MethodsX最新文献_第5页

Evaluating LiDAR technology for accurate measurement of tree metrics and carbon sequestration 评估激光雷达技术对树木指标和碳封存的精确测量

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

Retinal fundus imaging-based diabetic retinopathy classification using transfer learning and fennec fox optimization 基于视网膜眼底成像的糖尿病视网膜病变的转移学习和耳廓狐优化分类

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

An enhanced light weight face liveness detection method using deep convolutional neural network 一种基于深度卷积神经网络的增强轻量级人脸活动性检测方法

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

Optimizing multimodal scene recognition through relevant feature selection approach for scene classification 通过相关特征选择方法优化多模态场景识别进行场景分类

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 StopSpamX：一种用于社交网络垃圾邮件检测的多模态融合方法

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

Conversion between the numerical simulation and the calculation using the Deformation Energy Ratio approach 用变形能比法进行数值模拟与计算的转换

IF 1.6

MethodsX Pub Date : 2025-02-15 DOI: 10.1016/j.mex.2025.103221

Kirill Golubiatnikov, František Wald

引用次数: 0

Eco-friendly pH monitoring in aquaculture: A comparative study of biomass waste extracts with different setup designs 水产养殖生态友好型pH监测：不同设置设计的生物质废弃物提取物的比较研究

IF 1.6

MethodsX Pub Date : 2025-02-15 DOI: 10.1016/j.mex.2025.103223

Farhad Nadi , Elham Fazel Najafabadi , Hajar Rastegari

{"title":"Eco-friendly pH monitoring in aquaculture: A comparative study of biomass waste extracts with different setup designs","authors":"Farhad Nadi , Elham Fazel Najafabadi , Hajar Rastegari","doi":"10.1016/j.mex.2025.103223","DOIUrl":"10.1016/j.mex.2025.103223","url":null,"abstract":"<div><div>Maintaining optimal water quality is critical for the success of aquaculture operations, where pH monitoring plays a pivotal role. This study presents a novel approach for pH monitoring in aquaculture ponds by harnessing biomass-based indicators and smartphone-based colorimetric sensing using different setups designs. Three biomass indicators including red cabbage, mango leaf, and used coffee grounds extracts were tested. Standard solutions across a pH range of 1–13 were tested using four setups: black and white polypropylene enclosures, a polyethylene pipe assembly, and a polystyrene tray configuration. The polystyrene tray configuration was determined to be the most effective, as its longer light path (4.5 cm) significantly enhanced color visibility and produced more vibrant color changes, making it ideal for further investigations. The method is as follows:<ul><li><span>•</span><span><div>Water samples were collected from aquaculture ponds. pH were analyzed using this method and standard pH meters.</div></span></li><li><span>•</span><span><div>Mango leaf extract showed strong pH sensitivity and correlation (R² =0.9654).</div></span></li><li><span>•</span><span><div>The mango leaf extract attained a quantification accuracy of 0.5 pH units within a pH range of 3–12.</div></span></li><li><span>•</span><span><div>This smartphone-based approach offers simplicity and ease of implementation empowering aquaculture farmers with a practical tool for monitoring water quality.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103223"},"PeriodicalIF":1.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454069","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

Advanced image preprocessing and context-aware spatial decomposition for enhanced breast cancer segmentation 先进的图像预处理和上下文感知空间分解增强乳腺癌分割

IF 1.6

MethodsX Pub Date : 2025-02-15 DOI: 10.1016/j.mex.2025.103224

G. Kalpana , N. Deepa , D. Dhinakaran

{"title":"Advanced image preprocessing and context-aware spatial decomposition for enhanced breast cancer segmentation","authors":"G. Kalpana , N. Deepa , D. Dhinakaran","doi":"10.1016/j.mex.2025.103224","DOIUrl":"10.1016/j.mex.2025.103224","url":null,"abstract":"<div><div>The segmentation of breast cancer diagnosis and medical imaging contains issues such as noise, variation in contrast, and low resolutions which make it challenging to distinguish malignant sites. In this paper, we propose a new solution that integrates with AIPT (Advanced Image Preprocessing Techniques) and CASDN (Context-Aware Spatial Decomposition Network) to overcome these problems. The preprocessing pipeline apply bunch of methods including Adaptive Thresholding, Hierarchical Contrast Normalization, Contextual Feature Augmentation, Multi-Scale Region Enhancement, and Dynamic Histogram Equalization for image quality. These methods smooth edges, equalize the contrasting picture and inlay contextual details in a way which effectively eliminate the noise and make the images clearer and with fewer distortions. Experimental outcomes demonstrate its effectiveness by delivering a Dice Coefficient of 0.89, IoU of 0.85, and a Hausdorff Distance of 5.2 demonstrating its enhanced capability in segmenting significant tumor margins over other techniques. Furthermore, the use of the improved preprocessing pipeline benefits classification models with improved Convolutional Neural Networks having a classification accuracy of 85.3 % coupled with AUC-ROC of 0.90 which shows a significant enhancement from conventional techniques.<ul><li><span>•</span><span><div>Enhanced segmentation accuracy with advanced preprocessing and CASDN, achieving superior performance metrics.</div></span></li><li><span>•</span><span><div>Robust multi-modality compatibility, ensuring effectiveness across mammograms, ultrasounds, and MRI scans.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103224"},"PeriodicalIF":1.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464843","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 efficient method of modulo adder design for Digital Signal Processing applications 一种用于数字信号处理的有效模加法器设计方法

IF 1.6

MethodsX Pub Date : 2025-02-15 DOI: 10.1016/j.mex.2025.103216

Subodh Kumar Singhal , Sumit Kumar , Sujit Kumar Patel , K. Anjali Rao , Gaurav Saxena

{"title":"An efficient method of modulo adder design for Digital Signal Processing applications","authors":"Subodh Kumar Singhal , Sumit Kumar , Sujit Kumar Patel , K. Anjali Rao , Gaurav Saxena","doi":"10.1016/j.mex.2025.103216","DOIUrl":"10.1016/j.mex.2025.103216","url":null,"abstract":"<div><div>Modulo adder is a widely used arithmetic component in many Digital Signal Processing (DSP) applications such as Finite Impulse Response (FIR), Infinite Impulse Response (IIR) filters, digital signal processors, image processing modules, discrete cosine transform, and cryptography. Therefore, in this paper, the critical path delay and area of modulo adder are analyzed. An optimized diminished-one modulo adder for <span><math><mrow><msup><mrow><mn>2</mn></mrow><mi>n</mi></msup><mo>+</mo><mn>1</mn></mrow></math></span> is proposed based on the analysis results.<ul><li><span>•</span><span><div>Theoretical comparison shows that the suggested modulo adder involves 23.41 % less area (transistors count) and 31.64 % less delay than the best existing design for an average bit-width.</div></span></li><li><span>•</span><span><div>Synthesis result reveals that the proposed modulo adder involves 13.71 % less area and 14.5 % less delay compared to the best existing modulo adder structure design in the literature for an average bit-width.</div></span></li><li><span>•</span><span><div>To observe the overall efficacy of the suggested modulo adder design, the area delay product (ADP) and power delay product (PDP) values of the proposed and existing modulo adder designs are computed using synthesis data. The values obtained for ADP and PDP reveal that the proposed design achieves a 26.2 % reduction in ADP and a 32.8 % improvement in PDP compared to the best available modulo-adder structure.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103216"},"PeriodicalIF":1.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464844","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 sEMG Gesture Recognition with Stacked Autoencoder Neural Network for Bionic Hand” "利用堆叠式自动编码器神经网络优化仿生手的 sEMG 手势识别"

IF 1.6

MethodsX Pub Date : 2025-02-15 DOI: 10.1016/j.mex.2025.103207

Mr. Amol Pandurang Yadav , Dr. Sandip.R. Patil

{"title":"“Optimizing sEMG Gesture Recognition with Stacked Autoencoder Neural Network for Bionic Hand”","authors":"Mr. Amol Pandurang Yadav , Dr. Sandip.R. Patil","doi":"10.1016/j.mex.2025.103207","DOIUrl":"10.1016/j.mex.2025.103207","url":null,"abstract":"<div><div>This study presents a novel deep learning approach for surface electromyography (sEMG) gesture recognition using stacked autoencoder neural network (SAE)s. The method leverages hierarchical representation learning to extract meaningful features from raw sEMG signals, enhancing the precision and robustness of gesture classification.<ul><li><span>•</span><span><div>Feature Extraction and Classification MODWT Decomposition: The sEMG signals were decomposed using the MODWT DECOMPOSITION(Maximal Overlap Discrete Wavelet Transform) to capture various frequency components.</div></span></li><li><span>•</span><span><div>Time Domain Parameters: A total of 28 features per subject were extracted from the time domain, including statistical and spectral features.</div></span></li><li><span>•</span><span><div>Classifier Evaluation: Initial evaluations involved Autoencoder and LDA (Linear Discriminant Analysis) classifiers, with Autoencoder achieving an average accuracy of 77.96 % ± 1.24, outperforming LDA's 65.36 % ± 1.09.</div></span></li></ul>Advanced Neural Network Approach: Stacked Autoencoder Neural Network: To address challenges in distinguishing similar gestures within grasp groups, a Stacked Autoencoder Neural Network was employed. This advanced neural network architecture improved classification accuracy to over 100 %, demonstrating its effectiveness in handling complex gesture recognition tasks. These findings emphasize the significant potential of deep learning models in enhancing prosthetic control and rehabilitation technologies. . To verify these findings, we developed a 3d hand module in ADAMS software that is simulated using Matlab-ADAMS cosimulation.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103207"},"PeriodicalIF":1.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471518","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