A Hybrid GSA-K-Mean Classifier Algorithm to Predict Diabetes Mellitus

Lots of research has been carried out globally to design a machine classifier which could predict it from some physical and bio-medical parameters. In this work a hybrid machine learning classifier has been proposed to design an artificial predictor to correctly classify diabetic and non-diabetic people. The classifier is an amalgamation of the widely used K-means algorithm and Gravitational search algorithm (GSA). GSA has been used as an optimization tool which will compute the best centroids from the two classes of training data; the positive class (who are diabetic) and negative class (who are non-diabetic). In K-means algorithm instead of using random samples as initial cluster head, the optimized centroids from GSA are used as the cluster centers. The inherent problem associated with k-means algorithm is the initial placement of cluster centers, which may cause convergence delay thereby degrading the overall performance. This problem is tried to overcome by using a combined GSA and K-means.

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A Meta-Heuristic Model for Data Classification Using Target Optimization

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2017070102 ◽

2017 ◽

Vol 8 (3) ◽

pp. 24-36 ◽

Cited By ~ 2

Author(s):

Rabindra K. Barik ◽

Rojalina Priyadarshini ◽

Nilamadhab Dash

Keyword(s):

Search Algorithm ◽

Gravitational Search Algorithm ◽

Data Classification ◽

Optimization Techniques ◽

Training Data ◽

Data Sets ◽

Training Process ◽

Swarm Optimization ◽

Benchmark Data ◽

The Neural Network

The paper contains an extensive experimental study which focuses on a major idea on Target Optimization (TO) prior to the training process of artificial machines. Generally, during training process of an artificial machine, output is computed from two important parameters i.e. input and target. In general practice input is taken from the training data and target is randomly chosen, which may not be relevant to the corresponding training data. Hence, the overall training of the neural network becomes inefficient. The present study tries to put forward TO as an efficient methodology which may be helpful in addressing the said problem. The proposed work tries to implement the concept of TO and compares the outcomes with the conventional classifiers. In this regard, different benchmark data sets are used to compare the effect of TO on data classification by using Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) optimization techniques.

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Optimal Selection of the Cluster Head in Wireless Sensor Networks by Combining the Multiobjective Genetic Algorithm and the Gravitational Search Algorithm

Journal of Sensors ◽

10.1155/2021/2292580 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Seyed Reza Nabavi ◽

Vahid Ostovari Moghadam ◽

Mohammad Yahyaei Feriz Hendi ◽

Amirhossein Ghasemi

Keyword(s):

Genetic Algorithm ◽

Wireless Sensor Networks ◽

Energy Consumption ◽

Sensor Networks ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Cluster Head ◽

Wireless Sensor ◽

Multiobjective Genetic Algorithm ◽

Gravitational Search

With the development of various applications of wireless sensor networks, they have been widely used in different areas. These networks are established autonomously and easily in most environments without any infrastructure and collect information of environment phenomenon for proper performance and analysis of events and transmit them to the base stations. The wireless sensor networks are comprised of various sensor nodes that play the role of the sensor node and the relay node in relationship with each other. On the other hand, the lack of infrastructure in these networks constrains the sources such that the nodes are supplied by a battery of limited energy. Considering the establishment of the network in impassable areas, it is not possible to recharge or change the batteries. Thus, energy saving in these networks is an essential challenge. Considering that the energy consumption rate while sensing information and receiving information packets from another node is constant, the sensor nodes consume maximum energy while performing data transmission. Therefore, the routing methods try to reduce energy consumption based on organized approaches. One of the promising solutions for reducing energy consumption in wireless sensor networks is to cluster the nodes and select the cluster head based on the information transmission parameters such that the average energy consumption of the nodes is reduced and the network lifetime is increased. Thus, in this study, a novel optimization approach has been presented for clustering the wireless sensor networks using the multiobjective genetic algorithm and the gravitational search algorithm. The multiobjective genetic algorithm based on reducing the intracluster distances and reducing the energy consumption of the cluster nodes is used to select the cluster head, and the nearly optimal routing based on the gravitational search algorithm is used to transfer information between the cluster head nodes and the sink node. The implementation results show that considering the capabilities of the multiobjective genetic algorithm and the gravitational search algorithm, the proposed method has improved energy consumption, efficiency, data delivery rate, and information packet transmission rate compared to the previous methods.

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GSA-CHSR: Gravitational Search Algorithm for Cluster Head Selection and Routing in Wireless Sensor Networks

Applications of Soft Computing for the Web ◽

10.1007/978-981-10-7098-3_13 ◽

2017 ◽

pp. 225-252 ◽

Cited By ~ 2

Author(s):

Praveen Lalwani ◽

Haider Banka ◽

Chiranjeev Kumar

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Cluster Head ◽

Wireless Sensor ◽

Cluster Head Selection ◽

Gravitational Search

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One-class classification: taxonomy of study and review of techniques

The Knowledge Engineering Review ◽

10.1017/s026988891300043x ◽

2014 ◽

Vol 29 (3) ◽

pp. 345-374 ◽

Cited By ~ 164

Author(s):

Shehroz S. Khan ◽

Michael G. Madden

Keyword(s):

Novelty Detection ◽

Training Data ◽

Future Research ◽

Open Research ◽

Research Themes ◽

Positive Class ◽

Research Problems ◽

Unified View ◽

Negative Class ◽

One Class Classification

AbstractOne-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper, we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.

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Cluster Head Optimization based on Gravitational Search Algorithm

International Journal of Computer Applications ◽

10.5120/ijca2017914619 ◽

2017 ◽

Vol 169 (3) ◽

pp. 8-11

Author(s):

Ekta Singh ◽

Vikas Gupta

Keyword(s):

Search Algorithm ◽

Gravitational Search Algorithm ◽

Cluster Head ◽

Gravitational Search

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Comparison of Meta-heuristic Algorithms on Benchmark Functions

Academic Perspective Procedia ◽

10.33793/acperpro.02.03.41 ◽

2019 ◽

Vol 2 (3) ◽

pp. 508-517

Author(s):

FerdaNur Arıcı ◽

Ersin Kaya

Keyword(s):

Heuristic Algorithms ◽

Optimization Problems ◽

Search Algorithm ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Differential Evolution Algorithm ◽

Population Based ◽

Time Interval ◽

Bee Colony ◽

Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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