HUMAN RIGHTS: PHILOSOPHICAL AND LEGAL ANALYSIS by ALBOV A.P.

“…Given that all doctrines note the importance of these rights for the formation of the political climate and quality of life, and their violation inevitably leads either to the stagnation of political life in the state, or is a prerequisite for the formation of destructive movements of various kinds; that therefore human rights, along with other socio-political algorithms, can claim the leading role in the formation of legal policy both within the state itself and in international relations; however, this does not mean that these rights can be considered unconditional and absolute, they directly depend on the various cultural, religious, moral values of a particular society, and that therefore they can be defined rather as a kind of sustainable agreement.…”
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Machine learning-based predictive modeling of angina pectoris in an elderly community-dwelling population: Results from the PoCOsteo study. by Shahrokh Mousavi, Zahrasadat Jalalian, Sima Afrashteh, Akram Farhadi, Iraj Nabipour, Bagher Larijani

“…We aimed to develop machine learning-based models using multiple algorithms to predict and identify the predictors of angina pectoris in an elderly community-dwelling population.…”
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Comparison between coronal FLASH and sagittal double echo steady state MRI in detecting longitudinal cartilage thickness change by fully automated segmentation – Data from the FNIH... by Felix Eckstein, Akshay S. Chaudhari, David J. Hunter, Wolfgang Wirth

“…Design: Convolutional neural network (CNN) algorithms were trained on 86 radiographically osteoarthritic knees with manual expert segmentation of the medial and lateral femorotibial cartilages (coronal FLASH and sagittal DESS). …”
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Study on the prediction model of corrosion and scaling in the external cooling water system of ultra-high voltage converter stations phase modulator by GU Xiantao, FAN Peipei, CHEN Xiaochun, GAO Yuxiang, ZHOU Zhongkang, ZHANG Junjie, MA Xiaowei, JI Qiaozhen, WU Yan, XU Yayan, DONG Haosheng, DUAN Zhongxin, YANG Lin, GAO Zhonghui

“…Based on this dataset, a scale prediction model of the circulating cooling water system was trained using backpropagation(BP) neural networks and machine learning algorithms, and the accuracy of the model was evaluated. …”
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Blocking the interaction between S100A9 protein and RAGE V domain using S100A12 protein. by Revansiddha Katte, Chin Yu

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Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Rami Albasha, Md Saifuzzaman, Maxime Leduc

“…Our findings showed that XGB and RF could predict alfalfa crop height with an R² of 0.79 and a mean absolute error (MAE) of around 4 cm Our findings indicated that SVR exhibited the lowest accuracy among the three algorithms tested, with R² of 0.69 and an MAE of 4.63 cm. …”
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A study on plant root apex morphology as a model for soft robots moving in soil. by Anand Kumar Mishra, Francesca Tramacere, Roberto Guarino, Nicola Maria Pugno, Barbara Mazzolai

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Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review by Émile Lemoine, Joel Neves Briard, Bastien Rioux, Oumayma Gharbi, Renata Podbielski, Bénédicte Nauche, Denahin Toffa, Mark Keezer, Frédéric Lesage, Dang K. Nguyen, Elie Bou Assi

“…Significance: We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.…”
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Swarm intelligence for energy-efficient heating, ventilation, and air conditioning (HVAC) systems: A case study in smart buildings by Vinoth Kanna I, Raja Subramani, Maher Ali Rusho, Shubham Sharma, Ramachandran T, Abinash Mahapatro, Deepak Gupta, Jasmina Lozanovic

“…This research utilizes swarm intelligence algorithms—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and hybrid PSO-ACO-to optimize energy efficiency and thermal comfort in smart building HVAC systems. …”
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Entangled in uncertainty: The experience of living with dementia from the perspective of family caregivers. by Els van Wijngaarden, Hugo van der Wedden, Zerline Henning, Rikke Komen, Anne-Mei The

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Explainable machine learning model for predicting the transarterial chemoembolization response and subtypes of hepatocellular carcinoma patients by Yunjie Zhang, Songjian Tong, Junhui Yang, Jiawei Lin, Yifan Kong, Deyu Lu, Yan Chen, Yingchao Li, Linfeng Xu, Xiuyan Kong, Guoqing Zhu, Hao Zhang, Pixu Liu, Zhijie Yu, Jinglin Xia

“…Through recursive feature elimination (RFE), we systematically developed prediction models employing ten distinct ML algorithms. The SHAP algorithm was implemented to enhance model interpretability, while patient stratification was subsequently performed using PCA and K-means clustering to facilitate comprehensive prognostic analysis. …”
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The influence of pH and temperature on benthic chlorophyll-a: Insights from SHAP-XGBoost and random forest models by Sangar Khan, Noël P.D. Juvigny-Khenafou, Tatenda Dalu, Paul J. Milham, Yasir Hamid, Kamel Mohamed Eltohamy, Habib Ullah, Bahman Jabbarian Amiri, Hao Chen, Naicheng Wu

“…In the SHAP XGBoost, pH was the most important characteristic, followed by mean average temperature (AT). The SVR demonstrated that AT is vital for the upper and middle catchment reaches, while pH is more important in the lower reaches. …”
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Improved Absorbing Aerosol Index Results for EMI on Board Atmospheric Environment Monitoring Satellite by Fuying Tang, Fuqi Si, Ang Li, Haijin Zhou, Minjie Zhao, Yuhan Luo, Yuanyuan Qian

“…The comparison of the EMI with TROPOMI shows a high correlation of 0.91 and a root mean square of 0.296.…”
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YOLOv8-TF: Transformer-Enhanced YOLOv8 for Underwater Fish Species Recognition with Class Imbalance Handling by Chiranjibi Shah, M M Nabi, Simegnew Yihunie Alaba, Iffat Ara Ebu, Jack Prior, Matthew D. Campbell, Ryan Caillouet, Matthew D. Grossi, Timothy Rowell, Farron Wallace, John E. Ball, Robert Moorhead

“…However, implementing fish species detection algorithms in underwater environments presents significant challenges due to factors such as varying lighting conditions, water turbidity, and the diverse appearances of fish species. …”
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Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients by Aref Andishgar, Sina Bazmi, Kamran B. Lankarani, Seyed Alireza Taghavi, Mohammad Hadi Imanieh, Gholamreza Sivandzadeh, Samira Saeian, Nazanin Dadashpour, Alireza Shamsaeefar, Mahdi Ravankhah, Hamed Nikoupour Deylami, Reza Tabrizi, Mohammad Hossein Imanieh

“…Missing data was imputed using mean and median. Data preprocessing involved feature scaling and one-hot encoding. …”
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Streamlining Thoracic Radiotherapy Quality assurance: One-Class Classification for Automated OAR Contour Assessment by Yihao Zhao BSc, Cuiyun Yuan MSc, Ying Liang PhD, Yang Li MSc, Chunxia Li MSc, Man Zhao MSc, Jun Hu PhD, Ningze Zhong Bsc, Wei Liu PhD, Chenbin Liu PhD

“…Purpose Automating quality assurance (QA) for contours generated by automatic algorithms is critical in radiotherapy treatment planning. …”
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Automatic location and recognition of horse freezing brand using rotational YOLOv5 deep learning network by Zhixin Hua, Yitao Jiao, Tianyu Zhang, Zheng Wang, Yuying Shang, Huaibo Song

“…As for the results of number recognition, the mAP (mean Average Accuracy) was 95.77 %, the weight size was 13.71 MB, and the detection speed was 68.6 fps. …”
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Feasibility of GPT-3.5 versus Machine Learning for Automated Surgical Decision-Making Determination: A Multicenter Study on Suspected Appendicitis by Sebastian Sanduleanu, Koray Ersahin, Johannes Bremm, Narmin Talibova, Tim Damer, Merve Erdogan, Jonathan Kottlors, Lukas Goertz, Christiane Bruns, David Maintz, Nuran Abdullayev

“…Overall agreement between the GPT-3.5 output and the reference standard was assessed by means of inter-observer kappa values as well as accuracy, sensitivity, specificity, and positive and negative predictive values with the “Caret” and “irr” packages. …”
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Spatial analysis of air pollutant exposure and its association with metabolic diseases using machine learning by Jingjing Liu, Chang Liu, Zhangdaihong Liu, Yibin Zhou, Xiaoguang Li, Yang Yang

“…This study introduces a novel machine learning (ML) pipeline, an Algorithm for Spatial Relationships Analysis between Exposome and Metabolic Diseases (ASEMD), to analyze spatial associations between APs and MDs at the prefecture-level city scale in China. …”
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The effect of Pressurised Intraperitoneal Aerosol Chemotherapy on the quality of life of patients with primary diagnosed ovarian cancer with peritoneal carcinomatosis during combin... by A. S. Dzasokhov, A. A. Kostin, V. L. Astashov, A. V. Turiev, A. D. Uskov

“…The homogeneity of the distribution in the groups was established by means of an accurate two–way Wilcoxon–Mann‑Whitney rank sum criterion, the actual significance level was p = 0.498. …”
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