Transformer Self-Attention Change Detection Network with Frozen Parameters by Peiyang Cheng, Min Xia, Dehao Wang, Haifeng Lin, Zikai Zhao

“…This paper proposes a frozen-parameter Transformer self-attention change detection network (ZAQNet). The network integrates four innovative modules: a GIAU (Generalized Image Attention Unit) which can effectively fuse the features of two remote sensing images and accurately focus on changing areas; a GSAU (Global Spatial Attention Unit) which performs self attention processing in the image spatial dimension to enhance the model’s ability to capture global change information; a GSCU (Global Semantic Context Unit) which performs self-attention operations in the channel dimension to enhance the model’s attention to feature maps containing changing information; and a PRU (Patch Refinement Unit) which extracts and refines spatial position information from the underlying feature map, optimizing the restoration effect of the feature map. …”
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Early detection of bacterial pneumonia by characteristic induced odor signatures by Kim Arnold, Alejandro Gómez-Mejia, Miguel de Figueiredo, Julien Boccard, Kapil Dev Singh, Serge Rudaz, Pablo Sinues, Annelies S. Zinkernagel

“…The infection status was confirmed using classical CFU enumeration and tissue histology. The detected VOCs were analyzed using a pre- and post-processing algorithm along with ANOVA and RASCA statistical evaluation methods. …”
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Transformer-based ECG classification for early detection of cardiac arrhythmias by Sunnia Ikram, Amna Ikram, Harvinder Singh, Malik Daler Ali Awan, Sajid Naveed, Isabel De la Torre Díez, Henry Fabian Gongora, Thania Candelaria Chio Montero

“…Electrocardiogram (ECG) classification plays a critical role in early detection and trocardiogram (ECG) classification plays a critical role in early detection and monitoring cardiovascular diseases. …”
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Integrating Time Series Anomaly Detection Into DevOps Workflows by Gustav Kanahols, Shahriar Hasan, Per Erik Strandberg

“…However, challenges remain regarding the lack of ground truth data from DevOps systems, as well as difficulties in storing, processing, and visualizing the collected data. Furthermore, most of these datasets are unlabeled, making it unclear what constitutes anomalous system behavior, and no generalized approach exists for selecting the most suitable AI algorithms for anomaly detection in such contexts. …”
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GNNMutation: a heterogeneous graph-based framework for cancer detection by Nuriye Özlem Özcan Şimşek, Arzucan Özgür, Fikret Gürgen

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Edge-Assisted Label-Flipping Attack Detection in Federated Learning by Nourah S. AlOtaibi, Muhamad Felemban, Sajjad Mahmood

“…Federated Learning (FL) has transformed machine learning by facilitating decentralized, privacy-focused data processing. Despite its advantages, FL remains vulnerable to data poisoning attacks, particularly Label-Flipping Attacks (LFA). …”
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An advanced deep learning method for pepper diseases and pests detection by Xuewei Wang, Jun Liu, Qian Chen

“…To address these challenges, this study introduces YOLO-Pepper, an enhanced model designed specifically for greenhouse pepper disease and pest detection, overcoming three key obstacles: small target recognition, multi-scale feature extraction under occlusion, and real-time processing demands. …”
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Detection of Plants Leaf Diseases using Swarm Optimization Algorithms by Saud Abdul Razzaq, Baydaa Khaleel

“…In this research, some artificial intelligence algorithms, represented by swarm optimization algorithms, were used to detect and classify plant diseases to healthy and unhealthy through images of different leaves of plants. …”
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SYSTEM FOR DETECTION AND IDENTIFICATION OF POTENTIALLY EXPLOSIVE OBJECTS IN OPEN AREA by Ivan Pakhnуts, Sofiia Khrustalova, Kirill Khrustalev

“… The subject of this research is the methods, means and systems for detecting potentially dangerous military objects in open terrain. …”
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Modified planar sensors for the separate detection of certain cephalosporin antibiotics by Kulapina, Elena Grigorievna, Mursalov, Ruslan K., Kulapina, Olga Ivanovna

“…The possibility of separate determination of cephalosporin antibiotics in two- and three-component mixtures by projection methods of multidimensional data processing PLS-1 and PLS-2 is shown. The standard errors of calibration and prediction are estimated, on the basis of which the optimal number of latent variables for these methods is selected. …”
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LLM-Based Cyberattack Detection Using Network Flow Statistics by Leopoldo Gutiérrez-Galeano, Juan-José Domínguez-Jiménez, Jörg Schäfer, Inmaculada Medina-Bulo

“…In this paper, we present a novel approach for cyberattack detection using an encoder–decoder pre-trained Large Language Model (T5), fine-tuned to adapt its classification scheme for the detection of cyberattacks. …”
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A Lightweight Citrus Object Detection Method in Complex Environments by Qiurong Lv, Fuchun Sun, Yuechao Bian, Haorong Wu, Xiaoxiao Li, Xin Li, Jie Zhou

“…Aiming at the limitations of current citrus detection methods in complex orchard environments, especially the problems of poor model adaptability and high computational complexity under different lighting, multiple occlusions, and dense fruit conditions, this study proposes an improved citrus detection model, YOLO-PBGM, based on You Only Look Once v7 (YOLOv7). …”
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Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…This study pioneers an innovative framework, using Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features, combined with a hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier, to enhance the detection of DR. Inspired by principles of randomization-based learning, our approach incorporates elements of stochastic modeling within the CNN-RBF architecture to optimize feature extraction and classification, mirroring the efficiency of non-iterative training processes. …”
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The Task of Detecting Unacceptable Information Security Events in the Information Infrastructure by Darya A. Evdokimova, Andrey A. Mikryukov

“…As cyber threats grow more complex and diverse, traditional detection methods are becoming increasingly ineffective, necessitating improvements in existing technologies to protect information systems.The novelty of the proposed solutions lies in improving the accuracy of detecting unacceptable events through the use of machine learning methods and a neural network classifier, as well as reducing response time by utilizing the Elastic Stack tool for data collection, processing, aggregation, and visualization.Materials and methods. …”
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BACK TO THE “GOLD STANDARD”: HOW PRECISE IS HEMATOCRIT DETECTION TODAY? by Leonid Livshits, Tal Bilu, Sari Peretz, Anna Bogdanova, Max Gassmann, Harel Eitam, Ariel Koren, Carina Levin

“… Introduction: The commonly used method for hematocrit detection, by visual examination of microcapillary tube, known as "micro-HCT", is subjective but still remains one of the key sources for false hematocrit evaluation. …”
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Classification of and detection techniques for RNAi-induced effects in GM plants by Cecilia Diaz, Steve U. Ayobahan, Samson Simon, Luise Zühl, Andreas Schiermeyer, Elke Eilebrecht, Sebastian Eilebrecht

“…The RNAi mechanism hinges on the introduction of double-stranded RNA (dsRNA), which is processed into short interfering RNAs (siRNAs) that degrade specific messenger RNAs (mRNAs). …”
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Enhancing Road Safety: Detection of Animals on Highways During Night by K. Parkavi, Agniv Ganguly, Shivansh Sharma, Ashesh Banerjee, Keshav Kejriwal

“…Conventional monitoring systems are rendered useless due to poor visibility and inadequate lighting, emphasizing the necessity for a sophisticated detecting system. Using YOLOv5, a cutting- edge object detection algorithm, this study presents a way to identify animals on highways at night and reduce collisions. …”
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Exploration of machine learning approaches for automated crop disease detection by Annu Singla, Ashima Nehra, Kamaldeep Joshi, Ajit Kumar, Narendra Tuteja, Rajeev K. Varshney, Sarvajeet Singh Gill, Ritu Gill

“…Recent advancements in machine learning (ML) offer promising alternatives by automating the disease detection processes with high precision and efficiency. …”
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Automatic detection and counting of wheat spike based on DMseg-Count by Hecang Zang, Yilong Peng, Meng Zhou, Guoqiang Li, Guoqing Zheng, Hualei Shen

“…Compared with other deep learning models, the proposed DMseg-Count model can detect wheat spike image in challenging situations, and has better computer vision processing capabilities and performance evaluation detection effect. …”
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AI-Driven Boost in Detection Accuracy for Agricultural Fire Monitoring by Akmalbek Abdusalomov, Sabina Umirzakova, Komil Tashev, Jasur Sevinov, Zavqiddin Temirov, Bahodir Muminov, Abror Buriboev, Lola Safarova Ulmasovna, Cheolwon Lee

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