EFFICIENT RETINAL IMAGE SEGMENTATION BY U-NET FOR AGE-RELATED MACULAR DEGENERATION DIAGNOSIS by S Sujatha, Pramod Pandey, D Gnana Rajesh

“…It is a specialized convolutional neural network that has shown significant potential in accurately segmenting intricate structures captured in medical images. …”
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Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction by Qisheng Yan, Shitong Wang, Bingqing Li

“…Experimental results from real uranium resource price data demonstrate that the proposed hybrid forecasting method outperforms RBF neural network (RBFNN) and single ELM in terms of RMSE, MAE, and DS.…”
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Monthly Precipitation Prediction Based on Attention-BiLSTM Model by CHENG Yuxiang, XIAO Liying, WANG Pinggen, LIU Xiangzhou, ZHANG Chenhui

“…Combined with the bidirectional long short-term memory neural network (BiLSTM), an improved attention-BiLSTM model was proposed to predict monthly precipitation. …”
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Simulation-Based Optimization on the System-of-Systems Model via Model Transformation and Genetic Algorithm: A Case Study of Network-Centric Warfare by Bong Gu Kang, Seon Han Choi, Se Jung Kwon, Jun Hee Lee, Tag Gon Kim

“…The method consists of two processes: (1) the transformation of the SoS-based model into an integrated model using the neural network to reduce the execution time and (2) the optimization of the integrated model using the genetic algorithm with ranking and selection to decrease the number of simulation runs. …”
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Predictive modeling and optimization of SI engine performance and emissions with GEM blends using ANN and RSM by Farooq Shaik, D. Vinay Kumar, N. Channa Keshava Naik, G. Radha Krishna, T. M. Yunus Khan, Abdul Saddique Shaik, Abdulrajak Buradi, Addisu Frinjo Emma

“…Abstract The study employed an Artificial Neural Network (ANN) to predict the performance and emissions of a single-cylinder SI engine using blends of Gasoline, Ethanol, and Methanol (GEM) ranging from E10 to E50 equivalence, achieving less than 5% error compared to experimental values. …”
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Autonomous deployment and energy efficiency optimization strategy of UAV based on deep reinforcement learning by Yi ZHOU, Xiaoyong MA, Fuxiao GAO, Wei LI, Nan CHENG, Ning LU

“…Utilizing a UAV to build aerial mobile small cell can provide more flexible and efficient access services for ground terminal users.Constrained by the coverage and limited energy of the UAV,it is necessary to study how to build a fast,efficient and energy-saving air-ground collaborative network.To deal with complex dynamic scenarios,the UAV needs to deploy an optimal coverage position,and meanwhile reduce both path loss and energy consumption in the deployment process.Based on the deep reinforcement learning,a strategy of autonomous UAV deployment and efficiency optimization was proposed.The coverage state set of UAV was established,and the energy efficiency was used as a reward function.Depth neural network and Q-learning were used to guide UAV to make autonomous decision and deploy the optimal position.The simulation results show that the deployment time of the proposed method can be effectively reduced by 60%,while the energy consumption can be reduced by 10%~20%.…”
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Machine learning‐assisted performance analysis of organic photovoltaics by Sijing Zhong, Jiayi Huang, Hengyu Meng, Zhuo Feng, Qianyue Wang, Zhenyu Huang, Lijie Zhang, Shiwei Li, Weiyang Gong, Yusen Huang, Lei Ying, Ning Li

“…We find that the fully connected neural network achieves a Pearson coefficient of up to 0.88 for predicting the efficiency of OSCs with different combinations of donors and acceptors. …”
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Gearbox Fault Diagnosis Based on ALIF-PE-GOLSSVM by Huang Ying, Li Ximei, Ye Renhu, Wang Rui

“…The results show that the proposed method has advantages in fault recognition accuracy compared with BP neural network and SVM.…”
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Compound Autoregressive Network for Prediction of Multivariate Time Series by Yuting Bai, Xuebo Jin, Xiaoyi Wang, Tingli Su, Jianlei Kong, Yutian Lu

“…Firstly, a compound neural network framework was designed with the primary and auxiliary networks. …”
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The Hadamard-PINN for PDE inverse problems: Convergence with distant initial guesses by Yohan Chandrasukmana, Helena Margaretha, Kie Van Ivanky Saputra

“…This paper presents the Hadamard-Physics-Informed Neural Network (H-PINN) for solving inverse problems in partial differential equations (PDEs), specifically the heat equation and the Korteweg–de Vries (KdV) equation. …”
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Verification of an artificial intelligence vision chip design for edge computing based on hardware simulation system by Xuanzhe XU, Ke NING, Xuemin ZHENG, Mingxin ZHAO, Mengmeng XU, Nanjian WU, Liyuan LIU

“…The rise of visual deep learning algorithms based on convolutional neural network (CNN) has promoted the rapid development of the artificial intelligence (AI) vision chip design research.The step of chip verification is a bottleneck in the development of AI vision chips.A software and hardware verification method for AI vision chip design based on hardware simulation system was introduced.Taking AI vision chip design for edge computing as an example, the chip was run on the hardware simulation system (ZeBu) and the simulation verification work of typical deep learning network MobileNet was completed.The results show that the network model implemented on the hardware chip architecture keeps accuracy while the detection time of a single frame is only 18.51 ms under a 200 MHz clock frequency.The spread of the hardware simulation is 7 times faster than than of the software simulation.…”
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Traffic Accident Prediction Based on LSTM-GBRT Model by Zhihao Zhang, Wenzhong Yang, Silamu Wushour

“…Compared with various regression models and neural network models, the experimental results show that the LSTM-GBRT model has a good fitting effect and robustness. …”
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Trajectory Tracking Control Study of a New Parallel Mechanism with Redundant Actuation by Haiqiang Zhang, Hairong Fang, Dan Zhang, Qi Zou, Xueling Luo

“…In this paper, an efficient trajectory tracking control scheme for the new redundantly actuated parallel mechanism by integrating force/position hybrid control with the combination of inertia feed-forward control and back propagation (BP) neural network PID control is proposed. The dynamic models including the joint space and task space are formulated explicitly in efficient and compact form by means of the principle of virtual work and d’Alembert formulations. …”
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Energy-Saving and Sustainable Separation of Bioalcohols by Adsorption on Bone Char by Oslery Becerra-Pérez, Stavros Georgopoulos, Maria Lanara, Hilda Elizabeth Reynel-Ávila, Maria Papadaki, Adrián Bonilla-Petriciolet, Didilia Ileana Mendoza-Castillo

“…A model based on an artificial neural network was proposed to correlate both single and binary adsorption isotherms of these bioalcohols with bone char. …”
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Optimising deep learning models for ophthalmological disorder classification by S. Vidivelli, P. Padmakumari, C. Parthiban, A. DharunBalaji, R. Manikandan, Amir H. Gandomi

“…In this article, we have used different deep learning models for the categorisation of ophthalmological disorders into multiple classes and multiple labels utilising transfer learning-based convolutional neural network (CNN) methods. The Ocular Disease Intelligent Recognition (ODIR) database is used for experiments, and it contains fundus images of the patient’s left and right eyes. …”
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A Five-Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images by Varun Srivastava, Ravindra Kumar Purwar

“…The reduced set of features is then fed into either K nearest neighbor algorithm or feed-forward artificial neural network, to classify images. The algorithm is compared with three other techniques in terms of accuracy. …”
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Design and Optimization of a Multiband Extra Wideband Ring Patch Antenna for 5G mm-Wave Communication at 28/60/80 GHz Using MLPNN based K-fold cross validation method by Sellak Lahcen, Khabba Asma, Amadid Jamal, Chabaa Samira, Ibnyaich Saida, Zeroual Abdelouhab

“…To optimize the antenna dimensions for resonance at 28 GHz, 60 GHz, and 80 GHz, a Multilayer Perceptron Neural Network (MLPNN) was employed with a k-fold cross-validation technique. …”
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A 2D cell segmentation protocol for monitoring multiple STAT signaling pathways by fluorescence microscopy by Gergő Szanda, Éva Wisniewski, László Barna, Gábor Turu, Ken Mackie

“…The approach performs indistinguishably from neural-network-based segmentation while requiring only conventional and cost-effective techniques. …”
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Fault Diagnosis Method Research of Mechanical Equipment Based on Sensor Correlation Analysis and Deep Learning by Tangbo Bai, Jianwei Yang, Lixiang Duan, Yanxue Wang

“…Meanwhile, a convolutional neural network-based deep learning method is developed to process the matrix and to discover the relationship between results and equipment states for fault diagnosis. …”
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Generative Adversarial Network-Based Edge-Preserving Superresolution Reconstruction of Infrared Images by Yuqing Zhao, Guangyuan Fu, Hongqiao Wang, Shaolei Zhang, Min Yue

“…The convolutional neural network has achieved good results in the superresolution reconstruction of single-frame images. …”
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