Machine Learning-Driven D-Glucose Prediction Using a Novel Biosensor for Non-Invasive Diabetes Management by Pardis Sadeghi, Shahriar Noroozizadeh, Rania Alshawabkeh, Nian Xiang Sun

“…Advanced models, such as Convolutional Neural Networks and Recurrent Neural Networks, were used to analyze resistance signals, while classical algorithms served as benchmarks. …”
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Comparison of ResNet-50, EfficientNet-B1, and VGG-16 Algorithms for Cataract Eye Image Classification by Ilham Santoso, Ayub Michaelangelo Manurung, Egia Rosi Subhiyakto

“…This study contributes significantly to the selection of robust models for building an automated cataract detection framework.…”
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TACO: Adversarial Camouflage Optimization on Trucks to Fool Object Detectors by Adonisz Dimitriu, Tamás Vilmos Michaletzky, Viktor Remeli

“…We present Truck Adversarial Camouflage Optimization (TACO), a novel framework that generates adversarial camouflage patterns on 3D vehicle models to deceive state-of-the-art object detectors. …”
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DRSegNet: A cutting-edge approach to Diabetic Retinopathy segmentation and classification using parameter-aware Nature-Inspired optimization. by Sundreen Asad Kamal, Youtian Du, Majdi Khalid, Majed Farrash, Sahraoui Dhelim

“…This is why the outcomes of the presented study can be viewed as promising in terms of the further development of the proposed approach for DR diagnosis, as well as in creating a new reference point within the framework of medical image analysis and providing more effective and timely treatments.…”
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An Ultra-Precision Smoothing Polishing Model for Optical Surface Fabrication with Morphology Gradient Awareness by Guohao Liu, Yonghong Deng, Zhibin Li

“…To improve the surface morphology quality of ultra-precision optical components, particularly in the suppression of mid-spatial frequency (MSF) errors, this paper proposes a morphology gradient-aware spatiotemporal coupled smoothing model based on convolutional material removal. By introducing the Laplacian curvature into the surface evolution framework, a curvature-sensitive “peak-priority” mechanism is established to dynamically guide the local dwell time. …”
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YOLOP-MVF: A Multi-Task Autonomous Driving Perception Detection Method Based on Multi Scale Feature Weighted Fusion by Yanqiu Niu, Jing Zhang

“…To address challenges such as large-scale variations, background interference, and occlusions in multi-task autonomous driving perception, this paper proposes YOLOP-MVF, a multi-task detection framework based on multi-scale feature weighting fusion. …”
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Deep learning-driven medical image analysis for computational material science applications by Li Lu, Mingpei Liang

“…Conventional machine learning approaches struggle with data heterogeneity and the need for extensive labeled datasets.MethodsTo overcome these limitations, we propose a deep learning-driven framework that integrates convolutional neural networks (CNNs) with transformer-based architectures for enhanced feature representation. …”
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Query-Based Instance Segmentation with Dual Attention Transformer for Autonomous Vehicles by Aya Taourirte, Li-Hong Juang

“…To address these challenges, we propose an enhanced QueryInst-based instance segmentation framework. First, we replace the traditional CNN backbone with the DaViT Transformer to extract richer, multi-scale features. …”
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Contrastive learning method for leak detection in water distribution networks by Rongsheng Liu, Tarek Zayed, Rui Xiao

“…The out-of-sample validation results indicate that the proposed leak detection model is robust and effective in unexplored pipelines. The proposed framework significantly advances ML-based leak detection research and supports sustainable water management practices.…”
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A Novel Deeply-Learned Image Quality Analysis Algorithm for Clustering by Zhongzhe Chen, Xing Gao

“…Addressing the limitations of existing deep clustering methods, which struggle with variations in image size and quality and are vulnerable to data noise and model deviations, we propose a deeply-learned clustering paradigm in an unsupervised context. This framework utilizes a multi-layer deep architecture, in which the standard fully-linked layers are replaced by the deep convolutional ones in order to intelligently calculate semantic visual representations. …”
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A Novel Deep Learning Model for Human Skeleton Estimation Using FMCW Radar by Parma Hadi Rantelinggi, Xintong Shi, Mondher Bouazizi, Tomoaki Ohtsuki

“…To address this challenge, we propose a novel deep learning framework integrating convolutional neural networks (CNNs), multi-head transformers, and Bi-LSTM networks to enhance spatiotemporal feature representations. …”
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A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer by Ziynet Pamuk, Hüseyin Erikçi

“…This study proposes a deep learning-based model for predicting microsatellite instability (MSI) in colorectal cancer using hematoxylin and eosin (H&E)-stained histopathological tissue slides. A classification framework was constructed using convolutional neural networks (CNN) and optimized through transfer learning techniques. …”
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A comprehensive model for concrete strength prediction using advanced learning techniques by Sagar Dhengare, Udaykumar Waghe, Ganesh Yenurkar, Anjana Shyamala

“…This study proposes a novel hybrid machine learning framework to predict the power of eco-friendly concrete containing eco-friendly concrete, copper slag and eggshell powder as partial cement replacement. …”
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BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification by Anderson P. Avila Santos, Breno L. S. de Almeida, Robson P. Bonidia, Peter F. Stadler, Polonca Stefanic, Ines Mandic-Mulec, Ulisses Rocha, Danilo S. Sanches, André C.P.L.F. de Carvalho

“…This study presents BioDeepFuse, a hybrid deep learning framework integrating convolutional neural networks (CNN) or bidirectional long short-term memory (BiLSTM) networks with handcrafted features for enhanced accuracy. …”
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LocPro: A deep learning-based prediction of protein subcellular localization for promoting multi-directional pharmaceutical research by Yintao Zhang, Lingyan Zheng, Nanxin You, Wei Hu, Wanghao Jiang, Mingkun Lu, Hangwei Xu, Haibin Dai, Tingting Fu, Ying Zhou

“…Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. …”
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Integrated pixel-level crack detection and quantification using an ensemble of advanced U-Net architectures by Rakshitha R, Srinath S, N Vinay Kumar, Rashmi S, Poornima B V

“…This study introduces a hybrid framework that combines convolutional and transformer-based architectures, leveraging their strengths to achieve reliable crack segmentation and pixel-level quantification. …”
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MFEM-CIN: A Lightweight Architecture Combining CNN and Transformer for the Classification of Pre-Cancerous Lesions of the Cervix by Peng Chen, Fobao Liu, Jun Zhang, Bing Wang

“…The core of the framework is the MFEM-CIN hybrid model, which combines Convolutional Neural Networks (CNN) and Transformer to aggregate the correlation between local and global features. …”
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Severity Classification of Parkinson’s Disease via Synthesis of Energy Skeleton Images from Videos Produced in Uncontrolled Environments by Nejib Ben Hadj-Alouane, Arav Dhoot, Monia Turki-Hadj Alouane, Vinod Pangracious

“…This paper proposes a novel framework for the diagnosis and severity classification of PD using video data captured in uncontrolled environments. …”
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Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction by Ivan-Daniel Sievering, Ortal Senouf, Thabo Mahendiran, David Nanchen, Stephane Fournier, Olivier Muller, Pascal Frossard, Emmanuel Abbe, Dorina Thanou

“…In this work, we propose a novel anatomy-informed multimodal deep learning framework to predict future MI from clinical data and Invasive Coronary Angiography (ICA) images. …”
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YUNet_LLMClaimReport: An Enhanced Automobile Insurance Fraud Detection and Automated Claim Report Generation Using Large Language Models by P. Anand Kumar, S. Sountharrajan

“…In this research, YUNet_LLMClaimReport, a new framework is proposed that combines YOLOv11, U-Net, and a fine-tuned GPT-3.5-turbo large language model to automatically generate claim reports based on the detections and segmentation. …”
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