Data Mining Practical Machine Learning Tools and Techniques

Statistical Analysis With Missing Data (2nd ed.) (Book)

The existing pavement performance prediction methods are limited to single-factor predictions, which often face the challenges of high cost, low efficiency, and poor accuracy. It is difficult to simultaneously solve the temporal, spatial, and exogenous dependencies between pavement performance data and maintenance, the service life of highways, the environment, and other factors. Digital twin technology based on the building information modeling (BIM) model, combined with machine learning, puts forward a new perspective and method for the accurate and timely prediction of pavement performance. In this paper, we propose a highway tunnel pavement performance prediction approach based on a digital twin and multiple time series stacking (MTSS). This paper (1) establishes an MTSS prediction model with heterogeneous stacking of eXtreme gradient boosting (XGBoost), the artificial neural network (ANN), random forest (RF), ridge regression, and support vector regression (SVR) component learners after exploratory data analysis (EDA); (2) proposes a method based on multiple time series feature extraction to accurately predict the pavement performance change trend, using the highway segment as the minimum computing unit and considering multiple factors; (3) uses grid search with the k-fold cross validation method to optimize hyperparameters to ensure the robustness, stability, and generalization ability of the prediction model; and (4) constructs a digital twin for pavement performance prediction to realize the real-time dynamic evolution of prediction. The method proposed in this study is applied in the life cycle management of the Dalian highway-crossing tunnel in Shanghai, China. A dataset covering 2010–2019 is collected for real-time prediction of the pavement performance. The prediction accuracy evaluation shows that the mean absolute error (MAE) is 0.1314, the root mean squared error (RMSE) is 0.0386, the mean absolute percentage error (MAPE) is 5.10%, and the accuracy is 94.90%. Its overall performance is better than a single model. The results verify that the prediction method based on digital twin and MTSS is feasible and effective in the highway tunnel pavement performance prediction.

/pdf/prediction-of-highway-tunnel-pavement-performance-based-on-7zmbcju16b.pdf

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Detecting human emotion based on facial expression is considered a hard task for the computer vision community because of many challenges such as the difference of face shape from a person to another, difficulty of recognition of dynamic facial features, low quality of digital images, etc. In this paper, we propose a face-sensitive convolutional neural network (FS-CNN) for human emotion recognition. The proposed FS-CNN is used to detect faces on large scale images then analyzing face landmarks to predict expressions for emotion recognition. The FS-CNN is composed form two stages, patch cropping, and convolutional neural networks. The first stage is used to detect faces in high-resolution images and crop the face for further processing. The second stage is a convolutional neural network used to predict facial expression based on landmarks analytics, it was applied on pyramid images to process scale invariance. The proposed FS-CNN was trained and evaluated on the UMD Faces dataset. High performance was achieved with a mean average precision of about 95%.

Human emotion recognition based on facial expressions via deep learning on high-resolution images

Deep neural networks have emerged as a leading approach towards handling many natural language processing (NLP) tasks. Deep networks initially conquered the problems of computer vision. However, dealing with sequential data such as text and sound was a nightmare for such networks as traditional deep networks are not reliable in preserving contextual information. This may not harm the results in the case of image processing where we do not care about the sequence, but when we consider the data collected from text for processing, such networks may trigger disastrous results. Moreover, establishing sentence semantics in a colloquial text such as Roman Urdu is a challenge. Additionally, the sparsity and high dimensionality of data in such informal text have encountered a significant challenge for building sentence semantics. To overcome this problem, we propose a deep recurrent architecture RU-BiLSTM based on bidirectional LSTM (BiLSTM) coupled with word embedding and an attention mechanism for sentiment analysis of Roman Urdu. Our proposed model uses the bidirectional LSTM to preserve the context in both directions and the attention mechanism to concentrate on more important features. Eventually, the last dense softmax output layer is used to acquire the binary and ternary classification results. We empirically evaluated our model on two available datasets of Roman Urdu, i.e., RUECD and RUSA-19. Our proposed model outperformed the baseline models on many grounds, and a significant improvement of 6% to 8% is achieved over baseline models.

/pdf/attention-based-ru-bilstm-sentiment-analysis-model-for-roman-278eaf8s.pdf

Attention-Based RU-BiLSTM Sentiment Analysis Model for Roman Urdu

In the recent few years, a lot of advancement has been made in Urdu linguistics. There are many portals and news websites that are generating a huge amount of data every day. However, there is still no publicly available dataset nor any framework available for automatic Urdu extractive summary generation. In an automatic extractive summary generation, the sentences with the highest weights are given importance to be included in the summary. The sentence weight is computed by the sum of the weights of the words in the sentence. There are two famous approaches to compute the weight of the words in the English language: local weights (LW) approach and global weights (GW) approach. The sensitivity of the weights depends on the contents of the text, the one word may have different weights in a different article, known as LW based approach. Whereas, in the case of GW, the weights of the words are computed from the independent dataset, which implies the weights of all words remain the same in different articles. In the proposed framework, LW and GW based approaches are modeled for the Urdu language. The sentence weight method and the weighted term-frequency method are LW based approaches that compute the weights of the sentences by the sum of important words and the sum of frequencies of the important words, respectively. Whereas, vector space model (VSM) is GW based approach, that computes the weight of the words from the independent dataset, and then remain the same for all types of the text; GW is widely used in the English language for various applications such as information retrieval and text classification. The extractive summaries are generated by LW and GW based approaches and evaluated with ground-truth summaries that are obtained by the experts. The VSM is used as a baseline framework for sentence weighting. Experiments show that LW based approaches are better for extractive summary generation. The F-score of the sentence weight method and the weighted term-frequency method are 80% and 76%, respectively. The VSM achieved only 62% accuracy on the same dataset. Both, the datasets with ground-truth, and the code are made publicly available for the researchers.

Extractive Text Summarization Models for Urdu Language

Sentiment analysis task has widely been studied for various languages such as English and French. However, Roman Urdu sentiment analysis yet requires more attention from peer-researchers due to the lack of Off-the-Shelf Natural Language Processing (NLP) solutions. The primary objective of this study is to investigate the diverse machine learning methods for the sentiment analysis of Roman Urdu data which is very informal in nature and needs to be lexically normalized. To mitigate this challenge, we propose a fine-tuned Support Vector Machine (SVM) powered by Roman Urdu Stemmer. In our proposed scheme, the corpus data is initially cleaned to remove the anomalies from the text. After initial pre-processing, each user review is being stemmed. The input text is transformed into a feature vector using the bag-of-word model. Subsequently, the SVM is used to classify and detect user sentiment. Our proposed scheme is based on a dictionary based Roman Urdu stemmer. The creation of the Roman Urdu stemmer is aimed at standardizing the text so as to minimize the level of complexity. The efficacy of our proposed model is also empirically evaluated with diverse experimental configurations, so as to fine-tune the hyper-parameters and achieve superior performance. Moreover, a series of experiments are conducted on diverse machine learning and deep learning models to compare the performance with our proposed model. We also introduced the largest dataset on Roman Urdu, i.e., Roman Urdu e-commerce dataset (RUECD), which contains 26K+ user reviews annotated by the group of experts. The RUECD is challenging and the largest dataset available of Roman Urdu. The experiments show that the newly generated dataset is quite challenging and requires more attention from the peer researchers for Roman Urdu sentiment analysis. 

Sentiment Analysis of Roman Urdu on E-Commerce Reviews Using Machine Learning

Sindhi language is script language like Arabic and Persian. It's origin is 2500 years old and spoken in various countries in Asia. In this paper, we propose an Optical Character Recognition (OCR) system which recognizes handwritten Sindhi numeral expressions (i.e. Sindhi handwritten numeral strings) without using common input devices such as keyboard and storage device memory. Our experiments focus on character recognition which later can be used for various applications such as tutoring, mathematical kids games, and automatic telephone number conversion from sign boards in India and Pakistan. In our research, we investigate the correlation between the numeral shapes and apply famous state-of-the art classifier based on correlation based template matching. We experimentally show that template matching gives poor performance as the shapes of numerals are highly correlated. There exists little volume of literature to address OCR on Sindhi language but unavailability of benchmark dataset makes it difficult for researchers around the world to re-implement the literature frameworks. We provide two sets of images which can be used for training and prediction.

Handwritten Optical Character Recognition system for Sindhi numerals

The turnover ratio of employees in organization is the most important concerns as employees switching of organization/ job leaves huge gape and affects the performance of that organization. Among many, job satisfaction is the prime reason of employees to quit/switch, which is also directly related to human resource management (HRM) practices of the organization. It is always difficult and sometime beyond the control of human resource (HR) department to retained their well-trained and skilled employees but Data mining can play role to predict those employees who are expected to quit/leave an organization such that the HR department can device intervention strategy or look for alternative. In this paper, we focus on similar problem, where we use data mining techniques such as J48, Naive Bayes, and Logistic Regression predict employees who will leave the organization. Our data consists of different indicator values and some other important features such as number of projects, supervisor evaluation score and experience. We show that J48 perform well with accuracy 98.84% and TP rate 0.984%. Conventional statistical analysis has been used in literature to identify important factors affecting employees satisfaction but there is not agreed set. We also apply data mining techniques to identify such factors using two approaches such as Bayesian Network and IR. Finally, we provide a decision tree based model for decision makers that can easily stimulate employees satisfaction level for better retention policy.

https://www.ijcjournal.org/index.php/InternationalJournalOfComputer/article/download/1354/525

Predictive Human Resource Analytics Using Data Mining Classification Techniques

Convolutional Neural Network (NN) has gained a lot of attention of the researchers due to its high accuracy in classification and feature learning. In this paper, we evaluated the performance of CNN used as feature for image retrieval with the gold standard feature, aka SIFT. Experiments are conducted on famous Oxford 5k data-set. The mAP of SIFT and CNN is 0.6279 and 0.5284, respectively. The performance of CNN is also compared with bag of visual word (BoVW) model. CNN achieves better accuracy than BoVW.

/pdf/performance-evaluation-of-sift-and-convolutional-neural-4x2af6p7ap.pdf

Performance Evaluation of SIFT and Convolutional Neural Network for Image Retrieval

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