Machine Learning-Potato Leaf Disease Detection App (MR-PoLoD) by Ahmad Fauzi, Annisya E Chandra, Sofyah Imammah, Malvin Zapata, Marza I Marzuki, Soni Prayogi

“…This application uses the CNN (Convolutional Neural Network) Machine Learning Algorithm because currently, CNN is recognized as the most efficient and effective model in pattern and image recognition tasks. …”
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MEIS-YOLO: Improving YOLOv11 for efficient aerial object detection with lightweight design by Yicheng Liu, Jinsong Wu, Li Chen

“…With the advancement of aerial technologies like drones and satellites, deep learning-driven object detection has seen considerable improvements in the processing of aerial images. Nevertheless, conventional object detection algorithms continue to encounter performance limitations, particularly when handling complex backgrounds and small objects. …”
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A Deep Neural Network Framework for Dynamic Two-Handed Indian Sign Language Recognition in Hearing and Speech-Impaired Communities by Vaidhya Govindharajalu Kaliyaperumal, Paavai Anand Gopalan

“…In order to improve both model generalization and image quality, preprocessing is applied to images prior to prediction, and the proposed dataset is organized to handle multiple dynamic words. …”
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Hard-coded backdoor detection method based on semantic conflict by Anxiang HU, Da XIAO, Shichen GUO, Shengli LIU

“…The current router security issues focus on the mining and utilization of memory-type vulnerabilities, but there is low interest in detecting backdoors.Hard-coded backdoor is one of the most common backdoors, which is simple and convenient to set up and can be implemented with only a small amount of code.However, it is difficult to be discovered and often causes serious safety hazard and economic loss.The triggering process of hard-coded backdoor is inseparable from string comparison functions.Therefore, the detection of hard-coded backdoors relies on string comparison functions, which are mainly divided into static analysis method and symbolic execution method.The former has a high degree of automation, but has a high false positive rate and poor detection results.The latter has a high accuracy rate, but cannot automate large-scale detection of firmware, and faces the problem of path explosion or even unable to constrain solution.Aiming at the above problems, a hard-coded backdoor detection algorithm based on string text semantic conflict (Stect) was proposed since static analysis and the think of stain analysis.Stect started from the commonly used string comparison functions, combined with the characteristics of MIPS and ARM architectures, and extracted a set of paths with the same start and end nodes using function call relationships, control flow graphs, and branching selection dependent strings.If the strings in the successfully verified set of paths have semantic conflict, it means that there is a hard-coded backdoor in the router firmware.In order to evaluate the detection effect of Stect, 1 074 collected device images were tested and compared with other backdoor detection methods.Experimental results show that Stect has a better detection effect compared with existing backdoor detection methods including Costin and Stringer: 8 hard-coded backdoor images detected from image data set, and the recall rate reached 88.89%.…”
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Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning by Younes Karmoude, Soufiane Idbraim, Souad Saidi, Antoine Masse, Manuel Arbelo

“…This study monitors changes in an argan forest near Agadir, Morocco, from 2017 to 2023 using Sentinel-2 satellite imagery and advanced image processing algorithms. Various machine learning models were evaluated for argan tree detection, with LightGBM achieving the highest accuracy when trained on a dataset integrating spectral bands, temporal features, and vegetation indices information. …”
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Research on motion planning for an indoor spray arm based on an improved potential field method. by Dongjie Zhao, Bin Zhang, Ying Zhao, Qun Sun, Chuanjun Li, Chong Wang

“…Simulation analysis shows that the algorithm can plan better motion trajectories than the servo controller based on image moments in previous studies. …”
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Detection of the Pin Defects of Power Transmission Lines Based on Improved TPH-MobileNetv3 by Mengxuan Li, Jingshan Han, Zhi Yang, Bin Zhao, Peng Liu

“…Moreover, compared with the mainstream algorithms with the same detection accuracy, this algorithm not only reduces the model size and significantly enhances detection efficiency but also satisfies the requirement of edge image processing of power inspection.…”
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Machine learning applications for early detection of esophageal cancer: a systematic review by Farhang Hosseini, Farkhondeh Asadi, Hassan Emami, Rayan Ebnali Harari

“…Recent advances in machine learning (ML) techniques, particularly in computer vision, have demonstrated promising applications in medical image processing, assisting clinicians in making more accurate and faster diagnostic decisions. …”
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A Driver Behavior Detection Model for Human-Machine Co-Driving Systems Based on an Improved Swin Transformer by Junhua Cui, Yunxing Chen, Zhao Wu, Huawei Wu, Wanghao Wu

“…First, the efficient channel attention (ECA) module is added after the self-attention mechanism of the Swin transformer model so that the channel features can be dynamically adjusted according to their importance, thus enhancing the model’s attention to the important channel features. Then, the image preprocessing of the public State Farm dataset and expansion of the original image dataset is carried out. …”
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Electrowetting display of multiscale Gamma based on dynamic histogram equilibrium by Mingzhen Chen, Zhixian Lin, Shanling Lin, Jianpu Lin, Tailiang Guo

“…More importantly, the images processed by this algorithm exhibit better brightness details on chromatic electrowetting displays.…”
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Urban tree species classification using multisource satellite remote sensing data and street view imagery by Shufan Wang, Chun Liu, Shanshan Wei, Hao Tang

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Lightweight construction safety behavior detection model based on improved YOLOv8 by Kan Huang, Mideth B. Abisado

“…YOLO (You Only Look Once) is an object detection algorithm that can achieve real-time and efficient object detection by dividing images into grids and predicting the bounding boxes and categories of objects in each grid. …”
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A Multi-Input Neural Network Model for Accurate MicroRNA Target Site Detection by Mohammad Mohebbi, Amirhossein Manzourolajdad, Ethan Bennett, Phillip Williams

“…For each feature derived from a microRNA target-site duplex, we create a corresponding image. These images are processed in parallel by the MINN algorithm, allowing it to learn a comprehensive and precise representation of the underlying biological mechanisms. (3) Results: Our method, on an experimentally validated test set, detects target sites with an AUPRC of 0.9373, Precision of 0.8725, and Recall of 0.8703 and outperforms several commonly used computational methods of microRNA target-site predictions. (4) Conclusions: Incorporating diverse biologically explainable features, such as duplex structure, substructures, their MFEs, and binding probabilities, enables our model to perform well on experimentally validated test data. …”
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Study of the Possibility to Combine Deep Learning Neural Networks for Recognition of Unmanned Aerial Vehicles in Optoelectronic Surveillance Channels by Vladislav Semenyuk, Ildar Kurmashev, Dmitriy Alyoshin, Liliya Kurmasheva, Vasiliy Serbin, Alessandro Cantelli-Forti

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Automated diagnosis of mild cognitive impairment through connectivity analysis of EEG signals and a DL scheme by Jiayi Lin, Wei Huang

“…To investigate the hypothesis, a new signal processing technique was utilized to transform intricate EEGs into input images for a DL framework. …”
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Development of a new method to track multiple honey bees with complex behaviors on a flat laboratory arena. by Toshifumi Kimura, Mizue Ohashi, Karl Crailsheim, Thomas Schmickl, Ryuichi Okada, Gerald Radspieler, Hidetoshi Ikeno

“…In addition, honeybees react to light and recordings must be made under special red-light conditions, which the eyes of bees perceive as darkness. The resulting video images are scarcely distinguishable. We have developed a new algorithm, K-Track, for tracking numerous bees in a flat laboratory arena. …”
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An Efficient Prediction System for Diabetes Disease Based on Deep Neural Network by Tawfik Beghriche, Mohamed Djerioui, Youcef Brik, Bilal Attallah, Samir Brahim Belhaouari

“…In this work, an efficient medical decision system for diabetes prediction based on Deep Neural Network (DNN) is presented. Such algorithms are state-of-the-art in computer vision, language processing, and image analysis, and when applied in healthcare for prediction and diagnosis purposes, these algorithms can produce highly accurate results. …”
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ECG Paper Digitization and R Peaks Detection Using FFT by Ibraheam Fathail, Vaishali D. Bhagile

“…After that, the system retrieves the signals for further processing of the raw signals. We used the fast Fourier transform (FFT) algorithm to calculate R peaks and calculate the heart rate. …”
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Quantification of greenhouse gas emissions from livestock using remote sensing & artificial intelligence by Evet Naturinda, Fortunate Kemigyisha, Anthony Gidudu, Isa Kabenge, Emmanuel Omia, Jackline Aboth

“…This research developed a remote sensing and Artificial Intelligence (AI) based approach to quantify GHG emissions from cattle in the Kisombwa Ranching Scheme in Mubende District, central Uganda.We trained a deep learning algorithm, You Only Look Once (YOLO) v4, to detect cattle from the Unmanned Aerial Vehicle (UAV) images of the study area and applied the Simple Online Real-time Tracker (SORT) algorithm for automated counting. …”
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Global Feature Focusing and Information Enhancement Network for Occluded Pedestrian Detection by ZHENG Kaikui, JI Kangyou, LI Jun, LI Qiming

“…The Mamba module first flattens the feature maps into one dimensional image patch vectors and then uses linear layers for feature extraction and transformation. …”
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