Estimation and validation of solubility of recombinant protein in E. coli strains via various advanced machine learning models by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…Key preprocessing techniques such as Histogram-Based Outlier Detection (HBOD) and Z-score normalization are employed to ensure data integrity and consistency. …”
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Advanced Trajectory Analysis of NASA’s Juno Mission Using Unsupervised Machine Learning: Insights into Jupiter’s Orbital Dynamics by Ashraf ALDabbas, Zaid Mustafa, Zoltan Gal

“…This study focuses on change detection in terms of Juno’s trajectory, leveraging cutting-edge data computing techniques to analyze its orbital dynamics. …”
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Kalman Filter-Based Fusion of LiDAR and Camera Data in Bird’s Eye View for Multi-Object Tracking in Autonomous Vehicles by Loay Alfeqy, Hossam E. Hassan Abdelmunim, Shady A. Maged, Diaa Emad

“…The proposed method introduces a novel association strategy that incorporates structural similarity into the cost function, enabling effective data fusion between 2D camera detections and 3D LiDAR detections for robust track recovery during short occlusions by leveraging LiDAR depth. …”
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Automatic Detection for Mining Subsidence Areas Using the CBAM-Enhanced VGG-UNet Model With Long Time Series InSAR Interferograms by Kegui Jiang, Keming Yang, Mengting Gao, Liuguo Zhu, Chuang Jiang

“…The rapid advancements in remote sensing technology and deep learning algorithms have enabled significant breakthroughs in monitoring and accurately identifying mining subsidence. …”
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ML-Based Quantitative Analysis of Linguistic and Speech Features Relevant in Predicting Alzheimer’s Disease by Tripti Tripathi, Rakesh Kumar

“…Among the five machine learning algorithms tested, the XGBoost classifier showed the highest accuracy of 75.59%. …”
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Prediction and Mapping of Soil Total Nitrogen Using GF-5 Image Based on Machine Learning Optimization Modeling by LIU Liqi, WEI Guangyuan, ZHOU Ping

“…[Conclusions]This study demonstrates the clear feasibility of using GF-5 satellite hyperspectral remote sensing data and machine learning algorithm for large-scale quantitative detection and visualization analysis of soil TN content. …”
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Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.) by Jorge A. Fernandez-Jibaja, Nilton Atalaya-Marin, Yeltsin A. Álvarez-Robledo, Victor H. Taboada-Mitma, Juancarlos Cruz-Luis, Daniel Tineo, Malluri Goñas, Darwin Gómez-Fernández

“…These results underscore the potential of integrating multisource data with machine learning techniques for high-resolution phenological monitoring and varietal performance assessment.…”
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A NOVEL DEEP LEARNING APPROACHES FOR MULTI-CLASS HISTOPATHOLOGICAL SUB-IMAGE CLASSIFICATION USING PRIOR KNOWLEDGE by Riyam Ali Yassin, Morteza Valizadeh, Alaa Hussein Abdulaal

“…Convolutional neural networks (CNNs) have shown significant promise in medical image analysis, aiding in the detection of cancer cells and the classification of histopathological images through advanced data processing techniques. …”
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Development of a Collision-Free Path Planning Method for a 6-DoF Orchard Harvesting Manipulator Using RGB-D Camera and Bi-RRT Algorithm by Zifu Liu, Rizky Mulya Sampurno, R. M. Rasika D. Abeyrathna, Victor Massaki Nakaguchi, Tofael Ahamed

“…Therefore, in this study, we introduced a collision-free path planning method for a 6-DoF orchard harvesting manipulator using an RGB-D camera and the Bi-RRT algorithm. First, by transforming the RGB-D camera’s point cloud data into collision geometries, we achieved 3D obstacle map reconstruction, allowing the harvesting robot to detect obstacles within its workspace. …”
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Assessment of PTSD in military personnel via machine learning based on physiological habituation in a virtual immersive environment by Gauthier Pellegrin, Nicolas Ricka, Denis A. Fompeyrine, Thomas Rohaly, Leah Enders, Heather Roy

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ST-YOLO: a deep learning based intelligent identification model for salt tolerance of wild rice seedlings by Qiong Yao, Qiong Yao, Pan Pan, Pan Pan, Xiaoming Zheng, Xiaoming Zheng, Guomin Zhou, Guomin Zhou, Guomin Zhou, Jianhua Zhang, Jianhua Zhang

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Dempster Shafer-Empowered Machine Learning-Based Scheme for Reducing Fire Risks in IoT-Enabled Industrial Environments by Jayameena Desikan, Sushil Kumar Singh, A. Jayanthiladevi, Saurabh Singh, Byungun Yoon

“…The proposed method aims to enhance fire prediction and detection accuracy through algorithms developed using sensor preprocessing, machine learning, and Dempster Shafer fusion handling uncertain and varying multi-sensor data and demonstrates real-time, data-driven decision-making in critical industrial environments. …”
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Comprehensive Performance Comparison of Signal Processing Features in Machine Learning Classification of Alcohol Intoxication on Small Gait Datasets by Muxi Qi, Samuel Chibuoyim Uche, Emmanuel Agu

“…However, until now, no work has systematically evaluated the performance of various categories of gait features for alcohol intoxication detection task using traditional machine learning algorithms. …”
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Advanced machine learning techniques reveal multidimensional EEG abnormalities in children with ADHD: a framework for automatic diagnosis by Ying Mao, Ying Mao, Xuchen Qi, Xuchen Qi, Xuchen Qi, Lingyan He, Shan Wang, Zhaowei Wang, Fang Wang, Fang Wang

“…Then, four widely-employed machine learning algorithms (including random forest (RF), XGBoost, CatBoost, and LightGBM) were used for classification calculations, and the SHAP algorithm was then used to assess the importance of the contributing features to interpret the model’s decision process.ResultsThe results showed that the highest classification accuracy of 99.58% for pediatric ADHD detection was obtained with the CatBoost model based on the optimal feature subset of 206 features (PSD/FuzEn/MI = 53/5/148). …”
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Multiclass Supervised Learning Approach for SAR-COV2 Severity and Scope Prediction: SC2SSP Framework by Shaik Khasim Saheb, B. Narayanan, T.V. Narayana Rao

“…Materials and Methods: In this manuscript, the proposed model SC2SSP is a multiclass supervised learning technique that aims to predict the scope and severity of the SAR-COV2 virus using data on confirmed cases and deaths. …”
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Enhanced prediction of ventilator-associated pneumonia in patients with traumatic brain injury using advanced machine learning techniques by Negin Ashrafi, Armin Abdollahi, Kamiar Alaei, Maryam Pishgar

“…Overall, the results demonstrate that advanced ensemble learning, meticulous feature selection, and effective class imbalance handling can significantly enhance early detection in traumatic brain injury cases. …”
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Identification of potential biomarkers for 2022 Mpox virus infection: a transcriptomic network analysis and machine learning approach by Joy Prokash Debnath, Kabir Hossen, Sabrina Bintay Sayed, Md. Sayeam Khandaker, Preonath Chondrow Dev, Saifuddin Sarker, Tanvir Hossain

“…Subsequently, gene expression network analyses pinpoint the key DEGs, followed by their candidate drug assessment using the Drug SIGnatures DataBase (DSigDB) and validation by multiple machine learning algorithms. …”
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Fortifying IoT Infrastructure Using Machine Learning for DDoS Attack within Distributed Computing-based Routing in Networks by Sharaf Aldeen Abdulkadhum Abbas, Abdullahi Abdu Ibrahim

“…In addition, Algorithms perform quality control to determine the optimal hyperplane for the given data, Find neighboring data points and preserve the structure of the tree. …”
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Research on Detection of Icing Cover Transmission Lines Under Different Weather Conditions Based on Wide-Field Dynamic Convolutional Network LDKA-NET by Xinsheng Dong, Yuanhao Wan, Yongcan Zhu, Chao Ji

“…To address the issue of low detection accuracy for icing transmission line defects with existing models, this paper proposes a defect detection algorithm for icing transmission line defects under different weather conditions based on a Large Dynamic Kernel Aggregation Net (LDKA-NET). …”
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Enhancing Cloud Job Failure Prediction With a Novel Multilayer Voting-Based Framework by Ahmed Elkaradawy, Ayman Elshenawy, Hany Harb

“…Once the failed jobs are detected, the second layer takes over to determine the failure type employing the Random Forest algorithm. …”
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