Forecasting Stock Market Volatility Using Housing Market Indicators: A Reinforcement Learning-Based Feature Selection Approach by Pourya Zareeihemat, Samira Mohamadi, Jamal Valipour, Seyed Vahid Moravvej

“…This study tackles the complex challenge of accurately predicting stock market volatility through indicators from the housing market. …”
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Machine-learning aided calibration and analysis of porous media CFD models used for rotating packed beds by Ahmed M. Alatyar, Abdallah S. Berrouk

“…The algorithm is used to improve CFD predictions of dry pressure drop in rotating packed beds (RPBs) for a wide range of operating conditions. …”
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Fuzzy logic based adaptive duty cycling for sustainability in energy harvesting sensor actor networks by Sai Krishna Mothku, Rashmi Ranjan Rout

“…In this work, current residual energy, predicted harvesting energy (for a futuristic time slot) and predicted residual energy parameters are considered as fuzzy input variables to estimate duty cycle for a sensor node. …”
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Definition of complex roots of the fourth degree algebraic equation by V. M. Ilyin

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Unveiling shadows: A data-driven insight on depression among Bangladeshi university students by Sanjib Kumar Sen, Md. Shifatul Ahsan Apurba, Anika Priodorshinee Mrittika, Md. Tawhid Anwar, A.B.M. Alim Al Islam, Jannatun Noor

“…Seven machine learning models, including Support Virtual Machine (SVM), K-Nearest Neighbor (K-NN), Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest Classifier (RFC), Artificial Neural Network (ANN), and Gradient Boosting (GB), were trained and tested using the collected data (n = 750) to identify the most effective method for predicting depression. After rigorous analysis, Random Forest emerged as the best-performing algorithm, exhibiting remarkable accuracy (87%), precision (78%), recall (95%), and f1-score (86%). …”
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Digital processing of images of nano-dimensional elements on nanostructured material surface using the ImageJ software by H. T. Dinh, N. V. Lushpa, K. V. Chernyakova, I. A. Vrublevsky

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Future Era of Accountants under the Impact of AI by Guan Ran

“…As AI reshapes the industry, accountants must develop expertise in data analytics, predictive modeling, and cybersecurity to remain competitive. …”
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Distinctive Behaviors of Druggable Proteins in Cellular Networks. by Costas Mitsopoulos, Amanda C Schierz, Paul Workman, Bissan Al-Lazikani

“…We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. …”
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EXTRAPOLATION BY ATTRACTION METHOD FOR IDENTIFICATION OF TRACKS AND PLOTS IN SYSTEM OF SECONDARY RADAR INFORMATION PROCESSING by U. A. Aparovich, R. V. Bondarenko

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A comparison of tools used for tuberculosis diagnosis in resource-limited settings: a case study at Mubende referral hospital, Uganda. by Adrian Muwonge, Sydney Malama, Barend M de C Bronsvoort, Demelash Biffa, Willy Ssengooba, Eystein Skjerve

“…Clinical variables from a questionnaire and DZM were used to predict TB status in multivariable logistic and Cox proportional hazard models, while optimization and visualization was done with receiver operating characteristics curve and algorithm-charts in Stata, R and Lucid-Charts respectively.…”
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Validation of eight endotypes of lupus based on whole-blood RNA profiles by Peter E Lipsky, Prathyusha Bachali, Amrie C Grammer, Erika Hubbard

“…Objective We previously described a classification system of persons with SLE based on whole blood RNA profiles and a random forest (RF) algorithm to predict individual patient endotypes. …”
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FORECASTING COHERENT VOLATILITY BREAKOUTS by A. S. Didenko, M. M. Dubovikov, B. A. Poutko

“…The paper develops an algorithm for making long-term (up to three months ahead) predictions of volatility reversals based on long memory properties of financial time series. …”
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Imbalanced feature generation based on bootstrap power spectral curve for estimating respiratory rate by Soojeong Lee, Gyanendra Prasad Joshi, Gangseong Lee

“…However, machine learning algorithm-related errors are unsuitable for medical decision-making processes because some data have a much larger sample size in the training set than in other sets. …”
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Investigating the Machining Behavior of the Additively Manufactured Polymer-Based Composite Using Adaptive Neuro-Fuzzy Learning by Anastasios Tzotzis, Dumitru Nedelcu, Simona-Nicoleta Mazurchevici, Panagiotis Kyratsis

“…Multi-objective optimization using the Non-Dominated Sorting Genetic Algorithm 2 (NSGA-II) algorithm was employed to minimize both <i>Ra</i> and <i>T</i> simultaneously. …”
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Re-identification assistance and multi-stage association for pedestrian multi-object tracking by Ye Li, Li Zhan, Lei Wu, Hongkun Liu, Jianli Min, Xinzhong Wang

“…Then, based on the Intersection over Union (IoU) between the predicted bounding box and the detection bounding box, the Hungarian algorithm is used to match the targets between frames. …”
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State of Charge Estimation on Lithium-Ion Batteries Using Particle Swarm Optimization Method by Muhammad Ridho Dewanto, Riza Hadi Saputra, Kharis Sugiarto, Agung Adi Saputra

“…The high accuracy of 87.972% of PSO also shows that this method is reliable for applications that require precise SoC predictions. It is hoped that the results of this research can become a basis for further research in the field of battery management and metaheuristic algorithm optimization. …”
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AN INTERDISCIPLINARY APPROACH TO TREATMENT OF TEMPOROMANDIBULAR PAIN-DYSFUNCTION SYNDROME AGAINST THE BACKDROP OF CERVICAL OSTEOCHONDROSIS. by V.L. Melnik, V.K. Shevchenko

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System Development for Liquid Chemicals Point Injection Based on Convolutional Neural Network Models by V. S. Semenyuk, E. A. Nikitin

“…They showed that the predicted error on the validation data was 0.18758. …”
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Molecular sub-classification of renal epithelial tumors using meta-analysis of gene expression microarrays. by Thomas Sanford, Paul H Chung, Ariel Reinish, Vladimir Valera, Ramaprasad Srinivasan, W Marston Linehan, Gennady Bratslavsky

“…These signatures were organized into an algorithm to sub-classify renal neoplasms. The use of these signatures according to our algorithm was validated on several independent datasets.…”
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Detection of Multiple Small Biased GPS Spoofing Attacks on Autonomous Vehicles Using Time Series Analysis by Ahmad Mohammadi, Reza Ahmari, Vahid Hemmati, Frederick Owusu-Ambrose, Mahmoud Nabil Mahmoud, Parham Kebria, Abdollah Homaifar

“…This detection is facilitated through time series analysis at 25 and 50 s intervals to build a profile of data errors and distribution to predict the probability of such attacks. To evaluate the algorithm&#x0027;s effectiveness, five different test datasets depicting four types of spoofing scenarios&#x2014;turn-by-turn, overshoot, stop, and multiple small biased attacks&#x2014;were created using data from the publicly accessible Honda Research Institute Driving Dataset (HDD). …”
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