A Novel Method for the Enhancement of Sunflower Growth from Animal Bones and Chicken Feathers.

The present study aimed at converting meat industry waste, particularly waste bones and chicken feathers, into biochar to recycle valuable nutrients present in it, which ultimately become part of the municipal waste. The bone biochar (BB) and feathers biochar (FB) were prepared at 550 °C, and their potential was evaluated as an organic amendment for the growth of sunflower. The ash content (AC) and fixed carbon (FC) improved significantly in prepared biochars as compared to raw feedstock.

An Intelligent Mechanism to Detect Multi-Factor Skin Cancer.

Deep learning utilizing convolutional neural networks (CNNs) stands out among the state-of-the-art procedures in PC-supported medical findings. The method proposed in this paper consists of two key stages. In the first stage, the proposed deep sequential CNN model preprocesses images to isolate regions of interest from skin lesions and extracts features, capturing the relevant patterns and detecting multiple lesions.

A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review.

The emergence of the Internet of Things (IoT) technology has brought about tremendous possibilities, but at the same time, it has opened up new vulnerabilities and attack vectors that could compromise the confidentiality, integrity, and availability of connected systems. Developing a secure IoT ecosystem is a daunting challenge that requires a systematic and holistic approach to identify and mitigate potential security threats. Cybersecurity research considerations play a critical role in this regard, as they provide the foundation for designing and implementing security measures that can address emerging risks.

A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models.

Due to increasing demands for ensuring the safety and reliability of a system, fault detection (FD) has received considerable attention in modern industries to monitor their machines. Bulk materials are transported worldwide using belt conveyors as an essential transport system. The majority of conveyor components are monitored continuously to ensure their reliability, but idlers remain a challenge to monitor due to the large number of idlers (rollers) distributed throughout the working environment.

A Proposed Framework for Early Prediction of Schistosomiasis.

Schistosomiasis is a neglected tropical disease that continues to be a leading cause of illness and mortality around the globe. The causing parasites are affixed to the skin through defiled water and enter the human body. Failure to diagnose Schistosomiasis can result in various medical complications, such as ascites, portal hypertension, esophageal varices, splenomegaly, and growth retardation.

Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures.

Dementia affects the patient's memory and leads to language impairment. Research has demonstrated that speech and language deterioration is often a clear indication of dementia and plays a crucial role in the recognition process. Even though earlier studies have used speech features to recognize subjects suffering from dementia, they are often used along with other linguistic features obtained from transcriptions.

Deep Ensemble Learning for the Automatic Detection of Pneumoconiosis in Coal Worker's Chest X-ray Radiography.

Globally, coal remains one of the natural resources that provide power to the world. Thousands of people are involved in coal collection, processing, and transportation. Particulate coal dust is produced during these processes, which can crush the lung structure of workers and cause pneumoconiosis.

An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset.

Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today's medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient's death in later stages. The diagnosis of skin cancer at an earlier stage is crucial for the success rate of complete cure.

A Hybrid Approach to Tea Crop Yield Prediction Using Simulation Models and Machine Learning.

Tea ( L.) is one of the most highly consumed beverages globally after water. Several countries import large quantities of tea from other countries to meet domestic needs.

Computer-Aided Diagnosis of Coal Workers' Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review.

Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers' pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly improve workers' survival rate. The development of the CAD systems will reduce risk in the workplace and improve the quality of chest screening for CWP diseases.

Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images.

Brain tumor classification is a very important and the most prominent step for assessing life-threatening abnormal tissues and providing an efficient treatment in patient recovery. To identify pathological conditions in the brain, there exist various medical imaging technologies. Magnetic Resonance Imaging (MRI) is extensively used in medical imaging due to its excellent image quality and independence from ionizing radiations.

A comprehensive dataset for bibliometric analysis of SARS and coronavirus impact on social sciences.

The year 2020 has changed the living style of people all around the world. Corona pandemic has affected the people in all fields of life economically, physically, and mentally. This dataset is a collection of published articles discussing the effect of COVID and SARS on the social sciences from 2003 to 2020.

A Bibliometric Analysis of Corona Pandemic in Social Sciences: A Review of Influential Aspects and Conceptual Structure.

Corona pandemic has affected the whole world, and it is a highly researched area in biological sciences. As the current pandemic has affected countries socially and economically, the purpose of this bibliometric analysis is to provide a holistic review of the corona pandemic in the field of social sciences. This study aims to highlight significant, influential aspects, research streams, and themes.

Production of EPS under Cr(VI) challenge in two indigenous bacteria isolated from a tannery effluent.

Indigenous Cr(VI) reducing bacterial strains Pseudomonas aeruginosa Rb-1 and Ochrobactrum intermedium Rb-2 were evaluated for EPS production under Cr(VI) challenged and free conditions. Strain Rb-2 was more efficient in total EPS production (13.63 mg g(-1)) than Rb-1 (4.