Automated interpretation of deep learning-based water quality assessment system for enhanced environmental management decisions by Javed Mallick, Saeed Alqadhi, Majed Alsubih, Mohamed Fatahalla Mohamed Ahmed, Hazem Ghassan Abdo

“…Methodologically, the study used CNN algorithms optimised by Bayesian techniques for the prediction of eight water quality indices, coupled with SHAPley Additive exPlanations (SHAP) analysis under XAI to interpret the complex decision-making processes of these models. …”
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Resilience evaluation of memristor based PUF against machine learning attacks by Hebatallah M. Ibrahim, Heorhii Skovorodnikov, Hoda Alkhzaimi

“…Our results yield low accuracy and ROC results of within $$0.49-0.52$$ 0.49 - 0.52 and $$0.49-0.52$$ 0.49 - 0.52 respectively, indicating failure in predicting random data demonstrates efficient randomness prediction resiliency of the MR-PUF. …”
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AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view by Mostafa Jalilifar, Mahdi Sadeghi, Alireza Emami-Ardekani, Ahmad Bitarafan-Rajabi, Kouhyar Geravand, Parham Geramifar

“…The MLP-predicted dose values across all organs represented superior performance with the lowest mean absolute error in the liver but higher in the spleen and salivary glands. …”
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Optimization of Flavor Quality of Lactic Acid Bacteria Fermented Pomegranate Juice Based on Machine Learning by Wenhui ZOU, Fei PAN, Junjie YI, Linyan ZHOU

“…Binary classification models of HWPS and LWPS were established by random forest (RF) and adaptive boosting (AdaBoost) algorithms, and RF algorithm had higher prediction precision and accuracy. …”
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Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India by Srikanth Bhoopathi, Nitish Kumar, Somesh, Manali Pal

“…Conversely, for a 15-day lead time in Zone-1, XGBoost better predicts temperature peaks in both phases. In Zone-3 (T30AMT: 30 °C–35 °C) and Zone-4 (T30AMT < 30 °C) for both lead times, the performance of both models decline, indicating models and input variables are more effective in predicting higher temperatures typical of Zone-1 and 2 but less so in Zone-3 and 4. …”
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Ground Based Cloud Recognition with an Anchor Free Method by LI Yichao, GUO Rui, ZHANG Shaodi, SHOU Zefeng, CHEN Jing, LIU Yifei

“…In order to solve the problems of cloud type recognition of the ground-based cloud such as complex target candidate box selection and slow detection speed, a recognition method of cloud types in ground-based cloud map based on anchor free is proposed.First, the paper takes Center Net as the basic architecture of cloud type recognition.Based on thermodynamic diagrams prediction, key point prediction, center point prediction and candidate box prediction, a anchor free ground-based cloud type detection process is constructed.And then, the main network, loss function and candidate box prediction method of cloud type recognition model are designed.Finally, take CenterNet Resdcn101 as the model, compared the algorithm recognition accuracy, candidate boxes predict confidence and identify speed with mainstream target recognition methods and cloud type recognition method The results showed that the cloud type recognition method of the paper has higher recognition accuracy and faster recognition speed.…”
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Toward sustainable machining of hardened SKD11: Machine learning-based evaluation and optimization of surface roughness, tool wear, and CO2 emissions by Van-Canh Nguyen, Dung Hoang Tien, Van-Hung Pham, Thinh-Viet Nguyen, Thuy-Duong Nguyen

“…Results indicated that the second-order model demonstrated superior predictive accuracy for Ra (R² = 0.997) and CE (R² = 0.994), whereas the Vb prediction model failed to achieve sufficient reliability. …”
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Adherence to Clinical Quidelines on Preoperative Assessment and Correction of Cardiovascular Risk in Non-cardiac Surgery by M. D. Nigmatkulova, E. B. Kleymenova, L. P. Yashina, V. A. Otdelenov, S. A. Payushchik, O. D. Konova, D. A. Sychev

“…The number of inappropriate ECGs and echocardiographies, as well as incorrect treatment with beta-blockers, HMG CoA reductase inhibitors and ACE inhibitors (ARBs) in perioperative period evidence that the adherence of physicians to the clinical guidelines on preoperative assessment and perioperative management of patients remains low.It is reasonably to develop risk-based interdisciplinary protocols for preoperative examination, algorithms for interdisciplinary communication and interaction between specialists and the healthcare levels, as well as physicians' education for better adherence to clinical guidelines.…”
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Transcriptomics-based exploration of ubiquitination-related biomarkers and potential molecular mechanisms in laryngeal squamous cell carcinoma by Qiu Chen, Zhimin Wu, Yifei Ma

“…Then, therapeutic agents for biomarkers were predict. In addition, the regulatory networks of the biomarkers were mapped. …”
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Harmonizing Landsat-8 OLI and Sentinel-2 MSI: an assessment of surface reflectance and vegetation index consistency by Jiaqi Zhang, Xiaocheng Zhou, Xueping Liu, Xiaoqin Wang, Guojin He, Youshui Zhang

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Integrated Ultrasound‐Enrichment and Machine Learning in Colorimetric Lateral Flow Assay for Accurate and Sensitive Clinical Alzheimer's Biomarker Diagnosis by Shuqing Wang, Yan Zhu, Zhongzeng Zhou, Yong Luo, Yan Huang, Yibiao Liu, Tailin Xu

“…The LFA device is integrated with a portable ultrasonic actuator to rapidly enrich microparticles using ultrasound, which is essential for sample pre‐enrichment to improve the sensitivity, followed by ML algorithms to classify and predict the enhanced colorimetric signals. …”
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Blockchain and Bigdata to Secure Data Using Hash and Salt Techniques by AlSelami Fudhah A.

“…In recent times amount of data is increasing rapidly and analysis of data is a must to come up with business decisions, predictions etc. It’s not just text are numbers which has to be stored properly. …”
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Housing Value Forecasting Based on Machine Learning Methods by Jingyi Mu, Fang Wu, Aihua Zhang

“…In this paper, support vector machine (SVM), least squares support vector machine (LSSVM), and partial least squares (PLS) methods are used to forecast the home values. And these algorithms are compared according to the predicted results. …”
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Privacy-Preserving Machine Learning (PPML) Inference for Clinically Actionable Models by Baris Balaban, Seyma Selcan Magara, Caglar Yilgor, Altug Yucekul, Ibrahim Obeid, Javier Pizones, Frank Kleinstueck, Francisco Javier Sanchez Perez-Grueso, Ferran Pellise, Ahmet Alanay, Erkay Savas, Cetin Bagci, Osman Ugur Sezerman

“…Machine learning (ML) refers to algorithms (often models) that are learned directly from data, germane to past experience. …”
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Application of Support Vector Machines in High Power Device Technology by RAO Wei, LI Yong, YAN Ji

“…The prediction model was tested and used to analyze the influence of process parameters on the qualified rate. …”
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Deep Learning-Based Postural Asymmetry Detection Through Pressure Mat by Iker Azurmendi, Manuel Gonzalez, Gustavo García, Ekaitz Zulueta, Elena Martín

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DMSA-Net: a deformable multiscale adaptive classroom behavior recognition network by Chunyu Dong, Jing Liu, Shenglong Xie

“…In the intelligent transformation of education, accurate recognition of students’ classroom behavior has become one of the key technologies for enhancing the quality of instruction and the efficacy of learning. …”
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Using Age- and Size-Corrected Measures of Technical Skill to Better Assess the Performances of Youth Soccer Players by Thiago V. Camata, Andrew H. Hunter, Nicholas M. A. Smith, Mathew S. Crowther, Paulo R. P. Santiago, Robbie S. Wilson

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Internet of Things Assisted Plant Disease Detection and Crop Management Using Deep Learning for Sustainable Agriculture by Eman A. Al-Shahari, Ghadah Aldehim, Mohammed Aljebreen, Jehad Saad Alqurni, Ahmed S. Salama, Sitelbanat Abdelbagi

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Overview of Tensor-Based Cooperative MIMO Communication Systems—Part 2: Semi-Blind Receivers by Gérard Favier, Danilo Sousa Rocha

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