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Usability is one of the most important characteristics of software applications, especially when it comes to mobile shopping applications. There is a great deal of shift from traditional shopping to online shopping because it benefits both parties i.e., customers as well as businessmen. In such a scenario, the usability factor can play a very vital role in the business industry. If a client stops using a mobile shopping app because it is not user-friendly, it can badly damage the annual revenues especially when hundreds of alternatives are available and there is tough competition. Therefore, to keep existing customers intact and to attract new customers, it is very important to provide a user-friendly mobile app to customers. This paper considers a large variety of online customers with diverse requirements. background and constraints and evaluate the usability of existing mobile e-commerce apps to identify the problems people face with existing applications and do a systematic review of existing shopping apps. Then, we propose a personalized and adaptive usability model for mobile commerce apps considering the neglected user type i.e., illiterates and people with tactile disabilities. The proposed model LRAP is a layered approach from generalization to specification and it can be considered an extension of the famous PACMAD usability model. Besides this, we also suggest a combining score tool which will be helpful in measuring the usability of any app. Participant-based usability evaluation is the major technique used to identify the design problems in existing mobile commerce apps. Major design issues identified in mobile commerce apps include poor navigation and lack of personalization for illiterates and people with tactile disabilities. [ABSTRACT FROM AUTHOR]

Citation creates a link between citing and the cited author, and the frequency of citation has been regarded as the basic element to measure the impact of research and knowledge-based achievements. Citation frequency has been widely used to calculate the impact factor, H index, i10 index, etc., of authors and journals. However, for a fair evaluation, the qualitative aspect should be considered along with the quantitative measures. The sentiments expressed in citation play an important role in evaluating the quality of the research because the citation may be used to indicate appreciation, criticism, or a basis for carrying on research. In-text citation analysis is a challenging task, despite the use of machine learning models and automatic sentiment annotation. Additionally, the use of deep learning models and word embedding is not studied very well. This study performs several experiments with machine learning and deep learning models using fastText, fastText subword, global vectors, and their blending for word representation to perform in-text sentiment analysis. A dimensionality reduction technique called principal component analysis (PCA) is utilized to reduce the feature vectors before passing them to the classifier. Additionally, a customized convolutional neural network (CNN) is presented to obtain higher classification accuracy. Results suggest that the deep learning CNN coupled with fastText word embedding produces the best results in terms of accuracy, precision, recall, and F1 measure. [ABSTRACT FROM AUTHOR]

Agriculture plays a vital role in the economy of Pakistan. Issues concerning irrigation have been always encumbering the development of the country. Water scarcity is becoming a big issue because of climate change, insufficient services and rising population. Farmers are not receiving the supply of equitable water efficiently due to current methods of irrigation, such as inequitable water distribution, manual reporting of mogha discharge by OFWM, change of water turn at late night and no need of water at his allocated time, tail-end user problems like either too much of water supply or no availability of water as per need of farmers. One solution to this problem is a smart irrigation system in which the system uses internet of things (IoT) based sensors to monitor water levels and communicates the water situation to the user. In this research, we elaborate on the applicability of the Internet of Things (IOT) in the irrigation system and propose an architecture of IoT based water irrigation system using a wireless sensor network to solve the problems of farmers. The IoT based smart irrigation system is based on the raspberry pi to improve the productivity of water and keep costs down. Farmer is informed about fields while there is any deviance from the expected water situation by a text message. [ABSTRACT FROM AUTHOR]

Social media has become one of the most popular sources of information. People communicate with each other and share their ideas, commenting on global issues and events in a multilingual environment. While social media has been popular for several years, recently, it has given an exponential rise in online data volumes because of the increasing popularity of local languages on the web. This allows researchers of the NLP community to exploit the richness of different languages while overcoming the challenges posed by these languages. Urdu is also one of the most used local languages being used on social media. In this paper, we presented the first-ever event detection approach for Urdu language text. Multiclass event classification is performed by popular deep learning (DL) models, i.e.,Convolution Neural Network (CNN), Recurrence Neural Network (RNN), and Deep Neural Network (DNN). The one-hot-encoding, word embedding, and term-frequency inverse document frequency- (TF-IDF-) based feature vectors are used to evaluate the Deep Learning(DL) models. The dataset that is used for experimental work consists of more than 0.15 million (103965) labeled sentences. DNN classifier has achieved a promising accuracy of 84% in extracting and classifying the events in the Urdu language script. [ABSTRACT FROM AUTHOR]

It is the age of the social web, where people express themselves by giving their opinions about various issues, from their personal life to the world's political issues. This process generates a lot of opinion data on the web that can be processed for valuable information, and therefore, semantic annotation of opinions becomes an important task. Unfortunately, existing opinion annotation schemes have failed to satisfy annotation challenges and cannot even adhere to the basic definition of opinion. Opinion holders, topical features and temporal expressions are major components of an opinion that remain ignored in existing annotation schemes. In this work, we propose OpinionML, a new Markup Language, that aims to compensate for the issues that existing typical opinion markup languages fail to resolve. We present a detailed discussion about existing annotation schemes and their associated problems. We argue that OpinionML is more robust, flexible and easier for annotating opinion data. Its modular approach while implementing a logical model provides us with a flexible and easier model of annotation. OpinionML can be considered a step towards "information symmetry". It is an effort for consistent sentiment annotations across the research community. We perform experiments to prove robustness of the proposed OpinionML and the results demonstrate its capability of retrieving significant components of opinion segments. We also propose OpinionML ontology in an effort to make OpinionML more inter-operable. The ontology proposed is more complete than existing opinion ontologies like Marl and Onyx. A comprehensive comparison of the proposed ontology with existing sentiment ontologies Marl and Onyx proves its worth. [ABSTRACT FROM AUTHOR]

We applied t-distributed stochastic neighbor embedding (t-SNE) to visualize Urdu handwritten numerals (or digits). The data set used consists of 28 × 28 images of handwritten Urdu numerals. The data set was created by inviting authors from different categories of native Urdu speakers. One of the challenging and critical issues for the correct visualization of Urdu numerals is shape similarity between some of the digits. This issue was resolved using t-SNE, by exploiting local and global structures of the large data set at different scales. The global structure consists of geometrical features and local structure is the pixel-based information for each class of Urdu digits. We introduce a novel approach that allows the fusion of these two independent spaces using Euclidean pairwise distances in a highly organized and principled way. The fusion matrix embedded with t-SNE helps to locate each data point in a two (or three-) dimensional map in a very different way. Furthermore, our proposed approach focuses on preserving the local structure of the high-dimensional data while mapping to a low-dimensional plane. The visualizations produced by t-SNE outperformed other classical techniques like principal component analysis (PCA) and auto-encoders (AE) on our handwritten Urdu numeral dataset. [ABSTRACT FROM AUTHOR]

The real-time availability of the Internet has engaged millions of users around the world. The usage of regional languages is being preferred for effective and ease of communication that is causing multilingual data on social networks and news channels. People share ideas, opinions, and events that are happening globally i.e ., sports, inflation, protest, explosion, and sexual assault, etc . in regional (local) languages on social media. Extraction and classification of events from multilingual data have become bottlenecks because of resource lacking. In this research paper, we presented the event classification task for the Urdu language text existing on social media and the news channels by using machine learning classifiers. The dataset contains more than 0.1 million (102,962) labeled instances of twelve (12) different types of events. The title, its length, and the last four words of a sentence are used as features to classify the events. The Term Frequency-Inverse Document Frequency ( tf-idf ) showed the best results as a feature vector to evaluate the performance of the six popular machine learning classifiers. Random Forest (RF) and K-Nearest Neighbor (KNN) are among the classifiers that out-performed among other classifiers by achieving 98.00% and 99.00% accuracy, respectively. The novelty lies in the fact that the features aforementioned are not applied, up to the best of our knowledge, in the event extraction of the text written in the Urdu language., Competing Interests: The authors declare that they have no competing interests., (© 2021 Awan et al.)