Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN by Nivea Kesav, M.G. Jibukumar

“…Brain tumor classification and detection is a subject of prime importance where Convolutional Neural Networks (CNN) find application. …”
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HE-BiDet: A Hardware Efficient Binary Neural Network Accelerator for Object Detection in SAR Images by Dezheng Zhang, Zehan Liang, Rui Cen, Zhihong Yan, Rui Wan, Dong Wang

“…To address this, we propose HE-BiDet, an ultra-lightweight Binary Neural Network (BNN) framework co-designed with hardware acceleration. First, we develop an ultra-lightweight SAR ship detection model. …”
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An Enhanced Bio-inspired GWO–DE Technique for Efficient Feature Selection in the EEG-RSVP Paradigm by S. Abinayaa, S. S. Sridhar

“…To resolve these issues, we present a bio-inspired hybrid optimization framework where Differential Evolution (DE) is integrated with Grey Wolf Optimization (GWO) to improve the efficiency of feature selections. …”
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Feature-informed machine learning for detecting material deformation and failure in aluminum pipes under bending load using acoustic emission sensors by Xiaowei Zuo, Nicholas Satterlee, Chang-Whan Lee, In-Gyu Choi, Choon-Wook Park, John S. Kang

“…Additionally, we propose a novel Feature-Informed Convolutional Neural Network (FI-CNN), which integrates the features into the CNN framework, yielding an accuracy of 92.7 %, outperforming the traditional machine learning methods. …”
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An integrated predictive model for Alzheimer’s disease progression from cognitively normal subjects using generated MRI and interpretable AI by Atefe Aghaei, Mohsen Ebrahimi Moghaddam

“…In this study, we propose an integrated framework that leverages ensemble transfer learning, generative modeling, and automatic ROI extraction techniques to predict the progression of Alzheimer’s disease from cognitively normal (CN) subjects. …”
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Deep learning-based surface deformation tracking with interferometric fringes: A case study in Taiwan by Shih-Teng Chang, Shih-Yuan Lin, Yu-Ching Lin

“…By enabling deformation detection across different magnitudes, time periods, and regions, the proposed framework offers a scalable and transferable solution for extending MT-InSAR-based surface hazard tracking.…”
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Multi-source data fusion-based knowledge transfer for unmanned aerial vehicle flight data anomaly detection and recovery by Lei Yang, Shaobo Li, Liya Yu, Caichao Zhu, Congbao Wang

“…First, a data-driven framework based on one-dimensional convolutional neural network and bi-directional long short-term memory (1D CNN-BiLSTM) with parameter selection and residual smoothing (1DCB-PSRS) is proposed. …”
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Benchmarking Deep Learning for Wetland Mapping in Denmark Using Remote Sensing Data by Muhammad Rizwan Asif

“…While remote sensing combined with deep learning (DL) offers a promising solution, inconsistencies in wetland classification systems—where different regions define wetland types based on their policy frameworks and conservation priorities—limit the applicability of these models. …”
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Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction by Jilong Zhong, Aigen Fan, Kuangang Fan, Wenjie Pan, Lu Zeng

“…Based on the MFCC feature extraction algorithm framework, an improved frequency band feature extraction method was proposed. …”
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Real-Time Quality Monitoring and Anomaly Detection for Vision Sensors in Connected and Autonomous Vehicles by Elena Politi, Charalampos Davalas, Christos Chronis, George Dimitrakopoulos, Dimitrios Michail, Iraklis Varlamis

“…This paper proposes a supervised video anomaly assessment framework for autonomous vehicles leveraging Image Quality Assessment (IQA) and Drift Detection techniques. …”
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Collaborative Forecasting of Multiple Energy Loads in Integrated Energy Systems Based on Feature Extraction and Deep Learning by Zhe Wang, Jiali Duan, Fengzhang Luo, Xiaoyu Qiu

“…Then, a multi-task parallel learning framework combining a regression convolutional neural network and long short-term memory networks is developed to predict reconstructed subsequences. …”
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Leveraging deep learning for risk prediction and resilience in supply chains: insights from critical industries by Waleed Abdu Zogaan, Nouran Ajabnoor, Abdullah Ali Salamai

“…To enhance SCR and mitigate risks, this paper proposes a deep learning (DL) framework that helps predict supply chain risks and provides companies with actionable insights to overcome them. …”
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Analysis of different IDS-based machine learning models for secure data transmission in IoT networks by Gladić Dejana, Petrovački Jelena, Sladojević Srdan, Arsenović Marko, Ristić Sonja

“…The RT-IoT2022 dataset was used for experimental analysis, providing realistic framework for testing ML models, including k-nearest neighbors, Random Forest, XGBoost, multilayer perceptron, and various 1D convolutional neural network architectures. …”
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Optimal Feature Selection and Classification for Parkinson’s Disease Using Deep Learning and Dynamic Bag of Features Optimization by Aarti, Swathi Gowroju, Mst Ismat Ara Begum, A. S. M. Sanwar Hosen

“…Advanced DL architectures are then used to classify the chosen features, guaranteeing excellent computational efficiency and accuracy. The framework’s adaptability to different datasets further highlights its versatility and potential for further medical applications. …”
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Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments by Małgorzata Przybyła-Kasperek, Jakub Sacewicz

“…In scenarios where data dispersion arises due to privacy constraints or decentralized data collection, traditional global modelling is impractical. We propose a framework where classifiers are trained locally on independent subsets of EEG data without requiring centralized access. …”
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Driving Behavior Classification Using a ConvLSTM by Alberto Pingo, João Castro, Paulo Loureiro, Sílvio Mendes, Anabela Bernardino, Rolando Miragaia, Iryna Husyeva

“…This work explores the classification of driving behaviors using a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks (ConvLSTM). …”
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PIABC: Point Spread Function Interpolative Aberration Correction by Chanhyeong Cho, Chanyoung Kim, Sanghoon Sull

“…Upon this understanding, we introduce a framework that utilizes densely interpolated Point Spread Functions (PSFs) to recover high-fidelity images. …”
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Leveraging Deep Learning and Internet of Things for Dynamic Construction Site Risk Management by Li-Wei Lung, Yu-Ren Wang, Yung-Sung Chen

“…The system employs the DIKW knowledge management framework to extract, transform, and load (ETL) high-quality labeled data and optimize worker and machinery recognition. …”
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Machine Learning-Potato Leaf Disease Detection App (MR-PoLoD) by Ahmad Fauzi, Annisya E Chandra, Sofyah Imammah, Malvin Zapata, Marza I Marzuki, Soni Prayogi

“…This application uses the Python programming language which is rich in library and framework availability so that it can meet the needs of machine learning and image classification tasks. …”
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Robust Face Recognition Using Deep Learning and Ensemble Classification by Pavani Chitrapu, Mahesh Kumar Morampudi, Hemantha Kumar Kalluri

“…Deep learning has become popular for automatically learning features through convolution layers. This study proposes a robust framework that integrates contrast-limited adaptive histogram equalization (CLAHE) and adaptive gamma correction for illumination normalization and multi-task cascaded convolutional networks (MTCNN) for precise face detection under varying poses and lighting conditions. …”
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