Pub Date : 2023-03-30DOI: 10.5121/mlaij.2023.10101
Maha Hany, Shaheera Rashwan, Neveen M. Abdelmotilib
Prediction of quality and consumers’ preferences is essential task for food producers to improve their market share and reduce any gap in food safety standards. In this paper, we develop a machine learning method to predict yogurt preferences based on the sensory attributes and analysis of samples’ images using image processing texture and color feature extraction techniques. We compare three unsupervised ML feature selection techniques (Principal Component Analysis and Independent Component Analysis and t-distributed Stochastic Neighbour Embedding) with one supervised ML feature selection technique (Linear Discriminant Analysis) in terms of accuracy of classification. Results show the efficiency of the supervised ML feature selection technique over the traditional feature selection techniques.
{"title":"A Machine Learning Method for Prediction of Yogurt Quality and Consumers Preferencesusing Sensory Attributes and Image Processing Techniques","authors":"Maha Hany, Shaheera Rashwan, Neveen M. Abdelmotilib","doi":"10.5121/mlaij.2023.10101","DOIUrl":"https://doi.org/10.5121/mlaij.2023.10101","url":null,"abstract":"Prediction of quality and consumers’ preferences is essential task for food producers to improve their market share and reduce any gap in food safety standards. In this paper, we develop a machine learning method to predict yogurt preferences based on the sensory attributes and analysis of samples’ images using image processing texture and color feature extraction techniques. We compare three unsupervised ML feature selection techniques (Principal Component Analysis and Independent Component Analysis and t-distributed Stochastic Neighbour Embedding) with one supervised ML feature selection technique (Linear Discriminant Analysis) in terms of accuracy of classification. Results show the efficiency of the supervised ML feature selection technique over the traditional feature selection techniques.","PeriodicalId":347528,"journal":{"name":"Machine Learning and Applications: An International Journal","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127300812","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}
Marco Oyarzo Huichaqueo, Renato Andrés Muñoz Orrego
With the increase in astronomical surveys, astronomers are faced with the challenging task of analyzing a large amount of data in order to classify observed objects into hard-to-distinguish classes. This article presents a machine learning-based method for the automatic spectral classification of stars from the latest release of the SDSS database. We propose the combinatorial use of spectral data, derived stellar data, and calculated data to create patterns. Using these patterns as inputs, we develop a Random Forest model that outputs the spectral class of the observed star. Our model is able to classify data into six complex classes: A, F, G, K, M, and Carbon stars. Due to the unbalanced nature of the data, we train our model considering three data use cases: using the original data, using under-sampling, and over-sampling data techniques. We further test our model by using a fixed dataset and a stratified dataset. From this, we analyze the performance of our model through statistical metrics. The experimental results showed that the combinatorial use of data as an input pattern contributes to improve the prediction scores in all data use cases, meanwhile, the model trained with augmented data outperforms the other cases. Our results suggest that machine learning-based spectral classification of stars may be useful for astronomers.
{"title":"Automatic Spectral Classification of Stars using Machine Learning: An Approach based on the use of Unbalanced Data","authors":"Marco Oyarzo Huichaqueo, Renato Andrés Muñoz Orrego","doi":"10.5121/mlaij.2022.9401","DOIUrl":"https://doi.org/10.5121/mlaij.2022.9401","url":null,"abstract":"With the increase in astronomical surveys, astronomers are faced with the challenging task of analyzing a large amount of data in order to classify observed objects into hard-to-distinguish classes. This article presents a machine learning-based method for the automatic spectral classification of stars from the latest release of the SDSS database. We propose the combinatorial use of spectral data, derived stellar data, and calculated data to create patterns. Using these patterns as inputs, we develop a Random Forest model that outputs the spectral class of the observed star. Our model is able to classify data into six complex classes: A, F, G, K, M, and Carbon stars. Due to the unbalanced nature of the data, we train our model considering three data use cases: using the original data, using under-sampling, and over-sampling data techniques. We further test our model by using a fixed dataset and a stratified dataset. From this, we analyze the performance of our model through statistical metrics. The experimental results showed that the combinatorial use of data as an input pattern contributes to improve the prediction scores in all data use cases, meanwhile, the model trained with augmented data outperforms the other cases. Our results suggest that machine learning-based spectral classification of stars may be useful for astronomers.","PeriodicalId":347528,"journal":{"name":"Machine Learning and Applications: An International Journal","volume":"26 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133320543","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}
Birds are everywhere around us and are easy to spot. However, for many beginner birders, identifying the birds is a hard task [8]. There are many apps that help the birder to identify the birds, but they are often too complicated and require good internet to give a result. A better app is needed so that birders can identify birds while not depending on internet connection. My app, AI_Bider, is mainly built in android studio using flutter and firebase, and the AI engine is coded with TensorFlow and trained with images from the internet [9]. To test my AI engine, I made six different prototypes, each having a different number of times that the code will train from the dataset of pictures. I then selected 5 birds that are in my dataset and found 5 pictures on the internet for each of them, which I then uploaded to the app. My app will then give me 3 bird species that most closely resemble the image, as well as the app’s confidence in its choices, which are listed as percentages [6]. I recorded the percentages of accuracy for each picture. After taking the average percentage of all the models, I selected the most successful model, which had an average percent of accuracy of 79%.
{"title":"Ai_Birder: Using Artificial Intelligence and Deep Learning to Create a Mobile Application that Automates Bird Classification","authors":"Charles Tian, Yu Sun","doi":"10.5121/mlaij.2022.9301","DOIUrl":"https://doi.org/10.5121/mlaij.2022.9301","url":null,"abstract":"Birds are everywhere around us and are easy to spot. However, for many beginner birders, identifying the birds is a hard task [8]. There are many apps that help the birder to identify the birds, but they are often too complicated and require good internet to give a result. A better app is needed so that birders can identify birds while not depending on internet connection. My app, AI_Bider, is mainly built in android studio using flutter and firebase, and the AI engine is coded with TensorFlow and trained with images from the internet [9]. To test my AI engine, I made six different prototypes, each having a different number of times that the code will train from the dataset of pictures. I then selected 5 birds that are in my dataset and found 5 pictures on the internet for each of them, which I then uploaded to the app. My app will then give me 3 bird species that most closely resemble the image, as well as the app’s confidence in its choices, which are listed as percentages [6]. I recorded the percentages of accuracy for each picture. After taking the average percentage of all the models, I selected the most successful model, which had an average percent of accuracy of 79%.","PeriodicalId":347528,"journal":{"name":"Machine Learning and Applications: An International Journal","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123086850","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}
The proposed methodology presented in the paper deals with solving the problem of multilingual speech recognition. Current text and speech recognition and translation methods have a very low accuracy in translating sentences which contain a mixture of two or more different languages. The paper proposes a novel approach to tackling this problem and highlights some of the drawbacks of current recognition and translation methods. The proposed approach deals with recognition of audio queries which contain a mixture of words in two different languages - Kannada and English. The novelty in the approach presented, is the use of a next Word Prediction model in combination with a Deep Learning speech recognition model to accurately recognise and convert the input audio query to text. Another method proposed to solve the problem of multilingual speech recognition and translation is the use of cosine similarity between the audio features of words for fast and accurate recognition. The dataset used for training and testing the models was generated manually by the authors as there was no pre-existing audio and text dataset which contained sentences in a mixture of both Kannada and English. The DL speech recognition model in combination with the Word Prediction model gives an accuracy of 71% when tested on the in-house multilingual dataset. This method outperforms other existing translation and recognition solutions for the same test set. Multilingual translation and recognition is an important problem to tackle as there is a tendency for people to speak in a mixture of languages. By solving this problem, the barrier of language and communication can be lifted and thus can help people connect better and more comfortably with each other.
{"title":"Multilingual Speech to Text using Deep Learning based on MFCC Features","authors":"P. Reddy","doi":"10.5121/mlaij.2022.9202","DOIUrl":"https://doi.org/10.5121/mlaij.2022.9202","url":null,"abstract":"The proposed methodology presented in the paper deals with solving the problem of multilingual speech recognition. Current text and speech recognition and translation methods have a very low accuracy in translating sentences which contain a mixture of two or more different languages. The paper proposes a novel approach to tackling this problem and highlights some of the drawbacks of current recognition and translation methods. The proposed approach deals with recognition of audio queries which contain a mixture of words in two different languages - Kannada and English. The novelty in the approach presented, is the use of a next Word Prediction model in combination with a Deep Learning speech recognition model to accurately recognise and convert the input audio query to text. Another method proposed to solve the problem of multilingual speech recognition and translation is the use of cosine similarity between the audio features of words for fast and accurate recognition. The dataset used for training and testing the models was generated manually by the authors as there was no pre-existing audio and text dataset which contained sentences in a mixture of both Kannada and English. The DL speech recognition model in combination with the Word Prediction model gives an accuracy of 71% when tested on the in-house multilingual dataset. This method outperforms other existing translation and recognition solutions for the same test set. Multilingual translation and recognition is an important problem to tackle as there is a tendency for people to speak in a mixture of languages. By solving this problem, the barrier of language and communication can be lifted and thus can help people connect better and more comfortably with each other.","PeriodicalId":347528,"journal":{"name":"Machine Learning and Applications: An International Journal","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127752550","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}
The research on affinity between drugs and targets (DTA) aims to effectively narrow the target search space for drug repurposing. Therefore, reasonable prediction of drug and target affinities can minimize the waste of resources such as human and material resources. In this work, a novel graph-based model called DSAGLSTM-DTA was proposed for DTA prediction. The proposed model is unlike previous graph-based drug-target affinity model, which incorporated self-attention mechanisms in the feature extraction process of drug molecular graphs to fully extract its effective feature representations. The features of each atom in the 2D molecular graph were weighted based on attention score before being aggregated as molecule representation and two distinct pooling architectures, namely centralized and distributed architectures were implemented and compared on benchmark datasets. In addition, in the course of processing protein sequences, inspired by the approach of protein feature extraction in GDGRU-DTA, we continue to interpret protein sequences as time series and extract their features using Bidirectional Long Short-Term Memory (BiLSTM) networks, since the context-dependence of long amino acid sequences. Similarly, DSAGLSTM-DTA also utilized a self-attention mechanism in the process of protein feature extraction to obtain comprehensive representations of proteins, in which the final hidden states for element in the batch were weighted with the each unit output of LSTM, and the results were represented as the final feature of proteins. Eventually, representations of drug and protein were concatenated and fed into prediction block for final prediction. The proposed model was evaluated on different regression datasets and binary classification datasets, and the results demonstrated that DSAGLSTM-DTA was superior to some state-ofthe-art DTA models and exhibited good generalization ability.
{"title":"DSAGLSTM-DTA: Prediction of Drug-Target Affinity using Dual Self-Attention and LSTM","authors":"Lyu Zhijian, Shaohua Jiang, Yonghao Tan","doi":"10.5121/mlaij.2022.9201","DOIUrl":"https://doi.org/10.5121/mlaij.2022.9201","url":null,"abstract":"The research on affinity between drugs and targets (DTA) aims to effectively narrow the target search space for drug repurposing. Therefore, reasonable prediction of drug and target affinities can minimize the waste of resources such as human and material resources. In this work, a novel graph-based model called DSAGLSTM-DTA was proposed for DTA prediction. The proposed model is unlike previous graph-based drug-target affinity model, which incorporated self-attention mechanisms in the feature extraction process of drug molecular graphs to fully extract its effective feature representations. The features of each atom in the 2D molecular graph were weighted based on attention score before being aggregated as molecule representation and two distinct pooling architectures, namely centralized and distributed architectures were implemented and compared on benchmark datasets. In addition, in the course of processing protein sequences, inspired by the approach of protein feature extraction in GDGRU-DTA, we continue to interpret protein sequences as time series and extract their features using Bidirectional Long Short-Term Memory (BiLSTM) networks, since the context-dependence of long amino acid sequences. Similarly, DSAGLSTM-DTA also utilized a self-attention mechanism in the process of protein feature extraction to obtain comprehensive representations of proteins, in which the final hidden states for element in the batch were weighted with the each unit output of LSTM, and the results were represented as the final feature of proteins. Eventually, representations of drug and protein were concatenated and fed into prediction block for final prediction. The proposed model was evaluated on different regression datasets and binary classification datasets, and the results demonstrated that DSAGLSTM-DTA was superior to some state-ofthe-art DTA models and exhibited good generalization ability.","PeriodicalId":347528,"journal":{"name":"Machine Learning and Applications: An International Journal","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127160823","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}
The discovery of habitable exoplanets has long been a heated topic in astronomy. Traditional methods for exoplanet identification include the wobble method, direct imaging, gravitational microlensing, etc., which not only require a considerable investment of manpower, time, and money, but also are limited by the performance of astronomical telescopes. In this study, we proposed the idea of using machine learning methods to identify exoplanets. We used the Kepler dataset collected by NASA from the Kepler Space Observatory to conduct supervised learning, which predicts the existence of exoplanet candidates as a three-categorical classification task, using decision tree, random forest, naïve Bayes, and neural network; we used another NASA dataset consisted of the confirmed exoplanets data to conduct unsupervised learning, which divides the confirmed exoplanets into different clusters, using k-means clustering. As a result, our models achieved accuracies of 99.06%, 92.11%, 88.50%, and 99.79%, respectively, in the supervised learning task and successfully obtained reasonable clusters in the unsupervised learning task.
{"title":"Exoplanets Identification and Clustering with Machine Learning Methods","authors":"Yucheng Jin, Lanyi Yang, Chia-En Chiang","doi":"10.5121/mlaij.2022.9101","DOIUrl":"https://doi.org/10.5121/mlaij.2022.9101","url":null,"abstract":"The discovery of habitable exoplanets has long been a heated topic in astronomy. Traditional methods for exoplanet identification include the wobble method, direct imaging, gravitational microlensing, etc., which not only require a considerable investment of manpower, time, and money, but also are limited by the performance of astronomical telescopes. In this study, we proposed the idea of using machine learning methods to identify exoplanets. We used the Kepler dataset collected by NASA from the Kepler Space Observatory to conduct supervised learning, which predicts the existence of exoplanet candidates as a three-categorical classification task, using decision tree, random forest, naïve Bayes, and neural network; we used another NASA dataset consisted of the confirmed exoplanets data to conduct unsupervised learning, which divides the confirmed exoplanets into different clusters, using k-means clustering. As a result, our models achieved accuracies of 99.06%, 92.11%, 88.50%, and 99.79%, respectively, in the supervised learning task and successfully obtained reasonable clusters in the unsupervised learning task.","PeriodicalId":347528,"journal":{"name":"Machine Learning and Applications: An International Journal","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125861261","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}
The use of Machine Learning in Artificial Intelligence is the inspiration that shaped technology as it is today. Machine Learning has the power to greatly simplify our lives. Improvement in speech recognition and language understanding help the community interact more naturally with technology. The popularity of machine learning opens up the opportunities for optimizing the design of computing platforms using welldefined hardware accelerators. In the upcoming few years, cameras will be utilised as sensors for several applications. For ease of use and privacy restrictions, the requested image processing should be limited to a local embedded computer platform and with a high accuracy. Furthermore, less energy should be consumed. Dedicated acceleration of Convolutional Neural Networks can achieve these targets with high flexibility to perform multiple vision tasks. However, due to the exponential growth in technology constraints (especially in terms of energy) which could lead to heterogeneous multicores, and increasing number of defects, the strategy of defect-tolerant accelerators for heterogeneous multi-cores may become a main micro-architecture research issue. The up to date accelerators used still face some performance issues such as memory limitations, bandwidth, speed etc. This literature summarizes (in terms of a survey) recent work of accelerators including their advantages and disadvantages to make it easier for developers with neural network interests to further improve what has already been established.
{"title":"A Survey of Neural Network Hardware Accelerators in Machine Learning","authors":"F. Jasem, Manar AlSaraf","doi":"10.5121/mlaij.2021.8402","DOIUrl":"https://doi.org/10.5121/mlaij.2021.8402","url":null,"abstract":"The use of Machine Learning in Artificial Intelligence is the inspiration that shaped technology as it is today. Machine Learning has the power to greatly simplify our lives. Improvement in speech recognition and language understanding help the community interact more naturally with technology. The popularity of machine learning opens up the opportunities for optimizing the design of computing platforms using welldefined hardware accelerators. In the upcoming few years, cameras will be utilised as sensors for several applications. For ease of use and privacy restrictions, the requested image processing should be limited to a local embedded computer platform and with a high accuracy. Furthermore, less energy should be consumed. Dedicated acceleration of Convolutional Neural Networks can achieve these targets with high flexibility to perform multiple vision tasks. However, due to the exponential growth in technology constraints (especially in terms of energy) which could lead to heterogeneous multicores, and increasing number of defects, the strategy of defect-tolerant accelerators for heterogeneous multi-cores may become a main micro-architecture research issue. The up to date accelerators used still face some performance issues such as memory limitations, bandwidth, speed etc. This literature summarizes (in terms of a survey) recent work of accelerators including their advantages and disadvantages to make it easier for developers with neural network interests to further improve what has already been established.","PeriodicalId":347528,"journal":{"name":"Machine Learning and Applications: An International Journal","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133693090","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}
Data available from web based sources has grown tremendously with growth of the internet. Users interested in information from such sources often use a search engine to obtain the data which they edit for presentation to their audience. This process can be tedious especially when it involves the generation of a summary. One way to ease the process is by automation of the summary generation process. Efforts by researchers towards automatic summarization have yielded several approaches among them machine learning. Thus, recommendations have been made on combining the algorithms with different strengths, also called hybridization, in order to enhance their performance. Therefore, this research sought to establish the impact of hybridization of Deep Belief Network (DBN) with Support Vector Machine (SVM) on precision, recall, accuracy and F-measure when used in the case of query oriented multi-document summarization. The experiments were carried out using data from National Institute of Standards and Technology (NIST), Document Understanding Conference (DUC) 2006. The data was split into training and test data and used appropriately in DBN, SVM, SVM-DBN hybrid and DBN-SVM hybrid. Results indicated that the hybridized algorithm has better precision, accuracy and F-measure as compared to DBN. Pre-classification hybridization of DBN with SVM (SVM-DBN) gives the best results. This research implies that use of DBN and SVM hybrid algorithms would enhance query oriented multi-document summarization.
{"title":"Hybridization of DBN with SVM and its Impact on Performance in Multi-Document Summarization","authors":"","doi":"10.5121/mlaij.2021.8304","DOIUrl":"https://doi.org/10.5121/mlaij.2021.8304","url":null,"abstract":"Data available from web based sources has grown tremendously with growth of the internet. Users interested in information from such sources often use a search engine to obtain the data which they edit for presentation to their audience. This process can be tedious especially when it involves the generation of a summary. One way to ease the process is by automation of the summary generation process. Efforts by researchers towards automatic summarization have yielded several approaches among them machine learning. Thus, recommendations have been made on combining the algorithms with different strengths, also called hybridization, in order to enhance their performance. Therefore, this research sought to establish the impact of hybridization of Deep Belief Network (DBN) with Support Vector Machine (SVM) on precision, recall, accuracy and F-measure when used in the case of query oriented multi-document summarization. The experiments were carried out using data from National Institute of Standards and Technology (NIST), Document Understanding Conference (DUC) 2006. The data was split into training and test data and used appropriately in DBN, SVM, SVM-DBN hybrid and DBN-SVM hybrid. Results indicated that the hybridized algorithm has better precision, accuracy and F-measure as compared to DBN. Pre-classification hybridization of DBN with SVM (SVM-DBN) gives the best results. This research implies that use of DBN and SVM hybrid algorithms would enhance query oriented multi-document summarization.","PeriodicalId":347528,"journal":{"name":"Machine Learning and Applications: An International Journal","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123051072","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}
This study aims to introduce a discussion platform and curriculum designed to help people understand how machines learn. Research shows how to train an agent through dialogue and understand how information is represented using visualization. This paper starts by providing a comprehensive definition of AI literacy based on existing research and integrates a wide range of different subject documents into a set of key AI literacy skills to develop a user-centered AI. This functionality and structural considerations are organized into a conceptual framework based on the literature. Contributions to this paper can be used to initiate discussion and guide future research on AI learning within the computer science community.
{"title":"A Development Framework for a Conversational Agent to Explore Machine Learning Concepts","authors":"Ayse Kok Arslan","doi":"10.5121/mlaij.2021.8301","DOIUrl":"https://doi.org/10.5121/mlaij.2021.8301","url":null,"abstract":"This study aims to introduce a discussion platform and curriculum designed to help people understand how machines learn. Research shows how to train an agent through dialogue and understand how information is represented using visualization. This paper starts by providing a comprehensive definition of AI literacy based on existing research and integrates a wide range of different subject documents into a set of key AI literacy skills to develop a user-centered AI. This functionality and structural considerations are organized into a conceptual framework based on the literature. Contributions to this paper can be used to initiate discussion and guide future research on AI learning within the computer science community.","PeriodicalId":347528,"journal":{"name":"Machine Learning and Applications: An International Journal","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127883930","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}
Depth estimation has made great progress in the last few years due to its applications in robotics science and computer vision. Various methods have been implemented and enhanced to estimate the depth without flickers and missing holes. Despite this progress, it is still one of the main challenges for researchers, especially for the video applications which have more complexity of the neural network which af ects the run time. Moreover to use such input like monocular video for depth estimation is considered an attractive idea, particularly for hand-held devices such as mobile phones, they are very popular for capturing pictures and videos, in addition to having a limited amount of RAM. Here in this work, we focus on enhancing the existing consistent depth estimation for monocular videos approach to be with less usage of RAM and with using less number of parameters without having a significant reduction in the quality of the depth estimation.
{"title":"An Enhancement for the Consistent Depth Estimation of Monocular Videos using Lightweight Network","authors":"Mohamed N. Sweilam, N. Tolstokulakov","doi":"10.5121/mlaij.2021.8302","DOIUrl":"https://doi.org/10.5121/mlaij.2021.8302","url":null,"abstract":"Depth estimation has made great progress in the last few years due to its applications in robotics science and computer vision. Various methods have been implemented and enhanced to estimate the depth without flickers and missing holes. Despite this progress, it is still one of the main challenges for researchers, especially for the video applications which have more complexity of the neural network which af ects the run time. Moreover to use such input like monocular video for depth estimation is considered an attractive idea, particularly for hand-held devices such as mobile phones, they are very popular for capturing pictures and videos, in addition to having a limited amount of RAM. Here in this work, we focus on enhancing the existing consistent depth estimation for monocular videos approach to be with less usage of RAM and with using less number of parameters without having a significant reduction in the quality of the depth estimation.","PeriodicalId":347528,"journal":{"name":"Machine Learning and Applications: An International Journal","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131078505","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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