Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00046
V. G, Deepa Gupta, Vani Kanjirangat
In this work, we propose a semi-supervised boot-strapping approach for relation extraction in domain specific texts, specifically focusing on agricultural domain. Our approach utilizes the BERT model with dependency parsing for relation extraction. The proposed model, focuses on identifying five inter subdomain relations viz., Soil_Location, Soil_Crop, Disease_Pathogen, Pathogen_Crop, and Chemical_Crop. We created a corpus of 30,000 sentences extracted from recognised agriculture sites to evaluate the model. The labeled relations were then manually checked to evaluate the prediction accuracy. We used a test corpus with 700 sentences that included 3500 triplets for the evaluation. The proposed approach presents an average macro F -Score of 86.4 %, which is quite promising for semi-supervised domain specific relation extraction systems. Experimental results show the efficacy of the proposed approach in classifying relational phrases in a semi-supervised set-up for the agricultural domain.
{"title":"Semi Supervised Approach for Relation Extraction in Agriculture Documents","authors":"V. G, Deepa Gupta, Vani Kanjirangat","doi":"10.1109/OCIT56763.2022.00046","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00046","url":null,"abstract":"In this work, we propose a semi-supervised boot-strapping approach for relation extraction in domain specific texts, specifically focusing on agricultural domain. Our approach utilizes the BERT model with dependency parsing for relation extraction. The proposed model, focuses on identifying five inter subdomain relations viz., Soil_Location, Soil_Crop, Disease_Pathogen, Pathogen_Crop, and Chemical_Crop. We created a corpus of 30,000 sentences extracted from recognised agriculture sites to evaluate the model. The labeled relations were then manually checked to evaluate the prediction accuracy. We used a test corpus with 700 sentences that included 3500 triplets for the evaluation. The proposed approach presents an average macro F -Score of 86.4 %, which is quite promising for semi-supervised domain specific relation extraction systems. Experimental results show the efficacy of the proposed approach in classifying relational phrases in a semi-supervised set-up for the agricultural domain.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131887959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00034
S. Nayak, Satchidananda Dehuri, Sung-Bae Cho
Time series data are correlated in a nonlinear fashion which makes the future data prediction challenging. Particularly, the correlation among data at the fluctuation points is insignificant and it is hard to capture the underlaying nonlinearity at those points by conventional prediction systems. The accuracy of time series forecasting (TSF) is vastly influenced by the current and immediate past data rather by far away data points. This article proposes an extreme learning-based method for exploration of virtual data positions (ELMVDP) from the training data and incorporates them to the original time series to intensify the TSF accuracy of a single hidden layer neural network. Specifically, this method is useful for the time series having less volume of data which may not suffice to train a TSF model. The effectiveness of ELMVDP method is evaluated on time series available in the literature, compared with few similar deterministic and stochastic approaches, and observations from simulation studies show that ELMVDP method yields better predictions than others.
{"title":"ELMVDP: extreme learning based virtual data position exploration and incorporation method for escalation of time series forecasting accuracy","authors":"S. Nayak, Satchidananda Dehuri, Sung-Bae Cho","doi":"10.1109/OCIT56763.2022.00034","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00034","url":null,"abstract":"Time series data are correlated in a nonlinear fashion which makes the future data prediction challenging. Particularly, the correlation among data at the fluctuation points is insignificant and it is hard to capture the underlaying nonlinearity at those points by conventional prediction systems. The accuracy of time series forecasting (TSF) is vastly influenced by the current and immediate past data rather by far away data points. This article proposes an extreme learning-based method for exploration of virtual data positions (ELMVDP) from the training data and incorporates them to the original time series to intensify the TSF accuracy of a single hidden layer neural network. Specifically, this method is useful for the time series having less volume of data which may not suffice to train a TSF model. The effectiveness of ELMVDP method is evaluated on time series available in the literature, compared with few similar deterministic and stochastic approaches, and observations from simulation studies show that ELMVDP method yields better predictions than others.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"256 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116876435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00042
Ch. Sanjeev Kumar Dash, A. K. Behera, S. Nayak, Satchidananda Dehuri, J. P. Mohanty
Economic activities have deteriorated the quality of air, which is a vital natural resource. There has been a lot of research on predicting when terrible air quality will occur, but much of it is limited by a lack of data collected, making it unable to account for periodic and other factors. This article develops and analyses the performances of two higher order neural networks-based forecasts such as pi-sigma neural network (PSNN) and functional link artificial neural network (FLANN) on estimating the air quality index (AQI) of Brarajanagar and Talcher industrial region of Odisha State, India. AQIs at the daily level of two cities are collected from the Kaggle source, preprocessed, and used for modeling and forecasting by the two higher-order neural networks. Simulation outcomes and comparative studies are in favor of PSNN and FLANN-based forecasting
{"title":"Estimation of Air Quality Index of Brajarajnagar and Talcher Industrial Region of Odisha State: A Higher Order Neural Network Approach","authors":"Ch. Sanjeev Kumar Dash, A. K. Behera, S. Nayak, Satchidananda Dehuri, J. P. Mohanty","doi":"10.1109/OCIT56763.2022.00042","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00042","url":null,"abstract":"Economic activities have deteriorated the quality of air, which is a vital natural resource. There has been a lot of research on predicting when terrible air quality will occur, but much of it is limited by a lack of data collected, making it unable to account for periodic and other factors. This article develops and analyses the performances of two higher order neural networks-based forecasts such as pi-sigma neural network (PSNN) and functional link artificial neural network (FLANN) on estimating the air quality index (AQI) of Brarajanagar and Talcher industrial region of Odisha State, India. AQIs at the daily level of two cities are collected from the Kaggle source, preprocessed, and used for modeling and forecasting by the two higher-order neural networks. Simulation outcomes and comparative studies are in favor of PSNN and FLANN-based forecasting","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116046344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00060
Prachi Vijayeeta, Parthasarathi Pattnayak
Deep Learning mechanisms can be leveraged for playing the type of music based on the emotions of an individual entity. This can be done by detecting the human facial expressions, color, posture, orientation, lightning, etc. An interface is designed which makes the system to analyze the possible variability of faces. The basic pre-requisite for emotion recognition is appropriate selection of facial features that helps in identifying the mood of a person. Traditionally, grouping songs into various playlist was manual interpreted that consumed lot of time and it was indeed a tedious task. However, the advent of Facial Expression Based Music System emphasizes an automatic creation of music playlist based on real time mental state of an individual. In this work we have employed Haar Cascade-CNN classifier and SVM classifier to detect the emotions in an image. Haar Cascade is a machine learning-based approach where a lot of positive and negative images are used to train the classifier. The learning algorithm keeps on training the input feature vector based on the image captured. The gray scale image of the face is used by the system to classify five basic emotions such as surprise, disgust, neutral, anger and happiness. The emotion classification is achieved by observing the parts of the face, like eyes, lips movement, etc. A comparative study of these two classifiers are conducted based on the trained datasets. This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.
{"title":"A Deep Learning approach for Emotion Based Music Player","authors":"Prachi Vijayeeta, Parthasarathi Pattnayak","doi":"10.1109/OCIT56763.2022.00060","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00060","url":null,"abstract":"Deep Learning mechanisms can be leveraged for playing the type of music based on the emotions of an individual entity. This can be done by detecting the human facial expressions, color, posture, orientation, lightning, etc. An interface is designed which makes the system to analyze the possible variability of faces. The basic pre-requisite for emotion recognition is appropriate selection of facial features that helps in identifying the mood of a person. Traditionally, grouping songs into various playlist was manual interpreted that consumed lot of time and it was indeed a tedious task. However, the advent of Facial Expression Based Music System emphasizes an automatic creation of music playlist based on real time mental state of an individual. In this work we have employed Haar Cascade-CNN classifier and SVM classifier to detect the emotions in an image. Haar Cascade is a machine learning-based approach where a lot of positive and negative images are used to train the classifier. The learning algorithm keeps on training the input feature vector based on the image captured. The gray scale image of the face is used by the system to classify five basic emotions such as surprise, disgust, neutral, anger and happiness. The emotion classification is achieved by observing the parts of the face, like eyes, lips movement, etc. A comparative study of these two classifiers are conducted based on the trained datasets. This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"373 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124673252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00013
R. Krishna, K. Prema
India's most widely utilized food crop is soybean, and deep learning techniques are frequently used in forecasting and classification tasks. The minute scenario shows that the classification of the soybean crop diseases is a well-used machine learning technique with the help of images. But the proposed work, for the first time, combines soybean physic crop properties, weather properties, and deep learning techniques for classification. As a result, Random Forest and Support Vector Machine classification algorithms are utilized and the accuracy is compared with and without feature selection. Disease classification is compared using deep learning techniques like Recurrent Neural Networks, Convolutional Neural Networks, and Multi-Layer Perceptrons, along with optimization techniques like Adam, RmsProp, and AdaGrad. Results indicate that the farmers can predict soybean crop disease based on weather and the physical crop properties, hence taking preventive action.
{"title":"Optimization methods for soybean crop disease classification: A comparative study","authors":"R. Krishna, K. Prema","doi":"10.1109/OCIT56763.2022.00013","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00013","url":null,"abstract":"India's most widely utilized food crop is soybean, and deep learning techniques are frequently used in forecasting and classification tasks. The minute scenario shows that the classification of the soybean crop diseases is a well-used machine learning technique with the help of images. But the proposed work, for the first time, combines soybean physic crop properties, weather properties, and deep learning techniques for classification. As a result, Random Forest and Support Vector Machine classification algorithms are utilized and the accuracy is compared with and without feature selection. Disease classification is compared using deep learning techniques like Recurrent Neural Networks, Convolutional Neural Networks, and Multi-Layer Perceptrons, along with optimization techniques like Adam, RmsProp, and AdaGrad. Results indicate that the farmers can predict soybean crop disease based on weather and the physical crop properties, hence taking preventive action.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122316154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00043
Shubhashree Sahoo, R. Dalei, S. Rath, U. Sahu
Evolutionary algorithms (EA) are well known algorithms and commonly used for trajectory optimization of missile. The present research work aims at comparative performance analysis of two different EAs such as genetic algorithm (GA) and differential evolution (DE) for optimization of missile gliding trajectory. The range of missile was maximized by optimizing gliding trajectory through descretization of angle of attack (AOA) as control parameter and problem solving. Evaluation of performance characteristics of GA and DE was carried out on the basis of computation time, accuracy of solution and convergence efficiency. Experimental results demonstrate the better performance of DE when compared to GA in terms of computation time, solution accuracy and convergence efficiency.
{"title":"Comparative Performance Analysis of Genetic Algorithm and Differential Evolution for Optimization of Missile Gliding Trajectory","authors":"Shubhashree Sahoo, R. Dalei, S. Rath, U. Sahu","doi":"10.1109/OCIT56763.2022.00043","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00043","url":null,"abstract":"Evolutionary algorithms (EA) are well known algorithms and commonly used for trajectory optimization of missile. The present research work aims at comparative performance analysis of two different EAs such as genetic algorithm (GA) and differential evolution (DE) for optimization of missile gliding trajectory. The range of missile was maximized by optimizing gliding trajectory through descretization of angle of attack (AOA) as control parameter and problem solving. Evaluation of performance characteristics of GA and DE was carried out on the basis of computation time, accuracy of solution and convergence efficiency. Experimental results demonstrate the better performance of DE when compared to GA in terms of computation time, solution accuracy and convergence efficiency.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126929393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00011
P. S. Chatterjee
Serious Speech and Motor Impairment (SSMI) affects a sizeable portion of the Indian population. They are unable to communicate normally because of their physical impairment. For persons who are physically disabled, such as those who have cerebral palsy, speech disorders, or other motor neuron difficulties, the Sanyog is a tool that helps them speak and communicate. The user communicates with icons in this Iconic Communication System (ICS). The collection of chosen icons is transformed into an instantiated representation, which resembles a frame. A natural language simple sentence generator accepts this intermediate representation. The proposed work aim to creates compound sentence from the subject's input in Sanyog in Bengali language. Two simple sentences are aggregated to form compound sentence. After that we applies the rules of pronolninalization to generate pronouns which makes the compound sentence more fluent. In this paper among the different categories of pronouns we have only concentrate on anaphoric pronoun generation. Lastly the correctness of the generated sentences are checked.
{"title":"Compound sentence and pronoun generation in Sanyog: An Iconic Communication System for People with Speech and Motor Impairments","authors":"P. S. Chatterjee","doi":"10.1109/OCIT56763.2022.00011","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00011","url":null,"abstract":"Serious Speech and Motor Impairment (SSMI) affects a sizeable portion of the Indian population. They are unable to communicate normally because of their physical impairment. For persons who are physically disabled, such as those who have cerebral palsy, speech disorders, or other motor neuron difficulties, the Sanyog is a tool that helps them speak and communicate. The user communicates with icons in this Iconic Communication System (ICS). The collection of chosen icons is transformed into an instantiated representation, which resembles a frame. A natural language simple sentence generator accepts this intermediate representation. The proposed work aim to creates compound sentence from the subject's input in Sanyog in Bengali language. Two simple sentences are aggregated to form compound sentence. After that we applies the rules of pronolninalization to generate pronouns which makes the compound sentence more fluent. In this paper among the different categories of pronouns we have only concentrate on anaphoric pronoun generation. Lastly the correctness of the generated sentences are checked.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127803229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00089
Saumya Jaipuria, R. Das
In Mobile Sensor Networks (MSN), covering targets with minimum movement is an important issue. We consider two related problems but with a limited mobility model where no sensor can move beyond a certain distance. In the first problem, we minimize the sum of the movements of all sensors. And in the other, we minimize their maximum. We solve the first problem by relaxing the equivalent Integer Linear Program (ILP) where the maximum allowable distance is a parameter. Experimental results show that our algorithm gives the solution very close to the optimal. For the second problem, we apply binary search and repeatedly execute the relaxed LP until we find the smallest value of the maximum distance that gives a feasible solution. We could find movements of sensors that satisfy the above limit in all our experiments with different random placements of sensors and targets.
{"title":"Coverage of Targets in Mobile Sensor Networks With Limited Mobility","authors":"Saumya Jaipuria, R. Das","doi":"10.1109/OCIT56763.2022.00089","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00089","url":null,"abstract":"In Mobile Sensor Networks (MSN), covering targets with minimum movement is an important issue. We consider two related problems but with a limited mobility model where no sensor can move beyond a certain distance. In the first problem, we minimize the sum of the movements of all sensors. And in the other, we minimize their maximum. We solve the first problem by relaxing the equivalent Integer Linear Program (ILP) where the maximum allowable distance is a parameter. Experimental results show that our algorithm gives the solution very close to the optimal. For the second problem, we apply binary search and repeatedly execute the relaxed LP until we find the smallest value of the maximum distance that gives a feasible solution. We could find movements of sensors that satisfy the above limit in all our experiments with different random placements of sensors and targets.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"14 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131342476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/ocit56763.2022.00005
{"title":"Message from the General Chairs: OCIT 2022","authors":"","doi":"10.1109/ocit56763.2022.00005","DOIUrl":"https://doi.org/10.1109/ocit56763.2022.00005","url":null,"abstract":"","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134012467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00035
Remya Sivan, Tripty Singh, P. Pati
Ancient manuscripts like palm leaves, available in museum libraries, are a rich source of knowledge. Digitization helps store this knowledge protected for the future & enables its global access. Varying writing styles, presence of currently discarded & rare characters, quality of imaging, and palm leaves are some of the challenges to be handled while building an offline handwritten recognition system for these manuscripts. This paper focuses on recognizing Malayalam characters available in palm leaves using deep learning techniques. With the help of the histogram and contour method, lines are segmented from palm leaves first. Subsequently, individual characters are extracted from the lines. A customized Convolution Neural Network (CNN) is employed to recognize these segmented characters. This trained CNN recognizes forty-eight classes of segmented characters with 86% accuracy. Additionally, this paper compares the results with other standard CNN models.
{"title":"Malayalam Character Recognition from Palm Leaves Using Deep-Learning","authors":"Remya Sivan, Tripty Singh, P. Pati","doi":"10.1109/OCIT56763.2022.00035","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00035","url":null,"abstract":"Ancient manuscripts like palm leaves, available in museum libraries, are a rich source of knowledge. Digitization helps store this knowledge protected for the future & enables its global access. Varying writing styles, presence of currently discarded & rare characters, quality of imaging, and palm leaves are some of the challenges to be handled while building an offline handwritten recognition system for these manuscripts. This paper focuses on recognizing Malayalam characters available in palm leaves using deep learning techniques. With the help of the histogram and contour method, lines are segmented from palm leaves first. Subsequently, individual characters are extracted from the lines. A customized Convolution Neural Network (CNN) is employed to recognize these segmented characters. This trained CNN recognizes forty-eight classes of segmented characters with 86% accuracy. Additionally, this paper compares the results with other standard CNN models.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129038037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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