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128 articles for “clustering algorithms”
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Database Intrusion Detection Using Genetic Support Vector Fuzzy Clustering Learning Model
Abstract: AbstractThe rapid development of computer networks and increasing dependency of almost all companies and government agencies on Internet and cloud computing lead to the problem of stability and security like intrusions in several forms which can cause huge loss to these organizations. During recent years, disaster in data due to intrusions has dramatically increased. The hindrance of such intrusions is entirely dependent on their detection part which can be possible …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 6, Issue 2, 2019 · pp. 32–40 Read article
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A Novel Approach for Adjustment of CH in Energy Efficient routing Protocol (LEACH) for Wireless Sensor Network
Abstract: Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. In this paper, author develop and analyze low-energy adaptive clustering hierarchy distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating …
Published in Journal Of Network security · Vol. 1, Issue 1, 2013 Read article
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An Innovative Fuzzy-Enhanced Black Widow Spider Optimization for Energy-Efficient Cluster Communication Through the Selection of the Ideal Cluster Head
Abstract: This paper presents a novel approach to enhance energy efficiency in cluster-based wireless sensor networks (WSNs) by integrating fuzzy logic with the black widow spider optimization (BWSO) algorithm. The focus of the suggested fuzzy-enhanced BWSO approach is on cluster head selection, which is essential for lowering energy consumption and enhancing network performance. The program uses fuzzy logic to assess a number of factors, including network density, node proximity, and energy …
Published in International Journal of Wireless Security and Networks · Vol. 2, Issue 2, 2024 · pp. 28–34 Read article
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Comparing Algorithm Performance in Machine Learning for Landslide Susceptibility Studies: An Overview
Abstract: Machine learning algorithms have gained popularity over the past years, and with this comes the rise in their efficiencies; this study demonstrated that machine learning algorithms could successfully be used to identify landslide susceptibility areas. Machine learning algorithms like decision trees, support vector machines, K-Means, Hierarchical Clustering, Self-Organizing Maps (SOMs), artificial neural networks, and Bayesian networks have been used in recent studies. This study thus reviews several studies and results …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 1, 2023 · pp. 71–89 Read article
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Geographical Knowledge report on Agriculture by Spatial Data Mining
Abstract: Spatial Data Mining is nothing but the integration of Geographical Information System and Data Mining. The main objective of the present study i.e., Spatial Data Mining for Agriculture is to find or identify the potentially useful and ultimately understandable patterns in data which will be visualized on the map. Data Mining Algorithms such as clustering, classification, and association rules etc are applied for the Past and Future trends visualization for …
Published in Journal of Advanced Database Management & Systems · Vol. 5, Issue 1, 2018 · pp. 8–19 Read article
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Intelligent Document Scanner
Abstract: AbstractThere is an irresistible trend in the present world for scanning the paper documents and convert it into a digitalized format. Most of these scanning treat the whole document as an entire image. In this scenario, we propose a novel “Intelligent Document Scanner” which automatically segment and classify the contents of the image document including texts, tables and images and store it as a PDF document with three sections that …
Published in Journal of Computer Technology & Applications · Vol. 7, Issue 2, 2016 · pp. 47–57 Read article
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Selection of Cluster Head in Wireless Sensor Network towards Extending Network Life Time
Abstract: Reduced energy consumption and extended lifetime are basic requirements of Wireless Sensor Network (WSN) with distributed nature and dynamic topological changes. The motes are arranged in clusters and only one mote is chosen as cluster head to synchronize and data routing. The proposed work introduces an innovative approach of choosing cluster head in artificially intelligent wireless sensor network. In the proposed work, the residual energy consumption plays the major role …
Published in Journal of Microcontroller Engineering and Applications · Vol. 3, Issue 3, 2016 · pp. 1–10 Read article
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Hybrid Approach for Community Detection Using Deep Learning Techniques
Abstract: Community detection in complex networks is a fundamental problem with applications across diverse domains, ranging from social networks to biological systems and beyond. Traditional methods based on graph theory have been widely used for identifying communities within networks. However, the intricate and evolving nature of modern networks demands more sophisticated approaches. This research work proposes a hybrid approach that combines the strengths of deep learning techniques with traditional community detection …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 3, 2024 · pp. 18–26 Read article
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Early Detection of Alzheimer’s Disease Using Machine Learning Techniques
Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative condition impacting a large global population. Detecting AD early is critical for timely intervention and effective management. Conventional diagnostic approaches involve cognitive assessments and neuroimaging, which are often lengthy, costly, and prone to human error. In this paper, we propose a novel approach for early detection of AD using machine learning techniques applied to multimodal data, including neuroimaging, cognitive assessments, and biomarkers. Our …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 14, Issue 2, 2024 · pp. 32–43 Read article
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An Enhanced DBSCAN Clustering Mechanism in Big Data
Abstract: Big data is the term for a gathering of data sets so immense and complex that it ends up hard to process utilizing present database administration devices or traditional data handling applications. Today, current data volumes are driven by both unstructured and semi-organized data. In this manner, end-to-end preparing can be blocked by the change between organized data in social frameworks of database administration and unstructured data for examination. In …
Published in Journal of Web Engineering & Technology · Vol. 5, Issue 2, 2018 · pp. 10–15 Read article
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Multiple Fragment Information Gathering Using Accumulating Framework
Abstract: The approach of use mobile sink, has been adopted in wireless sensor networks ( WSN) and wireless sensor and actor network( WSAN) to achieve advanced efficiency in terms of gathering data from sensors. Mobility-assisted data collection brings in new opportunity to improve the energy efficiency sensor nodes. However, mobile sink also introduces new challenges such as large data collection tansy. A lot of research efforts have been devoted to reduce …
Published in Journal of Web Engineering & Technology · Vol. 2, Issue 3, 2015 · pp. 14–18 Read article
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Compact Method for Analyzing Big Data
Abstract: Big Data Analysis (BDA) method is proposed in this paper. A compact method (K-Means Cluster, Generalised K-Means Cluster, C-Means, Fuzzy Means, Fuzzy Numbers, Fuzzy Set, Fuzzy Logic, Neural Network, Genetic Algorithm) based big data analysis is presented in this paper. There are four models are presented as, probabilistic model, possibility model, fuzzy model and variance model. A compact algorithm is illustrated also in this paper.Keywords: Big Data, K-Means Cluster, Generalised …
Published in Journal of Advanced Database Management & Systems · Vol. 7, Issue 2, 2020 · pp. 5–14 Read article
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Big Data Management: Theory & Practice
Abstract: In this paper, Big Data Management is studied. There is finite number of information in Big Data and correspondingly its method of analysis exists. Big Data Analysis (BDA) method is proposed in this paper. A compact method (K-Means Cluster, Generalised K-Means Cluster, C-Means, Fuzzy Means, Fuzzy Numbers, Fuzzy Set, Fuzzy Logic, Neural Network, Genetic Algorithm) based big data analysis is presented in this paper. There are four models are presented …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 7, Issue 2, 2020 · pp. 13–22 Read article
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Covariance based Clustering for Digital Image Compression
Abstract: Traditionally clustering is done based on distance between the data points and the cluster centroids. A new type of clustering based on distance between the covariance matrices of the clustered data points was recently introduced. This allows clustering data points according to their spatial distribution model. This paper presents an improved algorithm for the computation of covariance based clustering with application to digital image compression. Karhunen-Loeve Transform is an optimal …
Published in Current Trends in Information Technology · Vol. 8, Issue 1, 2018 · pp. 16–21 Read article
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A Machine Learning-based Analysis of Climate Change
Abstract: Climatic variations are a pressing global challenge that demands immediate and comprehensive attention. A wealth of articles has been published on climate change mitigation and adaptation, yet there remains a need for innovative methods to explore the complexities of climatic variations and to devise more efficient and effective strategies for adjustment and alleviation. With technological advancements, machine learning (ML) and deep learning (DL) approaches have derived significant popularity across various …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 13, Issue 2, 2024 · pp. 1–10 Read article
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A Survey of Fraud Detection Techniques for Credit Card Based Transaction Processing
Abstract: The wide emergence of electronic-commerce has widened the extensive usage of credit card for online transactions. However, there is also a high rise in malicious transaction and fraudulent associated with the credit cards. In this study, we present several models and algorithm used in data mining for the detection of such malicious fraudulent or thefts. Such algorithm learns the transaction patterns and clusters the pattern of sequences usually involving with …
Published in Recent Trends in Parallel Computing · Vol. 2, Issue 1, 2015 · pp. 10–15 Read article
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Enhancing Pattern Recognition in Social Networking Dataset using Data Mining Techniques
Abstract: Databases today can range in size more than terabytes. Within these masses of data lies hidden information of strategic importance. But when there are so many trees, how do we draw meaningful conclusions about the forest? The newest answer is data mining, which is being used both to increase revenues and to reduce costs. Data mining is a process that uses a variety of data analysis tools to discover patterns …
Published in Journal Of Network security · Vol. 1, Issue 2-3, 2013 · pp. 1–7 Read article
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A Novel Segmentation Method of Cervical Cancer affected Surface Tissues based on Superpixels
Abstract: AbstractCervical Cancer is one of the pervasive forms of cancer afflicting the female population worldwide. A Digital Colposcope is a self-illuminated powerful microscope which acquires the image of the affected cervix. The raw cervix image acquired by the colposcope is known as a cervigram. The raw cervigram is preprocessed by removing the specular reflections and then the region of interest is sought. Different novel segmentation algorithms which were proposed in …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 4, Issue 3, 2017 · pp. 1–7 Read article
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Representative Feature Selection for Efficient Clustering
Abstract: AbstractClustering is one of the widely used data mining techniques. In machine learning, it is an unsupervised learning method that needs training data to be used. The problem with clustering is that it is computationally expensive and takes more time for grouping high-dimensional data. Therefore it is necessary to reduce number of features in the high-dimensional data in order to make the search space reduced. Many techniques came into existance …
Published in Journal Of Network security · Vol. 5, Issue 2, 2017 · pp. 19–27 Read article
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Classification and Detection of Brain Tumor using Convolutional Neural Network
Abstract: Tumors are masses created when brain cells multiply uncontrollably. A brain tumor is the medical term for this condition. Brain tumors are a serious and aggressive disease that can lead to a reduced life expectancy. Developing a treatment plan is essential to raising a patient's standard of living. Tumors in different regions of the body are evaluated using a variety of imaging techniques, with MRI pictures being utilized mostly for …
Published in International Journal of Cheminformatics · Vol. 1, Issue 1, 2023 · pp. 8–13 Read article