Precision neuropsychology in the area of AI by Astri J. Lundervold

“…The paper outlines the technological evolution from basic computerized testing to sophisticated machine learning applications that could enable clinicians to more accurately detect subtypes of neuropsychological conditions. …”
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A review of recent artificial intelligence for traditional medicine by Chengbin Hou, Yanzhuo Gao, Xinyu Lin, Jinchao Wu, Ning Li, Hairong Lv, William Cheng-Chung Chu

“…By leveraging advanced algorithms and models, AI can improve decision-making efficiency, optimize diagnosis accuracy, enhance patient experience, and reduce costs. …”
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THE CURRENT STATE OF ARTIFICIAL INTELLIGENCE IN RADIOLOGY – A REVIEW OF THE BASIC CONCEPTS, APPLICATIONS, AND CHALLENGES by Mariana Yordanova

“…Results and Discussion: Machine learning in radiology focuses on developing algorithms that analyze medical images without explicitly programmed rules, divided into supervised and unsupervised learning. …”
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A Robust Framework for Bamboo Forest AGB Estimation by Integrating Geostatistical Prediction and Ensemble Learning by Lianjin Fu, Qingtai Shu, Cuifen Xia, Zeyu Li, Hailing He, Zhengying Li, Shaoyang Ma, Chaoguan Qin, Rong Wei, Qin Xiang, Xiao Zhang, Yiran Zhang, Huashi Cai

“…Subsequently, a stacked ensemble model, integrating four machine learning algorithms, predicted AGB from the full suite of continuous variables. …”
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Real-time monitoring to predict depressive symptoms: study protocol by Yu-Rim Lee, Jong-Sun Lee

“…Passive data will be collected through sensors on the wearable-device, while EMA data will be collected four times a day through a smartphone app. A machine learning algorithm and multilevel model will be used to construct a predictive model for depressive symptoms using the collected data.DiscussionThis study explores the potential of wearable devices and smartphones to improve the understanding and treatment of depression in young adults. …”
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Abnormal heart sound recognition using SVM and LSTM models in real-time mode by Moy’awiah A. Al-Shannaq, Areen Nasrawi, Abed Al-Raouf K. Bsoul, Ahmad A. Saifan

“…Digital signal processing methods, by applying the fast Fourier transform, filtering techniques, and the dual-tree complex wavelet transform, with machine learning classification algorithms are employed to segment the input phonocardiogram signal, extract meaningful features, and find the appropriate class for the input signal. …”
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Convolutional neural network approach for fault detection and characterization in medium voltage distribution networks by Atefeh Pour Shafei, J.Fernando A. Silva, J. Monteiro

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Object-gaze distance: Quantifying near-peripheral gaze behavior in real-world applications by Felix Wang, Julian Wolf, Mazda Farshad, Mirko Meboldt, Quentin Lohmeyer

“…The algorithm uses machine learning for area of interest (AOI) detection and computes the minimal 2D Euclidean pixel distance to the gaze point, creating a continuous gaze-based time-series. …”
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A novel integrated TDLAVOA-XGBoost model for tool wear prediction in lathe and milling operations by Zhongyuan Che, Chong Peng, Chi Wang, Jikun Wang

“…Machine learning models, particularly eXtreme Gradient Boosting (XGBoost), demonstrate pattern recognition capabilities for such predictions. …”
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Application of Concentration-Area fractal modeling and artificial neural network to identify Cu, Zn±Pb geochemical anomalies in Hashtjin area, NW of Iran by Ali Imamalipour, Hamed Ebrahimi, Amir reza Abdollahpur

“…Recent research investigations have shown that Machine Learning (ML) algorithms can identify geochemical anomalies associated with mineralization that represent targets for mineral exploration. …”
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A comprehensive review of flood damage in mountainous regions: challenges, solutions, and advanced management technologies by Mohammad Roohi, Jila Dehghani, Maryam Irani, Paramiss Mina

“…This work employs techniques such as LiDAR for precise topographic models, integrating remote sensing with hydrological/hydraulic models, and analyzing satellite imagery to study flood patterns and land cover changes. Furthermore, a precise and useful understanding of the effects of floods in these areas has been made possible by the use of machine learning algorithms to forecast flood episodes and evaluate damage, in conjunction with field research and community participation, such as citizen science initiatives for data collection and local knowledge integration. …”
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Mobile Robot Positioning with Wireless Fidelity Fingerprinting and Explainable Artificial Intelligence by Hüseyin Abacı, Ahmet Çağdaş Seçkin

“…This paper proposes a machine learning-based method for Wi-Fi-enabled robot positioning in indoor environments. …”
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WGCNA-ML-MR integration: uncovering immune-related genes in prostate cancer by Jing Lv, Jing Lv, Yuhua Zhou, Yuhua Zhou, Shengkai Jin, Shengkai Jin, Chaowei Fu, Chaowei Fu, Yang Shen, Yang Shen, Bo Liu, Bo Liu, Menglu Li, Yuwei Zhang, Yuwei Zhang, Ninghan Feng, Ninghan Feng, Ninghan Feng

“…The intersection of differentially expressed genes and driver genes was taken, and Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were performed. Furthermore, a machine learning algorithm was used to screen for core genes and construct a diagnostic model, which was then validated in an external validation dataset. …”
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Seismic clusters and fluids diffusion: a lesson from the 2018 Molise (Southern Italy) earthquake sequence by Stefania Gentili, Piero Brondi, Giuliana Rossi, Monica Sugan, Giuseppe Petrillo, Jiancang Zhuang, Stefano Campanella

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Exploring the Role of Artificial Intelligence in Detecting Advanced Persistent Threats by Pedro Ramos Brandao

“…This paper explores the pivotal role of Artificial Intelligence (AI) in enhancing the detection and mitigation of APTs. By leveraging machine learning algorithms and data analytics, AI systems can identify patterns and anomalies that are indicative of sophisticated cyber-attacks. …”
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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

“… Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. 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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Integrating Model‐Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation by Karthik Raman, Rukmini Kumar, Cynthia J. Musante, Subha Madhavan

“…Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug–target interactions from big data, enabling more accurate predictions and novel hypothesis generation. …”
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Integrated single-cell and transcriptome sequencing data reveal the value of IL1RAP in gastric cancer microenvironment and prognosis by Weifeng Yang, Xiaohua Wu, Jian Wang, Wenquan Ou, Xing Huang

“…The CIBERSORT and ssGSEA algorithms elucidated immune infiltration patterns, while TIDE and TCGA predicted immune-related outcomes. …”
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Revolutionizing Sperm Analysis with AI: A Review of Computer-Aided Sperm Analysis Systems by Francisco J. Baldán, Diego García-Gil, Carlos Fernandez-Basso

“…However, limitations persist, such as the dependency on large, high-quality annotated datasets for training DL models, potential challenges in model generalizability across diverse clinical settings, and the “black-box” nature of some complex algorithms, alongside crucial needs for rigorous clinical validation, data standardization, and ethical management of sensitive information. …”
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Melanoma Skin Lesion Classification Using Neural Networks: A systematic review by ahmed hammo, Mohammed Younis

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