Modified Fuzzy Hypersphere Neural Network for Pattern Classification using Supervised Clustering

Mane, Deepak; Kulkarni, Uday

doi:10.1016/j.procs.2018.10.399

Cited by 10 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the proposed methodology, the customized Supervised Hypersphere Neural Network (SHNN) technique is presented as an extension to the MFHSNN which was proposed by D. T. Mane [11]. A supervised learning approach can be used to classify every pattern to its corresponding hypersphere.…”

Section: Proposed Shnn Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Pattern Classification Using Supervised Hypersphere Neural Network0

Mane¹,

Moorthy²,

Kumbharkar³

et al. 2022

IJETAE

View full text Add to dashboard Cite

Pattern classification is of significant demand in the field of machine learning. Its applications range from a simple problem of speech recognition to a complex and important problem of medical diagnosis. Fuzzy based algorithms have been one of the most important methods which have contributed in solving the pattern classification problem. A customized Fuzzy based Supervised Hypersphere Neural Network (SHNN) is presented for the use of pattern classification. Here, a modernized pattern classification method has been presented by considering the fuzzy hypersphere neural network concept at the back end and using a modified version of the membership function aiming to solve the pattern classification problems and boost the performance of the algorithm. The proposed SHNN model creates supervised hypersphere using measurements obtained from intra-class distance techniques along with individual class pattern choice, over the fuzzy membership function. The previous modified fuzzy approaches presented an inherent drawback of ambiguous assignment of classes, losing the fuzzy nature during the assigning of classes in the testing phase and over fitting of the model during the training phase. The proposed approach solves this problem by adding a non linearity to the output of the membership function to maintain its fuzzy nature. Additionally, a new weighted Euclidean distance equation has been designed to enhance the performance of the algorithm. The performance of the proposed model of SHNN has been examined on four standard datasets namely - Pima, liver, glass and monks-3. The results obtained were superior to the previous proposed approaches. Thus, presenting a new state result of the art on the datasets.

show abstract

Section: Proposed Shnn Methodsmentioning

confidence: 99%

“…Table 3 shows the tabulation of the performance metric with respect to each of the existing models [11][12][13]. These readings are used to plot comparison graphs shown in figure 3 to figure 6.…”

Section: Glassmentioning

confidence: 99%

Pattern Classification Using Supervised Hypersphere Neural Network0

Mane¹,

Moorthy²,

Kumbharkar³

et al. 2022

IJETAE

View full text Add to dashboard Cite

show abstract

“…About Dataset: To accurately classify yoga poses, it is important to have a diverse and representative dataset that captures the variations in pose execution and context [10]. However, existing datasets for yoga pose classification are limited in size and diversity, and may not be suitable for all use cases [16][17] [18][19]. To address this limitation, we developed a custom dataset of yoga poses that is tailored to our specific use case.…”

Section: Compute the Confusion Matrixmentioning

confidence: 99%

Smart Yoga Assistant: SVM-based Real-time Pose Detection and Correction System

Mane,

Upadhye,

Gite

et al. 2023

IJRITCC

View full text Add to dashboard Cite

SVM-based Real-time Pose Detection and Correction System refer to a computer system that uses machine learning techniques to detect and correct a person's yoga pose in real-time. This system can act as a virtual yoga assistant, helping people improve their yoga practice by providing immediate feedback on their form and helping to prevent injury. This paper presents a yoga tracker and correction system that uses computer vision and machine learning algorithms to track and correct yoga poses. The system comprises a camera and a computer vision module that captures images of the yoga practitioner and identifies the poses being performed. The machine learning module analyzes the images to provide feedback on the quality of the poses and recommends corrections to improve form and prevent injuries. This paper proposed a customized support vector machine (SVM) based real-time pose detection and correction system that suggests yoga practices based on specific health conditions or diseases. Paper aims to provide a reliable and accessible resource for individuals seeking to use yoga as a complementary approach to managing their health conditions. The system also includes a practitioner’s interface that enables practitioners to receive personalized recommendations for their yoga practice. The system is developed using Python and several open-source libraries, and was tested on a dataset of yoga poses. The hyper parameter gamma tuned to optimize the classification accuracy on our dataset produced 87% which is better than other approaches. The experiment results demonstrate the effectiveness of the system in tracking and correcting yoga poses, and its potential to enhance the quality of yoga practice.

show abstract

“…Pattern classification with neural networks entails developing an algorithm that maps input feature variables to binomial class output space. [102] A neural network is a set of optimization techniques that attempts to recognize hidden patterns or relationships in the dataset using a technique similar to how the human brain works. In this context, neural networks are systems of neurons that might be artificial or synthetic in nature.…”

Section: Neural Networkmentioning

confidence: 99%

Detection of Anomaly using Machine Learning: A Comprehensive Survey

Mane¹,

Sangve²,

Upadhye³

et al. 2022

IJETAE

View full text Add to dashboard Cite

Anomaly detection is an important element in the domain of security. As a result, we undertook a literature review on ML algorithms that identify abnormalities. In this paper, we are presenting a review of the 101 research articles describing ML techniques for anomaly detection published between 2015 - 2022.The goal of this paper is to review research papers that have used machine learning to develop anomaly detection algorithmThe forms of anomaly detection examined in this study include system log anomaly detection, network anomaly detection, cloud-based anomaly detection, and anomaly detection in the medical profession. After assessing the selected research articles, we present more than 10 applications of anomaly detection. Also, we have shared a range of datasets used in anomaly detection research, in addition to revealing 30+ new ML models employed in anomaly detection. We have discovered 55 new datasets for anomaly detection. We've noticed that the majority of researchers utilize real-life datasets and an unsupervised learning technique to detect anomalies. Many ML methods may be applied in this subject, so we present a summary of all work done in the previous six years. Keywords Intrusion detection, Artificial intelligence, Anomaly detection, security, Machine learning.

show abstract

Modified Fuzzy Hypersphere Neural Network for Pattern Classification using Supervised Clustering

Cited by 10 publications

References 14 publications

Pattern Classification Using Supervised Hypersphere Neural Network0

Pattern Classification Using Supervised Hypersphere Neural Network0

Smart Yoga Assistant: SVM-based Real-time Pose Detection and Correction System

Detection of Anomaly using Machine Learning: A Comprehensive Survey

Contact Info

Product

Resources

About