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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Sanjeev Kumar Pippal
GL Bajaj Institute of Technology and Management
Computer Science & IT

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Real time Indian sign language recognition using transfer learning with VGG16 Sumit Kumar; Ruchi Rani; Sanjeev Kumar Pippal; Ulka Chaudhari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.26498

Abstract

Normal people’s interaction and communication are easier than those with disabilities such as hearing and speech, which are very complicated; hence, the use of sign language plays a crucial role in bridging this gap in communication. While previous attempts have been made to solve this problem using deep learning techniques, including convolutional neural networks (CNNs), support vector machine (SVM), and K-nearest neighbours (KNN), these have low accuracy or may not be employed in real time. This work addresses both issues: improving upon prior limitations and extending the challenge of classifying characters in Indian sign language (ISL). Our system, which can recognize 23 hand gestures of ISL through a purely camera-based approach, eliminates expensive hardware like hand gloves, thus making it economical. The system yields an accuracy of 97.5% on the training dataset, utilizing a pre-trained VGG16 CNN optimized by the Adam optimizer and cross-entropy loss function. These results clearly show how effective transfer learning is in classifying ISL and its possible real-world applications.
Customer segmentation in e-commerce: K-means vs hierarchical clustering Sumit Kumar; Ruchi Rani; Sanjeev Kumar Pippal; Riya Agrawal
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 1: February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i1.26384

Abstract

Customer segmentation is important for e-commerce companies to understand and target different customers. The primary focus of this work is the application and comparison of K-means clustering and hierarchical clustering, unsupervised machine learning techniques, in customer segmentation for e-commerce platforms. Clustering leverages customer search behavior, reflecting brand preferences, and identifying distinct customer segments. The proposed work explores the K-means algorithm and hierarchical clustering. It uses them to classify customers in a standard e-commerce customer dataset, mainly focused on frequently searched brands. Both techniques are compared based on silhouette scores and cluster visualizations. K-means clustering yielded well-separated segments compared to hierarchical clustering. Then, using the K-means algorithm, customers are classified into different segments based on brand search patterns. Further, targeted marketing strategies are discussed for each segment. Results show three customer segments: high searchers-low buyers, loyal customers, and moderate engagers. The proposed work provides valuable insights into customers that could be used for developing targeted marketing campaigns, product recommendations, and customer engagement strategies to enhance the conversion rate, customer satisfaction, and, in turn, the growth of an e-commerce platform.
Optimizing multi-tenant database architecture for efficient software as a service delivery Sanjeev Kumar Pippal; Sumit Kumar; Ruchi Rani
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 5: October 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i5.26385

Abstract

A multi-tenant database (MTDB) is the backbone for any cloud app that employs a software as a service (SaaS) delivery paradigm. Every cloud-based SaaS delivery strategy relies heavily on the architecture of multitenant databases. The hardware and performance costs for quicker query execution and space savings provided by the architecture of MTDBs are implementation costs. All tenants’ data may be kept in a single table with a common schema and database format, making it the most cost-effective MTDB configuration. The arrangement becomes congested if tenants have varying storage needs. In this research, we present a space-saving architecture that improves transactional query execution while avoiding the waste of space due to different attribute needs. Extensible markup language (XML) and JavaScript object notation (JSON) compare the proposed system against the state of the art. The suggested multitenant database architecture reduces unnecessary space and improves query performance. The experimental findings show that the suggested system outperforms the state-ofthe-art extension table method.
Co-Authors Riya Agrawal Ruchi Rani Sumit Kumar Ulka Chaudhari