A Reinforcement Learning-based Intelligent Learning Method for Anti-active Jamming in Frequency Agility Radar by J. Wei, L. Yu, Y. Wei, R. Xu

“…We develop jamming signal models and design four jamming strategies based on two common types of active jamming, providing essential data for the FAR intelligent learning method. To enhance FAR’s adaptive anti-jamming and target detection performance, we propose an RL-based intelligent learning model. …”
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A dataset for vineyard disease detection via multispectral imagingZenodo by Alessio Saccuti, Filippo Graziosi, Dario Lodi Rizzini

“…Potential applications include the development of machine learning algorithms for automated disease detection, image alignment techniques, and background removal methods. …”
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CAFiKS: Communication-Aware Federated IDS With Knowledge Sharing for Secure IoT Connectivity by Ogobuchi Daniel Okey, Demostenes Zegarra Rodriguez, Frederico Gadelha Guimaraes, Joao Henrique Kleinschmidt

“…Additionally, to cater for robust security measures to protect the vast number of devices and data in IoT networks, federated learning-based methods are increasingly being employed to develop intrusion detection systems (IDS) that monitor IoT traffic for malicious activities. …”
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An Algorithm of Train Operation Environment Recognition Based onEmbedded GPU Platform by XIONG Minjun, LI Chen, ZHANG Huiyuan, PENG Liantie, SU Zhen

“…Aiming at the problems of low efficiency, poor accuracy and weak robustness of traditional train operation environment sensing algorithms, a real-time detection algorithm of train operation environment based on image instance segmentation is proposed. …”
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Real-time diagnosis of multi-category skin diseases based on IR-VGG by Ling TAN, Shanshan RONG, Jingming XIA, Sarker SAJIB, Wenjie MA

“…Malignant skin lesions have a very high cure rate in the early stage.In recent years, dermatological diagnosis research based on deep learning has been continuously promoted, with high diagnostic accuracy.However, computational resource consumption is huge and it relies on large computing equipment in hospitals.In order to realize rapid and accurate diagnosis of skin diseases on Internet of things (IoT) mobile devices, a real-time diagnosis system of multiple categories of skin diseases based on inverted residual visual geometry group (IR-VGG) was proposed.The contour detection algorithm was used to segment the lesion area of skin image.The convolutional block of the first layer of VGG16 was replaced with reverse residual block to reduce the network parameter weight and memory overhead.The original image and the segmented lesion image was inputed into IR-VGG network, and the dermatological diagnosis results after global and local feature extraction were outputed.The experimental results show that the IR-VGG network structure can achieve 94.71% and 85.28% accuracy in Skindata-1 and Skindata-2 skin diseases data sets respectively, and can effectively reduce complexity, making it easier for the diagnostic system to make real-time skin diseases diagnosis on IoT mobile devices.…”
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SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals by Davide Lillini, Carlo Aironi, Lucia Migliorelli, Leonardo Gabrielli, Stefano Squartini

“…To address the limitations of PSG, we propose a decision support system, which uses a tracheal microphone for data collection and a deep learning (DL) approach—namely SiCRNN—to detect apnea events during overnight sleep recordings. …”
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Object-gaze distance: Quantifying near-peripheral gaze behavior in real-world applications by Felix Wang, Julian Wolf, Mazda Farshad, Mirko Meboldt, Quentin Lohmeyer

“…The algorithm uses machine learning for area of interest (AOI) detection and computes the minimal 2D Euclidean pixel distance to the gaze point, creating a continuous gaze-based time-series. …”
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The abnormal traffic detection scheme based on PCA and SSH by Zhenhui Wang, Dezhi Han, Ming Li, Han Liu, Mingming Cui

“…In order to solve the problems that the existing detection methods cannot fully learn the spatio-temporal characteristics of data, the classification accuracy is not high, and the detection time and accuracy are susceptible to the influence of redundant data in the sample. …”
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Random Forest-Based Prediction of the Optimal Solid Ink Density in Offset Lithography by Laihu Peng, Hao Fan, Yubao Qi, Jianqiang Li

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On the effect of sampling frequency on the electricity theft detection performance by Fatemeh Soleimani Nasab, Foad Ghaderi

“…However, based on technological and operational limitations, the rate of metre reading is too low in many countries, which can affect detection performance. To investigate the effect of sampling frequency on the performance of detection methods, we designed a processing framework to evaluate different classification algorithms on versions of a challenging dataset obtained by down‐sampling the original data at various rates. …”
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Bilingual hate speech detection on social media: Amharic and Afaan Oromo by Teshome Mulugeta Ababu, Michael Melese Woldeyohannis, Emuye Bawoke Getaneh

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Real-Time Recognition and Localization of Kiwifruit Based on Improved YOLOv5s Algorithm by Jin-Sui Dai, Zhi-Qin He

“…First, data enhancement is implemented in the training strategy to ensure the model’s efficient learning and generalization ability in complex environments; then the Coordinate Attention mechanism is introduced to highlight the key features of the image and improve detail detection; Secondly, the Bidirectional Feature Pyramid Network structure is used to optimize the feature fusion and strengthen the information exchange between different layers through bidirectional connectivity; then, the loss function is optimized to improve the bounding box localization accuracy; and finally, the combined with the binocular vision stereo matching algorithm Semi-Global Block Matching to obtain the spatial location information of Kiwifruit. …”
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A novel Smishing defense approach based on meta-heuristic optimization algorithms by Mohammad Alshinwan, Osama A. Khashan, Zakwan Alarnaout, Salam Salameh Shreem, Ahmed Younes Shdefat, Nader Abdel Karim

“…Most SMS phishing messages are short text with simple messages, new writing characteristics, and oversampling techniques for unbalanced data, so they are easy to spot. This paper introduces an approach for detecting SMS phishing based on machine learning algorithms. …”
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A new aggregation and riming discrimination algorithm based on polarimetric weather radars by A. Blanke, M. Gergely, S. Trömel, S. Trömel

“…Several machine learning methods have been tested to detect riming from the corresponding QVPs of polarimetric variables. …”
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A proficient approach for the classification of Alzheimer’s disease using a hybridization of machine learning and deep learning by Hafiz Ahmed Raza, Shahab U. Ansari, Kamran Javed, Muhammad Hanif, Saeed Mian Qaisar, Usman Haider, Paweł Pławiak, Iffat Maab

“…Recent studies have employed machine learning to detect and classify AD. Deep learning models have also been increasingly utilized with varying degrees of success. …”
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Robust Corner Detection Using Local Extrema Differences by Reza Yazdi, Hassan Khotanlou, Hosna Khademfar

“…Corner detection, crucial for many computer vision tasks due to corner's distinct structural properties, often relies on traditional intensity-based detectors developed before 2000. …”
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Advances and Challenges in Automated Drowning Detection and Prevention Systems by Maad Shatnawi, Frdoos Albreiki, Ashwaq Alkhoori, Mariam Alhebshi, Anas Shatnawi

“…Automatic drowning detection approaches could be further categorized into computer vision-based approaches, where camera-captured images are analyzed by machine learning algorithms to detect instances of drowning, and sensing-based approaches, where sensing instruments are attached to swimmers to monitor their physical parameters. …”
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A new classification algorithm for low concentration slurry based on machine vision by Chuanzhen Wang, Xinyi Wang, Andile Khumalo, Fengcheng Jiang, Jintao Lv

“…Subsequently, a new low concentration classification model was systematically developed, encompassing aspects such as original image acquisition, data augmentation, dataset partitioning, classification algorithm design, and model evaluation. …”
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Exploring Machine Learning and Deep Learning Approaches for Battery Management Systems in EVs: A Comprehensive Review by Sathish J., Ramash Kumar K., Saraswathi D.

“…Machine learning and deep learning algorithms mimic humans by focusing on statistical data and algorithms on a real-time basis. …”
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Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process by Jianjun Zhao, Junwu Zhoub, Weixing Su, Fang Liu

“…Given the difficulty of accurate online detection for massive data collecting real-timely in a strong noise environment during the complex geological mineral grade analysis process, an order self-learning ARHMM (Autoregressive Hidden Markov Model) algorithm is proposed to carry out online outlier detection in the geological mineral grade analysis process. …”
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