Integrate the Temporal Scheme for Unsupervised Video Summarization via Attention Mechanism by Vo Quoc Bang, Vo Hoai Viet

“…In this work, we present a novel unsupervised scheme named SegSum, designed for video summarization through the creation of video skims. …”
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Unsupervised Anomaly Detection of Forceps Force by Localizing the Region of Interest by Wenhui Zhuang, Kimihiko Masui, Naoto Kume, Megumi Nakao

“…In this study, we propose an alternative approach by formulating the force feedback problem as an image-based anomaly detection task. By leveraging unsupervised learning, we overcome the limitations posed by the scarcity of labeled abnormal force data and present a novel abnormal force anomaly detection method. …”
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Unsupervised iris image segmentation by E. Maksimenko, E. Pavelyeva

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Unsupervised Deep Learning for Synthetic CT Generation from CBCT Images for Proton and Carbon Ion Therapy for Paediatric Patients by Matteo Pepa, Siavash Taleghani, Giulia Sellaro, Alfredo Mirandola, Francesca Colombo, Sabina Vennarini, Mario Ciocca, Chiara Paganelli, Ester Orlandi, Guido Baroni, Andrea Pella

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A Supervised Approach for Land Use Identification in Trento Using Mobile Phone Data as an Alternative to Unsupervised Clustering Techniques by Manuel Mendoza-Hurtado, Gonzalo Cerruela-García, Domingo Ortiz-Boyer

“…Located in an alpine environment, Trento presents unique geographic challenges, including varied terrain and sparse network coverage, making it an ideal case for testing the robustness of supervised learning approaches. …”
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Zero-Shot Recon2Recon: Data-Free Unsupervised Denoiser Learning for Plug-and-Play Magnetic Resonance Imaging Reconstruction by Tae Hyung Kim

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Unsupervised Binary Classifier-Based Object Detection Algorithm with Integrated Background Subtraction Suitable for Use with Aerial Imagery by Gabija Veličkaitė, Ignas Daugėla, Ivan Suzdalev

“…Unlike conventional supervised models, SARGAS is trained in a partially unsupervised manner, learning to recognize feature patterns without requiring labeled data. …”
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Active Contours Connected Component Analysis Segmentation Method of Cancerous Lesions in Unsupervised Breast Histology Images by Vincent Majanga, Ernest Mnkandla, Zenghui Wang, Donatien Koulla Moulla

“…Once all binary-masked groups have been determined, a deep-learning recurrent neural network (RNN) model from the Keras architecture uses this information to automatically segment nuclei objects having cancerous lesions on the image via the active contours method. …”
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Water Leakage Pre-Localization in Drinking Water Networks via the Cosmic-Ray Neutron Sensing Technique by Luca Morselli, Federica Lorenzi, Andrea Basso, Luca Stevanato

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Automated note annotation after bioacoustic classification: Unsupervised clustering of extracted acoustic features improves detection of a cryptic owl by Callan Alexander, Robert Clemens, Paul Roe, Susan Fuller

“…Manual validation of outputs is time consuming, and additional fine-scale annotation of individual notes is impractical for large datasets and difficult to automate when using passive field recordings. This research presents an acoustic monitoring pipeline which employs a multi-stage hybrid approach: initial detection using a convolutional neural network classifier, followed by segmentation and iterative unsupervised clustering of extracted acoustic features using UMAP and HDBSCAN to remove label noise. …”
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Machine Learning in Intelligent Networks: Architectures, Techniques, and Use Cases by Elias Dritsas, Maria Trigka

“…It delves into key ML methodologies, supervised, unsupervised, reinforcement, and deep learning (DL), and highlights their transformative impact on network operations, such as resource allocation, fault management, and traffic optimization. …”
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Sustainable Selection of Machine Learning Algorithm for Gender-Bias Attenuated Prediction by Raik Orbay, Evelina Wikner

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Network-based analyses of multiomics data in biomedicine by Rachit Kumar, Joseph D. Romano, Marylyn D. Ritchie

“…This review will present various existing approaches in using network representations and analysis of data in multiomics in the framework of deep learning and machine learning approaches, subdivided into supervised and unsupervised approaches, to identify benefits and drawbacks of various approaches as well as the possible next steps for the field.…”
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Generating Anticancer Peptides Sequences Using Seq2Seq Modeling and Machine Learning Methods by Muhammad Sohail Ibrahim, Saheed Ademola Bello, Yunsang Kwak, Minseok Kim, Shujaat Khan

“…Our method begins with peptide sequence generation via a recurrent neural network (RNN) trained on the acp740 dataset. The generated sequences undergo rigorous filtration: Tier-1 employs three deep learning-based classifiers (ACP-DL, ACP-MHCNN, ACP-LSE) to identify potential ACPs; Tier-2 uses a nearest centroid classifier to filter out statistically less relevant sequences; Tier-3 involves a final filtration using unsupervised nearest neighbor learning based on fused feature encoding schemes (CKSAAP, k-Mer, and BPF). …”
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Translational medicine research on the role of key gene network modulation mediated by procyanidin B2 in the precise diagnosis and treatment of multiple sclerosis by Jian Liu, Meng Pu, Di Guo, Ying Xiao, Jin-zhu Yin, Dong Ma, Cun-gen Ma, Qing Wang

“…R language was employed for the identification of differentially expressed genes (DEGs), unsupervised clustering analysis, immune infiltration analysis, weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) analysis, and gene set enrichment analysis (GSEA). …”
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Automatic Blob Detection Method for Cancerous Lesions in Unsupervised Breast Histology Images by Vincent Majanga, Ernest Mnkandla, Zenghui Wang, Donatien Koulla Moulla

“…Nevertheless, the early detection of breast cancer lesions is a key determinant for diagnosis and treatment. In this study, we present a deep learning-based technique for breast cancer lesion detection, namely blob detection, which automatically detects hidden and inaccessible cancerous lesions in unsupervised human breast histology images. …”
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Sound-Based Unsupervised Fault Diagnosis of Industrial Equipment Considering Environmental Noise by Jeong-Geun Lee, Kwang Sik Kim, Jang Hyun Lee

“…This study proposes a fault diagnosis method using a variational autoencoder (VAE) and domain adaptation neural network (DANN), both of which are based on unsupervised learning, to address this problem. …”
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Machine Learning-Based Security Solutions for IoT Networks: A Comprehensive Survey by Abdullah Alfahaid, Easa Alalwany, Abdulqader M. Almars, Fatemah Alharbi, Elsayed Atlam, Imad Mahgoub

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Deep Learning and fMRI-Based Pipeline for Optimization of Deep Brain Stimulation During Parkinson’s Disease Treatment: Toward Rapid Semi-Automated Stimulation Optimization by Jianwei Qiu, Afis Ajala, John Karigiannis, Jurgen Germann, Brendan Santyr, Aaron Loh, Luca Marinelli, Thomas Foo, Radhika Madhavan, Desmond Yeo, Alexandre Boutet, Andres Lozano

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A Survey of Machine Learning Techniques for Optimal Capacitor Placement and Sizing in Smart Distribution Networks by Kwabena Addo, Katleho Moloi, Musasa Kabeya, Evans Eshiemogie Ojo

“…Traditional optimization techniques, while effective, often struggle with dynamic system behaviors, nonlinear loads, and real-time operational constraints. This paper presents a comprehensive review of machine learning (ML)-based methodologies for optimal capacitor placement and sizing, focusing on their ability to enhance voltage stability, minimize power losses, and improve overall grid efficiency in smart distribution networks. …”
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