Smart Watch Sensors for Tremor Assessment in Parkinson’s Disease—Algorithm Development and Measurement Properties Analysis by Giulia Palermo Schifino, Maira Jaqueline da Cunha, Ritchele Redivo Marchese, Vinicius Mabília, Luis Henrique Amoedo Vian, Francisca dos Santos Pereira, Veronica Cimolin, Aline Souza Pagnussat

“…This study aimed to develop and validate a tremor-detection algorithm using smartwatch sensors. Data were collected from 21 individuals with PD and 27 healthy controls using both a commercial inertial measurement unit (G-Sensor, BTS Bioengineering, Italy) and a smartwatch (Apple Watch Series 3). …”
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Advanced Signal Processing Algorithm-Based State Estimation and Denoising of ECG Signals for Biomedical Applications by Saad Arif, Mohammad Maroof Maroof Siddiqui, Nayef Alqahtani, Kamran Shah, Muhammad Attique Attique Khan, Naoufel Kraiem

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Wearable Fall Detection System with Real-Time Localization and Notification Capabilities by Chin-Kun Tseng, Shi-Jia Huang, Lih-Jen Kau

“…Despite significant progress in fall detection systems, many of the proposed algorithms remain difficult to implement in real-world applications. …”
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Improved YOLOv8 Network of Aircraft Target Recognition Based on Synthetic Aperture Radar Imaging Feature by Xing Wang, Wen Hong, Yunqing Liu, Guanyu Yan, Dongmei Hu, Qi Jing

“…First, we employed the Shi–Tomasi corner detection algorithm and the Enhanced Lee filtering algorithm to convert grayscale images into RGB images, thereby improving detection accuracy and efficiency. …”
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Anomaly detection in virtual machine logs against irrelevant attribute interference. by Hao Zhang, Yun Zhou, Huahu Xu, Jiangang Shi, Xinhua Lin, Yiqin Gao

“…Firstly, the log parsing algorithm is used to parse the logs. Then, the feature extraction algorithm, which combines Long Short-Term Memory and Autoencoder-Decoder, is applied to extract features. …”
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Recurrent Neural Network Optimized by Grasshopper for Accurate Audio Data-Based Diagnosis of Parkinson's Disease by Saif Wali Ali Alsudani, Ghassan Khudair Saud

“… Proposed here is a speech-based diagnostic framework for detecting Parkinson's disease that utilizes a Long Short-Term Memory neural network and the Grasshopper Optimization Algorithm. …”
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Research on dust identification and concentration detection method based on machine vision by Luyang TU, Qinghua CHEN, Yingsong CHENG, Bingyou JIANG

“…Aiming at the problem that the current machine vision algorithm fails to combine position information with concentration value in the field of dust detection, we propose an algorithm that combines improved YOLOv5 with multivariate model. …”
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An automatic approach to detect skin cancer utilizing active infrared thermography by Ricardo F. Soto, Sebastián E. Godoy

“…These features were implemented in a support-vector machine classifier to detect malignancy. …”
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Visual Positioning Detection of EMU Brake Pad Based on Deep Learning by GUAN Chunling, XU Yingjie, SONG Yuechao

“…To address the issue of indistinct image features in real-world scenarios, based on the Faster R-CNN (region conventional neural network) algorithm, an edge detection branch is introduced, and a target edge loss function is added to the loss function, integrating edge information from an auxiliary network. …”
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DCE-YOLOv8: Lightweight and Accurate Object Detection for Drone Vision by Jinsu An, Dong Hee Lee, Muhamad Dwisnanto Putro, Byeong Woo Kim

“…Object detection using drones is a sophisticated technology that employs a camera mounted on a drone in conjunction with a computer vision algorithm to pinpoint the precise location of an object and ascertain its type. …”
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A network intrusion detection method designed for few-shot scenarios by Weichen HU, Congyuan XU, Yong ZHAN, Guanghui CHEN, Siqing LIU, Zhiqiang WANG, Xiaolin WANG

“…Existing intrusion detection techniques often require numerous malicious samples for model training.However, in real-world scenarios, only a small number of intrusion traffic samples can be obtained, which belong to few-shot scenarios.To address this challenge, a network intrusion detection method designed for few-shot scenarios was proposed.The method comprised two main parts: a packet sampling module and a meta-learning module.The packet sampling module was used for filtering, segmenting, and recombining raw network data, while the meta-learning module was used for feature extraction and result classification.Experimental results based on three few-shot datasets constructed from real network traffic data sources show that the method exhibits good applicability and fast convergence and effectively reduces the occurrence of outliers.In the case of 10 training samples, the maximum achievable detection rate is 99.29%, while the accuracy rate can reach a maximum of 97.93%.These findings demonstrate a noticeable improvement of 0.12% and 0.37% respectively, in comparison to existing algorithms.…”
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Detecting and Explaining Postpartum Depression in Real-Time with Generative Artificial Intelligence by Silvia García-Méndez, Francisco de Arriba-Pérez

“…Moreover, it addresses the black box problem since the predictions are described to the end users thanks to the combination of LLMS with interpretable ML models (i.e. tree-based algorithms) using feature importance and natural language. …”
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Automated weed and crop recognition and classification model using deep transfer learning with optimization algorithm by K. Gopalakrishnan, R. Sivaraj, M. Vijayakumar

“…The ShuffleNetV2 approach is exploited in the AWRC-DLMLO method to ascertain feature vector. Next, the lemurs optimization algorithm (LOA) is applied to increase the hyperparameter and fine-tune the DL technique, further enhancing its performance. …”
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Foreign object detection on coal conveyor belt enhanced by attention mechanism by ZHANG Yang, CHENG Zhiyu, CHEN Yunjiang, ZHANG Jiannan, YUAN Wensheng, ZHANG Hui

“…In order to solve this problem, the network structure of the original YOLOv8 algorithm was optimized and a YOLOv8-CPCA detection method was proposed. …”
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Automated Seizure Detection through EEG Analysis and Deep Learning Technique by Srinivas Nowduri, M. Madhusudhana Subramanyam

“…However, one of the challenges of automatic seizure detection using EEG analysis is extracting optimal features that can distinguish between different states of epilepsy. …”
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Method for detecting cracks in retaining walls based on improved YOLOv5s by Yanhai Wang, Chenxin Guo, Guoyong Duan, Yuhao Zhang, Chao Yang, Huafeng Deng

“…Specifically, the BotNet module was introduced into the Backbone to enhance the extraction of long-term dependence and global features. The GhostNetV2 module was also utilized to reduce the network complexity, making it suitable for edge-based detection. …”
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Deep learning based gasket fault detection: a CNN approach by S. Arumai Shiney, R. Seetharaman, V. J. Sharmila, S. Prathiba

“…The suggested method uses deep learning approaches to recognize and evaluate radiator images, with a focus on identifying misaligned or incorrectly installed gaskets. Deep learning algorithms are specific for feature extraction and classification together with a convolutional neural network (CNN) module that allows for seamless connection. …”
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MRD: A Linear-Complexity Encoder for Real-Time Vehicle Detection by Kaijie Li, Xiaoci Huang

“…Vehicle detection algorithms constitute a fundamental pillar in intelligent driving systems and smart transportation infrastructure. …”
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Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet by Yu Zhang, Yangyang Jiao, Yinke Dou, Liangliang Zhao, Qiang Liu, Yang Liu

“…The thickness detection error is constrained within 7.2%. These results confirm that the proposed algorithm can provide a control basis for the high-precision operation of de-icing robots.…”
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Spiking Residual ShuffleNet-Based Intrusion Detection in IoT Environment by Sneha Leela Jacob, H. Parveen Sultana

“…In this context, a new model called Spiking Residual ShuffleNet (SR-ShuffleNet) is introduced for intrusion detection (ID) in an IoT environment. The Multi-objective Fractional Artificial Bee Colony (MFABC) algorithm is used for routing. …”
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