Traffic light detection and recognition based on deep learning for autonomous-rail rapid tram by XIONG Qunfang, LIN Jun, YUAN Xiwen, XU Yanghan, YUE Wei, LI Yuanzhengyu

“…This paper presents study efforts in this field through the application of a deep learning algorithm. Firstly, regions of interest (RoIs) for traffic lights were determined using high-precision map information to narrow the detection range and improve the detection speed. …”
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Machine Learning-Potato Leaf Disease Detection App (MR-PoLoD) by Ahmad Fauzi, Annisya E Chandra, Sofyah Imammah, Malvin Zapata, Marza I Marzuki, Soni Prayogi

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Detecting Fraudulent Transaction in Banking Sector Using Rule-Based Model and Machine Learning by Cut Dinda Rizki Amirillah

“…This research aims to develop an effective fraud detection model in banking transactions using the rule-based model (RBM) approach and the isolation forest (IF) machine learning algorithm. …”
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T‐SNERF: A novel high accuracy machine learning approach for Intrusion Detection Systems by Mohamed Hammad, Nabil Hewahi, Wael Elmedany

“…Abstract In the last few decades, Intrusion Detection System (IDS), in particular, machine learning‐based anomaly detection, has gained importance over Signature Detection Systems (SDSs) in the novel attacks detection. …”
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Physiological signal-based mental stress detection using hybrid deep learning models by Nandini Modi, Yogesh Kumar, Kapil Mehta, Neelam Chaplot

“…In this study, a hybrid deep learning model combining Convolution neural network (CNN) with Multilayer perceptron (MLP) is proposed to classify mental stress levels based on physiological signal data. …”
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Reinforcement Learning With Deep Features: A Dynamic Approach for Intrusion Detection in IoT Networks by Mohamad Khayat, Ezedin Barka, Mohamed Adel Serhani, Farag Sallabi, Khaled Shuaib, Heba M. Khater

“…A hybrid optimization approach was also introduced for feature selection using green anaconda optimization and the chaotic learning osprey optimization algorithm. Furthermore, an RL-based IDS (RL-IDS) that incorporates RNNs and autoencoders with a deep Q-network was proposed to enhance threat detection. …”
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HEDL: Deep learning multiple approaches for early detection of depression using sarcastic text by Vanita Kshirsagar, Nishant Pachpor, Ashwini Brahme, Ravindra Aapre, Shubhangi Suryawanshi, Digvijay Bhosale

“…Results of the experiment do support that the HEDL model is indeed much more accurate and reliable sarcastic detection methodology; it can have applications such as monitoring mental health or analysing sentiment. • Proposed the Hybrid Ensemble Deep Learning Algorithm (HEDL) for text data. • The proposed model outperforms traditional models in cognitive skill impairment detection. • Demonstrated scalability for diverse healthcare datasets.…”
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Research review on intelligent object detection technology for coal mines based on deep learning by Fan ZHANG, Jiarong ZHANG, Haixing CHENG

“…Firstly, a brief overview of object detection technology was provided, and the evolution process and algorithm classification of object detection technology based on deep learning were introduced. …”
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A Robust and Efficient Machine Learning Framework for Enhancing Early Detection of Android Malware by Fandi Kurniawan, Deris Stiawan, Darius Antoni, Mohd Yazid Idris, Rahmat Budiarto

“…The dataset used consists of 2,084 Android applications, including 1,314 malware samples and 770 benign applications, obtained through a reverse engineering process. Data pre-processing, feature extraction, and training using supervised learning are carried out to optimize detection accuracy. …”
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Aircraft Skin Machine Learning-Based Defect Detection and Size Estimation in Visual Inspections by Angelos Plastropoulos, Kostas Bardis, George Yazigi, Nicolas P. Avdelidis, Mark Droznika

“…Additionally, an algorithm is proposed to estimate the size of the detected defect. …”
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Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers by Raja Azlina Raja Mahmood, AmirHossien Abdi, Masnida Hussin

“…The results show that using optimal features coupling with a good classifier in a detection system able to reduce the classifier model building time, reduce the computational burden to analyze data, and consequently attain high detection rate.…”
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Network Intrusion Detection Using Knapsack Optimization, Mutual Information Gain, and Machine Learning by Akindele S. Afolabi, Olubunmi A. Akinola

“…Protection against such attacks may be achieved through the use of an intrusion detection system (IDS), which can be designed by training machine learning models to detect cyberattacks. …”
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Implementation of Several Data Mining Strategies on Electronic Nose Data for Identifying Gluten in Cheese by Mohammad Nasiri-Galeh, Mahdi Ghasemi-Varnamkhasti

“…Electronic nose is an electronic device for smell detection. The data obtained from this device are stored in the form of numbers in different columns, which are related to the data of two types of cheese namely gluten-free cheese and cheese with gluten. …”
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Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data by Jiali Wang, Jia Chen

“…Firstly, analyze and preprocess the big data uploaded by the battery. Subsequently, the importance of indicators in the data was analyzed using the Random Forest algorithm (RF). …”
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Comparison of Machine Learning and Statistical Approaches of Detecting Anomalies Using a Simulation Study by Klaudia Lenart

“…There are diverse methods of anomaly detection. In particular two groups of methods have been developed independently – statistical methods and machine learning algorithms. …”
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Deep and hybrid learning of MRI diagnosis for early detection of the progression stages in Alzheimer’s disease by Ibrahim Abunadi

“…The fourth proposed system diagnoses the data set using ANN and FFNN algorithms based on the hybrid features of ResNet-18 and AlexNet deep learning models and traditional algorithms (LBP, DWT, and GLCM). …”
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An analytical assessment of credit card fraud detection techniques: Supervised, Unsupervised, and Reinforcement Learning by Abdourahman Djamal Djama

“…Method. information-theoretical analysis of methods for detecting fraud with bank cards, machine learning algorithms in improving the accuracy of fraud detection. …”
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Plastic Litter Detection in the Environment Using Hyperspectral Aerial Remote Sensing and Machine Learning by Marco Balsi, Monica Moroni, Soufyane Bouchelaghem

“…Data were preprocessed to simply equalize the spectra across the whole band and across different environmental conditions, and machine learning techniques were applied to detect plastics even in real-time. …”
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Trust-driven approach to enhance early forest fire detection using machine learning by Tayyab Khan, Karan Singh, Bhoopesh Singh Bhati, Khaleel Ahmad, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

“…This renders it a promising solution for the imperative need to promptly detect and prevent forest fires. Our technique combines trust mechanisms with machine learning algorithms to create a very advanced forest fire detection system.…”
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Deep learning-based time series prediction in multispectral and hyperspectral imaging for cancer detection by Lijun Hao, Changmin Wang, Jinshan Che, Mingming Sun, Yuhong Wang

“…Traditional approaches for cancer detection rely on handcrafted features and conventional machine learning algorithms, which struggle with high-dimensional spectral data, noise interference, and domain adaptation challenges. …”
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