Improved Conservation of Energy in Fog IOT Services Using Machine Learning Model

Prakash, M. Viju; Porkodi, V.; Rajanarayanan, Sasweth C.; Khan, Mujeebudheen; Ibrahim, Banar Fareed; Sivaram, M.

doi:10.1109/iccit-144147971.2020.9213719

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…In paper (Prakash et al, 2020), the author proposes a machine learning model to reduce the consumption of energy demand in fog computing Internet of Things (IoT) services. The machine learning model adopts network density, latency, and mobility as the energy constraints and designs an objective function in order to support the lower energy consumption in the network.…”

Section: Work Related To Energy Optimizationmentioning

confidence: 99%

(Offloading) QoE-Aware Application Mapping and Energy-Aware Module Placement in Fog Computing + Offloading

Keat

Ang

Chong

et al. 2022

International Journal of Web Services Research

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Fog computing is a potential solution for the Internet of Things in close connection with things and end-users. Fog computing will easily transfer sensitive data without delaying distributed devices. Moreover, fog computing is more in real-time streaming applications, sensor networks, IoT which need high speed and reliable internet connectivity. Due to the heterogeneous and distributed characteristics, finley distributing the task with computation offloading is a challenging task. Developing an efficient QoE-aware application mapping policy is challenging due to the different user interests. The energy consumption would usually increase after such an algorithm and policy are implemented. In this paper, we enhanced the future from the previous QoE paper by proposing a computation offloading algorithm. The proposed algorithm is to prevent overloading on fog devices. Our proposed solution has been evaluated and compared with other existing solutions, the results show that our proposed solution performs better in terms of execution time, energy consumption, and network usage.

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Section: Work Related To Energy Optimizationmentioning

confidence: 99%

(Offloading) QoE-Aware Application Mapping and Energy-Aware Module Placement in Fog Computing + Offloading

Keat

Ang

Chong

et al. 2022

International Journal of Web Services Research

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“…Machine learning [13,15,16,17], bio-inspired computation approaches [17,18,19,20,21,22,23], deep learning [24], and other diverse techniques have recently found use in the field of medical prediction. Despite the validation of a variety of strategies, none have consistently produced exact and trustworthy results.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Breast Cancer Detection with Ensemble Machine Learning Models

Dawood Salim Mohammed

2024

jes

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Breast cancer remains a significant public health issue worldwide, underlining the need for accurate and efficient diagnostic methods. In this paper, we propose a new technique to enhance breast cancer diagnosis through the integration of multiple machine-learning models. Our strategy employs a combination of the Naive Bayes classifier, Stochastic Gradient Descent (SGD), Bagging, and the ZeroR classifier, alongside Bayes Network learning. The cornerstone of our approach is Bayes Network learning, a probabilistic graphical model designed to map out the intricate interconnections among various diagnostic factors. This is significant in the way that it can help to uncover complex relationships in the data for the sake of leading to more accurate predictions. Added to the above, we use the Naïve Bayes classifier, a classifier showing good validity in classification tasks and based on probabilistic reasoning, for the screening of breast cancer. Further, a refined model's parameter is included using the SGD and leads to enhancement of the generalization and overall performance of the model. In addition, as part of controlling overfitting, one can also use Bagging. It is an ensemble method in the sense that it considers several models. ZeroR classifier is a very basic classifier and is just used to compare its performance with our composite approach. We are comparing complex ensemble results to its simplicity. We will validate the ability of our proposed methodology to compare the performance of our integrated models against ZeroR.

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“…Several methods have been developed for detecting breast cancer. Breast imaging is a type of mammography [22]. Breast imaging is sometimes known as mammography.…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of data mining algorithms in breast cancer

Alsudani

Fakhruldeen

Yousif

2023

IJEECS

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<span lang="EN-US">Unchecked breast cell growth is one of the leading causes of death in women globally and is the cause of breast cancer. The only method to avoid breast cancer-related deaths is through early detection and treatment. The proper classification of malignancies is one of the most significant challenges in the medical industry. Due to their high precision and accuracy, machine learning techniques are extensively employed for identifying and classifying various forms of cancer. The authors of this review studied numerous data mining algorithms and implemented them such that clinicians might use them to accurately detect cancer cells early on. This article introduces several techniques, including support vector machine (SVM), K star (K*) classifier, additive regression (AR), back propagation (BP) neural network, and Bagging. These algorithms are trained using a set of data that contains tumor parameters from breast cancer patients. Comparing the results, the authors found that SVM and Bagging had the highest precision and accuracy, respectively. Also assess the number of studies that provide machine learning techniques for breast cancer detection.</span>

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Improved Conservation of Energy in Fog IOT Services Using Machine Learning Model

Cited by 6 publications

References 13 publications

(Offloading) QoE-Aware Application Mapping and Energy-Aware Module Placement in Fog Computing + Offloading

(Offloading) QoE-Aware Application Mapping and Energy-Aware Module Placement in Fog Computing + Offloading

Enhancing Breast Cancer Detection with Ensemble Machine Learning Models

Comparative evaluation of data mining algorithms in breast cancer

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