Enhancement of Breast Cancer Classification Using Bat Feature Selection with Recurrent Deep Learning by Ali Nafaa Jaafar

“…DNA is a valuable tool for classifying expression of genes in detection of breast cancer. Gene expression data are biological data that extract valuable hidden information from gene datasets. …”
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GEOMAPLEARN 1.2: detecting structures from geological maps with machine learning – the case of geological folds by D. Oakley, D. Oakley, C. Loiselet, T. Coowar, T. Coowar, V. Labbe, J.-P. Callot

“…In this paper, we present automated workflows for detecting geological folds from map data using both unsupervised and supervised machine learning. …”
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Multi-Level Foreground Prompt for Incremental Object Detection by Jianwen Mo, Ronghua Zou, Hua Yuan

“…In the study of incremental object detection, knowledge distillation and data replay are effective methods to mitigate catastrophic forgetting. …”
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Blockchain enabled deep learning model with modified coati optimization for sustainable healthcare disease detection and classification by Heba G. Mohamed, Fadwa Alrowais, Fahd N. Al-Wesabi, Mesfer Al Duhayyim, Anwer Mustafa Hilal, Abdelwahed Motwakel

“…For the optimal subset of features, the spotted hyena optimization algorithm (SHOA) model is used. Furthermore, the attention bidirectional gated recurrent unit (ABiGRU) method is implemented for disease detection and classification. …”
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Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine by Debendra Muduli, Rani Kumari, Adnan Akhunzada, Korhan Cengiz, Santosh Kumar Sharma, Rakesh Ranjan Kumar, Dinesh Kumar Sah

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Detection and classification of hypertensive retinopathy based on retinal image analysis using a deep learning approach by Bambang Krismono Triwijoyo, Ahmat Adil, Muhammad Zulfikri

“…The dataset is divided into 60 % training and 40 % validation data. The next step is the image analysis process, which involves extracting retinal blood vessels using the Otsu segmentation algorithm. …”
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Automated Detection of Central Retinal Artery Occlusion Using OCT Imaging via Explainable Deep Learning by Ansgar Beuse, Daniel Alexander Wenzel, MD, Martin Stephan Spitzer, MD, Karl Ulrich Bartz-Schmidt, MD, Maximilian Schultheiss, MD, Sven Poli, MD, Carsten Grohmann, PhD, MD

“…Objective: To demonstrate the capability of a deep learning model to detect central retinal artery occlusion (CRAO), a retinal pathology with significant clinical urgency, using OCT data. …”
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Improving detection of Parkinson’s disease with acoustic feature optimization using particle swarm optimization and machine learning by Elmoundher Hadjaidji, Mohamed Cherif Amara Korba, Khaled Khelil

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Deep learning and support vector machine-recursive feature elimination-based network intrusion detection model by YE Qing, ZHANG Yannian, WU Hao

“…Moreover, both oversampling and under-sampling techniques were utilized to tackle the unbalance problem of data sample distribution. Three deep learning algorithms were used to build the base learner of the ensemble framework, and deep neural network was used to build the meta-learner, so as to improve the performance of DLRF model to detect network attacks. …”
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Automated lung cancer detection using novel genetic TPOT feature optimization with deep learning techniques by Mohamed Hammad, Mohammed ElAffendi, Muhammad Asim, Ahmed A. Abd El-Latif, Radwa Hashiesh

“…However, previous deep learning models for lung cancer detection have faced challenges such as limited data, inadequate feature extraction, interpretability issues, and susceptibility to data variability. …”
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Enhancing intrusion detection in IoT networks using machine learning-based feature selection and ensemble models by Ayoob Almotairi, Samer Atawneh, Osama A. Khashan, Nour M. Khafajah

“…The model employs feature selection and ensemble modelling to investigate and enhance key classification metrics for intrusion detection of IoT data. This approach comprises two core components: the utilization of the K-Best algorithm for feature selection, extracting the top 15 critical features and the construction of an ensemble model incorporating various traditional machine learning models. …”
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Tennis Timing Assessment by a Machine Learning-Based Acoustic Detection System: A Pilot Study by Lucio Caprioli, Amani Najlaoui, Francesca Campoli, Aatheethyaa Dhanasekaran, Saeid Edriss, Cristian Romagnoli, Andrea Zanela, Elvira Padua, Vincenzo Bonaiuto, Giuseppe Annino

“…<b>Methods:</b> Based on a machine learning algorithm, the proposed acoustic detection system classifies the sound of the ball’s impact on the racket and the ground to measure the time between them and give immediate feedback to the player. …”
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An Evolutionary Deep Reinforcement Learning-Based Framework for Efficient Anomaly Detection in Smart Power Distribution Grids by Mohammad Mehdi Sharifi Nevisi, Mehrdad Shoeibi, Francisco Hernando-Gallego, Diego Martín, Sarvenaz Sadat Khatami

“…The increasing complexity of modern smart power distribution systems (SPDSs) has made anomaly detection a significant challenge, as these systems generate vast amounts of heterogeneous and time-dependent data. …”
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Secondary System Fault Detection Method Based on Association Rules and Reconstruction Error by Yang WANG, Weidong MA, Jiming LIU, Boshi WANG, Kai YAO, Wei HAN, Juan YU

“…The ensemble learning model is used to quantify the current fault detection probability of the equipment. …”
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Early Detection of Dementia in Populations With Type 2 Diabetes: Predictive Analytics Using Machine Learning Approach by Phan Thanh Phuc, Phung-Anh Nguyen, Nam Nhat Nguyen, Min-Huei Hsu, Nguyen Quoc Khanh Le, Quoc-Viet Tran, Chih-Wei Huang, Hsuan-Chia Yang, Cheng-Yu Chen, Thi Anh Hoa Le, Minh Khoi Le, Hoang Bac Nguyen, Christine Y Lu, Jason C Hsu

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Breast cancer detection and classification with digital breast tomosynthesis: a two-stage deep learning approach by Yazeed Alashban

“…CLINICAL SIGNIFICANCE: The proposed two-tier DL algorithm, combining a modified VGG19 model for image classification and YOLOv5-CBAM for lesion detection, can improve the accuracy, efficiency, and reliability of breast cancer screening and diagnosis through innovative artificial intelligence-driven methodologies.…”
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Automatic detection and prediction of epileptic EEG signals based on nonlinear dynamics and deep learning: a review by Shixiao Tan, Zhen Tang, Qiang He, Ying Li, Yuliang Cai, Jiawei Zhang, Di Fan, Zhenkai Guo

“…These methods reveal dynamic patterns in signals, thereby substantially improving epilepsy detection and prediction accuracy. This survey reviews research progress in automatic detection and prediction of epileptic EEG signals based on nonlinear dynamics and deep learning, evaluating key techniques including Lyapunov exponents, fractal dimensions, and entropy metrics. …”
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A novel EEG artifact removal algorithm based on an advanced attention mechanism by Rui Jiang, Shen Tong, Jiawei Wu, Haowei Hu, Ran Zhang, Heng Wang, Yan Zhao, Weixin Zhu, Shuyan Li, Xiao Zhang

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An early warning system based on machine learning detects huge forest loss in Ukraine during the war by Roberto Cazzolla Gatti, Rocìo Beatriz Cortès Lobos, Michele Torresani, Duccio Rocchini

“…We employed Random Forest, a supervised machine learning classification algorithm, in conjunction with high-quality satellite imagery, to quantify the forest loss in Ukraine during the war, between 2022 and 2023. …”
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A MACHINE LEARNING DISTRACTED DRIVING PREDICTION MODEL by Samira AHANGARI, Mansoureh JEIHANI, Abdollah DEHZANGI

“…In this study, we use a Bayesian Network classifier as a robust machine learning algorithm on our trained data (80%) and tested (20%) with the data collected from a driving simulator, in which the 92 participants drove six scenarios of handheld calling, hands-free calling, texting, voice command, clothing, and eating/drinking on four different road classes (rural collector, freeway, urban arterial, and local road in a school zone). …”
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