bluuebunny/crossref_metadata_2025 huggingface dataset:
Supplementary Figure S1 from Next-Generation Sequencing–Based MSI Scoring Predicts Benefit in Mismatch Repair–Deficient Tumors Treated with Nivolumab: Follow-up on NCI-MATCH Arm Z1D
Authors: Schoenfeld Jonathan D. , Azad Nilofer S. , Gross Jacob , Chen Li , Overman Michael J. , Kao Katrina , Jackson Latifa , Brunnquell Donna , Bu Xiangning , Coppola Christina , Guan Ping , Lee Jennifer , Sims David , Fuchs Rebecca , Weirather Jason L. , Pfaff Kathleen L. , Gunasti Lauren , Ranasinghe Srin , Hamilton Stanley R. , Wang Victoria , O’Dwyer Peter J. , Wu Catherine J. , Rodig Scott J. , Patton David R. , Harris Lyndsay
Published: February 2025

Subject MSI and MANTIS score correlation
Supplementary Figure S1 from Smoking-Associated Carcinogen–Induced Inflammation Promotes Lung Carcinogenesis via IRAK4 Activation
Authors: Aggarwal Ritesh K. , Sidoli Simone , Wang Jingli , Sahu Srabani , Sanawar Rahul , Gupta Varun , Aluri Srinivas , Sukrithan Vineeth , Vegivinti Charan T.R. , Zavras Phaedon D. , Verma Divij , Gordon-Mitchell Shanisha , Agarwal Beamon , Verma Tanya , Starczynowski Daniel T. , Steidl Ulrich G. , Shastri Aditi , Halmos Balazs , LaFave Lindsay M. , Cheng Haiying , Verma Amit , Zou Yiyu
Published: February 2025

Supplementary Figure S1. Uncropped Western Blots for Figure 4C data presented in the manuscript. Also see Data Availability Statement.
Supplementary Figures 1 from Circulating Tumor DNA Dynamics and Clinical Outcome in Metastatic Colorectal Cancer Patients Undergoing Front-Line Chemotherapy
Authors: Ghidini Michele , Hahne Jens Claus , Senti Chiara , Heide Timon , Proszek Paula Z. , Shaikh Ridwan , Carter Paul , Hubank Mike , Trevisani Francesco , Garrone Ornella , Cappelletti Maria Rosa , Generali Daniele , Cattaneo Monica , Gnocchi Nicoletta , Donati Gianvito , Gobbi Angela , Grizzi Giulia , Lampis Andrea , Elghadi Raghad , Tanzi Giulia , Tomasello Gianluca , Ratti Margherita , Pinato David J. , Fassan Matteo , Vlachogiannis Georgios , Sottoriva Andrea , Cortellini Alessio , Passalacqua Rodolfo , Valeri Nicola
Published: February 2025

Supplementary Figures S1-S6
Supplemental Methods 1 from Phase I/II Trial of Exportin 1 Inhibitor Selinexor plus Docetaxel in Previously Treated, Advanced KRAS-Mutant Non–Small Cell Lung Cancer
Authors: von Itzstein Mitchell S. , Burns Timothy F. , Dowell Jonathan E. , Horn Leora , Camidge D. Ross , York Sally J. , Eaton Keith D. , Kyle Kelly , Fattah Farjana , Liu Jialiang , Mu-Mosley Hong , Gupta Arjun , Nadeem Urooba , Gao Ang , Zhang Song , Gerber David E.
Published: February 2025

Supplemental Methods 1. Next-generation sequencing.
Supplemental Figure 6 from Phase I/II Trial of Exportin 1 Inhibitor Selinexor plus Docetaxel in Previously Treated, Advanced KRAS-Mutant Non–Small Cell Lung Cancer
Authors: von Itzstein Mitchell S. , Burns Timothy F. , Dowell Jonathan E. , Horn Leora , Camidge D. Ross , York Sally J. , Eaton Keith D. , Kyle Kelly , Fattah Farjana , Liu Jialiang , Mu-Mosley Hong , Gupta Arjun , Nadeem Urooba , Gao Ang , Zhang Song , Gerber David E.
Published: February 2025

Supplemental Figure 6. Post-treatment serum LDH changes.
Supplemental Figure 4 from Phase I/II Trial of Exportin 1 Inhibitor Selinexor plus Docetaxel in Previously Treated, Advanced KRAS-Mutant Non–Small Cell Lung Cancer
Authors: von Itzstein Mitchell S. , Burns Timothy F. , Dowell Jonathan E. , Horn Leora , Camidge D. Ross , York Sally J. , Eaton Keith D. , Kyle Kelly , Fattah Farjana , Liu Jialiang , Mu-Mosley Hong , Gupta Arjun , Nadeem Urooba , Gao Ang , Zhang Song , Gerber David E.
Published: February 2025

Supplemental Figure 4. Cytokine changes (post-selinexor, pre-docetaxel) significantly associated with clinical outcomes.
Supplemental Figure 3 from Phase I/II Trial of Exportin 1 Inhibitor Selinexor plus Docetaxel in Previously Treated, Advanced KRAS-Mutant Non–Small Cell Lung Cancer
Authors: von Itzstein Mitchell S. , Burns Timothy F. , Dowell Jonathan E. , Horn Leora , Camidge D. Ross , York Sally J. , Eaton Keith D. , Kyle Kelly , Fattah Farjana , Liu Jialiang , Mu-Mosley Hong , Gupta Arjun , Nadeem Urooba , Gao Ang , Zhang Song , Gerber David E.
Published: February 2025

Supplemental Figure 3. Baseline cytokines significantly associated with clinical outcomes.
Table S9 from Multiomics Reveals the Immunologic Features and the Immune Checkpoint Blockade Potential of Colorectal Medullary Carcinoma
Authors: Liu Chao , Zou Haoyi , Ruan Yuli , Fang Lin , Wang Bojun , Cui Luying , Wu Tong , Chen Zhuo , Dang Tianjiao , Lan Ya , Zhao Wenyuan , Zhang Chunhui , Meng Hongxue , Zhang Yanqiao
Published: February 2025

Supplementary Table 9. The pathologic classification method according to 2019 WHO
Table S16 from Multiomics Reveals the Immunologic Features and the Immune Checkpoint Blockade Potential of Colorectal Medullary Carcinoma
Authors: Liu Chao , Zou Haoyi , Ruan Yuli , Fang Lin , Wang Bojun , Cui Luying , Wu Tong , Chen Zhuo , Dang Tianjiao , Lan Ya , Zhao Wenyuan , Zhang Chunhui , Meng Hongxue , Zhang Yanqiao
Published: February 2025

Supplementary Table 16. Immune landscape analysis between MeC and nMeC
Table S11 from Multiomics Reveals the Immunologic Features and the Immune Checkpoint Blockade Potential of Colorectal Medullary Carcinoma
Authors: Liu Chao , Zou Haoyi , Ruan Yuli , Fang Lin , Wang Bojun , Cui Luying , Wu Tong , Chen Zhuo , Dang Tianjiao , Lan Ya , Zhao Wenyuan , Zhang Chunhui , Meng Hongxue , Zhang Yanqiao
Published: February 2025

Supplementary Table 11. Differentially expressed genes results between groups
Table S10 from Multiomics Reveals the Immunologic Features and the Immune Checkpoint Blockade Potential of Colorectal Medullary Carcinoma
Authors: Liu Chao , Zou Haoyi , Ruan Yuli , Fang Lin , Wang Bojun , Cui Luying , Wu Tong , Chen Zhuo , Dang Tianjiao , Lan Ya , Zhao Wenyuan , Zhang Chunhui , Meng Hongxue , Zhang Yanqiao
Published: February 2025

Supplementary Table 10. Onco PanScan (Tissue) gene list
Figure S6 from Multiomics Reveals the Immunologic Features and the Immune Checkpoint Blockade Potential of Colorectal Medullary Carcinoma
Authors: Liu Chao , Zou Haoyi , Ruan Yuli , Fang Lin , Wang Bojun , Cui Luying , Wu Tong , Chen Zhuo , Dang Tianjiao , Lan Ya , Zhao Wenyuan , Zhang Chunhui , Meng Hongxue , Zhang Yanqiao
Published: February 2025

sFigure6. Clonality and diversity analysis results.
Table S4 from Human Pancreatic Cancer Single-Cell Atlas Reveals Association of CXCL10+ Fibroblasts and Basal Subtype Tumor Cells
Authors: Loveless Ian M. , Kemp Samantha B. , Hartway Kailee M. , Mitchell Jacob T. , Wu Yuesong , Zwernik Samuel D. , Salas-Escabillas Daniel James , Brender Sydney , George Madison , Makinwa Yetunde , Stockdale Thais , Gartrelle Kendyll , Reddy Rohit G. , Long Daniel W. , Wombwell Allison , Clark Julie M. , Levin Albert M. , Kwon David , Huang Ling , Francescone Ralph , Vendramini-Costa Débora B. , Stanger Ben Z. , Alessio Adam , Waters Andrew M. , Cui Yuehua , Fertig Elana J. , Kagohara Luciane T. , Theisen Brian , Crawford Howard C. , Steele Nina G.
Published: February 2025

Supplemental Table S4
Table S3 from Human Pancreatic Cancer Single-Cell Atlas Reveals Association of CXCL10+ Fibroblasts and Basal Subtype Tumor Cells
Authors: Loveless Ian M. , Kemp Samantha B. , Hartway Kailee M. , Mitchell Jacob T. , Wu Yuesong , Zwernik Samuel D. , Salas-Escabillas Daniel James , Brender Sydney , George Madison , Makinwa Yetunde , Stockdale Thais , Gartrelle Kendyll , Reddy Rohit G. , Long Daniel W. , Wombwell Allison , Clark Julie M. , Levin Albert M. , Kwon David , Huang Ling , Francescone Ralph , Vendramini-Costa Débora B. , Stanger Ben Z. , Alessio Adam , Waters Andrew M. , Cui Yuehua , Fertig Elana J. , Kagohara Luciane T. , Theisen Brian , Crawford Howard C. , Steele Nina G.
Published: February 2025

Supplemental Table S3
Supplementary Table S2 from Complete Remissions of HER2-Positive Trastuzumab-Resistant Xenografts Using a Potent [225Ac]Ac-Labeled Anti-HER2 Antibody–Drug Radioconjugate
Authors: Pougoue Ketchemen Jessica , Njotu Fabrice Ngoh , Babeker Hanan , Monzer Alissar , Nwangele Emmanuel , Tikum Anjong Florence , Henning Nikita , Hassani Nava , Frye Sarah , Perron Randy , Byrne Chris , Didychuk Candice , Qi Qi , Bannister Laura , Doroudi Alireza , Fonge Humphrey
Published: February 2025

Supplementary Table S2: CBC toxicity studies of [225Ac]Ac-Macropa-T-PEG6-DM1 in non-tumor bearing Balb/C mice
Supplementary Figure S2 from Complete Remissions of HER2-Positive Trastuzumab-Resistant Xenografts Using a Potent [225Ac]Ac-Labeled Anti-HER2 Antibody–Drug Radioconjugate
Authors: Pougoue Ketchemen Jessica , Njotu Fabrice Ngoh , Babeker Hanan , Monzer Alissar , Nwangele Emmanuel , Tikum Anjong Florence , Henning Nikita , Hassani Nava , Frye Sarah , Perron Randy , Byrne Chris , Didychuk Candice , Qi Qi , Bannister Laura , Doroudi Alireza , Fonge Humphrey
Published: February 2025

Supplementary Fig. S2: Size exclusion high performance liquid chromatography (SEC-HPLC) analysis with ultraviolet (uv) detection at 280 nM showing chromatograms of trastuzumab and its conjugates. The chromatograms confirmed the chemical purity of the constructs was >95%.
Supplementary Figure S1 from Dynamic Tracking of Tumor Microenvironment Modulation Using Kaede Photoconvertible Transgenic Mice Unveils New Biological Properties of Viral Immunotherapy
Authors: Diers Anne R. , Guo Qiuchen , Li Zhi , Richardson Erin , Idris Suaad , Willis Claire , Tak Paul P. , Withers David R. , Barone Francesca
Published: February 2025

Supplemental Figure 1 shows individual tumor growth of animals treated with and without CAN-2409 and prodrug
Supplemental Figure 4 from Dynamic Tracking of Tumor Microenvironment Modulation Using Kaede Photoconvertible Transgenic Mice Unveils New Biological Properties of Viral Immunotherapy
Authors: Diers Anne R. , Guo Qiuchen , Li Zhi , Richardson Erin , Idris Suaad , Willis Claire , Tak Paul P. , Withers David R. , Barone Francesca
Published: February 2025

Supplemental Figure 4 shows the flow cytometry gating scheme applied to tumor samples
Figure 5 from Dynamic Tracking of Tumor Microenvironment Modulation Using Kaede Photoconvertible Transgenic Mice Unveils New Biological Properties of Viral Immunotherapy
Authors: Diers Anne R. , Guo Qiuchen , Li Zhi , Richardson Erin , Idris Suaad , Willis Claire , Tak Paul P. , Withers David R. , Barone Francesca
Published: February 2025

CAN-2409 + prodrug treatment reinvigorates tumor-retained, exhausted CD8+ T cells, and tumor-experienced CD8+ T cells, which egress tumors, have enhanced functionality. The effect of CAN-2409 + prodrug treatment on terminal exhaustion of CD8+ T cells was assessed in the tumor using CX3CR1 as a marker coupled with the granzyme B expression. A, Representative flow cytometry plots for total, KG+, and KR+ populations of total and TAA-specific (MC38 pentamer+) CD8+ T cells and quantification are shown in B. N = 4–6 mice per group. Two-tailed t test, *, P < 0.05.
Figure 4 from Dynamic Tracking of Tumor Microenvironment Modulation Using Kaede Photoconvertible Transgenic Mice Unveils New Biological Properties of Viral Immunotherapy
Authors: Diers Anne R. , Guo Qiuchen , Li Zhi , Richardson Erin , Idris Suaad , Willis Claire , Tak Paul P. , Withers David R. , Barone Francesca
Published: February 2025

No significant changes of CD8+ T cell in the dLN following CAN-2409 treatment. A, Quantification of total CD8+ T cell in the tumor dLN. B, Representative flow cytometry plots for KG+ and KR+ populations of CD8+ T cells and quantification are shown in C for KR+ CD8+ T cells. D, Quantification of Ki67+ percentage in the KG+CD8+T cells. E, Quantification of total TAA–specific (CD44hi MC38 pentamer+) CD8+ T cell in the tumor dLN. F, Quantification of Ki67+ percentage in TAA–specific KG+CD8+T cells. G, KR+ percent of TAA–specific (MC38 pentamer+) CD8+ T cell. All data are measured by flow cytometry. N = 4–6 mice per group. Two-tailed t test, **, P < 0.01.
Figure 3 from Dynamic Tracking of Tumor Microenvironment Modulation Using Kaede Photoconvertible Transgenic Mice Unveils New Biological Properties of Viral Immunotherapy
Authors: Diers Anne R. , Guo Qiuchen , Li Zhi , Richardson Erin , Idris Suaad , Willis Claire , Tak Paul P. , Withers David R. , Barone Francesca
Published: February 2025

Expansion of immune cells within tumors is, in part, due to CAN-2409 + prodrug–mediated stimulation of proliferation of T-cell subsets in the TME (total, neoantigen-specific, and stem-like CD8+ T cells). A, The effect of CAN-2409 + prodrug treatment on proliferative status of T-cell subsets was assessed using Ki67 as a marker. Representative flow cytometry plots for total, KG+, and KR+ populations of total, TAA–specific (MC38 pentamer+), and stem-like (PD-1+Ly108+) CD8+ T cells and quantification are shown in B–D. N = 4–6 mice per group. Two-tailed t test, *, P < 0.05; **, P < 0.01; ***, P < 0.001.
Figure 2 from Dynamic Tracking of Tumor Microenvironment Modulation Using Kaede Photoconvertible Transgenic Mice Unveils New Biological Properties of Viral Immunotherapy
Authors: Diers Anne R. , Guo Qiuchen , Li Zhi , Richardson Erin , Idris Suaad , Willis Claire , Tak Paul P. , Withers David R. , Barone Francesca
Published: February 2025

CAN-2409 + prodrug induces expansion of immune cells and neoantigen-specific T cells retained in tumors. A, Summary of MC38 implanted subcutaneous on the flank of C57BL/6 Kaede mice and photoconverted by violet light exposure. B, Study timeline for experiments assessing the effect of CAN-2409 + prodrug on TME modulation in Kaede photoconvertible mice. C, Representative flow cytometry plots showing KR+ and KG+ expression in immune subpopulations within tumors. MC38 pentamers were used to identify antigen-specific CD8+ T cells. D, Quantification of newly entering (KG+) cells for each T-cell subset. N = 4–6 mice per group. Two-tailed t test, *, P < 0.05; **, P < 0.01; ***, P < 0.001.
Predicting tourist arrivals to a tropical island using artificial intelligence
Authors: Suharto Bambang , Edi Suharno Novianto , Sinatriya Marjianto Rachman , Firdaus Aji Akbar , Suprapto Sena Sukmananda , Andria Kusuma Vicky , Amalia Sinulingga Rizky
Published: February 2025

This research leverages artificial intelligence (AI) techniques to develop a predictive model for forecasting tourist arrivals in East Java Province, Indonesia, using a comprehensive dataset encompassing historical tourism statistics from 2018 to 2020, seasonal trends, promotional campaigns, and various economic and social variables. The study evaluates three AI methodologies: artificial neural network (ANN), extreme learning machine (ELM), and Jordan recurrent neural network (JRNN), each known for their distinct strengths in processing complex data and adapting to changing trends. The comparative analysis reveals that the JRNN model outperforms others with the highest precision, achieving an average prediction deviation of just 2.98% from actual data, effectively capturing temporal and seasonal trends. The ANN follows closely with a deviation of 3.31%, showing strong capabilities in handling complex, nonlinear relationships. In contrast, the ELM, though fastest in training, exhibits a larger deviation of 10.51%, indicating a trade-off between speed and accuracy. These results highlight the potential of AI to significantly enhance the accuracy and operational efficiency of tourism forecasts, offering robust tools for stakeholders to engage in informed strategic planning and resource allocation in dynamic market conditions.
Review of recent advances in non-invasive hemoglobin estimation
Authors: Sutikno Tole , Handayani Lina , Ruliyandari Rochana , Wijaya Oktomi , Satrian Purnama Hendril , Arsadiando Watra , Pamungkas Anggit
Published: February 2025

Hemoglobin is essential for diagnosing conditions like anemia and respiratory issues. Traditionally, the assessment of hemoglobin necessitates invasive techniques that involve blood draws, which can induce discomfort and present possible complications for patients. Recent advancements in non-invasive technologies have light-emitting diode (LED) to the development of smartphone applications and machine learning algorithms that allow real-time hemoglobin level estimation, eliminating the need for blood sampling. This not only improves patient comfort but also enhances access to ongoing health monitoring. This review aims to delve into the newest developments in smartphone-oriented strategies for hemoglobin estimation, highlighting their importance within contemporary healthcare practices and the potential implications they might have for more expansive clinical applications. Technological advancements have combined smartphones and artificial intelligence (AI) for non-invasive hemoglobin estimation, offering a promising alternative to traditional methods. These solutions optimize data collection and analysis processes, enhance diagnoses' accuracy, and facilitate timely medical interventions. Advancements in technology have revolutionized medical diagnostics, particularly in estimating hemoglobin levels non-invasively. AI methodologies have demonstrated significant results in accurately forecasting hemoglobin concentrations through a variety of analytical strategies. Future research should focus on the best configurations for these networks and the physiological concepts underpinning spectral data interpretations.
Enhancing facial recognition accuracy through feature extractions and artificial neural networks
Authors: Kusnadi Adhi , Pane Ivranza Zuhdi , Tobing Fenina Adline Twince
Published: February 2025

Facial recognition is a biometric system used to identify individuals through faces. Although this technology has many advantages, it still faces several challenges. One of the main challenges is that the level of accuracy has yet to reach its maximum potential. This research aims to improve facial recognition performance by applying the discrete cosine transform (DCT) and Gaussian mixture model (GMM), which are then trained with backward propagation of errors (backpropagation) and convolutional neural networks (CNN). The research results show low DCT and GMM feature extraction accuracy with backpropagation of 4.88%. However, the combination of DCT, GMM, and CNN feature extraction produces an accuracy of up to 98.2% and a training time of 360 seconds on the Olivetti Research Laboratory (ORL) dataset, an accuracy of 98.9% and a training time of 1210 seconds on the Yale dataset, and 100% accuracy and training time 1749 seconds on the Japanese female facial expression (JAFFE) dataset. This improvement is due to the combination of DCT, GMM, and CNN's ability to remove noise and study images accurately. This research is expected to significantly contribute to overcoming accuracy challenges and increasing the flexibility of facial recognition systems in various practical situations, as well as the potential to improve security and reliability in security and biometrics.
A mixed integer nonlinear programming model for site-specific management zone problem
Authors: Urban-Rivero Luis Eduardo , Velasco Jonás
Published: February 2025

Precision agriculture employs sophisticated tools to optimize decision-making in farming, aiming to simultaneously improve crop yields and manage resources more effectively in a context of increasing scarcity and rising costs. A key aspect of precision agriculture is the delineation of site-specific management zones (SSMZs), which involves segmenting a field into areas that are homogeneous in terms of soil physicochemical properties. The problem of delineating SSMZ have been approached using a wide variety of methodologies, all of which, heuristic, focus on finding feasible solutions. Until this work, there was no exact algorithm or mathematical model that would allow for a point of comparison. This paper introduces a novel approach to tackle the delineation of SSMZ with orthogonal shapes through the development of a mixed integer nonlinear programming (MINLP) model. Small instances with different scenarios show the scope of the proposed approach and the significance of the results. It provides a structure for the SSMZ problem with orthogonal shapes and establishes a benchmark for evaluating the performance of heuristic solutions, metaheuristics, or hybrid approaches.
Machine learning based COVID-19 study performance prediction
Authors: Rahman Md. Ataur , Rahman Md. Sadekur , Islam Mohammad Monirul , Hasan Mahady , Habib Md. Tarek
Published: February 2025

COVID-19 has impacted education worldwide. In this troublesome situation, it is hard enough for an institution to predict a student’s performance. Students’ performance prediction has always been a complex task and this pandemic situation has led this task to be more complex. The main focus of this work is to come up with a machine learning model based on a classical machine learning technique to predict the change in students’ performance due to COVID-19. Initially, some relevant features are selected, based on which the data are collected from students of some private universities in Bangladesh. After the entire data set is formed, we preprocessed the dataset to remove redundancy and noise. These preprocessed data are used for testing and training using the proposed model. The model is extensively evaluated in this way using three separate classical machine learning techniques, namely linear regression, k-nearest neighbors (k-NN), and decision tree. Finally, the results of the entire experiment follow, demonstrating the power of the machine learning model in such an application. It is observed that the proposed model with linear regression exhibits the best performance with an R2 error of 0.07% and an accuracy of 99.84%.
Optimizing the gallstone detection process with feature selection statistical analysis algorithm
Authors: Yanto Musli , Yuhandri Yuhandri , Tajuddin Muhammad , Septiana Vina Tri
Published: February 2025

Early detection is one form of early anticipation in treating gallstone disease patients using medical images. However, the problem that exists is that there are still many shortcomings in medical images, such as noise in the image that causes the detection process to not run optimally. Based on this, this study aims to carry out the process of detecting gallstone objects in magnetic resonance cholangiopancreatography (MRCP) images by optimizing the performance of extraction techniques for feature selection. Optimization of extraction techniques in feature selection is carried out using the performance of the feature selection statistics analysis (FSSA) algorithm. The performance of the FSSA algorithm can provide improvements in the feature selection process by excelling in the performance of classification methods such as k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN), and the Pearson correlation (PC) method. Based on the tests that have been carried out, the performance of the FSSA algorithm in the detection process provides an accuracy level of 95.69%, a sensitivity of 89.65%, and a specificity of 98.43%. Overall, this study can contribute to the development of extraction and provide a significant technical impact on optimizing the gallstone detection process.
A hybrid feature selection with data-driven approach for cardiovascular disease prediction using machine learning
Authors: Shilpa Thoutireddy , Debnath Rajib
Published: February 2025

Affecting various disorders of heart and blood vessels mainly cardiovascular diseases (CVDs) is the leading cause of human mortality on the planet. A number of machine learning (ML) based supervised learning approaches existing in the literature have been found useful in the clinical decision support system (CDSS) for detecting CVDs automatically. The challenge, however, is that their performance tends to decline unless the training data is of a certain standard. Several approaches to solving this problem are known as feature selection techniques. Despite several notable advancements in the CVD modeling literature, a weak compendium of research exists in an area which supports the integration of the feature selection approach as a means of enhancing the training quality and thus the prediction accuracy. Against this background, in this paper, we proposed a framework called the cardiovascular disease prediction framework (CVDPF) that integrates ML methods. To support this, we designed and proposed a new hybrid feature selection (HFS) algorithm that aims to reduce the number of parameters. This algorithm adopts several filter methods in order to enhance its performance for the task of feature selection. To improve the prediction accuracy of CVDs, a number of ML tools using the HFS approach has been designed and is termed as machine learning based cardiovascular disease prediction (ML-CVDP). The validation of the framework and the algorithms discussed has been done on the basis of a CVD dataset. The experimental findings demonstrated that CVDPF in combination with HFS outperforms other methods of feature selection available.
A comparative analysis of optical character recognition models for extracting and classifying texts in natural scenes
Authors: Prakash Puneeth , Yeliyur Hanumanthaiah Sharath Kumar
Published: February 2025

This research introduces prior-guided dynamic tunable network (PDTNet), an efficient model designed to improve the detection and recognition of text in complex environments. PDTNet’s architecture combines advanced preprocessing techniques and deep learning methods to enhance accuracy and reliability. The study comprehensively evaluates various optical character recognition (OCR) models, demonstrating PDTNet’s superior performance in terms of adaptability, accuracy, and reliability across different environmental conditions. The results emphasize the need for a context-aware approach in selecting OCR models for specific applications. This research advocates for the development of hybrid OCR systems that leverages multiple models, aiming to arrive at a higher accuracy and adaptability in practical applications. With a precision of 85%, the proposed model showed an improved performance of 1.7% over existing state of the arts model. These findings contribute valuable insights into addressing the technical challenges of text extraction and optimizing OCR model selection for real-world scenarios.
Accuracy of neural networks in brain wave diagnosis of schizophrenia
Authors: Sukemi Sukemi , Cahyadi Gabriel Ekoputra Hartono , Samsuryadi Samsuryadi , Akbar Muhammad Agung
Published: February 2025

This research explores the application of a modified deep learning model for electroencephalography (EEG) signal classification in the context of schizophrenia diagnosis. This study aims to utilize the temporal and spatial characteristics of EEG data to improve classification accuracy. Four popular convolutional neural network (CNN) architectures, namely LeNet-5, AlexNet, VGG16, and ResNet-18, are adapted to handle 1D EEG signals. In addition, a hybrid architecture of CNN-gated recurrent unit (GRU) and CNN-long short-term memory (LSTM) is proposed to capture spatial and temporal dynamics. The model was evaluated on a dataset consisting of EEG recordings from 14 patients with paranoid schizophrenia and 14 healthy controls. The results show high accuracy and F1 scores for all modified models, with CNN-LSTM and CNN-GRU achieving the highest performance with scores of 0.96 and 0.97, respectively. Receiver operating characteristic (ROC) curves demonstrate the model's ability to distinguish between healthy controls and schizophrenia patients. The proposed model offers a promising approach for automated schizophrenia diagnosis based on EEG signals, potentially assisting clinicians in early detection and intervention. Future work will focus on larger data sets and explore transfer learning techniques to improve the generalization ability of the model.
Enhancing fire detection capabilities: Leveraging you only look once for swift and accurate prediction
Authors: Nugroho Agung , Agastya I Made Artha , Kusrini Kusrini
Published: February 2025

Detecting fires is crucial to prevent potentially catastrophic outcomes. Traditional fire detection methods, relying on electronic, chemical, or mechanical sensors, often suffer from time delays in activation due to threshold parameters. An emerging alternative utilizes artificial intelligence, particularly image-based fire detection, using convolutional neural networks (CNNs). You only look once (YOLO) is a state-of-the-art object detection framework prized for speed and real-time capabilities. In our research, we conducted multiple training experiments employing various deep neural network (DNN) architectures as feature extractors for object detection within the YOLOv5 framework. These architectures included MobileNetV3, ResNet, and CSP-Darknet53. Among these configurations, YOLOv5 with CSP-Darknet53 (scale s) emerged as the most accurate, boasting mAP@50 of 0.88 and an impressive FPS of 73, with training model size of 14.50 MB. Furthermore, we integrated the selected model with the streamlit package to create a user-friendly web application interface for fire detection testing. The resulting model demonstrates remarkable efficiency, detecting fires within 0.01 seconds. This research represents a significant advancement in fire detection technology, offering both rapid detection and enhanced accuracy, with potential applications in various settings, from indoor facilities to outdoor environments.
A transfer learning-based deep neural network for tomato plant disease classification
Authors: Lachhab Fadwa , Aboulmanadel El Mahdi
Published: February 2025

The agriculture sector plays a significant role in Morocco's economy, and tomato farming is an essential component of this industry. However, tomato plants are prone to various diseases that can adversely affect productivity and quality. A novel approach to detect tomato plant diseases is proposed int this study, by modeling and developing a transfer learning-based convolution neural network (CNN) model that processes real-time images. The model is trained and validated with a deep CNN using a private dataset of 18,159 annotated tomato leaf images collected from experimental farms over five months. The performance of our residual neural network (ResNet-50) model is evaluated using stochastic gradient descent (SGD) and adaptive moment estimation (Adam) optimizers to demonstrate superior efficiency. Farmers can simply send images of their tomato leaves through our platform, and the trained model will identify accurately the disease. The developed model demonstrates exceptional performance, achieving a 0.96 F1 score and an 97% accuracy rate when tested on a dataset generated from real-world fields. This approach not only improves disease detection but also contributes to sustainable farming practices and enhanced productivity.
Enhancing accessibility with long short-term memory-based sign language detection systems
Authors: Wadmare Jyoti , Lokare Reena , Wadmare Ganesh , Kolte Dakshita , Bhatia Kapil , Singh Jyotika , Agrawal Sakshi
Published: February 2025

Individuals who are deaf or experience difficulties with hearing and speech predominantly rely on sign language as their medium to communicate, which is not universally comprehended leading to obstacles in achieving effective communication. Advances in deep learning technologies in recent years have enabled the development of systems intended to autonomously interpret gestures in sign language and translate them into spoken language. This paper introduces a system built on deep learning methodologies for recognizing sign language. It uses long short-term memory (LSTM) architecture to distinguish and classify hand gestures which are static and dynamic. The system is divided into three primary components, including dataset collection, neural network assessment, and sign detection component that encompasses hand gesture extraction and sign language classification. The module to extract hand gestures makes use of recurrent neural networks (RNNs) for the detection and tracking of hand movements in video sequences. Another RNN that is incorporated by classification module categorizes these gestures into established sign language classes. Upon evaluation on a custom dataset, the proposed system attains an accuracy rate of 99.42%, thus making it visualize its promise as an assistive technology for handicapped hearing individuals. This system can further be enhanced by including further classes on sign language and real-time gesture interpretation.
U-Net for wheel rim contour detection in robotic deburring
Authors: Ait El Attar Hicham , Samri Hassan , Ech-Chhibat Moulay El Houssine , Mansouri Khalifa , Bahani Abderrahim , Bahrar Tarek
Published: February 2025

Automating robotic deburring in the automotive sector demands extreme precision in contour detection, particularly for complex components like wheel rims. This article presents the application of the U-Net architecture, a deep learning technique, for the precise segmentation of the outer contour of wheel rims. By integrating U-Net's capabilities with OpenCV, we have developed a robust system for wheel rim contour detection. This system is particularly well-suited for robotic deburring environments. Through training on a diverse dataset, the model demonstrates exceptional ability to identify wheel rim contours under various lighting and background conditions, ensuring sharp and accurate segmentation, crucial for automotive manufacturing processes. Our experiments indicate that our method surpasses conventional techniques in terms of precision and efficiency, representing a significant contribution to the incorporation of deep learning in industrial automation. Specifically, our method reduces segmentation errors and improves the efficiency of the deburring process, which is essential for maintaining quality and productivity in modern production lines.
A novel scalable deep ensemble learning framework for big data classification via MapReduce integration
Authors: Varadharajan Kesavan Mettur , Prem Kumar Josephine , Ashwin Nanda
Published: February 2025

Big data classification involves the systematic sorting and analysis of extensive datasets that are aggregated from a variety of sources. These datasets may include but are not limited to, electronic records, digital imaging, genetic information sequences, transactional data, research outputs, and data streams from wearable technologies and connected devices. This paper introduces the scalable deep ensemble learning framework for big data classification (SDELF-BDC), a novel methodology tailored for the classification of large-scale data. At its core, SDELF-BDC leverages a Hadoop-based map-reduce framework for feature selection, significantly reducing feature-length and enhancing computational efficiency. The methodology is further augmented by a deep ensemble model that judiciously applies a variety of deep learning classifiers based on data characteristics, thereby ensuring optimal performance. Each classifier's output undergoes a rigorous optimization-based ensemble approach for refinement, utilizing a sophisticated algorithm. The result is a robust classification system that excels in predictive accuracy while maintaining scalability and responsiveness to the dynamic requirements of big data environments. Through a strategic combination of classifiers and an innovative reduction phase, SDELF-BDC emerges as a comprehensive solution for big data classification challenges, setting new benchmarks for predictive analytics in diverse and data-intensive domains.
A comparative analysis of exponential smoothing method and deep learning models for bitcoin price prediction
Authors: Tripathy Nrusingha , Mishra Debahuti , Hota Sarbeswara , Priyadarshani Behera Mandakini , Chandra Das Gobinda , Sekhar Dalai Sasanka , Nayak Subrat Kumar
Published: February 2025

Blockchain technology is the foundation of cryptocurrencies, which are virtual currencies. The decentralized nature of cryptocurrencies has resulted in a significant reduction of central authority over them, which has implications for global trade and relations. The need for an effective model to anticipate the price of cryptocurrencies is essential due to their wide variations in value. Due to the shortcomings of conventional production forecasting, in this work, four distinct models were used. The deep learning models are the long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), and both the Facebook-Prophet and Silverkite support the exponential smoothing technique. Silverkite is designed to handle a wide range of time series forecasting tasks. Considering past bitcoin information from January 2012 to March 2021, a period of nine years, we looked at the models. The Bi-LSTM model yields a 7.073 mean absolute error (MAE) and a 3.639 root mean squared error (RMSE). The Bi-LSTM model identifies the deviations that might draw attention and avert any problems.
Squeeze-excitation half U-Net and synthetic minority oversampling technique oversampling for papilledema image classification
Authors: Wiharto Wiharto , Syaifuddin Angga Exca Pradipta
Published: February 2025

The emergence of various convolutional neural networks (CNN) architectures indicates progress in the computer vision field. However, most of the architectures have large parameters, which tends to increase the computational cost of the training process. Additionaly, imbalanced data sources are often encountered, causing the model to overfit. The aim of this study is to evaluate a new method to classify retinal fundus images from imbalanced data into the corresponding classes by using fewer parameters than the previous method. To achieve this, squeeze-excitation half U-Net (SEHUNET) architecture, a modification of half U-Net with squeeze-excite process to provide attention mechanism on each feature maps channel of the model, in combination with synthetic minority oversampling technique (SMOTE) is proposed. The test accuracy of SEHUNET is 98.52% with area under the curve of receiver operation characteristic (AUROC) of 0.999. This result outperforms the previous study that used CNN with Bayesian optimization, achieving accuracy of 95.89% and AUROC of 0.992. SEHUNET is also able to compete with the transfer learning methods used in previous research such as InceptionV3 with 96.35% accuracy, visual geometry group (VGG) with 96.8%, and ResNet with 98.63%. This performance can be achieved by SEHUNET with only 0.268 million parameters compared to the architecture parameters used in previous research ranging from 11 million to 33 million.
Deep ensemble learning with uncertainty aware prediction ranking for cervical cancer detection using Pap smear images
Authors: Sreelatha Sreelatha , Shivashetty Vrinda
Published: February 2025

This paper proposes a novel deep ensemble learning framework designed for the efficient detection and classification of cervical cancer from Pap smear images. The proposed study implements three advanced learning models namely DenseNet201, Xception, and a classical convolutional neural network (CNN) customized with optimal hyperparameters to automate feature extraction and cervical cancer detection process. The proposed study also introduces a novel ensemble learning to enhance the classification of cervical cancer. The proposed ensemble mechanism is based on the confidence aggregation followed by uncertainty quantification and prediction ranking scheme, thus ensuring that more reliable predictions have a proportionally greater influence on the final outcome. The primary goal is to leverage the collective intelligence of the ensemble in a manner that prioritizes reliability and minimizes the impact of less certain predictions. The experimental analysis is carried out on two dataset one with whole slide images (WSI) and another on cropped images. The proposed ensemble model achieves an accuracy rate 100 and 97% for dataset with WSI and with cropped images respectively.
Primary phase Alzheimer's disease detection using ensemble learning model
Authors: Dasarwar Priya , Yadav Uma , Chavhan Nekita
Published: February 2025

Alzheimer's disease (AD) is a noteworthy problem for public health. Older people are most impacted by this neurological disease. It leads to memory loss and various cognitive impairments, eventually hindering communication. As a result, research on early AD detection has intensified in recent years. In current research work, we propose an ensemble learning strategy to identify AD by classifying brain images into two groups: AD brain and normal brain. Researchers have recently explored various machine learning (ML) and deep learning techniques to improve early disease detection. Patients with AD can recover from it more successfully and with less damage if they receive early diagnosis and therapy. This research presents an ensemble learning model to predict AD using decision trees (DT), logistic regression (LR), support vector machines (SVM), and convolutional neural networks (CNN). The open access series of imaging studies (OASIS) dataset is used for model training, and performance is measured in terms of various kinds of outcome namely accuracy, precision, recall, and F1 score. Our results demonstrated that, for the AD dataset, the CNN achieved the maximum validation accuracy of 90.32%. Thus, by accurately detecting the condition, ensemble algorithms can potentially significantly reduce the annual mortality rates associated with AD.
Graph-based methods for transaction databases: a comparative study
Authors: AlZoubi Wael Ahmad , Alturani Ibrahim Mahmoud , Ali Aloglah Roba Mahmoud
Published: February 2025

There has been an increased demand for structured data mining. Graphs are among the most extensively researched data structures in discrete mathematics and computer science. Thus, it should come as no surprise that graph-based data mining has gained popularity in recent years. Graph-based methods for a transaction database are necessary to transform all the information into a graph form to conveniently extract more valuable information to improve the decision-making process. Graph-based data mining can reveal and measure process insights in a detailed structural comparison strategy that is ready for further analysis without the loss of significant details. This paper analyzes the similarities and differences among four of the most popular graph-based methods that is applied to mine rules from transaction databases by abstracting them out as a concrete high-level interface and connecting them into a common space.
Survey and comparative analysis of phishing detection techniques: current trends, challenges, and future directions
Authors: Jadhav Ashvini , Chandre Pankaj R.
Published: February 2025

In the age of digital communication, scams such as phishing continue to be a problem, necessitating the need for ever-more-advanced detection techniques to safeguard sensitive data. Examining several methods now in use, this review article groups them according to the application (email, web server, mail server, or browser-based). It explores the advantages and disadvantages of behavior-based, heuristic-based, machine learning (ML)-based, and signature-based techniques and offers a comparative evaluation of their efficacy. The essay delves deeper into the latest developments in phishing detection research, such as ML-powered social media exploration and real-time website analysis. The evaluation goes beyond just identifying detecting techniques; it also includes a data-driven analysis. In particular, random forest and support vector machines are ML algorithms that regularly produce results with high accuracy for detecting phishing attempts. Metrics like as recall, F1-score, and precision show how well these algorithms. Furthermore, specialised techniques such as heuristic-based and cantina-based approaches provide remarkable performance, underscoring the possibility of additional research in this field. Future research explores improved phishing detection through: better accuracy with ML, integrating new technologies, analyzing user behavior. A hybrid approach combining these techniques offers a stronger defense.
A portfolio optimization model for return trend rate and risk trend rate based on machine learning
Authors: Zhu Chunman , Yahya Dawod Ahmad , Xi Yu , Chen Gongsuo
Published: February 2025

This paper presents a machine learning-based portfolio optimization model alongside a trading strategy algorithm. There are two distinct steps to the approach. Firstly, the long short-term memory (LSTM) neural network model was used to predict the closing price of stocks in the following 4 days. The average rise and fall rate over these four days is then calculated as the stock's return trend rate, which can measure the direction and intensity of the stock's rise and fall. The same method is used to predict the average of the industry index's rise and fall rate over the next four days as the risk trend rate. In the second step, the improved mean–variance model (IMV) model is used to provide customers with the stock portfolio purchasing strategy based on the return trend rate and risk trend rate. The experimental results demonstrate that the approach has a certain application value and outperforms the traditional method in terms of annual returns and Sharpe ratio, using the Shanghai Stock Exchange and the Shenzhen Stock Exchange as study samples. The model shows approximately 1% improvement in prediction accuracy. The latest advancements in machine learning provide substantial prospects for tactics involving the purchase of portfolios.
A fuzzy inference system for hand injury level classification using surface electromyography signals
Authors: Enojas Mark Joseph Bullo
Published: February 2025

The surface electromyography (SEMG) is extensively used in assessing injuries in the musculoskeletal parts of the body. Integrating intelligence in such applications impacted the development of intelligent medical devices. The conventional way of assessing hand injury level is manually and subjectively done by experts to identify the type of rehabilitation program recommended to the patient. This work uses SEMG data to classify hand injury levels through a fuzzy inference system (FIS). Three of the many features of the SEMG signal were selected based on its high distinction levels, namely, the root-mean-square, enhanced mean-absolute value, and the waveform length. Segmentation through a sliding window method is used for feature extraction. The FIS rules were designed based on the assessment guide of the experts. A Mamdani-type FIS classifier was used with membership functions which are a combination of trapezoidal and triangular types. A MATLAB Simulink model was also designed to test the FIS system. The setup effectively identified injury levels through tests with a healthy subject, wherein no muscle activation means an injury, while the full fist, as a full muscle activation or healthy. In between signal values vary with different injury levels. In the future, this setup will be tested on patients in a rehabilitation clinic for validation.
Long-range radio and Internet of things-inspired smart road reflectors for smart highways
Authors: Singh Rajesh , Gehlot Anita , Akram Shaik Vaseem , Singh Vivek Kumar , Iqbal Mohammed Ismail , Mahala Rahul
Published: February 2025

The Internet of things (IoT) has been proven as an efficient technology for real-time monitoring of physical things through the Internet from any location. With the advancement in sensors and communication technologies, the implementation of IoT is adopted in wide extensions. Road reflectors on highway roads need to be automated and also powered with intelligence. With this motivation, we have proposed and implemented IoT and long range (LoRa) based architecture for the realization of smart road reflectors on the highway. To realize the proposed architecture, the hardware of the smart reflector and gateway is implemented on the university campus. During our implementation of the hardware, we observed the light intensity values that are sensed by smart reflectors on the server through LoRa and internet connectivity. In the future, we will be integrating additional sensors and also power the smart reflector with artificial intelligence to predict the fog status of a particular road.