Investigating the relationship between thought interference, somatic passivity and outcomes in patients with psychosis: a natural language processing approach using a clinical reco... by Robert Stewart, Anna Kolliakou, Jyoti Sanyal, Rashmi Patel, Thibault Thierry Magrangeas

“…Objectives We aimed to apply natural language processing algorithms in routine healthcare records to identify reported somatic passivity (external control of sensations, actions and impulses) and thought interference symptoms (thought broadcasting, insertion, withdrawal), first-rank symptoms traditionally central to diagnosing schizophrenia, and determine associations with prognosis by analysing routine outcomes.Design Four algorithms were developed on deidentified mental healthcare data and applied to ascertain recorded symptoms over the 3 months following first presentation to a mental healthcare provider in a cohort of patients with a primary schizophreniform disorder (ICD-10 F20-F29) diagnosis.Setting and participants From the electronic health records of a large secondary mental healthcare provider in south London, 9323 patients were ascertained from 2007 to the data extraction date (25 February 2020).Outcomes The primary binary dependent variable for logistic regression analyses was any negative outcome (Mental Health Act section, >2 antipsychotics prescribed, >22 days spent in crisis care) over the subsequent 2 years.Results Final adjusted models indicated significant associations of this composite outcome with baseline somatic passivity (prevalence 4.9%; adjusted OR 1.61, 95% CI 1.37 to 1.88), thought insertion (10.7%; 1.24, 95% CI 1.15 to 1.55) and thought withdrawal (4.9%; 1.36, 95% CI 1.10 to 1.69), but not independently with thought broadcast (10.3%; 1.05, 95% CI 0.91 to 1.22).Conclusions Symptoms traditionally central to the diagnosis of schizophrenia, but under-represented in current diagnostic frameworks, were thus identified as important predictors of short-term to medium-term prognosis in schizophreniform disorders.…”
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Evaluating the feasibility of automating dataset retrieval for biodiversity monitoring by Alexandre Fuster-Calvo, Sarah Valentin, William C. Tamayo, Dominique Gravel

“…Automating these tasks through advanced algorithms holds immense potential to revolutionize biodiversity monitoring. …”
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Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection by Kuan-Yu Chen, Yen-Chun Huang, Chih-Kuang Liu, Shao-Jung Li, Mingchih Chen

“…Additionally, six machine-learning algorithms and five cross-validation techniques were employed to identify key features and construct the evaluation model. …”
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A Low-Complexity Diversity-Preserving Universal Bit-Flipping Enhanced Hard Decision Decoder for Arbitrary Linear Codes by Praveen Sai Bere, Mohammed Zafar Ali Khan, Lajos Hanzo

“…The simulation results provided for various known codes and decoding algorithms validate the performance versus complexity benefits of the proposed decoder.…”
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Research on new energy power plant network traffic anomaly detection method based on EMD by Danni Liu, Shengda Wang, YutongLi, Ji Du, Jia Li

“…Methodology This research propose Network Quality Assessment (NQA) traffic management algorithms to prevent illegal access and data breaches, this involves strong security measures such as encryption, firewalls, and encrypted communication methods. …”
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Inductive Power Transfer Coil Misalignment Perception and Correction for Wirelessly Recharging Underground Sensors by John Sanchez, Juan Arteaga, Cody Zesiger, Paul Mitcheson, Darrin Young, Shad Roundy

“…On average, the alignment algorithms required less than four sensor measurements for localization. …”
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A Novel Approach to Faster Convergence and Improved Accuracy in Deep Learning-Based Electrical Energy Consumption Forecast Models for Large Consumer Groups by A. Jayanth Balaji, Binoy B. Nair, D. S. Harish Ram, Kuruvachan Kalluvelil George

“…Eight deep learning algorithms trained using eleven mini-batch combinations, for two forecasting horizons resulted in 176 workflows. …”
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Context aware machine learning techniques for brain tumor classification and detection – A review by Usman Amjad, Asif Raza, Muhammad Fahad, Doaa Farid, Adnan Akhunzada, Muhammad Abubakar, Hira Beenish

“…Competitive analysis reveals that CNN and outperform others algorithms on publicly available datasets, suggesting their potential for precise tumor diagnosis and treatment planning. …”
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Perovskite Solar Cell: Chemical Composition and Bandgap Energy via Machine Learning by Filipi França dos Santos, Kelly Cristine Da Silveira, Gesiane Mendonça Ferreira, Daniella Herdi Cariello, Mônica Calixto de Andrade

“…By deploying machine learning algorithms, this work paves the way for targeted optimizations and considerable strides in the manufacturing of perovskite-based photovoltaic cells. …”
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Advances in Light Field Spatial Super-Resolution: A Comprehensive Literature Survey by Wenqi Lyu, Hao Sheng, Wei Ke, Xiao Ma

“…Future research should focus on enhancing the robustness, generalization, and capability of algorithms to handle complex scenarios. This review offers valuable direction for future research on light field image super-resolution and provides a solid foundation for its applications in virtual reality, augmented reality, and autonomous driving.…”
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Quick Determination of Soil Quality Using Portable Spectroscopy and Efficient Multivariate Techniques by Ernest Teye, Charles L. Y. Amuah, Kofi Atiah, Ransford Opoku Darko, Thomas Abindaw, Kwadwo Kusi Amoah, Michael Miyittah, Emmanuel Afutu, Rebecca Owusu

“…In this study, a pocket-sized shortwave NIR spectroscopy (740–1070 nm) and multivariate statistics were used to classify soil from different land-use types and simultaneously predict nitrogen (N), phosphorus (P), potassium (K), calcium (Ca2+), magnesium (Mg2+), and pH in Ghana. Different Algorithms. Linear discriminant analysis (LDA), support vector machine (SVM), and partial least squares algorithms (full-range partial least square, FrPLS; interval partial least squares, IPLS; synergy interval partial least squares, Si-PLS) were attempted for building a suitable classification and quantification model. …”
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Adaptive Cut Selection in Mixed-Integer Linear Programming by Turner, Mark, Koch, Thorsten, Serrano, Felipe, Winkler, Michael

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Mood Detection from Physical and Neurophysical Data Using Deep Learning Models by Zeynep Hilal Kilimci, Aykut Güven, Mitat Uysal, Selim Akyokus

“…Another novelty is that the emotion classification task is performed by both conventional machine learning algorithms and deep learning models. For this purpose, Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) neural network are employed as deep learning methodologies. …”
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Taylor Expansion of Surface Potential in MOSFET: Application to Pao-Sah Integral by Hugues Murray, Patrick Martin, Serge Bardy

“…The solution is presented analytically wherever possible, and the integration is made from simple numerical methods (Simpson, Romberg) or adaptative algorithms and can be implemented in simple C-program or in usual mathematical software. …”
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Classification of Agricultural Emissions Among OECD Countries with Unsupervised Techniques by Adam Andrzejuk

“…Two methods were used in the analysis: K-means and HDBSCAN algorithms. Both techniques are part of unsupervised learning tasks, which group data into multiple clusters. …”
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A Review of Integration Strategies to Support Gene Regulatory Network Construction by Hailin Chen, Vincent VanBuren

“…Basically, the process of integration of different data sources is divided into two phases: the first phase is collection of the required data and the second phase is data processing with advanced algorithms to infer the GRNs. In this paper these two phases are called “structural integration” and “analytic integration,” respectively. …”
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Machine Learning Classifiers Based Classification For IRIS Recognition by Bahzad Taha Chicho, Adnan Mohsin Abdulazeez, Diyar Qader Zeebaree, Dilovan Assad Zebari

“…The study employs K-nearest neighbors, decision tree (j48), and random forest algorithms, and then compares their performance using the IRIS dataset. …”
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Drug-Induced Liver Injury Associated with the Use of Everolimus in a Liver Transplant Patient by Serena Patel, Michel H. Mendler, Mark A. Valasek, Shirley M. Tsunoda

“…This report includes a brief review of the pharmacokinetic properties of everolimus, a review of the relevant literature, and an analysis using the RUCAM and Naranjo algorithms.…”
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USE OF CORPORATE ACCOUNTING AND CONTROL SYSTEM IN MANAGEMENT by T. M. Rogulenko, A. V. Bodyko

“…According to the results of the study a conclusion has been made, that the corporate accounting and control system is one of the modern algorithmized and automated tools of the corporate management system of economic development strategy in a highly competitive environment and the unpredictability of the behavior of economic entities in the market of goods, works, services, currency, securities, etc., which is integrated with other management systems in the mode of their parallel automation.…”
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A New Approach to General Interpolation Formulae for Bivariate Interpolation by Le Zou, Shuo Tang

“…We discuss the interpolation theorem, algorithms, dual interpolation, and special cases and give many kinds of interpolation scheme. …”
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