Enhancing IoT Network Security Through Deep Learning-Based Intrusion Detection by MOHAMMED FAWWAZ ALI MOHAMMED FAWWAZ ALI

“…It proposes a new deep learning-based Legitimate Load Testing (LLT) attack detection algorithm implemented in Python and supported by libraries such as TensorFlow, scikit-learn, and Seaborn. …”
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Multi-Task Water Quality Colorimetric Detection Method Based on Deep Learning by Shenlan Zhang, Shaojie Wu, Liqiang Chen, Pengxin Guo, Xincheng Jiang, Hongcheng Pan, Yuhong Li

“…Current research on colorimetric detection using deep learning algorithms predominantly focuses on single-target classification. …”
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An Integrated Framework for Cryptocurrency Price Forecasting and Anomaly Detection Using Machine Learning by Hani Alnami, Muhammad Mohzary, Basem Assiri, Hussein Zangoti

“…A Random Forest ensemble learning algorithm, a Gradient Boosting model, and a feedforward neural network were implemented to handle the complexities in cryptocurrency data. …”
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Detection of DDoS Attacks in SDN Switches with Deep Learning and Swarm Intelligence Approach by Mohsen Eghbali, Mohammadreza Mollkhalili Maybodi

“…Experimental results obtained in MATLAB, using the NSL-KDD dataset, demonstrate the proposed method’s effectiveness, achieving an accuracy of 99.34%, a sensitivity of 99.16%, and a precision of 98.93% in attack detection. The proposed method outperforms feature selection methods based on WOA, HHO, and AO algorithms, and deep learning methods like LSTM, RNN, and CNN, particularly in detecting DDoS attacks.…”
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Adaptive Convolution Kernels Construction Based on Unsupervised Learning for Underwater Acoustic Detection by Hao Yin, Chao Li, Haibin Wang, Jun Wang, Fan Yin, Zili Qin, Chuxian Wang

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Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia by Mahwish Ilyas, Muhammad Bilal, Nadia Malik, Hikmat Ullah Khan, Muhammad Ramzan, Anam Naz

“…To accomplish this task, we use digital image processing techniques and then apply the convolutional neural network (CNN) deep learning algorithm to blood sample images. This research employs a multi-stage methodology, including data preparation, data preprocessing, feature extraction, and then classification. …”
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An Automatic Damage Detection Method Based on Adaptive Theory-Assisted Reinforcement Learning by Chengwen Zhang, Qing Chun, Yijie Lin

“…The aim of this paper is to construct a real-time automated damage detection method by developing a theory-assisted adaptive mutiagent twin delayed deep deterministic (TA2-MATD3) policy gradient algorithm. …”
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Ensemble Deep Learning Object Detection Fusion for Cell Tracking, Mitosis, and Lineage by Imad Eddine Toubal, Noor Al-Shakarji, D. D. W. Cornelison, Kannappan Palaniappan

“…EDNet detections are used in our M2Track multiobject tracking algorithm for tracking cells, detecting cell mitosis (cell division) events, and cell lineage graphs. …”
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Learning by precedents based on the analysis of the features properties by V. V. Krasnoproshin, V. G. Rodchanka

“…An algorithm that allows paralleling of the learning process and performing it in the automatic mode has been developed in this approach. …”
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Coordinated Jamming and Poisoning Attack Detection and Mitigation in Wireless Federated Learning Networks by Sofia Barkatsa, Maria Diamanti, Panagiotis Charatsaris, Stefanos Voikos, Eirini Eleni Tsiropoulou, Symeon Papavassiliou

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AYOLO: Development of a Real-Time Object Detection Model for the Detection of Secretly Cultivated Plants by Ali Yılmaz, Yüksel Yurtay, Nilüfer Yurtay

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On Explainability of Reinforcement Learning-Based Machine Learning Agents Trained with Proximal Policy Optimization That Utilizes Visual Sensor Data by Tomasz Hachaj, Marcin Piekarczyk

“…In this paper, we address the issues of the explainability of reinforcement learning-based machine learning agents trained with Proximal Policy Optimization (PPO) that utilizes visual sensor data. …”
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Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Saad Ahmed Dheyab, Shaymaa Mohammed Abdulameer, Salama Mostafa

“…This model relies mainly on dimensionality reduction and machine learning algorithms. The principal component analysis (PCA) and the linear discriminant analysis (LDA) techniques perform the dimensionality reduction in individual and hybrid modes to process and improve the data. …”
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Automatic detection of trapping events of postnatal piglets in loose housing pen: comparison of YOLO versions 4, 5, and 8 by Taeyong Yun, Jinsul Kim, Jinhyeon Yun, Tai-Won Um

“…This study employed three deep learning object recognition algorithms–– You Only Look Once (YOLO)v4-Tiny, YOLOv5s and YOLOv8s––followed by a performance analysis. …”
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Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques by Uwe Knauer, Sebastian Warnemünde, Patrick Menz, Bonito Thielert, Lauritz Klein, Katharina Holstein, Miriam Runne, Wolfgang Jarausch

“…Therefore, the potential of hyperspectral imaging in combination with data analysis by machine learning algorithms was investigated to detect the symptoms solely based on the spectral signature of collected leaf samples. …”
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PREDICTIVE MODELS FOR EARLY DETECTION OF PARKINSON’S DISEASE: A MACHINE LEARNING APPROACH by S. Jeyantha Jafna Juliet, D. Jasmine David, J. S. Raj Kumar, Angelin Jeba P., R. Golden Nancy, M. Selvarathi, T. Jemima Jebaseeli

“…To diagnose PD, the proposed method uses two different data sets. Algorithms for machine learning are also capable of helping in producing specific details from such data. …”
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Transportation Mode Detection Using Learning Methods and Self-Contained Sensors: Review by Ilhem Gharbi, Fadoua Taia-Alaoui, Hassen Fourati, Nicolas Vuillerme, Zebo Zhou

“…Existing TMD review papers until now offer overviews of applications and algorithms without tackling the specific issues faced with real-world data collection and classification. …”
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A Synergy Between Machine Learning and Formal Concept Analysis for Crowd Detection by Anas M. Al-Oraiqat, Oleksandr Drieiev, Sattam Almatarneh, Mohammadnoor Injadat, Karim A. Al-Oraiqat, Hanna Drieieva, Yassin M. Y. Hasan

“…Recent systems take advantage of the synergy between machine learning, data mining, and image processing to extract/analyze features from crowded zones and recognize patterns and anomalies from the crowd behavior. …”
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Recent advances in machine learning for defects detection and prediction in laser cladding process by X.C. Ji, R.S. Chen, C.X. Lu, J. Zhou, M.Q. Zhang, T. Zhang, H.L. Yu, Y.L. Yin, P.J. Shi, W. Zhang

“…Addressing the reliability and reproducibility of cladding quality is a paramount concern within laser cladding technology. Leveraging data-driven machine learning algorithms enables the monitoring and detection of defects throughout the laser cladding process. …”
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Explainable few-shot learning workflow for detecting invasive and exotic tree species by Caroline M. Gevaert, Alexandra Aguiar Pedro, Ou Ku, Hao Cheng, Pranav Chandramouli, Farzaneh Dadrass Javan, Francesco Nattino, Sonja Georgievska

“…This can cause difficulties in practical situations where models should be trained for new applications for which very little data is available. While few-shot learning algorithms can address the first problem, they still lack sufficient explanations for the results. …”
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