Control over biopower in cognitive and surveillance capitalism by Stanković-Pejnović Vesna

“…Predictive behavioral surplus sources are increased and enhanced to guide, advise and lead people to behaviors, which they believe free, which actually aim for the greater profit of surveillance capitalists.…”
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Integration of graph neural networks and transcriptomics analysis identify key pathways and gene signature for immunotherapy response and prognosis of skin melanoma by Maodong Ye, Shuai Ren, Huanjuan Luo, Xiumin Wu, Hongwei Lian, Xiangna Cai, Yingchang Ji

“…Methods GNNs models were developed to predict the response to immunotherapy and to pinpoint key pathways. …”
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The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1) by M. Buehner, J.-F. Caron, E. Lapalme, A. Caya, P. Du, Y. Rochon, S. Skachko, M. Bani Shahabadi, S. Heilliette, M. Deshaies-Jacques, W. Chang, M. Sitwell

“…In addition to describing the current MIDAS applications, a sample of the results from these systems is presented to demonstrate their performance in comparison with either systems from before the switch to using MIDAS software or similar systems at other numerical weather prediction (NWP) centres. The modular software design also allows the code that implements high-level components (e.g. observation operators, error covariance matrices, state vectors) to easily be used in many different ways depending on the application, such as for both variational and ensemble DA algorithms, for estimating the observation impact on short-term forecasts, and for performing various observation pre-processing procedures. …”
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Machine learning based identification of suicidal ideation using non-suicidal predictors in a university mental health clinic by Muhammed Ballı, Asli Ercan Dogan, Sevin Hun Senol, Hale Yapici Eser

“…Mental disorders are closely linked to suicidal ideation, but predicting suicide remains complex due to the multifaceted nature of contributing factors. …”
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Adjusted imbalance ratio leads to effective AI-based drug discovery against infectious disease by Ons Masmoudi, Afef Abdelkrim, Emna Harigua-Souiai

“…We trained five machine learning and six deep learning algorithms to predict the anti-pathogen activity of chemical compounds, derived from PubChem Bioassays targeting four infectious diseases. …”
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MRI-based brain tumor ensemble classification using two stage score level fusion and CNN models by Oussama Bouguerra, Bilal Attallah, Youcef Brik

“…In the second stage, ensemble learning algorithms like weighted sum, fuzzy rank, and majority vote are used to combine the scores from the trained models, enhancing prediction results. …”
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A Hybrid Approach to Call Admission Control in 5G Networks by Mohammed Al-Maitah, Olena O. Semenova, Andriy O. Semenov, Pavel I. Kulakov, Volodymyr Yu. Kucheruk

“…Such artificial intelligence techniques as neural networks, fuzzy systems, and genetic and evolutionary algorithms are widely used for communication systems management, optimization, and prediction. …”
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The Development of Digital Twin Baby Incubators for Fault Detection and Performance Analysis by Hatice Kabaoğlu, Emine Uçar, Fecir Duran

“…The digital twin employs a hybrid model integrating Long Short-Term Memory (LSTM) and Random Forest (RF) algorithms to predict potential errors and alarms. The LSTM algorithm was trained using sensor data provided by a health technology company to predict future measurements. …”
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INTERGENIC INTERACTIONS OF TUMOR NECROSIS FACTOR ALPHA, INTERLEUKIN 17, AND OSTEOPROTEGERIN IN THE IMMUNOPATHOGENESIS OF RHEUMATOID ARTHRITIS IN THE RUSSIAN POPULATION OF THE CHELY... by Yulia Chumacheva, Daria Stashkevich, Tatiana Suslova, Daria Shmelkova, Aleksandra Burmistrova

“…To analyse the data we applied the multifactor dimensionality reduction (MDR) algorithm, which constructs predictive case–control models and evaluates their robustness through ten-fold cross-validation and permutation testing.The algorithm identified three most informative combinations comprising four to six SNPs each; every combination showed statistical significance and high predictive accuracy. …”
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Transforming tabular data into images via enhanced spatial relationships for CNN processing by Hameedah A. Alenizy, Jawad Berri

“…The results showed that NCTD consistently surpassed the majority of competing algorithms in nine out of ten datasets, indicating its potential as a robust tool for extending CNN applicability beyond conventional image-based domains, particularly in complex classification and prediction.…”
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Machine Learning Ensemble Classifiers for Feature Selection in Rice Cultivars by Chandrakumar Thangavel, D Sakthipriya

“…Machine Learning is crucial for seed prediction, germination, crop production, soil moisture, and land suitability evaluation. …”
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Deep reinforcement learning for inverse inorganic materials design by Christopher Karpovich, Elton Pan, Elsa A. Olivetti

“…We apply template-based crystal structure prediction to suggest feasible crystal structure matches for target inorganic compositions identified by our machine learning (ML) algorithms to highlight the plausibility of the identified target compositions. …”
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Development of the automatic system for switching on the generating equipment for parallel operation with the electric power system by V. A. Fyodorova, V. F. Kirichenko, G. V. Glazyrin

“…The system has extended functionality compared to analogs, allowing the use of traditional methods and the developed method of accelerated synchronization. The results predict reduction of capital costs for automation systems, as one device provides synchronization on several circuit breakers and reduction of generator operation costs, as using this system, machine parts exposed to unacceptable thermal and mechanical effects. …”
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Scientists yet to consider spatial correlation in assessing uncertainty of spatial averages and totals by Alexandre M.J.-C. Wadoux, Gerard B.M. Heuvelink

“…The remote sensing data typically serve as covariates to deliver spatially explicit information using machine learning algorithms. Often the uncertainty associated with the maps is also quantified, for instance by prediction error variance maps or by maps of the lower and upper limits of a prediction interval. …”
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LSTM-MM: Efficient LSTM-Based Mobility Management for Power Inspection Vehicles in Smart Grids by Xiaoyan Wang, Xianfeng Xie, Shengnan Zhao

“…This scheme leverages Software-Defined Networking (SDN) technology to centrally manage vehicle communication and network traffic, integrating trajectory prediction algorithms to forecast the future movements of power inspection vehicles, optimizing route planning, and improving operational efficiency and network performance. …”
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Biomarker discovery by sparse canonical correlation analysis of complex clinical phenotypes of tuberculosis and malaria. by Juho Rousu, Daniel D Agranoff, Olugbemiro Sodeinde, John Shawe-Taylor, Delmiro Fernandez-Reyes

“…We show that using clinical view projections we obtain remarkable improvements in diagnostic class prediction, up to 11% in tuberculosis and up to 5% in malaria. …”
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Artificial intelligence in diabetology by V. V. Klimontov, V. B. Berikov, O. V. Saik

“…Machine learning algorithms are applied for personalized prediction of glucose trends, in the closed-loop insulin delivery systems and decision support systems for lifestyle modification and diabetes treatment. …”
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WisdomModel: convert data into wisdom by Israa Mahmood, Hasanen Abdullah

“…Purpose – Traditional classification algorithms always have an incorrect prediction. As the misclassification rate increases, the usefulness of the learning model decreases. …”
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Electric Vehicle Charging Load Forecasting Based on K-Means++-GRU-KSVR by Renxue Shang, Yongjun Ma

“…Due to the fluctuations in EV charging loads, particularly the significant load variation between commercial and non-commercial areas, global models often suffer from prediction errors when forecasting loads. To address this issue, this paper proposes a regional forecasting method based on K-means++ clustering and deep learning algorithms. …”
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Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

“…In this study, we investigate the predictive efficacy of distinguishing between benign and malignant thyroid nodules by employing traditional machine learning algorithms and a deep transfer learning model, aiming to advance the diagnostic paradigm in this field. …”
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