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Journal articles on the topic 'Temporary Convolution Neural Network'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Ngoc, Quang Tran, Seunghyun Lee, and Byung Cheol Song. "Facial Landmark-Based Emotion Recognition via Directed Graph Neural Network." Electronics 9, no. 5 (2020): 764. http://dx.doi.org/10.3390/electronics9050764.

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Facial emotion recognition (FER) has been an active research topic in the past several years. One of difficulties in FER is the effective capture of geometrical and temporary information from landmarks. In this paper, we propose a graph convolution neural network that utilizes landmark features for FER, which we called a directed graph neural network (DGNN). Nodes in the graph structure were defined by landmarks, and edges in the directed graph were built by the Delaunay method. By using graph neural networks, we could capture emotional information through faces’ inherent properties, like geom

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Zanjad, Sagar. "Diabetic Retinopathy Detection from Retinal Images." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35218.

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Diabetic Retinopathy (DR) is an eye disease that can occur in people with diabetes causing permanent or temporary blindness, where the blood vessels of retina, a layer in rear interior eye and so called light sensitive part effected by high sugar damages nerves. As I mentioned above, DR can present in various ways; Some people with sick retinas experience blurred vision, while others have difficulty seeing colors or eye floaters. At first, DR might cause no symptoms or only mild vision problems. But it can lead to blindness if left undiagnosed and untreated. This study presents a pipeline for

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Tang, Xinkun, Ying Xu, Feng Ouyang, and Ligu Zhu. "A Lightweight Reconstruction Model via a Neural Network for a Video Super-Resolution Model." Applied Sciences 13, no. 18 (2023): 10165. http://dx.doi.org/10.3390/app131810165.

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Super-resolution in image and video processing has been a challenge in computer vision, with its progression creating substantial societal ramifications. More specifically, video super-resolution methodologies aim to restore spatial details while upholding the temporal coherence among frames. Nevertheless, their extensive parameter counts and high demand for computational resources challenge the deployment of existing deep convolutional neural networks on mobile platforms. In response to these concerns, our research undertakes an in-depth investigation into deep convolutional neural networks a

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Wenjuan Xiao, Wenjuan Xiao, and Xiaoming Wang Wenjuan Xiao. "Attention Mechanism Based Spatial-Temporal Graph Convolution Network for Traffic Prediction." 電腦學刊 35, no. 4 (2024): 093–108. http://dx.doi.org/10.53106/199115992024083504007.

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<p>Considering the complexity of traffic systems and the challenges brought by various factors in traffic prediction, we propose a spatial-temporal graph convolutional neural network based on attention mechanism (AMSTGCN) to adapt to these dynamic changes and improve prediction accuracy. The model combines the spatial feature extraction capability of graph attention network (GAT) and the dynamic correlation learning capability of attention mechanism. By introducing the attention mechanism, the network can adaptively focus on the dependencies between different time steps and different nod

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Sato, Daisaku. "NEURAL NETWORK MODEL FOR IDENTIFYING THE COASTAL SAND AREA USING THE AERIAL PHOTOGRAPHS PICTURED BY UAV." Coastal Engineering Proceedings, no. 36v (December 28, 2020): 10. http://dx.doi.org/10.9753/icce.v36v.sediment.10.

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Small island states formed by atolls such as Marshall Islands, Kiribati and Tuvalu require useful and efficiency method of coastal monitoring for coastal management because of lack of human resources and budget. In atoll islands, the identification of shape of sandy beach and temporally accumulation area of sand has high importance in coastal management. In this study, neural network model to classify the aerial photographs pictured by UAV was established for identifying the sand area in the coastal zone of Fongafale island in Funafuti atoll, Tuvalu. Photographs of coastal sediments especially

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Liu, Xiang, Hongyuan Wang, Xinlong Chen, Weichun Chen, and Zhengyou Xie. "Exploiting Temporal–Spatial Feature Correlations for Sequential Spacecraft Depth Completion." Remote Sensing 15, no. 19 (2023): 4786. http://dx.doi.org/10.3390/rs15194786.

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The recently proposed spacecraft three-dimensional (3D) structure recovery method based on optical images and LIDAR has enhanced the working distance of a spacecraft’s 3D perception system. However, the existing methods ignore the richness of temporal features and fail to capture the temporal coherence of consecutive frames. This paper proposes a sequential spacecraft depth completion network (S2DCNet) for generating accurate and temporally consistent depth prediction results, and it can fully exploit temporal–spatial coherence in sequential frames. Specifically, two parallel convolution neura

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Fajri, Diny Melsye Nurul, Wayan Firdaus Mahmudy, and Titiek Yulianti. "Detection of Disease and Pest of Kenaf Plant using Convolutional Neural Network." Journal of Information Technology and Computer Science 6, no. 1 (2021): 18–24. http://dx.doi.org/10.25126/jitecs.202161195.

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Kenaf fiber is mainly used for forest wood substitute industrial products. Thus, the kenaf fiber can be promoted as the main composition of environmentally friendly goods. Unfortunately, there are several Kenaf gardens that have been stricken with the disease-causing a lack of yield. By utilizing advances in technology, it was felt to be able to help kenaf farmers quickly and accurately detect which pests or diseases attacked their crops. This paper will discuss the application of the machine learning method which is a Convolutional Neural Network (CNN) that can provide results for inputting l

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Anwar, Batool, Mohamed M. Morsey, Islam Hegazy, Zaki T. Fayed, and Taha El-Arif. "ENHANCED POLSAR IMAGE CLASSIFICATION USING DEEP CONVOLUTIONAL AND TEMPORAL CONVOLUTIONAL NETWORKS." ORESTA 7, no. 2 (2024): 196–218. https://doi.org/10.5281/zenodo.15086559.

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<em>A new framework in the form of Polarimetric Synthetic Aperture Radar (PolSAR) image classification, where deep Convolutional Neural Networks (CNNs) were integrated with the traditional Machine Learning (ML) techniques under a Temporal Convolutional Network (TCN) architecture, was introduced in the paper. The main aim behind this new approach is to overcome the severe limitations inherent in both deep CNN and conventional ML approaches. The application of the sliding-window strategy eliminates the necessity of requiring extensive feature extraction procedures while reducing computational co

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Jain, Vinesh Kumar, Arka Prokash Mazumdar, and Mahesh Chandra Govil. "Congestion Prediction in Internet of Things Network using Temporal Convolutional Network A Centralized Approach." Defence Science Journal 72, no. 6 (2022): 810–23. http://dx.doi.org/10.14429/dsj.72.17447.

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The unprecedented ballooning of network traffic flow, specifically, Internet of Things (IoT) network traffic, has big stressed of congestion on todays Internet. Non-recurring network traffic flow may be caused by temporary disruptions, such as packet drop, poor quality of services, delay, etc. Hence, the network traffic flow estimation is important in IoT networks to predict congestion. As the data in IoT networks is collected from a large number of diversified devices which have unlike format of data and also manifest complex correlations, so the generated data is heterogeneous and nonlinear

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Bogolub, Kyren R., Jackson P. Bell, Enrique R. Chon, Robert M. Kirkham, and Anne F. Sheehan. "Earthquake swarm near Great Sand Dunes, Colorado, investigated with temporary seismic network and machine learning sesmic phase analysis." Mountain Geologist 60, no. 3 (2023): 81–101. http://dx.doi.org/10.31582/rmag.mg.60.3.81.

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In November of 2018, residents living in the Zapata Subdivision south of Great Sand Dunes National Park and Preserve reported hearing and feeling multiple small earthquakes. Reports of additional earthquakes continued, escalating in late February of 2019, when the USGS recorded over 27 magnitude 0.9 and larger earthquakes over a two-day period. Subdivision residents became concerned that these could be foreshocks to a future, larger earthquake. To further study these earthquakes, we installed a temporary network of seismometers in the area during 2019 and used a convolution neural network seis

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Min, Deokki, Hyeonuk Nam, and Yong-Hwa Park. "Sound Event Detection utilizing Spectro-Temporal Receptive Field." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 268, no. 5 (2023): 3768–77. http://dx.doi.org/10.3397/in_2023_0537.

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Sound event detection (SED) is a task of recognizing the target sound events with their respective onset and offset time in a recorded audio clip. SED is closely related to auditory scene analysis, the process by which human auditory systems perceive sound into perceptually meaningful components. While human auditory system is trained to perform SED well enough, the way sound is transformed into neural response within human auditory system still remain as the subject of ongoing research. One of efforts to describe the relationship between sound and neural response is the spectro-temporal recep

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Yuferev, Leonid Yu, and Oleg V. Maschev. "DEVELOPMENT OF AN ALGORITHM FOR HIERARCHICAL CLUSTERING OF CROP DEFECTS BASED ON NEURAL NETWORK DATA." Elektrotekhnologii i elektrooborudovanie v APK 71, no. 4 (2024): 103–9. https://doi.org/10.22314/2658-4859-2024-71-4-103-109.

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The research is devoted to the development and application of methods for automated monitoring of the state of crops using unmanned aerial vehicles equipped with high-resolution cameras with triaxial stabilization. During the survey of the field at a height of 3 meters, video is taken to analyze the vegetation cover. The developed algorithms, which include hierarchical clustering of diseases and pests, make it possible to systematize the detected objects and localize the foci of their spread, which increases the accuracy of measures to combat quarantine objects and minimizes damage to the crop

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Askari, Elham, Sara Motamed, and Safoura Ashori Ghale Koli. "Recognition of Alzheimer’s Patients in Emotional States Based on the Optimal Convolutional Neural Network and Electroencephalography." Journal of Health and Biomedical Informatics 10, no. 2 (2023): 175–84. http://dx.doi.org/10.34172/jhbmi.2023.23.

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Introduction: Accurate diagnosis of Alzheimer’s disease in the early stages plays an important role in patient care, and preventive measures should be taken before irreversible brain damage occurs. With increasing age, there are changes in memory, which is normal, but the symptoms of Alzheimer’s disease are more than temporary forgetfulness. Early and intelligent diagnosis of Alzheimer’s disease in different situations can greatly help patients and physicians. Method: In the proposed method, a convolutional neural network will be used to improve the recognition of people with Alzheimer’s disea

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Ramachandran, Dhanagopal, R. Suresh Kumar, Ahmed Alkhayyat, et al. "Classification of Electrocardiography Hybrid Convolutional Neural Network-Long Short Term Memory with Fully Connected Layer." Computational Intelligence and Neuroscience 2022 (July 11, 2022): 1–10. http://dx.doi.org/10.1155/2022/6348424.

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Electrocardiography (ECG) is a technique for observing and recording the electrical activity of the human heart. The usage of an ECG signal is common among clinical professionals in the collection of time data for the examination of any rhythmic conditions associated with a subject. The investigation was carried out in order to computerize the assignment by exhibiting the issue using encoder-decoder techniques, creating the information that was simply typical of it, and utilising misfortune appropriation to anticipate standard or anomalous information. On a broad variety of applications such a

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Lim, Kim, Kim, Hong, and Han. "Payload-Based Traffic Classification Using Multi-Layer LSTM in Software Defined Networks." Applied Sciences 9, no. 12 (2019): 2550. http://dx.doi.org/10.3390/app9122550.

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Recently, with the advent of various Internet of Things (IoT) applications, a massive amount of network traffic is being generated. A network operator must provide different quality of service, according to the service provided by each application. Toward this end, many studies have investigated how to classify various types of application network traffic accurately. Especially, since many applications use temporary or dynamic IP or Port numbers in the IoT environment, only payload-based network traffic classification technology is more suitable than the classification using the packet header

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Paukaeva, Anastasiia A., Tsuyoshi Setoguchi, Norihiro Watanabe, and Vera I. Luchkova. "Temporary Design on Public Open Space for Improving the Pedestrian’s Perception Using Social Media Images in Winter Cities." Sustainability 12, no. 15 (2020): 6062. http://dx.doi.org/10.3390/su12156062.

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Due to the severe climate, residents of winter cities tend not to utilize public open spaces inwinter. Temporary design interventions such as emblematic events are always proposed in wintercities to enhance pedestrian activity by celebrating the season and improving the perception ofwinter. In this study, we clarify the impact of the event on pedestrians’ perception to determinethe role of temporary design in improving the perception of public open spaces in winter cities.Using the example of event known as “Ice Town” on the Lenin Square in Khabarovsk, the contentof the Instagram images was an

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Saadun Naif1, Kwakib, Kadhim Mahdi Hashim2, and Kadhim Mahdi Hashim2. "Decimal Digits Recognition from Lip Movement Using GoogleNet network." Journal of Education for Pure Science- University of Thi-Qar 12, no. 2 (2023): 297–307. http://dx.doi.org/10.32792/jeps.v12i2.195.

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Lip reading is a visual way to communicate with people through the movement of the lips, especially thehearing impaired and people who are in noisy environments such as stadiums and airports. Lip reading isnot easy to face many difficulties, especially when taking a video of the person, including lighting,rotation, the person’s position and different skin colors...etc. As researchers are constantly looking fornew techniques for lip-reading.The main objective of the paper is to design and implement an effective system for identifying decimaldigits by movement. Our proposed system consists of tw

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Badamshin, Alexey E., Maxim E. Ruzanov, Fanur A. Bayazitov, and Ekaterina V. Filippova. "A HYBRID APPROACH TO IMAGE ENHANCEMENT IN LOW-LIGHT CONDITIONS BASED ON RETINEX AND RECURRENT CONVOLUTIONAL NETWORKS." Electrical and Data Processing Facilities and Systems 21, no. 2 (2025): 111–22. https://doi.org/10.17122/1999-5458-2025-21-2-111-122.

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Relevance Modern computer vision tasks often face problems with noise, color distortion, and loss of detail, which limits their applicability in real-world scenarios. Existing approaches, including histogram transformations and deep learning-based methods, demonstrate either insufficient adaptability to variable lighting levels or high computational complexity. This creates the need for hybrid solutions that combine the advantages of classical and neural network methods to increase efficiency and versatility. Aim of research The main aim of the research is to investigate a hybrid image enhance

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Wang, Chao, Xi Chen, and Ying Wang. "Research on Automatic Unattended Bill Collection, Paste and Verifiction Integrated Robot Equipment and Control Platform based on Deep Convolutional Neural Network." Scalable Computing: Practice and Experience 25, no. 4 (2024): 3126–38. http://dx.doi.org/10.12694/scpe.v25i4.2997.

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A new solution for fully automated and unmanned ticket pasting verification based on deep convolutional neural networks is designed to address the issues of low efficiency, error-proneness, and wastage of manpower in the supplier service hall. The technology makes full use of machine vision and image processing, AI precise positioning correction algorithm and other methods to build an automatic unattended bill collection, paste and verification platform. Through the technologies of high-speed identification of invoice information, 3D vision-guidance planning, control of the path of robotic arm

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CHENG, Shou-ye, Feng GAO, Guo-ye JING, Ming ZHOU, Bo HAN, and Zheng TANG. "An Intelligent Temporary While-Boring Support Technology For Raise Boring Method." E3S Web of Conferences 233 (2021): 01075. http://dx.doi.org/10.1051/e3sconf/202123301075.

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Ensuring the stability of the shaft structure is one of the key technologies for the application of raise boring method. In the process of reaming through rock formations with water disintegration characteristics, the impact of water gushing and drenching may induce partial collapse. To solve this problem, an intelligent temporary while-boring support technology is proposed in this paper. Firstly, the main characteristics of the technology are introduced. Utilizing the space inside the raise boring pipes, the material conveying pipes and nozzle can reach the lower part of the reamer to realize

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Rajabrundha, A., A. Lakshmisangeetha, and A. Balajiganesh. "Analysis of Sleep apnea Considering Electrocardiogram Data Using Deep learning Algorithms." Journal of Physics: Conference Series 2318, no. 1 (2022): 012009. http://dx.doi.org/10.1088/1742-6596/2318/1/012009.

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Abstract Sleep is a vital component of every human being. Adequate restful and restorative sleep reenergizes the body, enhances overall health and psychological well-being. Sleep hygiene, chaotic lifestyles, disorder breathing, stress, and anxiety contribute to poor sleep quality. Obstructive sleep apnea (OSA) sleep respiratory disorder causes temporary lapses of breathing results in gasping, choking, snoring sounds during sleep. The individual does not consciously wake up, but the brain has to start breathing again which disrupts the sleep quality. Polysomnography (PSG) sleep study is employe

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Loiola, Saulo Hudson Nery, Felipe Lemes Galvão, Bianca Martins dos Santos, et al. "Development of New Staining Procedures for Diagnosing Cryptosporidium spp. in Fecal Samples by Computerized Image Analysis." Microscopy and Microanalysis 27, no. 6 (2021): 1518–28. http://dx.doi.org/10.1017/s1431927621012903.

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Interpretation errors may still represent a limiting factor for diagnosing Cryptosporidium spp. oocysts with the conventional staining techniques. Humans and machines can interact to solve this problem. We developed a new temporary staining protocol associated with a computer program for the diagnosis of Cryptosporidium spp. oocysts in fecal samples. We established 62 different temporary staining conditions by studying 20 experimental protocols. Cryptosporidium spp. oocysts were concentrated using the Three Fecal Test (TF-Test®) technique and confirmed by the Kinyoun method. Next, we built a b

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Shuqi Zhang, Shuqi Zhang. "Cascade Attention-based Spatial-temporal Convolutional Neural Network for Motion Image Posture Recognition." 電腦學刊 33, no. 1 (2022): 021–30. http://dx.doi.org/10.53106/199115992022023301003.

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<p>The traditional motion posture recognition methods cannot capture the temporal relationship in a video sequence, which leads to the problem that the recognition effect of time-dependent behaviors is not ideal. Therefore, this paper proposes a cascade attention-based spatial-temporal convolutional neural network for motion posture recognition. Firstly, the convolutional neural network is used to model the time sequence relationship in the video, so as to capture the spatial-temporal information in the video efficiently. At the same time, the cascade attention mechanism is used to impro

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Sharath, Kumar Shetty, Kumara M. Vijayananda, and G. Panicker Visakh. "Artificial Intelligence in Orthodontics: A Review Article." International Journal of Innovative Science and Research Technology 7, no. 11 (2023): 2041–45. https://doi.org/10.5281/zenodo.7547674.

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This review aims to determine the applications of ArtificialIntelligence (AI) that are extensively employed in the field of Orthodontics, to evaluate its benefits, and to discuss its potential implications in this speciality. Recentdecades have witnessed enormous changes in our profession. The arrival of newand more aesthetic options in orthodontic treatment, the transition to a fully digitalworkflow, the emergence of temporary anchorage devices and new imaging methods all provide both patients and professionals with a new focus in orthodontic care.A scoping review of the literature was carrie

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Bai, Yanbing, Wenqi Wu, Zhengxin Yang, et al. "Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets." Remote Sensing 13, no. 11 (2021): 2220. http://dx.doi.org/10.3390/rs13112220.

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Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in recent years has demonstrated the excellent potential of multi-source data fusion and deep learning algorithms in improving flood detection, while this field has only been studied initially due to the lack of large-scale labelled remote sensing images of flood events. Here, we pre

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Wiskes, William, Leonhard Blesius, and Ellen Hines. "Identification of Abandoned Logging Roads in Point Reyes National Seashore." Remote Sensing 15, no. 13 (2023): 3369. http://dx.doi.org/10.3390/rs15133369.

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Temporary roads are often placed in mountainous regions for logging purposes but then never decommissioned and removed. These abandoned forest roads often have unwanted environmental consequences. They can lead to altered hydrological regimes, excess erosion, and mass wasting events. These events can affect sediment budgets in streams, with negative consequences for anadromous fish populations. Maps of these roads are frequently non-existent; therefore, methods need to be created to identify and locate these roads for decommissioning. Abandoned logging roads in the Point Reyes National Seashor

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McGettigan, Carolyn, Jane E. Warren, Frank Eisner, Chloe R. Marshall, Pradheep Shanmugalingam, and Sophie K. Scott. "Neural Correlates of Sublexical Processing in Phonological Working Memory." Journal of Cognitive Neuroscience 23, no. 4 (2011): 961–77. http://dx.doi.org/10.1162/jocn.2010.21491.

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This study investigated links between working memory and speech processing systems. We used delayed pseudoword repetition in fMRI to investigate the neural correlates of sublexical structure in phonological working memory (pWM). We orthogonally varied the number of syllables and consonant clusters in auditory pseudowords and measured the neural responses to these manipulations under conditions of covert rehearsal (Experiment 1). A left-dominant network of temporal and motor cortex showed increased activity for longer items, with motor cortex only showing greater activity concomitant with addin

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Hakim, Heba, and Ali Marhoon. "Indoor Low Cost Assistive Device using 2D SLAM Based on LiDAR for Visually Impaired People." Iraqi Journal for Electrical and Electronic Engineering 15, no. 2 (2019): 115–21. http://dx.doi.org/10.37917/ijeee.15.2.12.

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Many assistive devices have been developed for visually impaired (VI) person in recent years which solve the problems that face VI person in his/her daily moving. Most of researches try to solve the obstacle avoidance or navigation problem, and others focus on assisting VI person to recognize the objects in his/her surrounding environment. However, a few of them integrate both navigation and recognition capabilities in their system. According to above needs, an assistive device is presented in this paper that achieves both capabilities to aid the VI person to (1) navigate safely from his/her c

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Gilcher, Mario, and Thomas Udelhoven. "Field Geometry and the Spatial and Temporal Generalization of Crop Classification Algorithms—A Randomized Approach to Compare Pixel Based and Convolution Based Methods." Remote Sensing 13, no. 4 (2021): 775. http://dx.doi.org/10.3390/rs13040775.

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With the ongoing trend towards deep learning in the remote sensing community, classical pixel based algorithms are often outperformed by convolution based image segmentation algorithms. This performance was mostly validated spatially, by splitting training and validation pixels for a given year. Though generalizing models temporally is potentially more difficult, it has been a recent trend to transfer models from one year to another, and therefore to validate temporally. The study argues that it is always important to check both, in order to generate models that are useful beyond the scope of

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Sineglazov, Victor, and Petro Chynnyk. "Quantum Convolution Neural Network." Electronics and Control Systems 2, no. 76 (2023): 40–45. http://dx.doi.org/10.18372/1990-5548.76.17667.

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In this work, quantum convolutional neural networks are considered in the task of recognizing handwritten digits. A proprietary quantum scheme for the convolutional layer of a quantum convolutional neural network is proposed. A proprietary quantum scheme for the pooling layer of a quantum convolutional neural network is proposed. The results of learning quantum convolutional neural networks are analyzed. The built models were compared and the best one was selected based on the accuracy, recall, precision and f1-score metrics. A comparative analysis was made with the classic convolutional neura

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Naufal, Mohammad Farid, Selvia Ferdiana Kusuma, Zefanya Ardya Prayuska, et al. "Comparative Analysis of Image Classification Algorithms for Face Mask Detection." Journal of Information Systems Engineering and Business Intelligence 7, no. 1 (2021): 56. http://dx.doi.org/10.20473/jisebi.7.1.56-66.

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Background: The COVID-19 pandemic remains a problem in 2021. Health protocols are needed to prevent the spread, including wearing a face mask. Enforcing people to wear face masks is tiring. AI can be used to classify images for face mask detection. There are a lot of image classification algorithm for face mask detection, but there are still no studies that compare their performance.Objective: This study aims to compare the classification algorithms of classical machine learning. They are k-nearest neighbors (KNN), support vector machine (SVM), and a widely used deep learning algorithm for ima

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Harahap, Salman Al Farizi, and Irmawan Irmawan. "Performance comparison of MobileNet, EfficientNet, and Inception for predicting crop disease." Sriwijaya Electrical and Computer Engineering Journal 1, no. 1 (2024): 30–36. http://dx.doi.org/10.62420/selco.v1i1.4.

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Disease plant food can cause significant loss in production agriculture since difficult to detect early symptoms of disease. Apart from that, the selection of Convolutional Neural Network (CNN) architecture for the detection of disease plants often faces the challenge of trade-offs between accuracy and efficiency. In this research, we propose a solution with compares the performance of three current CNN architectures, ie MobileNet, EfficientNet, and Inception, in context predictions of disease plant food. We implement a transfer learning approach to increase efficiency and performance model pr

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Mruthyunjaya and Suresh Kumar Mandala. "A brain tumor identification using convolution neural network and fully convolution neural network." MATEC Web of Conferences 392 (2024): 01130. http://dx.doi.org/10.1051/matecconf/202439201130.

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Brain tumor identification, along with an investigation, is harmful to the patient. Segmentation, therefore, of paying attention to near-neighborhood growth remains accurate, effective, and healthy. Fully Convolution Neural Network (FCNN) is a reliable picture model to capitulate the hide quality. The form of the multifaceted with the incessant pixels taught with the crest state and the symbolic picture taught. In this research, the making of a totally convoluted method to obtain the participation of a random element and the production of correspondingly large-scale output with a resourceful a

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Aguilar I., Luis, Miguel Ibáñez-Reluz, Juan C. Z. Aguilar, Elí W. Zavaleta-Aguilar, and L. Antonio Aguilar. "Forecasting SARS-CoV-2 in the peruvian regions: a deep learning approach using temporal convolutional neural networks." Selecciones Matemáticas 8, no. 1 (2021): 12–26. http://dx.doi.org/10.17268/sel.mat.2021.01.02.

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Wang, Jubo, and Ruixin Wang. "Multi-UAV Area Coverage Track Planning Based on the Voronoi Graph and Attention Mechanism." Applied Sciences 14, no. 17 (2024): 7844. http://dx.doi.org/10.3390/app14177844.

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Drone area coverage primarily involves using unmanned aerial vehicles (UAVs) for extensive monitoring, surveying, communication, and other tasks over specific regions. The significance and value of this technology are multifaceted. Firstly, UAVs can rapidly and efficiently reach remote or inaccessible areas to perform tasks such as terrain mapping, disaster monitoring, or search and rescue, significantly enhancing response speed and execution efficiency. Secondly, drone area coverage in agricultural monitoring, forestry conservation, and urban planning offers high-precision data support, aidin

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Hsing-Chung Chen, Hsing-Chung Chen, Karisma Trinanda Putra Hsing-Chung Chen, Chien-Erh Weng Karisma Trinanda Putra, and Jerry Chun-Wei Lin Chien-Erh Weng. "A Novel Predictor for Exploring PM2.5 Spatiotemporal Propagation by Using Convolutional Recursive Neural Networks." 網際網路技術學刊 23, no. 1 (2022): 167–78. http://dx.doi.org/10.53106/160792642022012301017.

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<p>The spread of PM2.5 pollutants that endanger health is difficult to predict because it involves many atmospheric variables. These micro particles could spread rapidly from their source to residential areas, increasing the risk of respiratory disease if exposed for long periods. However, the existing prediction systems do not take into account the geographical correlation among neighboring nodes spatially and temporally resulting in loss of important information, lack of PM2.5 propagation resolution, and lower forecasting accuracy. In this paper, a novel scheme is proposed to generate

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M, Venkata Krishna Reddy, and Pradeep S. "Envision Foundational of Convolution Neural Network." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 6 (2021): 54–60. https://doi.org/10.35940/ijitee.F8804.0410621.

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Profound learning's goes to the achievement of spurs in a large number and understudies to find out about the energizing innovation. At this regular process of novices to venture the multifaceted nature of comprehension and applying profound learning. We present Convolution Neural Network (CNN) EXPLAINER, an intelligent representation instrument intended for non-specialists to learn and inspect (CNN)-Convolution Neural Network a fundamental profound learning model engineering. Our apparatus tends to key difficulties that fledglings face in finding out about Convolution Neural Network, it c

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Reddy*, M. Venkata Krishna, and Pradeep S. "Envision Foundational of Convolution Neural Network." International Journal of Innovative Technology and Exploring Engineering 10, no. 6 (2021): 54–60. http://dx.doi.org/10.35940/ijitee.f8804.0410621.

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1. Bilal, A. Jourabloo, M. Ye, X. Liu, and L. Ren. Do Convolutional Neural Networks Learn Class Hierarchy? IEEE Transactions on Visualization and Computer Graphics, 24(1):152–162, Jan. 2018. 2. M. Carney, B. Webster, I. Alvarado, K. Phillips, N. Howell, J. Griffith, J. Jongejan, A. Pitaru, and A. Chen. Teachable Machine: Approachable Web-Based Tool for Exploring Machine Learning Classification. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI ’20. ACM, Honolulu, HI, USA, 2020. 3. A. Karpathy. CS231n Convolutional Neural Networks for Visual Recognition

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Sarker, Goutam. "Some Studies on Convolution Neural Network." International Journal of Computer Applications 182, no. 21 (2018): 13–22. http://dx.doi.org/10.5120/ijca2018917965.

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Sharanya S, Sridhar PA, Poornakala J, Muppala Vasishta, and Tharani U. "Convolution Neural Network Based Ecg Classifier." International Journal of Research in Pharmaceutical Sciences 10, no. 3 (2019): 1626–30. http://dx.doi.org/10.26452/ijrps.v10i3.1327.

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Classification of Electrocardiogram (ECG) signals plays a significant role in the identification of the functioning of the heart. This work pertains with the ECG signals, where the classifier is developed for identification of normal or abnormal conditions of the heart. The raw ECG signals are collected from an online database (www.physioNet.org) for classification. The raw ECG signal is pre-processed for noise removal, and the frequency spectrum is analysed to compare raw and denoised ECG signal. Attributes (P, Q, R, S, T time intervals) from denoised ECG signal is analysed and classified usi

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Muhson, Meryem H., and Ayad A. Al-Ani. "BLIND RESTORATION USING CONVOLUTION NEURAL NETWORK." Iraqi Journal of Information and Communications Technology 1, no. 1 (2021): 25–32. http://dx.doi.org/10.31987/ijict.1.1.178.

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Image restoration is a branch of image processing that involves a mathematical deterioration and restoration model to restore an original image from a degraded image. This research aims to restore blurred images that have been corrupted by a known or unknown degradation function. Image restoration approaches can be classified into 2 groups based on degradation feature knowledge: blind and non-blind techniques. In our research, we adopt the type of blind algorithm. A deep learning method (SR) has been proposed for single image super-resolution. This approach can directly learn an end-to-end map

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Hamid, Sofia, and Mrigana Walia. "Convolution Neural Network Based Image Recognition." International Journal of Science and Research (IJSR) 10, no. 2 (2021): 1673–77. https://doi.org/10.21275/sr21225214136.

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Rere, L. M. Rasdi, Mohamad Ivan Fanany, and Aniati Murni Arymurthy. "Metaheuristic Algorithms for Convolution Neural Network." Computational Intelligence and Neuroscience 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/1537325.

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A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that c

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Wan Khairul Hazim Wan Khairul Amir, Afiqah Bazlla Md Soom, Aisyah Mat Jasin, Juhaida Ismail, Aszila Asmat, and Rozeleenda Abdul Rahm an. "Sales Forecasting Using Convolution Neural Network." Journal of Advanced Research in Applied Sciences and Engineering Technology 30, no. 3 (2023): 290–301. http://dx.doi.org/10.37934/araset.30.3.290301.

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Sales forecasting is an essential component of business management, providing insight into future sales and revenue. It is critical for effective inventory management, cash flow, and business growth planning. While many retailers rely on simple Excel functions or subjective guesses from management, the industry is increasingly turning to machine learning techniques to develop more accurate and reliable prediction models. Among these techniques, Convolutional Neural Networks (CNN) emerged as a suitable option due to their ability to learn and improve accuracy over time. CNN applies several laye

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Dhingra, Gaurav, Supreeth S, Neha K. R, Amruthashree R. V, and Eshitha D. "Traffic Management using Convolution Neural Network." International Journal of Engineering and Advanced Technology 8, no. 5s (2019): 146–49. http://dx.doi.org/10.35940/ijeat.e1031.0585s19.

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Traffic is one of the major problems in most of the metropolitan cities. Classifying the traffic conditions are important for determining traffic control strategies and management. Traffic congestions have negative impact on society, as a lot of time is wasted in it and controlling the congestions is necessary. By classification we can get to know which lane has traffic, from which we can further check the reasons for traffic and to take appropriate decisions to improve the performance. Video on traffic data is suitable source for traffic analysis. In this paper, video surveillance data is use

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Khan, Maleika Heenaye Mamode, Chonnoo Abubakar Siddick Khan, and Rengony Mohammad Oumeir. "Vehicle recognition using convolution neural network." International Journal of Biometrics 15, no. 3/4 (2023): 344. http://dx.doi.org/10.1504/ijbm.2023.130638.

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Gaurav, Dhingra, S. Supreeth, K. R. Neha, R. V. Amruthashree, and D. Eshitha. "Traffic Management using Convolution Neural Network." International Journal of Engineering and Advanced Technology (IJEAT) 8, no. 5S, May, 2019 (2019): 146–49. https://doi.org/10.5281/zenodo.7027893.

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Traffic is one of the major problems in most of the metropolitan cities. Classifying the traffic conditions are important for determining traffic control strategies and management. Traffic congestions have negative impact on society, as a lot of time is wasted in it and controlling the congestions is necessary. By classification we can get to know which lane has traffic, from which we can further check the reasons for traffic and to take appropriate decisions to improve the performance. Video on traffic data is suitable source for traffic analysis. In this paper, video surveillance data is use

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Yuvan Feberian and Desti Fitriati. "KLASIFIKASI RIMPANG MENGGUNAKAN CONVOLUTION NEURAL NETWORK." Journal of Informatics and Advanced Computing (JIAC) 3, no. 1 (2022): 10–14. https://doi.org/10.35814/jiac.v3i1.3849.

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Rimpang dalam ilmu botani dapat didefinisikan sebagai tanaman yang tumbuh di bawah permukaan tanah seperti jahe, kencur, kunyit, lengkuas dan temulawak. Rimpang dapat digunakan sebagai pengobatan tradisional di Indonesia untuk mengobati beberapa penyakit seperti karminativa, diaforetika, stimulansia, kolagoga dan lain-lain. Peneliti membuat survei dalam bentuk kuisioner untuk mengetahui apakah orang dapat membedakan rimpang dengan responden sebanyak 56 orang dan hasil dari kuisioner tersebut menunjukan bahwa 12 orang menjawab dengan benar, 16 orang menjawab ragu-ragu, 28 orang menjawab tidak b

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Ayyappa, Moraboina. "Fingerprint Recognition Using Convolution Neural Network." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49476.

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Abstract - Fingerprint recognition has emerged as a highly reliable biometric technique for identity verification due to the uniqueness and permanence of fingerprint patterns. This project proposes a deep learning-based approach for fingerprint recognition using Convolutional Neural Networks (CNNs), enhanced by image inversion and data augmentation techniques. The CNN architecture is trained to automatically extract and learn robust features from fingerprint images, eliminating the need for manual feature engineering.To improve model performance and generalization, image preprocessing through

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Wang, Lei. "Application Research of Deep Convolutional Neural Network in Computer Vision." Journal of Networking and Telecommunications 2, no. 2 (2020): 23. http://dx.doi.org/10.18282/jnt.v2i2.886.

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<p>As an important research achievement in the field of brain like computing, deep convolution neural network has been widely used in many fields such as computer vision, natural language processing, information retrieval, speech recognition, semantic understanding and so on. It has set off a wave of neural network research in industry and academia and promoted the development of artificial intelligence. At present, the deep convolution neural network mainly simulates the complex hierarchical cognitive laws of the human brain by increasing the number of layers of the network, using a lar

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