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Journal articles on the topic '360 neural networks'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Jitendra, Sunte. "Scientific Reason for Divorce in Couples of Marriage Life." Journal of Applied Nursing Research and Education 3, no. 2 (2025): 35–38. https://doi.org/10.5281/zenodo.15516598.

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<em>In the day-to-day present scenario in social life, divorce is going to see a lot of statistics. This is a bad remark in social life; however, one needs to resolve the issue and make it into a balanced situation. In the current scenario, there will be a lot of records of marriages that break up and lead to divorce. In this paper one can identify the major reason for the problem and rectify the same issue through the scientific way. In understanding the universe, there will be 360-degree artificial neural networks whose origin and destination are from the bottom lower sky and the top upper s

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Sychev, D. A. "Pharmacotherapy Safety 360°: NOLI NOCERE!" Pharmacogenetics and Pharmacogenomics, no. 1 (July 19, 2023): 3–5. http://dx.doi.org/10.37489/2588-0527-2023-1-3-5.

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The Russian Congress «Pharmacotherapy Safety 360°: NOLI NOCERE!» was successfully held at the Russian Ministry of Health in May 2023, providing a high-level, expert platform to discuss current and topical issues of pharmacovigilance and pharmacotherapy safety for different patient groups, including pediatrics, gerontology and geriatrics, pregnant women, patients with orphan and oncological diseases. Extensive scientific topics covered the most significant aspects of the pharmacotherapy safety in various fields, including cardiology, gastroenterology, pulmonology and allergology, endocrinology,

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Deepali, M. Bongulwar, and N. Talbar S. "Robust Convolutional Neural Network Model For Recognition of Fruits." Indian Journal of Science and Technology 14, no. 45 (2021): 3318–34. https://doi.org/10.17485/IJST/v14i45.1493.

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<strong>Objectives:</strong> To develop a model for the automatic recognition of fruits utilizing deep learning techniques. <strong>Methods:</strong> We have designed a fruit classification and recognition Model using Convolutional Neural Networks (CNN). We have used excellent quality ImageNet dataset of fruit images for evaluation purpose. It contains 9,130 images of 11 different categories. The classification is challenging as the images comprise different fruits of the same color and shape, overlapped fruits, the background is not homogenous, and with different light effects

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Liu, Kevin. "Comparison of different Convolutional Neural Network models on Fruit 360 Dataset." Highlights in Science, Engineering and Technology 34 (February 28, 2023): 85–94. http://dx.doi.org/10.54097/hset.v34i.5385.

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Numerous Convolutional Neural Networks emerged in the past decade, each varies in accuracy, speed, and architecture. From AlexNet to ResNet, CNN models have been developing rapidly, and the architecture of the models become more complicated. These models are known for their accuracy on ImageNet, so the topic of this research is to explore how CNN models can perform differently on the Fruit 360 dataset. A model constructed specifically in this research and three significant models developed in the past decade are applied to the Fruit 360 dataset for result comparison: VGG-16, ResNet-50, MobileN

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Kumar, Shashwat, Lalit Bhagat, Antony Franklin A., and Jiong Jin. "Multi-neural network based tiled 360°video caching with Mobile Edge Computing." Journal of Network and Computer Applications 201 (May 2022): 103342. http://dx.doi.org/10.1016/j.jnca.2022.103342.

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Beaucamp, B., T. Leduc, V. Tourre, and M. Servières. "THE WHOLE IS OTHER THAN THE SUM OF ITS PARTS: SENSIBILITY ANALYSIS OF 360° URBAN IMAGE SPLITTING." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2022 (May 18, 2022): 33–40. http://dx.doi.org/10.5194/isprs-annals-v-4-2022-33-2022.

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Abstract. 360° imagery has been increasingly used to estimate the subjective qualities of the urban space, such as the feeling of safety or the liveliness of a place. These spherical panoramas offer an immersive view of the urban scene, close to the experience of a pedestrian. In recent years, Deep Learning approaches have been developed for this estimation task, only using flat images because these images are easier to annotate and process with standard CNNs. Thus to qualify the whole urban space, the panoramic images are divided into four flat sub-images that can be processed by the trained

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Alvarez-Rodríguez, Sergio, and Francisco G. Peña-Lecona. "Artificial Neural Networks with Machine Learning Design for a Polyphasic Encoder." Sensors 23, no. 20 (2023): 8347. http://dx.doi.org/10.3390/s23208347.

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Artificial neural networks are a powerful tool for managing data that are difficult to process and interpret. This article presents the design and implementation of backpropagated multilayer artificial neural networks, structured with a vector input, hidden layers, and an output node, for information processing generated by an optical encoder based on the polarization of light. A machine learning technique is proposed to train the neural networks such that the system can predict with remarkable accuracy the angular position in which the rotating element of the neuro-encoder is located based on

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Lee, Dongwon, Minji Choi, and Joohyun Lee. "Prediction of Head Movement in 360-Degree Videos Using Attention Model." Sensors 21, no. 11 (2021): 3678. http://dx.doi.org/10.3390/s21113678.

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In this paper, we propose a prediction algorithm, the combination of Long Short-Term Memory (LSTM) and attention model, based on machine learning models to predict the vision coordinates when watching 360-degree videos in a Virtual Reality (VR) or Augmented Reality (AR) system. Predicting the vision coordinates while video streaming is important when the network condition is degraded. However, the traditional prediction models such as Moving Average (MA) and Autoregression Moving Average (ARMA) are linear so they cannot consider the nonlinear relationship. Therefore, machine learning models ba

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Barmpoutis, Panagiotis, Tania Stathaki, Kosmas Dimitropoulos, and Nikos Grammalidis. "Early Fire Detection Based on Aerial 360-Degree Sensors, Deep Convolution Neural Networks and Exploitation of Fire Dynamic Textures." Remote Sensing 12, no. 19 (2020): 3177. http://dx.doi.org/10.3390/rs12193177.

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The environmental challenges the world faces have never been greater or more complex. Global areas that are covered by forests and urban woodlands are threatened by large-scale forest fires that have increased dramatically during the last decades in Europe and worldwide, in terms of both frequency and magnitude. To this end, rapid advances in remote sensing systems including ground-based, unmanned aerial vehicle-based and satellite-based systems have been adopted for effective forest fire surveillance. In this paper, the recently introduced 360-degree sensor cameras are proposed for early fire

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Zhang, Xin, Degang Yang, Tingting Song, Yichen Ye, Jie Zhou, and Yingze Song. "Classification and Object Detection of 360° Omnidirectional Images Based on Continuity-Distortion Processing and Attention Mechanism." Applied Sciences 12, no. 23 (2022): 12398. http://dx.doi.org/10.3390/app122312398.

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The use of 360° omnidirectional images has occurred widely in areas where comprehensive visual information is required due to their large visual field coverage. However, many extant convolutional neural networks based on 360° omnidirectional images have not performed well in computer vision tasks. This occurs because 360° omnidirectional images are processed into plane images by equirectangular projection, which generates discontinuities at the edges and can result in serious distortion. At present, most methods to alleviate these problems are based on multi-projection and resampling, which ca

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Gao, Hong Li, Xiao Hui Shi, Ling Cong Feng, and Li Ping Xu. "Condition Monitoring and Life Prediction of Rolling Guide Based on Hybrid Intelligence." Applied Mechanics and Materials 44-47 (December 2010): 2045–49. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.2045.

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To evaluate accurately working condition of guide, make maintenance strategy, and predict its residual life in the process of machining operation, a rolling guide rail condition monitoring system based on neural networks was constructed after key factors to guide life were investigated carefully. Eight B&K 4321 three-way vibration sensor were installed on slider surface to monitor the on-line condition of four guides and eight sliders. Vibration signals were processed by wavelet packet decomposition and the most sensitive features to guide life were selected by fuzzy clustering method. The

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Ha, Van Kha Ly, Rifai Chai, and Hung T. Nguyen. "A Telepresence Wheelchair with 360-Degree Vision Using WebRTC." Applied Sciences 10, no. 1 (2020): 369. http://dx.doi.org/10.3390/app10010369.

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This paper presents an innovative approach to develop an advanced 360-degree vision telepresence wheelchair for healthcare applications. The study aims at improving a wide field of view surrounding the wheelchair to provide safe wheelchair navigation and efficient assistance for wheelchair users. A dual-fisheye camera is mounted in front of the wheelchair to capture images which can be then streamed over the Internet. A web real-time communication (WebRTC) protocol was implemented to provide efficient video and data streaming. An estimation model based on artificial neural networks was develop

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HAMILTON, ALISTER, STEPHEN CHURCHER, PETER J. EDWARDS, GEOFFREY B. JACKSON, ALAN F. MURRAY, and H. MARTIN REEKIE. "PULSE STREAM VLSI CIRCUITS AND SYSTEMS: THE EPSILON NEURAL NETWORK CHIPSET." International Journal of Neural Systems 04, no. 04 (1993): 395–405. http://dx.doi.org/10.1142/s0129065793000328.

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An analogue CMOS VLSI neural processing chip has been designed and fabricated. The device employs "pulse-stream" neural state signalling and is capable of computing some 360 million synaptic connections per second. In addition to basic characterisation results, the performance of the chip in solving "real-world" problems is also demonstrated. The experience gained from the development of this device has resulted in the design of a second "pulse-stream" chip with improved performance and features. It is anticipated that this second device will be integrated into a standard bus-based system and

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Ekpar, Frank Edughom. "A Baseline Electroencephalography Motor Imagery Brain-Computer Interface System Using Artificial Intelligence and Deep Learning." European Journal of Electrical Engineering and Computer Science 8, no. 3 (2024): 46–53. http://dx.doi.org/10.24018/ejece.2024.8.3.632.

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This paper presents a baseline or reference (single channel, single subject, single trial) electroencephalography (EEG) motor imagery (MI) brain computer interface (BCI) that harnesses deep learning artificial neural networks (ANNs) for brainwave signal classification. The EEG electrode or sensor is placed on the scalp within the frontal lobe of the right hemisphere of the brain and approximately above the motor cortex. Signal classification discriminates among three MI classes, namely, right first closed event, neutral event and left first closed event and the measured accuracy of the deep le

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Stepanenko, S. O., and P. Y. Yakimov. "Using high-performance deep learning platform to accelerate object detection." Information Technology and Nanotechnology, no. 2416 (2019): 354–60. http://dx.doi.org/10.18287/1613-0073-2019-2416-354-360.

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Object classification with use of neural networks is extremely current today. YOLO is one of the most often used frameworks for object classification. It produces high accuracy but the processing speed is not high enough especially in conditions of limited performance of a computer. This article researches use of a framework called NVIDIA TensorRT to optimize YOLO with the aim of increasing the image processing speed. Saving efficiency and quality of the neural network work TensorRT allows us to increase the processing speed using an optimization of the architecture and an optimization of calc

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Barreto-Cubero, Andres J., Alfonso Gómez-Espinosa, Jesús Arturo Escobedo Cabello, Enrique Cuan-Urquizo, and Sergio R. Cruz-Ramírez. "Sensor Data Fusion for a Mobile Robot Using Neural Networks." Sensors 22, no. 1 (2021): 305. http://dx.doi.org/10.3390/s22010305.

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Mobile robots must be capable to obtain an accurate map of their surroundings to move within it. To detect different materials that might be undetectable to one sensor but not others it is necessary to construct at least a two-sensor fusion scheme. With this, it is possible to generate a 2D occupancy map in which glass obstacles are identified. An artificial neural network is used to fuse data from a tri-sensor (RealSense Stereo camera, 2D 360° LiDAR, and Ultrasonic Sensors) setup capable of detecting glass and other materials typically found in indoor environments that may or may not be visib

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Nuñez-Piña, Federico, Joselito Medina-Marin, Juan Carlos Seck-Tuoh-Mora, Norberto Hernandez-Romero, and Eva Selene Hernandez-Gress. "Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks." Complexity 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/1254794.

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The problem of assigning buffers in a production line to obtain an optimum production rate is a combinatorial problem of type NP-Hard and it is known as Buffer Allocation Problem. It is of great importance for designers of production systems due to the costs involved in terms of space requirements. In this work, the relationship among the number of buffer slots, the number of work stations, and the production rate is studied. Response surface methodology and artificial neural network were used to develop predictive models to find optimal throughput values. 360 production rate values for differ

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Jeng, Chyuan-Hwan, and Y. L. Mo. "Quick Seismic Response Estimation of Prestressed Concrete Bridges Using Artificial Neural Networks." Journal of Computing in Civil Engineering 18, no. 4 (2004): 360–72. http://dx.doi.org/10.1061/(asce)0887-3801(2004)18:4(360).

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Tiwari, Saurabh, Seongjun Heo, Nokeun Park, and Nagireddy Gari S. Reddy. "Modeling Mechanical Properties of Industrial C-Mn Cast Steels Using Artificial Neural Networks." Metals 15, no. 7 (2025): 790. https://doi.org/10.3390/met15070790.

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This study develops a comprehensive artificial neural network (ANN) model for predicting the mechanical properties of carbon–manganese cast steel, specifically, the yield strength (YS), tensile strength (TS), elongation (El), and reduction of area (RA), based on the chemical composition (16 alloying elements) and heat treatment parameters. The neural network model, employing a 20-44-44-4 architecture and trained on 400 samples from an industrial dataset of 500 samples, achieved 90% of test predictions within a 5% deviation from actual values, with mean prediction errors of 3.45% for YS and 4.9

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Karim, Muh Nasirudin, Ricardus Anggi Pramunendar, Moch Arief Soeleman, Purwanto Purwanto, and Bahtiar Imran. "Classification of Lombok Pearls using GLCM Feature Extraction and Artificial Neural Networks (ANN)." ILKOM Jurnal Ilmiah 14, no. 3 (2022): 209–17. http://dx.doi.org/10.33096/ilkom.v14i3.1317.209-217.

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This study used the second-order Gray Level Co-occurrence Matrix (GLCM) and pearl image classification using the Artificial Neural Network (ANN). No previous research combines the GLCM method with artificial neural networks in pearl image classification. The number of images used in this study is 360 images with three labels, including 120 A images, 120 AA images, and 120 AAA images. The epochs used in this study were 10, 20, 30, 40, 50, 60, 70, and 80. The test results at epoch 10 got 80.00% accuracy, epoch 20 got 90.00% accuracy, epoch 30 got 93.33% accuracy, and epoch 40 got 94.44% accuracy

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Fan, Ching-Ling, Shou-Cheng Yen, Chun-Ying Huang, and Cheng-Hsin Hsu. "Optimizing Fixation Prediction Using Recurrent Neural Networks for 360$^{\circ }$ Video Streaming in Head-Mounted Virtual Reality." IEEE Transactions on Multimedia 22, no. 3 (2020): 744–59. http://dx.doi.org/10.1109/tmm.2019.2931807.

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Castro-Correa, Jhon A., Mohsen Badiey, Jhony H. Giraldo, and Fragkiskos D. Malliaros. "Graph Neural Networks for source localization using Ships-of-Opportunity spectrograms." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A305. http://dx.doi.org/10.1121/10.0023611.

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Traditional underwater source localization methods have primarily relied on optimization techniques, matched-field processing, beamforming, and, more recently, deep learning approaches. However, these methods often fail to exploit the spatial correlation of data due to their representation in a regular domain. Nowadays, data collection commonly occurs in complex domains, like sensor networks, where signals and features are best represented as graphs based on feature similarity metrics. In a graph representation setting, each sensor or feature corresponds to a node in the graph, accompanied by

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Kusnadi, Adhi, Ivranza Zuhdi Pane, and Fenina Adline Twince Tobing. "Enhancing facial recognition accuracy through feature extractions and artificial neural networks." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1056. https://doi.org/10.11591/ijai.v14.i2.pp1056-1066.

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Facial recognition is a biometric system used to identify individuals through faces. Although this technology has many advantages, it still faces several challenges. One of the main challenges is that the level of accuracy has yet to reach its maximum potential. This research aims to improve facial recognition performance by applying the discrete cosine transform (DCT) and Gaussian mixture model (GMM), which are then trained with backward propagation of errors (backpropagation) and convolutional neural networks (CNN). The research results show low DCT and GMM feature extraction accuracy with b

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Adhi, Kusnadi, Zuhdi Pane Ivranza, and Adline Twince Tobing Fenina. "Enhancing facial recognition accuracy through feature extractions and artificial neural networks." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1056–66. https://doi.org/10.11591/ijai.v14.i2.pp1056-1066.

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Facial recognition is a biometric system used to identify individuals through faces. Although this technology has many advantages, it still faces several challenges. One of the main challenges is that the level of accuracy has yet to reach its maximum potential. This research aims to improve facial recognition performance by applying the discrete cosine transform (DCT) and Gaussian mixture model (GMM), which are then trained with backward propagation of errors (backpropagation) and convolutional neural networks (CNN). The research results show low DCT and GMM feature extraction accuracy with b

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Marchesi, Giulia, Christian Eichhorn, David A. Plecher, Yuta Itoh, and Gudrun Klinker. "EnvSLAM: Combining SLAM Systems and Neural Networks to Improve the Environment Fusion in AR Applications." ISPRS International Journal of Geo-Information 10, no. 11 (2021): 772. http://dx.doi.org/10.3390/ijgi10110772.

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Augmented Reality (AR) has increasingly benefited from the use of Simultaneous Localization and Mapping (SLAM) systems. This technology has enabled developers to create AR markerless applications, but lack semantic understanding of their environment. The inclusion of this information would empower AR applications to better react to the surroundings more realistically. To gain semantic knowledge, in recent years, focus has shifted toward fusing SLAM systems with neural networks, giving birth to the field of Semantic SLAM. Building on existing research, this paper aimed to create a SLAM system t

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Chen, Dong, and Younghoon Joo. "Novel Approach to 2D DOA Estimation for Uniform Circular Arrays Using Convolutional Neural Networks." International Journal of Antennas and Propagation 2021 (July 8, 2021): 1–15. http://dx.doi.org/10.1155/2021/5516798.

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This paper presents a novel efficient high-resolution two-dimensional direction-of-arrival (2D DOA) estimation method for uniform circular arrays (UCA) using convolutional neural networks. The proposed 2D DOA neural network in the single source scenario consists of two levels. At the first level, a classification network is used to classify the observation region into two subregions (0°, 180°) and (180°, 360°) according to the azimuth angle degree. The second level consists of two parallel DOA networks, which correspond to the two subregions, respectively. The input of the 2D DOA neural networ

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Chen, Joy Iong-Zong, and Chung-Sheng Pi. "Assessment for Different Neural Networks with FeatureSelection in Classification Issue." Sensors 22, no. 8 (2022): 3099. http://dx.doi.org/10.3390/s22083099.

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In general, the investigation of NN (neural network) computing systems requires the management of a significant number of simultaneous distinct algorithms, such as parallel computing, fault tolerance, classification, and data optimization. Supervised learning for NN originally comes from certain parameters, such as self-revised learning, input learning datasets, and multiple second learning processes. Specifically, the operation continues to adjust the NN connection synapses’ weight to achieve a self-learning computer system. The current article is aimed at developing the CC (correlation coeff

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Liu, Ting-Wei, Chun-Tat Chan, and Rih-Teng Wu. "Deep-Learning-Based Acoustic Metamaterial Design for Attenuating Structure-Borne Noise in Auditory Frequency Bands." Materials 16, no. 5 (2023): 1879. http://dx.doi.org/10.3390/ma16051879.

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In engineering acoustics, the propagation of elastic flexural waves in plate and shell structures is a common transmission path of vibrations and structure-borne noises. Phononic metamaterials with a frequency band gap can effectively block elastic waves in certain frequency ranges, but often require a tedious trial-and-error design process. In recent years, deep neural networks (DNNs) have shown competence in solving various inverse problems. This study proposes a deep-learning-based workflow for phononic plate metamaterial design. The Mindlin plate formulation was used to expedite the forwar

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Han, Weixing, Guang Yang, Chunsheng Hao, Zhengqi Wang, Dejing Kong, and Yu Dong. "A Data-Driven Model of Cable Insulation Defect Based on Convolutional Neural Networks." Applied Sciences 12, no. 16 (2022): 8374. http://dx.doi.org/10.3390/app12168374.

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The insulation condition of cables has been the focus of research in power systems. To address the problem that the electric field is not easily measured under the operating condition of 10 kV transmission cables with insulation defects, this paper proposes a data-driven cable insulation defect model based on a convolutional neural network approach. The electric field data during cable operation is obtained by finite element calculation, and a multi-dimensional input feature quantity and a data set with the electric field strength as the output feature quantity are constructed. A convolutional

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Wei, Wei, Chaolong Yuan, Rendong Wu, and Wei Jiao. "Prediction of breakthrough extruding force in large-scale extrusion process using artificial neural networks." Science Progress 104, no. 1 (2021): 003685042199260. http://dx.doi.org/10.1177/0036850421992609.

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Accurate prediction of breakthrough extruding force is very important for extrusion production, especially for the large-scale extrusion process, which directly affects the production costs and safety. In this paper, based on the production data of the 360-million-newton-tonnage extruding machine, an artificial neural network (ANN) algorithm is used to establish the breakthrough extruding force prediction model for the large-scale extrusion process, and the calculation results are validated. Results show that the proposed model has high accuracy, and the average relative error between the pred

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Mohammed, Foziya Ahmed, Kula Kekeba Tune, Juhar Ahmed Mohammed, Tizazu Alemu Wassu, and Seid Muhie. "Early Cervical Cancer Diagnosis with SWIN-Transformer and Convolutional Neural Networks." Diagnostics 14, no. 20 (2024): 2286. http://dx.doi.org/10.3390/diagnostics14202286.

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Introduction: Early diagnosis of cervical cancer at the precancerous stage is critical for effective treatment and improved patient outcomes. Objective: This study aims to explore the use of SWIN Transformer and Convolutional Neural Network (CNN) hybrid models combined with transfer learning to classify precancerous colposcopy images. Methods: Out of 913 images from 200 cases obtained from the Colposcopy Image Bank of the International Agency for Research on Cancer, 898 met quality standards and were classified as normal, precancerous, or cancerous based on colposcopy and histopathological fin

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Mostafa, Almetwally M., Swarn Avinash Kumar, Talha Meraj, Hafiz Tayyab Rauf, Abeer Ali Alnuaim, and Maram Abdullah Alkhayyal. "Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants." Applied Sciences 12, no. 1 (2021): 239. http://dx.doi.org/10.3390/app12010239.

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Food production is a growing challenge with the increasing global population. To increase the yield of food production, we need to adopt new biotechnology-based fertilization techniques. Furthermore, we need to improve early prevention steps against plant disease. Guava is an essential fruit in Asian countries such as Pakistan, which is fourth in its production. Several pathological and fungal diseases attack guava plants. Furthermore, postharvest infections might result in significant output losses. A professional opinion is essential for disease analysis due to minor variances in various gua

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Mohd Hussin, Najihah, Muhammad Noorazlan Shah Zainudin, Wira Hidayat Mohd Saad, Muhammad Raihaan Kamarudin, Sufri Muhammad, and Muhd Shah Jehan Abd Razak. "Chili fruits maturity estimation using various convolutional neural network architecture." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 1 (2024): 557. http://dx.doi.org/10.11591/ijeecs.v33.i1.pp557-567.

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<span>Agricultural robots recently become popular by helping the farmer to conduct their daily chores. A slow process of picking and grading will leads to an inaccurate result thus increasing the production cost. This study represents an innovative and economical alternative for farmers who require to undergone the process of estimating their maturity categories. A total of 1,200 chili images with 256×256 pixel are used, where 840 is used for training and the remaining 360 being served for testing. The maturity is determined by measuring the length of chili structure between the calyx an

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Gingrich, Oliver, Evgenia Emets, Alain Renaud, Sean Soraghan, and Dario Villanueva Ablanedo. "KIMA: The Wheel–Voice Turned into Vision: A Participatory, Immersive Visual Soundscape Installation." Leonardo 53, no. 5 (2020): 479–84. http://dx.doi.org/10.1162/leon_a_01698.

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Over the last five years, KIMA, an art and research project on sound and vision, has investigated visual properties of sound. Previous iterations of KIMA focused on digital representations of cymatics—physical sound patterns—as media for performance. The most recent development incorporated neural networks and machine learning strategies to explore visual expressions of sound in participatory music creation. The project, displayed on a 360-degree canvas at the London Roundhouse, prompted the audience to explore their own voice as intelligent, real-time visual representation. Machine learning a

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Alfaro, Luis, Claudia Rivera, Jorge Luna-Urquizo, Antonio Arroyo-Paz, Lucy Delgado, and Elisa Castañeda. "Experiential Marketing Tourism and Hospitality Tours Generation Hybrid Model." MENDEL 29, no. 2 (2023): 273–82. http://dx.doi.org/10.13164/mendel.2023.2.273.

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The emergence of immersive technologies presents unprecedented opportunities for both users and organizations. This paper explores the future of digital marketing as a new ecosystem wherein innovative marketing strategies enable organizations to communicate with their customer base in ways previously unattainable, reshaping traditional marketing concepts into novel and unimaginable actions. This study proposes an experiential marketing tourism and hospitality tours generation hybrid model. The model focuses on generating virtual tours based on 360° VR videos, specifically designed for hotel en

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Mahmoodzada, Abdulsaboor, Suhrab Ahadi, and Abdul Basir Mahmoodzada. "Comparison performance of Artificial Neural Networks and Fuzzy Inference systems in forecasting precious metals price Case Study: Gold, Silver, Platinum and Palladium." Academic Perspective Procedia 3, no. 1 (2020): 749–62. http://dx.doi.org/10.33793/acperpro.03.01.129.

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Awareness about the price of precious metals and the correct prediction on the process of taking decision can bring facilities, and purchasing them in the global market and recognizing the specific time of dealing can cause investment. In this article comparison of the performance of Artificial Neural Networks and Fuzzy Inference Systems in predicting the price of the precious metals (Case Study: Gold, Silver, Platinum and Palladium).has been pointed. The information about each of these metals (Gold, Silver, Platinum and Palladium) is monthly considered from 1998 until 2018 including 360 data.

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Jin, Pengfei, and Zhuoyuan Yu. "Research on 3D Visualization of Drone Scenes Based on Neural Radiance Fields." Electronics 13, no. 9 (2024): 1682. http://dx.doi.org/10.3390/electronics13091682.

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Neural Radiance Fields (NeRFs), as an innovative method employing neural networks for the implicit representation of 3D scenes, have been able to synthesize images from arbitrary viewpoints and successfully apply them to the visualization of objects and room-level scenes (<50 m2). However, due to the capacity limitations of neural networks, the rendering of drone-captured scenes (>10,000 m2) often appears blurry and lacks detail. Merely increasing the model’s capacity or the number of sample points can significantly raise training costs. Existing space contraction methods, designed for f

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ГУРКОВСЬКА, С. С., та Д. Ю. МІХЄЄНКО. "АВТОМАТИЗОВАНА ПОБУДОВА 2D-КРЕСЛЕНЬ З 3D-МОДЕЛЕЙ ІЗ ВИКОРИСТАННЯМ ІНСТРУМЕНТІВ КОМП’ЮТЕРНОЇ ГРАФІКИ". Вісник Херсонського національного технічного університету, № 4(91) (30 грудня 2024): 267–72. https://doi.org/10.35546/kntu2078-4481.2024.4.34.

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У статті досліджується ефективність сучасного програмного забезпечення для автоматизації створення кресленників із тривимірних моделей, що є актуальним завданням для інженерної та архітектурної галузей. Основна увага приділена аналізу можливостей програм AutoCAD, SolidWorks, Fusion 360 та алгоритмів, заснованих на згорткових нейронних мережах (Convolutional Neural Networks, CNN). Дослідження спрямоване на визначення рівня ефективності цих інструментів за такими ключовими параметрами, як швидкість обробки, адаптація до складних форм і гнучкість у налаштуванні кресленників. Дослідження показало,

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Kshivets, Oleg. "Lymph node metastases of gastric cancer and blood cell circuit." Journal of Clinical Oncology 39, no. 15_suppl (2021): e16024-e16024. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e16024.

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e16024 Background: Significance of blood cell circuit in terms of detection of gastric cancer (GC) patients (GCP) with lymph node metastases was investigated. Methods: We analyzed data of 793 consecutive GCP (age = 57±9.4 years; tumor size = 5.4±3.1 cm) radically operated (R0) and monitored in 1975-2021 (m = 555, f = 238; distal gastrectomies = 460, proximal gastrectomies = 163, total gastrectomies = 170, combined gastrectomies with resection of pancreas, liver, diaphragm, colon transversum, esophagus, duodenum, splenectomy, small intestine, kidney, adrenal gland = 244; D2-lymphadenectomy = 51

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Chen, Xiaolei, Baoning Cao, and Ishfaq Ahmad. "Lightweight Neural Network-Based Viewport Prediction for Live VR Streaming in Wireless Video Sensor Network." Mobile Information Systems 2021 (November 9, 2021): 1–12. http://dx.doi.org/10.1155/2021/8501990.

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Live virtual reality (VR) streaming (a.k.a., 360-degree video streaming) has become increasingly popular because of the rapid growth of head‐mounted displays and 5G networking deployment. However, the huge bandwidth and the energy required to deliver live VR frames in the wireless video sensor network (WVSN) become bottlenecks, making it impossible for the application to be deployed more widely. To solve the bandwidth and energy challenges, VR video viewport prediction has been proposed as a feasible solution. However, the existing works mainly focuses on the bandwidth usage and prediction acc

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Wang, Daobin, Kun Wen, Tiantian Bai, Ruiyang Xia, Zanshan Zhao, and Guanjun Gao. "Convolutional Neural Network-Based Fiber Optic Channel Emulator and Its Application to Fiber-Longitudinal Power Profile Estimation." Photonics 12, no. 3 (2025): 271. https://doi.org/10.3390/photonics12030271.

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This paper proposes an accuracy enhancement method for fiber-longitudinal power profile estimation (PPE) based on convolutional neural networks (CNN). Two types of CNNs are designed. The first network treats different polarization streams identically and is denoted as CNN. The second network considers the difference between the contributions of different polarization streams to the nonlinear phase shift and is denoted as enhanced CNN (ECNN). The numerical simulation results confirm the effectiveness of the method for a 64 Gbaud/s quadrature phase-shift keying (QPSK) polarization-division-multi

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Yang, Xiao, Jinchang Zhang, Bidur Paneru, et al. "Precision Monitoring of Dead Chickens and Floor Eggs with a Robotic Machine Vision Method." AgriEngineering 7, no. 2 (2025): 35. https://doi.org/10.3390/agriengineering7020035.

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Modern poultry and egg production is facing challenges such as dead chickens and floor eggs in cage-free housing. Precision poultry management strategies are needed to address those challenges. In this study, convolutional neural network (CNN) models and an intelligent bionic quadruped robot were used to detect floor eggs and dead chickens in cage-free housing environments. A dataset comprising 1200 images was used to develop detection models, which were split into training, testing, and validation sets in a 3:1:1 ratio. Five different CNN models were developed based on YOLOv8 and the robot’s

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Dong, Yun, Elena Spinei, and Anuj Karpatne. "A feasibility study to use machine learning as an inversion algorithm for aerosol profile and property retrieval from multi-axis differential absorption spectroscopy measurements." Atmospheric Measurement Techniques 13, no. 10 (2020): 5537–50. http://dx.doi.org/10.5194/amt-13-5537-2020.

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Abstract. In this study, we explore a new approach based on machine learning (ML) for deriving aerosol extinction coefficient profiles, single-scattering albedo and asymmetry parameter at 360 nm from a single multi-axis differential optical absorption spectroscopy (MAX-DOAS) sky scan. Our method relies on a multi-output sequence-to-sequence model combining convolutional neural networks (CNNs) for feature extraction and long short-term memory networks (LSTMs) for profile prediction. The model was trained and evaluated using data simulated by Vector Linearized Discrete Ordinate Radiative Transfe

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Gulzar, Yonis, Zeynep Ünal, Shahnawaz Ayoub, Faheem Ahmad Reegu, and Alhanouf Altulihan. "Adaptability of deep learning: datasets and strategies in fruit classification." BIO Web of Conferences 85 (2024): 01020. http://dx.doi.org/10.1051/bioconf/20248501020.

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This review aims to uncover the multifaceted landscape of methodologies employed by researchers for accurate fruit classification. The exploration encompasses an array of techniques and models, each tailored to address the nuanced challenges presented by fruit classification tasks. From convolutional neural networks (CNNs) to recurrent neural networks (RNNs), and transfer learning to ensemble methods, the spectrum of approaches underscores the innovative strategies harnessed to achieve precision in fruit categorization. A significant facet of this review lies in the analysis of the various dat

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Tabkha, A., R. Hajji, R. Billen, and F. Poux. "SEMANTIC ENRICHMENT OF POINT CLOUD BY AUTOMATIC EXTRACTION AND ENHANCEMENT OF 360° PANORAMAS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W17 (November 29, 2019): 355–62. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w17-355-2019.

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Abstract. The raw nature of point clouds is an important challenge for their direct exploitation in architecture, engineering and construction applications. Particularly, their lack of semantics hinders their utility for automatic workflows (Poux, 2019). In addition, the volume and the irregularity of the structure of point clouds makes it difficult to directly and automatically classify datasets efficiently, especially when compared to the state-of-the art 2D raster classification. Recently, with the advances in deep learning models such as convolutional neural networks (CNNs) , the performan

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Jungclaus, Nils, Markus von der Heyde, Helge Ritter, and Gerhard Sagerer. "An Architecture for Distributed Visual Memory." Zeitschrift für Naturforschung C 53, no. 7-8 (1998): 550–59. http://dx.doi.org/10.1515/znc-1998-7-809.

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Abstract The development of autonomous as well as situated robots is one of the great remaining challenges and involves a number of different scientific disciplines. In spite of recent dramatic progress, it remains worthwhile to examine natural systems, because their abilities are still out of reach. Motivated by research work done in the fields of cognitive systems, visual perception, and psychology of memory we designed and implemented a memory architecture for visual tasks. Structural and functional concepts of the memory architecture were modeled on the ones found in natural systems. We pr

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Munjal, Rashmi, William Liu, Xue Jun Li, and Jairo Gutierrez. "A Neural Network-Based Sustainable Data Dissemination through Public Transportation for Smart Cities." Sustainability 12, no. 24 (2020): 10327. http://dx.doi.org/10.3390/su122410327.

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In recent years, there has been a big data revolution in smart cities dues to multiple disciplines such as smart healthcare, smart transportation, and smart community. However, most services in these areas of smart cities have become data-driven, thus generating big data that require sharing, storing, processing, and analysis, which ultimately consumes massive amounts of energy. The accumulation process of these data from different areas of a smart city is a challenging issue. Therefore, researchers have started aiming at the Internet of vehicles (IoV), in which smart vehicles are equipped wit

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Yu, Xianjia, Sahar Salimpour, Jorge Peña Queralta, and Tomi Westerlund. "General-Purpose Deep Learning Detection and Segmentation Models for Images from a Lidar-Based Camera Sensor." Sensors 23, no. 6 (2023): 2936. http://dx.doi.org/10.3390/s23062936.

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Over the last decade, robotic perception algorithms have significantly benefited from the rapid advances in deep learning (DL). Indeed, a significant amount of the autonomy stack of different commercial and research platforms relies on DL for situational awareness, especially vision sensors. This work explored the potential of general-purpose DL perception algorithms, specifically detection and segmentation neural networks, for processing image-like outputs of advanced lidar sensors. Rather than processing the three-dimensional point cloud data, this is, to the best of our knowledge, the first

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Traore, Adama, Syed Tahir Ata-Ul-Karim, Aiwang Duan, Mukesh Kumar Soothar, Seydou Traore, and Ben Zhao. "Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques." Remote Sensing 13, no. 21 (2021): 4476. http://dx.doi.org/10.3390/rs13214476.

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The equivalent water thickness (EWT) is an important biophysical indicator of water status in crops. The effective monitoring of EWT in wheat under different nitrogen and water treatments is important for irrigation management in precision agriculture. This study aimed to investigate the performances of machine learning (ML) algorithms in retrieving wheat EWT. For this purpose, a rain shelter experiment (Exp. 1) with four irrigation quantities (0, 120, 240, 360 mm) and two nitrogen levels (75 and 255 kg N/ha), and field experiments (Exps. 2–3) with the same irrigation and rainfall water levels

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Yahya, Asma Salim, and Naktal Moaid Edan. "ECG Signal Classification Using Hybrid and Non-Hybrid Learning Technologies." International Journal on Perceptive and Cognitive Computing 11, no. 1 (2025): 114–21. https://doi.org/10.31436/ijpcc.v11i1.503.

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Most arrhythmias caused by cardiovascular disorders disrupt the electrical activity of the heart, resulting in changes in the morphology of electrocardiogram (ECG) recordings. By analyzing different ECG patterns and comparing machine learning and deep learning techniques, this research aims to accurately identify twenty-nine different cardiac problems and sinus rhythm. The database contains 48 heart rate recordings at a frequency of 360 Hz for about 25 minutes for five classes, namely “N”, “S”, “V”, “F”, and “Q”. Support Vector Machine (SVM), k-nearest neighbor (k-nearest neighbor) classifier,

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