Corrigendum to "Development of an explainable machine learning model for Alzheimer's disease prediction using clinical and behavioural features" [MethodsX 15 (2025) 103491].

[This corrects the article DOI: 10.1016/j.mex.

Effective demineralization of malachite green, and Janus green blue dyes with nickel oxide nanoparticles: a novel green chemistry method.

Solution combustion method (SCM), with urea as a fuel, is a convenient method to synthesize nickel oxide (NiO) nanoparticles. NiO nanoparticles have been characterized using UV-visible spectroscopy (UV-Vis), X-ray diffraction (XRD), scanning electron microscopy (SEM) with EDX, transmission electron microscopy (TEM), and BET surface area determination. The average size of the NiO nanoparticles is 22 nm, and it has a band gap of 3.

Development of an explainable machine learning model for Alzheimer's disease prediction using clinical and behavioural features.

This article presents a reproducible machine learning methodology for the early prediction of Alzheimer's disease (AD) using clinical and behavioural data. A comparative analysis of multiple classification algorithms was conducted, with the Gradient Boosting classifier yielding the best performance (accuracy: 93.9 %, F1-score: 91.

A novel hybrid convolutional and transformer network for lymphoma classification.

Lymphoma poses a critical health challenge worldwide, demanding computer aided solutions towards diagnosis, treatment, and research to significantly enhance patient outcomes and combat this pervasive disease. Accurate classification of lymphoma subtypes from Whole Slide Images (WSIs) remains a complex challenge due to morphological similarities among subtypes and the limitations of models that fail to jointly capture local and global features. Traditional diagnostic methods, limited by subjectivity and inconsistencies, highlight the need for advanced, Artificial Intelligence (AI)-driven solutions.

Optimized disease prediction in healthcare systems using HDBN and CAEN framework.

Classification and segmentation play a pivotal role in transforming decision-making processes in healthcare, IoT, and edge computing. However, existing methodologies often struggle with accuracy, precision, and specificity when applied to large, heterogeneous datasets, particularly in minimizing false positives and negatives. To address these challenges, we propose a robust hybrid framework comprising three key phases: feature extraction using a Hybrid Deep Belief Network (HDBN), dynamic prediction aggregation via a Custom Adaptive Ensemble Network (CAEN), and an optimization mechanism ensuring adaptability and robustness.

Mathematical model of the lumpy skin disease using Caputo fractional-order derivative via invariant point technique.

The aim of this paper is to study the fractional model of Lumpy Skin Disease, aiming to enhance our understanding of this disease. Specifically, we employ the recently introduced Caputo-Fabrizio fractional (CFF) derivative to analyze the Lumpy Skin Disease model in detail. To comprehensively study the model's solutions, we utilize the Picard-Lindelof approach to assess their existence and uniqueness.

Cognitive neurodynamic approaches to adaptive signal processing in wireless sensor networks.

In recent years, Wireless Sensor Networks (WSN) have become vital because of their versatility in numerous applications. Nevertheless, the attain problems like inherent noise, and limited node computation capabilities, result in reduced sensor node lifespan as well as enhanced power consumption. To tackle such problems, this study develops a Modified-Distributed Arithmetic-Offset Binary Coding-based Adaptive Finite Impulse Response (MDA-OBC based AFIR) framework.

Segmentation of breast lesion using fuzzy thresholding and deep learning.

Breast cancer is a major cause of morbidity and mortality in women. In breast cancer screening, Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has shown promise as a technique, providing enhanced temporal patterns of breast tissues. This study proposes an enhanced segmentation method for identifying breast lesions.

Environmental water quality prediction based on COOT-CSO-LSTM deep learning.

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model's performance.

Plastic pyrolytic oils as renewable fuel: improving its physical properties and ignition patterns by waste renewable source-an experimental analysis.

The increase in plastic products and disposal poses a severe environmental challenge because of their poor biodegradability and undesirable disposal by landfilling. Recycling is the best possible solution to the environmental challenges implemented by the plastic industry. Pyrolysis is a process that converts waste plastics into pyrolytic oil, and it can be used as fuel in a blended form.

Environment impact assessment of agricultural diesel engines utilizing biodiesel derived from phoenix sylvestris oil.

Uncontrolled emissions, massive price increases, and other factors encourage searching for a suitable diesel engine fuel alternative. In its processed form, vegetable oil biodiesel is an appealing green alternative fuel for compression ignition engines. Vegetable oil esters have qualities comparable to those of standard diesel fuel.

An efficient and low complex model for optimal RBM features with weighted score-based ensemble multi-disease prediction.

Multi-disease prediction is regarded as the capacity to simultaneously identify various diseases that are expected to be affected an individual at a certain period. These multiple diseases are seemed to be at various progression levels and need to be detected in the patient at the time of clinical visits. Diverse studies in the literature have included the predictive models for particular diseases yet, it is unable to notice humans with multiple diseases since humans are mostly suffered not only from a single disease but also from multiple diseases.

Autonomous service for managing real time notification in detection of COVID-19 virus.

In today's world, the most prominent public issue in the field of medicine is the rapid spread of viral sickness. The seriousness of the disease lies in its fast spreading nature. The main aim of the study is the proposal of a framework for the earlier detection and forecasting of the COVID-19 virus infection amongst the people to avoid the spread of the disease across the world by undertaking the precautionary measures.

Thermal and chemical exhaust gas recirculation potential of punnai oil biodiesel-fuelled diesel engine for environmental sustainability.

Major energy production all over the world depends on fossil fuels. Recent research on alternative energy sources has raised major concerns about environmental impacts, future availability, and cost. Pollution from diesel engines also affects the environment negatively.

Performance and environmental impact assessment of diesel engine operating on high viscous punnai oil-diesel blends.

The increasing demand for energy consumption because of the growing population and environmental concerns has motivated the researchers to ponder about alternative fuel that could replace diesel fuel. A new fuel should be cheaply available, clean, efficient, and environmentally friendly. In this paper, the engine operated with neat punnai oil blends with diesel was investigated at various engine load conditions, keeping neat punnai oil and diesel as base fuels.

Detection of turner syndrome using hand X-ray using anchor based links segmentation method.

Turner Syndrome (TS) is a chromosomal disorder, wherein the female's growth is impacted. Immature ovaries, low stature, and heart abnormalities are a range of developmental and medical issues due to TS. The condition of TS might be detected prior to birth, throughout infancy or in the early years of life.

Universal Keyword Classifier on Public Key Based Encrypted Multikeyword Fuzzy Search in Public Cloud.

Cloud computing has pioneered the emerging world by manifesting itself as a service through internet and facilitates third party infrastructure and applications. While customers have no visibility on how their data is stored on service provider's premises, it offers greater benefits in lowering infrastructure costs and delivering more flexibility and simplicity in managing private data. The opportunity to use cloud services on pay-per-use basis provides comfort for private data owners in managing costs and data.

A Hybrid Swarm Algorithm for optimizing glaucoma diagnosis.

Glaucoma is among the most common causes of permanent blindness in human. Because the initial symptoms are not evident, mass screening would assist early diagnosis in the vast population. Such mass screening requires an automated diagnosis technique.