The advances from the last few decades in the fields of ML (Machine Learning), DL (Deep Learning), and semantic computing are now changing the shape of the healthcare system. But, unlike physical health problems, diag...
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Message Queuing Telemetry Transport (MQTT) has emerged as the widely adopted application layer protocol for IoT environments because of its lightweight header, minimal power, and bandwidth requirements. Despite its po...
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Message Queuing Telemetry Transport (MQTT) has emerged as the widely adopted application layer protocol for IoT environments because of its lightweight header, minimal power, and bandwidth requirements. Despite its popularity, the earlier version of the protocol, MQTT v3.1.1, encounters performance issues in large-scale implementations and required an update to handle the growing requirements of modern IoT applications. In response to these concerns, MQTT v5.0 was released with several significant features designed to enhance the reliability, user experience, and performance of IoT systems. While the MQTT protocol features were intended to facilitate robust and efficient communications, adversaries could exploit these features to mount various types of attacks in IoT deployments. More specifically, the Denial of Service (DoS) attacks towards the MQTT protocol have recently gained a lot of attention from the research community. However, the existing works primarily focus only on exploring the possibilities of misusing the MQTT v3.1.1 protocol features to generate DoS attacks in IoT realms. In this work, we attempt to extensively investigate the advanced protocol features of MQTT v5.0 that can be exploited to launch DDoS attacks impacting the IoT paradigm. We present the first critical evaluation of Distributed Denial of Service (DDoS) attacks on the MQTT v5.0 protocol by analyzing three significant features: CONNECT Properties, User Properties, and Flow Control. Moreover, we systematically propose attack scenarios based on the adversary's capabilities, thus illustrating the practicality of proposed attacks in real-world scenarios. Furthermore, we built a real-world testbed for IoT healthcare application to evaluate the severity of the identified attacks. The experimental results demonstrate the effectiveness of these attacks in impacting the availability of guaranteed IoT services to legitimate users, even in times of need. Additionally, we disclose the insightful fi
In the current era of smart technology, integrating the Internet of Things (IoT) with Artificial Intelligence has revolutionized several fields, including public health and sanitation. The smart lavatory solution prop...
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Vehicular Named Data Networks (VNDN) is a content centric approach for vehicle networks. The fundamental principle of addressing the content rather than the host, suits vehicular environment. There are numerous challe...
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Cervical cell segmentation is a significant task in medical image analysis and can be used for screening various cervical diseases. In recent years, substantial progress has been made in cervical cell segmentation tec...
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Representation of compound information in a truthful, coarse way forms the layout of the granular computing paradigm. In granular computing, the continuous variables are mapped into intervals to be utilized in the ext...
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Load forecasting plays a crucial role in mitigating risks for utilities by predicting future usage of commodity markets transmission or supplied by the utility. To achieve this, various techniques such as price elasti...
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Load forecasting plays a crucial role in mitigating risks for utilities by predicting future usage of commodity markets transmission or supplied by the utility. To achieve this, various techniques such as price elastic demand, climate and consumer response, load analysis, and sustainable energy generation predictive modelling are used. As both supply and demand fluctuate, and weather and power prices can rise significantly during peak periods, accurate load forecasting becomes critical for utilities. By providing brief demand forecasts, load forecasting can assist in estimating load flows and making decisions that prevent overloading. Therefore, load forecasting is crucial in helping electric utilities make informed decisions related to power, load switching, voltage regulation, switching, and infrastructure development. Forecasting is a methodology used by electricity companies to forecast the amount of electricity or power production needed to maintain constant supply as well as load demand balance. It is required for the electrical industry to function properly. The smart grid is a new system that enables electricity providers and customers to communicate in real-time. The precise energy consumption sequence of the consumers is required to enhance the demand schedule. This is where predicting the future comes into play. Forecasting future power system load (electricity consumption) is a critical task in providing intelligence to the power grid. Accurate forecasting allows utility companies to allocate resources and assume system control in order to balance the same demand and availability for electricity. In this article, a study on load forecasting algorithms based on deep learning, machine learning, hybrid methods, bio-inspired techniques, and other techniques is carried out. Many other algorithms based on load forecasting are discussed in this study. Different methods of load forecasting were compared using three performance indices: RMSE (Root Mean Square Err
Sequence-to-sequence models are fundamental building blocks for generating abstractive text summaries, which can produce precise and coherent summaries. Recently proposed, different text summarization models aimed to ...
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1 Introduction Automatic bug assignment has been well-studied in the past *** textual bug reports usually describe the buggy phenomena and potential causes,engineers highly depend on these reports to fix ***,researche...
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1 Introduction Automatic bug assignment has been well-studied in the past *** textual bug reports usually describe the buggy phenomena and potential causes,engineers highly depend on these reports to fix ***,researchers spend much effort on processing bug reports,aiming for obtaining key information and/or clues for reproducing bugs,analyzing their root causes,assigning them to developers/maintainers,and fixing these ***,our previous research[1]reveals that noises in texts bring adverse impacts to automatic bug assignments unexpectedly,mainly due to insufficiency of classical Natural Language Processing(NLP)techniques.
In the field of Human Activity Recognition (HAR), the precise identification of human activities from time-series sensor data is a complex yet vital task, given its extensive applications across various industries. Th...
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