Integrating machine learning and reliability analysis: A novel approach to predicting heavy metal removal efficiency using biochar by Mohammad Sadegh Barkhordari, Chongchong Qi

“…The framework addresses key challenges by employing data imputation to manage missing information, data augmentation to overcome limitations of small datasets, and reliability analysis to assess predictive uncertainties, thereby improving the model’s reliability and generalization capability. …”
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A Hybrid Approach of DenseNet121 with Attention and Bi-LSTM for Yoga Pose Estimation by Aarthy K., Alice Nithya

“…The system is designed to integrate advanced AI techniques, providing an innovative approach to pose recognition that leverages several sophisticated machine learning models and algorithms to enhance performance. The pre-processing stage involves applying a Wiener Filter (WF) for effective noise removal, ensuring that the data is clean and ready for analysis. …”
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Cloud-edge collaborative data anomaly detection in industrial sensor networks. by Tao Yang, Xuefeng Jiang, Wei Li, Peiyu Liu, Jinming Wang, Weijie Hao, Qiang Yang

“…To solve the limitations above, this paper develops a cloud-edge collaborative data anomaly detection approach for industrial sensor networks that mainly consists of a sensor data detection model deployed at individual edges and a sensor data analysis model deployed in the cloud. …”
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Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques by Mohammed Qader Kheder, Aree Ali Mohammed

“…Test results indicate that the proposed models have significant improvements in terms of accuracy. …”
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Mapping County-Level Rice Planting Areas by Joint Use of High-Resolution Optical and Time Series SAR Imagery by Jia Xu, Haojie Wang, Lin Qiu, Hui Wang, Yang Mu

“…Subsequently, the long short-term memory (LSTM)-based temporal classification model was utilized to acquire rice cultivation information at parcel scale using time-series Sentinel-1 SAR data. …”
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Species-specific variation in predicted distribution and habitat suitability of phlebotomine sand flies in Italy under different climate change scenarios by Camila González, Johan Manuel Calderón, Ana María López, Ilaria Bernardini, Luigi Gradoni, Marco Pombi, Gioia Bongiorno, Simona Gabrielli

“…This study evaluated the potential distribution of six phlebotomine sand fly species, known or suspected vectors of L. infantum, under climate change scenarios using ecological niche modeling and the maximum entropy (MaxEnt v. 3.4.1) modeling algorithm. …”
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Multimodal data integration in early-stage breast cancer by Arnau Llinas-Bertran, Maria Butjosa-Espín, Vittoria Barberi, Jose A. Seoane

“…However, existing knowledge does not fully encompass the diverse nature of breast cancer, particularly in triple-negative tumors.The integration of multi-omics and multimodal data has the potential to provide new insights into biological processes, to improve breast cancer patient stratification, enhance prognosis and response prediction, and identify new biomarkers.This review presents a comprehensive overview of the state-of-the-art multimodal (including molecular and image) data integration algorithms developed and with applicability to breast cancer stratification, prognosis, or biomarker identification. …”
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“Image-Spectral” fusion monitoring of peanut leaf spot disease level based on deep learning by Yongda Lin, Jiangtao Tan, Hong Li, Xi Li, Jianguo Wang, Tingting Chen, Lei Zhang

“…To address these limitations, this study proposes a robust multi-source feature fusion model for peanut leaf spot detection, integrating ResNet101 for RGB image feature extraction and an improved 1D-CNN for hyperspectral feature extraction. …”
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Proximal remote sensing of dissolved organic matter in aqua-culture ponds via multi-temporal spectral correction by Wenxu Lv, Yancang Wang, Huiqiong Cao, Peng Cheng, Xiaohe Gu, Zhuoran Ma, Mengjie Li, Ruiyin Tang, Qichao Zhao, Xuqing Li, Lan Zhang, Shuaifei Liu

“…Among the three algorithms, the Random Forest model yielded the best performance, with an R2 of 0.82, RMSE of 3.1 mg/L, and MAE of 2.37 mg/L on the test set. …”
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Deep learning-based framework for Mycobacterium tuberculosis bacterial growth detection for antimicrobial susceptibility testing by Hoang-Anh T. Vo, Sang Nguyen, Ai-Quynh T. Tran, Han Nguyen, Hai Bich Ho, Philip W. Fowler, Timothy M. Walker, Thuy Thi Nguyen

“…Automated Mycobacterial Growth Detection Algorithm (AMyGDA) is a software package that uses image processing techniques to read plates, but struggles with plates that exhibit low growth or images of low quality. …”
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Enhancing seizure detection with hybrid XGBoost and recurrent neural networks by Santushti Santosh Betgeri, Madhu Shukla, Dinesh Kumar, Surbhi B. Khan, Muhammad Attique Khan, Nora A. Alkhaldi

“…An accurate and timely prediction system can help mitigate these risks by enabling preventive measures and improving patient safety. This study investigates machine learning and deep learning algorithms for seizure prediction, comparing their effectiveness on a large EEG dataset of epileptic patients. …”
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Application of artificial intelligence technologies for the detection of early childhood caries by Priyanka A, Rishi Sreekumar, S Namasivaya Naveen

“…This study examined various approaches, datasets, methodologies, and algorithms. The inclusion criteria are the accuracy of models, the investigation of different risk factors, and the applicability of ML and DL in caries prediction. …”
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Deep-Learning-CNN for Detecting Covered Faces with Niqab by Abdulaziz A. Alashbi, Mohd Shahrizal Sunar, Zieb Alqahtani

“…An experimental test was conducted to evaluate the performances of the proposed model using the Niqab-Face dataset. The experiment showed encouraging results and improved accuracy compared with state-of-the-art face detection algorithms…”
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Guidance Law Design for a Class of Dual-Spin Mortars by Qing-wei Guo, Wei-dong Song, Yi Wang, Zhi-cai Lu

“…After the transform function of the actuator was obtained, the control model of the shell was improved. The results of the Monte Carlo simulation demonstrate that the guidance law is suitable and the mortar can be effectively controlled.…”
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A probabilistic gap based condition prediction approach for desulfurization slurry circulating pump by Jiaxing Zhu, Buyun Sheng, Junlan Hu, Yanfei Li, Ruiping Luo, Yue Shi

“…This paper proposes a solution using probabilistic gap positive-learning (PGPU) and biased SVM algorithms. Key contributions include: (1) a comprehensive feature model based on expert experience and vibration signal extraction for condition classification, (2) a PGPU and bias-SVM method that updates the model by leveraging probability gaps between true and known samples, and (3) cross-comparisons with other classifiers like SVM and neural networks. …”
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Mental Health Classification Using Machine Learning with PCA and Logistics Regression Approaches for Decision Making by Hendra Hendra, Mustafa Mat Deris, Ika Safitri Windiarti

“…Reducing bias within these datasets is essential to enhance the fairness and accuracy of the models and algorithms they support. Research on mental health classification using machine learning techniques, particularly PCA and logistic regression, is significant because it has the potential to improve decision-making in mental health care.…”
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A Lightweight Direction-Aware Network for Vehicle Detection by Luxia Yang, Yilin Hou, Hongrui Zhang, Chuanghui Zhang

“…The mechanism can fully perceive the details and salient information of input features in multiple directions, thus improving the ability of the model to capture critical features. …”
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Quantum ensemble learning with a programmable superconducting processor by Jiachen Chen, Yaozu Wu, Zhen Yang, Shibo Xu, Xuan Ye, Daili Li, Ke Wang, Chuanyu Zhang, Feitong Jin, Xuhao Zhu, Yu Gao, Ziqi Tan, Zhengyi Cui, Aosai Zhang, Ning Wang, Yiren Zou, Tingting Li, Fanhao Shen, Jiarun Zhong, Zehang Bao, Zitian Zhu, Zixuan Song, Jinfeng Deng, Hang Dong, Pengfei Zhang, Wei Zhang, Hekang Li, Qiujiang Guo, Zhen Wang, Ying Li, Xiaoting Wang, Chao Song, H. Wang

“…Based on the probabilistic nature of quantum measurement, the algorithm improves the prediction accuracy by refining the attention mechanism during the adaptive training and combination of quantum classifiers. …”
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In silico assessment of biocompatibility and toxicity: molecular docking and dynamics simulation of PMMA-based dental materials for interim prosthetic restorations by Ravinder S. Saini, Rayan Ibrahim H. Binduhayyim, Vishwanath Gurumurthy, Abdulkhaliq Ali F. Alshadidi, Shashit Shetty Bavabeedu, Rajesh Vyas, Doni Dermawan, Punnoth Poonkuzhi Naseef, Seyed Ali Mosaddad, Artak Heboyan

“…The HADDOCK standalone version was utilized for docking calculations, employing a Lamarckian genetic algorithm to explore the conformational space of ligand-receptor interactions. …”
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Combining Physical and Network Data for Attack Detection in Water Distribution Networks by Côme Frappé - - Vialatoux, Pierre Parrend

“…Water distribution infrastructures are increasingly incorporating the IoT in the form of sensing and computing power to improve control over the system and achieve greater adaptability to water demand. …”
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