A review of plant leaf disease identification by deep learning algorithms by Junmin Zhao, Laixiang Xu, Zizhen Ma, Juncai Li, Xiaowei Wang, Yunchang Liu, Xiaojie Du

“…The proposed work aims to combine plant leaf disease datasets from various countries, review current research and progress in deep learning algorithms for plant disease recognition, and explain how different types of data are developed and used in this area using different deep learning networks. …”
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Smart Agriculture: Predicting Diseases in olive using Deep Learning Algorithms by Rahman F., Raghatate Kapesh Subhash

“…In this study, olive tree diseases are managed by improving prediction and management using a deep learning algorithm, with the aim of efficiency and accuracy of the process. …”
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Random Oversampling-Based Diabetes Classification via Machine Learning Algorithms by G. R. Ashisha, X. Anitha Mary, E. Grace Mary Kanaga, J. Andrew, R. Jennifer Eunice

“…The proposed model consists of the random oversampling method to balance the range of classes, the interquartile range technique-based outlier detection to eliminate outlier data, and the Boruta algorithm for selecting the optimal features from the datasets. …”
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A Robust Enhanced Ensemble Learning Method for Breast Cancer Data Diagnosis on Imbalanced Data by Zhenzhen Wang, Junde Xie, Jia Zhang

“…Addressing class imbalance in breast cancer data is essential for enhancing detection accuracy, yet traditional machine learning methods often overlook this imbalance, limiting their classification performance. …”
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PREDICTION OF TELECOM SERVICES CONSUMERS CHURN BY USING MACHINE LEARNING ALGORITHMS by Edin Osmanbegović, Anel Džinić, Mirza Suljić

“…The name "machine learning" refers to the automated detection of meaningful patterns in large data sets.  …”
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PREDICTION OF TELECOM SERVICES CONSUMERS CHURN BY USING MACHINE LEARNING ALGORITHMS by Edin Osmanbegović, Anel Džinić, Mirza Suljić

“…The name "machine learning" refers to the automated detection of meaningful patterns in large data sets.  …”
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An Elderly Fall Detection Method Based on Federated Learning and Extreme Learning Machine (Fed-ELM) by Zhigang Yu, Jiahui Liu, Mingchuan Yang, Yanmin Cheng, Jie Hu, Xinchi Li

“…To solve the above issue, this paper proposes a fall detection algorithm combining Federated Learning and Extreme Learning Machine (Fed-ELM). …”
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Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine by Ningsang Jiang, Peng Li, Zhiming Feng

“…The first part of the Continuous Change Detection and Classification (CCDC) algorithm holds promising potential in capturing abrupt changes. …”
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Learning-based early detection of post-hepatectomy liver failure using temporal perioperative data: a nationwide multicenter retrospective study in ChinaResearch in context by Kai Wang, Qian Yang, Kang Li, Shanhua Tang, Baoluhe Zhang, Xiangyun Liao, Shunda Du, Wenguang Fu, Zhiwei Li, Huanwei Chen, Haorong Xie, Pengxiang Huang, Jieyuan Li, Qiuting Wang, Haiqing Liu, Zhiwei Huang, Pheng Ann Heng, Xueshuai Wan, Chuanjiang Li, Weixin Si

“…PHLF was diagnosed by concurrent elevated prothrombin time/INR and hyperbilirubinemia on or after postoperative day 5 and graded according to the International Study Group of Liver Surgery criteria. The proposed algorithm employed a powerful foundation model (Bio-Clinical Bidirectional Encoder Representation from Transformers) and a context-aware transformer module to perform in-depth temporal feature investigation of perioperative data to enable early detection of PHLF. …”
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Quantum-enhanced beetle swarm optimized ELM for high-dimensional smart grid intrusion detection by Na Cheng, Shuqing Wang, Lihong Zhao, Yan Hu

“…Abstract This study proposes a novel smart grid intrusion detection model, combining a quantum-enhanced beetle swarm optimization algorithm with extreme learning machine (QBOA-ELM), with the aim of improving detection accuracy, efficiency, and robustness. …”
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AI-Driven Predictive Maintenance in Mining: A Systematic Literature Review on Fault Detection, Digital Twins, and Intelligent Asset Management by Luis Rojas, Álvaro Peña, José Garcia

“…The findings highlight the increasing adoption of deep learning, reinforcement learning, and digital twins for anomaly detection and process optimization. …”
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Hybrid machine learning algorithms accurately predict marine ecological communities by Luciana Erika Yaginuma, Luciana Erika Yaginuma, Fabiane Gallucci, Danilo Cândido Vieira, Paula Foltran Gheller, Simone Brito de Jesus, Thais Navajas Corbisier, Gustavo Fonseca

“…Data was analyzed by means of a hybrid machine learning (ML) approach, which combines unsupervised and supervised methods. …”
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Application of Real-Time Palm Imaging with Nelder–Mead Particle Swarm Optimization/Regression Algorithms for Non-Contact Blood Pressure Detection by Te-Jen Su, Ya-Chung Hung, Wei-Hong Lin, Wen-Rong Yang, Qian-Yi Zhuang, Yan-Xiang Fei, Shih-Ming Wang

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Evaluation of deep learning and convolutional neural network algorithms accuracy for detecting and predicting anatomical landmarks on 2D lateral cephalometric images: A systematic... by Jimmy Londono, Shohreh Ghasemi, Altaf Hussain Shah, Amir Fahimipour, Niloofar Ghadimi, Sara Hashemi, Zohaib Khurshid, Mahmood Dashti

“…Introduction: Cephalometry is the study of skull measurements for clinical evaluation, diagnosis, and surgical planning. Machine learning (ML) algorithms have been used to accurately identify cephalometric landmarks and detect irregularities related to orthodontics and dentistry. …”
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Development of a novel sustainable, portable, fast, and non-invasive platform based on ATR-FTIR technology coupled with machine learning algorithms for Helicobacter pylori detectio... by Ghabriel Honório-Silva, Marco Guevara-Vega, Nagela Bernadelli Sousa Silva, Marcelo Augusto Garcia-Júnior, Deborah Cristina Teixeira Alves, Luiz Ricardo Goulart, Mario Machado Martins, André Luiz Oliveira, Rui Miguel Pinheiro Vitorino, Thulio Marquez Cunha, Carlos Henrique Gomes Martins, Murillo Guimarães Carneiro, Robinson Sabino-Silva

“…In this context, it is critical to develop novel alternative non-invasive platforms for the portable, fast, accessible through self-collection and reagent-free detection of H. pylori. Here, we used attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) supported by Machine Learning algorithms to identify infrared vibrational modes of H. pylori diluted in human saliva. …”
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Advancing Alzheimer’s disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis by Md Nurul Ahad Tawhid, Siuly Siuly, Enamul Kabir, Yan Li

“…To address these issues, a deep learning-based framework is proposed to detect AD using EEG data, focusing on determining the optimal segment length for classification. …”
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Enhancing retinal disease diagnosis through AI: Evaluating performance, ethical considerations, and clinical implementation by Maryam Fatima, Praveen Pachauri, Wasim Akram, Mohd Parvez, Shadab Ahmad, Zeinebou Yahya

“…Deep learning algorithms showed a sensitivity of 90 % and specificity of 98 % for diabetic retinopathy detection. …”
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Precise prediction of choke oil rate in critical flow condition via surface data by Qing Wang, Muntadher Abed Hussein, Bhavesh Kanabar, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Mohammad Mahtab Alam, Hojjat Abbasi

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Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis. by Mwenge Mulenga, Arutchelvan Rajamanikam, Suresh Kumar, Saharuddin Bin Muhammad, Subha Bhassu, Chandramathi Samudid, Aznul Qalid Md Sabri, Manjeevan Seera, Christopher Ifeanyi Eke

“…This innovative approach markedly enhances the Area Under the Curve (AUC) performance of the Deep Neural Network (DNN) algorithm in colorectal cancer (CRC) detection using gut microbiome data, elevating it from 0.800 to 0.923. …”
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Plasticulture detection at the country scale by combining multispectral and SAR satellite data by Alessandro Fabrizi, Peter Fiener, Thomas Jagdhuber, Kristof Van Oost, Florian Wilken

“…The algorithm detected 103 103 ha of PMF and 37 103 ha of PCV in 2020, while a combination of agricultural statistics and surveys estimated a smaller plasticulture cover of around 100 103 ha in 2019. …”
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