Journal articles on the topic 'DBN-CO'
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A. Geetha, Geetha, and Gomathi N. Gomathi. "A Robust Grey Wolf-based Deep Learning for Brain Tumour Detection in MR Images." International Journal of Engineering Education 1, no. 1 (June 15, 2019): 9–23. http://dx.doi.org/10.14710/ijee.1.1.9-23.
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In recent times, the detection of brain tumour is a common fatality in the field of the health community. Generally, the brain tumor is an abnormal mass of tissue where the cells grow up and increase uncontrollably, apparently unregulated by mechanisms that control cells. A number of techniques have been developed so far; however, the time consumption in detecting brain tumor is still a challenge in the field of image processing. This paper intends to propose a new detection model even accurately. The model includes certain processes like Preprocessing, Segmentation, Feature Extraction and Classification. Particularly, two extreme processes like contrast enhancement and skull stripping are processed under initial phase, in the segmentation process, this paper uses Fuzzy Means Clustering (FCM) algorithm. Both Gray Level Co-occurrence Matrix (GLCM) as well as Gray-Level Run-Length Matrix (GRLM) features are extracted in feature extraction phase. Moreover, this paper uses Deep Belief Network (DBN) for classification. The DBN is integrated with the optimization approach, and hence this paper introduces the optimized DBN, for which Grey Wolf Optimization (GWO) is used here. The proposed model is termed as GW-DBN model. The proposed model compares its performance over other conventional methods in terms of Accuracy, Specificity, Sensitivity, Precision, Negative Predictive Value (NPV), F1Score and Matthews Correlation Coefficient (MCC), False negative rate (FNR), False positive rate (FPR) and False Discovery Rate (FDR), and proven the superiority of proposed work.
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Geetha, A., and N. Gomathi. "A robust grey wolf-based deep learning for brain tumour detection in MR images." Biomedical Engineering / Biomedizinische Technik 65, no. 2 (April 28, 2020): 191–207. http://dx.doi.org/10.1515/bmt-2018-0244.
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AbstractIn recent times, the detection of brain tumours has become more common. Generally, a brain tumour is an abnormal mass of tissue where the cells grow uncontrollably and are apparently unregulated by the mechanisms that control cells. A number of techniques have been developed thus far; however, the time needed in a detecting brain tumour is still a challenge in the field of image processing. This article proposes a new accurate detection model. The model includes certain processes such as preprocessing, segmentation, feature extraction and classification. Particularly, two extreme processes such as contrast enhancement and skull stripping are processed under the initial phase. In the segmentation process, we used the fuzzy means clustering (FCM) algorithm. Both the grey co-occurrence matrix (GLCM) as well as the grey-level run-length matrix (GRLM) features were extracted in the feature extraction phase. Moreover, this paper uses a deep belief network (DBN) for classification. The optimized DBN concept is used here, for which grey wolf optimisation (GWO) is used. The proposed model is termed the GW-DBN model. The proposed model compares its performance over other conventional methods in terms of accuracy, specificity, sensitivity, precision, negative predictive value (NPV), the F1Score and Matthews correlation coefficient (MCC), false negative rate (FNR), false positive rate (FPR) and false discovery rate (FDR), and proves the superiority of the proposed work.
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Maylawati, Dian Sa'adillah, Yogan Jaya Kumar, Fauziah Kasmin, and Muhammad Ali Ramdhani. "Deep sequential pattern mining for readability enhancement of Indonesian summarization." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (February 1, 2024): 782. http://dx.doi.org/10.11591/ijece.v14i1.pp782-795.
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In text summarization research, readability is a great issue that must be addressed. Our hypothesis is readability can be accomplished by using text representations that keep the meaning of text documents intact. Therefore, this study aims to combine sequential pattern mining (SPM) in producing a sequence of a word as text representation with unsupervised deep learning to produce an Indonesian text summary called DeepSPM. This research uses PrefixSpan as an SPM algorithm and deep belief network (DBN) as an unsupervised deep learning method. This research uses 18,774 Indonesian news text from IndoSum. The readability aspect is evaluated by recall-oriented understudy for gisting evaluation (ROUGE) as a co-selection-based analysis; Dwiyanto Djoko Pranowo metrics, Gunning fog index (GFI), and Flesch-Kincaid grade level (FKGL) as content-based analysis; and human readability evaluation with two experts. The experiment result shows that DeepSPM yields better than DBN, with the F-measure value of ROUGE-1 enhanced to 0.462, ROUGE-2 is 0.37, and ROUGE-L is 0.41. The significance of ROUGE results also be tested using T-Test. The content-based analysis and human readability evaluation findings are conformable with the findings of co-selection-based analysis that generated summaries are only partially readable or have a medium level of readability aspect.
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Jia, Meng, and Zhiqiang Zhao. "Change Detection in Synthetic Aperture Radar Images Based on a Generalized Gamma Deep Belief Networks." Sensors 21, no. 24 (December 11, 2021): 8290. http://dx.doi.org/10.3390/s21248290.
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Change detection from synthetic aperture radar (SAR) images is of great significance for natural environmental protection and human societal activity, which can be regarded as the process of assigning a class label (changed or unchanged) to each of the image pixels. This paper presents a novel classification technique to address the SAR change-detection task that employs a generalized Gamma deep belief network (gΓ-DBN) to learn features from difference images. We aim to develop a robust change detection method that can adapt to different types of scenarios for bitemporal co-registered Yellow River SAR image data set. This data set characterized by different looks, which means that the two images are affected by different levels of speckle. Widely used probability distributions offer limited accuracy for describing the opposite class pixels of difference images, making change detection entail greater difficulties. To address the issue, first, a gΓ-DBN can be constructed to extract the hierarchical features from raw data and fit the distribution of the difference images by means of a generalized Gamma distribution. Next, we propose learning the stacked spatial and temporal information extracted from various difference images by the gΓ-DBN. Consequently, a joint high-level representation can be effectively learned for the final change map. The visual and quantitative analysis results obtained on the Yellow River SAR image data set demonstrate the effectiveness and robustness of the proposed method.
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Whitney, Ralph A., Anca Penciu, J. Scott Parent, Rui Resendes, and William Hopkins. "Cross-Linking of Brominated Poly(isobutylene-co-isoprene) by N-Alkylation of the Amidine Bases DBU and DBN." Macromolecules 38, no. 11 (May 2005): 4625–29. http://dx.doi.org/10.1021/ma047850+.
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David, Leo Gertrude, Raj Kumar Patra, Przemysław Falkowski-Gilski, Parameshachari Bidare Divakarachari, and Lourdusamy Jegan Antony Marcilin. "Tool Wear Monitoring Using Improved Dragonfly Optimization Algorithm and Deep Belief Network." Applied Sciences 12, no. 16 (August 14, 2022): 8130. http://dx.doi.org/10.3390/app12168130.
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In recent decades, tool wear monitoring has played a crucial role in the improvement of industrial production quality and efficiency. In the machining process, it is important to predict both tool cost and life, and to reduce the equipment downtime. The conventional methods need enormous quantities of human resources and expert skills to achieve precise tool wear information. To automatically identify the tool wear types, deep learning models are extensively used in the existing studies. In this manuscript, a new model is proposed for the effective classification of both serviceable and worn cutting edges. Initially, a dataset is chosen for experimental analysis that includes 254 images of edge profile cutting heads; then, circular Hough transform, canny edge detector, and standard Hough transform are used to segment 577 cutting edge images, where 276 images are disposable and 301 are functional. Furthermore, feature extraction is carried out on the segmented images utilizing Local Binary Pattern (LBPs) and Speeded up Robust Features (SURF), Harris Corner Detection (HCD), Histogram of Oriented Gradients (HOG), and Grey-Level Co-occurrence Matrix (GLCM) feature descriptors for extracting the texture feature vectors. Next, the dimension of the extracted features is reduced by an Improved Dragonfly Optimization Algorithm (IDOA) that lowers the computational complexity and running time of the Deep Belief Network (DBN), while classifying the serviceable and worn cutting edges. The experimental evaluations showed that the IDOA-DBN model attained 98.83% accuracy on the patch configuration of full edge division, which is superior to the existing deep learning models.
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Bhardwaj, Shikha, Gitanjali Pandove, and Pawan Kumar Dahiya. "A Web Application-Based Secured Image Retrieval System With an IoT-Cloud Network." International Journal of Web Services Research 18, no. 1 (January 2021): 1–20. http://dx.doi.org/10.4018/ijwsr.2021010101.
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Many encryption and searching techniques have been used, but they did not prove effective to support smart devices in order to provide input image. Therefore, based on these facts, an effective and novel system has been developed in this paper which is based on content-based search concentrated on encrypted images. Four type of features, namely color moment (CM), Gray level co-occurrence matrix (GLCM), hybrid of CM and GLCM, and lastly, a deep belief network (DBN) has been used here. This deep neural network is based on clustering in combination with indexing and the developed model is called as cluster-based deep belief network (CBDBN) in the present work. A web based application has also been developed using Apache Tomcat server and MATLAB engine. Analysis of many parameters like precision, recall, entropy, correlation coefficient, and time has been done here on benchmark datasets, namely WANG and COIL.
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Yao, Yunhao, Xiaoxing Zhou, and Merle Parmak. "Risk assessment for yachting tourism in China using dynamic Bayesian networks." PLOS ONE 18, no. 8 (August 23, 2023): e0289607. http://dx.doi.org/10.1371/journal.pone.0289607.
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Scientific evaluation of yachting tourism safety risks (YTSRs) is crucial to reducing accidents in this sector. This paper is based on the data of 115 yachting tourism accidents in China’s coastal areas from 2008 to 2021. A fishbone diagram and the analytic hierarchy process (AHP) were used to identify the risk factors of yachting tourism from four aspects human, yachting, environmental, and management risk and to construct an evaluation index system. To perform dynamic evaluation, a dynamic evaluation model of YTSRs was built using dynamic Bayesian networks (DBN). The results indicate that human factors, such as the unsafe behavior of yachtsmen and tourists, are the primary risk factors; the risk is higher in summer than in winter, and the Pearl River Delta region has a greater risk of yachting tourism. It is suggested to improve the normal safety risk prevention and control system of yachting tourism, to advocate for multi-subject coordination and co-governance, and to improve the insurance service system so as to provide a guarantee for the safe and healthy development of yachting tourism in China. The findings provide theoretical and practical guidance for marine and coastal tourism safety management, as well as the prevention and control of YTSRs.
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Sainudeen, Jinu P., Ceronmani Sharmila V, and Parvathi R. "Skin cancer detection: Improved deep belief network with optimal feature selection." Multiagent and Grid Systems 19, no. 2 (October 6, 2023): 187–210. http://dx.doi.org/10.3233/mgs-230040.
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During the past few decades, melanoma has grown increasingly prevalent, and timely identification is crucial for lowering the mortality rates linked to this kind of skin cancer. Because of this, having access to an automated, trustworthy system that can identify the existence of melanoma may be very helpful in the field of medical diagnostics. Because of this, we have introduced a revolutionary, five-stage method for detecting skin cancer. The input images are processed utilizing histogram equalization as well as Gaussian filtering techniques during the initial pre-processing stage. An Improved Balanced Iterative Reducing as well as Clustering utilizing Hierarchies (I-BIRCH) is proposed to provide better image segmentation by efficiently allotting the labels to the pixels. From those segmented images, features such as Improved Local Vector Pattern, local ternary pattern, and Grey level co-occurrence matrix as well as the local gradient patterns will be retrieved in the third stage. We proposed an Arithmetic Operated Honey Badger Algorithm (AOHBA) to choose the best features from the retrieved characteristics, which lowered the computational expense as well as training time. In order to demonstrate the effectiveness of our proposed skin cancer detection strategy, the categorization is done using an improved Deep Belief Network (DBN) with respect to those chosen features. The performance assessment findings are then matched with existing methodologies.
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SHANAHAN, TIMOTHY. "DESIGN BY NATURE." BioScience 54, no. 11 (2004): 1044. http://dx.doi.org/10.1641/0006-3568(2004)054[1044:dbn]2.0.co;2.
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Soroka, B. S., and V. V. Horupa. "SCIENTIFIC AND ENGINEERING PRINCIPLES OF EFFICIENT FUEL USE AND ENVIRONMENTALLY FRIENDLY GAS COMBUSTION IN STOVE PLATES. PART 1. MODERN STATE-OF-THE-ART AND DIRECTIONS FOR IMPROVEMENT THE GAS BURNING IN DOMESTIC GAS COOKERS." Energy Technologies & Resource Saving, no. 3 (March 20, 2017): 3–18. http://dx.doi.org/10.33070/etars.3.2017.01.
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Natural gas NG consumption in industry and energy of Ukraine, in recent years falls down as a result of the crisis in the country’s economy, to a certain extent due to the introduction of renewable energy sources along with alternative technologies, while in the utility sector the consumption of fuel gas flow rate enhancing because of an increase the number of consumers. The natural gas is mostly using by domestic purpose for heating of premises and for cooking. These items of the gas utilization in Ukraine are already exceeding the NG consumption in industry. Cooking is proceeding directly in the living quarters, those usually do not meet the requirements of the Ukrainian norms DBN for the ventilation procedures. NG use in household gas stoves is of great importance from the standpoint of controlling the emissions of harmful components of combustion products along with maintenance the satisfactory energy efficiency characteristics of NG using. The main environment pollutants when burning the natural gas in gas stoves are including the nitrogen oxides NOx (to a greater extent — highly toxic NO2 component), carbon oxide CO, formaldehyde CH2O as well as hydrocarbons (unburned UHC and polyaromatic PAH). An overview of environmental documents to control CO and NOx emissions in comparison with the proper norms by USA, EU, Russian Federation, Australia and China, has been completed. The modern designs of the burners for gas stoves are considered along with defining the main characteristics: heat power, the natural gas flow rate, diameter of gas orifice, diameter and spacing the firing openings and other parameters. The modern physical and chemical principles of gas combustion by means of atmospheric ejection burners of gas cookers have been analyzed from the standpoints of combustion process stabilization and of ensuring the stability of flares. Among the factors of the firing process destabilization within the framework of analysis above mentioned, the following forms of unstable combustion/flame unstabilities have been considered: flashback, blow out or flame lifting, and the appearance of flame yellow tips. Bibl. 37, Fig. 11, Tab. 7.
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Lindberg, Anton, and Neil Vasdev. "Ring-opening of non-activated aziridines with [11C]CO2via novel ionic liquids." RSC Advances 12, no. 33 (2022): 21417–21. http://dx.doi.org/10.1039/d2ra03966d.
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Ostendorf, Martin, Sandor van der Neut, Floris P. J. T. Rutjes, and Henk Hiemstra. "Enantioselective Synthesis of Hydroxy-Substituted DBN-Type Amidines as Potential Chiral Catalysts." European Journal of Organic Chemistry 2000, no. 1 (January 2000): 105–13. http://dx.doi.org/10.1002/(sici)1099-0690(200001)2000:1<105::aid-ejoc105>3.0.co;2-n.
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Shi, Min, and Yu-Mei Shen. "A Novel Reaction of 1,8-Diazabicyclo[5.4.0]undec-7-ene (DBU) or 1,5-Diazabicyclo[4.3.0]non-5-ene (DBN) with Benzyl Halides in the Presence of Water." Helvetica Chimica Acta 85, no. 5 (May 2002): 1355. http://dx.doi.org/10.1002/1522-2675(200205)85:5<1355::aid-hlca1355>3.0.co;2-m.
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Bhatt, Abhishek, and Vandana Thakur. "An Optimized Deep Belief Network for Land Cover Classification Using Synthetic-Aperture Radar Images and Landsat Images." Computer Journal, July 24, 2022. http://dx.doi.org/10.1093/comjnl/bxac077.
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Abstract This paper intends to propose an automated deep learning-based land cover classification model of remote sensing images. The model includes (i) pre-processing, (ii) feature extraction and (iii) classification. The captured synthetic-aperture radar (SAR) and Landsat-8 images are initially pre-processed using the Gabor filtering model. Subsequently, from SAR images the gray-level-co-occurrence matrix-based texture characteristics are extracted, and temperature vegetation index-based characteristics, normalized vegetation index-based features, normalized difference index-based features and coloration index features are extracted from Landsat-8 images. Finally, the extracted features are subjected to an optimized deep belief network (DBN), where the weight is fine-tuned by the optimization logic. For this, a new Sunflower adopted Red Deer (SARD) algorithm is introduced in this work that hybrids the concept of Red Deer algorithm and Sunflower optimization. The performance of the proposed classification model is compared over other conventional models concerning different measures. Especially, the accuracy of the presented work (SARD+DBN) for Testcase3 is 5, 7, 6 and 30% better than existing DA + DBN, JA + DBN, SLnO+DBN and LA + DBN methods, respectively.
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Li, Zuohong, Feng Li, Ruoping Liu, Mengze Yu, Zhiying Chen, Zihao Xie, and Zhaobin Du. "A Data-Driven Genetic Algorithm for Power Flow Optimization in the Power System With Phase Shifting Transformer." Frontiers in Energy Research 9 (January 31, 2022). http://dx.doi.org/10.3389/fenrg.2021.793686.
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Phase-shifting transformer (PST) is one of the flexible AC transmission technologies to solve the problem of uneven power transmission. Considering that PST can also be used as a regulation means for the economic operation of the system, it is necessary to study the power flow optimization of power systems with PST. In order to find a more efficient power flow optimization method, an improved genetic algorithm including a data-driven module is proposed. This method uses the deep belief network (DBN) to train the sample set of the power flow and obtains a high-precision proxy model. Then, the calculation of the DBN model replaces the traditional adaptation function calculation link which is very time-consuming due to a great quantity of AC power flow solution work. In addition, the sectional power flow reversal elimination mechanism in the genetic algorithm is introduced and appropriately co-designed with DBN to avoid an unreasonable power flow distribution of the grid section with PST. Finally, by comparing with the traditional model-driven genetic algorithm and traditional mathematical programming method, the feasibility and the validity of the method proposed in this paper are verified on the IEEE 39-node system.
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Lan, Lijun, Ying Liu, and Wen Feng Lu. "Automatic Discovery of Design Task Structure Using Deep Belief Nets." Journal of Computing and Information Science in Engineering 17, no. 4 (May 16, 2017). http://dx.doi.org/10.1115/1.4036198.
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With the arrival of cyber physical world and an extensive support of advanced information technology (IT) infrastructure, nowadays it is possible to obtain the footprints of design activities through emails, design journals, change logs, and different forms of social data. In order to manage a more effective design process, it is essential to learn from the past by utilizing these valuable sources and understand, for example, what design tasks are actually carried out, their interactions, and how they impact each other. In this paper, a computational approach based on the deep belief nets (DBN) is proposed to automatically uncover design tasks and quantify their interactions from design document archives. First, a DBN topic model with real-valued units is developed to learn a set of intrinsic topic features from a simple word-frequency-based input representation. The trained DBN model is then utilized to discover design tasks by unfolding hidden units by sets of strongly connected words, followed by estimating the interactions among tasks on the basis of their co-occurrence frequency in a hidden topic space. Finally, the proposed approach is demonstrated through a real-life case study using a design email archive spanning for more than 2 yr.
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Abdulmunem, Inas Ali. "Brain MR Images Classification for Alzheimer’s Disease." Iraqi Journal of Science, June 30, 2022, 2725–40. http://dx.doi.org/10.24996/ijs.2022.63.6.37.
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Alzheimer’s Disease (AD) is the most prevailing type of dementia. The prevalence of AD is estimated to be around 5% after 65 years old and is staggering 30% for more than 85 years old in developed countries. AD destroys brain cells causing people to lose their memory, mental functions and ability to continue daily activities. The findings of this study are likely to aid specialists in their decision-making process by using patients’ Magnetic Resonance Imaging (MRI) to distinguish patients with AD from Normal Control (NC). Performance evolution was applied to 346 Magnetic Resonance images from the Alzheimer's Neuroimaging Initiative (ADNI) collection. The Deep Belief Network (DBN) classifier was used to fulfill classification function. Weights were used to test the proposed method's recognition capacity, and the network was trained with a sample training set. As a result, this study offeres a new method for identifying Alzheimer's disease utilizing automated categorization. In tests, it performed admirably With 98.46% accuracy achieved for AD and NC studied classes when combining Gray Level Co-occurrence Matrix (GLCM) features with a DBN.
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"Preface." Journal of Physics: Conference Series 2632, no. 1 (November 1, 2023): 011001. http://dx.doi.org/10.1088/1742-6596/2632/1/011001.
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The 2023 4th International Conference on Internet of Things, Artificial Intelligence and Mechanical Automation (IoTAIMA 2023) was held via hybrid form in Guangzhou, China during July 21st to 23rd, 2023, following the successes of previous events held in Hangzhou (2020, 2021), and Shanghai (2022). This year the IoTAIMA 2023 aimed to bring together researchers, developers, and users in both industry and academia in the world to discuss emerging issues on Internet of Things, artificial intelligence and mechanical automation, for sharing state-of-art results, and for exploring new areas of research and development. The Conference was organized by Peking University-Wuhan Institute for Artificial Intelligence and co-organized by Central South University School of Automation. More than 80 delegates from home and abroad attended the Conference. The IoTAIMA 2023 was featured with four keynote speeches (by Prof. Deyu Qi, Prof. Jinping Ao, Prof. Ming Jiang, Assoc. Prof. Wei Wei, respectively), and several oral and poster presentations, in which a wide range of topics were covered and the most recent significant results were presented. Prof. Ming Jiang from Sun Yat-sen University, China addressed his speech Massive MIMO Aided Multinetwork 3D Positioning. Exploiting a distributed M-MIMO framework, his team proposed to employ a deep belief network (DBN) to analyze the received signal strengths (RSS) generated by a diffraction model. Next, the preliminary DBN estimates were forwarded to a long-short term memory network (LSTMN), where the trajectory information of the targeted UE can be extracted based on much less historical trajectory information than existing solutions. Then, the three-dimension (3D) coordinates of the UE’s positions can be estimated with a back propagation neural network (BPNN) which combines the outputs of DBN and LSTMN. Finally, extensive simulation results were provided to demonstrate the effectiveness of the proposed BPNN scheme. Quantities of excellent papers evaluated based on their originality, technical or research content, correctness, relevance to conference, contributions, and readability were presented in the Conference Proceedings published by Journal of Physics: Conference Series (JPCS). The topics of these papers include Radio Frequency Identification, Micro-Electro-Mechanical Systems, New Sensing Technology, Unmanned System Control Technology, Industrial Robots and Automatic Production line, etc. With the excellent quality of all the presentations, the IoTAIMA 2023 was a great success. We wish to thank the sponsors of the Conference, and particularly the Technical Program Committee. We would also like to extend our gratitude to all the speakers and authors for sharing scientific ideas and presenting new perspectives with us on related topics. Our appreciation also goes to the editors and other staff of JPCS for the assistance it provided for the publication of this paper volume. The Committee of IoTAIMA 2023 List of Committee Member is available in this Pdf.