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Handwritten Amharic Character Recognition through Transfer Learning: Integrating CNN Models and Machine Learning Classifiers
Natenaile Asmamaw Shiferaw;Zefree Lazarus Mayaluri;Prabodh Kumar Sahoo;Ganapati Panda;Prince Jain;Adyasha Rath;Md. Shabiul Islam;Mohammad Tariqul Islam
IEEE Access
Year: 2025 | Early Access Article |
Handwritten Amharic character recognition presents significant challenges due to the script’s syllabic nature and variations in handwriting styles. This study investigates a hybrid approach that integrates convolutional neural networks (CNNs) with machine learning classifiers to enhance recognition accuracy. Transfer learning is applied using four CNN architectures: AlexNet, VGG16, VGG19, and ResN...Show More
Handwritten Amharic Character Recognition through Transfer Learning: Integrating CNN Models and Machine Learning Classifiers
Natenaile Asmamaw Shiferaw;Zefree Lazarus Mayaluri;Prabodh Kumar Sahoo;Ganapati Panda;Prince Jain;Adyasha Rath;Md. Shabiul Islam;Mohammad Tariqul Islam
Year: 2025 | Early Access Article |
Improved Voice Activity Detection for Smart Healthcare of Speech-based Contagious Diseases
Jagannath Dayal Pradhan;Komal Tripathy;Tusar Kanti Dash;Ganapati Panda;Prabodh Kumar Sahoo;Heli A. Shah
2024 Parul International Conference on Engineering and Technology (PICET)
Year: 2024 | Conference Paper |
In recent years, development of smart health care systems, automated monitoring of medical equipment plays a crucial role specially in the case of contagious diseases. This paper deals with this problem from the context of developing smart saline bottle management of patients in contagious diseases and related with the manual error of the hospital staffs. In the first step, a speech based contagio...Show More
An Improved VGG-19 Network Induced Enhanced Feature Pooling for Precise Moving Object Detection in Complex Video Scenes
Prabodh Kumar Sahoo;Manoj Kumar Panda;Upasana Panigrahi;Ganapati Panda;Prince Jain;Md. Shabiul Islam;Mohammad Tariqul Islam
Background subtraction is a crucial stage in many visual surveillance systems. The prime objective of any such system is to detect local changes, and the system could be utilized to face many real-life challenges. Most of the existing methods have addressed the problems of moderate and fast-moving object detection. However, very few literature have addressed the issues of slow moving object detect...Show More
Mitigating Information Interruptions by COVID-19 Face Masks: A Three-Stage Speech Enhancement Scheme
Tusar Kanti Dash;Chinmay Chakraborty;Satyajit Mahapatra;Ganapati Panda
The coronavirus disease 2019 (COVID-19) preventive measures have resulted in significant lifestyle changes. One of the COVID-19 new normal is the usage of face masks for protection against airborne aerosol which creates distractions and interruptions in voice communication. It has a different influence on speech than the standard concept of noise affecting speech communication. Furthermore, it has...Show More
Integrating Advanced Deep Learning Features with SVM for Pathological Brain Detection: A Novel Hybrid Approach
Debendra Muduli;Santosh Kumar Sharma;Adyasha Rath;Ram Chandra Barik;Ganapati Panda
2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The objective of this study is to create an automated method for detecting pathological conditions in the brain, which can aid radiologists in accurately identifying brain diseases more efficiently. By integrating magnetic resonance imaging (MRI) into the proposed system, it is anticipated that more precise information regarding brain soft tissues can be obtained. We propose a novel hybrid approac...Show More
Improved Breast Cancer Detection from Ultrasound Images Using YOLOv8 Model
Pravin Kumar Samanta;Aadiptya Basuli;Nirmal Kumar Rout;Ganapati Panda
2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The number of cases of breast cancer has steadily risen during the past few decades. It is one of the major causes of death in women. There is a good chance of recovery if it is identified early. In order to discover and classify breast cancers, a modified version of the YOLOv8 network is employed, which overcomes the drawbacks of earlier models. Publicly accessible data sets of ultrasound breast ...Show More
Performance Evaluation and Ranking of Deep Learning Feature Extraction Models for Thyroid Cancer Diagnosis using D-CRITIC TOPSIS
2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The nodules in the thyroid region can be cancerous or non-cancerous, present even in healthy humans. Early diagnosis of thyroid cancer is helpful for prevention and treatment. Diagnosing thyroid cancer using traditional approaches is a hard-working task due to the considerable burden on the healthcare community. In this paper, we analyzed the performance of the AI models (Swin Transformer, Data Ef...Show More
A Novel Approach for the Detection of Tuberculosis and Pneumonia Using Chest X-Ray Images for Smart Healthcare Applications
Subrat Kumar Kabi;Rajesh Kumar Tripathy;Dipti Patra;Ganapati Panda
The early discrimination of pneumonia and tuber-culosis is difficult for radiologists due to similar pathological symptoms in the chest X-ray images. Therefore, there is a requirement for automated methods to detect and classify these two diseases accurately. This letter proposes the multiscale eigendomain gradient boosting (MEGB)-based approach to detect pneumonia and tuberculosis from chest X-ra...Show More
Cyber Secure Remote Sensing Image Encryption Scheme using Blockchain and Dual Chaotic Map
Ram Chandra Barik;Ganapati Panda;Adyasha Ratha;Saroj Padhan;Suvamoy Changder
2023 3rd International Conference on Range Technology (ICORT)
Year: 2023 | Conference Paper |
We address the issue of Satellite image security with blockchain enabled secure high dimensional chaos maps. Millions of Satellite image are acquired daily basis for weather forecasting and also for many other prominent application areas. Securing the remote sensing image is still a challenging issue. This paper depicts two phase image encryption technique to Barr cyberattack. At the first phase t...Show More
Dynamic Spectrum Allocation in Cognitive Radar: A Brief Overview
2023 3rd International Conference on Range Technology (ICORT)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The use of mobile phones and wireless devices will face spectrum scarcity issues in near future, because of the explosive growth of wireless applications. Spectrum congestion and interference during communication are the upcoming challenges in cognitive radar, so the researchers are trying to put their enormous efforts towards mitigating the spectrum scarcity issues. According to the literature re...Show More
Adversarial Defense with Local Robust Principal Component Analysis and Wavelet Denoising
Aryaman Sinha;Niladri B. Puhan;Soumya P. Dash;Guda Mrudhul;Ganapati Panda
2023 3rd International Conference on Range Technology (ICORT)
Year: 2023 | Conference Paper |
In this work, a local robust principal component analysis (RPCA)-based adversarial defense methodology is presented. The proposed defense is inspired by recent works of input reconstruction paradigm. The proposed method uses localised processing to apply RPCA towards removal of adversarial perturbations. The reconstructed image is guided by applying wavelet denoising to remove residual perturbatio...Show More
A novel deep CNN model for improved breast cancer detection using ultrasound images
2023 International Conference on Communication, Circuits, and Systems (IC3S)
Year: 2023 | Conference Paper |
Cited by: Papers (6)
The biggest cause of cancer related fatalities in women globally is breast cancer. So, the early detection of this disease is beneficial to the patient since it allows for the adaptation of appropriate care. Breast ultrasonography images with complex artifacts and high noise levels make diagnosis extremely difficult for radiologists. Automatic breast cancer classification is therefore our top obje...Show More
Comparative Performance Analysis of Heart Sound based Heart Disease Classifiers Using Machine Learning Models from Balanced Datasets
Adyasha Rath;Ganapati Panda;Debendra Muduli;Ram Chandra Barik
2023 International Conference on Microwave, Optical, and Communication Engineering (ICMOCE)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
Accurate heart disease (HD) detection and follow up treatment at an early stage can save lot of human lives. In recent years, many research efforts have been made to achieve this objective. The HD detection using machine learning (ML) approach can be made either from various test reports, electrocardiogram (ECG) signal or heart murmurs. In recent past many ML based classification models have been ...Show More
Use of Blockchain Technology to Protect Privacy in Electronic Health Records- A Review
Meraj Farheen Ansari;Bibhu Dash;Swati Swayamsiddha;Ganapati Panda
2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Patients' medical files are electronically preserved and accessible through a network such as Electronic Health Records (EHRs). Numerous opportunities exist for EHRs to enhance patient care, clinical practice performance indicators, and potential future clinical research contributions. The techniques used to preserve EHRs have proved incredibly unsafe in the contemporary era of smart homes and urb...Show More
Detection of Pneumonia from X-ray Images using Eigen Decomposition and Machine Learning techniques
2022 International Conference on Signal and Information Processing (IConSIP)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
The mortality rate due to pneumonia is very high across the world and particularly in India. This respiratory and lung-based disease can be controlled by early detection and treatment. Many reported articles are available on this topic. In the current investigation an attempt has been made to develop and test k-nearest neighbor (k-NN), random forest (RF), and light gradient boosting machine (LGBM)...Show More
Efficient Spectrum Allocation in Cognitive Radio using Yellow Saddle Goatfish Algorithm
2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Spectrum shortage and spectrum scarcity are the burning issues in wireless communication. To overcome these challenges, Cognitive Radio Networks (CRNs) can be deployed as an alternative technology to gainfully employ the underutilized radio spectrum of licensed users. In the current research work, multi-objective optimization based approaches are introduced for spectrum allocation in CRNs by minim...Show More
Multiscale Domain Gradient Boosting Models for the Automated Recognition of Imagined Vowels Using Multichannel EEG Signals
Shaswati Dash;Rajesh Kumar Tripathy;Dinesh Kumar Dash;Ganapati Panda;Ram Bilas Pachori
This letter proposes the multiscale domain gradient boosting-based approach for the automated recognition of imagined vowels using the multichannel electroencephalogram (MCEEG) signals. The multiscale analysis of the MCEEG signals is performed using multivariate automatic singular spectrum analysis and multivariate fast and adaptive empirical mode decomposition methods. The features such as bubble...Show More
Fourier-Bessel Domain based Discrete Stockwell Transform for the Analysis of Non-stationary Signals
Shaswati Dash;Samit Kumar Ghosh;Rajesh Kumar Tripathy;Ganapati Panda;Ram Bilas Pachori
2022 IEEE India Council International Subsections Conference (INDISCON)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
In this article, a novel time-frequency (TF) analysis approach based on the Fourier-Bessel series expansion (FBSE) domain discrete Stockwell transform (DST) is proposed. The Toeplitz matrix is formulated using the FBSE coefficients of the non-stationary signal. The Gaussian matrix is multiplied by the Toeplitz matrix to obtain the windowed Fourier-Bessel Toeplitz (WFBT) matrix. The FBSE-DST time-f...Show More
Gradient Boosting Machine and Efficient Combination of Features for Speech-Based Detection of COVID-19
Tusar Kanti Dash;Chinmay Chakraborty;Satyajit Mahapatra;Ganapati Panda
In recent times, speech-based automatic disease detection systems have shown several promising results in biomedical and life science applications, especially in the case of respiratory diseases. It provides a quick, cost-effective, reliable, and non-invasive potential alternative detection option for COVID-19 in the ongoing pandemic scenario since the subject's voice can be remotely recorded and ...Show More
Automated Heart Sound Activity Detection From PCG Signal Using Time–Frequency-Domain Deep Neural Network
Samit Kumar Ghosh;R. N. Ponnalagu;Rajesh Kumar Tripathy;Ganapati Panda;Ram Bilas Pachori
The phonocardiogram (PCG) signal deciphers the mechanical activity of the heart, and it consists of the fundamental heart sounds (FHSs) (S1 and S2), murmurs, and other associated sounds (S3 and S4). Detection of FHS activity (FHSA) is vital for the automated analysis of PCG signals to diagnose various heart valve diseases. This article proposes a time–frequency-domain (TFD) deep neural network (DN...Show More
Enhanced Prediction of Thyroid Disease Using Machine Learning Method
2022 IEEE VLSI Device Circuit and System (VLSI DCS)
Year: 2022 | Conference Paper |
Cited by: Papers (11)
Thyroid disease is becoming increasingly in men, women and children but commonly occurring among women over the age of 30. It causes heart problem, eye problem, fertility and pregnancy problems over its effect for long time. As a result, it is critical to evaluate the thyroid information in order to forecast the early prediction of disease and take steps to avoid the deadly condition of thyroid ca...Show More
Automated Detection of Pulmonary Diseases From Lung Sound Signals Using Fixed-Boundary-Based Empirical Wavelet Transform
Rajesh Kumar Tripathy;Shaswati Dash;Adyasha Rath;Ganapati Panda;Ram Bilas Pachori
In this letter, a promising method is proposed to automatically detect pulmonary diseases (PDs) from lung sound (LS) signals. The modes of the LS signal are evaluated using empirical wavelet transform with fixed boundary points. The time-domain (Shannon entropy) and frequency-domain (peak amplitude and peak frequency) features have been extracted from each mode. The classifiers, such as support ve...Show More
Automated Recognition of Imagined Commands From EEG Signals Using Multivariate Fast and Adaptive Empirical Mode Decomposition Based Method
Shaswati Dash;Rajesh Kumar Tripathy;Ganapati Panda;Ram Bilas Pachori
In this letter, a novel automated approach for recognizing imagined commands using multichannel electroencephalogram (MEEG) signals is presented. The multivariate fast and adaptive empirical mode decomposition method decomposes the MEEG signals into various modes. The slope domain entropy and $L_1$-norm features are obtained from the modes of MEEG signals. The machine learning models such as k-nea...Show More
A Cybertwin-Based 6G Cooperative IoE Communication Network: Secrecy Outage Analysis
Soumya P. Dash;Sandeep Joshi;Suresh Chandra Satapathy;Shishir Kumar Shandilya;Ganapati Panda
The sixth-generation (6G) communication networks being highly data-intensive and enabled by the Internet of Everything (IoE) are envisaged to find applications in various domains including smart healthcare, smart industry, and gaming. In this article, a novel hybrid 6G-based cybertwin cooperative architecture is studied and an analytical framework for the secrecy outage analysis is presented. The ...Show More
On the Development and Performance Evaluation of Improved Radial Basis Function Neural Networks
This article deals with the development of four modified radial basis function neural network (RBFNN) models. The corresponding learning algorithms associated with the updating of internal parameters of the models are derived. The conventional inputs are used in the first and second modified RBFNN models (models 3 and 4) whereas exponential nonlinear inputs are used in the fifth and sixth RBFNN mo...Show More
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