Machine Learning and Applications: An International Journal最新文献

A Machine Learning Method for Prediction of Yogurt Quality and Consumers Preferencesusing Sensory Attributes and Image Processing Techniques 利用感官属性和图像处理技术预测酸奶质量和消费者偏好的机器学习方法

Machine Learning and Applications: An International Journal Pub Date : 2023-03-30 DOI: 10.5121/mlaij.2023.10101

Maha Hany, Shaheera Rashwan, Neveen M. Abdelmotilib

引用次数: 0

Automatic Spectral Classification of Stars using Machine Learning: An Approach based on the use of Unbalanced Data 基于机器学习的恒星光谱自动分类:一种基于非平衡数据的方法

Machine Learning and Applications: An International Journal Pub Date : 2022-12-30 DOI: 10.5121/mlaij.2022.9401

Marco Oyarzo Huichaqueo, Renato Andrés Muñoz Orrego

{"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}

引用次数: 0

Ai_Birder: Using Artificial Intelligence and Deep Learning to Create a Mobile Application that Automates Bird Classification Ai_Birder:使用人工智能和深度学习创建一个自动鸟类分类的移动应用程序

Machine Learning and Applications: An International Journal Pub Date : 2022-09-30 DOI: 10.5121/mlaij.2022.9301

Charles Tian, Yu Sun

{"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}

引用次数: 0

Multilingual Speech to Text using Deep Learning based on MFCC Features 基于MFCC特征的深度学习多语言语音到文本

Machine Learning and Applications: An International Journal Pub Date : 2022-06-30 DOI: 10.5121/mlaij.2022.9202

P. Reddy

{"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}

引用次数: 0

DSAGLSTM-DTA: Prediction of Drug-Target Affinity using Dual Self-Attention and LSTM DSAGLSTM-DTA:利用双自注意和LSTM预测药物-靶标亲和力

Machine Learning and Applications: An International Journal Pub Date : 2022-06-30 DOI: 10.5121/mlaij.2022.9201

Lyu Zhijian, Shaohua Jiang, Yonghao Tan

{"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}

引用次数: 0

Exoplanets Identification and Clustering with Machine Learning Methods 用机器学习方法识别和聚类系外行星

Machine Learning and Applications: An International Journal Pub Date : 2022-03-31 DOI: 10.5121/mlaij.2022.9101

Yucheng Jin, Lanyi Yang, Chia-En Chiang

引用次数: 0

A Survey of Neural Network Hardware Accelerators in Machine Learning 机器学习中的神经网络硬件加速器综述

Machine Learning and Applications: An International Journal Pub Date : 2021-12-31 DOI: 10.5121/mlaij.2021.8402

F. Jasem, Manar AlSaraf

{"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}

引用次数: 0

Hybridization of DBN with SVM and its Impact on Performance in Multi-Document Summarization DBN与SVM的杂交及其对多文档摘要性能的影响

Machine Learning and Applications: An International Journal Pub Date : 2021-09-30 DOI: 10.5121/mlaij.2021.8304

{"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}

引用次数: 1

A Development Framework for a Conversational Agent to Explore Machine Learning Concepts 探索机器学习概念的对话代理开发框架

Machine Learning and Applications: An International Journal Pub Date : 2021-09-30 DOI: 10.5121/mlaij.2021.8301

Ayse Kok Arslan

引用次数: 0

An Enhancement for the Consistent Depth Estimation of Monocular Videos using Lightweight Network 基于轻量级网络的单目视频一致深度估计增强

Machine Learning and Applications: An International Journal Pub Date : 2021-09-30 DOI: 10.5121/mlaij.2021.8302

Mohamed N. Sweilam, N. Tolstokulakov

引用次数: 0