PROACTIVE MITIGATION OF DDoS IoT-RELATED ATTACK USING MACHINE LEARNING AND SOFTWARE DEFINED NETWORKING TECHNIQUES by Emmanuel J. Ebong, Samuel N. John, Dominic S. Nyitamen, Samuel F. Kolawole

“…The large dataset was scaled down using Min Max Scaler before the Machine Learning (ML) classification stage. Four (4) ML algorithms namely, Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF) were used to classify the models. …”
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AlgAlert: A two-level approach for algae bloom prediction using deep learning by Areej Alsini, Amina Saeed, Dawood Amin

“…Chlorophyll-a (Chl-a) is essential to detect harmful algae blooms that can damage aquatic ecosystems and cause economic losses. …”
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Precision Soil Moisture Monitoring Through Drone-Based Hyperspectral Imaging and PCA-Driven Machine Learning by Milad Vahidi, Sanaz Shafian, William Hunter Frame

“…Our results demonstrate that PCA effectively detected critical variables for soil moisture estimation, with the ANN model outperforming other machine learning algorithms, including Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting (XGBoost). …”
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Development and external validation of machine learning-based models to predict patients with cellulitis developing sepsis during hospitalisation by Li Hu, Xilingyuan Chen, Rentao Yu

“…The results could provide a tool for clinicians to detect hospitalised patients with cellulitis developing sepsis early.…”
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An inductive learning-based method for predicting drug-gene interactions using a multi-relational drug-disease-gene graph by Jian He, Yanling Wu, Linxi Yuan, Jiangguo Qiu, Menglong Li, Xuemei Pu, Yanzhi Guo

“…Computational analysis can accurately detect drug-gene interactions (DGIs) cost-effectively. …”
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OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis. by Greg Finak, Jacob Frelinger, Wenxin Jiang, Evan W Newell, John Ramey, Mark M Davis, Spyros A Kalams, Stephen C De Rosa, Raphael Gottardo

“…Here we present OpenCyto, a new BioConductor infrastructure and data analysis framework designed to lower the barrier of entry to automated flow data analysis algorithms by addressing key areas that we believe have held back wider adoption of automated approaches. …”
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Experimental Study of Coherent Collaborative Processing Method in Distributed Automotive Radar by A. A. Kuzin, S. E. Kuznetsov, A. V. Miakinkov, R. S. Fadeev, S. A. Shabalin

“…Intelligent driver assistance systems are increasingly employing radar systems to detect, resolve, and track various classes of targets. …”
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Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree‐Based Machine Learning Model by Md. Siddikur Rahman, Miftahuzzannat Amrin, Md. Abu Bokkor Shiddik

“…Results Our study findings detected a nonlinear effect of climatic parameters on dengue at different thresholds such as mean (27°C), minimum (22°C), maximum temperatures (32°C), and relative humidity (82%). …”
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The Specific Features of the Pathogenesis and Correction of Critical Conditions in Obstetric Care by Yu. S. Podolsky

“…Water sectoral disorders were detected in three steps: on days 1, 3, and 5; these were identified in four steps in case of eclamptic coma (EC), regional anesthesia, and impaired oxygen status. …”
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"Proof-of-concept" evaluation of an automated sputum smear microscopy system for tuberculosis diagnosis. by James J Lewis, Violet N Chihota, Minty van der Meulen, P Bernard Fourie, Katherine L Fielding, Alison D Grant, Susan E Dorman, Gavin J Churchyard

“…<h4>Background</h4>"TBDx" is an innovative smear microscopy system that automatically loads slides onto a microscope, focuses and digitally captures images and then classifies smears as positive or negative using computerised algorithms.<h4>Objectives</h4>To determine the diagnostic accuracy of TBDx, using culture as the gold standard, and compare this to a microscopist's diagnostic performance.…”
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Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma by LI Chaoqun, ZHENG Hongliang, HUANG Ping

“…A prognostic model of CARGs was constructed by incorporating 12 machine learning algorithms, and univariate and multivariate Cox regression analyses were applied to evaluate whether the 12 models as an independent prognostic factor or not. …”
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Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan. by Ana Torres, Brima Musa Younis, Samuel Tesema, Jose Carlos Solana, Javier Moreno, Antonio J Martín-Galiano, Ahmed Mudawi Musa, Fabiana Alves, Eugenia Carrillo

“…<h4>Conclusions</h4>A combination of 14 biochemical variables identified by unsupervised ML was able to detect a worsening PKDL state in Sudanese patients. …”
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Comprehensive evaluation of machine learning models for predicting the cognitive status of Alzheimer's disease subjects and susceptible by Lucien Gnegne Meteumba, Vaghawan Prasad Ojha, Shantia Yarahmadian

“…This requires strong predictive models to tease apart what we can do to detect and introduce early-prevention. In the same vain, machine learning models could be effective on the prediction of cognitive status of subjects from the real study, thus early-identifying possible deterioration of cognitive ability of individuals who are susceptible. …”
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Advances in ECG and PCG-based cardiovascular disease classification: a review of deep learning and machine learning methods by Asmaa Ameen, Ibrahim Eldesouky Fattoh, Tarek Abd El-Hafeez, Kareem Ahmed

“…This involves the use of advanced tools to detect the disease early on and aid in the reduction of fatality rates. …”
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Bidimensional Increment Entropy for Texture Analysis: Theoretical Validation and Application to Colon Cancer Images by Muqaddas Abid, Muhammad Suzuri Hitam, Rozniza Ali, Hamed Azami, Anne Humeau-Heurtier

“…Entropy algorithms are widely applied in signal analysis to quantify the irregularity of data. …”
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Analysis of mid-infrared spectrum characteristics of sandstone with different acidification degrees based on fusion model by Lu Chen, Longfei Chang, Huiqing Lian, En Wang, Bixing Zhang, Jia Kang

“…The proposed fusion model achieves an accuracy of 95% in detecting the acidification degree of red sandstone, surpassing independent RF, KNN, and SVM models. …”
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Multi-Temporal Assessment of Soil Erosion After a Wildfire in Tuscany (Central Italy) Using Google Earth Engine by Francesco Barbadori, Pierluigi Confuorto, Bhushan Chouksey, Sandro Moretti, Federico Raspini

“…The results suggest the need to plan mitigation strategies towards reducing soil erodibility, directly and indirectly, with a continuous monitoring of erosion rates and the application of machine learning algorithms to thoroughly understand the relationships between variables.…”
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Klasifikasi Multilabel Pada Gaya Belajar Siswa Sekolah Dasar Menggunakan Algoritma Machine Learning by I Kadek Nicko Ananda, Ni Putu Novita Puspa Dewi, Ni Wayan Marti, Luh Joni Erawati Dewi

“…The machine learning algorithms used to build the model are Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). …”
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Attention-enhanced StrongSORT for robust vehicle tracking in complex environments by Wei Xu, Xiaodong Du, Ruochen Li, Bingjie Li, Yuhu Jiao, Lei Xing

“…Abstract While multi-object tracking is critical for autonomous driving systems, traditional algorithms exhibit three fundamental limitations in complex scenarios: (1) blurred feature representation under occlusion and re-identification scenarios causing identity switches, (2) insufficient sensitivity to scale-variant targets due to fixed geometric constraints in conventional IoU-based loss functions, and (3) gradient degradation in deep convolutional layers hindering discriminative feature learning. …”
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Production monitoring and machine tracking in underground mines based on a collision avoidance system: A case study by Artur Skoczylas, Natalia Duda-Mróz, Wioletta Koperska, Paweł Stefaniak, Paweł Śliwiński

“…Consequently, the development of validation algorithms, including error correction and adaptive filtering, was imperative. …”
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