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Predicting a trip's travel time is essential for route planning and navigation applications. The majority of research is based on international data that does not apply to Pakistan's road conditions. We designed a complete pipeline for mining trajectories from sensors data. On this data, we employed state-of-the-art approaches, including a shallow artificial neural network, a deep multi-layered perceptron, and a long-short-term memory, to explore the issue of travel time prediction on frequent routes. The experimental results demonstrate an average prediction error ranging from 30 seconds to 1.2 minutes on trips lasting 10 minutes to 60 minutes on six most frequent routes in regions of Islamabad, Pakistan., Comment: N/A

Discovering what other people think has always been a key aspect of our information-gathering strategy. People can now actively utilize information technology to seek out and comprehend the ideas of others, thanks to the increased availability and popularity of opinion-rich resources such as online review sites and personal blogs. Because of its crucial function in understanding people's opinions, sentiment analysis (SA) is a crucial task. Existing research, on the other hand, is primarily focused on the English language, with just a small amount of study devoted to low-resource languages. For sentiment analysis, this work presented a new multi-class Urdu dataset based on user evaluations. The tweeter website was used to get Urdu dataset. Our proposed dataset includes 10,000 reviews that have been carefully classified into two categories by human experts: positive, negative. The primary purpose of this research is to construct a manually annotated dataset for Urdu sentiment analysis and to establish the baseline result. Five different lexicon- and rule-based algorithms including Naivebayes, Stanza, Textblob, Vader, and Flair are employed and the experimental results show that Flair with an accuracy of 70% outperforms other tested algorithms., Comment: Sentiment Analysis, Opinion Mining, Urdu language, polarity assessment, lexicon-based method

Event Tree (ET) analysis is a widely used forward deductive safety analysis technique for decision-making at a system design stage. Existing ET tools usually provide Graphical Users Interfaces (GUI) for users to manually draw system-level ET diagrams, which consist of nodes and branches, describing all possible success and failure scenarios. However, these tools do not include some important ET analysis steps, e.g., the automatic generation and reduction of a complete system ET diagram. In this paper, we present a new Event Trees Modeling and Analysis (ETMA) tool to facilitate users to conduct a complete ET analysis of a given system. Some key features of ETMA include: (i) automatic construction of a complete ET model of real-world systems; (ii) deletion/reduction of unnecessary ET nodes and branches; (iii) partitioning of ET paths; and (iv) probabilistic analysis of the occurrence of a certain event. For illustration purposes, we utilize our ETMA tool to conduct the ET analysis of a protective fault trip circuit in power grid transmission lines. We also compared the ETMA results with Isograph, which is a well-known commercial tool for ET analysis., Comment: 21 pages, 10 figures, 4 tables, The 14th Annual IEEE International Systems Conference (SYSCON 2020)

Event Tree (ET) analysis is widely used as a forward deductive safety analysis technique for decision-making at the critical-system design stage. ET is a schematic diagram representing all possible operating states and external events in a system so that one of these possible scenarios can occur. In this report, we propose to use the HOL4 theorem prover for the formal modeling and step-analysis of ET diagrams. To this end, we developed a formalization of ETs in higher-order logic, which is based on a generic list datatype that can: (i) construct an arbitrary level of ET diagrams; (ii) reduce the irrelevant ET branches; (iii) partition ET paths; and (iv) perform the probabilistic analysis based on the occurrence of certain events. For illustration purposes, we conduct the formal ET stepwise analysis of an electrical power grid and also determine its System Average Interruption Frequency Index (SAIFI), which is an important indicator for system reliability., Comment: 17 pages, 3 figures

This study presents a novel method to recognize human physical activities using CNN followed by LSTM. Achieving high accuracy by traditional machine learning algorithms, (such as SVM, KNN and random forest method) is a challenging task because the data acquired from the wearable sensors like accelerometer and gyroscope is a time-series data. So, to achieve high accuracy, we propose a multi-head CNN model comprising of three CNNs to extract features for the data acquired from different sensors and all three CNNs are then merged, which are followed by an LSTM layer and a dense layer. The configuration of all three CNNs is kept the same so that the same number of features are obtained for every input to CNN. By using the proposed method, we achieve state-of-the-art accuracy, which is comparable to traditional machine learning algorithms and other deep neural network algorithms., Comment: IEEE ICET 2019