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Journal articles on the topic 'Recognizing complete classifications'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Rocha, T. Coelho, J. Cunha, S. Torres, and A. Lopes. "The universe of brief psychosis." European Psychiatry 64, S1 (2021): S514—S515. http://dx.doi.org/10.1192/j.eurpsy.2021.1377.

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IntroductionNowadays, ‘Acute and transient psychotic disorders’ in ICD-10 and ‘Brief psychotic disorders’ in DSM-5 are both classifications of the same clinical entity. Over the years, several concepts have been formulated to define the same syndrome.ObjectivesTo explore the historical evolution of brief psychotic disorders and relate them to current nosologies.MethodsLiterature review, using the most relevant papers, with the keywords “brief psychosis”, “bouffée délirante”, “cycloid psychosis”, “psychogenic psychosis”, “atypical psychosis” and “holodysphrenia”.ResultsInitially, in 1896, Kahlb

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Wang, Yu, Quanjun Song, Tingting Ma, Yong Chen, Hao Li, and Rongkai Liu. "Transformation classification of human squat/sit-to-stand based on multichannel information fusion." International Journal of Advanced Robotic Systems 19, no. 4 (2022): 172988062211037. http://dx.doi.org/10.1177/17298806221103708.

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In existing rehabilitation training, research on the accuracy of recognizing completed actions has achieved good results; however, the reduction in the misjudgment rate in the action conversion process needs further research. This article proposes a multichannel information fusion method for the movement conversion process of squat/sit-to-stand, which can help online movement conversion classification during rehabilitation training. We collected a training dataset from a total of eight subjects performing three different motions, including half squat, full squat, and sitting, equipped with pla

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Kislitsyna, Maria Yurievna, and Yurii Nikolaevich Orlov. "Statistical analysis of the complete corpus of fiction in Russian and recognition of the author." Keldysh Institute Preprints, no. 17 (2024): 1–24. http://dx.doi.org/10.20948/prepr-2024-17.

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Statistics of reference trigrams for the complete corpus of literary texts in Russian, including translated foreign authors, have been collected. Distributions of distances from individual texts to standards are constructed. The nearest reference method for recognizing the author of the text has been tested. The error was determined by genres, subgroups of authors and by the corpus as a whole. A classification of errors has been carried out to develop a correction method.

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Kislitsyna, Maria Yurievna, and Yurii Nikolaevich Orlov. "The structure of errors in recognizing the author of the text by the trigram method." Keldysh Institute Preprints, no. 60 (2024): 1–24. http://dx.doi.org/10.20948/prepr-2024-60.

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Statistics of reference trigrams for the complete corpus of literary texts in Russian, including translated foreign authors, have been collected. Distributions of distances from individual texts to standards are constructed. The nearest reference method for recognizing the author of the text has been tested. The error was determined by genres, subgroups of authors and by the corpus as a whole. A classification of errors has been carried out to develop a correction method.

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BALA, J. W., and P. W. PACHOWICZ. "RECOGNIZING NOISY PATTERNS VIA ITERATIVE OPTIMIZATION AND MATCHING OF THEIR DESCRIPTIONS." International Journal of Pattern Recognition and Artificial Intelligence 06, no. 04 (1992): 513–37. http://dx.doi.org/10.1142/s0218001492000291.

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This paper presents an approach to the recognition of noisy patterns. We assume that learned concept descriptions contain noisy components. These descriptions can be optimized to gain better performance through the elimination of some less significant noisy components. Parametric control of such an optimization, however, is very difficult and a parameter value has to be found through the training process. This value can change over concept variations and time, and in consequence, it can decrease the system recognition effectiveness. We found that the classification decision can be based on the

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Leonardi, Matilde, Andrea Martinuzzi, Paolo Meucci, et al. "A Population Survey in Italy Based on the ICF Classification: Recognizing Persons with Severe Disability." Scientific World Journal 2012 (2012): 1–9. http://dx.doi.org/10.1100/2012/189097.

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Aim of this paper is to describe functioning of subjects with “severe disability” collected with a protocol based on the International Classification of Functioning, Disability, and Health. It included sections on body functions and structures (BF and BS), activities and participation (A&P), and environmental factors (EF). In A&P, performance without personal support (WPS) was added to standard capacity and performance. Persons with severe disability were those reporting a number of very severe/complete problems in BF or in A&P-capacity superior to mean + 1SD. Correlations between

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Yaqoub, Muntazir, and Dr Gurinder Kaur Sodhi. "Classification of Diabetic Retinopathy Disease Using Retina Images: A Review." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 2260–66. http://dx.doi.org/10.22214/ijraset.2022.48454.

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Abstract: Retinal lesions that impair vision are a typical symptom of diabetic retinopathy, result of diabetes mellitus. Blindness may develop if it is not detected in time. Tragically, there is no known cure for DR, and therapy only works to stop vision loss. DR diagnosis and treatment can significantly lower risk of eyesight loss. Contrary to computer-aided methods, physical examination in case of DR visual image acquisition through optometrists is time-, effort-, and premium and prone to error. Deep learning recently emerged as popular techniques in boosting expertise in number of fields, i

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Setiawan, Johan, Dita Amalia, and Iwan Prasetiawan. "Data Mining Techniques for Predictive Classification of Anemia Disease Subtypes." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 8, no. 1 (2024): 10–17. http://dx.doi.org/10.29207/resti.v8i1.5445.

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Anemia, characterized by insufficient red blood cells or reduced hemoglobin, hinders oxygen transport in the body. Understanding the different types of anemia is vital to tailor effective prevention and treatment. This research explores the role of data mining in predicting and classifying anemia types, focusing on complete blood count (CBC) and demographic data. Data mining is the key to building models that help healthcare professionals diagnose and treat anemia. Employing the cross-industry standard process for data mining (CRISP-DM), with its six phases, facilitates this effort. Our study

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Kong, Guanghui, Zhiyong Wang, Xiuchao Wan, and Fengjun Xue. "Automatic segmentation and recognition of red and white cells in stool microscopic images of human." E3S Web of Conferences 185 (2020): 03024. http://dx.doi.org/10.1051/e3sconf/202018503024.

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Aiming to solve the problem of low efficiency in manually recognizing the red and white cells in stool microscopic images, we propose an automatic segmentation method based on iterative corrosion with marker-controlled watershed segmentation and an automatic recognition method based on support vector machine (SVM) classification. The method first obtains saliency map of the images in HSI and Lab color spaces through saliency detection algorithm, then fuses the salient images to complete the initial segmentation. Next, we segment the red and white cells completely based on the initial segmentat

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Valentyn, Yesilevskyi, Tevyashev Andriy, and Koliadin Anton. "A method of air object recognition based on the normalized contour descriptors and a complex-valued neural network." Eastern-European Journal of Enterprise Technologies 6, no. 4(108) (2020): 48–57. https://doi.org/10.15587/1729-4061.2020.220035.

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This paper reports a study into the methods for recognizing the type of an air object on a digital image acquired from an air situation video monitoring system. A method has been proposed that is based on the application of a specific neural network, which solves the problem of categorizing multidimensional complex vectors of objects’ features based on complex calculations. In this case, a feature vector for recognizing the type of an air object is built on the basis of a Fourier transform for the sequence of coordinates of its two-dimensional contour. A technique has been proposed to tr

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Zhang, Xinyu, Xinyan Qin, Jin Lei, Zhiyuan Zhai, Jianglong Zhang, and Zhi Wang. "A Novel Method on Recognizing Drum Load of Elastic Tooth Drum Pepper Harvester Based on CEEMDAN-KPCA-SVM." Agriculture 14, no. 7 (2024): 1114. http://dx.doi.org/10.3390/agriculture14071114.

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The operational complexities of the elastic tooth drum pepper harvester (ETDPH), characterized by variable drum loads that are challenging to recognize due to varying pepper densities, significantly impact pepper loss rates and mechanical damage. This study proposes a novel method integrating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), kernel principal component analysis (KPCA), and a support vector machine (SVM) to enhance drum load recognition. The method consists of three principal steps: the initial experiments with ETDPHs to identify the critical factors

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Luz, Inês Romão, Olinda Rebelo, José Augusto Costa, and Alice Carvalho. "HGG-55. An adolescent with a High-Grade Glioma: persuing a specific diagnosis." Neuro-Oncology 24, Supplement_1 (2022): i74. http://dx.doi.org/10.1093/neuonc/noac079.270.

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Abstract BACKGROUND: Recognizing the value of molecular parameters provides evidence with repercussions on diseases classification. CLINICAL CASE: A thirteen-year-old girl was admitted in coma at the Intensive Care Unit due to a hemorrhagic stroke. CT revealed an expansive left frontal lobe lesion with perilesional edema and compression signs by an extensive hematoma. She underwent an emergency decompressive craniotomy for hematoma drainage, and the lesion was biopsied. The subsequent MRI showed a large heterogeneous mass with a cystic component. A first subtotal resection of the lesion was pe

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Povhan, Igor. "Method of algorithmic classification trees based on constraints." Bulletin of the National Technical University "KhPI" A series of "Information and Modeling", no. 1 (5) (October 25, 2021): 17–38. http://dx.doi.org/10.20998/2411-0558.2021.01.02.

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The general problem of constructing algorithmic recognition (classification) trees based on a limited method in the theory of artificial intelligence is considered. The object of this research is the concept of an algorithmic classification tree based on a bounded method. The subject of the research is actual methods, algorithms and schemes (limited method) for constructing algorithmic classification trees. A limited method for constructing algorithmic classification trees is proposed, which for a given initial training sample of any size builds a tree structure (algorithm tree model), which c

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Touré, S. A., M. Seck, A. B. Diallo, et al. "Diagnosis Dilemma of Angioimmunoblastic T-Cell Lymphoma in Tuberculosis Endemic Region." Case Reports in Hematology 2020 (October 7, 2020): 1–4. http://dx.doi.org/10.1155/2020/8824843.

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Angioimmunoblastic T-cell lymphoma (AITL) is a rare hematologic malignancy recognized in the WHO 2016 classification as a clinical and histological entity. It is a very poorly described disease in Africa due to its rarity and diagnostic difficulties, particularly differential diagnosis with tuberculosis. Here, we report a 57-year-old man who presented with fever, weight loss, and lymphadenopathies. The diagnosis of tuberculosis was carried out based on lymph node fine needle aspiration showing the image of tuberculous adenitis and CT images in favor of necrotic lymphadenopathies. The presence

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Asif, Amna, Hamid Mukhtar, Fatimah Alqadheeb, Hafiz Farooq Ahmad, and Abdulaziz Alhumam. "An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning." Applied Sciences 12, no. 1 (2021): 238. http://dx.doi.org/10.3390/app12010238.

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A mispronunciation of Arabic short vowels can change the meaning of a complete sentence. For this reason, both the students and teachers of Classical Arabic (CA) are required extra practice for correcting students’ pronunciation of Arabic short vowels. That makes the teaching and learning task cumbersome for both parties. An intelligent process of students’ evaluation can make learning and teaching easier for both students and teachers. Given that online learning has become a norm these days, modern learning requires assessment by virtual teachers. In our case, the task is about recognizing th

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Raja, Hiral, Aarti Gupta, and Rohit Miri. "Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques." European Journal of Engineering and Technology Research 6, no. 4 (2021): 37–44. http://dx.doi.org/10.24018/ejers.2021.6.4.2460.

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The purpose of this study is to create an automated framework that can recognize similar handwritten digit strings. For starting the experiment, the digits were separated into different numbers. The process of defining handwritten digit strings is then concluded by recognizing each digit recognition module's segmented digit. This research utilizes various machine learning techniques to produce a strong performance on the digit string recognition challenge, including SVM, ANN, and CNN architectures. These approaches use SVM, ANN, and CNN models of HOG feature vectors to train images of digit st

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Raja, Hiral, Aarti Gupta, and Rohit Miri. "Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques." European Journal of Engineering and Technology Research 6, no. 4 (2021): 37–44. http://dx.doi.org/10.24018/ejeng.2021.6.4.2460.

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The purpose of this study is to create an automated framework that can recognize similar handwritten digit strings. For starting the experiment, the digits were separated into different numbers. The process of defining handwritten digit strings is then concluded by recognizing each digit recognition module's segmented digit. This research utilizes various machine learning techniques to produce a strong performance on the digit string recognition challenge, including SVM, ANN, and CNN architectures. These approaches use SVM, ANN, and CNN models of HOG feature vectors to train images of digit st

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Pallavi, Sahay, Sahay Bhawna, and K. Karn S. "A Morphometric Study to Assess the Lumbosacral Transitional Vertebrae and its Clinical Implications." International Journal of Current Pharmaceutical Review and Research 15, no. 12 (2023): 966–69. https://doi.org/10.5281/zenodo.13253939.

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AbstractAim: The aim of the present study was to assess the lumbosacral transitional vertebrae and its clinicalimplications.Methods: The present study included an examination of 100 adult sacra (75 male and 25 female) available in theDepartment of Anatomy, Darbhanga Medical College and Hospital, Laheriasarai, Darbhanga, Bihar, India for thepresence of LSTV. Duration of study from June 2020 to May 2021Results: In the present study of 100 sacra, 75 (75%) were male and 25 (25%) were female. Only 20 showedLumbosacral Transitional Vertebrae and 80 were normal sacra. Out of 75 male sacra, 12 and out

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Priyanka, Raj, and Kumar Barun. "A Morphometric Evaluation of Lumbosacral Transitional Vertebrae: An Observational Study." International Journal of Current Pharmaceutical Review and Research 16, no. 05 (2024): 308–11. https://doi.org/10.5281/zenodo.12786854.

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AbstractAim: The aim of the present study was to assess the lumbosacral transitional vertebrae and its clinicalimplications.Methods: The present study included an examination of 100 adult sacra (75 male and 25 female) available in theDepartment of Anatomy, Netaji Subhas Medical College and Hospital, Bihta, Patna, Bihar, India for the presenceof LSTV.Results: In the present study of 100 sacra, 75 (75%) were male and 25 (25%) were female. Only 20 showedLumbosacral Transitional Vertebrae and 80 were normal sacra. Out of 75 male sacra, 12 and out of 25 femalesacra, 8 were found to be Lumbosacral T

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Hong, Zhong, Kunhong Li, Mingjun Su, and Guangmin Hu. "Improved spectral clustering approach — A new tool for unsupervised seismic facies analysis of variable window length." Interpretation 9, no. 2 (2021): T407—T420. http://dx.doi.org/10.1190/int-2020-0059.1.

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The traditional constant time window-based waveform classification method is a robust tool for seismic facies analysis. However, when the interval thickness is seismically variable, the fixed time window is not able to contain the complete geologic information of interest. Therefore, the constant time window-based waveform classification method is inapplicable to conduct seismic facies analysis. To expand the application scope of seismic waveform classification in the strata with varying thickness, we have proposed a novel scheme for unsupervised seismic facies analysis of variable window leng

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Jaber, Ali K., and Ikhlas Abdel-Qader. "A Hybrid Feature Extraction Framework for Face Recognition." International Journal of Handheld Computing Research 8, no. 1 (2017): 1–13. http://dx.doi.org/10.4018/ijhcr.2017010101.

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A hybrid framework, that can be used in face recognition applications, to enhance system recognition efficiency and speed by extracting the most efficient features of face images is proposed. The proposed framework is based on features obtained using Histograms of Oriented Gradients (HOG) descriptor and compressive sensing (CS). The HOG feature descriptor has the advantage of extracting face feature vectors even with changes in face appearance and is fully capable of handling variations in illumination. CS is used to reduce the density of the resulting HOG face features which has a significant

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Sepúlveda Rodríguez, Luis Eduardo, Julio C. Chavarro-Porras, John A. Sanabria-Ordoñez, Harold E. Castro, and Jeanna Matthews. "A Survey of Virtualization Technologies: Towards a New Taxonomic Proposal." Ingeniería e Investigación 42, no. 3 (2022): e97363. http://dx.doi.org/10.15446/ing.investig.97363.

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At present, there is a proliferation of virtualization technologies (VTs), which are part of the basic and underlying infrastructure of popular cloud computing. Those interested in VTs are faced with a non-unified volume of information and various approaches to modes of operation, classification structures, and the performance implications of these technologies. This makes it difficult to decide which type of VT is appropriate for a particular context. Therefore, this paper reviews the state of the art on VT taxonomic models. Methodologically, a literature review is carried out to identify VT

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Wang, Chunsheng, Yongmin Wang, Yue Zhang, Hua Xu, and Zixuan Zhang. "Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory." Applied Sciences 13, no. 6 (2023): 3878. http://dx.doi.org/10.3390/app13063878.

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Much research has focused on classification within a closed set of emitters, while emitters outside this closed set are misclassified. This paper proposes an open-set recognition model based on prototypical networks and extreme value theory to solve the problem of specific emitter identification in open-set scenes and further improve the recognition accuracy and robustness. Firstly, a one-dimensional convolutional neural network was designed for recognizing I/Q signals, and a squeeze-and-excitation block with an attention mechanism was added to the network to increase the weights of the featur

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Choukhan, C. F., I. Lasri, R. El Hatimi, M. R. Lemnaouar, and M. Esghir. "SARS-CoV-2 Prediction Strategy Based on Classification Algorithms from a Full Blood Examination." Scientific World Journal 2023 (August 22, 2023): 1–15. http://dx.doi.org/10.1155/2023/3248192.

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A fast and efficient diagnosis of serious infectious diseases, such as the recent SARS-CoV-2, is necessary in order to curb both the spread of existing variants and the emergence of new ones. In this regard and recognizing the shortcomings of the reverse transcription-polymerase chain reaction (RT-PCR) and rapid diagnostic test (RDT), strategic planning in the public health system is required. In particular, helping researchers develop a more accurate diagnosis means to distinguish patients with symptoms with COVID-19 from other common infections is what is needed. The aim of this study was to

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Lim, Joshua H. X., and Swee King Phang. "Classification and Detection of Obstacles for Rover Navigation." Journal of Physics: Conference Series 2523, no. 1 (2023): 012030. http://dx.doi.org/10.1088/1742-6596/2523/1/012030.

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Abstract In this research project, the author aims to achieve Level 3 conditional automation whereby the researched Unmanned Ground Vehicle (UGV) is bound to classify and detect its own obstacle with human assistance as it cruises through a plantation field. Recognizing the different classes of obstacles enable the UGV to plan out the most efficient path to meet its desired goal. The purpose of this research project was to develop a classification and detection of obstacle and an optimal path planning algorithm suitable to be implemented for relieving the working process in an extreme conditio

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Ibarra-Pérez, Teodoro, Ramón Jaramillo-Martínez, Hans C. Correa-Aguado, et al. "A Performance Comparison of CNN Models for Bean Phenology Classification Using Transfer Learning Techniques." AgriEngineering 6, no. 1 (2024): 841–57. http://dx.doi.org/10.3390/agriengineering6010048.

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The early and precise identification of the different phenological stages of the bean (Phaseolus vulgaris L.) allows for the determination of critical and timely moments for the implementation of certain agricultural activities that contribute in a significant manner to the output and quality of the harvest, as well as the necessary actions to prevent and control possible damage caused by plagues and diseases. Overall, the standard procedure for phenological identification is conducted by the farmer. This can lead to the possibility of overlooking important findings during the phenological dev

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Yu, Zheqi, Adnan Zahid, Shuja Ansari, et al. "Hardware-Based Hopfield Neuromorphic Computing for Fall Detection." Sensors 20, no. 24 (2020): 7226. http://dx.doi.org/10.3390/s20247226.

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With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, traditional machine-learning methods for data classification, especially in real time, are computationally intensive. The deployment of Artificial Intelligence algorithms on embedded hardware for fast data classification and accurate fall detection poses a huge challenge in achieving power-efficient embedded systems. Therefore, by exploiting the associative memory feature of Hopfield Neural Network, a hardware module has been designed to simulate

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Chang, Aimee Caye G., Qiang Lai, Tao Chen, et al. "The complete chloroplast genome of Microcycas calocoma (Miq.) A. DC. (Zamiaceae, Cycadales) and evolution in Cycadales." PeerJ 8 (January 13, 2020): e8305. http://dx.doi.org/10.7717/peerj.8305.

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Cycadales is an extant group of seed plants occurring in subtropical and tropical regions comprising putatively three families and 10 genera. At least one complete plastid genome sequence has been reported for all of the 10 genera except Microcycas, making it an ideal plant group to conduct comprehensive plastome comparisons at the genus level. This article reports for the first time the plastid genome of Microcycas calocoma. The plastid genome has a length of 165,688 bp with 134 annotated genes including 86 protein-coding genes, 47 non-coding RNA genes (39 tRNA and eight rRNA) and one pseudog

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KHALAJZADEH, HURIEH, MOHAMMAD MANSOURI, and MOHAMMAD TESHNEHLAB. "HIERARCHICAL STRUCTURE BASED CONVOLUTIONAL NEURAL NETWORK FOR FACE RECOGNITION." International Journal of Computational Intelligence and Applications 12, no. 03 (2013): 1350018. http://dx.doi.org/10.1142/s1469026813500181.

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In this paper, a hierarchical structure based convolutional neural network is proposed to provide the ability for robust information processing. The weight sharing ability of convolutional neural networks (CNNs) is considered as a level of hierarchy in these networks. Weight sharing reduces the number of free parameters and improves the generalization ability. In the proposed structure, a small CNN which is used for feature extractor is shared between the whole input image pixels. A scalable architecture for implementing extensive CNNs is resulted using a smaller and modularized trainable netw

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Di Ruberto, Cecilia, Andrea Loddo, and Giovanni Puglisi. "Blob Detection and Deep Learning for Leukemic Blood Image Analysis." Applied Sciences 10, no. 3 (2020): 1176. http://dx.doi.org/10.3390/app10031176.

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In microscopy, laboratory tests make use of cell counters or flow cytometers to perform tests on blood cells, like the complete blood count, rapidly. However, a manual blood smear examination is still needed to verify the counter results and to monitor patients under therapy. Moreover, the manual inspection permits the description of the cells’ appearance, as well as any abnormalities. Unfortunately, manual analysis is long and tedious, and its result can be subjective and error-prone. Nevertheless, using image processing techniques, it is possible to automate the entire workflow, both reducin

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Cheng, Yingxue, Zhe Xi, Aobo Zhuang, et al. "A case of H-type double gallbladder anomaly: A case report." Medicine 103, no. 35 (2024): e39284. http://dx.doi.org/10.1097/md.0000000000039284.

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Rationale: Biliary system anomalies, such as duplicated gallbladders, are rare congenital conditions that present significant diagnostic challenges. Dr. Boyden’s classification system, especially the H-type anomaly, offers vital insight into these variations. Failure to detect these anomalies preoperatively can increase the risk of surgical complications, making early identification crucial for surgical planning. Patient concern: A 42-year-old male, asymptomatic, was incidentally found to have a gallbladder mass during routine imaging. An upper abdominal magnetic resonance imaging showed gallb

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Gu, Chihui, and Yinxing Li. "Analysis of Art Classroom Teaching Behavior Based on Intelligent Image Recognition." Mobile Information Systems 2022 (August 29, 2022): 1–11. http://dx.doi.org/10.1155/2022/5736407.

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To solve the problem of intelligent image recognition in classroom behavior, this paper proposes a fast target detection based on FFmpeg CODEC, extracts MHI-HOG joint features according to the located foreground target area, and finally completes the behavior recognition model through a BP neural network support vector machine joint classifier based on the look-up table. The results are as follows: the motion detection method based on H.264 FFmpeg CODEC video has the highest detection accuracy, which can reach 95%. The foreground detection method takes about 10 ms and saves 90% of the time. Th

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Alnabhan, Mohammad Q., and Paula Branco. "BERTGuard: Two-Tiered Multi-Domain Fake News Detection with Class Imbalance Mitigation." Big Data and Cognitive Computing 8, no. 8 (2024): 93. http://dx.doi.org/10.3390/bdcc8080093.

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In an era where misinformation and fake news undermine social well-being, this work provides a complete approach to multi-domain fake news detection. Multi-domain news refers to handling diverse content across various subject areas such as politics, health, research, crime, and social concerns. Recognizing the lack of systematic research in multi-domain fake news detection, we present a fundamental structure by combining datasets from several news domains. Our two-tiered detection approach, BERTGuard, starts with domain classification, which uses a BERT-based model trained on a combined multi-

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Adriano, Bruno, Junshi Xia, Gerald Baier, Naoto Yokoya, and Shunichi Koshimura. "Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia." Remote Sensing 11, no. 7 (2019): 886. http://dx.doi.org/10.3390/rs11070886.

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This work presents a detailed analysis of building damage recognition, employing multi-source data fusion and ensemble learning algorithms for rapid damage mapping tasks. A damage classification framework is introduced and tested to categorize the building damage following the recent 2018 Sulawesi earthquake and tsunami. Three robust ensemble learning classifiers were investigated for recognizing building damage from Synthetic Aperture Radar (SAR) and optical remote sensing datasets and their derived features. The contribution of each feature dataset was also explored, considering different co

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Meng, Zhichao, Man Zhang, and Hongxian Wang. "CNN with Pose Segmentation for Suspicious Object Detection in MMW Security Images." Sensors 20, no. 17 (2020): 4974. http://dx.doi.org/10.3390/s20174974.

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Millimeter-wave (MMW) imaging scanners can see through clothing to form a three-dimensional holographic image of the human body and suspicious objects, providing a harmless alternative for non-contacting searches in security check. Suspicious object detection in MMW images is challenging, since most of them are small, reflection-weak, shape, and reflection-diverse. Conventional detectors with artificial neural networks, like convolution neural network (CNN), usually take the problem of finding suspicious objects as an object recognition task, yielding difficulties in developing large-amount an

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Santos, David, Eric Lopez-Lopez, Xosé M. Pardo, Roberto Iglesias, Senén Barro, and Xosé R. Fdez-Vidal. "Robust and Fast Scene Recognition in Robotics Through the Automatic Identification of Meaningful Images." Sensors 19, no. 18 (2019): 4024. http://dx.doi.org/10.3390/s19184024.

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Scene recognition is still a very important topic in many fields, and that is definitely the case in robotics. Nevertheless, this task is view-dependent, which implies the existence of preferable directions when recognizing a particular scene. Both in human and computer vision-based classification, this actually often turns out to be biased. In our case, instead of trying to improve the generalization capability for different view directions, we have opted for the development of a system capable of filtering out noisy or meaningless images while, on the contrary, retaining those views from whi

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Zhang, Dongli. "A Study of Library Mobile Robot Book Classification and Transportation by Integrating DA and RMM." Journal of Advanced Computational Intelligence and Intelligent Informatics 28, no. 5 (2024): 1169–77. http://dx.doi.org/10.20965/jaciii.2024.p1169.

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As the complexity of modern library management tasks increases, it is difficult for traditional mobile robots to meet the task of moving and classifying books. In order to design a mobile robot that can autonomously classify and transport books, the study realizes the tasks of book classification and transportation in libraries by fusing the differential speed algorithm and the robot motion model algorithm. First, the robot operating system is utilized to scan the books, classify the books, and obtain the category information of the books. Then, the differential speed algorithm is used to cont

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Ananth, Kavitha, and Kirubanand V. B. "Extraction of Handwritten Text using Word Beam Search Algorithm and Language Modeling." International Journal of Membrane Science and Technology 10, no. 2 (2023): 2786–95. http://dx.doi.org/10.15379/ijmst.v10i2.2970.

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The challenge of recognizing handwriting in mortgage records is covered in this article. Businesses trying to digitize huge numbers of hand-marked scanned documents or reports have a significant challenge: offline handwritten text recognition from images. In order to translate a picture into a series of characters that match to the text that is contained in the image, this research suggests an innovative language model in combination with a deep convolutional network and a recurrent encoder-decoder network. Using the principles of Deep Learning and Word Beam Search, the complete model is train

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Lukman, Achmad, Erni Seniwati, and Eko Riswanto. "Citra Sitentik Untuk Klasifikasi Buah Menggunakan Algoritma SIFT Descriptor, Bag of Features dan Support Vector Machine." Building of Informatics, Technology and Science (BITS) 6, no. 3 (2024): 1627–37. https://doi.org/10.47065/bits.v6i3.6296.

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Recognizing specific objects assigned to a computer using artificial intelligence of course goes through a training and testing process using machine learning methods, the limited number of datasets makes it difficult for deep learning methods to carry out classification, so to overcome this, other methods are needed, including Scale Invariant Features Transform ( SIFT) which is a method of image processing to extract features from a limited amount of data and combined with a method in machine learning. To overcome the inability of deep learning to use limited datasets, this research uses a co

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Zahid, Hira, Munaf Rashid, Samreen Hussain, Fahad Azim, Sidra Abid Syed, and Afshan Saad. "Recognition of Urdu sign language: a systematic review of the machine learning classification." PeerJ Computer Science 8 (February 18, 2022): e883. http://dx.doi.org/10.7717/peerj-cs.883.

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Background and Objective Humans communicate with one another using language systems such as written words or body language (movements), hand motions, head gestures, facial expressions, lip motion, and many more. Comprehending sign language is just as crucial as learning a natural language. Sign language is the primary mode of communication for those who have a deaf or mute impairment or are disabled. Without a translator, people with auditory difficulties have difficulty speaking with other individuals. Studies in automatic recognition of sign language identification utilizing machine learning

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Liu, Lijing. "A Deep Neural Network-Based Target Recognition Algorithm for Robot Scenes." Scientific Programming 2022 (January 11, 2022): 1–14. http://dx.doi.org/10.1155/2022/4037625.

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Intelligent robots are a key vehicle for artificial intelligence and are widely employed in all aspects of everyday life and work, not just in the industry. One of the talents required for intelligent robots to complete their jobs is the capacity to identify their environment, which is a crucial obstacle to be overcome. Deep learning-based target identification algorithms currently do not fully leverage the link between high-level semantic and low-level detail information in the prediction step and hence are less successful in recognizing tiny target objects. Target recognition via vision sens

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Hou, Xiangfeng, and Qing Ji. "Research on the Recognition Algorithm of Basketball Technical Action Based on BP Neural System." Scientific Programming 2022 (January 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/7668425.

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Vision-based intelligent human action recognition is the most challenging direction in the field of computer vision in recent years. It detects human actions in video sequences, extracts action features and learns action features, and then recognizes human actions in videos. This paper is based on BP neural network’s basketball technique action recognition and experimental verification. First, design a basketball technique action recognition method based on BP neural network, analyze basketball actions, collect relevant test data, and divide the methods of basketball action recognition. Finall

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Mahajan, Ayush, Ved Upadhye, Vaidehi Dhobale, Rohit Dhongade, and Anuja Ghasad. "Review on Automated Accident Detection System." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 679–82. http://dx.doi.org/10.22214/ijraset.2023.56056.

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Abstract: Major accidents on highways, freeways and local roads can lead to huge social and economic impacts. Minor accidents may be resolved by the passengers themselves and do not require escorting to hospitals whereas major accidents where airbags are deployed require the immediate attention of authorities. Automated Accident Detection System [AADS] is an auto-detection unit system that immediately notifies an Emergency Contact through an alert alarm when someone presses the trigger button present on poles having CCTV cameras in the end vehicle is detected by the system, detailing the locat

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Song, Yang, Shanna Lv, Feilu Wang, and Mingkun Li. "Hardness-and-Type Recognition of Different Objects Based on a Novel Porous Graphene Flexible Tactile Sensor Array." Micromachines 14, no. 1 (2023): 217. http://dx.doi.org/10.3390/mi14010217.

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Accurately recognizing the hardness and type of different objects by tactile sensors is of great significance in human–machine interaction. In this paper, a novel porous graphene flexible tactile sensor array with great performance is designed and fabricated, and it is mounted on a two-finger mechanical actuator. This is used to detect various tactile sequence features from different objects by slightly squeezing them by 2 mm. A Residual Network (ResNet) model, with excellent adaptivity and feature extraction ability, is constructed to realize the recognition of 4 hardness categories and 12 ob

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Espíndola, Norma Rafaella Uchôa, Rayanne Cruz Rodrigues, Iara Marques Tavares, et al. "Psychological and emotional aspects of children undergoing elective surgery." CONTRIBUCIONES A LAS CIENCIAS SOCIALES 17, no. 2 (2024): e5035. http://dx.doi.org/10.55905/revconv.17n.2-060.

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Within the scope of pediatric surgical care, a multitude of emotional and psychological factors intertwine, especially during the preoperative phase. Both children and their parents are susceptible to a wide range of emotions, including fear, anxiety and stress. In addition, a child's age, temperament, and previous encounters with medical procedures may also influence their emotional reaction to the impending surgery. To analyze through literature how emotional and psychological factors can impact pediatric surgical procedures. This is a literature review designed to highlight what has already

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Abbas, Rizwan, Gehad Abdullah Amran, Irshad Hussain, and Shengjun Ma. "A Soft Computing View for the Scientific Categorization of Vegetable Supply Chain Issues." Logistics 6, no. 3 (2022): 39. http://dx.doi.org/10.3390/logistics6030039.

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Over the most recent couple of years, the Internet of Things and other empowering innovations have been logically utilized for digitizing the vegetable supply chain (VSC). Background: The unpredictable examples and complexity inserted in enormous data dimensions present a test for an orderly human master examination. Hence in an information-driven setting, soft computing (SC) has accomplished critical energy to investigate, mine, and concentrate confidential information data, or tackle complex improvement issues, finding some harmony between good productivity and maintainability of vegetable s

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Sidorenko, Oleksandr, Tetiana Klymenko, and Valentina Glivenko. "SEPARATE ASPECTS OF ACCOUNTING OF THE RECEIVABLES AND DOUBTFUL DEBTS RESERVE." Problems and prospects of economics and management, no. 3(31) (2022): 201–12. http://dx.doi.org/10.25140/2411-5215-2022-3(31)-201-212.

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In the article, a scientific approach to distinguishing (highlighting) the types of receivables for which accruel of the reserve for doubtful debts is possible, that allows for a reliable assessment of re-ceivables and the provision of complete, true and unbiased information to users of financial statements for decision-making is proposed. The relevance of the issue of distinguishing the types of receivables for which it is possible to ac-crue a reserve for doubtful debts is justified, and the need to develop and implement the detailing of the relevant sub-accounts of the next order to accumul

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Ali, Shereen H. "A Novel Intrusion Detection Framework (IDF) using Machine Learning Methods." Journal of Cybersecurity and Information Management 10, no. 01 (2023): 43–54. http://dx.doi.org/10.54216/jcim.100103.

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An intrusion detection system is a critical security feature that analyses network traffic in order to avoid serious unauthorized access to network resources. For securing networks against potential breaches, effective intrusion detection is critical. In this paper, a novel Intrusion Detection Framework (IDF) is proposed. The three modules that comprise the suggested IDF are: (i) Data Pre-processing Module (DPM), (ii) Feature Selection Module (FSM), and Classification Module (CM). DPM collects and processes network traffic in order to prepare data for training and testing. The FSM seeks to ide

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Beguin, Y., GK Clemons, P. Pootrakul, and G. Fillet. "Quantitative assessment of erythropoiesis and functional classification of anemia based on measurements of serum transferrin receptor and erythropoietin." Blood 81, no. 4 (1993): 1067–76. http://dx.doi.org/10.1182/blood.v81.4.1067.1067.

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Abstract We evaluated the quantitative value of a simple model of erythropoiesis, based on the basic assumptions that the red blood cell (RBC) mass determines erythropoietin (Epo) production, which in turn stimulates erythropoietic activity. The RBC mass was quantitated by direct isotopic measurement (RCM), Epo production by serum Epo levels, and erythropoiesis by the ferrokinetic measurement of the erythron transferrin uptake (ETU), the serum transferrin receptor (TfR) level, and the reticulocyte (retic) index, and was completed by an evaluation of overall marrow erythron cellularity. We stud

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50

Beguin, Y., GK Clemons, P. Pootrakul, and G. Fillet. "Quantitative assessment of erythropoiesis and functional classification of anemia based on measurements of serum transferrin receptor and erythropoietin." Blood 81, no. 4 (1993): 1067–76. http://dx.doi.org/10.1182/blood.v81.4.1067.bloodjournal8141067.

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We evaluated the quantitative value of a simple model of erythropoiesis, based on the basic assumptions that the red blood cell (RBC) mass determines erythropoietin (Epo) production, which in turn stimulates erythropoietic activity. The RBC mass was quantitated by direct isotopic measurement (RCM), Epo production by serum Epo levels, and erythropoiesis by the ferrokinetic measurement of the erythron transferrin uptake (ETU), the serum transferrin receptor (TfR) level, and the reticulocyte (retic) index, and was completed by an evaluation of overall marrow erythron cellularity. We studied a tot

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