MethodsX最新文献

{"title":"Optimized disease prediction in healthcare systems using HDBN and CAEN framework","authors":"G. Prabaharan ,&nbsp;S.M. Udhaya Sankar ,&nbsp;V. Anusuya ,&nbsp;K. Jaya Deepthi ,&nbsp;Rayappan Lotus ,&nbsp;R. Sugumar","doi":"10.1016/j.mex.2025.103338","DOIUrl":"10.1016/j.mex.2025.103338","url":null,"abstract":"<div><div>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. Extensive evaluations on four diverse datasets demonstrate the framework’s superior performance, achieving 93 % accuracy, 87 % precision, 95 % specificity, and 91 % recall. Advanced metrics, including a Matthews Correlation Coefficient of 0.8932, validate its reliability. The proposed framework establishes a new benchmark for scalable, high-performance classification and segmentation, offering robust solutions for real-world applications and paving the way for future integration with explainable AI and real-time systems.<ul><li><span>•</span><span><div>Designed a novel hybrid framework integrating HDBN and CAEN for adaptive feature extraction and prediction.</div></span></li><li><span>•</span><span><div>Proposed dynamic prediction aggregation and optimization strategies enhancing robustness across diverse data scenarios.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103338"},"PeriodicalIF":1.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parkinson’s disease detection using inceptionV3: A Deep learning approach","authors":"Pallavi M. Shanthappa,&nbsp;Madhwesh Bayari,&nbsp;G.B. Abhilash,&nbsp;K.V. Gokul,&nbsp;P.J. Ashish","doi":"10.1016/j.mex.2025.103333","DOIUrl":"10.1016/j.mex.2025.103333","url":null,"abstract":"<div><div>Parkinson's disease (PD) is a neurodegenerative condition that progressively affects motor function and causes tremors, rigidity, and bradykinesia. Detection of PD at an early stage is important to ensure timely intervention and better patient outcomes. This study uses deep learning algorithms to classify spiral images traced by patients as an inexpensive diagnostic technique for the detection of PD. A database consists of spiral images drawn manually by PD patients and normal individuals, divided into training and testing sets. To discriminate between spiral drawings of Parkinsonian and healthy cases four Convolutional Neural Network (CNN) architecture like DenseNet121, InceptionV3, VGG16, and LeNet are used. Followed by transfer learning which is employed to improve model performance by extracting fine motor impairment patterns in the spirals. DenseNet121 and InceptionV3 achieve competitive performance with 98.44 % accuracy, whereas VGG16 demonstrates excellent feature extraction performance. The study emphasizes the relevance of deep learning in non-invasive PD diagnosis, as a consistent, efficient, and automated method of early detection. The future can be directed towards the combination of spiral images with other biomarkers or a broader data set with other motor measures in a wider disease assessment.<ul><li><span>•</span><span><div>The study focuses on enhancing features extraction by leveraging hybrid deep learning models, improving classification performance.</div></span></li><li><span>•</span><span><div>Implementation of features scaling leads to better model performance, with improved accuracy.</div></span></li><li><span>•</span><span><div>The comparative analysis of CNN architecture provides valuable insights into balancing computational efficiency and classification performance.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103333"},"PeriodicalIF":1.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incremental capacity analysis of battery under dynamic load conditions","authors":"Urvashi Saini ,&nbsp;Sindhuja Renganathan","doi":"10.1016/j.mex.2025.103331","DOIUrl":"10.1016/j.mex.2025.103331","url":null,"abstract":"<div><div>The inconsistent charge and discharge patterns of electric vehicle batteries, coupled with their operation across varying voltage and current levels, pose a challenge for accurate capacity and state of health (SOH) assessment. Traditional methods rely on regular calibration, requiring controlled charge and discharge cycles, which are impractical in real-world scenarios. This research demonstrates an analysis-based method to obtain labeled capacity and SOH values in such conditions. This method not only provides labeled SOH values but also extracts health features that can be used for data-driven prediction of capacity or SOH.<ul><li><span>•</span><span><div>Incremental capacity analysis (ICA) method has been presented to be used with electric vehicle (EV) battery data.</div></span></li><li><span>•</span><span><div>The approach to extract health features from a EV battery using ICA method as a function of age of the battery has been presented which can be used along with a machine learning or deep learning model.</div></span></li><li><span>•</span><span><div>State of health has been calculated for a vehicle battery using the proposed method.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103331"},"PeriodicalIF":1.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sampling and metabarcoding of arthropod environmental DNA traces from flowers","authors":"David Wari ,&nbsp;Yoshinobu Kusumoto ,&nbsp;Toshio Kitamura","doi":"10.1016/j.mex.2025.103329","DOIUrl":"10.1016/j.mex.2025.103329","url":null,"abstract":"<div><div>Screening and selection of insectary plants that promote natural enemies has been mostly approached via conventional methods that employ mundane man-hours of manual surveys, sampling, sorting, and viewing under microscope. In this digital age, mundane man-hours in ecological surveys can be approached with revolutionary sequencing technology, i.e. the Next Generation Sequencing (NGS). Ecological scientist, especially marine biologists have been tracing environmental DNA (eDNA) by utilizing the NGS technology to study/monitor micro- and macro-organisms in aquatic conditions. The eDNA technology has now been adopted by applied entomologists and ecologists to survey and monitor arthropod biodiversity through space and time. Several advancements have been made in detecting arthropod eDNA traces cryopreserved in plant tissues such as stems, branches, leaves, and flowers. Using the techniques developed thus far, we adopted, and slightly modified the method originally intended for aquatic studies to evaluate arthropod eDNA traces on flowers of flowering plants. Corroborating the method, we showed that eDNA traces washed off from floral parts revealed plethora of arthropod species. Furthermore, data sets generated from eDNA analysis may assist in improving the data gathered using conventional methods.<ul><li><span>•</span><span><div>Conventional methods used in surveying arthropod fauna (especially indigenous natural enemies) on flowering plants can be a tedious exercise. Here, we adopted and optimized the method initially designed for aquatic environmental DNA analysis to supplement conventional methods in arthropod studies.</div></span></li><li><span>•</span><span><div>The optimized method showed that traces of arthropod eDNA materials on floral parts are detectable.</div></span></li><li><span>•</span><span><div>The eDNA technology can be used to generate qualitative data, especially data on the cryptic and unknown species of flowering plant-associated-natural enemies, that can supplement the quantitative data gathered from conventional methods.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103329"},"PeriodicalIF":1.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ZnOCZnO sandwich structures: Fabrication and photocatalytic applications","authors":"Thi Ly Tran ,&nbsp;Thi Le Na Vo ,&nbsp;Hung-Anh Tran Vu ,&nbsp;Quoc Viet Ho ,&nbsp;Anh Tuan Duong ,&nbsp;Viet Huong Nguyen ,&nbsp;Huu Tuan Nguyen","doi":"10.1016/j.mex.2025.103326","DOIUrl":"10.1016/j.mex.2025.103326","url":null,"abstract":"<div><div>This study investigates the development of ZnO<img>C<img>ZnO sandwich structures using ZnO thin films fabricated via the spatial atomic layer deposition (SALD) technique under atmospheric pressure. Carbon powders obtained from candle soot were introduced to modify the structural, optical, and photocatalytic properties of ZnO. The influence of this carbon layer on the structural, optical, and photocatalytic characteristics of the materials was comprehensively analyzed. The results indicate that incorporating carbon significantly enhances light absorption and charge carrier separation, leading to superior photocatalytic activity under UV light. The ZnO<img>C<img>ZnO structures exhibited a reduced bandgap (3.20 eV) compared to bare ZnO (3.27 eV), facilitating improved photon absorption. X-ray diffraction (XRD) analysis revealed weaker and broader peaks in ZnO<img>C<img>ZnO, suggesting reduced crystallite size and increased structural disorder due to carbon incorporation. The photocatalytic efficiency was assessed via methylene blue degradation under UV–Vis irradiation. The ZnO<img>C<img>ZnO structures achieved an 88.2 % degradation rate after 180 min, surpassing the 62.9 % degradation observed for bare ZnO film. This enhancement is attributed to improved charge separation and suppressed recombination facilitated by the carbon interlayer. The findings highlight the potential of ZnO<img>C<img>ZnO structures for environmental remediation and energy applications.<ul><li><span>•</span><span><div>Development of ZnO<img>C<img>ZnO sandwich structures using SALD under atmospheric conditions.</div></span></li><li><span>•</span><span><div>Integration of a candle soot-derived carbon layer to improve material properties.</div></span></li><li><span>•</span><span><div>Achieved enhanced photocatalytic efficiency through enhanced surface area and improved charge separation.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103326"},"PeriodicalIF":1.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum machine learning: A comprehensive review of integrating AI with quantum computing for computational advancements","authors":"Raghavendra M Devadas ,&nbsp;Sowmya T","doi":"10.1016/j.mex.2025.103318","DOIUrl":"10.1016/j.mex.2025.103318","url":null,"abstract":"<div><div>Quantum Machine Learning (QML) is the emerging confluence of quantum computing and artificial intelligence that promises to solve computational problems inaccessible to classical systems. Using quantum principles such as superposition, entanglement, and interference, QML promises exponential speed-ups and new paradigms for data processing in machine learning tasks. This review gives an overview of QML, from advancements in quantum-enhanced classical ML to native quantum algorithms and hybrid quantum-classical frameworks. It varies from applications in optimization, drug discovery, and quantum-secured communications, showcasing how QML can change healthcare, finance, and logistics industries. Even though this approach holds so much promise, significant challenges remain to be addressed-noisy qubits, error correction, and limitations in data encoding-that must be overcome by interdisciplinary research soon. The paper tries to collate the state of the art of QML in theoretical underpinnings, practical applications, and directions into the future.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103318"},"PeriodicalIF":1.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mining privacy-preserving association rules using transaction hewer allocator and facile hash algorithm in multi-cloud environments","authors":"D. Dhinakaran ,&nbsp;S. Gopalakrishnan ,&nbsp;D. Selvaraj ,&nbsp;M.S. Girija ,&nbsp;G. Prabaharan","doi":"10.1016/j.mex.2025.103317","DOIUrl":"10.1016/j.mex.2025.103317","url":null,"abstract":"<div><div>In this era of data-driven decision-making, it is important to securely and efficiently extract knowledge from distributed datasets. However, in outsourced data for tasks like frequent itemset mining, privacy is an important issue. The difficulty is to secure sensitive data while delivering the insights of the data. First, this paper proposes a new multi-cloud approach to preserve privacy, which includes two main components, named the Transaction Hewer and Allocator module and the Facile Hash Algorithm (FHA), in extracting the frequent itemset. All these components work together to protect the privacy of the data, wherever it is, during the transmission phase or the computation phase, even if it is raw data or processed data, on the different distributed cloud platforms. The complexities involved in the mining of frequent itemsets led us to introduce the Apriori with Tid Reduction (ATid) algorithm considering scalability and computational operational improvements to the mining process due to the Tid Reduction concept. We conduct performance evaluation on several datasets and show that our proposed framework achieves higher performance than existing methods, and encryption and decryption processes reduce the computational time by up to 25 % compared to the best alternative. It also exhibits approximately 15 % reduction in communication costs and displays scalability with the growing number of transactions, compared to the state-of-the-art evaluation metrics that indicate improved communication overhead.<ul><li><span>•</span><span><div>Introduces a multi-cloud privacy framework with Facile Hash Algorithm and Transaction Hewer and Allocator.</div></span></li><li><span>•</span><span><div>Enhances scalability using ATid algorithm with Tid Reduction.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103317"},"PeriodicalIF":1.6,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep recurrent neural network with fractional addax optimization algorithm for influenza virus host prediction","authors":"Shweta Ashish Koparde ,&nbsp;Sonali Kothari ,&nbsp;Sharad Adsure ,&nbsp;Kapil Netaji Vhatkar ,&nbsp;Vinod V. Kimbahune","doi":"10.1016/j.mex.2025.103319","DOIUrl":"10.1016/j.mex.2025.103319","url":null,"abstract":"<div><div>The accurate prediction of the host of influenza viruses is a significant challenge in bioinformatics, as it is crucial for understanding viral transmission dynamics and host-virus interactions. This research<ul><li><span>•</span><span><div>Introduces a novel approach for predicting the host of influenza viruses by leveraging protein sequences.</div></span></li><li><span>•</span><span><div>Extraction of features, including sequence length, Amino Acid Composition (AAC), Dipeptide Composition (DPC), Tripeptide Composition (TPC), aromaticity, secondary structure fraction, and entropy from protein sequence.</div></span></li><li><span>•</span><span><div>Addresses the data imbalance and improves model generalization, the oversampling technique is applied for data augmentation.</div></span></li></ul></div><div>The prediction model employs a Deep Recurrent Neural Network (DRNN) optimized by Fractional Addax Optimization 34 Algorithm (FAOA), a hybrid of Addax Optimization Algorithm (AOA) and Fractional Concept (FC), designed to perform 35 influenza virus host prediction. The model's performance is evaluated using metrics, such as Matthews's Correlation 36 Coefficient (MCC), F1-Score, and Mean Squared Error (MSE). Experimental results demonstrate that the DRNN_FAOA 37 model significantly outperforms existing methods, achieving the highest MCC of 0.937, F1-Score of 0.917, and the 38 lowest MSE of 0.038. The proposed DRNN_FAOA model's ability to accurately predict influenza virus hosts suggests its 39 potential as a robust model in virus-host prediction.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103319"},"PeriodicalIF":1.6,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing an efficient protocol for RNA extraction from Morelet's crocodile caudal scute biopsies","authors":"Asela Marisol Buenfil-Rojas ,&nbsp;Mauricio González-Jáuregui ,&nbsp;Mari Ochiai ,&nbsp;Hisato Iwata","doi":"10.1016/j.mex.2025.103315","DOIUrl":"10.1016/j.mex.2025.103315","url":null,"abstract":"<div><div>Addressing the challenge of RNA extraction from hard tissues of wild animals is crucial, especially given the species' conservation and the ethical imperative to avoid lethal sampling methods. This study focuses on optimizing a protocol for non-invasive RNA extraction from the caudal scutes of <em>Crocodylus moreletii</em>, an endemic species in the Yucatan Peninsula, Mexico, highlighting the significance of conducting research in tropical areas with limited laboratory access. Accompanying with RNA preservation buffer for the scute tissue, we explored various tissue disruption and homogenization techniques to facilitate RNA isolation and purification. The purity and integrity of RNA were assessed to determine the best extraction method. The optimized protocol involved ultrasonication of 75 mg samples, followed by a 3-hour Proteinase K incubation, yielding RNA with concentrations from 18.7 to 154.7 ng/µL, satisfactory purity (260/280 ratio ∼2), and integrity (RNA Integrity Number &gt;5.5). Further validation through quantitative PCR analyses confirmed the suitability of the extracted RNA for studies on gene expression levels and were sufficient for next-generation sequencing (NGS). This protocol may provide a basis for developing similar methodologies for other non-model species with hard tissues.<ul><li><span>•</span><span><div>This study optimizes non-invasive RNA extraction from crocodile scutes, enabling conservation research and transcriptomic analysis.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103315"},"PeriodicalIF":1.6,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Evaluation of Modified Wasserstein GAN-GP and State-of-the-Art GAN Models for Synthesizing Agricultural Weed Images in RGB and Infrared Domain","authors":"Shubham Rana,&nbsp;Matteo Gatti","doi":"10.1016/j.mex.2025.103309","DOIUrl":"10.1016/j.mex.2025.103309","url":null,"abstract":"<div><div>This study investigates the application of modified Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic RGB and infrared (IR) datasets to meet the annotation requirements for wild radish (Raphanus raphanistrum). The RafanoSet dataset was used for evaluation. Traditional WGAN models struggle with vanishing gradients and poor convergence, affecting data quality. Customizations in WGAN-GP improved synthetic image quality, especially in maintaining SSIM for RGB datasets. However, generating high-quality IR images remains challenging due to spectral complexities, with lower SSIM scores. Architectural enhancements including transposed convolutions, dropout, and selective batch normalization improved SSIM scores from 0.5364 to 0.6615 for RGB and from 0.3306 to 0.4154 for IR images. This study highlights the customized model's key features:<ul><li><span>•</span><span><div>Produces a 128 × 7 × 7 tensor, optimizes feature map size for subsequent layers, with two layers using 4 × 4 kernels and 128 and 64 filters for upsampling.</div></span></li><li><span>•</span><span><div>Uses 3 × 3 kernels in all convolutional layers to capture fine-grained spatial features, incorporates batch normalization for training stability, and applies dropout to reduce overfitting and improve generalization.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103309"},"PeriodicalIF":1.6,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}