Multi-omics analysis reveals that neutrophil extracellular traps related gene TIMP1 promotes CRC progression and influences ferroptosis by Yuzhao Jin, Luyu Liao, Qianping Chen, Bufu Tang, Jin Jiang, Ji Zhu, Minghua Bai

“…Method We jointly screened three major NETs genes through machine learning. Large-sample RNA transcriptome and single-cell transcriptome analysis further confirmed that TIMP1 is a core gene in NETs. …”
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Sensor Image, Anomaly Detection Method for Hydroelectric Dam Structure Using Sensors Measurements and Deep Learning by Van-Phuong Ha, Dinh-Van Nguyen, Trong-Chuong Trinh, Duc-Cuong Quach, Van HuyBui

“…To better prevent future disasters, machine-learning algorithms have been employed. Often, these algorithms are trained on historical sensor data to predict future events. …”
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A study on the global patterns in the design and development of ventricular assist devices: a visualization approach by Ajay K. Sood, A. K. Prasada Rao

“…Future studies can be conducted to explore the use of artificial intelligence and machine learning that can learn from data about patients and then adapt as per the requirements of the patients.…”
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Classification of Non-Seismic Tsunami Early Warning Level Using Decision Tree Algorithm by Elmo Juanara, Chi Yung Lam

“…Objective: This study explored the potential of machine learning algorithms in supporting early warning level issuing for non-seismic tsunamis, specifically volcanic tsunamis. …”
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Impact of glioma metabolism-related gene ALPK1 on tumor immune heterogeneity and the regulation of the TGF-β pathway by YaoFeng Hu, Sen Qin, RuCui Deng

“…Core genes were identified through WGCNA, and a total of 101 machine learning models were constructed, with LASSO+GBM selected as the optimal model, demonstrating robust validation performance. …”
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Enhancing generalization in a Kawasaki Disease prediction model using data augmentation: Cross-validation of patients from two major hospitals in Taiwan. by Chuan-Sheng Hung, Chun-Hung Richard Lin, Jain-Shing Liu, Shi-Huang Chen, Tsung-Chi Hung, Chih-Min Tsai

“…However, KD is frequently misdiagnosed as a common fever in clinical settings, and the inherent data imbalance further complicates accurate prediction when using traditional machine learning and statistical methods. This paper introduces two advanced approaches to address these challenges, enhancing prediction accuracy and generalizability. …”
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Intelligent lithium plating detection and prediction method for Li-ion batteries based on random forest model by Guangying Zhu, Jianguo Chen, Xuyang Liu, Tao Sun, Xin Lai, Yuejiu Zheng, Yue Guo, Rohit Bhagat

“…The study of intelligent machine learning-based lithium plating detection and warning algorithms for LIBs is of great importance. …”
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A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients by Alla Ahmad Hassan, Tarik A Rashid

“…Diseased people experience severe symptoms in more severe cases. such as shortness of breath, which can lead to respiratory failure and death. Machine learning techniques for detection and classification are commonly used in current medical diagnoses. …”
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Identifying Top-Performing Students via VKontakte Social Media Communities Using Advanced NLP Techniques by Sergei S. Gorshkov, Dmitry I. Ignatov, Anastasia Yu. Chernysheva, Vyacheslav L. Goiko, Vitaliy V. Kashpur

“…Additionally, the machine learning pipeline incorporated stacking to combine predictions from multiple models, enhancing robustness and classification performance. …”
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Optimizing Lightweight Recurrent Networks for Solar Forecasting in TinyML: Modified Metaheuristics and Legal Implications by Gradimirka Popovic , Zaklina Spalevic , Luka Jovanovic , Miodrag Zivkovic , Lazar Stosic , Nebojsa Bacanin 

“…Therefore, the accurate prediction of solar power production is vital for efficient grid management and energy trading. Machine learning models have emerged as a prospective solution, as they are able to handle immense datasets and model complex patterns within the data. …”
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Klasifikasi Penyakit Alzheimer Dari Scan Mri Otak Menggunakan Convnext by Yehezkiel Stephanus Austin, Haikal Irfano, Juan Young Christopher, Lintang Cahyaning Sukma, Octo Perdana Putra, Riyadh Ilham Ardhanto, Novanto Yudistira

“…Penanganan penyakit ini dapat dilakukan melalui deteksi dini untuk meningkatkan kualitas kehidupan pasien melalui perawatan medis yang efisien dan tepat waktu. Teknologi machine learning dan neural network dapat mendukung deteksi dini melalui penggunaan model ConvNeXt yang telah dilatih dengan metode transfer learning menggunakan bobot awal dari ImageNet, dan di-fine-tune untuk mengklasifikasikan empat tingkat keparahan Alzheimer berdasarkan hasil pemindaian MRI otak, yaitu Mild Demented, Moderate Demented, Non Demented, dan Very Mild Demented. …”
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A novel method for estrous cycle staging using supervised object detection by Benjamin Babaev, Saachi Goyal, Tushar Arora, Anita Autry, Rachel A. Ross

“…An object detection-based machine learning model, Object Detection Estrous Staging (ODES), was employed to identify cell types throughout the estrous cycle in mice. …”
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Adapting to evolving MRI data: A transfer learning approach for Alzheimer’s disease prediction by Rosanna Turrisi, Sarthak Pati, Giovanni Pioggia, Gennaro Tartarisco

“…Integrating 3D magnetic resonance imaging (MRI) with machine learning has shown promising results in healthcare, especially in detecting Alzheimer’s Disease (AD). …”
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Network analyses of brain tumor multiomic data reveal pharmacological opportunities to alter cell state transitions by Brandon Bumbaca, Jonah R. Huggins, Marc R. Birtwistle, James M. Gallo

“…Combining the simulation results and the machine learning predictions, we generated hypotheses for clinically relevant causal mechanisms of cell state transitions. …”
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Review on heterogeneous transfer learning by Yingzhao ZHU

“…Heterogeneous transfer learning breaks through the boundary that the feature space of source domain and target domain must be the same.It realizes analysis mining and knowledge migration of heterogeneous data.It further promotes data reuse and opens up a wider range of applications for the field of machine learning.Firstly,the definition and classification of transfer learning was introduced.Then,the research status of heterogeneous transfer learning was elaborated and its application scenarios were analyzed.At last,the existing problems and the possible research direction in the future were pointed out.…”
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Application of artificial intelligence in NFV by Zengyi LIU, Bo LEI, Mingchuan YANG

“…How to deal with the complicated management problem after network function virtualization,how to flexibly schedule the network,all these problems need to be solved in order to develop the NFV.The rise of artificial intelligence provides a new solution for the management and orchestration in the NFV system.The present situations of NFV and artificial intelligence were summarily introduced,and use-cases of artificial intelligence in NFV were discussed.In addition,a solution for the management of VNF lifecycle management based on machine learning was proposed to provide reference for artificial intelligence technology in the application of NFV.…”
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Internet of things and pan-healthcares by Jianguo MA, Hengyue JIN

“…The Internet of things is the result of the informatization.The purpose of the Internet of things is to let things speak.The health of both man’s body and the structures being used daily needs to talk urgently.A novel IoT architecture based on the deep machine-learning was proposed.The Internet of things through embedded depth learning,realizes the flow from the data stream to the streams of events,and then to the flow of knowledge the value stream.The core technology for IoT is prediction,protection and prevention.…”
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ARTIFICIAL INTELLIGENCE AS A TECHNOLOGICAL BASIS OF THE DEVELOPMENT OF BANKS by A. Berdyshev

“…Thanks to the development of machine learning, artificial intelligence and cognitive computing, banks can more quickly process huge amounts of information, build more accurate models that can anticipate the needs of customers, create personalized offers and automate their services. …”
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Foreword by Tapiwa Chagonda

“…Artificial intelligence (AI), robotics, mobile commerce, and advanced machine learning (ML) are no longer emerging phenomena in distant economies; they are deeply embedded in Africa’s sociopolitical, economic, and cultural fabric. …”
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A FUZZY LOGIC MODEL FOR HUMAN DISTRESS DETECTION by DIMPLE OGUNBIYI, IBRAHIM OGUNDOYIN, CALEB AKANBI

“…Existing research in distress detection arising from physical attacks focused mainly on the use of machine learning techniques. To extend research efforts, this study proposes an alternate approach using fuzzy logic. …”
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