Search

Machine Learning (ML) and Automated Machine Learning (Auto-ML) had attracted more attention and interest in industrial applications in recent years. Machine learning pipeline includes repetitive tasks such as data pre-processing, feature engineering, model selection and hyper-parameter optimization. Building a machine learning model requires extensive time for development, stress-testing, and multiple experiments. Besides, building a model with a small search space of pipeline steps with multiple algorithms and hyper-parameters takes hours. Hence, Auto-ML has been widely adapted to save time and efforts on such tasks. Nevertheless, there are many limitations in the existing Auto-ML frameworks. Most of the existing Auto-ML pipeline frameworks focus only on a part of the ML pipeline which does not always result in the optimum overall pipeline for the given dataset. Moreover, several Auto-ML pipeline frameworks ignore integrating meta-learning, thus they recommend a well-performing pipeline for a single task and not a global and generic optimal pipeline. Thus, for new tasks they must search for a new pipeline. Furthermore, although there are Auto-ML frameworks that consider the complete pipeline but imbalanced datasets did not receive much attention from the proposed Auto-ML frameworks. To tackle these challenges, we propose a new efficient Auto-ML framework that is equipped with a meta learning mechanism for automated algorithm selection which also handles imbalanced datasets efficiently. This paper also demonstrates how the proposed framework outperforms the-state-of-the-art frameworks.

Internet of Things (IoT) is a network which connects different communication devices with the internet to attain quick, robust and real-time information transfer and communication, achieving intelligent management. IoT is still in its infancy so it faces numerous challenges varying from data management to security concerns. Sensors generate enormous quantities of data that need to be handled efficiently to have successful deployment of IoT applications. Concerning data management, a great challenge that faces the IoT environment is the detection of contextual anomalies. Contextual anomaly detection is a sophisticated task because the context has to be taken into consideration in the anomaly detection process rather than checking only the deviation of the data value as in point anomaly detection. As a result, in this paper, a novel clustering based algorithm is proposed to detect contextual anomalies in Internet of Things. Attributes were separated into two different categories, namely contextual attributes and behavioral attributes. K-Means clustering technique was applied on the contextual and behavioral attributes separately, then the intersection between the contextual and behavioral clusters was used to detect the contextual anomalies. Moreover, the algorithm was applied on a real room occupation dataset of size around 20,000 records and the experiments showed promising results.

People’s opinions are considered as the most powerful source of market research. Popularly, Social Media has become a tool that is used in an easy way including huge number of users who can share their opinions about products or services and their thoughts about current problems of the society and express their views on political and religious issues. The knowledge extracted from social media contains sentiment data – that is not included in corporate database – that can be used to improve the marketing campaigns to retain customers and meet their needs in a better way. The integration and merging between both social media data and corporate data can lead to better insights that would not have been possible to gain without such integration. In this paper, we will use Twitter as a social media source platform to do a feature based level sentiment analysis using tweets including opinions about a specific product. . The research discussed three different ways to extract (feature/opinion) pairs from each text including: Normal Tokenization, N-gram Modeling Extraction, and Noun Chunking Extraction. The extracted opinion phrase related to each extracted feature is being classified using sentiment classification algorithm. A decision is taken about the best between the three ways according to the resulted measurements. SCDJF had been evaluated using multiple techniques. The best results occurred from Noun Chunking Extraction with accuracy 77%. Summarization of the results will show how this can be used to enhance decision making process of the organization. Summarization of the results will show how this can be used to enhance decision making process of the organization.

Inspired by the great success of information retrieval (IR) style keyword search on the web, keyword search on XML has emerged recently. Existing methods cannot resolve challenges addressed by using keyword search in Temporal XML documents. We propose a way to evaluate temporal keyword search queries over Temporal XML documents. Moreover, we propose a new ranking method based on the time-aware IR ranking methods to rank temporal keyword search queries results. Extensive experiments have been conducted to show the effectiveness of our approach.