Intelligent multi-modeling reveals biological relationships and adaptive phenotypes for dairy cow adaptation to climate change by Robson Mateus Freitas Silveira, Angela Maria de Vasconcelos, Concepta McManus, Luiz Paulo Fávero, Iran José Oliveira da Silva

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Leveraging a disulfidptosis-based signature to characterize heterogeneity and optimize treatment in multiple myeloma by Bingxin Zhang, Dong Zheng, Shuxia Zhu, Xinyi Zhang, Quanqiang Wang, Zhili Lin, Ziwei Zheng, Shujuan Zhou, Zixing Chen, Sisi Zheng, Enqing Lan, Luning Cui, Hansen Ying, Yu Zhang, Xuanru Lin, Qiang Zhuang, Honglan Qian, Xudong Hu, Yan Zhuang, Qianying Zhang, Zhouxiang Jin, Songfu Jiang, Yongyong Ma, Yongyong Ma, Yongyong Ma

“…Finally, in vitro experiments examined the expression patterns of disulfidptosis-related genes (DRGs) in MM.ResultsBy cluster analysis, we obtained three subtypes with C2 having a worse prognosis than C3. …”
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The Impact of AI-Driven Application Programming Interfaces (APIs) on Educational Information Management by David Pérez-Jorge, Miriam Catalina González-Afonso, Anthea Gara Santos-Álvarez, Zeus Plasencia-Carballo, Carmen de los Ángeles Perdomo-López

“…However, the review also identifies concerns about privacy, algorithmic bias, and limited methodological rigor in existing research. …”
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Multivariate determinants of self-management in Health Care: assessing Health Empowerment Model by comparison between structural equation and graphical models approaches by Filippo Trentini, Matteo Malgaroli, Anne Linda Camerini, Clelia Di Serio, Peter Schulz

“…</strong> This  paper aims at investigating the consistency of Health Empowerment Model by means of both graphical models approach, which is a “data driven” method and a Structural Equation Modeling (SEM) approach, which is instead “theory driven”, showing the different information pattern that can be revealed in a health care research context.…”
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Application of deep learning in malware detection: a review by Yafei Song, Dandan Zhang, Jian Wang, Yanan Wang, Yang Wang, Peng Ding

“…There is an urgent need to adopt advanced tools for early detection of malware and its variants to help researchers take early steps to defend against it. Its broad approach will help the early malware to detect and identify the behavioral patterns of large amounts of malicious data, and the discipline of artificial intelligence offers broad research potential. …”
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Identifying Suicidal Ideation Through Automatic Extraction of Emotional Traces in Suicide Notes by Angel Hernandez-Castaneda, Rene Arnulfo Garcia-Hernandez, Yulia Ledeneva

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THE IMPROVEMENT OF PROFESSIONAL TRAINING ORGANIZATION OF THE X-RAY SCREENING SYSTEMS OPERATORS BY USING THE EYE MOVEMENTS REGISTRATION SYSTEM AND METHODS OF CLUSTER AND DISCRIMINAN... by A. K. Volkov, V. V. Ionov

“…The X-ray screening systems operators’ professional training is based on the CBT (computer-based training) principle, which has algorithms of adaptive training. These algorithms in existing computer simulators include feedback mechanisms on the basis of trainability exponents – such as the frequency of detecting dangerous objects, the frequency of false alarms and detection time. …”
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Computational intelligence investigations on evaluation of salicylic acid solubility in various solvents at different temperatures by Adel Alhowyan, Wael A. Mahdi, Ahmad J. Obaidullah

“…Abstract This research shows the utilization of various tree-based machine learning algorithms with a specific focus on predicting Salicylic acid solubility values in 13 solvents. …”
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AI-Driven Framework for Evaluating Climate Misinformation and Data Quality on Social Media by Zeinab Shahbazi, Rezvan Jalali, Zahra Shahbazi

“…Data quality is defined using key dimensions of credibility, accuracy, relevance, and sentiment polarity, and a pipeline is developed using transformer-based NLP models, sentiment classifiers, and misinformation detection algorithms. The system processes user-generated content to detect sentiment drift, engagement patterns, and trustworthiness scores. …”
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Enhancing privacy in clustering and data mining: A novel approach for sensitive data protection by Haythem Hayouni

“… In the era of big data, clustering and data mining have become essential tools for uncovering patterns and insights from vast datasets. However, these processes often involve the use of sensitive data, raising significant concerns about privacy, security, and trustworthiness. …”
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Review of Methods and Models for Forecasting Electricity Consumption by Kamil Misiurek, Tadeusz Olkuski, Janusz Zyśk

“…The authors conducted a comparative analysis of various models, such as autoregressive models, neural networks, fuzzy logic systems, hybrid models, and evolutionary algorithms. Particular attention was paid to the effectiveness of these methods in the context of variable input data, such as weather conditions, seasonal fluctuations, and changes in energy consumption patterns. …”
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Integrating AI-generated content tools in higher education: a comparative analysis of interdisciplinary learning outcomes by Zhang Yan, Tang Qianjun

“…Using a mixed-methods approach, we analyzed implementation patterns and learning outcomes across humanities, STEM, and social sciences programs at multiple institutions. …”
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On the effect of sampling frequency on the electricity theft detection performance by Fatemeh Soleimani Nasab, Foad Ghaderi

“…Recently, machine and deep learning techniques are being used widely to detect thieves by analysing the consumption patterns. While the prediction accuracy of these methods depends on the number and quality of the existing samples used for training models, the majority of previous research work focussed on data with high sampling frequencies, for example, data from smart grids. …”
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Calculation Model of Multi-roll Straightening Process Based on Bilinear Hardening and Power Hardening by ZHU Xiaoyu, CHENG Zixing, WANG Xiaogang, HAN Peisheng

“…The bilinear model shows a linear increase in error, while the power model displays a nonlinear pattern. The magnitude of the hardening coefficient affects only the degree of curvature error, not the underlying behavior or material applicability. …”
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Human-based metaheuristics and non-parametric learning for groundwater-prone area mapping by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Seyedeh Zeinab Shogrkhodaei, Biswajeet Pradhan, Soo-Mi Choi

“…This study addresses these challenges by integrating human-inspired metaheuristics with non-parametric machine-learning techniques to enhance groundwater potential prediction. This research introduces a novel approach combining human-based metaheuristics—Teaching Learning Based Optimization (TLBO) and Cultural Algorithms (CA)—with non-parametric Decision Tree (DT) models. …”
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Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network by Aythem Khairi Kareem, Mohammed M. AL-Ani, Ahmed Adil Nafea

“…Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. …”
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Recent advances in machine learning for defects detection and prediction in laser cladding process by X.C. Ji, R.S. Chen, C.X. Lu, J. Zhou, M.Q. Zhang, T. Zhang, H.L. Yu, Y.L. Yin, P.J. Shi, W. Zhang

“…As a fundamental component of artificial intelligence, machine learning has gained considerable prominence within the domain of laser cladding in recent years. By employing algorithms to analyze data, discern patterns and regularities, rendering predictions and decisions, machine learning has significantly influenced various aspects of laser cladding processes. …”
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Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance by Firgiawan Faira, Dandy Pramana Hostiadi

“…Industry 4.0 requires secure networks as the advancements in IoT and AI exacerbate the challenges and vulnerabilities in data security. This research focuses on detecting Bot-IoT activity using the Bot-IoT UNSW Canberra 2018 dataset. …”
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Melanoma Skin Lesion Classification Using Neural Networks: A systematic review by ahmed hammo, Mohammed Younis

“…Given neural networks' evolutionary patterns, updated, changed, and integrated networks are expected to increase the performance of such systems. …”
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Development of an Optimal Machine Learning Model to Predict CO₂ Emissions at the Building Demolition Stage by Gi-Wook Cha, Choon-Wook Park

“…The GBM model also showed excellent results in generalization performance, and it effectively learned the data patterns without overfitting in residual analysis and mean absolute error (MAE) evaluation. …”
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