Green synthesis of zinc ferrite nanoparticles from Nyctanthes arbor-tristis: unveiling larvicidal potential, protein binding affinity and photocatalytic activities.

Environmental pollution, being a major concern worldwide, needs a unique and ecofriendly solution. To answer this, researchers are aiming in utilizing plant extracts for the synthesis of nanoparticles. These NPs synthesized using plant extracts provide a potential, environmentally benign technique for biological and photocatalytic applications.

Deep learning and optimization enabled multi-objective for task scheduling in cloud computing.

In cloud computing (CC), task scheduling allocates the task to best suitable resource for execution. This article proposes a model for task scheduling utilizing the multi-objective optimization and deep learning (DL) model. Initially, the multi-objective task scheduling is carried out by the incoming user utilizing the proposed hybrid fractional flamingo beetle optimization (FFBO) which is formed by integrating dung beetle optimization (DBO), flamingo search algorithm (FSA) and fractional calculus (FC).

A study on microstructural, mechanical properties, and optimization of wear behaviour of friction stir processed AZ31/TiC composites using response surface methodology.

The primary objective of this study is to investigate the microstructural, mechanical, and wear behaviour of AZ31/TiC surface composites fabricated through friction stir processing (FSP). TiC particles are reinforced onto the surface of AZ31 magnesium alloy to enhance its mechanical properties for demanding industrial applications. The FSP technique is employed to achieve a uniform dispersion of TiC particles and grain refinement in the surface composite.

Implementing heuristic-based multiscale depth-wise separable adaptive temporal convolutional network for ambient air quality prediction using real time data.

In many emerging nations, rapid industrialization and urbanization have led to heightened levels of air pollution. This sudden rise in air pollution, which affects global sustainability and human health, has become a significant concern for citizens and governments. While most current methods for predicting air quality rely on shallow models and often yield unsatisfactory results, our study explores a deep architectural model for forecasting air quality.

Characterization of plant leaves biomass-based bioplasticizers for biofilm applications.

The present surge in environmental consciousness has pushed for the use of biodegradable plasticizers, which are sustainable and abundant in plant resources. As a result of their biocompatibility and biodegradability, leaf plasticizers (CLP) serve as viable alternatives to chemical plasticizers. First time, the natural plasticizers from the Calotropis leaves were extracted for this study using a suitable chemical approach that was also environmentally friendly.

An XAI-enhanced efficientNetB0 framework for precision brain tumor detection in MRI imaging.

Background: Accurately diagnosing brain tumors from MRI scans is crucial for effective treatment planning. While traditional methods heavily rely on radiologist expertise, the integration of AI, particularly Convolutional Neural Networks (CNNs), has shown promise in improving accuracy. However, the lack of transparency in AI decision-making processes presents a challenge for clinical adoption.

Gastrointestinal tract disease detection via deep learning based structural and statistical features optimized hexa-classification model.

Background: Gastrointestinal tract (GIT) diseases impact the entire digestive system, spanning from the mouth to the anus. Wireless Capsule Endoscopy (WCE) stands out as an effective analytic instrument for Gastrointestinal tract diseases. Nevertheless, accurately identifying various lesion features, such as irregular sizes, shapes, colors, and textures, remains challenging in this field.

Many-objective ant lion optimizer (MaOALO): A new many-objective optimizer with its engineering applications.

Many-objective optimization (MaO) is an important aspect of engineering scenarios. In many-objective optimization algorithms (MaOAs), a key challenge is to strike a balance between diversity and convergence. MaOAs employs various tactics to either enhance selection pressure for better convergence and/or implements additional measures for sustaining diversity.

Investigation of the influence of Candida tropicalis on bioethanol production using sugarcane bagasse: stochastic and in silico analysis.

This study investigated the impact of Candida tropicalis NITCSK13 on sugarcane bagasse (SCB) consolidated bioprocessing (CSB) using various parameters, such as pH, steam explosion (STEX) pretreatment, and temperature (at two different temperatures, cellulose hydrolysis and ethanol fermentation). The backpropagation neural network (BPNN) method simulated the optimal CSB conditions, achieving a maximum ethanol yield of 44 ± 0.32 g/L (0.

Ataxia-Telangiectasia Mutated (ATM) gene signaling pathways in human cancers and their therapeutic implications.

Cancer is a multifaceted disease driven by abnormal cell growth and poses a significant global health threat. The multifactorial causes, differences in individual susceptibility to therapeutic drugs, and induced drug resistance pose major challenges in addressing cancers effectively. One of the most important aspects in making cancers highly heterogeneous in their physiology lies in the genes involved and the changes occurring to some of these genes in malignant conditions.

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.

Enhancing radiographic image interpretation: WARES-PRS model for knee bone tumour detection.

The early diagnosis of tumour is significant in biomedical research field to lower the severity level and restrict the process extension from cancer. Moreover, the detection of early sign of cancer is undertaken with extensive research efforts that dedicated to the disclosure and recognition of tumours. However, the limited data size as well as diverse appearance of images lowered the detection performance and failed to detect complex stage of tumour.

A Dynamic Traffic Light Control Algorithm to Mitigate Traffic Congestion in Metropolitan Areas.

This paper proposes a convolutional neural network (CNN) model of the signal distribution control algorithm (SDCA) to maximize the dynamic vehicular traffic signal flow for each junction phase. The aim of the proposed algorithm is to determine the reward value and new state. It deconstructs the routing components of the current multi-directional queuing system (MDQS) architecture to identify optimal policies for every traffic scenario.

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.

Detection of Parkinson disease using multiclass machine learning approach.

Parkinson's Disease (PD) is a prevalent neurological condition characterized by motor and cognitive impairments, typically manifesting around the age of 50 and presenting symptoms such as gait difficulties and speech impairments. Although a cure remains elusive, symptom management through medication is possible. Timely detection is pivotal for effective disease management.

Wearable biosensors in cardiovascular disease.

This review provides a comprehensive overview of the latest advancements in wearable biosensors, emphasizing their applications in cardiovascular disease monitoring. Initially, the key sensing signals and biomarkers crucial for cardiovascular health, such as electrocardiogram, phonocardiography, pulse wave velocity, blood pressure, and specific biomarkers, are highlighted. Following this, advanced sensing techniques for cardiovascular disease monitoring are examined, including wearable electrophysiology devices, optical fibers, electrochemical sensors, and implantable cardiac devices.

Antimicrobial efficiency against fish pathogens on the green synthesized silver nanoparticles.

Fish-borne pathogens such as A. hydrophila and F. aquidurense are the most resistant strains in pisciculture farming.

Effective prediction of human skin cancer using stacking based ensemble deep learning algorithm.

Automated diagnosis of cancer from skin lesion data has been the focus of numerous research. Despite that it can be challenging to interpret these images because of features like colour illumination changes, variation in the sizes and forms of the lesions. To tackle these problems, the proposed model develops an ensemble of deep learning techniques for skin cancer diagnosis.

Investigation on ultrasound images for detection of fetal congenital heart defects.

Congenital heart defects (CHD) are one of the serious problems that arise during pregnancy. Early CHD detection reduces death rates and morbidity but is hampered by the relatively low detection rates (i.e.

Optimized encoder-decoder cascaded deep convolutional network for leaf disease image segmentation.

Nowadays, Deep Learning (DL) techniques are being used to automate the identification and diagnosis of plant diseases, thereby enhancing global food security and enabling non-experts to detect these diseases. Among many DL techniques, a Deep Encoder-Decoder Cascaded Network (DEDCNet) model can precisely segment diseased areas from the leaf images to differentiate and classify multiple diseases. On the other hand, the model training depends on the appropriate selection of hyperparameters.

Recent advances in and studies on and its derivatives.

The marine red algae (KA), commonly known as , is a valuable resource with diverse applications. Research has primarily focused on its primary metabolites such as amino acids, proteins, plant hormones etc. Additionally, KA also produces secondary metabolites under stress, some of which exhibit antioxidant and antibacterial properties.

Nucleic acid-based electrochemical biosensors.

Colorectal cancer, breast cancer, oxidative DNA damage, and viral infections are all significant and major health threats to human health, presenting substantial challenges in early diagnosis. In this regard, a wide range of nucleic acid-based electrochemical platforms have been widely employed as point-of-care diagnostics in health care and biosensing technologies. This review focuses on biosensor design strategies, underlying principles involved in the development of advanced electrochemical genosensing devices, approaches for immobilizing DNA on electrode surfaces, as well as their utility in early disease diagnosis, with a particular emphasis on cancer, leukaemia, oxidative DNA damage, and viral pathogen detection.

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models.

Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis.