Early Diagnosis of Alzheimer’s Disease Using Adaptive Neuro K-Means Clustering Technique by Karan Kumar, Shweta Agrawal, Isha Suwalka, Celestine Iwendi, Cresantus N. Biamba

“…The approach integrates the Adaptive Moving Self-Organizing Map (AMSOM), a neural network technique for unsupervised training and tissue segmentation, with K-means clustering and Principal Component Analysis (PCA) for feature selection. …”
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Enhanced Lithographic Hotspot Detection via Multi-Task Deep Learning With Synthetic Pattern Generation by Xinguang Zhang, Shiyang Chen, Zhouhang Shao, Yongjie Niu, Li Fan

“…Our key contributions include: (1) A synthetic pattern generation method based on early design space exploration (EDSE) to augment training data and improve TNSB hotspot detection; (2) A multi-task convolutional neural network architecture that jointly performs hotspot classification and localization; and (3) An adaptive loss function that balances hotspot detection accuracy and false alarm reduction. …”
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MambaShadowDet: A High-Speed and High-Accuracy Moving Target Shadow Detection Network for Video SAR by Xiaowo Xu, Tianwen Zhang, Xiaoling Zhang, Wensi Zhang, Xiao Ke, Tianjiao Zeng

“…Existing convolution neural network (CNN)-based video synthetic aperture radar (SAR) moving target shadow detectors are difficult to model long-range dependencies, while transformer-based ones often suffer from greater complexity. …”
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A Prediction Model of Structural Settlement Based on EMD-SVR-WNN by Xianglong Luo, Wenjuan Gan, Lixin Wang, Yonghong Chen, Xue Meng

“…Aiming at the problems in the structural deformation prediction model and considering the internal characteristics of deformation monitoring data and the influence of different components in the data on the prediction accuracy, a combined prediction model based on the Empirical Mode Decomposition, Support Vector Regression, and Wavelet Neural Network (EMD-SVR-WNN) is proposed. EMD model is used to decompose the structure settlement monitoring data, and the settlement data can be effectively divided into relatively stable trend terms and residual components of random fluctuation by energy matrix. …”
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Loss Architecture Search for Few-Shot Object Recognition by Jun Yue, Zelang Miao, Yueguang He, Nianchun Du

“…In this paper, we investigate the problem of designing an optimal loss function for few-shot object recognition and propose a novel few-shot object recognition system that includes the following three steps: (1) generate a loss function architecture using a recurrent neural network (generator); (2) train a base embedding network with the generated loss function on a training set; (3) fine-tune the base embedding network using the few-shot instances from a validation set to obtain the accuracy and use it as a reward signal to update the generator. …”
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Review on operation control of cold thermal energy storage in cooling systems by Huan Wang, Baoshan Xie, Chuanchang Li

“…Two types of cold load predictions, parametric regression and artificial neural network method, are introduced. Three aspects of economic costs are summarized in terms of initial equipment investment cost, operational cost, and life-cycle cost are summarized. …”
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Coordination of preventive, emergency and restorative trading strategies under uncertain sequential extreme weather events by Xuemei Dai, Jing Zhou, Xu Zhang, Kaifeng Zhang, Wei Feng

“…First, a two-layer graph neural network (GNN) is employed to predict the probability distribution of system outages caused by SEWEs. …”
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Enhancing early lung cancer detection with MobileNet: A comprehensive transfer learning approach by Raquel Ochoa-Ornelas, Alberto Gudiño-Ochoa, Julio Alberto García-Rodríguez, Sofia Uribe-Toscano

“…This study investigates the application of MobileNetV2, a state-of-the-art, lightweight convolutional neural network, for the accurate classification of lung adenocarcinoma (LAC), benign lung tissue (BLT), and lung squamous cell carcinoma (LUSC). …”
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Multitask Learning for Estimation of Magnetic Parameters Using Pattern Recognition by Anubha Sehgal, Shipra Saini, Hemkant Nehete, Kunal Kranti Das, Sourajeet Roy, Brajesh Kumar Kaushik

“…Using these techniques, we introduce a multi-task convolutional neural network (CNN) model and support vector regression (SVR) model that is intended to precisely estimate two important parameters of magnetic systems such as the Dzyaloshinskii-Moriya interaction (DMI) constant and the exchange constant (A_ex). …”
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Enhancing Particulate Matter Estimation in Livestock-Farming Areas with a Spatiotemporal Deep Learning Model by Dohyeong Kim, Heeseok Kim, Minseon Hwang, Yongchan Lee, Choongki Min, Sungwon Yoon, Sungchul Seo

“…Using a 200 m × 200 m prediction grid, forecasts were generated for both 1 h and 24 h intervals using the Graz Lagrangian model (GRAL) and a one-dimensional convolutional neural network combined with the long short-term memory algorithm (1DCNN-LSTM). …”
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TXtreme: transformer-based extreme value prediction framework for time series forecasting by Hemant Yadav, Amit Thakkar

“…This paper proposes a TXtreme framework that uses Long-Short memory network, feed-forward neural network, and transformer to improve time series forecasting under extreme values. …”
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An approach for load frequency control enhancement in two-area hydro-wind power systems using LSTM + GA-PID controller with augmented lagrangian methods by Ritesh Dash, Kalvakurthi Jyotheeswara Reddy, Bhabasis Mohapatra, Mohit Bajaj, Ievgen Zaitsev

“…Abstract This paper proposes an advanced Load Frequency Control (LFC) strategy for two-area hydro-wind power systems, using a hybrid Long Short-Term Memory (LSTM) neural network combined with a Genetic Algorithm-optimized PID (GA-PID) controller. …”
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Study of Microstructure and Wear Resistance of AA5052/B4C Nanocomposites as a Function of Volume Fraction Reinforcement to Particle Size Ratio by ANN by D. Dinesh Kumar, A. Balamurugan, K. C. Suresh, R. Suresh Kumar, N. Jayanthi, T. Ramakrishnan, S. K. Hasane Ahammad, S. Mayakannan, S. Venkatesa Prabhu

“…This research examines a model function developed from an artificial neural network (ANN). AA5052/B4C composites bent using a powder metallurgy technique to hardness and ball-on-disc wear testing. …”
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Monitoring and Simulation of Dynamic Spatiotemporal Land Use/Cover Changes by Andong Guo, Yuqing Zhang, Qing Hao

“…In this study, we preprocessed multiperiod land use and socioeconomic data, combined with spatial zoning, multilayer perception (MLP) artificial neural network, and Markov chain (MC), to construct a cellular automaton model of spatial zoning. …”
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A Fatigue Driving Detection Algorithm Based on Facial Motion Information Entropy by Feng You, Yunbo Gong, Haiqing Tu, Jianzhong Liang, Haiwei Wang

“…First, we introduce an improved YOLOv3-tiny convolutional neural network to capture the facial regions under complex driving conditions, eliminating the inaccuracy and affections caused by artificial feature extraction. …”
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Characterization of Neural Interaction During Learning and Adaptation from Spike-Train Data by Liqiang Zhu, Ying-Cheng Lai, Frank C. Hoppensteadt, Jiping He

“…Our computation and analysis indicated that theadaptation tends to alter the connection topology of theunderlying neural network, yet the average interaction strength inthe network is approximately conserved before and after theadaptation. …”
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Water Pipeline Leak Detection and Localization With an Integrated AI Technique by Uma Rajasekaran, Mohanaprasad Kothandaraman, Chang Hong Pua

“…This approach uses a one-dimensional convolutional neural network (1DCNN) for feature extraction. This paper tunes the AdaBoost to have support vector machines (SVM), Decision Trees (DT), and multi-layer perceptron (MLP) instead of the inbuilt weak estimators to give improved performance. …”
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Research on a Surface Roughness Measurement Under ResNet-Based Roughness Classification and Light-Section With Seam-Driven Image Stitching (RCLS) by Huashen Guan, Qiushen Cai, Xiaobin Li, Guofu Sun

“…First, the images were classified with ResNet neural network, then stitched and enhanced by scale invariant feature transform (SIFT) and optimized random sample consensus (RANSAC) algorithm for the best visual effect. …”
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A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale by Ziyao Zhao, Yi Zhang, Yi Zhang

“…Two forecasting methods, FM1 and FM2, and four predicting models, linear regression (LR), support vector machine (SVR), backpropagation neural network (BPNN), and autoregressive integrated moving average (ARIMA), were proposed to build models that can predict the parking occupancy of different parking lots. …”
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Polarimetric image recovery method with domain-adversarial learning for underwater imaging by Fei Tian, Jiuming Xue, Zhedong Shi, Hongling Luo, Wanyuan Cai, Wei Tao

“…In this paper, we collect, to the best of our knowledge, the richest polarization color image dataset with different water types and present a specially designed neural network UPD-Net firstly employing the domain-adversarial learning strategy to recover the degraded color images. …”
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