Body Sensor 5 G Networks Utilising Deep Learning Architectures for Emotion Detection Based On EEG Signal Processing

Vairachilai, S.; Bostani, Ali; Mehbodniya, Abolfazl; Webber, Julian; Hemakesavulu, O.; Vijayakumar, P.

doi:10.1016/j.ijleo.2022.170469

Cited by 22 publications

(11 citation statements)

References 14 publications

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“…Artificial intelligence (AI) and its subsets, including machine learning, deep learning, and neural networks, constitute the intelligence powerhouse within the cloud computing ecosystem. AI algorithms leverage the computational prowess of cloud servers to analyze vast datasets, recognize patterns, and make data-driven predictions [20]. Embedded within cloud-based applications, machine learning algorithms continuously refine their models based on new data, fostering adaptability and enhancing decision-making capabilities [21,22].…”

Section: Introductionmentioning

confidence: 99%

Incentive approaches for cloud computing: challenges and solutions

Yunlong,

Jie

2024

J. Eng. Appl. Sci.

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Cloud computing enables highly configurable and reliable computing resources on a rentable per-use scheme, facilitating quick and cost-effective provisioning of large-scale applications. Thanks to the fast-paced evolution of cutting-edge technologies and the rapid spread of cloud-based solutions, the cloud computing ecosystem is now part of our everyday lives. Nevertheless, cloud computing relies on highly sophisticated data centers comprising energy-consuming servers and equipment that require much energy. Stimulating cloud services for active participation and network contributions presents several challenges. Strategies based on artificial intelligence (AI), game theory, and blockchain have great potential to create an economically sustainable cloud ecosystem. This paper explores strategies grounded in AI, game theory, and blockchain to foster an economically sustainable cloud ecosystem. Informed by a survey study, our research delves into incentive approaches within cloud computing. Theoretical foundations, motivations, and enabling techniques are comprehensively examined to provide valuable insights for a broad audience. The primary contributions of this work lie in elucidating the application of AI, game theory, and blockchain to address challenges in incentivizing cloud services, paving the way for a more sustainable and efficient cloud computing landscape.

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Section: Introductionmentioning

confidence: 99%

Incentive approaches for cloud computing: challenges and solutions

Yunlong,

Jie

2024

J. Eng. Appl. Sci.

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“…ML techniques, particularly reinforcement learning, empower robots to learn from their experiences and optimize path planning strategies over time [19][20][21]. NNs, inspired by the human brain's structure, excel at pattern recognition and can efficiently process vast amounts of sensor data to make real-time navigation decisions [22][23][24]. Together, these technologies enhance the adaptability, efficiency, and reliability of robot path planning systems.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Whale Optimization Algorithm with Differential Evolution and Lévy Flight for Robot Path Planning

TANG,

ZHAO

2024

ijacsa

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Path planning is a prominent and essential part of mobile robot navigation in robotics. It allows robots to determine the optimal path from a given beginning point to a desired end goal. Additionally, it enables robots to navigate around obstacles, recognize secure pathways, and select the optimal route to follow, considering multiple aspects. The Whale Optimization Algorithm (WOA) is a frequently adopted approach to planning mobile robot paths. However, conventional WOA suffers from drawbacks such as a sluggish convergence rate, inefficiency, and local optimization traps. This study presents a novel methodology integrating WOA with Lévy flight and Differential Evolution (DE) to plan robot paths. As WOA evolves, the Levy flight promotes worldwide search capabilities. On the other hand, DE enhances WOA's ability to perform local searches and exploitation while also maintaining a variety of solutions to avoid getting stuck in local optima. The simulation results demonstrate that the proposed approach offers greater planning efficiency and enhanced route quality.

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“…The emergence of Deep Learning (DL) has revolutionized biomedical signal processing by providing the capability to automatically learn hierarchical features from raw data with little human intervention [13,14]. DL models, including Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), have achieved remarkable performance in a wide range of applications, such as extracting ECG arrhythmias, braincomputer interface based on EEG signals, and noise suppression in EMG signals [15,16]. The turning point in the DL era encourages researchers to gradually shift from expert-designed feature engineering to data-driven end-to-end learning, leading to more precise, efficient, and flexible analysis of biological signals [17].…”

Section: Introductionmentioning

confidence: 99%

Exploring Cutting-Edge Developments in Deep Learning for Biomedical Signal Processing

Zhu,

Zhang,

Liu

et al. 2024

ijacsa

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Biomedical condition monitoring devices are progressing quickly by incorporating cost-effective and noninvasive sensors to track vital signs, record medical circumstances, and deliver meaningful responses. These sophisticated innovations rely on breakthrough technology to provide intelligent platforms for health monitoring, quick illness recognition, and precise treatment. Biomedical signal processing determines patterns of signals and serves as the backbone for reliable applications, medical diagnostics, and research. Deep Learning (DL) methods have brought significant innovation in biomedical signal processing, leading to the transformation of the health sector and medical diagnostics. This article covers an entire range of technical innovations evolved for DL-based biomedical signal processing where different modalities have been considered, including Electrocardiography (ECG), Electromyography (EMG), and Electroencephalography (EEG). A vast amount of biomedical data in various forms is available, and DL concepts are required to extract and model this data in order to identify hidden complex patterns that can be utilized to improve the diagnosis, prognosis, and personalized treatment of diseases in an individual. The nature of this developing topic certainly gives rise to a number of challenges. First, the application of sensitive and noisy time series data requires truly robust models. Second, many inferences made at the bedside must have interpretability by design. Third, the field will require that processing be performed in real-time if used for therapeutic interventions. We systematically evaluate these challenges and highlight areas where continued research is needed. The general expansion of DL technologies into the biomedical domain gives rise to novel concerns about accountability and transparency of algorithmic decision-making, a subject which we briefly touch upon as well.

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Body Sensor 5 G Networks Utilising Deep Learning Architectures for Emotion Detection Based On EEG Signal Processing

Cited by 22 publications

References 14 publications

Incentive approaches for cloud computing: challenges and solutions

Incentive approaches for cloud computing: challenges and solutions

Enhancing Whale Optimization Algorithm with Differential Evolution and Lévy Flight for Robot Path Planning

Exploring Cutting-Edge Developments in Deep Learning for Biomedical Signal Processing

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