Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments

Lucky, Lucky; Girsang, Abba Suganda

doi:10.31449/inf.v44i1.2672

Cited by 6 publications

(4 citation statements)

References 24 publications

(43 reference statements)

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“…KNN is a simple and versatile technique applicable to various applications, although it may be computationally costly for big datasets and high-dimensional feature spaces. KNN has been implemented in several domains, including bioinformatics, image processing, and recommendation systems (El Houby et al, 2017).…”

Section: K-nearest Neighborsmentioning

confidence: 99%

An Improved Ensemble Machine Learning Approach for Diabetes Diagnosis

Mohammed Rashid,

Yaseen,

Riyadh Saeed

et al. 2024

JST

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Diabetes is recognized as one of the most detrimental diseases worldwide, characterized by elevated levels of blood glucose stemming from either insulin deficiency or decreased insulin efficacy. Early diagnosis of diabetes enables patients to initiate treatment promptly, thereby minimizing or eliminating the risk of severe complications. Although years of research in computational diagnosis have demonstrated that machine learning offers a robust methodology for predicting diabetes, existing models leave considerable room for improvement in terms of accuracy. This paper proposes an improved ensemble machine learning approach using multiple classifiers for diabetes diagnosis based on the Pima Indians Diabetes Dataset (PIDD). The proposed ensemble voting classifier amalgamates five machine learning algorithms: Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forests (RF), and XGBoost. We obtained the individual model accuracies and used the ensemble method to improve accuracy. The proposed approach uses a pre-processing stage of standardization and imputation and applies the Local Outlier Factor (LOF) to remove data anomalies. The model was evaluated using sensitivity, specificity, and accuracy criteria. With a reported accuracy of 81%, the proposed approach shows promise compared to prior classification techniques.

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Section: K-nearest Neighborsmentioning

confidence: 99%

An Improved Ensemble Machine Learning Approach for Diabetes Diagnosis

Mohammed Rashid,

Yaseen,

Riyadh Saeed

et al. 2024

JST

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“…Related works on ACO-based data stream algorithms have mainly focused on clustering, [52][53][54][55] an unsupervised † learning task where the goal is to group data instances based on their features into homogeneous clusters. The only exception, to the best of our knowledge, is the Stream Ant Colony Decision Forrest (strACDF) algorithm proposed in Reference [56] for solving the data stream e-mail foldering problem.…”

Section: Classification With Acomentioning

confidence: 99%

Data stream classification with ant colony optimisation

Helal

Otero

2022

Int J of Intelligent Sys

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Data stream mining has recently emerged in response to the rapidly increasing continuous data generation. While the majority of Ant Colony Optimisation (ACO) rule induction algorithms have proved to be successful in producing both accurate and comprehensive classification models in nonstreaming (batch) settings, currently ACO‐based algorithms for classification problems are not suited to be applied to data stream mining. One of the main challenges is the iterative nature of ACO algorithms, where many procedures—for example, heuristic calculation, selection of continuous attributes, pruning—require multiple passes through the data to create a model. In this paper, we present a new ACO‐based algorithm for data stream classification. The proposed algorithm, called Stream Ant‐Miner (sAnt‐Miner), uses a novel hybrid pheromone model combining both a traditional construction graph and solution archives models to efficiently handle a large number of mixed‐type (nominal and continuous) attributes directly without the need for additional procedures, reducing the computational time required to complete an iteration of the algorithm. Our results show that sAnt‐Miner produces statistically significant concise models compared with state‐of‐the‐art rule induction data stream algorithms, without negative effects on their predictive accuracy.

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“…Clustering Protocol) [19] The authors proposed a clustering protocol for RCSFHs, based on a dual phase CH election scheme. So we have 2 steps to choose the CH of the cluster, the first step is the initial and temporary CH selection based on the primary and residual energy level, and the second step is the process of replacing the temporary CHs with high power CHs to form the final CH of the cluster.…”

Section: Dce (Distributed Energy-efficientmentioning

confidence: 99%

“…A connected object has value when it is connected to other objects and software bricks, for example: a connected watch is only of interest within a health/wellness oriented ecosystem, which goes far beyond knowing the time. An CO with three key elements: The data produced or received, stored or transmitted, the algorithms to process this data, the ecosystem in which it will react and integrate.The usage properties of a CO [18], [19]: Ergonomics (usability, handiness).Aesthetics (shapes /colors/ sounds/ sensations). Meta-Morphism(adaptability, personalization modulation).…”

Section: Connected Object (Co)mentioning

confidence: 99%

IoT based traffic congestion control for environmental applications

Gherbi¹,

Roumaissa²

2021

IJCAI

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Recently, the demands of the Internet of Things (IoT) in environmental application (e-applications) have been steadily growing. The main idea of the Internet of Things (IoT) is to revolutionize the current Internet by enriching it with a large number of intelligent objects that communicate with each other. Heterogeneous communication technologies are integrated into the Internet to achieve the IoT application. The function of these objects is to collect data from sensors, process it, and communicate it. Therefore, sensors are main features of IoT. The Heterogeneous Wireless sensor network (HWSN) in environmental application (e-applications) is a key technology element of the Internet of Things (IoT), which is considered as the future evolution of the Internet. The integration of HWSNs into IoT makes communication with any type of object possible and opens the gates to a multitude of application areas. We have proposed: LBCMH. After having detailed the functioning of our protocol, we present the realization of the simulations by the NS3 simulator, whose objective is to allow us to evaluate the performances of the proposed protocol by comparing the results with the two protocols: WBCHN and DEACP. We thus take into account several metrics for the performance of evaluation. We were able to show through the simulation results obtained that the objectives have been reached. Povzetek: V prispevku je predstavljena metoda na osnovi IoT za nadzorovanje prometnih zamaškov z namenom varovanja okolja.

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Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments

Cited by 6 publications

References 24 publications

An Improved Ensemble Machine Learning Approach for Diabetes Diagnosis

An Improved Ensemble Machine Learning Approach for Diabetes Diagnosis

Data stream classification with ant colony optimisation

IoT based traffic congestion control for environmental applications

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