A Review of Accessing Big Data with Significant Ontologies

Ontology Based Data Access (OBDA) is a recently proposed approach which is able to provide a conceptual view on relational data sources. It addresses the problem of the direct access to big data through providing end-users with an ontology that goes between users and sources in which the ontology is connected to the data via mappings. We introduced the languages used to represent the ontologies and the mapping assertions technique that derived the query answering from sources. Query answering is divided into two steps: (i) Ontology rewriting, in which the query is rewritten with respect to the ontology into new query; (ii) mapping rewriting the query that obtained from previous step reformulating it over the data sources using mapping assertions. In this survey, we aim to study the earlier works done by other researchers in the fields of ontology, mapping and query answering over data sources

Text Modeling in Adaptive Educational Chat Room Based on Madamira Tool

his paper discusses how to enhance the ability of text modeling in Arabic during chat sessions. Hanini and Jabari et al. modeled the text in chat sessions, but there is still a problem when using Arabic , because the Arabic language is very difficult to comprehend, has complex derivative and many ambiguities. This paper enhanced the previous study and added MADAMIRA tool to analyze the Arabic text. Monitoring and modeling has been completed through the text modeling process by evaluating the student expressions within the chat session using MADAMIRA tool and machine learning. MADAMIRA tool enables the modeling process to categorize Arabic text into different categories, which makes it easier to use the levels of the used expressions and discover the importance of the chat session between two peers. The process of the student modeling using MADAMIRA and Machine learning will update the student model which gathers information about the student achievements within the AVCM

Comparing LSTM and CNN methods in case study on public discussion about Covid-19 in Twitter

This study compares two Deep Learning model methods, which include the Long Short-Term Memory (LSTM) method and the Convolution Neural Network (CNN) method. The aim of the comparison is to discover the performance of two different fundamental deep learning approaches which are based on convolutional theory (CNN) and deal with the vanishing gradient problem (LSTM). The purpose of this study is to compare the accuracy of the two methods using a dataset of 4169 obtained by crawling social media using the Twitter API. The Tweets data we've obtained are based on a specific hashtag keyword, namely "covid-19 pandemic”. This study attempts to assess the sentiment of all tweets about the Covid-19 viral epidemic to determine whether tweets about Covid-19 contain positive or negative thoughts. Before classification, the Preprocessing and Word Embedding steps are completed, and this study has determined that the epoch used is 20 and the hidden layer is 64. Following the classification process, this study concludes that the two methods are appropriate for classifying public conversation sentences against Covid-19. According to this study, the LSTM method is superior, with an accuracy of 83.3%, a precision of 85.6%, a recall of 90.6%, and an f1-score of 88.5%. While the CNN method achieved an accuracy of 81%, precision of 71.7%, recall of 72%, and f1-score of 72

Comparing LSTM and CNN methods in case study on public discussion about Covid-19 in Twitter

This study compares two Deep Learning model methods, which include the Long Short-Term Memory (LSTM) method and the Convolution Neural Network (CNN) method. The aim of the comparison is to discover the performance of two different fundamental deep learning approaches which are based on convolutional theory (CNN) and deal with the vanishing gradient problem (LSTM). The purpose of this study is to compare the accuracy of the two methods using a dataset of 4169 obtained by crawling social media using the Twitter API. The Tweets data we've obtained are based on a specific hashtag keyword, namely "covid-19 pandemic”. This study attempts to assess the sentiment of all tweets about the Covid-19 viral epidemic to determine whether tweets about Covid-19 contain positive or negative thoughts. Before classification, the Preprocessing and Word Embedding steps are completed, and this study has determined that the epoch used is 20 and the hidden layer is 64. Following the classification process, this study concludes that the two methods are appropriate for classifying public conversation sentences against Covid-19. According to this study, the LSTM method is superior, with an accuracy of 83.3%, a precision of 85.6%, a recall of 90.6%, and an f1-score of 88.5%. While the CNN method achieved an accuracy of 81%, precision of 71.7%, recall of 72%, and f1-score of 72

Impact of Delay and Queue on the Length of Left-Turn Storage at Palestine Intersections in Baghdad city, Iraq

At intersections, Left-turning vehicles seek to occupy the same physical space as close to the stop line as possible. These result in high conflicts, delays, and blockage of vehicles by turning vehicles and vice versa. The impact of the lengths of Left-turn lanes on intersection delays is considered to optimize the lengths of the Left-turn lanes. Data for traffic counts, queue lengths, and signal timing are collected from three intersections in Baghdad city in Iraq. The methodology involves the development of estimation models using traffic Simulation SIDRA INTERSECTION 8.0 and simulating various scenarios by varying traffic signal conditions to evaluate delays and queues caused by varying lengths of the Left-turn Lane. Optimal lengths are computed and compared to existing lengths in intersections. No differences in delay, queue, and lengths of the Left turn lane are found using t-test analysis for significance. Outputs from the three models were compared to the maximum observed in the field from the selected intersections. Data analysis involved determining the R2 and the standard error mean between the model output and the observed data. In general, SIDRA INTERSECTION 8.0 overestimated queue vehicles and length of storage for approaches with a high degree of saturation ratios and underestimated it for those with a high degree of saturation ratios

A blockchain-secure mobility data in smart campus

Acquiring knowledge of the patterns of human mobility within a university setting is a critical endeavor that can facilitate the development of effective strategies for future work programs. It is essential to ascertain the positions of campus inhabitants as they engage in daily activities that align with the institution’s work plan framework. Nonetheless, the paramount challenge associated with the presence of personal data on human movement is ensuring the utmost security of this sensitive information. Blockchain technology offers a solution by enabling the safeguarding of personal data through the decentralization of information, wherein individuals act as controllers in a distributed cloud network. In the present study, a straight-forward system comprising GPS sensors and a Raspberry Pi is employed to detect personnel’s location data. The SHA256 algorithm is utilized to generate a hash that connects the constituent blocks, thereby significantly enhancing data security. The intricate hash computation is validated through the implementation of proof-of-work, which generates pertinent binary data at an expeditious block mining time of 16.64 milliseconds. This approach effectively thwarts cyberattacks and ensures the maximum protection of dat

Vasorelaxing effects and inhibition of nitric oxide in macrophages by new iron-containing carbon monoxide-releasing molecules (CO-RMs)

Carbon monoxide-releasing molecules (CO-RMs) are a class of organometallo carbonyl complexes capable of delivering controlled quantities of CO gas to cells and tissues thus exerting a broad spectrum of pharmacological effects. Here we report on the chemical synthesis, CO releasing properties, cytotoxicity profile and pharmacological activities of four novel structurally related iron-allyl carbonyls. The major difference among the new CO-RMs tested was that three compounds (CORM-307, CORM-308 and CORM-314) were soluble in dimethylsulfoxide (DMSO), whereas a fourth one (CORM-319) was rendered water-soluble by reacting the iron-carbonyl with hydrogen tetrafluoroborate. We found that despite the fact all compounds liberated CO, CO-RMs soluble in DMSO caused a more pronounced toxic effect both in vascular and inflammatory cells as well as in isolated vessels. More specifically, iron carbonyls

E-learning and adaptive e-learning review

This paper presents a brief overview of the e-learning concept, elearning history, technology and future, Virtual Classes, adaptive e-learning and case study. The development in the cyber learning is being so rapid in these systems. The features of adaptive learning are emphasized by comparing it with the other elearning systems. It is clear from the paper that adaptive learning has various aspects and will lead to a new era in learning. Using the best suitable e-learning tool depends on the learning group and the space of learning. Adaptive learning enhances the learning ability by customizing the learning objects to the student needs and mapping it directly to the learning domain

E-learning and adaptive e-learning review

This paper presents a brief overview of the e-learning concept, elearning history, technology and future, Virtual Classes, adaptive e-learning and case study. The development in the cyber learning is being so rapid in these systems. The features of adaptive learning are emphasized by comparing it with the other elearning systems. It is clear from the paper that adaptive learning has various aspects and will lead to a new era in learning. Using the best suitable e-learning tool depends on the learning group and the space of learning. Adaptive learning enhances the learning ability by customizing the learning objects to the student needs and mapping it directly to the learning domain