Pub Date : 2018-06-27DOI: 10.5772/INTECHOPEN.75546
Linpeng Jin, Jun Dong
With the development of telemedicine systems, collected ECG records are accumulated on a large scale. Aiming to lessen domain experts ’ workload, we propose a new method based on lead convolutional neural network (LCNN) and rule inference for classification of normal and abnormal ECG records with short duration. First, two different LCNN models are obtained through different filtering methods and different training methods, and then the multipoint-prediction technology and the Bayesian fusion method are successively applied to them. As beneficial complements, four newly developed disease rules are also involved. Finally, we utilize the bias-average method to output the predictive value. On the Chinese Cardiovascular Disease Database with more than 150,000 ECG records, our proposed method yields an accuracy of 86.22% and 0.9322 AUC (Area under ROC curve), comparable to the state-of-the-art results for this subject.
{"title":"Normal Versus Abnormal ECG Classification by the Aid of Deep Learning","authors":"Linpeng Jin, Jun Dong","doi":"10.5772/INTECHOPEN.75546","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75546","url":null,"abstract":"With the development of telemedicine systems, collected ECG records are accumulated on a large scale. Aiming to lessen domain experts ’ workload, we propose a new method based on lead convolutional neural network (LCNN) and rule inference for classification of normal and abnormal ECG records with short duration. First, two different LCNN models are obtained through different filtering methods and different training methods, and then the multipoint-prediction technology and the Bayesian fusion method are successively applied to them. As beneficial complements, four newly developed disease rules are also involved. Finally, we utilize the bias-average method to output the predictive value. On the Chinese Cardiovascular Disease Database with more than 150,000 ECG records, our proposed method yields an accuracy of 86.22% and 0.9322 AUC (Area under ROC curve), comparable to the state-of-the-art results for this subject.","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123342797","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 : 2018-06-27DOI: 10.5772/INTECHOPEN.74932
Y. Ali
The subject of machine condition monitoring and fault diagnosis as a part of system maintenance has gained a lot of interest due to the potential benefits to be learned from reduced maintenance budgets, enhanced productivity and improved machine availabil- ity. Artificial intelligence (AI) is a successful method of machine condition monitoring and fault diagnosis since these techniques are used as tools for routine maintenance. This chapter attempts to summarize and review the recent research and developments in the field of signal analysis through artificial intelligence in machine condition monitoring and fault diagnosis. Intelligent systems such as artificial neural network (ANN), fuzzy logic system (FLS), genetic algorithms (GA) and support vector machine (SVM) have pre - viously developed many different methods. However, the use of acoustic emission (AE) signal analysis and AI techniques for machine condition monitoring and fault diagnosis is still rare. In the future, the applications of AI in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature.
{"title":"Artificial Intelligence Application in Machine Condition Monitoring and Fault Diagnosis","authors":"Y. Ali","doi":"10.5772/INTECHOPEN.74932","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.74932","url":null,"abstract":"The subject of machine condition monitoring and fault diagnosis as a part of system maintenance has gained a lot of interest due to the potential benefits to be learned from reduced maintenance budgets, enhanced productivity and improved machine availabil- ity. Artificial intelligence (AI) is a successful method of machine condition monitoring and fault diagnosis since these techniques are used as tools for routine maintenance. This chapter attempts to summarize and review the recent research and developments in the field of signal analysis through artificial intelligence in machine condition monitoring and fault diagnosis. Intelligent systems such as artificial neural network (ANN), fuzzy logic system (FLS), genetic algorithms (GA) and support vector machine (SVM) have pre - viously developed many different methods. However, the use of acoustic emission (AE) signal analysis and AI techniques for machine condition monitoring and fault diagnosis is still rare. In the future, the applications of AI in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature.","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115805526","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 : 2018-06-27DOI: 10.5772/INTECHOPEN.76944
Evon M. O. Abu-Taieh, A. Alfaries, Nabeel Zanoon, Issam Alhadid, Alia Abu-Tayeh
Handheld devices are flooding the market, and their use is becoming essential among people. Hence, the need for fast and accurate character recognition methods that ease the data entry process for users arises. There are many methods developed for hand - writing character recognition especially for Latin-based languages. On the other hand, character recognition methods for Arabic language are lacking and rare. The Arabic language has many traits that differentiate it from other languages: first, the writing process is from right to left; second, the letter changes shape according to the position in the work; and third, the writing is cursive. Such traits compel to produce a special character recognition method that helps in producing applications for Arabic language. This research proposes a deterministic algorithm that recognizes Arabic alphabet let -ters. The algorithm is based on four categorizations of Arabic alphabet letters. Then, the research suggested a deterministic algorithm composed of 34 rules that can predict the character based on the use of all of categorizations as attributes assembled in a matrix for this purpose.
{"title":"A Deterministic Algorithm for Arabic Character Recognition Based on Letter Properties","authors":"Evon M. O. Abu-Taieh, A. Alfaries, Nabeel Zanoon, Issam Alhadid, Alia Abu-Tayeh","doi":"10.5772/INTECHOPEN.76944","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.76944","url":null,"abstract":"Handheld devices are flooding the market, and their use is becoming essential among people. Hence, the need for fast and accurate character recognition methods that ease the data entry process for users arises. There are many methods developed for hand - writing character recognition especially for Latin-based languages. On the other hand, character recognition methods for Arabic language are lacking and rare. The Arabic language has many traits that differentiate it from other languages: first, the writing process is from right to left; second, the letter changes shape according to the position in the work; and third, the writing is cursive. Such traits compel to produce a special character recognition method that helps in producing applications for Arabic language. This research proposes a deterministic algorithm that recognizes Arabic alphabet let -ters. The algorithm is based on four categorizations of Arabic alphabet letters. Then, the research suggested a deterministic algorithm composed of 34 rules that can predict the character based on the use of all of categorizations as attributes assembled in a matrix for this purpose.","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129746163","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 : 2018-06-27DOI: 10.5772/INTECHOPEN.75694
Rafael Rivera-López, Juana Canul-Reich
In this chapter, the application of a differential evolution-based approach to induce oblique decision trees (DTs) is described. This type of decision trees uses a linear combination of attributes to build oblique hyperplanes dividing the instance space. Oblique decision trees are more compact and accurate than the traditional univariate decision trees. On the other hand, as differential evolution (DE) is an efficient evolutionary algo- rithm (EA) designed to solve optimization problems with real-valued parameters, and since finding an optimal hyperplane is a hard computing task, this metaheuristic (MH) is chosen to conduct an intelligent search of a near-optimal solution. Two methods are described in this chapter: one implementing a recursive partitioning strategy to find the most suitable oblique hyperplane of each internal node of a decision tree, and the other conducting a global search of a near-optimal oblique decision tree. A statistical analysis of the experimental results suggests that these methods show better performance as decision tree induction procedures in comparison with other supervised learning approaches.
{"title":"Differential Evolution Algorithm in the Construction of Interpretable Classification Models","authors":"Rafael Rivera-López, Juana Canul-Reich","doi":"10.5772/INTECHOPEN.75694","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75694","url":null,"abstract":"In this chapter, the application of a differential evolution-based approach to induce oblique decision trees (DTs) is described. This type of decision trees uses a linear combination of attributes to build oblique hyperplanes dividing the instance space. Oblique decision trees are more compact and accurate than the traditional univariate decision trees. On the other hand, as differential evolution (DE) is an efficient evolutionary algo- rithm (EA) designed to solve optimization problems with real-valued parameters, and since finding an optimal hyperplane is a hard computing task, this metaheuristic (MH) is chosen to conduct an intelligent search of a near-optimal solution. Two methods are described in this chapter: one implementing a recursive partitioning strategy to find the most suitable oblique hyperplane of each internal node of a decision tree, and the other conducting a global search of a near-optimal oblique decision tree. A statistical analysis of the experimental results suggests that these methods show better performance as decision tree induction procedures in comparison with other supervised learning approaches.","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121173108","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 : 2018-06-27DOI: 10.5772/INTECHOPEN.74930
V. Phu, Vo Thi Ngoc Tran
Sentiment classification has already been studied for many years because it has had many crucial contributions to many different fields in everyday life, such as in political activi -ties, commodity production, and commercial activities. There have been many kinds of the sentiment analysis such as machine learning approaches, lexicon-based approaches, etc., for many years. The today tendency of the sentiment classification is as follows: (1) Processing many big data sets with shortening execution times (2) Having a high accuracy (3) Integrating flexibly and easily into many small machines or many different approaches. We will present each category in more details.
{"title":"The Today Tendency of Sentiment Classification","authors":"V. Phu, Vo Thi Ngoc Tran","doi":"10.5772/INTECHOPEN.74930","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.74930","url":null,"abstract":"Sentiment classification has already been studied for many years because it has had many crucial contributions to many different fields in everyday life, such as in political activi -ties, commodity production, and commercial activities. There have been many kinds of the sentiment analysis such as machine learning approaches, lexicon-based approaches, etc., for many years. The today tendency of the sentiment classification is as follows: (1) Processing many big data sets with shortening execution times (2) Having a high accuracy (3) Integrating flexibly and easily into many small machines or many different approaches. We will present each category in more details.","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"84 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134475679","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 : 2018-06-27DOI: 10.5772/INTECHOPEN.75575
M. Salleh, Noureen Talpur, Kashif HussainTalpur
The impact of innovated Neuro-Fuzzy System (NFS) has emerged as a dominant technique for addressing various difficult research problems in business. ANFIS (Adaptive Neuro-Fuzzy Inference system) is an efficient combination of ANN and fuzzy logic for modeling highly non-linear, complex and dynamic systems. It has been proved that, with proper number of rules, an ANFIS system is able to approximate every plant. Even though it has been widely used, ANFIS has a major drawback of computational complexities. The number of rules and its tunable parameters increase exponentially when the numbers of inputs are large. Moreover, the standard learning process of ANFIS involves gradient based learning which has prone to fall in local minima. Many researchers have used meta-heuristic algorithms to tune parameters of ANFIS. This study will modify ANFIS architecture to reduce its complexity and improve the accuracy of classification problems. The experiments are carried out by trying different types and shapes of membership functions and meta-heuristics Artificial Bee Colony (ABC) algorithm with ANFIS and the training error results are measured for each combination. The results showed that modified ANFIS combined with ABC method provides better training error results than common ANFIS model.
{"title":"A Modified Neuro-Fuzzy System Using Metaheuristic Approaches for Data Classification","authors":"M. Salleh, Noureen Talpur, Kashif HussainTalpur","doi":"10.5772/INTECHOPEN.75575","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75575","url":null,"abstract":"The impact of innovated Neuro-Fuzzy System (NFS) has emerged as a dominant technique \u0000for addressing various difficult research problems in business. ANFIS (Adaptive \u0000Neuro-Fuzzy Inference system) is an efficient combination of ANN and fuzzy logic for \u0000modeling highly non-linear, complex and dynamic systems. It has been proved that, \u0000with proper number of rules, an ANFIS system is able to approximate every plant. Even \u0000though it has been widely used, ANFIS has a major drawback of computational complexities. \u0000The number of rules and its tunable parameters increase exponentially when the \u0000numbers of inputs are large. Moreover, the standard learning process of ANFIS involves \u0000gradient based learning which has prone to fall in local minima. Many researchers have \u0000used meta-heuristic algorithms to tune parameters of ANFIS. This study will modify \u0000ANFIS architecture to reduce its complexity and improve the accuracy of classification \u0000problems. The experiments are carried out by trying different types and shapes of membership \u0000functions and meta-heuristics Artificial Bee Colony (ABC) algorithm with ANFIS \u0000and the training error results are measured for each combination. The results showed \u0000that modified ANFIS combined with ABC method provides better training error results \u0000than common ANFIS model.","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"637 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122952325","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 : 2018-06-27DOI: 10.5772/INTECHOPEN.75602
M. A. Azqhandi, M. Shekari
In recent years, discharge of synthetic dye waste from different industries leading to aquatic and environmental pollution is a serious global problem of great concern. Hence, the removal of dye prediction plays an important role in wastewater management and conservation of nature. Artificial intelligence methods are popular owing due to its ease of use and high level of accuracy. This chapter proposes a detailed review of artificial intelligence-based removal dye prediction methods particularly multiple linear regression (MLR), artificial neural networks (ANNs), and least squares-support vector machine (LS-SVM). Furthermore, this chapter will focus on ensemble prediction models (EPMs) used for removal dye prediction. EPMs improve the prediction accuracy by integrating several prediction models. The principles, advantages, disadvantages, and applications of these artificial intelligence-based methods are explained in this chapter. Furthermore, future directions of the research on artificial intelligence-based removal dye prediction methods are discussed. process [49], Fenton process [50], and adsorption [51] by applying ANNs. M. Ahmadi and Kh. Naderi applied general regression neural network (GRNN) to predict the removal of methylene blue (MB) and Basic Yellow 28 (BY28) from aqueous solution. Their findings indicated that a well-designed GRNN is able to predict the removal of azo dye based on sonication time, initial dye concentration, and adsorbent mass. Ahmadi and J. Pooralhossini used backpropagation neural network (BPNN) to predict the decolorization of sunset yellow (SY) and disulfine blue (DB) [52]. The obtained results show that the BPNN model outperforms the classical statistical model in terms of R 2 , RMSE, MAE, and AAD for both dyes. Ahmadi and team used BPNN to predict the efficiency of two carcinogenic dye (methylene blue (MB) and malachite green (MG)) adsorption onto Mn@ CuS/ZnS nanocomposite-loaded activated carbon (Mn@ CuS/ZnS-NC-AC) as a novel adsorbent to identify the model parameters in order to improve the prediction performance [35]. Ahmadi and Dastkhoon used neural network to predict Safranin-O (SO) and indigo car-mine (IC) adsorption onto Ni:FeO(OH)-NWs-AC. In this work, the influence of process variables (initial dye concentration, adsorbent mass, and sonication time) on the removal of both dyes was investigated by central composite rotatable design (CCRD) of RSM, multilayer per-ceptron (MLP) neural network, and Doolittle factorization algorithm (DFA). The ANN model
近年来，不同行业的合成染料废水排放造成的水体和环境污染是一个备受关注的全球性问题。因此，染料去除率预测在废水管理和自然保护中发挥着重要作用。人工智能方法因其易于使用和高水平的准确性而受到欢迎。本章详细回顾了基于人工智能的去除染料预测方法，特别是多元线性回归(MLR)、人工神经网络(ANNs)和最小二乘支持向量机(LS-SVM)。此外，本章将重点介绍用于去除染料预测的集合预测模型(epm)。epm通过集成多个预测模型来提高预测精度。本章解释了这些基于人工智能的方法的原理、优缺点和应用。展望了基于人工智能的去除率染料预测方法的未来研究方向。[49]工艺，Fenton工艺[50]，以及应用人工神经网络吸附[51]。艾哈迈迪先生和赫。Naderi应用广义回归神经网络(GRNN)预测了水溶液中亚甲基蓝(MB)和碱性黄28 (BY28)的去除效果。他们的研究结果表明，设计良好的GRNN能够根据超声时间、初始染料浓度和吸附剂质量来预测偶氮染料的去除。Ahmadi和J. Pooralhossini利用反向传播神经网络(BPNN)预测了日落黄(SY)和二硫胺蓝(DB)[52]的脱色效果。结果表明，BPNN模型在两种染料的r2、RMSE、MAE和AAD方面都优于经典统计模型。Ahmadi和团队利用BPNN预测了两种致癌染料(亚甲基蓝(MB)和孔雀石绿(MG))在Mn@ cu /ZnS纳米复合负载活性炭(Mn@ cu /ZnS- nc - ac)上作为新型吸附剂的吸附效率，以确定模型参数，以提高预测性能[35]。Ahmadi和Dastkhoon利用神经网络预测了Safranin-O (SO)和靛蓝car-mine (IC)在Ni:FeO(OH)-NWs-AC上的吸附。在这项工作中，通过RSM的中心复合旋转设计(CCRD)、多层感知器(MLP)神经网络和Doolittle分解算法(DFA)研究了工艺变量(染料初始浓度、吸附剂质量和超声时间)对两种染料去除的影响。人工神经网络模型
{"title":"Application of AI in Modeling of Real System in Chemistry","authors":"M. A. Azqhandi, M. Shekari","doi":"10.5772/INTECHOPEN.75602","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75602","url":null,"abstract":"In recent years, discharge of synthetic dye waste from different industries leading to aquatic and environmental pollution is a serious global problem of great concern. Hence, the removal of dye prediction plays an important role in wastewater management and conservation of nature. Artificial intelligence methods are popular owing due to its ease of use and high level of accuracy. This chapter proposes a detailed review of artificial intelligence-based removal dye prediction methods particularly multiple linear regression (MLR), artificial neural networks (ANNs), and least squares-support vector machine (LS-SVM). Furthermore, this chapter will focus on ensemble prediction models (EPMs) used for removal dye prediction. EPMs improve the prediction accuracy by integrating several prediction models. The principles, advantages, disadvantages, and applications of these artificial intelligence-based methods are explained in this chapter. Furthermore, future directions of the research on artificial intelligence-based removal dye prediction methods are discussed. process [49], Fenton process [50], and adsorption [51] by applying ANNs. M. Ahmadi and Kh. Naderi applied general regression neural network (GRNN) to predict the removal of methylene blue (MB) and Basic Yellow 28 (BY28) from aqueous solution. Their findings indicated that a well-designed GRNN is able to predict the removal of azo dye based on sonication time, initial dye concentration, and adsorbent mass. Ahmadi and J. Pooralhossini used backpropagation neural network (BPNN) to predict the decolorization of sunset yellow (SY) and disulfine blue (DB) [52]. The obtained results show that the BPNN model outperforms the classical statistical model in terms of R 2 , RMSE, MAE, and AAD for both dyes. Ahmadi and team used BPNN to predict the efficiency of two carcinogenic dye (methylene blue (MB) and malachite green (MG)) adsorption onto Mn@ CuS/ZnS nanocomposite-loaded activated carbon (Mn@ CuS/ZnS-NC-AC) as a novel adsorbent to identify the model parameters in order to improve the prediction performance [35]. Ahmadi and Dastkhoon used neural network to predict Safranin-O (SO) and indigo car-mine (IC) adsorption onto Ni:FeO(OH)-NWs-AC. In this work, the influence of process variables (initial dye concentration, adsorbent mass, and sonication time) on the removal of both dyes was investigated by central composite rotatable design (CCRD) of RSM, multilayer per-ceptron (MLP) neural network, and Doolittle factorization algorithm (DFA). The ANN model","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114505096","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 : 2018-06-27DOI: 10.5772/INTECHOPEN.75311
Md. Mohaiminul Islam, Yang Wang, P. Hu
Computational analysis of high-throughput omics data, such as gene expressions, copy number alterations and DNA methylation (DNAm), has become popular in disease studies in recent decades because such analyses can be very helpful to pre- dict whether a patient has certain disease or its subtypes. However, due to the high-dimensional nature of the data sets with hundreds of thousands of variables and very small number of samples, traditional machine learning approaches, such as support vector machines (SVMs) and random forests, have limitations to analyze these data efficiently. In this chapter, we reviewed the progress in applying deep learning algo rithms to solve some biological questions. The focus is on potential software tools and public data sources for the tasks. Particularly, we show some case studies using deep neural network (DNN) models for classifying molecular subtypes of breast cancer and DNN-based regression models to account for interindividual variation in triglyceride concentrations measured at different visits of peripheral blood samples using DNAm profiles. We show that integration of multi-omics profiles into DNN-based learning methods could improve the prediction of the molecular subtypes of breast cancer. We also demonstrate the superiority of our proposed DNN models over the SVM model for predicting triglyceride concentrations. brief
{"title":"Deep Learning Models for Predicting Phenotypic Traits and Diseases from Omics Data","authors":"Md. Mohaiminul Islam, Yang Wang, P. Hu","doi":"10.5772/INTECHOPEN.75311","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75311","url":null,"abstract":"Computational analysis of high-throughput omics data, such as gene expressions, copy number alterations and DNA methylation (DNAm), has become popular in disease studies in recent decades because such analyses can be very helpful to pre- dict whether a patient has certain disease or its subtypes. However, due to the high-dimensional nature of the data sets with hundreds of thousands of variables and very small number of samples, traditional machine learning approaches, such as support vector machines (SVMs) and random forests, have limitations to analyze these data efficiently. In this chapter, we reviewed the progress in applying deep learning algo rithms to solve some biological questions. The focus is on potential software tools and public data sources for the tasks. Particularly, we show some case studies using deep neural network (DNN) models for classifying molecular subtypes of breast cancer and DNN-based regression models to account for interindividual variation in triglyceride concentrations measured at different visits of peripheral blood samples using DNAm profiles. We show that integration of multi-omics profiles into DNN-based learning methods could improve the prediction of the molecular subtypes of breast cancer. We also demonstrate the superiority of our proposed DNN models over the SVM model for predicting triglyceride concentrations. brief","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114866818","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 : 2018-06-27DOI: 10.5772/INTECHOPEN.75136
S. Sugiono, R. Soenoko, R. Lukodono
Additional information
额外的信息
{"title":"Static/Dynamic Zoometry Concept to Design Cattle Facilities Using Back Propagation Neural Network (BPNN)","authors":"S. Sugiono, R. Soenoko, R. Lukodono","doi":"10.5772/INTECHOPEN.75136","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75136","url":null,"abstract":"Additional information","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130961415","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 : 2018-06-27DOI: 10.5772/INTECHOPEN.74562
López-Orozco, Florencia-Juárez
This chapter presents a relatively new approach to show how a web usability classi- cal paradigm can benefit from quantitative data of a nonclassical approach. In the pilot stage, we used experimental eye tracking data acquired from 11 participants faced to a web page to perform three simple tasks. Results show advantages by using eye tracking data to identify and verify some usability problems of such a web page. In this chapter, some hints are presented for people interested in measuring web usability by using such an approach. However, a deeper study should be carried out in order to generalize our results toward the construction of a methodology to be followed by a web developer or interested people in such a field of research.
{"title":"A Quantitative Approach for Web Usability Using Eye Tracking Data","authors":"López-Orozco, Florencia-Juárez","doi":"10.5772/INTECHOPEN.74562","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.74562","url":null,"abstract":"This chapter presents a relatively new approach to show how a web usability classi- cal paradigm can benefit from quantitative data of a nonclassical approach. In the pilot stage, we used experimental eye tracking data acquired from 11 participants faced to a web page to perform three simple tasks. Results show advantages by using eye tracking data to identify and verify some usability problems of such a web page. In this chapter, some hints are presented for people interested in measuring web usability by using such an approach. However, a deeper study should be carried out in order to generalize our results toward the construction of a methodology to be followed by a web developer or interested people in such a field of research.","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123724070","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：