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Tytuł:
Shifting to blended online learning and its impact on student performance: A case study for students enrolled in economic courses prior to COVID-19 emergency remote instruction
Autorzy:
Tila, Dorina
Powiązania:
https://bibliotekanauki.pl/articles/1827672.pdf
Data publikacji:
2020-11
Wydawca:
Szkoła Główna Handlowa w Warszawie
Tematy:
blended learning
hybrid learning
distance learning
online learning
students’ performance
Opis:
This study explores whether student academic performance differs between the face-to-face and online hybrid sections in an undergraduate introductory macroeconomic course offered at a US community college. The data was collected from 414 students enrolled in various sections of the course during five semesters from spring 2016 to fall 2018. The findings show no statistical difference in student performance between face-to-face and online hybrid courses and contribute to the literature specific to the discipline of economics, which unlike other disciplines, has shown discord in findings. The usefulness of such results may extend to US higher education institutions to help them make data-informed decisions about their future investments in online teaching modalities and course design in the discipline of economics.
Źródło:
e-mentor. Czasopismo naukowe Szkoły Głównej Handlowej w Warszawie; 2020, 86, 4; 62-71
1731-6758
1731-7428
Pojawia się w:
e-mentor. Czasopismo naukowe Szkoły Głównej Handlowej w Warszawie
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hybrid Synchronous and Asynchronous Language Learning in Writing Class: The Learners’ Psychosocial Perspectives in Indonesia
Autorzy:
Tusino, -
Sukarni, Semi
Rokhayati, Titi
Powiązania:
https://bibliotekanauki.pl/articles/1964301.pdf
Data publikacji:
2021-09-30
Wydawca:
Wydawnictwo Adam Marszałek
Tematy:
Hybrid learning
synchronous and asynchronous language learning
writing class
learners’ perspectives
Opis:
Hybrid synchronous and asynchronous language learning remains under-explored in writing class. The purpose of this study is to describe learners’ perceptions toward hybrid synchronous and asynchronous language learning model in EFL writing. A qualitative case study was employed. The respondents were undergraduate learners in English major. The research instruments were close-response questionnaires and semi-structured interviews in the academic writing course. Findings showed that psychological and social factors were crucial in the learners’ online writing. It revealed that teachers needed to provide comprehensible input, challenging group-work activity, and constructive feedback during online writing. Hybrid synchronous and asynchronous language learning enhanced writing competence, but it encountered a problem with internet connectivity. The study discloses that hybrid synchronous and asynchronous language learning has the potential to teach EFL writing.
Źródło:
The New Educational Review; 2021, 65; 190-199
1732-6729
Pojawia się w:
The New Educational Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Effect of Hybrid Task-Based Language Teaching and Critical Thinking on Writing Performance in Indonesia
Autorzy:
Tusino, -
Faridi, Abdurrachman
Saleh, Mursid
Fitriati, Sri Wuli
Powiązania:
https://bibliotekanauki.pl/articles/1963612.pdf
Data publikacji:
2020-09-30
Wydawca:
Wydawnictwo Adam Marszałek
Tematy:
hybrid learning
task-based language teaching
critical thinking
writing performance
Opis:
This study aims to describe the effect of hybrid task-based language teaching and critical thinking skills on writing performance among Indonesian learners. This study employed experimental research with a factorial design. The participants were Indonesian undergraduate learners majoring in an English program. The instruments used were critical thinking questionnaires and writing tests in the genre-based writing course. The results of the study showed that hybridtask-based language teaching was effective for improving EFL learners’ writing performance. Also, it revealed that learners with high critical thinking achieved better writing performance than learners with low critical thinking after being taught by hybrid task-based language teaching. The results indicate that hybrid task-based language teaching and critical thinking have a significant effect on EFL writing performance.
Źródło:
The New Educational Review; 2020, 61; 109-118
1732-6729
Pojawia się w:
The New Educational Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The impact of personality traits and study mode on mental health and stimulant use among university students during Covid-19 pandemic
Autorzy:
Markiewicz, Katarzyna
Kaczmarek, Bożydar L.J.
Gaś, Zbigniew B.
Powiązania:
https://bibliotekanauki.pl/articles/28762799.pdf
Data publikacji:
2023-09-11
Wydawca:
Fundacja Edukacji Medycznej, Promocji Zdrowia, Sztuki i Kultury Ars Medica
Tematy:
the COVID-19 pandemic
stimulants
personality traits
emotions
university students
hybrid learning
Opis:
The COVID-19 pandemic has significantly increased feelings of insecurity and anxiety for the health of oneself and those of one’s loved ones, as well as for the future, which has led to an increased level of stress. This has resulted in a tendency to use alcohol and drugs. Studies show that such behaviors are triggered both by external and internal factors. Therefore, the study has looked for interrelations between personality traits, mental state, and learning mode (online versus hybrid) and the frequency of stimulants and tranquilizers consumption in the last 6-12 months of the COVID-19 pandemic. The study involved 113 university students aged 19-34. Due to pandemic-related limitations, 51.3% of students took online courses, while 48.7% were involved in hybrid learning. The participants were all asked to complete an online questionnaire that included 17 questions regarding mental health, drug and alcohol use. Additionally, the TIPI questionnaire was used to assess personality traits. The study found that online learning as well as feelings of loneliness and emptiness resulted in increased use of alcohol, antidepressants and sleeping pills. On the other hand, personality traits such as extraversion, agreeableness and emotional stability helped to limit the use of this type of stimulants. Online learners more often reported deterioration in their mental state, related to difficulties in adapting to pandemic-related conditions. This group was also more likely to use sedatives, sleeping pills, and antidepressants, with a significant difference in means, compared to hybrid students. In contrast, hybrid learners frequently reported a sense of the loss of meaning as well as worrying about the fate of loved ones, thinking back to a situation no longer under their control, and difficulties in making decisions. At the same time, most respondents of this group reported a positive vision of their future and a high sense of responsibility.
Źródło:
Acta Neuropsychologica; 2023, 21(4); 373-386
1730-7503
2084-4298
Pojawia się w:
Acta Neuropsychologica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hybrid Learning of Interval Type-2 Fuzzy Systems Based on Orthogonal Least Squares and Back Propagation for Manufacturing Applications
Autorzy:
Mendez, G.
Hernandez, A.
Powiązania:
https://bibliotekanauki.pl/articles/384517.pdf
Data publikacji:
2008
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
type-2 fuzzy inference systems
type-2 neuro-fuzzy systems
hybrid learning
uncertain rule-based fuzzy logic systems
Opis:
This paper presents a novel learning methodology based on the hybrid algorithm for interval type-2 (IT2) fuzzy logic systems (FLS). Since in the literature only back-propagation method has been proposed for tuning of both antecedent and consequent parameters of type-2 fuzzy logic systems, a hybrid learning algorithm has been developed. The hybrid method uses recursive orthogonal least-squares method for tuning of consequent parameters as well as the back-propagation method for tuning of antecedent parameters. The systems were tested for three types of inputs: a) interval singleton b) interval type-1 (T1) non-singleton, c) interval type-2 non-singleton. The experimental results of the application of the hybrid interval type-2 fuzzy logic systems for scale breaker entry temperature prediction in a real hot strip mill were carried out for three different types of coils. They proved the feasibility of the systems developed here for scale breaker entry temperature prediction. Comparison with type-1 fuzzy logic systems shows that the hybrid learning interval type-2 fuzzy logic systems improve performance in scale breaker entry temperature prediction under the tested condition.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2008, 2, 1; 23-32
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A hybrid geometallurgical study using coupled Historical Data (HD) and Deep Learning (DL) techniques on a copper ore mine
Autorzy:
Gholami, Alireza
Asgari, Kaveh
Khoshdast, Hamid
Hassanzadeh, Ahmad
Powiązania:
https://bibliotekanauki.pl/articles/2146884.pdf
Data publikacji:
2022
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
hybrid geometallurgy
historical data
deep learning
copper ore
flotation
Opis:
This research work introduces a novel hybrid geometallurgical approach to develop a deep and comprehensive relationship between geological and mining characteristics with metallurgical parameters in a mineral processing plant. This technique involves statistically screening mineralogical and operational parameters using the Historical Data (HD) method. Further, it creates an intelligent bridge between effective parameters and metallurgical responses by the Deep Learning (DL) simulation method. In the HD method, the time and cost of common approaches in geometallurgical studies were minimized through the use of available archived data. Then, the generated DL-based predictive model was enabled to accurately forecast the process behavior in the mineral processing units. The efficiency of the proposed method for a copper ore sample was practically evaluated. For this purpose, six representative samples from different active mining zone were collected and used for flotation tests organized using a randomizing code. The experimental results were then statistically analyzed using HD method to assess the significance of mineralogical and operational parameters, including the proportions of effective minerals, particle size, collector and frother concentration, solid content and pH. Based on the HD analysis, the metallurgical responses including the copper grade and recovery, copper kinetics constant and iron grade in concentrate were modeled with an accuracy of about 90%. Next, the geometallurgical model of the process was developed using the long short-term memory neural network (LSTM) algorithm. The results showed that the studied metallurgical responses could be predicted with more than 95% accuracy. The results of this study showed that the hybrid geometallurgy approach can be used as a promising tool to achieve a reliable relationship between the mining and mineral processing sectors, and sustainable and predictable production.
Źródło:
Physicochemical Problems of Mineral Processing; 2022, 58, 3; art. no. 147841
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial intelligence approach for detecting material deterioration in hybrid building constructions
Autorzy:
Chesnokov, Andrei V.
Mikhailov, Vitalii V.
Dolmatov, Ivan V.
Powiązania:
https://bibliotekanauki.pl/articles/29520106.pdf
Data publikacji:
2021
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
hybrid construction
material deterioration
artificial neural network
semi-supervised machine learning
Opis:
Hybrid constructions include heterogeneous materials with different behaviors under load. The aim is to achieve a so-called synergistic effect when the advantages of particular structural elements complement each other in a unified system. The building constructions considered in the research include high-strength steel cables, fiberglass rods, and flexible polymer membranes. The membrane is attached to the rods which have been elastically bent from the initially straight shape into an arch-like form. Structural materials inevitably deteriorate during a long operational period. The present study focuses on detecting material deterioration using Artificial Neural Networks (ANNs), which belong to the scope of intelligent techniques for data analysis. Appropriate ANN structures and required features are proposed. A semi-supervised learning strategy is used. The approach allows the training of the networks with normal data only derived from the construction without defects. Material degradationis detected by the level of reconstruction error produced by the network given the input data. The work contributes to the field of structural health monitoring of hybrid building constructions. It provides the opportunity to detect material deterioration given the forces in particular structural elements.
Źródło:
Computer Methods in Materials Science; 2021, 21, 2; 83-94
2720-4081
2720-3948
Pojawia się w:
Computer Methods in Materials Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hybrid education in higher education on the example of academic teachers experiences in post-pandemic reality
Autorzy:
Romaniuk, Miłosz Wawrzyniec
Łukasiewicz-Wieleba, Joanna
Powiązania:
https://bibliotekanauki.pl/articles/2124747.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
hybrid education
higher education
academic teacher
blended learning
COVID-19
SARS-CoV-2
Opis:
The article concerns the experiences of academic teachers related to hybrid education at the end of the SARS-CoV-2 coronavirus pandemic. The aim of the study was to understand the lecturers' perspective on hybrid education implemented in the first semester of the 2021/2022 academic year at The Maria Grzegorzewska University and an attempt to compare it with traditional education and distance education. The subject of the research was, among others, readiness to implement hybrid teaching, university support for lecturers in the field of hybrid teaching and the diversity of experiences of academic teachers. The research used the method of diagnostic survey. The obtained results indicate that the lecturers declare their readiness to conduct hybrid teaching, especially in the case of their own or students' illness, or random factors that make it impossible to conduct full-time classes or top-down legal regulations. They appreciate the organizational support of their immediate supervisor and the opportunity to make up for classes that have not taken place in a hybrid form. The lecturers highly assess the level of their own involvement in the preparation and conduct of classes, as well as the quality of their didactic work. They see the possibility of using a hybrid approach not only in teaching but also in their self-improvement, work organization and maintaining health. At the same time, they indicate the shortcomings and difficulties related to didactics, social, technical, and organizational aspects, as well as systemic deficiencies. Based on the results, recommendations related to the use of hybrid education in post-pandemic academic education were developed.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 3; 489--496
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hybrid education in higher education on the example of students experiences in post-pandemic reality
Autorzy:
Romaniuk, Miłosz Wawrzyniec
Łukasiewicz-Wieleba, Joanna
Powiązania:
https://bibliotekanauki.pl/articles/2124755.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
hybrid education
higher education
academic teacher
blended learning
COVID-19
SARS-CoV-2
Opis:
The article concerns the experiences of students related to hybrid education conducted in the first semester of the academic year 2021/2022. The aim of the study was to find out the opinions of students on hybrid education conducted at The University and to compare it with traditional education and distance education. The subject of the research was, among others, the readiness of students to participate in hybrid learning, assessing its quality and other related experiences. The research used the method of diagnostic survey. The obtained results indicate that students rate their readiness to participate in hybrid education higher than the readiness of lecturers to conduct it. They see the possibility of using a hybrid approach to education and science, organization of education and health. They indicate convenience, organization and health safety as the most important advantages and social costs, student attitudes and technical problems as the most important disadvantages of hybridization. The article also presents the expectations of students in relation to the systemic sanctioning of hybrid education. It was suggested to use the lessons learned by developing and testing the effectiveness of a hybrid approach, the potential of which is undeniable and scientifically proven.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 3; 497--504
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Enhancing the performance of deep learning technique by combining with gradient boosting in rainfall-runoff simulation
Autorzy:
Abdullaeva, Barno S.
Powiązania:
https://bibliotekanauki.pl/articles/28411647.pdf
Data publikacji:
2023
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
deep learning
gradient boosting
hybrid model
multi-step ahead forecasting
rainfall-runoff simulation
Opis:
Artificial neural networks are widely employed as data mining methods by researchers across various fields, including rainfall-runoff (R-R) statistical modelling. To enhance the performance of these networks, deep learning (DL) neural networks have been developed to improve modelling accuracy. The present study aims to improve the effectiveness of DL networks in enhancing the performance of artificial neural networks via merging with the gradient boosting (GB) technique for daily runoff data forecasting in the river Amu Darya, Uzbekistan. The obtained results showed that the new hybrid proposed model performed exceptionally well, achieving a 16.67% improvement in determination coefficient (R2) and a 23.18% reduction in root mean square error (RMSE) during the training phase compared to the single DL model. Moreover, during the verification phase, the hybrid model displayed remarkable performance, demonstrating a 66.67% increase in R2 and a 50% reduction in RMSE. Furthermore, the hybrid model outperformed the single GB model by a significant margin. During the training phase, the new model showed an 18.18% increase in R2 and a 25% reduction in RMSE. In the verification phase, it improved by an impressive 75% in R2 and a 33.33% reduction in RMSE compared to the single GB model. These findings highlight the potential of the hybrid DL-GB model in improving daily runoff data forecasting in the challenging hydrological context of the Amu Darya River basin in Uzbekistan.
Źródło:
Journal of Water and Land Development; 2023, 59; 216--223
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolution-fuzzy rule based system with parameterized consequences
Autorzy:
Czekalski, P.
Powiązania:
https://bibliotekanauki.pl/articles/908394.pdf
Data publikacji:
2006
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
strategia ewolucyjna
system rozmyty
system hybrydowy
evolutionary strategy
fuzzy inference system
off-line learning
hybrid system
Opis:
While using automated learning methods, the lack of accuracy and poor knowledge generalization are both typical problems for a rule-based system obtained on a given data set. This paper introduces a new method capable of generating an accurate rule-based fuzzy inference system with parameterized consequences using an automated, off-line learning process based on multi-phase evolutionary computing and a training data covering algorithm. The presented method consists of the following steps: obtaining an initial set of rules with parameterized consequences using the Michigan approach combined with an evolutionary strategy and a covering algorithm for the training data set; reducing the obtained rule base using a simple genetic algorithm; multi-phase tuning of the fuzzy inference system with parameterized consequences using the Pittsburgh approach and an evolutionary strategy. The paper presents experimental results using popular benchmark data sets regarding system identification and time series prediction, providing a reliable comparison to other learning methods, particularly those based on neuro-fuzzy, clustering and \epsilon-insensitive methods. An examplary fuzzy inference system with parameterized consequences using the Reichenbach implication and the minimum t-norm was implemented to obtain numerical results.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2006, 16, 3; 373-385
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial intelligence-based hybrid forecasting models for manufacturing systems
Autorzy:
Rosienkiewicz, Maria
Powiązania:
https://bibliotekanauki.pl/articles/1841698.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
artificial neural network
support vector machine
extreme learning machine
hybrid forecasting
production planning
maintenance
quality control
Opis:
The paper addresses the problem of forecasting in manufacturing systems. The main aim of the research is to propose new hybrid forecasting models combining artificial intelligencebased methods with traditional techniques based on time series – namely: Hybrid econometric model, Hybrid artificial neural network model, Hybrid support vector machine model and Hybrid extreme learning machine model. The study focuses on solving the problem of limited access to independent variables. Empirical verification of the proposed models is built upon real data from the three manufacturing system areas – production planning, maintenance and quality control. Moreover, in the paper, an algorithm for the forecasting accuracy assessment and optimal method selection for industrial companies is introduced. It can serve not only as an efficient and costless tool for advanced manufacturing companies willing to select the right forecasting method for their particular needs but also as an approach supporting the initial steps of transformation towards smart factory and Industry 4.0 implementation.
Źródło:
Eksploatacja i Niezawodność; 2021, 23, 2; 263-277
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A hybrid control strategy for a dynamic scheduling problem in transit networks
Autorzy:
Liu, Zhongshan
Yu, Bin
Zhang, Li
Wang, Wensi
Powiązania:
https://bibliotekanauki.pl/articles/2172126.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
service reliability
transit network
proactive control method
deep reinforcement learning
hybrid control strategy
niezawodność usług
sieć tranzytowa
uczenie głębokie
kontrola hybrydowa
Opis:
Public transportation is often disrupted by disturbances, such as the uncertain travel time caused by road congestion. Therefore, the operators need to take real-time measures to guarantee the service reliability of transit networks. In this paper, we investigate a dynamic scheduling problem in a transit network, which takes account of the impact of disturbances on bus services. The objective is to minimize the total travel time of passengers in the transit network. A two-layer control method is developed to solve the proposed problem based on a hybrid control strategy. Specifically, relying on conventional strategies (e.g., holding, stop-skipping), the hybrid control strategy makes full use of the idle standby buses at the depot. Standby buses can be dispatched to bus fleets to provide temporary or regular services. Besides, deep reinforcement learning (DRL) is adopted to solve the problem of continuous decision-making. A long short-term memory (LSTM) method is added to the DRL framework to predict the passenger demand in the future, which enables the current decision to adapt to disturbances. The numerical results indicate that the hybrid control strategy can reduce the average headway of the bus fleet and improve the reliability of bus service.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2022, 32, 4; 553--567
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fuzzy interpretation for temporal-difference learning in anomaly detection problems
Autorzy:
Sukhanov, A. V.
Kovalev, S. M.
Stýskala, V.
Powiązania:
https://bibliotekanauki.pl/articles/200233.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
anomaly prediction
Markov reward model
hybrid fuzzy-stochastic rules
temporal-difference learning for intrusion detection
przewidywanie anomalii
model Markova
wykrywanie włamań
hybrydowy algorytm stochastyczny
Opis:
Nowadays, information control systems based on databases develop dynamically worldwide. These systems are extensively implemented into dispatching control systems for railways, intrusion detection systems for computer security and other domains centered on big data analysis. Here, one of the main tasks is the detection and prediction of temporal anomalies, which could be a signal leading to significant (and often critical) actionable information. This paper proposes the new anomaly prevent detection technique, which allows for determining the predictive temporal structures. Presented approach is based on a hybridization of stochastic Markov reward model by using fuzzy production rules, which allow to correct Markov information based on expert knowledge about the process dynamics as well as Markov’s intuition about the probable anomaly occurring. The paper provides experiments showing the efficacy of detection and prediction. In addition, the analogy between new framework and temporal-difference learning for sequence anomaly detection is graphically illustrated.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2016, 64, 3; 625-632
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hybrid deep learning method for detection of liver cancer
Autorzy:
Deshmukh, Sunita P.
Choudhari, Dharmaveer
Amalraj, Shankar
Matte, Pravin N.
Powiązania:
https://bibliotekanauki.pl/articles/38701864.pdf
Data publikacji:
2023
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
liver cancer detection
deep learning
fully convolutional neural network
hybrid approach
discrete wavelet transform
wykrywanie raka wątroby
uczenie głębokie
neuronowa sieć konwulcyjna
podejście hybrydowe
dyskretna transformata falkowa
Opis:
Liver disease refers to any liver irregularity causing its damage. There are several kinds of liver ailments. Benign growths are rarely life threatening and can be removed by specialists. Liver malignant tumor is leading causes of cancer death. Identifying malignant growth tissue is a troublesome and tedious task. There is significantly less information and statistical analysis presented related to cholangiocarcinoma and hepatoblastoma. This research focuses on the image analysis of these two types of cancer. The framework’s performance is evaluated using 2871 images, and a dual hybrid model is used to accomplish superb exactness. The aftereffects of both neural networks are sent into the result prioritizer that decides the most ideal choice for image arrangement. The relevance of elements appears to address the appropriate imaging rules for each class, and feature maps matching the original picture voxel features. The significance of features represents the most important imaging criteria for each class. This deep learning system demonstrates the concept of illuminating elements of a pre-trained deep neural network’s decision-making process by an examination of inner layers and the description of attributes that contribute to predictions.
Źródło:
Computer Assisted Methods in Engineering and Science; 2023, 30, 2; 151-165
2299-3649
Pojawia się w:
Computer Assisted Methods in Engineering and Science
Dostawca treści:
Biblioteka Nauki
Artykuł
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