MethodsX最新文献_第3页

Fraction collection of bioactive compounds from ion chromatography: No longer mission impossible

IF 1.6

MethodsX Pub Date : 2025-02-21 DOI: 10.1016/j.mex.2025.103243

Hans S.A. Yates , James F. Carter , Mary T. Fletcher , Viviene S. Santiago , Ondrea Thompson , Natasha L. Hungerford

引用次数: 0

Assessing banana stalk susceptibility to pathogens and their virulence

IF 1.6

MethodsX Pub Date : 2025-02-20 DOI: 10.1016/j.mex.2025.103244

T. Desmarez , P. Brat , L. Lassois , B. Barral , O. Hubert

引用次数: 0

Desing and methodological process for assessing quasi-experiments in virtual reality environments for deepfake recognition in the artificial intelligence era

IF 1.6

MethodsX Pub Date : 2025-02-20 DOI: 10.1016/j.mex.2025.103242

Alberto Sanchez-Acedo, Alejandro Carbonell-Alcocer, Manuel Gertrudix

{"title":"Desing and methodological process for assessing quasi-experiments in virtual reality environments for deepfake recognition in the artificial intelligence era","authors":"Alberto Sanchez-Acedo, Alejandro Carbonell-Alcocer, Manuel Gertrudix","doi":"10.1016/j.mex.2025.103242","DOIUrl":"10.1016/j.mex.2025.103242","url":null,"abstract":"<div><div>Nowadays, the impact of artificial intelligence tools in the professional field must be analyzed, as well as their influence on the field of journalism and information. One of the aspects that has generated most concern in this area is the use of these tools, which can generate audiovisual content, for the creation of deepfakes. This article presents the methodology used to carry out a quasi-experiment designed to study and analyze the behaviour of young people in the face of possible exposure to deepfakes generated with artificial intelligence tools, as well as their ability to identify them. The experiment is conducted in a virtual environment in which participants are immersed in an interact with the environment in which they visualize newspaper front pages that include contextual elements. Participants must review the information included in the virtual environment to determine whether the images displayed correspond to real people or people generated with artificial intelligence tools. In addition, the influence and importance of the contextual elements accompanying an image in determining whether it is fake or real is analyzed. This article aims to detail the methodology used in this experiment to promote its replicability.<ul><li><span>•</span><span><div>This article proposes the method of a detailed guide to be replicated and reproduced in future academic research to understand the media diet of different population groups.</div></span></li><li><span>•</span><span><div>Datasets are provided with results that allow for comparative, longitudinal and replication studies.</div></span></li><li><span>•</span><span><div>The A-Frame framework for the design of virtual environments is introduced and can be used for the design of quasi-experiments.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103242"},"PeriodicalIF":1.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced leukemia prediction using hybrid ant colony and ant lion optimization for gene selection and classification

IF 1.6

MethodsX Pub Date : 2025-02-20 DOI: 10.1016/j.mex.2025.103239

Santhakumar D , Gnanajeyaraman Rajaram , Elankavi R , Viswanath J , Govindharaj I , Raja J

{"title":"Enhanced leukemia prediction using hybrid ant colony and ant lion optimization for gene selection and classification","authors":"Santhakumar D , Gnanajeyaraman Rajaram , Elankavi R , Viswanath J , Govindharaj I , Raja J","doi":"10.1016/j.mex.2025.103239","DOIUrl":"10.1016/j.mex.2025.103239","url":null,"abstract":"<div><div>Gene selection plays a crucial role in the pre-processing of microarray data, aiming to identify a small set of genes that enhances classification accuracy and reduces costs. Traditional methods, such as Genetic Algorithms (GA) and Maximum Relevance Minimum Redundancy (MRMR), have been widely used, but bio-inspired algorithms like Ant Colony Optimization (ACO) and Ant Lion Optimizer (ALO) have shown promising results. These algorithms are based on natural processes: ACO mimics the foraging behavior of ants, while ALO models the hunting strategy of ant-lion larvae. However, both approaches face challenges like premature convergence and inefficient feature space mapping when used individually. To address these issues, this work introduces a hybrid ACO-ALO method, combining the strengths of both algorithms. The proposed hybrid approach enhances feature selection by improving accuracy, reducing computational complexity, and boosting classifier performance. The proposed model, which identifies the optimal feature set for classification using Support Vector Machine (SVM), has achieved an impressive prediction accuracy of 93.94 %. Results on microarray datasets for leukemia prediction show that the hybrid approach outperforms other methods in terms of both effectiveness and efficiency. This work demonstrates the potential of hybrid optimization techniques in bioinformatics for better gene selection and cancer diagnosis.<ul><li><span>•</span><span><div>Hybrid ACO-ALO approach combines strengths of both algorithms for better feature selection.</div></span></li><li><span>•</span><span><div>Enhances classifier performance while reducing computational complexity.</div></span></li><li><span>•</span><span><div>Outperforms traditional methods on leukemia prediction datasets.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103239"},"PeriodicalIF":1.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing SPI and SPEI for drought forecasting through the power law process: A case study in South Sulawesi, Indonesia

IF 1.6

MethodsX Pub Date : 2025-02-19 DOI: 10.1016/j.mex.2025.103235

Nurtiti Sunusi, Nur Hikmah Auliana

{"title":"Assessing SPI and SPEI for drought forecasting through the power law process: A case study in South Sulawesi, Indonesia","authors":"Nurtiti Sunusi, Nur Hikmah Auliana","doi":"10.1016/j.mex.2025.103235","DOIUrl":"10.1016/j.mex.2025.103235","url":null,"abstract":"<div><div>This study presents a method for assessing drought events by integrating Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) into the Power Law Process (PLP) model. The method begins with identifying drought events based on SPI and SPEI, followed by the Cramér–von Mises goodness-of-fit test to ensure the drought data meets PLP assumptions. Parameter estimation is performed using Maximum Likelihood Estimation (MLE) with a time-truncated approach, treating drought as a random process within a defined observation period. Model validation is conducted by comparing actual drought events with predictions from the cumulative PLP function, while event probabilities are determined using the Nonhomogeneous Poisson Process. Applied to 24 regencies/cities in South Sulawesi, the method showed that 14 regions fit the PLP based on SPI, and 13 regions based on SPEI. Predictions indicate that over the next 12 months, drought will occur for one month based on SPI and two months based on SPEI. This method contributes to the development of drought monitoring and early warning systems, supporting mitigation and adaptation strategies in South Sulawesi.</div><div>The main contributions of this study include:<ul><li><span>•</span><span><div>The development of a novel methodological framework by integrating SPI and SPEI into the PLP for drought analysis</div></span></li><li><span>•</span><span><div>Practical applications in drought early warning systems and drought risk management in South Sulawesi</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103235"},"PeriodicalIF":1.6,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating LiDAR technology for accurate measurement of tree metrics and carbon sequestration

IF 1.6

MethodsX Pub Date : 2025-02-18 DOI: 10.1016/j.mex.2025.103237

Suradet Tantrairatn , Auraluck Pichitkul , Nutchanan Petcharat , Pawarut Karaked , Atthaphon Ariyarit

{"title":"Evaluating LiDAR technology for accurate measurement of tree metrics and carbon sequestration","authors":"Suradet Tantrairatn , Auraluck Pichitkul , Nutchanan Petcharat , Pawarut Karaked , Atthaphon Ariyarit","doi":"10.1016/j.mex.2025.103237","DOIUrl":"10.1016/j.mex.2025.103237","url":null,"abstract":"<div><div>Carbon credits play a crucial role in mitigating climate change by incentivizing reductions in greenhouse gas emissions and providing a measurable way to balance carbon dioxide output, fostering sustainable environmental practices. However, conventional methods of measuring carbon credits are often time-consuming and lack accuracy. This research examines carbon credit measurement in a 40 × 40 <em>m<sup>2</sup></em> rubber forest, evaluating the effectiveness of LiDAR technology in measuring Tree Height (TH) and Diameter at Breast Height (DBH) using a dataset of 100 samples. The method is as follows:<ul><li><span>•</span><span><div>Three measurement methods were compared: conventional techniques using diameter tape and hypsometers, manual LiDAR measurements, and automated measurements using 3D Forest Inventory software with the CloudCompare plugin.</div></span></li><li><span>•</span><span><div>The Mean Absolute Percentage Error (MAPE) for carbon sequestration was 4.276 % for manual LiDAR measurements and 6.901 % for the 3D Forest Inventory method.</div></span></li><li><span>•</span><span><div>Root Mean Square Error (RMSE) values for carbon sequestration using LiDAR measurements were 33.492 kgCO<sub>2</sub>e, whereas RMSE values for the 3D Forest Inventory method were significantly higher. This indicates that manual LiDAR measurements are more accurate and consistent, while the higher RMSE in the 3D Forest Inventory method reflects greater variability and potential estimation errors.</div></span></li></ul></div><div>The findings suggest that LiDAR technology, particularly manual measurements, provides a reliable and efficient alternative for carbon sequestration assessments in forest management.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103237"},"PeriodicalIF":1.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An enhanced light weight face liveness detection method using deep convolutional neural network

IF 1.6

MethodsX Pub Date : 2025-02-17 DOI: 10.1016/j.mex.2025.103229

Swapnil R. Shinde , Anupkumar M. Bongale , Deepak Dharrao , Sudeep D. Thepade

{"title":"An enhanced light weight face liveness detection method using deep convolutional neural network","authors":"Swapnil R. Shinde , Anupkumar M. Bongale , Deepak Dharrao , Sudeep D. Thepade","doi":"10.1016/j.mex.2025.103229","DOIUrl":"10.1016/j.mex.2025.103229","url":null,"abstract":"<div><div>Authentication plays a pivotal role in contemporary security frameworks, with various methods utilized including passwords, hardware tokens, and biometrics. Biometric authentication and face recognition hold significant application potential, albeit susceptible to forgery, termed as face spoofing attacks. These attacks, encompassing 2D and 3D modalities, pose challenges through fake photos, warped images, video displays, and 3D masks. The existing counter measures are attack specific and use complex architecture adding to the computational cost. The deep transfer learning models such as AlexNet, ResNet, VGG, and Inception V3 can be used, but they are computationally expensive. This article proposes LwFLNeT, a lightweight deep CNN method that leverages parallel dropout layers to prevent over fitting and achieves excellent performance on 2D and 3D face spoofing datasets. The proposed methods is validated through the Cross-dataset train test evaluation. The methodology proposed in the article has the following key contributions:<ul><li><span>•</span><span><div>Design of Light Weight Dual Stream CNN architecture with a parallel dropout layer to minimize over fitting issue.</div></span></li><li><span>•</span><span><div>Design of Generalized and Robust deep CNN architecture that detects both 2D and 3D attacks with higher efficiency compared to existing methodology.</div></span></li><li><span>•</span><span><div>Method validation done with State-of-the-Art methods using the standard performance metrics for face spoofing attack detection.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103229"},"PeriodicalIF":1.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Retinal fundus imaging-based diabetic retinopathy classification using transfer learning and fennec fox optimization

IF 1.6

MethodsX Pub Date : 2025-02-17 DOI: 10.1016/j.mex.2025.103232

Indresh Kumar Gupta , Shruti Patil , Supriya Mahadevkar , Ketan Kotecha , Awanish Kumar Mishra , Joel J.P.C. Rodrigues

{"title":"Retinal fundus imaging-based diabetic retinopathy classification using transfer learning and fennec fox optimization","authors":"Indresh Kumar Gupta , Shruti Patil , Supriya Mahadevkar , Ketan Kotecha , Awanish Kumar Mishra , Joel J.P.C. Rodrigues","doi":"10.1016/j.mex.2025.103232","DOIUrl":"10.1016/j.mex.2025.103232","url":null,"abstract":"<div><div>Diabetic retinopathy (DR) is a serious complication of diabetes that can result in vision loss if untreated, often progressing silently without warning symptoms. Elevated blood glucose levels damage the retina's microvasculature, initiating the condition. Early detection through retinal fundus imaging, supported by timely analysis and treatment, is critical for managing DR effectively. However, manually inspecting these images is a labour-intensive and time-consuming process, making computer-aided diagnosis (CAD) systems invaluable in supporting ophthalmologists.</div><div>This research introduces the Fundus Imaging Diabetic Retinopathy Classification using Deep Learning and Fennec Fox Optimization (FIDRC-DLFFO) model, which automates the identification and classification of DR. The model integrates several advanced techniques to enhance performance and accuracy.<ul><li><span>1.</span><span><div>The proposed FIDRC-DLFFO model automates DR detection and classification by combining median filtering for noise reduction, Inception-ResNet-v2 for feature extraction, and a gated recurrent unit (GRU) for classification.</div></span></li><li><span>2.</span><span><div>Fennec Fox Optimization (FFO) fine-tunes the GRU hyperparameters, boosting classification accuracy, with its effectiveness demonstrated on benchmark datasets.</div></span></li><li><span>3.</span><span><div>The results provide insights into the model's effectiveness and potential for real-world application.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103232"},"PeriodicalIF":1.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing multimodal scene recognition through relevant feature selection approach for scene classification

IF 1.6

MethodsX Pub Date : 2025-02-17 DOI: 10.1016/j.mex.2025.103226

Sumathi K , Pramod Kumar S , H R Mahadevaswamy , Ujwala B S

引用次数: 0

StopSpamX: A multi modal fusion approach for spam detection in social networking

IF 1.6

MethodsX Pub Date : 2025-02-16 DOI: 10.1016/j.mex.2025.103227

Dasari Siva Krishna , Gorla Srinivas

{"title":"StopSpamX: A multi modal fusion approach for spam detection in social networking","authors":"Dasari Siva Krishna , Gorla Srinivas","doi":"10.1016/j.mex.2025.103227","DOIUrl":"10.1016/j.mex.2025.103227","url":null,"abstract":"<div><div>Social networking platforms like Twitter, Instagram, Youtube, Facebook, Whatsapp have completely changed people's daily routine. Users of these social media networks have total freedom to upload anything that has political, commercial, or entertainment value. The data collected from these sources can be genuine or fake. There are no concerns or problems if the data published is true and relevant. The main difficulty arises while we deal with the spam data. So, this problem of spam data should be properly handled. In order to achieve a spam free environment, researchers have proposed numerous methods and algorithms for spam detection. Out of them few algorithms are implemented to detect the spam data in twitter.<ul><li><span>•</span><span><div>We compare the outcomes in each scenario using various state-of-the-art word embedding techniques, such as Word2Vecv, GloVe, and FastText.</div></span></li><li><span>•</span><span><div>To account for the restrictions, two deep learning hybrid fusion classifier techniques—Text-based classifier and Combined classifier—are used in this work. These classifiers are built using deep learning techniques including GRU, LSTM, and CNN.</div></span></li><li><span>•</span><span><div>These methods will be evaluated using a range of measures, including F1-score, accuracy, recall, and precision. These actions could enhance the performance of the hybrid fusion approach.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103227"},"PeriodicalIF":1.6,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0