping human behaviour.

Linking the trust of industrial entrepreneurs on elements of ecosystem with entrepreneurial success: Determining startup behavior as mediator and entrepreneurial strategy as moderator.

Purpose: This study aimed to apply "multi-criteria decision approach and attitude-change theory" to examine post-COVID-19 impact on entrepreneurial mindset by investigating the link between entrepreneurs social capital (trust on three elements of ecosystem i.e., experts & enterprises, media, and government) and entrepreneurial success (both individual and organizational).