A Review of CNN Applications in Smart Agriculture Using Multimodal Data by Mohammad El Sakka, Mihai Ivanovici, Lotfi Chaari, Josiane Mothe

“…A comparative analysis shows how CNNs perform with respect to other techniques that involve traditional machine learning and recent deep learning models in image processing, particularly when applied to high-dimensional or temporal data. …”
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Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier by Ranjana Agrawal, Sucheta Kulkarni, Madan Deshpande, Anita Gaikwad, Rahee Walambe, Ketan V. Kotecha

“…Stages 1–3 were classified using machine Learning (ML) models. • This study aims to improve accuracy of Stages 1–3 classification and identify Pre-plus/ Plus disease using MultiCNN_LSTM networks. …”
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A review of electric vehicle technology: Architectures, battery technology and its management system, relevant standards, application of artificial intelligence, cyber security, an... by Vincent M. Macharia, Vijay K. Garg, Dinesh Kumar

“…Furthermore, this article gives a comprehensive overview of various emerging technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI) & Machine Learning (ML), to improve EVs' performance through digitalisation. …”
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Perceptions and attitudes towards AI among trainee and qualified radiologists at selected South African training hospitals by Ayanda I. Nciki, Linda T. Hlabangana

“…Participants agreed that AI could reliably detect pathological conditions (89%), reach reliable diagnosis (89%), improve daily work (78%), and 89% favoured AI practice; 89% believed that in the future, machine learning will not be independent of the radiologist. …”
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The data-intensive research paradigm: challenges and responses in clinical professional graduate education by Chunhong Yang, Yijing Chen, Changshun Qian, Fangmin Shi, Fangmin Shi, You Guo, You Guo, You Guo, You Guo, You Guo

“…With the widespread application of big data, artificial intelligence, and machine learning technologies in the medical field, a new paradigm of data-intensive clinical research is emerging as a key force driving medical advancement. …”
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Anxiety classification in virtual reality using biosensors: A mini scoping review. by Deniz Mevlevioğlu, Sabin Tabirca, David Murphy

“…Our inclusion criteria were peer-reviewed studies which take place in a Virtual Reality environment and assess the user's anxiety using machine learning classification models and biosensors.<h4>Results</h4>1749 records were identified and out of these, 11 (n = 237) studies were selected. …”
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Ankrd1 as a potential biomarker for the transition from acute kidney injury to chronic kidney disease by Hailin Li, Lemei Hu, Changqing Zheng, Ying Kong, Ming Liang, Quhuan Li

“…Analysis of intercellular crosstalk, trajectory and machine learning algorithms revealed hub cell clusters and genes. …”
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Transfer learning for non-image data in clinical research: A scoping review. by Andreas Ebbehoj, Mette Østergaard Thunbo, Ole Emil Andersen, Michala Vilstrup Glindtvad, Adam Hulman

“…<h4>Background</h4>Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. …”
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A Multi-Faceted Approach to Trending Topic Attack Detection Using Semantic Similarity and Large-Scale Datasets by Insaf Kraidia, Afifa Ghenai, Samir Brahim Belhaouari

“…We evaluated our approach using a comprehensive framework that assessed textual, image, and overall similarity. Five machine learning models&#x2014;Random Forest, Decision Tree, K-Neighbors, Gradient Boosting, and XGBoost&#x2014;were tested, with results benchmarked against nine baseline methods across different linguistic datasets and learning scenarios. …”
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Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization by Guosheng Cui, Ye Li, Jianzhong Li, Jianping Fan

“…Nonnegative Matrix Factorization (NMF) is one of the most popular feature learning technologies in the field of machine learning and pattern recognition. It has been widely used and studied in the multi-view clustering tasks because of its effectiveness. …”
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Nonlinearity of the post-spinel transition and its expression in slabs and plumes worldwide by Junjie Dong, Rebecca A. Fischer, Lars P. Stixrude, Matthew C. Brennan, Kierstin Daviau, Terry-Ann Suer, Katlyn M. Turner, Yue Meng, Vitali B. Prakapenka

“…Combining our data with results from the literature, and using a global analysis based on machine learning, we find a pronounced nonlinearity in the post-spinel boundary, with its slope ranging from –4 MPa/K at 2100 K, to –2 MPa/K at 1950 K, and to 0 MPa/K at 1600 K. …”
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Integrating Modern Technologies into Traditional Anterior Cruciate Ligament Tissue Engineering by Aris Sopilidis, Vasileios Stamatopoulos, Vasileios Giannatos, Georgios Taraviras, Andreas Panagopoulos, Stavros Taraviras

“…Finally, we highlight the benefits of incorporating new technologies like artificial intelligence and machine learning that could revolutionize tissue engineering.…”
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Künstliche Intelligenz im Englischunterricht – Grundwissen und Praxisbeispiele by Inez De Florio-Hansen

“…What are the meanings of fundamental concepts such as algorithms, machine learning, and artificial neural networks? What are the best practices for inputting prompts (prompt engineering), and how can prompt quality be improved to achieve desired outcomes? …”
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A data-driven latent variable approach to validating the research domain criteria framework by S. K. L. Quah, B. Jo, C. Geniesse, L. Q. Uddin, J. A. Mumford, D. M. Barch, D. A. Fair, I. H. Gotlib, R. A. Poldrack, M. Saggar

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GroupFound: An effective approach to detect suspicious accounts in online social networks by Bo Feng, Qiang Li, Xiaowen Pan, Jiahao Zhang, Dong Guo

“…Most existing works mainly utilize machine learning based on features. However, once the spammers disguise the key features, the detection method will soon fail. …”
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An Enhanced Deep Neural Network for Predicting Workplace Absenteeism by Syed Atif Ali Shah, Irfan Uddin, Furqan Aziz, Shafiq Ahmad, Mahmoud Ahmad Al-Khasawneh, Mohamed Sharaf

“…The efficacy of the proposed method is tested with traditional machine learning techniques, and the results indicate 90.6% performance in Deep Neural Network as compared to 73.3% performance in a single-layer Neural Network and 82% performance in Decision Tree, SVM, and Random Forest. …”
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A hybrid approach for intrusion detection in vehicular networks using feature selection and dimensionality reduction with optimized deep learning. by Fayaz Hassan, Zafi Sherhan Syed, Aftab Ahmed Memon, Saad Said Alqahtany, Nadeem Ahmed, Mana Saleh Al Reshan, Yousef Asiri, Asadullah Shaikh

“…The intended use of CFS and PCA in the machine learning pipeline serves two folds benefit, first is that the resultant feature matrix contains attributes that are most useful for recognizing malicious traffic, and second that after CFS and PCA, the feature matrix has a smaller dimensionality which in turn means that smaller number of weights need to be trained for the dense layers (connections are required for the dense layers) which resulting in smaller model size. …”
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TurkMedNLI: a Turkish medical natural language inference dataset through large language model based translation by İskender Ülgen Oğul, Fatih Soygazi, Belgin Ergenç Bostanoğlu

“…To ensure quality, we conducted comprehensive evaluations using both machine learning models and medical expert review. Our results show that BERTurk achieved 75.17% accuracy on TurkMedNLI test data and 76.30% on the normalized test set, while BioBERTurk demonstrated comparable performance with 75.59% accuracy on test data and 72.29% on the normalized dataset. …”
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Consensus Mechanism of IoT Based on Blockchain Technology by Yue Wu, Liangtu Song, Lei Liu, Jincheng Li, Xuefei Li, Linli Zhou

“…Offline fast election, which is the node that wins the election, becomes the block node. Machine learning methods are also introduced to identify or remove outliers in the sensor data before such data are uploaded to the chain. …”
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VisualSAF-A Novel Framework for Visual Semantic Analysis Tasks by Antonio V. A. Lundgren, Byron L. D. Bezerra, Carmelo J. A. Bastos-Filho

“…The framework leverages semantic variables extracted using machine learning algorithms to provide additional high-level information, augmenting the capabilities of the primary task model. …”
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