North, Iran-Contra, and the Doomsday Project: The Original Congressional Cover Up of Continuity-of-Government Planning by Peter Dale Scott

“…(One of the two Committee Chairs was Lee Hamilton, later co-chair of the similarly evasive 9/11 Commission Report).Recently I have written about the extraordinary power of the COG network Doomsday planners, or what CNN in 1991 described as a "shadow government...about which you know nothing." …”
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Intelligent Image Recognition System for Marine Fouling Using Softmax Transfer Learning and Deep Convolutional Neural Networks by C. S. Chin, JianTing Si, A. S. Clare, Maode Ma

“…The proposed system utilizes transfer learning and deep convolutional neural network (CNN) to perform image recognition on the fouling image by classifying the detected fouling species and the density of fouling on the surface. …”
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Advancements in Machine Learning and Deep Learning Techniques for Crop Yield Prediction: A Comprehensive Review by V. Ramesh and P. Kumaresan

“…The survey results show that a hybrid CNN DNN and RNN model with optimization algorithms outperforms the other existing traditional models.…”
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The role of the Indonesian child protection commission in the Papua province conflict by Yuspani Asemki, Titin Purwaningsih

“…Employing qualitative methods, data sourced from prominent media outlets including Kompas, Suara Papua, Jubi, Detik, and CNN are meticulously analyzed using the Nvivo 12 Plus Application. …”
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Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system. by Yuan Wang, Shaolin Hu

“…The results show that this method effectively overcomes the problems of false alarms and missed alarms based on fixed threshold alarm methods, and achieves 100% classification of two types of faults: non starting of the drive machine and low oil pressure by constructing a PCA (Principal Component Analysis)-SPE (Square Prediction Error)-CNN (Convolutional Neural Network) classifier. Combined with dynamic knowledge graph and NLP (Natural Language Processing) inference, it achieves good diagnostic results.…”
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Multidisciplinary ML Techniques on Gesture Recognition for People with Disabilities in a Smart Home Environment by Christos Panagiotou, Evanthia Faliagka, Christos P. Antonopoulos, Nikolaos Voros

“…The results highlight the strengths and weaknesses of each approach, revealing that while some methods excel in specific scenarios, the integrated solution of MoveNet and CNN provides a robust framework for real-time gesture recognition.…”
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Rapid Focused Spot Scanning Imaging Using Multimode Fiber with a Convolutional Neural Network-Based Phase Modulation by Rongjing Tong, Anni Xu, Hua Shen

“…In this paper, we present a rapid beam-focusing method for multimode fiber (MMF) that integrates a Convolutional Neural Network (CNN) with a Spatial Light Modulator (SLM). This approach efficiently focuses light fields by training neural networks to approximate the transmission matrix of the optical system, eliminating the need for iterative calculations. …”
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A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods by Meng Meng, Kun Zhu, Keqin Chen, Hang Qu

“…With the development of artificial intelligence especially the combination of deep learning and computer vision, greater advantages have been brought to the concrete crack detection based on convolutional neural network (CNN) over the traditional methods. However, these machine learning (ML) methods still have some defects, such as it being inaccurate or not strong, having poor generalization ability, or the accuracy still needs to be improved, and the running speed is slow. …”
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Application of deep residual networks to predict the effective properties of fiber-reinforced composites with voids by Mahdi Karimian, Seyed Ali Hosseini Kordkheili

“…To train, four different CNN (i.e. from a simple to deeper one) together with MSE loss function are used to increase the accuracy. …”
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Investigation of Nonlinear Vibrational Analysis of Circular Sector Oscillator by Using Cascade Learning by Naveed Ahmad Khan, Muhammad Sulaiman, Jamel Seidu, Fahad Sameer Alshammari

“…A data set for the supervised learning of the CNN-BLM algorithm for different angles α and radius R are generated by Runge–Kutta (RK-4) method. …”
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UTR-Insight: integrating deep learning for efficient 5′ UTR discovery and design by Saichao Pan, Hanyu Wang, Hang Zhang, Zan Tang, Lianqiang Xu, Zhixiang Yan, Yong Hu

“…We developed UTR-Insight, a model integrating a pretrained language model with a CNN-Transformer architecture, explaining 89.1% of the mean ribosome load (MRL) variation in random 5′ UTRs and 82.8% in endogenous 5′ UTRs, surpassing existing models. …”
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Dilated SE-DenseNet for brain tumor MRI classification by Yuannong Mao, Jiwook Kim, Lena Podina, Mohammad Kohandel

“…Abstract In the field of medical imaging, particularly MRI-based brain tumor classification, we propose an advanced convolutional neural network (CNN) leveraging the DenseNet-121 architecture, enhanced with dilated convolutional layers and Squeeze-and-Excitation (SE) networks’ attention mechanisms. …”
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BLSENet: A Novel Lightweight Bilinear Convolutional Neural Network Based on Attention Mechanism and Feature Fusion Strategy for Apple Leaf Disease Classification by Tianyu Fang, Jialin Zhang, Dawei Qi, Mingyu Gao

“…To solve this problem, a lightweight bilinear convolutional neural network (CNN) model named BLSENet based on attention mechanism is designed. …”
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Enhancing Rehabilitation Assessment with Artificial Intelligence: A Comprehensive Investigation of Posture Quality Prediction Using Machine Learning by Zhang Wenxi

“…AI techniques, including Support Vector Machines (SVM), decision trees, random forests, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), show great potential in improving the accuracy and personalization of rehabilitation assessment. …”
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Rancang Bangun Purwarupa Pemilah Sampah Pintar Berbasis Deep Learning by Kahlil Muchtar, Nyak Twoman Anshari, Chairuman Chairuman, Khalid Alhabibie, Khairul Munadi

“…Deep Learning method applied here is using Convolutional Neural Network (CNN). The algorithm is like human nerves and is one of supervised learning. …”
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Global Detection of Live Virtual Machine Migration Based on Cellular Neural Networks by Kang Xie, Yixian Yang, Ling Zhang, Maohua Jing, Yang Xin, Zhongxian Li

“…Through analyzing the detection process, the parameter relationship of CNN is mapped as an optimization problem, in which improved particle swarm optimization algorithm based on bubble sort is used to solve the problem. …”
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Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep Learning by Chunjie Li, Pan Liu, Zhenlong Xie, Zhibin Li, Huan Huan

“…Then a combined model of road adhesion coefficient estimation based on self-attention (SA), convolutional neural network (CNN), and long short-term memory (LSTM) is established, to reduce the instability of the prediction, Q-learning is used to optimize the weight of the model. …”
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Enhancing Credit Risk Decision-Making in Supply Chain Finance With Interpretable Machine Learning Model by Guanglan Zhou, Shiru Wang

“…Specifically, we applied Extreme Gradient Boosting (XGBoost), Random Forest (RF), Least Squares Support Vector Machine (LSSVM) and Convolutional Neural Network (CNN) models for risk assessment. Our methodology included an ablation experiment along with utilizing Shapley Additive Explanation (SHAP) to elucidate the contribution and significance of specific risk factors. …”
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Classification of Buried Objects From Ground Penetrating Radar Images by Using Second-Order Deep Learning Models by Douba Jafuno, Ammar Mian, Guillaume Ginolhac, Nickolas Stelzenmuller

“…These thumbnails are then inputs to the first layers of a classical CNN, which then produces a covariance matrix using the outputs of the convolutional filters. …”
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Integrating Machine Learning for Predictive Maintenance on Resource-Constrained PLCs: A Feasibility Study by Riccardo Mennilli, Luigi Mazza, Andrea Mura

“…Using acoustic data, a convolutional neural network (CNN) is deployed to infer the rotational speed of a mechanical test bench. …”
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