Machine learning reveals sex differences in distinguishing between conduct-disordered and neurotypical youth based on emotion processing dysfunction by Gregor Kohls, Erik M. Elster, Peter Tino, Graeme Fairchild, Christina Stadler, Arne Popma, Christine M. Freitag, Stephane A. De Brito, Kerstin Konrad, Ruth Pauli

“…Here, we used novel analytic techniques such as machine learning (ML) to uncover potentially sex-specific differences in emotion dysfunction among girls and boys with CD compared to their neurotypical peers. …”
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Comparative study of long short-term memory (LSTM), bidirectional LSTM, and traditional machine learning approaches for energy consumption prediction by Hamed Alizadegan, Behzad Rashidi Malki, Arian Radmehr, Hossein Karimi, Mohsen Asghari Ilani

“…Additionally, individual models based on LSTM, Bi-LSTM, and other machine learning methods are implemented for a comprehensive assessment. …”
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A novel hybrid methodology for wind speed and solar irradiance forecasting based on improved whale optimized regularized extreme learning machine by S. Syama, J. Ramprabhakar, R Anand, V. P. Meena, Josep M. Guerrero

“…Then, a unique swarm intelligence technique, the non-linear dimension learning Hunting Whale Optimization Algorithm (NDLHWOA), is devised to optimize regularized extreme learning machine model parameters to capture the implicit information of each reconstructed sub-series. …”
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Solar Energy Forecasting Framework Using Prophet Based Machine Learning Model: An Opportunity to Explore Solar Energy Potential in Muscat Oman by Mazhar Baloch, Mohamed Shaik Honnurvali, Adnan Kabbani, Touqeer Ahmed, Sohaib Tahir Chauhdary, Muhammad Salman Saeed

“…Nevertheless, with the advancement in the field of artificial intelligence (AI), one can predict the availability of solar and wind energy in the short, medium, and long term with fairly high accuracy. As such, this research work aims to develop a machine-learning-based framework for forecasting global horizontal irradiance (GHI) for Muscat, Oman. …”
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Robust ensemble of handcrafted and learned approaches for DNA-binding proteins by Loris Nanni, Sheryl Brahnam

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Exploring the most important factors related to self-perceived health among older men in Sweden: a cross-sectional study using machine learning by David C Currow, Magnus Per Ekström, Max Olsson

“…Objective To evaluate which factors are the most strongly related to self-perceived health among older men and describe the shape of the association between the related factors and self-perceived health using machine learning.Design and setting This is a cross-sectional study within the population-based VAScular and Chronic Obstructive Lung disease study (VASCOL) conducted in southern Sweden in 2019.Participants A total of 475 older men aged 73 years from the VASCOL dataset.Measures Self-perceived health was measured using the first item of the Short Form 12. …”
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Hate Speech Detection Using Large Language Models: A Comprehensive Review by Aish Albladi, Minarul Islam, Amit Das, Maryam Bigonah, Zheng Zhang, Fatemeh Jamshidi, Mostafa Rahgouy, Nilanjana Raychawdhary, Daniela Marghitu, Cheryl Seals

“…Traditional methods for detecting hate speech, such as keyword matching, rule-based systems, and machine learning algorithms, often struggle to capture the subtle and context-dependent nature of hateful content. …”
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Boruta-grid-search least square support vector machine for NO2 pollution prediction using big data analytics and IoT emission sensors by Habeeb Balogun, Hafiz Alaka, Christian Nnaemeka Egwim

“…The purpose of this paper is to pre-process a relatively large data of NO2 from Internet of Thing (IoT) sensors with time-corresponding weather and traffic data and to use the data to develop NO2 prediction models using BA-GS-LSSVM and popular standalone algorithms to allow for a fair comparison. Design/methodology/approach – This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO2 pollution concentration. …”
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Pengembangan Auto-AI Model Generatif Analisis Kompleksitas Waktu Algoritma Untuk Data Multi-Sensor IoT Pada Node-RED Menggunakan Extreme Learning Machine by Imam Cholissodin, Dahnial Syauqy, Dwi Ady Firmanda, Ibrahim Aji, Edy Rahman, Syazwandy Harahap, Fernando Septino

“…Namun dengan perkembangan teknologi komputer untuk AI, Machine Learning maupun Deep Learning, algoritma dengan basis AI tersebut, dalam penelitian ini dikembangkan untuk menemukan solusi general persamaan model T(n) secara otomatis dari desain algoritma sederhana atau kompleks. …”
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Attention-enhanced optimized deep ensemble network for effective facial emotion recognition by Taimoor Khan, Muhammad Yasir, Chang Choi

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Research and application of adaptive algorithm for 5G voice quality evaluation by Yuxiang ZHAO, Yaxin JI, Li YU, Tianyi ZHOU, Hang ZHOU

“…MOS (mean opinion score) is usually used to evaluate voice quality in the industry.It can objectively and fairly reflect the user’s voice service perception.It is difficult and costly to obtain data by road test, so a trained supervised learning model is usually used to predict the MOS score.However, the operator voice data has the characteristics of low percentage of MOS low score data and time sequence change, which affects the accuracy and generalization of the model prediction.Based on the study of existing data acquisition systems and machine learning algorithms of operators, an adaptive algorithm for MOS evaluation of 5G speech quality was proposed.Firstly, POLQA algorithm test equipment based on full parameter evaluation obtained training data to ensure the accuracy of training samples.Secondly, by means of data enhancement, the difficulty of acquiring poor quality samples was solved.Finally, based on the adaptive algorithm selection, the optimal MOS prediction model could be selected periodically and dynamically according to the timing changes of data features, so as to achieve large-scale and intelligent evaluation of 5G voice quality.…”
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‘We have opened a can of worms’: using collaborative ethnography to advance responsible artificial intelligence innovation by Andrés Domínguez Hernández, Richard Owen

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The Data Artifacts Glossary: a community-based repository for bias on health datasets by Rodrigo R. Gameiro, Naira Link Woite, Christopher M. Sauer, Sicheng Hao, Chrystinne Oliveira Fernandes, Anna E. Premo, Alice Rangel Teixeira, Isabelle Resli, An-Kwok Ian Wong, Leo Anthony Celi

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A Framework for Evaluating Geomagnetic Indices Forecasting Models by Armando Collado‐Villaverde, Pablo Muñoz, Consuelo Cid

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ATTRIBUTION OF MEDIA TEXTS BASED ON A TRAINED NATURAL LANGUAGE MODEL AND LINGUISTIC ASSESSMENT OF IDENTIFICATION QUALITY by Vladimir A. Klyachin, Ekaterina V. Khizhnyakova

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Crime Prediction Using Decision Tree (J48) Classification Algorithm. by Ivan, Niyonzima, Emmanuel Ahishakiye, Elisha Opiyo Omulo, Danison Taremwa

“…This study considered the development of crime prediction prototype model using decision tree (J48) algorithm because it has been considered as the most efficient machine learning algorithm for prediction of crime data as described in the related literature. …”
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Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools—a scoping review of potential applications by Anuja Phalle, Devaki Gokhale

“…IntroductionRecent developments in Artificial Intelligence (AI) and Machine Learning (ML) technologies have opened new avenues for their applications in dietary assessments. …”
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Advanced R-GAN: Generating anomaly data for improved detection in imbalanced datasets using regularized generative adversarial networks by Junhak Lee, Dayeon Jung, Jihoon Moon, Seungmin Rho

“…The discriminator is meticulously designed, leveraging the CELU (short for continuously differentiable exponential linear unit) activation for optimal feature extraction, ensuring diverse and representative sample generation. To ensure fairness and validate the effectiveness of our data generation process, we used PyCaret's automated machine learning framework to rigorously test different machine learning models, ultimately identifying the light gradient boosting machine as the most effective. …”
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A Review of Reinforcement Learning for Fixed-Wing Aircraft Control Tasks by David J. Richter, Ricardo A. Calix, Kyungbaek Kim

“…A lot of that can be attributed to the recent advancements in machine learning (ML) and deep learning (DL) as a whole, the power of deep neural networks and the incorporation of them into reinforcement learning algorithms and techniques. …”
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USING ARTIFICIAL INTELLIGENCE (AI) AND DEEP LEARNING TECHNIQUES IN FINANCIAL RISK MANAGEMENT by Joseph Olorunfemi AKANDE

“…Despite these advances, there is still much work to be done regarding the explainability and fairness of machine learning models used in financial risk management. …”
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