Efficient Intrusion Detection System Data Preprocessing Using Deep Sparse Autoencoder with Differential Evolution by Saranya N., Anandakumar Haldorai

“…The efficiency of the transformation methods is evaluated with recursive Pearson correlation-based feature selection and graphical convolution neural network (G-CNN) methods.…”
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Analysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning by Lin Zhou

“…Then, combining data enhancement (Mixup) with three-dimensional convolutional neural network (3D-CNN), an emotion-related EEG topographic map classification method based on M-3DCNN is proposed. …”
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Image Recognition and Simulation Based on Distributed Artificial Intelligence by Tao Fan

“…By analyzing the existing digital image recognition methods, an improved BP neural network algorithm is proposed. Under the premise of ensuring accuracy, the recognition speed of digital images is accelerated, the time required for recognition is reduced, real-time performance is guaranteed, and the effectiveness of the algorithm is verified.…”
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Automatic Evaluation of Internal Combustion Engine Noise Based on an Auditory Model by Kai Liang, Haijun Zhao

“…To improve the accuracy and efficiency of the objective evaluation of noise quality from internal combustion engines, an automatic noise quality classification model was constructed by introducing an auditory model-based acoustic spectrum analysis method and a convolutional neural network (CNN) model. A band-pass filter was also designed in the model to automatically extract the features of the noise samples, which were later used as input data. …”
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A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device by Debajyoti Pal, Vajirasak Vanijja

“…We also use an Artificial Neural Network approach for building the model and compare its performance with the regressive approach.…”
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Haptic Perception with Self-Organizing ANNs and an Anthropomorphic Robot Hand by Magnus Johnsson, Christian Balkenius

“…The four systems differ in the kind of self-organizing neural network used for clustering. For the mapping of the explored objects, one system uses a Self-Organizing Map (SOM), one uses a Growing Cell Structure (GCS), one uses a Growing Cell Structure with Deletion of Neurons (GCS-DN), and one uses a Growing Grid (GG). …”
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Research on FTTR WLAN indoor wireless location algorithm based on frequency response by Zhifeng LONG, Jing ZHANG

“…Highly accurate and reliable indoor wireless positioning services have been widely used.In order to obtain good positioning accuracy, the design of positioning algorithms needs to be matched with wireless positioning facilities.fiber to the room (FTTR) is an indoor access network solution based on IEEE 802.11 ax, a new generation of wireless local area network (WLAN) standard.Compared with the existing Wi-Fi networks, FTTR has a much larger available band width.However, FTTR WLAN also lacks of a public valid data set to support localization functions, which makes the localization research based on FTTR scenarios face huge obstacles.In order to solve the above problems, firstly, a frequency response-based FTTR scene dataset generation method was proposed, which uses the existing Wi-Fi localization dataset to generate the frequency response matrix within the available band width of FTTR.Then, the parallel path principal component analysis (PCA) method was used to generate the classification matrix.And the generated dataset was trained using a fully connected neural network to improve the accuracy.The experimental results on the real measurement dataset show that the proposed localization algorithm can achieve a localization accuracy of less than 1 m, which is not only more accurate than the traditional location estimation algorithm, but also basically meets the fine-grained localization requirements for practical applications.…”
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Bifurcation Analysis and Synchronous Patterns between Field Coupled Neurons with Time Delay by Li Zhang, Xinlei An, Jiangang Zhang, Qianqian Shi

“…Exploring the biophysical properties of coupling channels is of great significance for further understanding the rhythm transitions of neural network electrical activity patterns and preventing neurological diseases. …”
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Integration and Fusion of Geologic Hazard Data under Deep Learning and Big Data Analysis Technology by Feng He, Chunxue Liu, Hongjiang Liu

“…On the premise of optimizing the processing of landslide images, first, the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) based on the natural statistical characteristics of the spatial domain is introduced, which is initially combined with Super-Resolution Convolutional Neural Network (SRCNN). Then, the AlexNet is fine-tuned and applied to highway landslide monitoring and surveying. …”
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Road Performance and Ice-Melting Characteristics of Steel Wool Asphalt Mixture by Yifeng Xia, Jie Jia, Qian Chen

“…A prediction model of the ice-melting rate of steel wool asphalt mixture based on a double-hidden layer backpropagation (BP) neural network was established. The results show that the road performance of the asphalt mixture mixed with steel wool mostly meets the requirements of the specification. …”
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Improving Electron Density Predictions in the Topside of the Ionosphere Using Machine Learning on In Situ Satellite Data by S. Dutta, M. B. Cohen

“…This research focuses on predicting the electron density in the topside of the ionosphere using satellite data, in particular from the Defense Meteorological Satellite Program, a collection of 19 satellites that have been polar orbiting the Earth for various lengths of times, fully covering 1982 to the present. An artificial neural network was developed and trained on two solar cycles worth of data (113 satellite‐years), along with global drivers and indices such as F10.7, interplanetary magnetic field, and Kp to generate an electron density prediction. …”
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A hybrid model for prediction of software effort based on team size by Prerana Rai, Dinesh Kumar Verma, Shishir Kumar

“…These techniques are mostly based on statistical methods (viz. simple linear regression (SLR), multi linear regression, support vector machine, cascade correlation neural network (CCNN) etc.) and some probability‐based models. …”
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Anti-Diabetic Therapeutic Medicinal Plant Identification Using Deep Fused Discriminant Subspace Ensemble (D2SE) by N. Sasikaladevi, S. Pradeepa, A. Revathi, S. Vimal, Gaurav Dhiman

“…They may act as an alternative source of antidiabetic agents. The fused deep neural network (DNN) model with Discriminant Subspace Ensemble is designed to identify the diabetic plants from VNPlant200 data set. …”
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Comparison of Fully Convolutional Networks and U-Net for Optic Disc and Optic Cup Segmentation by Jin Zixiao

“…This paper aims to compare two well-known convolutional neural network (CNN) structures, namely Fully Convolutional Networks (FCNs) and U-Net for the segmentation of the optic disc (OD) and optic cup (OC) from retinal fundus images which play an important role in glaucoma diagnosis. …”
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Transcranial Alternating Current and Random Noise Stimulation: Possible Mechanisms by Andrea Antal, Christoph S. Herrmann

“…Such findings are further supported by neural network simulations and knowledge from physics on entraining physical oscillators in the human brain. …”
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Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise. by Shamik Tiwari, Akhilesh Kumar Sharma, Izzatdin Abdul Aziz, Deepak Gupta, Antima Jain, Hairulnizam Mahdin, Senthil Athithan, Rahmat Hidayat

“…The SFTF and statistical moments-based handcrafted features are passed to a multilayer feed-forward neural network for classification. These models are evaluated on natural textures from Kylberg Texture Dataset 1.0. …”
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Gradient Enhancement Techniques and Motion Consistency Constraints for Moving Object Segmentation in 3D LiDAR Point Clouds by Fangzhou Tang, Bocheng Zhu, Junren Sun

“…In this paper, we introduce a novel deep neural network designed to enhance the performance of 3D LiDAR point cloud moving object segmentation (MOS) through the integration of image gradient information and the principle of motion consistency. …”
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A Semi-supervised Deep Learning Method for Cervical Cell Classification by Siqi Zhao, Yongjun He, Jian Qin, Zixuan Wang

“…Cervical cell classification is a key technology in the intelligent cervical cancer diagnosis system. Training a deep neural network-based classification model requires a large amount of data. …”
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Pointer meters recognition method in the wild based on innovative deep learning techniques by Jiajun Feng, Haibo Luo, Rui Ming

“…Abstract This study presents a novel approach to identifying meters and their pointers in modern industrial scenarios using deep learning. We developed a neural network model that can detect gauges and one or more of their pointers on low-quality images. …”
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Perception Analysis and Early Warning of Home-Based Care Health Information Based on the Internet of Things by Yi Mao, Lei Zhang, Xin Wu

“…In order to improve the accuracy of prediction, the DS evidence theory is used to optimize the traditional BP neural network (BPNN) algorithm and conduct experimental tests. …”
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