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The urban road networks and vehicles generate exponential amount of spatio-temporal big-data, which invites researchers from diverse fields of interest. Global positioning system devices may transceive data every second thus producing huge amount of trajectory data. Subsequently, it requires optimized computing for various operations such as visualization and mining hidden patterns. This sporadically stored big-data contains invaluable information, which is useful for a number of real-time applications. Compression is a highly important, but knotty task. Optimized compression enables us achieve the desired results in efficient and effective manner by using minimum energy and computational resources without compromising on important information. We present two versions of a compression technique based on the points of intersections (PoI) of urban roads networks. Based on intelligent mining paradigm, we created a compressed lookup lexicon to store the PoIs of dynamically selected region of interests (ROI). An important feature of our lexicon is the key pattern, which is intelligently computed based on the relative geographic position of a spatial geodetic vertex with respect to Euclidean space origin in a given ROI. This compresses trajectories in linear time, making it feasible for mission critical real world applications. Our experimental dataset contained 959 547, 517 436, and 231 740 trajectories for Bikes, Cars, and Taxis, respectively. The Compr 10 reduced these trajectories to 17 428, 11 084, and 6565, respectively. Results of Compr 15 and Compr 20 show promising results. We define the quality of the compression in context of the considered problem. The results show that the proposed technique achieved satisfactory quality of the compression. INTRODUCTIONThe urban roads networks and many types of vehicles running over them generate exponential amount of spatio-temporal big-data, which attracts the keen interest of researchers from diverse fields of research such as power and energy, geographical information systems, data mining, artificial intelligence, and so on. This big-data contains invaluable This is a companion to [10.1002/ett.3886].

The use of deep learning (DL) techniques for mobile learning is an emerging field aimed at developing methods for finding mobile learners' learning behavior and exploring important learning features. The learning features (learning time, learning location, repetition rate, content types, learning performance, learning time duration, and so on) act as fuel to DL algorithms based on which DL algorithms can classify mobile learners into different learning groups. In this study, a powerful and efficient m-learning model is proposed based on DL techniques to model the learning process of m-learners. The proposed m-learning model determines the impact of independent learning features on the dependent feature i.e. learners' performance. The m-learning model dynamically and intuitively explores the weights of optimum learning features on learning performance for different learners in their learning environment. Then it split learners into different groups based on features differences, weights, and interrelationships. Because of the high accuracy of the DL technique, it was used to classify learners into five different groups whereas random forest (RF) ensemble method was used in determining each feature importance in making adaptive m-learning model. Our experimental study also revealed that the m-learning model was successful in helping m-learners in increasing their performance and taking the right decision during the learning flow.

Mobile learning (M-learning) has gained tremendous attention in the educational environment in the past decade. For effective M-learning, it is important to create an efficient M-learning model that can identify the exact requirements of mobile learners (M-learners). M-learning model is composed of features that are generated during M-learners' interaction with mobile devices. For an adaptive M-learning model, not only learning features are required, but it is also important to determine how they differ for various M-learners, their weights, and interrelationship. This study proposes a robust and adaptive Mlearning model that is based on machine learning and deep learning (ML/DL) techniques. The proposed M-learning model dynamically explores learning features, their corresponding weights, and association for M-learners. Based on learning features, the M-learning model categorizes M-learners into different performance groups. The M-learning model then provides adaptive content, suggestions, and recommendations to M-learners in order to make learning adaptive and stimulating. For comparative analysis, the prediction accuracy of five baseline ML models was compared with the deep Artificial Neural Network (deep ANN). The results demonstrated that deep ANN and Random Forest (RF) models exhibited better prediction accuracy. Subsequently, both models were selected for developing the M-learning model which included the performance categorization of M-learners under a five-level classification scheme and assigning weights to various features for providing adaptive help and support to M-learners. Our explanatory analysis has shown that behavioral features besides contextual features also influence the learning performance of M-learners. As a direct outcome of this research, more efficient, interactive, and useful mobile learning applications can be developed that accurately predict learning objectives and requirements of diverse Mlearners thus helping M-learners in enhancing their study behavior.

Abstract--NaturalLanguage Processing is the multidisciplinary area of Artificial Intelligence, Machine Learning and Computational Linguistic for processing human language automatically. It involves understanding and processing of human language. The way through which we share our contents or feelings have always great importance in understanding and processing of language. Parsing is the most suited approach in identifying and scanning what the available sentences expressed? Parsing is the process in which syntactic structure of sentence is identified using grammatical tags. The syntactically correct sentence structure is achieved by assigning grammatical labels to its constituents using lexicon and syntactic rules. Phrase and Dependency are two main structure formalisms for parsing natural language sentences. The growing use of web 2.0 has produced novel research challenges as people from different geographical areas are using this channel and sharing contents in their native languages. Urdu is one of such free word order native language which is widely shared over social media sites but identification and summarization of Urdu sentences is challenging task. In this review paper we present an overview to recent work in parsing of fixed order (i.e. English) and free word order languages (i.e Urdu) in order to reveal the most suited method for Urdu Language Parsing. This survey explored that dependency parsing is more appropriate for Urdu and other free word order languages and parsers of English language are not useful in parsing Urdu sentence due to its morphological, syntactical and grammatical differences.