G Sunil Reddy
@sru.edu.in
Assistant Professor, Department of Computer Science and Engineering
SR University ( formally S R Engineering College)
RESEARCH INTERESTS
Cyber Security, Computer Forensics, Data Mining, IoT, Network security,
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
G. Sunil, Malatesh Akkur, and Dhiraj Kapila
Springer Nature Singapore
Goli Sunil, Srinivas Aluvala, Chinthala Sujitha, Akarapu Mahesh, Areefa, Kanegonda Ravi Chythanya, and Gadde Aruna
AIP Publishing
Kothandaraman Dhandapani, Rajchandar Kannan, Alavandar Arulmurugan, Goli Sunil, Karuppasamy Kannapiran, Durgunala Ranjith, and Swetha Mucha
AIP Publishing
Goli Sunil, Areefa, Kota Pragathi, Koyyada Rishitha, Sambari Praveen Kumar, Kothandaraman Dhandapani, and Rajasri Reddy
AIP Publishing
Vishali Sivalenka, Srinivas Aluvala, Khaja Mannanuddin, G. Sunil, J. Vedika, and V. Pranathi
AIP Publishing
Sajja Suneel, R. Krishnamoorthy, Anandbabu Gopatoti, Lakshmana Phaneendra Maguluri, Prathyusha Kuncha, and G. Sunil
Springer Science and Business Media LLC
Sahil Bhatia, Mansi Sharma, Shweta Pandey, Ruchi Tripathi, G Sunil, and Mansi Sahu
IEEE
Abdul Rahim, Ankur Sharma, Gaurav Sethi, Azad Kumar, G Sunil, and Muntather Almusawi
IEEE
The way we connect to and engage with different things and urban infrastructure has been completely transformed by the Internet of Things (IoT), a paradigm shift in technology. IoT entails providing internet connectivity to various items, such as electrical poles and home appliances, in order to facilitate data collecting and communication. Real-time applications and monitoring are made possible by this connectivity since data is collected and delivered instantaneously or with no delay. The real-time data produced by IoT is crucial in the context of urban development and safety because it provides timely insights into the state of key city assets, ultimately enhancing environmental sustainability and public security. Monitoring the tilt status of electric poles in urban areas in real time is one specialized application of the IoT. To precisely gauge the angle or inclination of these essential infrastructure components, tilt sensing technology is used. The device can determine the stability and safety of electric poles by continuously measuring the tilt angle. An IoT-based solution is suggested to accomplish this, utilizing the NodeMCU development board. NodeMCU is a good option for IoT applications since it easily blends a microcontroller and Wi-Fi module. The NodeMCU serves as the key element in this system and is in charge of gathering data from tilt sensors and sending it over the internet.
S. Vinod Kumar, G. Sunil, Ramy Riad Hussein, S. Manju Vidhya, and S. Meenakshi Sundaram
IEEE
Facial Emotion Recognition (FER) plays a crucial role in analysing and understanding individual’s emotions in the form of non-verbal communication. Existing Deep Learning (DL) based FER methods offered diverse contribution in analysing human emotions but several challenges like extracting accurate spatial and temporal features for accurate classification of emotions is a major concern. To address this, a DL based Facial Recognition and Emotion Detection (FRED) system is proposed by using extended Cohn-Kanade (CK+) dataset as the input. The data is pre-processed first where the Histogram Equalization (HE) is applied and then Convolutional Neural Network (CNN) is used to extract the spatial features while reducing the dimensionality. The extracted features are given to Recurrent Neural Network (RNN) to learn long term dependencies from a temporal sequence of data. The final classification is performed where the Attention Mechanism (AM) is integrated in the RNN to determine the higher weights for better outcome. The results of proposed AMConVnet-RNN showed a superior classification accuracy of 99.54%, precision of 98.63%, and f1-score of 97.39% when compared to existing FER methods like Residual Network-50 (ResNet-50) and Channel Based Attention Mechanism (CBAM) based ResNet50.
Sri Lavanya Sajja, Zaid Alsalami, G Sunil, Sunil Kumar V, and Ghazi Mohamad Ramadan
IEEE
Deep Learning (DL) based identification and classification of archaeological sites are being used progressively from satellite images obtained from Geographic Information System (GIS). Even though, there were promising results from the existing researches, there is still need to enhance the classification with distinct features of the satellite images. The methods found challenging to accurately or partially identify an archaeological feature especially in a complex landscape due to lack of distinction between archaeological and non-archaeological features. For this, the geo-referenced satellite images are considered in this research, which are pre-processed using min-max normalization for efficient data analysis and to remove unwanted data. The Selective kernel spatial Feature Extraction (SFE) module is used to improve the feature extraction process where the spatial features are extracted. The extracted features are then classified using Fully Convoluted Deep Neural Network (FCDNN) for effective identification and classification of archaeological sites. The results showed that the proposed SFE-FCDNN achieved better classification accuracy with 98.83%, precision of 97.41%, and an error rate of 1.71 when compared to the existing methods such as Neural Network (NN), and Backpropagation Neural Network (BNN).
Mukesh Soni, G Sunil, N Rajesh, Zaid Alsalami, and Papiya Dutta
IEEE
Wireless Sensor Networks (WSNs) are a substantial part of the Internet of Things (IoT) utilized for cluster and routing path applications to monitor computational fields. The major challenge of the Firefly Algorithm (FA) is its sensitivity to parameters such as the attractiveness coefficient and the randomization factor. Selecting appropriate parameter values can be challenging and may require fine-tuning. The proposed method utilizes energy levels in the IoT to efficiently manage routing data transmission, enhancing information transfer through the cluster router. Aquila Optimization (AO) often involves trade-offs between different parameters. Improving one aspect may negatively impact another, and finding the right balance can be difficult. Although the proposed FA and AO method exhibit high performance compared to existing methods such as Black Widow Optimization (BWO), Fixed Parameter Tractability (FPT), and Multipath Link Routing Protocol (MLRP). The proposed FA and AO method yield high results, achieving a Network Lifetime 1900 rounds, Packet Delivery Ratio (PDR) of 0.98, and an End-To-End Delay (ETED) of 0.80 ms. These outcomes outperform existing methods in the context of WSNs.
Layth Hussein, P Rashmi, G Sunil, Sheeba Armoogum, and S Meenakshi Sundaram
IEEE
Early detection of problems can save wind turbines from significant damage, reducing maintenance costs and downtime. Operators can save millions of dollars in repair and replacement expenses by implementing an efficient fault detection system. Identifying the wind turbine’s issues is challenging for the model to be trained on. This work presents an effective method for detecting wind turbine faults using the Gated Recurrent Unit (GRU) classifier. The SCADA historical wind turbine dataset was first preprocessed using the sliding window approach. Subsequently, the auto-encoder method extracts the spatial features from the processed data. The Multi-Objective Evolutionary Algorithm (MOEA) is then used to select the optimal features. Each individual and the initial population representation consists of fixed-length arrays of bits, with the size matching the number of attributes in the problem that needs to be solved. Finally, the problem is solved using MOEA and the GRU’s effective classifier. The model was evaluated using several metrics, including F1-Score, Accuracy, Precision, and Recall, with numerical values of 1.00, 0.997, 0.805, and 0.999, respectively. A comparative analysis with Convolutional Neural Network-based 2Dimentional Long-Short Term Memory (3DSE-CNN-2DLSTM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Light Gradient-Boosting Machine (LightGBM) illustrates the current approaches.
Khushi Mittal, Kanwarpartap Singh Gill, Priyanshi Aggarwal, Ramesh Singh Rawat, and G Sunil
IEEE
This research study investigates the use of Deep Convolutional Neural Networks (CNNs) and Transfer Learning in the field of coral reef conservation. It does this by introducing a dataset consisting of 923 photographs that classify corals as either healthy or bleached. The paper aims to solve the labor-intensive process of manually identifying coral reefs by providing an automated picture classification model based on Convolutional Neural Networks (CNNs). This is particularly important due to the crucial function of coral reefs and the issues they face, such as coral bleaching. The approach utilises the VGG 19 pre-trained CNN model and includes the use of training, validation, and testing datasets. Model monitoring is implemented via the use of callbacks. The hyperparameters of the model, such as batch size, epochs, and input shape, are explicitly defined. Common evaluation metrics used in machine learning include accuracy, confusion matrix, and classification report. The study highlights the advantages of the suggested paradigm in terms of efficiency, scalability, and real-time monitoring. The achieved accuracy of 74% demonstrates encouraging outcomes, highlighting the capability of CNNs in promptly identifying and tracking the well-being of coral reefs. This is vital for making well-informed decisions and undertaking conservation initiatives.
Khushi Mittal, Kanwarpartap Singh Gill, Priyanshi Aggarwal, Ramesh Singh Rawat, and G Sunil
IEEE
This study focuses on the crucial matter of identifying and categorising bone fractures via the use of sophisticated image analysis methods. Timely detection of bone fractures is crucial for appropriate treatment due to their prevalence and considerable impact on patient outcomes. Prompt identification is vital to minimise problems and optimise overall recuperation. The research uses a dataset of 4906 X-ray pictures that are categorised into two classes: fractured and not-fractured. It employs a Sequential Convolutional Neural Network (CNN) model. The CNN model achieves an amazing accuracy of 98% in fracture classification using a training set of 4099 pictures, a testing set of 401 photos, and an additional 406 images for validation. The technique highlights the capacity of deep learning in medical imaging to provide precise and efficient fracture diagnosis, demonstrating the potential of this approach to improve healthcare results.
Muskan Agarwal, Priyanshi Aggarwal, G Sunil, Kanwarpartap Singh Gill, and Ramesh Singh Rawat
IEEE
The abstract of this research paper outlines the overarching objectives, methodologies, findings, and implications of the study, focusing on the development of a robust machine learning model for precise classification of various ovarian cancer subtypes. The primary goal was to leverage the VGG19 architecture, a deep convolutional neural network, to accurately identify subtypes including endometrioid carcinoma (EC), clear cell carcinoma (CC), mucinous carcinoma (MC), high-grade serous carcinoma (HGSC), and low-grade serous carcinoma (LGSC). Employing advanced image processing algorithms and feature extraction techniques, the model was trained on a dataset comprising histology images of ovarian tissue samples. The achieved overall accuracy of 75% signifies the effectiveness of the proposed strategy in subtype identification. A comprehensive evaluation of the model’s performance, including accuracy, recall, and F1-score, was conducted to assess its classification skills across diverse subtypes. The study not only presents the model’s achievements but also sheds light on potential areas for growth, future research prospects, and an analysis of the model’s strengths and weaknesses. The research contributes significantly to the field by demonstrating the ability to differentiate between ovarian cancer subgroups, offering valuable insights that could enhance the precision of diagnosis and, consequently, the formulation of tailored treatment regimens. The abstract concludes by emphasizing the increasing trend in the utilization of computational approaches, particularly machine learning, for enhanced accuracy in ovarian cancer diagnosis and treatment. Overall, the abstract serves as a concise and informative preview of the research, inviting readers to delve deeper into the study’s details.
Muskan Agarwal, Priyanshi Aggarwal, G Sunil, Kanwarpartap Singh Gill, and Ramesh Singh Rawat
IEEE
The abstract of this research paper encapsulates the essence of a comprehensive investigation into stellar classification using linear regression. The study explores the predictive power of various features, including Absolute Temperature, Relative Luminosity, Relative Radius, Absolute Magnitude, Star Color, and Spectral Class, with a focus on predicting Star Type. The dataset encompasses a diverse range of stars, from Red Dwarfs to HyperGiants, providing a rich foundation for analysis. The linear regression model demonstrates a remarkable 90% accuracy in predicting star types, underscoring the efficacy of this approach in the context of stellar classification. Precision, recall, and F1-score metrics are employed to evaluate the model’s performance across different star types, revealing insights into its strengths and limitations. The abstract sets the stage for the paper, summarizing the key findings and emphasizing the significance of the research in contributing to our understanding of stars and the broader field of astronomy.
M. Sangeetha, G Sunil, Ammar Hameed Shnain, Shruthi B S, and A U Shabeer Ahamed
IEEE
In building the navigation software application, the Landmark plays an important factor in visual place recognition. There is major problem for performing the classification to visual recognition, due to change of environments like summer, winter, rainy season the place changes. To overcome the problem, the paper proposes a vision transformer as classification for place recognition. In vision transformer the first stage is considerations images are divided into various parts, then it converts into sequence embedding by moving it. To keep information at correct position, the embedding position is used in the parts and the sequence result is sent to various multihead attention layers for final result generation. For validating the proposal, we performed the vision transformer model on the two different datasets such as paris and oxford datasets. The result obtained by performing the vision transformer is demonstrated the other existing model in terms of accuracy in two datasets the given values are 90.63% and 87.30% respectively.
Khushi Mittal, Kanwarpartap Singh Gill, Priyanshi Aggarwal, Ramesh Singh Rawat, and G Sunil
IEEE
This study article focuses on dysarthria, a speech problem that is often seen in patients with cerebral palsy (CP) or amyotrophic lateral sclerosis (ALS). Early identification of dysarthria is crucial for successful treatments and better patient outcomes, since it has a substantial influence on communication ability. Using the TORGO database, which consists of 2000 samples of persons with and without dysarthria, spanning different genders, the research use a Convolutional Neural Network (CNN) to classify speech. The dataset includes both dysarthric and non-dysarthric individuals of both genders, with 500 samples each, captured during separate sessions. The CNN-based technique demonstrates an impressive 96% accuracy in differentiating between dysarthric and non-dysarthric instances, highlighting the promise of deep learning technology in assisting with the early detection of dysarthria and enabling prompt therapies for afflicted people. This study makes significant contributions to the advancement of diagnostic capacities and presents a potential opportunity to improve the quality of life for those with speech difficulties.
Khushi Mittal, Kanwarpartap Singh Gill, Priyanshi Aggarwal, Ramesh Singh Rawat, and G Sunil
IEEE
This research study introduces a novel method for predicting the condition of high-speed bearings in wind turbines. The method utilises unprocessed data collected from sensors that measure vibrations. The process entails calculating a Kurtosis Spectrogram for each of the 50 samples and using a 1D-CNN to categorise bearings into two groups: Medium-life Expectancy (more than 15 days) or Short-life Expectancy (less than 15 days). The research highlights the importance of early identification in forecasting the Remaining-Useful Life (RUL) of machine components, which is vital for averting failures and optimising maintenance choices. The 1D-CNN, implemented using Keras, has remarkable accuracy, achieving an overall accuracy of almost 90% based on recall. This study emphasises the prospective utilisation of this solution in alert systems, offering vital assistance for technical determinations about component upkeep, guaranteeing the dependability and effectiveness of wind turbine systems.
Muskan Agarwal, Kanwarpartap Singh Gill, Priyanshi Aggarwal, Ramesh Singh Rawat, and G Sunil
IEEE
This research introduces a comprehensive approach for enzyme classification, merging Exploratory Data Analysis (EDA) with a Neural Network (NN) model. Leveraging a dataset of 858,777 labeled amino acid sequences, originating from ten organisms, the model predicts enzyme classes in a test set of 253,146 samples while excluding sequences above a set length. Preprocessing involves omitting rare amino acids (X, U, B, Z) and accounting for amino acids (B, Z) exclusive to the training set. The Neural Network architecture, comprising an embedding layer, bidirectional LSTM layers, and a dense output layer, yields promising results with 79% accuracy on the test set after 20 epochs. A detailed classification report and confusion matrix showcase the model's efficacy across 20 enzyme classes. This study advances enzyme classification methodologies, demonstrating the significance of EDA and NN integration in bioinformatics and molecular biology. Future directions involve exploring additional features and optimization strategies for continued model enhancement.
Muskan Agarwal, Kanwarpartap Singh Gill, Priyanshi Aggarwal, Ramesh Singh Rawat, and G Sunil
IEEE
This research study investigates the use of Convolutional Neural Network (CNN) Autoencoders for detecting anomalies in electrocardiogram (ECG) data utilising the PTB Diagnostic ECG Database. The dataset consists of 14,552 samples that are classified into two categories: normal heartbeats and heartbeats influenced by cardiac abnormalities. The use of Transposed Convolution has greatly enhanced the performance of the model. The work highlights the crucial importance of promptly identifying cardiac abnormalities and introduces a CNN Autoencoder model specifically developed to effectively encode and decode ECG data, facilitating the identification of irregular patterns. The process entails constructing a resilient Autoencoder including encoder and decoder components, which are taught to minimise mistakes in reconstruction. The assessment measures demonstrate the model's exceptional accuracy (76.93%), precision (55.23%), recall (89.81%), and F1 score (65.40%). This study emphasises the significance of using deep learning methods to identify anomalies in ECG data at an early stage. This approach shows great potential for enhancing diagnostic skills and improving patient outcomes.
Ricky Rajora, Deepak Banerjee, Deepak Upadhyay, Sarishma Dangi, and G Sunil
IEEE
Their abstract presents a novel method for identifying cassava root disease by creating a sophisticated model. Using Convolutional Neural Networks (CNNs) in combination with Random Forest, the model achieves high recall, high precision, and F1-Score values of more than 97.7% for a few disease classes: cassava Mosaic Disease, cassava Brown Streak Disease, cassava Bacterial Blight, cassava Anthracnose Disease, or cassava Root Rot. The macro-, weighted--, & micro-average accuracy metrics, which add up to an astounding 97.80%, all demonstrate how resilient the model is. With its thorough insights into the model's classification performance, Figure 3 offers a nuanced breakdown of true positives, false positives, false negatives, and true negatives. Furthermore, the complex CNN architecture behind this achievement is explored in Table 3. The research involved examining a dataset of 6810 images. The model demonstrated impressive performance, achieving an accuracy of 97.79% in overall classification. The model's dependability across a range of disease cases is highlighted by these metrics, even though macro, weighted, and micro-average support and support proportion values were missing. The cumulative effect of these results highlights the revolutionary effect of the model on the identification of diseases in cassava production. Enhancing crop health and advancing precision agriculture is made possible by the precision and accuracy of the methodology. The useful tool for farmers to maximize cassava production and secure a more secure and resilient agricultural future, this creative integration of cutting-edge technologies not only transforms disease identification but also makes a significant contribution to the larger goal of sustainable agricultural practices.
Gunjan Shandilya, Vatsala Anand, Rahul Chauhan, Hemant Singh Pokhariya, Sheifali Gupta, and G Sunil
IEEE
The world market for spices is dominated by the important spice crop, cinnamon. Achieving excellent yields and production standards in the cultivation of cinnamon depends on the accurate classification of diseases affecting the plant, such as rough bark and stripe canker. To minimize yield loss and maintain healthy yields of crops, early recognition of plant disease is crucial for prompt action. Deep learning approaches for automated classification make it easier to identify sick plants quickly and take appropriate action. Convolutional Neural Networks (CNNs), one of the deep learning techniques, have greatly improved automated diagnosis of plant diseases. In this work, a CNN-based method has been utilized to classify two common diseases in cinnamon plants: rough bark and stripe canker. This study uses a carefully selected dataset of 1600 high-resolution photos of impacted cinnamon plants. After a great deal of testing and optimization, the suggested CNN architecture performs admirably, with an astounding accuracy of 94.99% and a negligible loss of 0.93 with precision and recall of approx 96.5%. Enhancing agricultural productivity, early disease diagnosis, affordable treatments, agricultural precision, global food security, research and development are all areas where this sort of classification is important. In addition to addressing current issues in agriculture, this study advances more general goals like sustainability, food safety, and agricultural technology innovation.
Gunjan Sharma, Vatsala Anand, Rahul Chauhan, Hemant Singh Pokhariya, Sheifali Gupta, and G Sunil
IEEE
A major global health concern, kidney infections are linked to rising death rates, especially when they worsen and cause the breakdown of the kidneys. Kidney function is seriously threatened by common kidney disorders such as nephrolithiasis, kidney tumors, and cyst development. Kidney failure, which can be brought on by conditions including tumors, stones, and cysts, can be avoided with prompt diagnosis and treatment. Computer-aided diagnostics are essential due to the rising incidence of chronic renal illness, the lack of specialists, and the increased need for evaluation and monitoring. Though artificial intelligence (AI) methods, such as machine and deep learning, have been investigated for the identification of renal illness, their effectiveness is still lacking. In order to fill this gap, this study implements a deep learning-based Convolutional Neural Network (CNN) model for kidney illness prognosis and classification using a benchmark kidney dataset from Computed Tomography. CNN uses data reprocessing to extract features from the CT images. The results show how effective the suggested method is in correctly classifying renal illness, with a noteworthy accuracy of 99.88%, precision of 99.8%, recall of 99.7%, and an F1-score of 0.98. This work represents a potential development in the field of computer-assisted renal health diagnostics by supporting the use of the refined CNN model as a trustworthy instrument for kidney illness identification. This research can be applied in the medical field for the diagnosis of kidney diseases.
Ricky Rajora, Deepak Banerjee, Aarju Rajora, Ramesh Singh Rawat, and G Sunil
IEEE
In the abstract, This research article addresses the essential issue of forging flaws in manufacturing processes by implementing an advanced categorization model. The model uses Convolutional Neural Networks, or Random Forest techniques to accurately detect and categorise various forging flaws such as Fold, Scale, Underfill, Overflow, Upsetting Defect, or Seams. The model's performance is evaluated using accuracy, recollection, and F1-score measures for every defect class, and the results are extraordinary. Precision ratings vary between 96.77% to 97.36%, indicating the model's ability to minimize false positives. Recall values of 96.83% to 97.32% demonstrate the model's ability to capture real positive events while minimizing false negatives. The F1-score, a standardized indicator of precision and recall, is consistently greater than 97%, demonstrating a fair trade-off between precision with sensitivity for each fault class. The support values, which indicate actual examples of every category in the data set, range from 1235 through 1330, demonstrating that the dataset is diverse and representative. The support proportion, which ranges from 0.16 to 0.17, offers information on the relative prevalence of each fault type. With a total precision of 99%, the algorithm demonstrates remarkable skill in automating the identification of forging flaws. The macro-average, weighted-average, all micro-average scores all equal 97.07%, indicating consistent performance across classes and evaluation parameters. This research advances flaw detection approaches in manufacturing, guaranteeing the manufacture of high-quality forged parts with increased safety, dependability, and economic feasibility.