Ramesh Kumar Bhukya

Section 01

Academic Profile

Dr. Ramesh Kumar Bhukya received the B.Tech. degree in Electronics and Communication Engineering from Mahatma Gandhi Institute of Technology (MGIT) Gandipet, Hyderabad, Telangana, India, in 2008, the M.Tech. degree in Electronics and Communication Engineering from Motilal Nehru National Institute of Technology (MNNIT) Allahabad, Prayagraj, Uttar Pradesh, India in 2011, and the Ph.D. degree in Electronics and Electrical Engineering from Indian Institute of Technology Guwahati (IITG), Guwahati, Assam, India in 2019. His Ph.D. work focuses on Approaches for robust text-dependent speaker verification under degraded conditions using speech-specific knowledge from the perspective of practical application-oriented systems. After completing doctoral studies, he joined as an Assistant Professor in Electronics and Communication Engineering from the National Institute of Technology (NIT), Hamirpur, Himachal Pradesh, India, in 2019-2020. In 2020, he joined in the Department of Electronics and Communication Engineering at National Institute of Technology Andhra Pradesh (NIT-AP), Tadepalligudem, Andhra Pradesh, India. He is currently working as an Assistant Professor in the Department of Electronics and Communication Engineering from the Indian Institute of Information Technology Allahabad (IIIT-A), Prayagraj, Uttar Pradesh, India. His research interests are speech signal processing, speaker verification, biomedical signal processing, machine learning and pattern recognition.

Section 02

Editorial Roles

This user currently does not serve as an editor for any ICCK journals.

Section 03

ICCK Publications

Free Access | Research Article | 01 October 2026

A Multimodal Ensemble Learning Framework for Early Detection of Adolescent Depression Using Multi-Source Real-World Data

by

Ramesh K. Bhukya

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Nirbhay Singh

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Aniket Tiwari

ICCK Transactions on Educational Data Mining | Volume 2, Issue 2: 63-84, 2026 | DOI: 10.62762/TEDM.2026.244050

Abstract

Depression and academic stress undermine students' attendance, performance, and retention, yet educational institutions lack reliable early-alert tools. Because about three-quarters of lifetime psychiatric conditions emerge before age 25, timely automated screening is critical for early intervention. This paper proposes a multi-source ensemble learning framework for distinguishing {\it depression} (DP) from {\it non-depression} (ND) using 1,547 harmonized samples drawn from six real-world sources: occupational surveys, student academic stress records, Twitter sentiment data, Reddit depression posts, fine-grained emotion corpora, and behavioural monitoring signals. Academic stress and behavio... More >

Graphical Abstract

A Multimodal Ensemble Learning Framework for Early Detection of Adolescent Depression Using Multi-Source Real-World Data

Open Access | Research Article | 18 December 2025 | Cited: 2

Robust Detection of Maize Foliage Fungal Diseases using Tree-Based Ensemble Methods

by

Prabhat Chandra Shrivastava

,

Mohammad Saheeb Masood

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,

,

Digital Intelligence in Agriculture | Volume 1, Issue 2: 79-95, 2025 | DOI: 10.62762/DIA.2025.672386

Abstract

Maize productivity in India, a major global producer, is severely threatened by leaf diseases. Accurate identification of Common Rust (CR), Northern Corn Leaf Blight (NCLB), and Gray Leaf Spot (GLS) remains challenging with traditional methods. This study evaluated traditional and ensemble-based classifiers for classifying these diseases alongside healthy (HL) leaves. Using accuracy, precision, recall, and F1-score, we assessed k-NN, DT, RF, ETs, AdaBoost, SGD, GB, XGBoost, LightGBM, and a Stacking model on a four-class dataset. Ensemble methods demonstrated clear superiority. The Stacking model achieved the highest accuracy (98.50%), followed by LightGBM (98.46%) and XGBoost (98.01%). Among... More >

Graphical Abstract

Robust Detection of Maize Foliage Fungal Diseases using Tree-Based Ensemble Methods