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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Jakati, Jagadish S.
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Speech enhancement by using novel multiband spectral subtraction method along with a reduction of the cross spectral component Jakati, Jagadish S.; Koti, Ramesh B.; Matad, Sidramayya; Jadhav, Jagannath; Mule, Shrishail Basvant; Bedakihale, Sanmati; Mathad, Vireshkumar G.; Bandekar, Amar R.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp933-941

Abstract

It is essential to enhance the speech signal's clarity and quality in order to maintain the message's content. By boosting the noisy voice signal, the speech signal quality can be raised. Two techniques are presented in this study to significantly minimize the additive background noise. In order to minimize non-stationary additive noise concerning the speech signal, the first approach employs modified multiband spectral subtraction. With this technique, spectral subtraction is carried out based on the signal to noise ratio (SNR) values in various noisy speech frames. When the noisy signal and noise signal are somewhat correlated, a second method is used to minimize the cross spectral components. These techniques are used to get over the drawbacks of the fundamental spectrum subtraction method. To improve the noisy speech signal, both techniques are combined.
Segmentation and classification of plant leaf disease using advanced deep learning approach and ensemble classifier Huddar, Suma S.; Rudagi, Jayashri; Jakati, Jagadish S.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1489-1502

Abstract

An essential component of maintaining global food production is plants. On other hand, a number of plant diseases can threaten agricultural output and cause large losses if left unchecked. Agricultural specialists and botanists physically track plant diseases in a labor-intensive, error-prone manner using a conventional method. AI can give evaluations that are quicker and more accurate than those made using conventional approaches by automating the identification and analysis of diseases. This technical development presents a viable way to lessen crop losses and lessen the severity of infections. As a result, we describe an ensemble machine learning strategy for plant disease classification in this study that is enabled by deep learning. Data augmentation is done in the first part of the study, and in the second step, we provide a modified Mask R-CNN model for plant leaf segmentation. Afterwards, a model to extract the deep features based on CNN is shown. Lastly, the ensemble classifier is built using support vector machine classifier (SVM), random forest (RF), and decision tree (DT) with the aid of majority voting. The suggested method's effectiveness is tested on plant village, apple, maize, and rice, yielding overall accuracy values of 99.45%, 96.30%, 96.85%, and 98.25%, in that order.
Co-Authors Bandekar, Amar R. Bedakihale, Sanmati Huddar, Suma S. Jadhav, Jagannath Koti, Ramesh B. Matad, Sidramayya Mathad, Vireshkumar G. Mule, Shrishail Basvant Rudagi, Jayashri