Hands‐On H2O Machine Learning Tool

Dulhare, Uma N.; Mubeen, Azmath; Ahmad, Khaleel

doi:10.1002/9781119654834.ch15

Cited by 11 publications

(11 citation statements)

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“…For this research article, the authors have chosen to replicate the experiment on the same physiological data as described above but using two regression models that rely on decision trees to produce more accurate prediction sets at the cost of requiring more energy from the part of the CSS [ 57 ]. Decision trees represent a more sophisticated regression mechanism for predicting missing values based on machine learning algorithms [ 58 ]. Wozniakowski et al [ 59 ] have revised gradient boosting in a way that allows it to produce a series of enhancements to a model that is not constant, potentially incorporating previous knowledge or a deeper understanding of the data-generation process.…”

Section: Prominent Challenges For Iomt In Cardiovascular Applicationsmentioning

confidence: 99%

An Optimal, Power Efficient, Internet of Medical Things Framework for Monitoring of Physiological Data Using Regression Models

Mishra,

Liberman,

Brahamanpally

2024

Sensors

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The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity.

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Section: Prominent Challenges For Iomt In Cardiovascular Applicationsmentioning

confidence: 99%

An Optimal, Power Efficient, Internet of Medical Things Framework for Monitoring of Physiological Data Using Regression Models

Mishra,

Liberman,

Brahamanpally

2024

Sensors

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“…This paper has taken the aforesaid vital parameters for the device classification framework into account and developed a distributed, robust, scalable framework based on stackensemble methods. The scaling of the computing resources is carried through distributed clusters of Docker containers equipped with H2O prediction models [15] in the AI/ML pipeline. The framework can handle latency and privacy issues.…”

Section: Memory and Computational Resourcesmentioning

confidence: 99%

A Novel Distributed Stack Ensembled Meta-Learning-Based Optimized Classification Framework for Real-time Prolific IoT Traffic Streams

Snehi

Bhandari

2022

Arab J Sci Eng

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The concurrence of state-of-the-art Industrial 5G, Cyber-Physical Systems, Smart-Systems, Industrial Internet of Things, and Additive Manufacturing paves the next-level digital remodeling. However, the transfiguration unwittingly tailpiece an operational onus on the smart-environment operators. The multiplicity and classes of IoT devices operating in the intelligent environment are myriad. The characterization of ingress network traffic and the accurate classification of devices is necessary to efficiently manage the devices and offer cutting-edge security solutions and quality of Service (QoS). The paper addresses these challenges by offering a novel intelligent framework for traffic classification leveraging behavioral attributes of IoT traffic. The paper’s contributions to the research community are fourfold. Firstly, the paper proposes a novel IoT classification framework based on Stack-Ensemble for real-time high-volume IoT traffic. The experimental results indicate that the proposed novel Stack Ensemble model can extract the best out of base models and demonstrate an accuracy of 99.94%. The intelligent models are evaluated over multiple dimensions to project the isometric view of the model performance and the experimental results. To achieve that goal, all the performance metrics that most researchers most often miss have been elucidated. Secondly, the paper comprehends the flow-level statistical characteristics of IoT devices. Third, the paper offers the distributed, scalable, and portable framework architecture. The architecture is horizontally scalable, distributing the computational load. The framework offers an end-to-end industry-grade machine-learning pipeline and triumphs the vulnerabilities of existing solutions. Finally, the paper discusses the statistical insights into the intelligent model and the results of the experimentation study. The proposed work paves the opportunities for researchers, smart-environment operators, and developers to unfold the architecture and supplement the security solutions against cyber-attacks.

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“…Because the two datasets used in this study have different formats, the preprocessing of the two datasets will have different stages as well. Data preprocessing involves the process of data cleaning and/or data integration and/or data reduction and/or data addition and/or data transformation [25]. In this study, we will transform the data where we will map categorical to numerical values in both datasets and change the shape of the columns in the second dataset.…”

Section: Figure 3 Second Dataset Data Distributionmentioning

confidence: 99%

Comparative Analysis of KNN, Naïve Bayes and SVM Algorithms for Movie Genres Classification Based on Synopsis.

Buslim¹,

Hardy

et al. 2022

j. Teknik inform.

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Text classification is a process of categorizing a text into the correct label. Text classification in natural language processing is a challenging task that requires accuracy to get the correct results, manual text classification tends to be inefficient because it requires a lot of time and also experts. The utilization of machine learning for automatic text classification can be a solution to this problem. KNN, Naive Bayes, and SVM are known as some of the most algorithms to solve classification problems, especially text classification. In this study, we are trying to compare the KNN, Naive Bayes, and SVM algorithms for text classification with the problem of classifying movie genres based on a synopsis using datasets obtained from Kaggle.com and IMDB Dataset. The results of this study indicate that of the 12 experiments, Support Vector Machine (SVM) is the bestperforming algorithm with an accuracy of 90%, 93%, 65%, and 63%. It is hoped that this research can help to determine the best algorithm in the text classification process.

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Hands‐On H2O Machine Learning Tool

Cited by 11 publications

References 0 publications

An Optimal, Power Efficient, Internet of Medical Things Framework for Monitoring of Physiological Data Using Regression Models

An Optimal, Power Efficient, Internet of Medical Things Framework for Monitoring of Physiological Data Using Regression Models

A Novel Distributed Stack Ensembled Meta-Learning-Based Optimized Classification Framework for Real-time Prolific IoT Traffic Streams

Comparative Analysis of KNN, Naïve Bayes and SVM Algorithms for Movie Genres Classification Based on Synopsis.

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