Using machine learning architecture to optimize and model the treatment process for saline water level analysis

Rajput, Sarvesh P. S.; Webber, Julian; Bostani, Ali; Mehbodniya, Abolfazl; Arumugam, Mahendran; Nanjundan, Preethi; Wendimagegen, Adimas

doi:10.2166/wrd.2022.069

Cited by 18 publications

(11 citation statements)

References 18 publications

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“…In classification tasks, assessing the performance of DL models necessitates the utilization of various metrics to accurately evaluate their effectiveness in classifying data. These metrics offer insights into different facets of the model's performance and aid in determining its efficacy in data classification [35]. Commonly employed metrics for evaluating DL models in classification tasks encompass accuracy, precision, recall, F1-score, area under the receiver operating characteristics curve, false alarm ratio, and misdetection ratio [36].…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

Zhu,

Zhang,

Liu

et al. 2024

ijacsa

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“…Furthermore, the cloud task scheduling process is highly uncertain because of multiple factors triggering the unpredictable cloud environment, including network connectivity [23], resource usage [24], peak network demands [25], and web service performance inherent to service models of the cloud [26]. Artificial intelligence and machine learning techniques offer intelligent and adaptive solutions by analyzing patterns and predicting future demands, leading to proactive load management [27,28].…”

Section: Motivationmentioning

confidence: 99%

Modified Artificial Bee Colony Algorithm for Load Balancing in Cloud Computing Environments

LI,

WANG

2024

ijacsa

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Task scheduling in cloud computing is a complex optimization problem influenced by the ever-changing user requirements and the different architectures of cloud systems. Efficiently distributing workloads across Virtual Machines (VMs) is critical to mitigate the negative consequences of inadequate and excessive workloads, such as higher power consumption and possible machine malfunctions. This paper presents a novel method for dynamic load balancing using a Modified Artificial Bee Colony (MABC) algorithm. The ABC algorithm has exceptional competence in solving complex nonlinear optimization problems based on bee colonies' foraging behavior. Nevertheless, the traditional version of the ABC algorithm cannot effectively use resources, resulting in a rapid decline in population diversity and an ineffective spread of knowledge about the best solution between generations. To address these limitations, this study integrates a genetic model into the algorithm, enhancing population diversity through crossover and mutation operators. The developed algorithm is compared with the prevailing algorithms to confirm its effectiveness. The results of the proposed MABC algorithm for the load balancing method are compared with the current ones, and it is observed that this algorithm is more beneficial in terms of cost and energy as well as resource utilization.

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“…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]. Deep learning, particularly with neural networks, thrives on the robust computational infrastructure offered by cloud services, enabling the training of complex models for image and speech recognition, natural language processing, and more [23,24].…”

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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Using machine learning architecture to optimize and model the treatment process for saline water level analysis

Cited by 18 publications

References 18 publications

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

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

Modified Artificial Bee Colony Algorithm for Load Balancing in Cloud Computing Environments

Incentive approaches for cloud computing: challenges and solutions

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