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Intelligent design of display space layout based on two-stage deep learning network

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2023年第6期23卷 3347-3362页

作者： Liu, Jiaxing Zhu, Yongchao Cui, Yin Jiangsu Ocean Univ Sch Art & Design Lianyungang Jiangsu Peoples R China City Univ Macau Fac Innovat & Design Taipa Macao Peoples R China Beijing Inst Technol Sch Design & Arts Beijing 100081 Peoples R China

In an age of big data and information overload, recommendation systems have evolved rapidly. Throughout the traditional design of interior spaces, the specialised nature of the work and the high rate of human involvement has led to high costs. With the continuous development of artificial intelligence technology, it provides a favourable environment for reducing the development cost of the system. This study proposes a two-stage modelling scheme based on deep learning networks for the intelligent design of display space layouts, divided into two parts: matching and layout, which greatly improves design efficiency. The research results show that through comparison tests, its prediction accuracy reaches more than 80%, which can well meet the matching requirements of household products. The training number of Epochs is between 15 and 30, its training curve tends to saturate and the best accuracy can reach 100%, while the running time of the hybrid algorithm proposed in this study is only 20.716 s, which is significantly better compared with other algorithms. The proposed hybrid algorithm has a running time of only 20.716 s, which is significantly better than other algorithms. The approach innovatively combines deep learning technology with computer-aided design (CAD), enabling designers to automatically generate display space layouts with good visibility and usability based on complex design constraints. This study presents an innovative application of the research methodology by combining quantitative and qualitative methods to analyse the data. The application of both methods provides a more comprehensive understanding of the problem under study and provides insight into the key factors that influence the results. The findings of this study can provide useful insights for policy makers and practitioners.

关键词： Recommendation systems artificial intelligence deep learning network display space layouts matching requirements

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Emotion Recognition Based On Electroencephalogram Signals Using deep learning network

JOURNAL OF APPLIED SCIENCE AND ENGINEERING

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2023年第1期27卷 1967-1974页

作者： Wu, Bin Chaohu Univ Sch Comp & Artificial Intelligence Chaohu 238000 Peoples R China

deep learning networks have a high calculation volume, which is one of their problems. To solve this defect, the data of intrinsic modes obtained from the application of empirical mode decomposition to Electroencephalograph signals were used for the first time in this study. The present paper presents a method for emotion recognition using a deep learning network and electroencephalogram signal. Based on the non-stationary nature of the electroencephalogram, the intrinsic mode functions are extracted using empirical mode decomposition before selecting the first three intrinsic mode functions. Then, electrode positions are converted into pixel positions in images using suitable mapping, and the extracted features are interpreted as pixel color components. Using a deep learning network, all generated images are input into the network to determine whether they belong to the high or low valence class. Similarly, the class of arousal has been determined using the same method. This method was evaluated using the DEAP database to assess its *** results show that by selecting the image with the size of 17 x 17, the proposed method can detect valence and arousal emotions with an accuracy of 82.3% and 78.4%, respectively, which is an acceptable superiority compared to previous research.

关键词： deep learning network electroencephalogram signal intrinsic mode functions empirical mode decomposition emotion recognition

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SEMANTIC KNOWLEDGE EMBEDDING deep learning network FOR LAND COVER CLASSIFICATION 5

SEMANTIC KNOWLEDGE EMBEDDING DEEP LEARNING NETWORK FOR LAND ...

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5th International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS) Geospatial Week (GSW)

作者： Chen, Jiage Du, Xiao Zhang, Junhui Wan, Yongtao Zhao, Wenzhi Natl Geomat Ctr China Beijing 100830 Peoples R China Beijing Normal Univ Inst Remote Sensing Sci & Engn Fac Geog Sci State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China

Land cover classification is essential basic information and key parameters for environmental change research, geographical and national monitoring, and sustainable development planning. deep learning can automatically and multi-level extract the features of complex features, which has been proven to be an effective method for information extraction. However, one of the major challenges of deep learning is its poor interpret-ability, which makes it difficult to understand and explain the reasoning behind its classification results. This paper proposes a deep cross-modal coupling model (CMCM) for integrating semantic features and visual features. The representation of knowledge map is indicatively introduced into remote sensing image classification. Compared to previous studies, the proposed method provides accurate descriptions of the complex semantic objects within a complex land cover environment. The results showed that the integration of semantic knowledge improved the accuracy and interpret-ability of land cover classification.

关键词： Semantic Knowledge deep learning network Land Cover Classification

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Hybrid deep learning network with Convolutional Attention for Detecting Epileptic Seizures from EEG Signals

Hybrid Deep Learning Network with Convolutional Attention fo...

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Intelligent Systems Conference

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit Univ Phayao Sch Informat & Commun Technol Dept Comp Engn Phayao Thailand King Mongkuts Univ Technol North Bangkok Fac Appl Sci Dept Math Intelligent & Nonlinear Dynam Innovat Res Ctr Bangkok Thailand

ISBN: (纸本)9783031663352;9783031663369

Rapid detection of epileptic seizures from electroencephalography (EEG) signals is vital for timely treatment. However, accurately identifying seizures automatically remains challenging due to the diversity of EEG data. Recently, deep learning approaches have shown promising performance. This research presents a hybrid deep neural network combining convolutional neural network layers to automatically extract spatial-temporal patterns, bidirectional long short-term memory layers to model long-term temporal relationships, and a convolutional block attention module to focus on informative features. We evaluated this model using a benchmark EEG dataset for classifying seizures into two groups. Our model achieved a peak accuracy of 97.70% and F1-score of 97.70%, surpassing state-of-the-art techniques. The attention module improved the extraction of valuable features from EEG channels and time segments related to seizures. We provide visualizations of EEG events that the model emphasizes during seizure and non-seizure periods. This hybrid model with channel and temporal attention enhances the efficacy and interpretability of EEG-based seizure detection, further enabling patient-specific seizure characterization. The model shows promise for assisting clinicians in safely identifying seizures from multichannel EEG data.

关键词： Epileptic seizure detection EEG signal deep learning network Convolutional block attention module

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Detection of moisture content in salted sea cucumbers by hyperspectral and low field nuclear magnetic resonance based on deep learning network framework

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FOOD RESEARCH INTERNATIONAL 2022年第0期156卷 111174-111174页

作者： Zeng, Fanyi Shao, Weidong Kang, Jiaming Yang, Jixin Zhang, Xu Liu, Yang Wang, Huihui Dalian Polytech Univ Sch Mech Engn & Automation Qinggongyuan 1 Dalian 116034 Peoples R China Dalian Polytech Univ Natl Engn Res Ctr Seafood Qinggongyuan 1 Dalian 116034 Peoples R China Minist Educ China Engn Res Ctr Seafood Dalian 116034 Peoples R China Collaborat Innovat Ctr Seafood Deep Proc Dalian 116034 Peoples R China

The accurate control of moisture content (MC) during the processing of sea cucumber is beneficial to improve the taste of sea cucumber and maintain its nutritional value, which is directly related to the quality and shelf life of sea cucumber. The purpose of this study is to explore the feasibility using deep learning (DL) to realize rapid nondestructive detection of MC in salted sea cucumbers based on hyperspectral imaging (HSI) and low field nuclear magnetic resonance (LF-NMR) data. Firstly, three Cuckoo Search (CS) dimensionality reduction algo-rithms (Traditional-CS, Binary-CS and Chaotic-CS) were combined with DL framework respectively using HSI and LF-NMR data to establish prediction models, which proved the feasibility of DL framework in predicting the MC of sea cucumbers, and Chaotic-CS algorithm was selected as the optimal dimensionality reduction algorithm. Then, the MC visualization based on HSI and LF-NMR data was realized respectively to detect the migration and decrease of MC. Finally, using both HSI and LF-NMR data, the advantages of the models based on Fusion-net DL (FDL) framework were discussed, which showed better performance than the single-data models, with R-C(2) of 0.9929, RMSEC of 0.0016, R-P(2) of 0.9936 and RPD of 12.5041. In summary, the rapid nondestructive detection of MC in salted sea cucumbers could be realized by HSI and LF-NMR data based on DL framework, and the advantage of data fusion detection based on FDL framework was verified.

关键词： Hyperspectral imaging Low field nuclear magnetic resonance Sea cucumber deep learning network

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An improved algorithm based on deep learning network for road image redundancy removal

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JOURNAL OF SUPERCOMPUTING 2022年第8期78卷 10385-10404页

作者： Yang, Shengli Wang, Haoliang Chen, Qiang Dongguan City Coll Sch Comp & Informat Dongguan 523419 Peoples R China

Road detection is defined as one of the core technology of Advanced Driving Assistance System (ADAS), and this problem is important for improving the recognition accuracy and speed. Though much work has been done concerning road detection, the related questions about non-road areas are not thoroughly considered. Understanding the question is of primary importance in ADAS, we proposed an improved algorithm based on deep learning network for road image redundancy removal. Compared with the most typical road recognition methods, the experimental results show that the proposed method improves the speed of road recognition greatly, while ensuring the accuracy of road recognition to high level.

关键词： Contour image Vanishing point deep learning network Image recognition Redundancy removal

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Semantic Pneumonia Segmentation and Classification for Covid-19 Using deep learning network

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Computers, Materials & Continua 2022年第10期73卷 1141-1158页

作者： M.M.Lotfy Hazem M.El-Bakry M.M.Elgayar Shaker El-Sappagh G.Abdallah M.I A.A.Soliman Kyung Sup Kwak Department of Mathematics Faculty of ScienceArish UniversityArish45511Egypt Department of Information Systems Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Department of Information Technology Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Centro Singular de Investigación en Tecnoloxías Intelixentes(CiTIUS) Universidade de Santiago de CompostelaSantiago de Compostela15705Spain Information Systems Department Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13512Egypt Department of Information and Communication Engineering Inha UniversityIncheon22212Korea

Early detection of the Covid-19 disease is essential due to its higher rate of infection affecting tens of millions of people,and its high number of deaths also by 7%.For that purpose,a proposed model of several stages was *** first stage is optimizing the images using dynamic adaptive histogram equalization,performing a semantic segmentation using deepLabv3Plus,then augmenting the data by flipping it horizontally,rotating it,then flipping it *** second stage builds a custom convolutional neural network model using several pre-trained ***,the model compares the pre-trained data to the new output,while repeatedly trimming the best-performing models to reduce complexity and improve memory *** experiments were done using different techniques and ***,the proposed model achieved an average accuracy of 99.6%and an area under the curve of 0.996 in the Covid-19 *** paper will discuss how to train a customized intelligent convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.

关键词： SARS-COV2 COVID-19 pneumonia deep learning network semantic segmentation smart classification

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A regional wind wave prediction surrogate model based on CNN deep learning network

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APPLIED OCEAN RESEARCH 2022年 126卷

作者： Huang, Limin Jing, Yu Chen, Hangyu Zhang, Lu Liu, Yuliang Harbin Engn Univ Qingdao Innovat & Dev Base Qingdao 266000 Peoples R China Harbin Engn Univ Coll Shipbuilding Engn Harbin 150001 Peoples R China

Accurate prediction of wave characteristics of large-scale waves is of great significance for ocean disaster prevention and shipping safety. The traditional method is mainly based on ocean numerical models using wind as input to predict regional waves. In recent years, machine learning methods have gradually been concerned and applied in wave prediction, providing an effective approach for the real-time forecasting of regional wind waves. This paper proposes a regional wind wave prediction surrogate model based on a convolutional neural network (CNN), which takes historical wind and wave data as input to realize the prediction of current waves. Herein, the wind datasets from the European Center for Medium-Term Weather Forecast (ECMWF) and validated wave datasets simulated by the Simulating Waves Nearshore (SWAN) model are employed in model verification and optimization. We investigate the influence of historical winds and waves on the prediction accuracy of current waves and verify the adaptability of the surrogate model to different computational regions. The results show that the prediction accuracy is significantly promoted by considering historical wind and waves data in model input. The proposed model has good adaptability to different computational regions, and the results indicate that the surrogate model can effectively realize regional wind wave prediction, which greatly improves the computational efficiency as compared to the SWAN model. In addition, its relative error with the verified SWAN simulation results is less than 5%.

关键词： Regional wind wave prediction Surrogate model deep learning network Convolutional neural network

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A recurrent skip deep learning network for accurate image segmentation

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期74卷 103533-103533页

作者： Shi, Ce Zhang, Juan Zhang, Xin Shen, Meixiao Chen, Hao Wang, Lei Wenzhou Med Univ Eye Hosp Sch Ophthalmol & Optometry Wenzhou 325027 Peoples R China

Accurate image segmentation plays a vital role in quantitatively assessing various diseases and their prognosis. In this study, we described a novel deep learning network termed Recurrent Skip network (RS-Net) by integrating a backward skip connection and an attention-aware convolutional block with the available BiO-Net. To validate its performance and merits, we applied it (1) for the segmentation of three corneal layers (i.e., epithelium layer, Bowman's layer, and stroma layer) depicted on optical coherence tomography (OCT) images and (2) for the segmentation of the optic disc (OD) and cup (OC) depicted on color fundus photography (CFP). Our experiments showed that RS-Net achieved an average Dice score of 0.9327 and 0.8868, respectively for the two different segmentation tasks, demonstrating a unique performance as compared with BiO-Net and other networks.

关键词： Image segmentation OCT Color fundus photography deep learning network Skip connection

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Integrated Image Sensor and deep learning network for Fabric Pilling Classification

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SENSORS AND MATERIALS 2022年第1期34卷 93-104页

作者： Shih, Chi-Huang Lin, Cheng-Jian Lee, Chin-Ling Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn Taichung 411 Taiwan Natl Taichung Univ Sci & Technol Coll Intelligence Taichung 404 Taiwan Natl Taichung Univ Sci & Technol Dept Int Business Taichung 404 Taiwan

Manufacturers' fabrics are tested for abrasion resistance before leaving the factory, and the fabrics are manually visually graded to ensure that there are no defects. However, manual visual classification consumes a lot of human resources. In addition, long-term visual inspections using the eyes often result in occupational injuries. As a result, the overall efficiency is reduced. To overcome and avoid such situations, we devised an image preprocessing technology and deep learning network for classifying the pilling level of knitted fabrics. In the first step, fabric images are collected using an image optical sensor. The fast Fourier transform (FFT) and Gaussian filter are used for image preprocessing to strengthen the pilling characteristics in the fabric images. In the second step, the characteristics and classification of fabric pilling are automatically captured and identified using a deep learning network. The experimental results show that the average accuracy of the proposed method for pilling level classification is 100%. The proposed method has 0.3% and 2.7% higher average accuracy than deep-principalcomponent-analysis-based neural networks (DPCANN) and the type-2 fuzzy cerebellar model articulation controller (T2FCMAC), respectively, demonstrating the superiority of the proposed model.

关键词： deep learning network fabric image pilling level classification image sensor

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