Evaluating Deep Learning Architectures for Breast Tumor Classification and Ultrasound Image Detection Using Transfer Learning by Christopher Kormpos, Fotios Zantalis, Stylianos Katsoulis, Grigorios Koulouras

“…The intersection of medical image classification and deep learning has garnered increasing research interest, particularly in the context of breast tumor detection using ultrasound images. Prior studies have predominantly focused on image classification, segmentation, and feature extraction, often assuming that the input images, whether sourced from healthcare professionals or individuals, are valid and relevant for analysis. …”
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Automatic Detection and Classification of Dental Anomalies and Tooth Types Using Transformer-Based Yolo With GA Optimization by Sanabam Bineshwor Singh, Anuradha Laishram, Khelchandra Thongam, Khumanthem Manglem Singh

“…This addition enabled the model to capture comprehensive contextual information and learn distinct feature representations from the dental images. Inspired by Transformer models, the multi-head self-attention mechanism improves the capacity of the model to intricate spatial relationships in images, enhancing detection and classification performance. …”
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Intelligent Stress Detection Using ECG Signals: Power Spectrum Imaging with Continuous Wavelet Transform and CNN by Rodrigo Mateo-Reyes, Irving A. Cruz-Albarran, Luis A. Morales-Hernandez

“…The proposed approach is characterized by its ability to transform ECG signals into time–frequency representations by means of short duration sampling, achieving an accurate classification of stress states without the need for complex feature extraction processes. This model is an efficient and accurate tool for stress analysis from biomedical signals.…”
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Windows Malware Detection via Enhanced Graph Representations with Node2Vec and Graph Attention Network by Nisa Vuran Sarı, Mehmet Acı, Çiğdem İnan Acı

“…This achievement highlights the framework’s ability to utilize contextual information for malware detection. The real-world dataset used provides a benchmark for future work, and this research lays a comprehensive foundation for advancing graph-based malware analysis.…”
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Detection of Multiple X-Ray Quasi-periodic Oscillations in IGR J19294+1816 with Insight-HXMT by Wen Yang, Wei Wang

“…Wavelet analysis also shows multiple QPOs at the frequencies of ∼30 mHz, 50 and 110 mHz, which have transient behaviors. …”
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Intrusion Detection Using Machine Learning for Risk Mitigation in IoT-Enabled Smart Irrigation in Smart Farming by Abhishek Raghuvanshi, Umesh Kumar Singh, Guna Sekhar Sajja, Harikumar Pallathadka, Evans Asenso, Mustafa Kamal, Abha Singh, Khongdet Phasinam

“…In the preprocessing of the NSL-KDD data set, first all symbolic features are converted to numeric features. Feature extraction is performed using principal component analysis. …”
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Analyzing the Efficacy of Computer-Aided Detection in Cerebral Aneurysm Diagnosis Using MRI Modality: A Review by Keerthi A. S. Pillai, Preena K. P., Madhu S. Nair

“…CNNs have proven to be a crucial component in improving the accuracy and efficiency of aneurysm detection by automatically learning features from raw imaging data, bypassing the need for manual feature extraction. …”
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Novel metrics and LSH algorithms for unsupervised, real-time anomaly detection in multi-aspect data streams by Samira Khodabandehlou, Alireza Hashemi Golpayegani

“…The main contributions include the Collusiveness metric, which detects group anomalies through statistical divergence analysis, and the RR-ISF, which prioritizes rare burst patterns. …”
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Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN by Nivea Kesav, M.G. Jibukumar

“…Later this same architecture is used as the feature extractor of an RCNN to detect the tumor regions of the Glioma MRI sample that has been classified from the previous stage and the tumor region is bounded using bounding boxes. …”
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ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection by N. I. Md. Ashafuddula, Rafiqul Islam

“…The model utilizes contour-based analysis, which is critical for object detection, precise segmentation, and capturing subtle variations in tumor morphology. …”
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A Systematic Literature Review of Machine Learning-Based Personality Trait Detection Using Electroencephalographic Data by Celina Rieck, Pascal Penava, Ricardo Buettner

“…We compare their performance by a meta-analysis of weighted data, highlighting strengths and limitations. …”
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Leveraging Deep Learning and Multimodal Large Language Models for Near-Miss Detection Using Crowdsourced Videos by Shadi Jaradat, Mohammed Elhenawy, Huthaifa I. Ashqar, Alexander Paz, Richi Nayak

“…At the event level, ViTs delivered a strong performance with a test accuracy of 77.27% and an F1-score of 67.37%, while CNN+LSTM, although lower in overall performance, demonstrated significant potential with a test accuracy of 78.1% and an F1-score of 68.69%. For a deeper analysis, we applied GPT-4o to process critical safety events (near-misses and crashes), utilizing both zero-shot and few-shot learning for narrative generation and feature extraction. …”
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Semantic and Geometric Fusion for Object-Based 3D Change Detection in LiDAR Point Clouds by Abderrazzaq Kharroubi, Fabio Remondino, Zouhair Ballouch, Rafika Hajji, Roland Billen

“…The model is evaluated on the Urb3DCD-v2 dataset, with feature importance analysis to identify important features. …”
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Polarimetric SAR Ship Detection Using Context Aggregation Network Enhanced by Local and Edge Component Characteristics by Canbin Hu, Hongyun Chen, Xiaokun Sun, Fei Ma

“…However, polarization SAR methods for ship detection still face challenges. The traditional constant false alarm rate (CFAR) detectors face sea clutter modeling and parameter estimation problems in ship detection, which is difficult to adapt to the complex background. …”
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Accurate and lightweight oral cancer detection using SE-MobileViT on clinically validated image dataset by Md Firoz Kabir, Md Yousuf Ahmad, Roise Uddin, Martin Cordero, Shashi Kant

“…Abstract Oral cancer poses a critical global health challenge, with early detection significantly improving patient survival rates and treatment outcomes. …”
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D₂X: Depression Detection System Through X Using Hybrid Machine Learning by Thara Angskun, Suda Tipprasert, Atitthan Thippongtorn, Jitimon Angskun

“…This article introduces a depression detection system through <inline-formula> <tex-math notation="LaTeX">$\mathbb {X}$ </tex-math></inline-formula> called <inline-formula> <tex-math notation="LaTeX">$D_{2}\mathbb {X}$ </tex-math></inline-formula>, which utilizes sentiment analysis of <inline-formula> <tex-math notation="LaTeX">$\mathbb {X}$ </tex-math></inline-formula> users&#x2019; tweets to predict their level of depression. …”
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Knowledge Improved Hybrid DNN&#x2013;KAN Framework for Intrusion Detection in Wireless Sensor Networks by M. Sriraghavendra, Muna Elsadig, Ines Hilali Jaghdam, S. Abdel-Khalek, B. Galeebathullah, Salem Alkhalaf

“…The proposed framework preprocesses and merges multiple datasets (WSN, NSL-KDD, and CICIDS2017), extracts features using Principal Component Analysis (PCA), and constructs a knowledge graph to embed expert-defined rules via Graph Convolutional Networks (GCNs). …”
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Association between dermoscopic features and tumor thickness, diameter, age, and sex in acral melanoma: A cross-sectional studyCapsule Summary by Yaei Togawa, MD, PhD, Yosuke Yamamoto, MD, PhD, Keisuke Suehiro, MD, Yuki Shiko, PhD, Kohei Takahashi, MS, Yohei Kawasaki, PhD, Hiroyuki Matsue, MD, PhD, Takashi Inozume, MD, PhD

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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

“…Using a dataset of 1000 clinically diagnosed images, we applied feature extraction techniques, including histogram bins and texture analysis with the gray level co-occurrence matrix (GLCM), alongside support vector machines (SVMs), for image quality assessment. …”
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Mamba-YOLO-ML: A State-Space Model-Based Approach for Mulberry Leaf Disease Detection by Chang Yuan, Shicheng Li, Ke Wang, Qinghua Liu, Wentao Li, Weiguo Zhao, Guangyou Guo, Lai Wei

“…Visualization analysis of the detection performance on the test set using GradCAM revealed that the enhanced Mamba-YOLO-ML model demonstrates earlier and more effective focus on characteristic regions of different diseases compared with its predecessor. …”
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