Yazar "Hussain, Walayat" seçeneğine göre listele

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    Blockchain-based IoT: An Overview
    (Ieee, 2021) Raza, Muhammad Raheel; Varol, Asaf; Hussain, Walayat
    The Internet of Things (IoT) has revolutionized the human world by transforming ordinary everyday objects into smart devices. These autonomous devices have reshaped our lives. The emerging technology is expanding day-by-day with the increasing need for smart devices as so the issues are also increasing w.r.t security, data reliability, maintenance and authentication. On the other hand, another innovative technology- Blockchain- has transformed our financial world by introducing sophisticated security. An integrated Blockchain-IoT system can resolve the problems they face individually and serve the technological world better. The paper provides a comprehensive study of both technologies by highlighting their features and challenges. The article further critically analyses existing approaches that discussed various issue about IoT and Blockchain.
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    Performance analysis of deep approaches on airbnb sentiment reviews
    (2022) Raza, Muhammad Raheel; Hussain, Walayat; Varol, Asaf
    Consumer reviews in the Airbnb marketplace are one of the key attributes to measure the quality of services and the main determinant of consumer rentals decisions. Such feedback can impact both a new and repeated consumer's choice decision. The way to manage poor reviews can help to save or damage the host's reputation. Sentiment analysis enables an Airbnb host to get an insight into the business, pinpoint degradation of the specific component of compound services and assist in managing it proactively. Multiple Deep Learning algorithms have been used for Natural Language Processing (NLP). For optimal sentiment management in the Airbnb marketplace, it is crucial to identify the right algorithm. The paper uses multiple Deep Learning algorithms to identify different aspects of guest reviews and analyze their accuracies. The paper uses four accuracy measurement benchmarks – Precision, Recall, F1-score and Support to analyze results. The analysis shows that the GRU method achieves the best results with the highest classification metrics values as compared to RNN and LSTM.
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    Sentiment Analysis using Deep Learning in Cloud
    (Ieee, 2021) Raza, Muhammad Raheel; Hussain, Walayat; Tanyıldızi, Erkan; Varol, Asaf
    Sentiments are the emotions or opinions of an individual encapsulated within texts or images. These emotions play a vital role in the decision-making process for a business. A cloud service provider and consumer are bound together in a Service Level Agreement (SLA) in a cloud environment. SLA defines all the rules and regulations for both parties to maintain a good relationship. For a long-lasting and sustainable relationship, it is vital to mine consumers' sentiment to get insight into the business. Sentiment Analysis or Opinion Mining refers to the process of extracting or predicting different point of views from a text or image to conclude. Various techniques, including Machine Learning and Deep Learning, strives to achieve results with high accuracy. However, most of the existing studies could not unveil hidden parameters in text analysis for optimal decision-making. This work discusses the application of sentiment analysis in the cloud-computing paradigm. The paper provides a comparative study of various textual sentiment analysis using different deep learning approaches and their importance in cloud computing. The paper further compares existing approaches to identify and highlight gaps in them.