Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan by Takeo Nakayama, Yusuke Tsugawa, Hiroshi Yoshihara, Memori Fukuda, Sho Okiyama

“…A deep learning-based algorithm that included a multi-instance convolutional neural network was trained to detect hypertension from pharyngeal images and demographic information. …”
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EF yolov8s: A Human–Computer Collaborative Sugarcane Disease Detection Model in Complex Environment by Jihong Sun, Zhaowen Li, Fusheng Li, Yingming Shen, Ye Qian, Tong Li

“…Subsequently, five basic instance segmentation models of YOLOv8 were used for comparative analysis, validated using nutrient deficiency condition videos, and a human–machine integrated detection model for nutrient deficiency symptoms at the top of sugarcane was constructed. …”
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Deep Transfer Learning Based on Hybrid Swin Transformers With LSTM for Intrusion Detection Systems in IoT Environment by Ibrahim A. Fares, Ahmed Gamal Abdellatif Ibrahim, Mohamed Abd Elaziz, Mansour Shrahili, Adham Ahmed Elmahallawy, Rana Muhammad Sohaib, Mahmoud A. Shawky, Syed Tariq Shah

“…These pre-trained weights are further transferred into another instance of the new model for subsequent fine-tuning. …”
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Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI by Tassilo Wald, Benjamin Hamm, Julius C. Holzschuh, Rami El Shafie, Andreas Kudak, Balint Kovacs, Irada Pflüger, Bastian von Nettelbladt, Constantin Ulrich, Michael Anton Baumgartner, Philipp Vollmuth, Jürgen Debus, Klaus H. Maier-Hein, Thomas Welzel

“…Moreover, volumetric instance sensitivity improved by 3.6–7.6 points (p < 0.001). …”
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A Combined Approach Of Adasyn And Tomeklink For Anomaly Network Intrusion Detection System Using Some Selected Machine Learning Algorithms by Nasiru Ige Salihu, Muhammed Nazeer Musa, Awujola J. Olalekan

“…Securing computer networks against malicious attacks requires an efficient Network Intrusion Detection System (IDS). While machine learning techniques are commonly used for anomaly-based intrusion detection, data imbalance challenges conventional algorithms, leading to biased predictions and reduced accuracy. …”
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Low-Shot Weakly Supervised Object Detection for Remote Sensing Images via Part Domination-Based Active Learning and Enhanced Fine-Tuning by Peng Liu, Boxue Huang, Tingting Jin, Hui Long

“…In low-shot weakly supervised object detection (LS-WSOD), a small number of strong (instance-level) labels are introduced to a weakly (image-level) annotated dataset, thus balancing annotation costs and model performance. …”
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Few-Shot Object Detection for Remote Sensing Images via Pseudo-Sample Generation and Feature Enhancement by Zhaoguo Huang, Danyang Chen, Cheng Zhong

“…Few-shot object detection (FSOD) based on fine-tuning is essential for analyzing optical remote sensing images. …”
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Vision transformer-enhanced thermal anomaly detection in building facades through fusion of thermal and visible imagery by Siyu Zheng, Jiaxin Zhang, Rui Zu, Yunqin Li

“…The thermal anomaly detection of building facades is critically important for the evaluation and upkeep of structures. …”
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Fast Islanding Detection for Distribution System including PV using Multi-Model Decision Tree Algorithm by Rasool Ebrahimi, Ghazanfar Shahgholian, Bahador Fani

“…Modern distribution system including Distributed Generation (DG) requires reliable and fast islanding detection algorithms in order to determine the grid status. …”
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Ocular Coherence Tomography Unveils Alport Syndrome: A Critical Tool in Detecting Collagen IV Nephropathies by Abdelrahman Ibrahim, Zena Altawallbeh, Monica Patricia Revelo, Martin Gregory, Laith Al-Rabadi

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SDFSD-v1.0: A Sub-Meter SAR Dataset for Fine-Grained Ship Detection by Peixin Cai, Bingxin Liu, Peilin Wang, Peng Liu, Yu Yuan, Xinhao Li, Peng Chen, Ying Li

“…We developed the “annotate entire image, then slice” workflow (AEISW) and constructed a sub-meter SAR fine-grained ship detection dataset (SDFSD) by using 846 sub-meter SAR images that include 96,921 ship instances of 15 ship types across 35,787 slices. …”
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Implementation of Blockchain Technology for Image Plagiarism Detection Using DCT, AES128, and SHA-1 Algorithms by Filbert Duran, Leonardo, Shickhem, Yennimar

“…Image plagiarism can be conceptualized as a broader category that encompasses the challenges of detecting copied images. Identifying instances of plagiarism is of paramount importance not only for graphic designers, professional photographers, and bloggers but also for publishing entities and legal practitioners seeking to uncover unauthorized reproductions of their creations. …”
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Sustainable phytoprotection: a smart monitoring and recommendation framework using Puma Optimization for potato pathogen detection by Amal H. Alharbi, Faris H. Rizk, Khaled Sh. Gaber, Marwa M. Eid, Marwa M. Eid, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy, Pushan Kumar Dutta, Doaa Sami Khafaga

“…The system is trained and evaluated on a real-world dataset derived from structured field experiments, comprising 52 instances and 42 agronomic, microbial, and ecological variables. …”
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Breast Cancer Detection Analysis Using Different Machine Learning Techniques: South Iraq Case Study by Salma Abdulbaki Mahmood, Myssar Jabbar Hammood Al-Battbootti, Saad Shaheen Hamadi, Iuliana Marin, Costin-Anton Boiangiu, Nicolae Goga

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Real-time detection and monitoring of public littering behavior using deep learning for a sustainable environment by Eaman Alharbi, Ghadah Alsulami, Sarah Aljohani, Waad Alharbi, Somayah Albaradei

“…Leveraging surveillance cameras and advanced computer vision technology, SAWN aims to identify and reduce instances of littering. Our study explores the use of the MoViNet video classification model to detect littering activities by vehicles and pedestrians, alongside the YOLOv8 object detection model to identify individuals responsible through facial recognition and license plate detection. …”
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Study on the Detection of Single and Dual Partial Discharge Sources in Transformers Using Fiber-Optic Ultrasonic Sensors by Feng Liu, Yansheng Shi, Shuainan Zhang, Wei Wang

“…Partial discharge is a fault that occurs at the site of insulation defects within a transformer. Dual instances of partial discharge origination discharging simultaneously embody a more intricate form of discharge, where the interaction between the discharge sources leads to more intricate and unpredictable insulation damage. …”
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A novel ensemble Wasserstein GAN framework for effective anomaly detection in industrial internet of things environments by Rubina Riaz, Guangjie Han, Kamran Shaukat, Naimat Ullah Khan, Hongbo Zhu, Lei Wang

“…Abstract Imbalanced datasets in Industrial Internet of Things (IIoT) environments pose a serious challenge for reliable pattern classification. Critical instances of minority classes (such as anomalies or system faults) are often vastly outnumbered by routine data, making them difficult to detect. …”
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Advancing Grapevine Disease Detection Through Airborne Imaging: A Pilot Study in Emilia-Romagna (Italy) by Virginia Strati, Matteo Albéri, Alessio Barbagli, Stefano Boncompagni, Luca Casoli, Enrico Chiarelli, Ruggero Colla, Tommaso Colonna, Nedime Irem Elek, Gabriele Galli, Fabio Gallorini, Enrico Guastaldi, Ghulam Hasnain, Nicola Lopane, Andrea Maino, Fabio Mantovani, Filippo Mantovani, Gian Lorenzo Mazzoli, Federica Migliorini, Dario Petrone, Silvio Pierini, Kassandra Giulia Cristina Raptis, Rocchina Tiso

“…Innovative applications of high-resolution airborne imaging are explored for detecting grapevine diseases. Driven by the motivation to enhance early disease detection, the method’s effectiveness lies in its capacity to identify isolated cases of grapevine yellows (Flavescence dorée and Bois Noir) and trunk disease (Esca complex), crucial for preventing the disease from spreading to unaffected areas. …”
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Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears by Jhonathan Sora-Cardenas, Wendy M. Fong-Amaris, Cesar A. Salazar-Centeno, Alejandro Castañeda, Oscar D. Martínez-Bernal, Daniel R. Suárez, Carol Martínez

“…The system achieved an F1-score of 95% for image quality evaluation, 88.92% for leukocyte detection, and 82.10% for parasite detection. The F1-score—a metric balancing precision (correctly identified positives) and recall (correctly detected instances out of actual positives)—is especially valuable for assessing models on imbalanced datasets. …”
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Hybrid Big Bang-Big crunch with cuckoo search for feature selection in credit card fraud detection by Mohd Shukri Ab Yajid, Nilesh Bhosle, Gadug Sudhamsu, Ali Khatibi, Sahil Sharma, Rubal Jeet, R. Sivaranjani, A. Bhowmik, A. Johnson Santhosh

“…Consequently, there has been a substantial rise in instances of credit card fraud that leads to monetary losses for both individuals and financial institutions. …”
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