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p-Index From 2021 - 2026
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This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Kavita Jhajharia
Manipal University Jaipur
Computer Science & IT Electrical & Electronics Engineering Engineering

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A comprehensive review on machine learning in agriculture domain Kavita Jhajharia; Pratistha Mathur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp753-763

Abstract

Agriculture is an essential part of sustaining human life. Population growth, climate change, resource competition are the key issues that increase food security and to handle such complex problems in agriculture production, intelligent or smart farming extends the incorporation of technology into traditional agriculture notion. Machine learning is a vitally used technology in agriculture to protect food security and sustainability. Crop yield production, water preservation, soil health and plant diseases can be addressed by machine learning. This paper has presented a compendious review of research papers that deployed machine learning in the agriculture domain. The observed sub-categories of the agriculture domain are crop yield prediction, soil management, pest management, weed management, and crop disease. The outcomes represent that machine learning provides better accuracy concerning classification or regression. Machine learning emerged with the internet of things, drones, robots, automated machinery, and satellite imagery motivates researchers for smart farming and food security.
Advanced crop yield prediction using machine learning and deep learning: a comprehensive review Ayush Anand; Kavita Jhajharia
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 2: April 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i2.26621

Abstract

The advancement of machine learning (ML) and deep learning (DL) techniques has significantly improved crop yield prediction, making it more accurate and reliable. In this review, the implementation of ML and DL algorithms for crop yield prediction is thoroughly investigated, focusing on their crucial role in enhancing crop productivity. Along with ML and DL algorithms examine, the review analyses the use of remote sensing technologies, such as satellite and drone data, in providing high-resolution inputs essential for accurate yield predictions. The study identifies the state of art algorithms, most used features, data sources and evaluation metrics, providing a comparison of ML and DL. The findings indicate that DL models are more effective with large datasets, while ML models remain robust for smaller datasets. The future directions are proposed to develop the generalised models for different crops and regions. The review aims to assist researchers by summarising state of art techniques and identifying the present.
Cross-lingual deep learning model for gender detection Kavita Jhajharia; Ginika Mahajan; Dhondi Samrudh; Koustubh Patel
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10985

Abstract

Speech recognition is transforming the way humans interact with technology and automatic gender recognition is an essential part of this evolution. This study develops a multilingual deep learning (DL) model for gender detection using three audio datasets: RAVDESS (English), Berlin EmoDB (German), and IITKGP-SEHSC (Hindi). These datasets provide linguistic diversity, enabling the development of a multi-lingual gender identification model. The mel-frequency cepstral coefficients (MFCC) and VGGish embeddings and other audio features were used to process raw audio data into something meaningful. The findings show the machine learning (ML) models (random forest (RF) and extreme gradient boosting) achieved good results in the monolingual (98.26% using Hindi and 96.90% using cross-lingual) setup. In DL models, convolutional neural network (CNN) outperformed other models in both monolingual and cross-lingual scenarios, with 99.33% accuracy for Hindi and 98.11% accuracy in cross-lingual setup. These findings show how well DL works for gender detection in multilingual and emotionally complex settings. It outperforms traditional models. The experiment describes the potential of DL in speech-based artificial intelligence (AI) applications, which enhances the performance in real-life scenarios.
Co-Authors Ayush Anand Dhondi Samrudh Ginika Mahajan Koustubh Patel Pratistha Mathur