MethodsX最新文献_第2页

Exploring the efficacy of various CNN architectures in diagnosing oral cancer from squamous cell carcinoma 探索各种 CNN 架构在从鳞状细胞癌诊断口腔癌方面的功效

IF 1.6

MethodsX Pub Date : 2024-11-05 DOI: 10.1016/j.mex.2024.103034

Prerna Kulkarni , Nidhi Sarwe , Abhishek Pingale , Yash Sarolkar , Rutuja Rajendra Patil , Gitanjali Shinde , Gagandeep Kaur

{"title":"Exploring the efficacy of various CNN architectures in diagnosing oral cancer from squamous cell carcinoma","authors":"Prerna Kulkarni , Nidhi Sarwe , Abhishek Pingale , Yash Sarolkar , Rutuja Rajendra Patil , Gitanjali Shinde , Gagandeep Kaur","doi":"10.1016/j.mex.2024.103034","DOIUrl":"10.1016/j.mex.2024.103034","url":null,"abstract":"<div><div>Oral cancer can result from mutations in cells located in the lips or mouth. Diagnosing oral cavity squamous cell carcinoma (OCSCC) is particularly challenging, often occurring at advanced stages. To address this, computer-aided diagnosis methods are increasingly being used. In this work, a deep learning-based approach utilizing models such as VGG16, ResNet50, LeNet-5, MobileNetV2, and Inception V3 is presented. NEOR and OCSCC datasets were used for feature extraction, with virtual slide images divided into tiles and classified as normal or squamous cell cancer. Performance metrics like accuracy, F1-score, AUC, precision, and recall were analyzed to determine the prerequisites for optimal CNN performance. The proposed CNN approaches were effective for classifying OCSCC and oral dysplasia, with the highest accuracy of 95.41 % achieved using MobileNetV2.</div></div><div><h3>Key findings</h3><div>Deep learning models, particularly MobileNetV2, achieved high classification accuracy (95.41 %) for OCSCC.</div><div>CNN-based methods show promise for early-stage OCSCC and oral dysplasia diagnosis. Performance parameters like precision, recall, and F1-score help optimize CNN model selection for this task.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103034"},"PeriodicalIF":1.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655311","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

Development of rapid, efficient and cost effective screening technique for testing arecanut against Phytophthora meadii incitant of fruit rot disease 开发快速、高效和成本效益高的筛选技术，用于检测油甘花生对引起果实腐烂病的蚧壳虫的抗性

IF 1.6

MethodsX Pub Date : 2024-11-04 DOI: 10.1016/j.mex.2024.103032

V. H. Prathibha , N. R. Nagaraja , M. K. Rajesh , Daliyamol , K. P. Thejasri , Rajkumar , Vinayaka Hegde , Uchoi Anok

{"title":"Development of rapid, efficient and cost effective screening technique for testing arecanut against Phytophthora meadii incitant of fruit rot disease","authors":"V. H. Prathibha , N. R. Nagaraja , M. K. Rajesh , Daliyamol , K. P. Thejasri , Rajkumar , Vinayaka Hegde , Uchoi Anok","doi":"10.1016/j.mex.2024.103032","DOIUrl":"10.1016/j.mex.2024.103032","url":null,"abstract":"<div><div>To accelerate identification of disease resistant arecanut germplasm or hybrids against <em>Phytophthora</em>, it is very much imperative to develop bioassays which could differentiate resistant and susceptible cultivars efficiently. Here, a cost effective and rapid technique, called the “Detached Leaf Assay”, was developed to identify resistant germplasm at the seedling stage itself. Zoospore production in highly virulent <em>Phytophthora meadii</em> (P19) was standardized by incubating under a 12 hours light and dark regime. Zoospore suspension was adjusted to 10<sup>5</sup> spores ml<sup>-1</sup> in Petri plates. Subsequently, surface sterilized arecanut leaves were floated in zoospore suspension and incubated at temperature of 24±1 °C. Disease symptoms, including water-soaked lesions, were recorded three days after inoculation. Infection lesion increased from 1 to 7.3 cm<sup>2</sup>. The pathogen was re-isolated and confirmed with the original culture. The assay was successfully validated to screen arecanut accessions, wild types and hybrids against <em>P. meadii</em>. This technique is the first to be developed, and it is simple, cost-effective, and faster. It also provides consistent infection and could be effectively utilized to screen arecanut germplasm or hybrids against <em>P. meadii</em> in the seedling stage itself.<ul><li><span>•</span><span><div>Developed a cost effective, efficient and rapid screening technique</div></span></li><li><span>•</span><span><div>The technique was validated to identify resistant arecanut genotypes against <em>Phytophthora meadii</em> at the seedling stage.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103032"},"PeriodicalIF":1.6,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654779","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

Ocean wave prediction using Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) in Tuban Regency for fisherman safety 使用长短期记忆（LSTM）和极端梯度提升（XGBoost）预测图班地区的海浪，保障渔民安全

IF 1.6

MethodsX Pub Date : 2024-11-02 DOI: 10.1016/j.mex.2024.103031

Riswanda Ayu Dhiya'ulhaq, Anisya Safira, Indah Fahmiyah, Mohammad Ghani

{"title":"Ocean wave prediction using Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) in Tuban Regency for fisherman safety","authors":"Riswanda Ayu Dhiya'ulhaq, Anisya Safira, Indah Fahmiyah, Mohammad Ghani","doi":"10.1016/j.mex.2024.103031","DOIUrl":"10.1016/j.mex.2024.103031","url":null,"abstract":"<div><div>The fishing industry has a large role in the Indonesian economy, with potential profits in 2020 of around US$ 1.338 billion. Tuban Regency is one of the regions in East Java that contributes to the fisheries sector. Fisheries relate to the work of fishermen. Accidents in shipping are still a major concern. One of the natural factors that influence shipping accidents is the height of the waves. Fisherman safety regulations have been established by the Ministry of Maritime Affairs and Fisheries and the Meteorology, Climatology and Geophysics Agency. Apart from regulations, the results of wave height predictions using the Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) methods can help fishermen determine shipping departures, thereby reducing the risk of accidents. In this study, the Grid Search hyperparameter tuning process was used for both methods which were carried out on four location coordinates. Based on the analysis results, LSTM is superior in predicting wave height for the next 30 days because it can predict wave height at all three locations, with results at the first location (RMSE 0.045; MAE 0.029; MAPE 8.671 %), second location (RMSE 0.051; MAE 0.035; MAPE 10.64 %), and third location (RMSE 0.044; MAE 0.027; MAPE 7.773 %), while XGBoost only has the best value at fourth location (RMSE 0.040; MAE 0.025; MAPE 7.286 %).<ul><li><span>•</span><span><div>Hyperparameter tuning with gridsearch is used in LSTM and XGBoost to obtain optimal accuracy</div></span></li><li><span>•</span><span><div>LSTM outperforms in three locations, while XGBoost outperforms in the fourth location.</div></span></li><li><span>•</span><span><div>Advanced prediction techniques such as LSTM and XGBoost improve fishermen's safety by providing accurate wave height estimates, thereby reducing the possibility of shipping accidents.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103031"},"PeriodicalIF":1.6,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655310","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 lncRNA-miRNA interaction analysis: Modified crosslinking and immunoprecipitation (M-CLIP) assay 优化 lncRNA-miRNA 相互作用分析：改良交联和免疫沉淀（M-CLIP）测定

IF 1.6

MethodsX Pub Date : 2024-10-30 DOI: 10.1016/j.mex.2024.103028

Revathy Nadhan , Rohini Gomathinayagam , Rangasudhagar Radhakrishnan , Ji Hee Ha , Muralidharan Jayaraman , Danny N. Dhanasekaran

{"title":"Optimizing lncRNA-miRNA interaction analysis: Modified crosslinking and immunoprecipitation (M-CLIP) assay","authors":"Revathy Nadhan , Rohini Gomathinayagam , Rangasudhagar Radhakrishnan , Ji Hee Ha , Muralidharan Jayaraman , Danny N. Dhanasekaran","doi":"10.1016/j.mex.2024.103028","DOIUrl":"10.1016/j.mex.2024.103028","url":null,"abstract":"<div><div>Defining lncRNA-miRNA interactions is critical for understanding their roles in cellular signaling and cancer biology. Capturing these interactions is challenging due to the inherent instability of RNAs. Our study focuses on the long non-coding RNA (lncRNA) UCA1, exploring its role in ovarian cancer progression through interactions with microRNAs (miRNAs). We hypothesized that UCA1 acts as a competing endogenous RNA (ceRNA), sequestering let-7 miRNAs to modulate the expression of let-7 targets, thereby driving cancer progression. Typically, miRNAs associate with ribonucleoprotein complexes that include Ago2 protein, pivotal in mediating miRNA activity and stability. Analyzing these complexes has proven effective in identifying lncRNAs and their miRNA partners. Inspired by previous RNA-protein crosslinking methodologies, we developed the Modified Crosslinking and Immunoprecipitation (M-CLIP) assay to capture UCA1-let-7 miRNA interactions through immunoprecipitation of Ago2, followed by qRT-PCR to detect the bound UCA1 and its associated let-7 miRNAs. This method includes:<ul><li><span>•</span><span><div>Formaldehyde-based crosslinking followed by cell lysis</div></span></li><li><span>•</span><span><div>Immunoprecipitation and isolation of RNAs bound to bait proteins</div></span></li><li><span>•</span><span><div>Characterization of bound lncRNA and target miRNAs</div></span></li></ul>Our findings demonstrate the efficacy of the M-CLIP assay in identifying UCA1-let-7 interactions, providing a robust tool to elucidate how UCA1 and similar lncRNAs influence cancer progression through miRNA sequestration.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103028"},"PeriodicalIF":1.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655308","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

A GIS-toolbox for a landscape structure based Wind Erosion Risk Assessment (WERA) 基于景观结构的风蚀风险评估（WERA）地理信息系统工具箱

IF 1.6

MethodsX Pub Date : 2024-10-30 DOI: 10.1016/j.mex.2024.103006

Roger Funk, Lidia Völker

{"title":"A GIS-toolbox for a landscape structure based Wind Erosion Risk Assessment (WERA)","authors":"Roger Funk, Lidia Völker","doi":"10.1016/j.mex.2024.103006","DOIUrl":"10.1016/j.mex.2024.103006","url":null,"abstract":"<div><div>The landscape structure influences the local wind field by lowering the wind speed and thus reducing the wind erosion risk. An important parameter is the height of each landscape element, as this determines the length of wind protection behind it. Further determining parameters are the wind speeds above a threshold value for initiating wind erosion and the corresponding wind directions. The presented method combines heights of landscape elements and the directional transport capacities of erosive wind speeds to derive a map of the spatial wind speed reduction by landscape structures. This map can be combined with the soil-derived erodibility map to get finally a wind erosion risk map which includes landscape effects.<ul><li><span>•</span><span><div>The ArcGIS toolbox “WERA” allows a detailed analysis of the landscape structure on wind erosion processes,.</div></span></li><li><span>•</span><span><div>The method allows both the identification of risk areas and needs for additional plantings of LE.</div></span></li><li><span>•</span><span><div>The area-specific query determines the wind erosion risk for field blocks, districts or municipalities.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103006"},"PeriodicalIF":1.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586510","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

ViT-HHO: Optimized vision transformer for diabetic retinopathy detection using Harris Hawk optimization ViT-HHO：利用哈里斯-霍克优化技术检测糖尿病视网膜病变的优化视觉转换器

IF 1.6

MethodsX Pub Date : 2024-10-23 DOI: 10.1016/j.mex.2024.103018

Vishal Awasthi , Namita Awasthi , Hemant Kumar , Shubhendra Singh , Prabal Pratap Singh , Poonam Dixit , Rashi Agarwal

{"title":"ViT-HHO: Optimized vision transformer for diabetic retinopathy detection using Harris Hawk optimization","authors":"Vishal Awasthi , Namita Awasthi , Hemant Kumar , Shubhendra Singh , Prabal Pratap Singh , Poonam Dixit , Rashi Agarwal","doi":"10.1016/j.mex.2024.103018","DOIUrl":"10.1016/j.mex.2024.103018","url":null,"abstract":"<div><div>Diabetic retinopathy (DR) is a significant cause of vision impairment globally, emphasizing the importance of timely and precise detection to prevent severe consequences. This study presents an optimized Vision Transformer (ViT) model that incorporates Harris Hawk Optimization (HHO) to improve the automated detection of diabetic retinopathy (DR). The ViT architecture utilizes self-attention mechanisms to capture local and global features in retinal images. Additionally, HHO optimizes key hyperparameters to maximize the performance of the model. The proposed ViT-HHO model achieved exceptional performance on the APTOS-2019 and IDRiD datasets. Specifically, it achieved 99.83 % accuracy, 99.78 % sensitivity, 99.85 % specificity, and 99.80 % AUC-ROC on the APTOS-2019 dataset, surpassing traditional CNNs and alternative optimization techniques. The model exhibited strong generalization on the IDRiID dataset, achieving an accuracy of 99.11 % and an AUC-ROC of 99.12 %. The ViT-HHO model demonstrates the potential for enhancing the clinical detection of diabetic retinopathy (DR), providing high precision and reliability.<ul><li><span>•</span><span><div>An optimized Vision Transformer (ViT) model was developed using HHO for improved detection of Diabetic Retinopathy (DR).</div></span></li><li><span>•</span><span><div>The model was validated on the APTOS-2019 and IDRiID datasets, demonstrating superior accuracy and AUC-ROC metrics.</div></span></li><li><span>•</span><span><div>The model's generalization and robustness were demonstrated through comprehensive performance evaluations.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103018"},"PeriodicalIF":1.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529066","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

The TOPSIS method: Figuring the landslide susceptibility using Excel and GIS TOPSIS 方法：使用 Excel 和 GIS 计算滑坡易发性

IF 1.6

MethodsX Pub Date : 2024-10-17 DOI: 10.1016/j.mex.2024.103005

Jonmenjoy Barman , Brototi Biswas , Syed Sadath Ali , Mohamed Zhran

{"title":"The TOPSIS method: Figuring the landslide susceptibility using Excel and GIS","authors":"Jonmenjoy Barman , Brototi Biswas , Syed Sadath Ali , Mohamed Zhran","doi":"10.1016/j.mex.2024.103005","DOIUrl":"10.1016/j.mex.2024.103005","url":null,"abstract":"<div><div>The current study introduced Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to enhance landslide susceptibility. It determines the relative distance of each alternative from the ideal best and ideal worst value. The ArcGIS environment was used to prepare eleven landslide conditioning factors, while raster values were extracted for the decision matrix preparation. We utilized subjective expert judgment to create a weighted matrix that considers the roles of each conditioning component. In addition, a Euclidean distance was measured from each alternative to the ideal best and worst values. The relative closeness value (R<sub>i</sub>) has been used to prepare the landslide susceptibility index by the inverse distance weighting (IDW) interpolation. Furthermore, the precision of the landslide susceptibility was justified by area under curve-receiver operating characteristic (AUC-ROC) which was 0.987. Hence, multi-criteria decision-making (MCDM) techniques like the TOPSIS method are very useful for natural hazard mapping.</div><div><ul><li><span>•</span><span><div>The simplified TOPSIS approach described by Hwang and Yoon (1981) is applied in this study. The criteria have been categorized and assigned weights based on expert judgment and previously published material.</div></span></li><li><span>•</span><span><div>The TOPSIS approach and GIS integration has significantly enhanced the creation of a landslide susceptibility map for a sensitive area.</div></span></li><li><span>•</span><span><div>The method is easiest and suitable for short term operation research.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103005"},"PeriodicalIF":1.6,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529067","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

Standardized lab-scale production of the recombinant fusion protein HUG for the nanoscale analysis of bilirubin 用于胆红素纳米级分析的重组融合蛋白 HUG 的标准化实验室规模生产

IF 1.6

MethodsX Pub Date : 2024-10-17 DOI: 10.1016/j.mex.2024.103001

Paola Sist, Suman Saeed, Federica Tramer, Antonella Bandiera, Sabina Passamonti

引用次数: 0

Automated prediction of phosphorus concentration in soils using reflectance spectroscopy and machine learning algorithms 利用反射光谱学和机器学习算法自动预测土壤中的磷浓度

IF 1.6

MethodsX Pub Date : 2024-10-15 DOI: 10.1016/j.mex.2024.102996

Fabio Eliveny Rivadeneira-Bolaños , Sandra Esperanza Nope-Rodríguez , Martha Isabel Páez-Melo , Carlos Rafael Pinedo-Jaramillo

{"title":"Automated prediction of phosphorus concentration in soils using reflectance spectroscopy and machine learning algorithms","authors":"Fabio Eliveny Rivadeneira-Bolaños , Sandra Esperanza Nope-Rodríguez , Martha Isabel Páez-Melo , Carlos Rafael Pinedo-Jaramillo","doi":"10.1016/j.mex.2024.102996","DOIUrl":"10.1016/j.mex.2024.102996","url":null,"abstract":"<div><div>A method is presented for predicting total phosphorus concentration in soils from Santander de Quilichao, Colombia, using a UV-VIS V-750 Spectrophotometer and machine learning techniques. A total of 152 soil samples, prepared with varying proportions of P<sub>2</sub>O<sub>5</sub> fertilizer and soil, were analyzed, obtaining reflectance spectra in the 200 to 900 nm range with 3501 wavelengths. Additionally, 152 laboratory results of total phosphorus concentration were used to train the prediction model. The spectra were filtered using a Savitzky-Golay filter. Key wavelengths were identified using Variable Importance in Projection - Partial Least Squares (VIP-PLS) and Random Forest (RF), reducing the spectral bands to 1085. Principal Component Analysis (PCA) further reduced data dimensionality. A feedforward artificial neural network was then trained to predict phosphorus concentration. This method is faster than traditional lab tests by leveraging advanced data analysis and machine learning, offering results in less time. While sample preparation remains consistent with standard spectroscopic analysis, the value added by the proposed method lies in its data processing and interpretation. Currently applied to a single soil type, future improvements will include more soil types and other macronutrients, enhancing nutrient management in agriculture. Accurate macronutrient measurements aid in better fertilizer uses planning.</div><div>• Filtering spectra and determining relevant wavelengths using VIP-PLS and RF.</div><div>• Dimensionality reduction with PCA.</div><div>• Training feedforward artificial neural networks.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 102996"},"PeriodicalIF":1.6,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529065","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

A method to improve binary forecast skill verification 改进二进制预报技能验证的方法。

IF 1.6

MethodsX Pub Date : 2024-10-15 DOI: 10.1016/j.mex.2024.103010

Thitithep Sitthiyot , Kanyarat Holasut

{"title":"A method to improve binary forecast skill verification","authors":"Thitithep Sitthiyot , Kanyarat Holasut","doi":"10.1016/j.mex.2024.103010","DOIUrl":"10.1016/j.mex.2024.103010","url":null,"abstract":"<div><div>To overcome the limitations of existing deterministic binary forecast skill verification methods that award a perfect score for forecasting events considered easy to forecast, an improvement factor is introduced. It comprises two components which are 1) a measure of the ease with which an event can be accurately forecasted and 2) a measure of frequency of event. By using two hypothetical datasets, this study demonstrates that an improvement factor could enhance the performance of existing deterministic binary forecast skill verification methods by awarding score that is close to score for no-skill forecast for the perfect forecasts of events considered easy to forecast. In addition, the forecast and actual data on annual inflation rate are used to demonstrate how an improvement factor could be used together with the existing deterministic binary forecast skill verification methods in order to assess skills of the forecasters in practice.<ul><li><span>•</span><span><div>Existing deterministic binary forecast skill verification methods fail to award correct score for events considered easy to forecast.</div></span></li><li><span>•</span><span><div>An improvement factor is developed in order to enhance performance of existing deterministic binary forecast skill verification methods.</div></span></li><li><span>•</span><span><div>Hypothetical and empirical data are used to validate how an improvement factor works.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103010"},"PeriodicalIF":1.6,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11513477/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142522301","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