Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers by S. B. G. Tilak Babu, Ch Srinivasa Rao

“…The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors, such as local ternary pattern, local phase quantization, local Gabor binary pattern histogram sequence, Weber local descriptor, and local monotonic pattern, and classifiers such as optimized support vector machine and optimized NBC. …”
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Using Features Extracted From Upper Limb Reaching Tasks to Detect Parkinson’s Disease by Means of Machine Learning Models by Giuseppe Cesarelli, Leandro Donisi, Francesco Amato, Maria Romano, Mario Cesarelli, Giovanni D'Addio, Alfonso M. Ponsiglione, Carlo Ricciardi

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Compression Sensitivity of the Burrows–Wheeler Transform and Its Bijective Variant by Hyodam Jeon, Dominik Köppl

“…The Burrows–Wheeler Transform (BWT) is a widely used reversible data compression method, forming the foundation of various compression algorithms and indexing structures. Prior research has analyzed the sensitivity of compression methods and repetitiveness measures to single-character edits, particularly in binary alphabets. …”
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MedSegBench: A comprehensive benchmark for medical image segmentation in diverse data modalities by Zeki Kuş, Musa Aydin

“…The datasets and source code are publicly available, encouraging further research and development in medical image analysis.…”
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Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics. by Alexandria M Jensen, Peter DeWitt, Brianne M Bettcher, Julia Wrobel, Katerina Kechris, Debashis Ghosh

“…Modeling the network topology of the human brain within the mesoscale has become an increasing focus within the neuroscientific community due to its variation across diverse cognitive processes, in the presence of neuropsychiatric disease or injury, and over the lifespan. Much research has been done on the creation of algorithms to detect these mesoscopic structures, called communities or modules, but less has been done to conduct inference on these structures. …”
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Practicality of training a quantum-classical machine in the noisy intermediate-scale quantum era by Tarun Dutta, Alex Jin, Clarence Liu Huihong, José Ignacio Latorre, Manas Mukherjee

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Validation of Self-reported Medical Condition in the Taiwan Biobank by Chi-Shin Wu, Le-Yin Hsu, Chen-Yang Shen, Wei J. Chen, Shi-Heng Wang

“…These findings underscore the need for further investigation, especially when these variables are crucial to research objectives. Integrating complementary databases, such as clinical diagnoses, prescription records, and medical procedures, can enhance accuracy through customized algorithms based on disease categories and participant characteristics and optimize sensitivity or positive predictive values to align with specific research objectives.…”
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A Novel Graphical Technique for Combinational Logic Representation and Optimization by Vedhas Pandit, Björn Schuller

“…The method is graphical in nature and provides complete ‘‘implementation-free” description of the logical functions, similar to binary decision diagrams (BDDs) and Karnaugh-maps (K-maps). …”
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Key Generation and Testing Based on Biometrics by Alaa AbdulRaheeM, shahd Abdulrhman Hasso

“…Iris recognition systems have received significant attention in biometrics for their ability to provide robust criteria for identifying individuals, thanks to the rich texture of the iris. In this research, the key generation process was created by converting biometrics (the iris) into a digital representation (a set of binary numbers from the two iris) that can be used in the encryption process. …”
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Influence of Explanatory Variable Distributions on the Behavior of the Impurity Measures Used in Classification Tree Learning by Krzysztof Gajowniczek, Marcin Dudziński

“…These probability distributions include the normal, Cauchy, uniform, exponential, and two beta distributions. This research assumes that the values of the binary responses are generated from the logistic regression model. …”
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Robust fault detection and classification in power transmission lines via ensemble machine learning models by Tahir Anwar, Chaoxu Mu, Muhammad Zain Yousaf, Wajid Khan, Saqib Khalid, Ahmad O. Hourani, Ievgen Zaitsev

“…Leveraging a comprehensive dataset of diverse fault scenarios, various machine learning algorithms—including Random Forest (RF), K-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM) networks—are evaluated. …”
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Identifying Suicidal Ideation Through Automatic Extraction of Emotional Traces in Suicide Notes by Angel Hernandez-Castaneda, Rene Arnulfo Garcia-Hernandez, Yulia Ledeneva

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Shot based keyframe extraction using edge-LBP approach by H.M. Nandini, H.K. Chethan, B.S. Rashmi

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An Interpretable Machine Learning-Based Hurdle Model for Zero-Inflated Road Crash Frequency Data Analysis: Real-World Assessment and Validation by Moataz Bellah Ben Khedher, Dukgeun Yun

“…This study validates the model’s performance with real-world crash data from 2011 to 2015 in South Korea, demonstrating superior accuracy in both the classification and regression stages compared to other machine learning algorithms and traditional models. These findings have significant implications for traffic safety research and policymaking, offering stakeholders a more accurate and interpretable tool for crash data analysis to develop targeted safety interventions.…”
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Terahertz Meta-Holograms Reconstruction Based on Compressed Sensing by Mengyuan Hu, Zhen Tian, Xieyu Chen, Xingye Yang, Zhihao Yi, Qiu Wang, Chunmei Ouyang, Jianqiang Gu, Jiaguang Han, Weili Zhang

“…At present, most single-pixel terahertz imaging developments are based on simple metal samples, researchers rarely study the reconstruction of complex structural samples with large attenuation in the terahertz domain, such as metasurface holographic images. …”
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Effectiveness of machine learning models in diagnosis of heart disease: a comparative study by Waleed Alsabhan, Abdullah Alfadhly

“…Our study employs a wide range of ML algorithms, such as Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neibors (KNN), AdaBoost (AB), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), CatBoost (CB), Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN) to assess the predictive performance of these algorithms in the context of heart disease detection. …”
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A rapid, affordable, and reliable method for profiling microbiome biomarkers from fecal images by Donghyeok Lee, Annemiek Maaskant, Huy Ngo, Roy C. Montijn, Jaco Bakker, Jan A.M. Langermans, Evgeni Levin

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Assessing reading fluency in elementary grades: A machine learning approach by Gabriel Candido da Silva, Rodrigo Lins Rodrigues, Américo N. Amorim, Lieny Jeon, Emilia X.S. Albuquerque, Vanessa C. Silva, Vinícius F. da Silva, André L.A. Pinheiro, João P.J.R. Nunes, Suzana X.M.G. de Souza, Maxsuel S. Silva, Igor Mauro, Alexandre Magno Andrade Maciel

“…Audio recordings from 2nd and 3rd grade students across 144 Brazilian schools were transcribed using the advanced Whisper ASR system, enabling automated extraction of fluency features. The research objective was to determine which algorithm best predicts fluency, considering diverse evaluation setups including binary classification (fluent versus non-fluent), multiclass classification (differentiated fluency levels), and regression analysis to estimate continuous fluency scores. …”
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Predictive value of ENLIGHT-DP in patients with metastatic lung adenocarcinoma treated with immune checkpoint inhibitors and platinum chemotherapy directly from histopathology slid... by Eli Pikarsky, Anna Elia, Gal Dinstag, Omer Tirosh, Tuvik Beker, Ranit Aharonov, Hovav Nechushtan, Leon Gugel, Yaron Kinar, Michael Tabi, Tzivia Gottlieb, Aron Popovtzer, Johnathan Arnon, Yakir Rottenberg, Philip Blumenfeld

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A combination of urinary biomarker panel and PancRISK score for earlier detection of pancreatic cancer: A case-control study. by Silvana Debernardi, Harrison O'Brien, Asma S Algahmdi, Nuria Malats, Grant D Stewart, Marija Plješa-Ercegovac, Eithne Costello, William Greenhalf, Amina Saad, Rhiannon Roberts, Alexander Ney, Stephen P Pereira, Hemant M Kocher, Stephen Duffy, Oleg Blyuss, Tatjana Crnogorac-Jurcevic

“…Here, we aimed to establish the accuracy of an improved panel, including REG1B instead of REG1A, and an algorithm for data interpretation, the PancRISK score, in additional retrospectively collected urine specimens. …”
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