A Monocyte-Driven Prognostic Model for Multiple Myeloma: Multi-Omics and Machine Learning Insights by Xie L, Gao M, Tan S, Zhou Y, Liu J, Wang L, Li X

“…Subsequently, based on 482 prognostic moDEGs, we developed and validated an optimal model, termed the Monocyte-related Gene Prognostic Signature (MGPS), by integrating 101 predictive models generated from 10 machine learning algorithms across multiple transcriptome sequencing datasets. …”
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Preliminary exploratory study on differential diagnosis between benign and malignant peripheral lung tumors: based on deep learning networks by Yuan Wang, Yuan Wang, Yutong Zhang, Yongxin Li, Tianyu She, Meiqing He, Hailing He, Dong Zhang, Dong Zhang, Jue Jiang

“…DeLong’s test was employed to compare the differences between the groups.ResultsAmong the five models, the one based on the ResNet18 algorithm demonstrated the highest performance. It exhibited statistically significant advantages in predictive accuracy (p < 0.05) compared to the models based on ResNet152, ResNet101, ResNet50, and ResNet34 algorithms. …”
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An Analysis of Semi-Supervised Machine Learning in Electrical Machines by V. Raju Arvind, S. Shyamsharan, Poorvajaa Gurunathan, Krishna Kumba, Nawin Ra

“…The research investigates important SSML algorithms such as self-training, co-training, generative models, and graph-based methods, highlighting their particular uses in fault diagnosis, condition monitoring, and predictive maintenance of electrical machines. …”
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Mitochondria-related genes as prognostic signature of endometrial cancer and the effect of MACC1 on tumor cells. by Xuefen Lin, Jianfeng Zheng, Yanhong Li, Linying Liu, Qinying Liu, Jie Lin, Yang Sun

“…The predictive performance, immune features, and anti-tumor response of the mitochondrial signature were evaluated through multiple algorithms. …”
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Integrating deep learning in public health: a novel approach to PICC-RVT risk assessment by Yue Li, Yue Li, Shengxiao Nie, Lei Wang, Dongsheng Li, Shengmiao Ma, Ting Li, Hong Sun

“…Stability varied with the number of predictive factors, with Cox-Time showing the highest ICC (0.974) with 16 predictive factors, and DeepSurv the most stable with 26 predictive factors (ICC: 0.983). …”
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Comparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data by Haniyeh Rafiepoor, Alireza Ghorbankhanloo, Kazem Zendehdel, Zahra Zangeneh Madar, Sepideh Hajivalizadeh, Zeinab Hasani, Ali Sarmadi, Behzad Amanpour‐Gharaei, Mohammad Amin Barati, Mozafar Saadat, Seyed‐Ali Sadegh‐Zadeh, Saeid Amanpour

“…The study found that AI algorithms alone did not significantly improve predictability compared to the Gail model. …”
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Development of Automatic Balancing Application forFashion Company Using Artificial Intelligence by May Alrasheed, Mohamed Jmali, Thouraya Hamdi

“…Therefore, ant colony algorithms are perfect for manufacturers pursuing cost reduction, improved product quality and facilitated production processes. …”
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On Simplifying Expressions with Mixed Boolean-Arithmetic by Yury V. Kosolapov

“…Based on the length reduction algorithm, an algorithm is constructed that allows to estimate the resistance of an MBA-expression to simplification. …”
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A Self-Adaptive Regression-Based Multivariate Data Compression Scheme with Error Bound in Wireless Sensor Networks by Jianming Zhang, Kun Yang, Lingyun Xiang, Yuansheng Luo, Bing Xiong, Qiang Tang

“…Theoretically and experimentally, it is concluded that the proposed algorithms can effectively exploit the correlations on the same sensor node and achieve significant reduction in data transmission. …”
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The application of machine learning in clinical microbiology and infectious diseases by Cheng Xu, Ling-Yun Zhao, Cun-Si Ye, Ke-Chen Xu, Ke-Chen Xu, Ke-Yang Xu

“…Machine learning in the field of microbiology mainly adopts supervised learning and unsupervised learning, involving algorithms from classification and regression to clustering and dimensionality reduction. …”
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Comparative study of IoT- and AI-based computing disease detection approaches by Wasiur Rhmann, Jalaluddin Khan, Ghufran Ahmad Khan, Zubair Ashraf, Babita Pandey, Mohammad Ahmar Khan, Ashraf Ali, Amaan Ishrat, Abdulrahman Abdullah Alghamdi, Bilal Ahamad, Mohammad Khaja Shaik

“…This study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms.…”
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A simultaneous post-LASIK sequential bilateral implantation of multifocal IOLs aimed at refraction correction. A clinical case by E. N. Eskina, A. V. Belogurova, A. I. Fisenko

“…The compliance with the modified algorithms of pre-and intraoperative behavior of the operating team, thorough preparation of the patient, careful calculation of the IOL allowed us to obtain the predictive refractive result with a high level of visual satisfaction and absence of undesirable postoperative phenomena. …”
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Neurotechnological Approaches to Cognitive Rehabilitation in Mild Cognitive Impairment: A Systematic Review of Neuromodulation, EEG, Virtual Reality, and Emerging AI Applications by Evgenia Gkintoni, Stephanos P. Vassilopoulos, Georgios Nikolaou, Apostolos Vantarakis

“…Emerging AI applications showed potential for personalized assessment and intervention through predictive modeling and adaptive algorithms. <i>Conclusions:</i> Neurotechnological approaches offer promising avenues for MCI rehabilitation, with the most substantial evidence for integrated interventions targeting multiple mechanisms. …”
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Advanced evaluation of performance of machine learning models for soapstock splitting optimisation under uncertainty by Bartosz Szeląg, Krzysztof Barbusiński, Michał Stachura, Przemysław Kowal, Adam Kiczko, Eldon R. Rene

“…This study proposes a computational framework for the prediction and optimisation of soapstock splitting under conditions of limited measurement data and input uncertainty. …”
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AI illuminates paths in oral cancer by Devesh U. Kapoor, Pushpendra Kumar Saini, Narendra Sharma, Ankul Singh, Bhupendra G. Prajapati, Gehan M. Elossaily, Summya Rashid

“…The role of artificial intelligence in prognosis of oral cancer including predictive modeling, identification of prognostic factors and risk stratification also discussed significantly in this review. …”
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Validation of Self-reported Medical Condition in the Taiwan Biobank by Chi-Shin Wu, Le-Yin Hsu, Chen-Yang Shen, Wei J. Chen, Shi-Heng Wang

“…Integrating complementary databases, such as clinical diagnoses, prescription records, and medical procedures, can enhance accuracy through customized algorithms based on disease categories and participant characteristics and optimize sensitivity or positive predictive values to align with specific research objectives.…”
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Optimizing additive manufacturing workflows using model-based systems engineering by Mohamed Amine Daoud, Khadija Bekkay Haouari, Meriem Hayani Mechkouri, Amine Ennawaoui, Hicham El hadraoui, Ilias Naser, Mustapha Ouardouz, Kamal Reklaoui

“…Future iterations of ASAM will incorporate advanced algorithms for real-time parameter optimization and predictive analytics. …”
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Integrated Platform for Bio-Image Processing and Identification of Cervical Cancer by Larisa-Dalina LAZĂR, Diogen BABUC, Todor IVAŞCU

“…The proposed methodology consists of several stages, starting with the preprocessing of medical images, including normalization, augmentation, and noise reduction. Following this, the cervical cells are segmented and classified using a combination of machine learning algorithms, such as shallow machine learning models and Convolutional Neural Networks (CNN). …”
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Feature Extraction and Diagnosis of Power-Shift System Faults in Unmanned Hydro-Mechanical Transmission Tractors by Ya Li, Kuan Liu, Xiaohan Chen, Kejia Zhai, Yangting Liu, Yehui Zhao, Guangming Wang

“…Both W_SCBAM and conventional algorithms were trained 20 times, and their performance was compared. …”
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AI-powered blockchain technology in industry 4.0, a review by Mohsen Soori, Roza Dastres, Behrooz Arezoo

“…Artificial Intelligence (AI) algorithms can be employed to enhance the security of the blockchain networks in the era of industry 4.0. …”
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