Early detection is vital in crop health, yet improvement in productivity faces time-consuming and inefficient challenges due to traditional manual techniques of plant disease detection. Thus, we present a deep learnin...
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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 toward 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 DoS (DDoS) attacks on the MQTT v5.0 protocol by analyzing three significant features: 1) CONNECT properties;2) user properties;and 3) 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 findings o
The conventional approach to scalp inspection in the hairdressing industry relies on manually interpreting scalp symptom images. Hairdressers provide treatments based on visual assessment, leading to potential inaccur...
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This study examines the use of experimental designs, specifically full and fractional factorial designs, for predicting Alzheimer’s disease with fewer variables. The full factorial design systematically investigates ...
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Automated speaker identification is an important research topic in recent advanced technologies. This process helps to analyze the speakers in their emergencies. Various existing approaches are used for speaker identi...
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Automatic emotion identification from speech is a difficult problem that significantly depends on the accuracy of the speech characteristics employed for categorization. The display of emotions seen in human speech is...
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Automatic emotion identification from speech is a difficult problem that significantly depends on the accuracy of the speech characteristics employed for categorization. The display of emotions seen in human speech is inherently integrated with hidden representations of several dimensions and the fundamentals of human behaviour. This illustrates the significance of using auditory data gathered from discussions between people to determine people's emotions. In order to engage with people more closely, next-generation artificial intelligence will need to be able to recognize and express emotional states. Even though recovery of emotions from verbal descriptions of human interactions has shown promising outcomes, the accuracy of auditory feature-based emotion recognition from speech is still lacking. This paper suggests a unique method for Speech-based Emotion Recognition (SER) that makes use of Improved and a Faster Region-based Convolutional Neural Network (IFR-CNN). IFR-CNN employs Improved Intersection over Unification (IIOU) in the positioning stage with better loss function for improving Regions of Interest (RoI). With the help of a Recurrent Neural Network (RNN)-based model that considers both the dialogue structure and the unique emotional states;modern categorical emotion forecasts may be created quickly. In particular, IFR-CNN was developed to learn and store affective states, as well as track and recover speech properties. The effectiveness of the proposed method is evaluated with the help of real-time prediction capabilities, empirical evaluation, and benchmark datasets. From the speech dataset, we have extracted the Mel frequency cepstral coefficients (MFCC), as well as spectral characteristics and temporal features. Emotion recognition using retrieved information is the goal of the IFR-development. Quantitative analysis on two datasets, the Berlin database of Emotional Speech (EMODB) and the Serbian Emotional Speech database (GEES), revealed encouraging r
To manage the huge data, cloud computing necessitates a significant number of disc I/O, network bandwidth and CPU cycles. To handle the massive volumes of data, the programming paradigm known as data flow integrates t...
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This paper describes a Plastic Waste Hotspot Detection System which has been developed in an international collaborative research project to realize an 'Environmental AI-Human Actions integration' with marine ...
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In recent years, there has been rapid development in vehicle safety technology, with the emergence of various active safety systems including blind spot information systems, adaptive cruise control, and front collisio...
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As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy conce...
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As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.
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