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Objectives: Sindhi language, historically rich belongs to Indo-Aryan language with diverse background and diverse dialects. Recent drive in globalization, e-commerce and e-literacy have influenced on languages as well. There are lots of magazines, Sindhi books, newspapers and web material available online, but unluckily still proper dataset is not designed for Sindhi information processing. This research study focuses on the Sindhi language news headline texts dataset and automated tool for the online texts’ classification based on the predefined label. Methods/Statistical Analysis: For the collection of datasets, the scraping tool is designed for extraction of the headline news from most popular newspapers: Awami Awaz and Daily Jhoongar. The dataset contains 2800 Sindhi headline news with five categories: 0. Entertainment, 1. Sports, 2. Science and Technology, 3. International, 4. National, 5. Sindhi news. The dataset is normalized by removing stop words and cleaning the spaces, punctuations and other unnecessary texts. Furthermore, the language feature is analyzed using TF-IDF and vector model. This paper presents Sindhi headline news classification model with implementation of the machine learning classification algorithms, namely. Multinomial NB, Linear SVC, Logistic Regression, MLP classifier, SGD Classifier, Random Forest Classifier, Ridge Classifier. Findings: The results show that the performance of the Linear SVC and MLP Classifier indicate better results on Sindhi headlines news categorization as compared to other classification techniques. This research study helps in improving the automatic classification of Sindhi text headline news. Application/Improvements: It is recommended that LSVC and MLP Classifiers should be used in Sindhi language news headline classification.

Objectives: The emotion detection is one of the important fields in computer human interaction and this study plays significant a role for identification facial expression from the images. To identify the single emotion, need a various variability of human shapes such as pose, color, texture, expression, posture and orientation. In this study, we implement Local Binary Pattern (LBP) based filters for identifying the dynamic face textures. And moreover, this approach also provides extension and simplification. Methods/Statistical Analysis: We used built-in FER2013 datasets, the database consisting seven classes (Surprise, Fear, Angry, Neutral, Sad, Disgust, Happy). The dataset is divided into three parts testing, validation and training (15% and 70%). The Convolution neural network is trained with feature Descriptor Local Binary Pattern. Findings: The experimental results have demonstrated that local LBP representations are effective in spatial dynamic feature extraction, as they encode the information of image texture configuration while providing local structure patterns. The advantages of our approach include local processing, robustness to monotonic grayscale changes and simple computation. The results show that, the performance LBP based Convolution Neural Network (CNN) model is better than conventional CNN. This research study further helps in image classification and image processing fields. Application/Improvements: It is recommended that LBP should be used for finding the local regions or pattern from the image. The LBP computation and local processing is quite better with robustness and monotonic changes. Keywords: Convolution Neural Network (CNN), Facial Emotion, Facial Expression, Face detection, Expressions, Local Binary Pattern (LBP)

Objectives: A new method is introduced in this study for Facial expression recognition using FER2013 database consisting seven classes consisting (Surprise, Fear, Angry, Neutral, Sad, Disgust, Happy) in past few decades, Exploration of methods to recognize facial expressions have been active research area and many applications have been developed for feature extraction and inference. However, it is still challenging due to the high-intra class variation. Methods/Statistical Analysis: we deeply analyzed the accuracy of both handcrafted and leaned aspects such as HOG. This study proposed two models; (1) FER using Deep Convolutional Neural Network (FER-CNN) and (2) Histogram of oriented Gradients based Deep Convolutional Neural Network (FER-HOGCNN). the training and testing accuracy of FER-CNN model set 98%, 72%, similarly Losses were 0.02, 2.02 respectively. On the other side, the training and testing accuracy of FER- HOGCNN model set 97%, 70%, similarly Losses were 0.04, 2.04. Findings: It has been found that the accuracy of FER- HOGCNN model is good overall but comparatively not better than Simple FER-CNN. In dataset the quality of images are low and small dimensions, for that reason, the HOG loses some important features during training and testing. Application/Improvements: The study helps for improving the FER System in image processing and furthermore, this work shall be extended in future, and order to extract the important features from images by combining LBP and HOG operator using Deep Learning models. Keywords: Deep Learning, Emotion Recognition, Facial Expression, CNN, FER, HOG

This paper focuses on the performance analysis of hyperparameters of the Sentiment Analysis (SA) model of a course evaluation dataset. The performance was analyzed regarding hyperparameters such as activation, optimization, and regularization. In this paper, the activation functions used were adam, adagrad, nadam, adamax, and hard_sigmoid, the optimization functions were softmax, softplus, sigmoid, and relu, and the dropout values were 0.1, 0.2, 0.3, and 0.4. The results indicate that parameters adam and softmax with dropout value 2.0 are effective when compared to other combinations of the SA model. The experimental results reveal that the proposed model outperforms the state-of-the-art deep learning classifiers.

This paper presents the automated tool for the classification of text with respect to predefined categories. It has always been considered as a vital method to manage and process a huge number of documents in digital forms which are widespread and continuously increasing. Most of the research work in text classification has been done in Urdu, English and other languages. But limited research work has been carried out on roman data. Technically, the process of the text classification follows two steps: the first step consists of choosing the main features from all the available features of the text documents with the usage of feature extraction techniques. The second step applies classification algorithms on those chosen features. The data set is collected through scraping tools from the most popular news websites Awaji Awaze and Daily Jhoongar. Furthermore, the data set splits in training and testing 70%, 30%, respectively. In this paper, the deep learning models, such as RNN, LSTM, and CNN, are used for classification of roman Urdu headline news. The testing accuracy of RNN (81%), LSTM (82%), and CNN (79%), and the experimental results demonstrate that the performance of the LSTM method is state-of-art method compared to CNN and RNN.