panduranga vital terlapu | JNTU College of Engineering, kakinada (original) (raw)

Papers by panduranga vital terlapu

Proceedings on Engineering SciencesVolume, 2024

Breast cancer (BC) ranks the second most prevalent cancer among women globally and is the leading... more Breast cancer (BC) ranks the second most prevalent cancer among women globally and is the leading cause of female mortality. The conventional method for BC detection primarily relies on biopsy; this might be time-consuming and error-prone. The substantial lives lost due to BC underscores its significant threat. Mitigating this threat focuses on early detection and prevention by adopting novel techniques. Many researchers have turned to Machine Learning algorithms to develop prognosis systems. We employ a combination of deep learning (DL) and machine learning (ML) algorithms for BC identification. Our approach is a hybrid Convolutional Neural Network (CNN) model, which performs better than other experimental and existing models. This model effectively categorizes histopathological images into either benign or malignant classes. We explored various methodologies, including CNN, CNN in conjunction with Support Vector Machine (SVM), CNN with Random Forest, and VGG-16 combined with XGBOOST. This research seeks to enhance the accuracy and efficiency of BC diagnosis. It contributes to more effective early detection and improved patient outcomes.

Institute of Advanced Engineering and Science (IAES), 2024

Wireless sensor networks (WSNs) are of significant importance in many applications; nevertheless,... more Wireless sensor networks (WSNs) are of significant importance in many applications; nevertheless, their operational efficiency and longevity might be impeded by energy limitations. The low energy adaptive clustering hierarchy (LEACH) protocol has been specifically developed with the objective of achieving energy consumption equilibrium and regularly rotating cluster heads (CHs). This study presents a novel technique, namely the hierarchical fuzzy logic controller (HFLC), which is integrated with the LEACH protocol to enhance the process of CH selection and effectively prolong the network's operational lifespan. The HFLC system employs fuzzy logic as a means to address the challenges posed by uncertainty and imprecision. It assesses many aspects, including residual energy, node proximity, and network density, in order to make informed decisions. The combination of HFLC with LEACH demonstrates superior performance compared to the conventional LEACH protocol in terms of energy efficiency, stability, and network durability. This study emphasizes the potential of intelligent and adaptive mechanisms in improving the performance of WSNs by improving the survivability of nodes by reducing the energy consumption of the nodes during the communication of network process. It also paves the way for future research that integrates soft computing approaches into network protocols.

Annals of data science, Jul 7, 2024

Indian journal of science and technology, May 14, 2024

Objectives: The research aims to enhance breast cancer detection accuracy and effectiveness using... more Objectives: The research aims to enhance breast cancer detection accuracy and effectiveness using deep transfer learning and pre-trained neural networks. It analyses breast ultrasound images and identifies important characteristics using pre-trained networks. The goal is to create a more efficient and accurate automated system for breast cancer detection. Methods: The study uses breast ultrasound cancer image data from the Kaggle Data Repository to extract informative features, identify cancer-related characteristics, and classify them into benign, malignant, and normal tissue. Pre-trained Deep Neural Networks (DNNs) extract these features and feed them into a 10-fold crossvalidation SVM classifier. The SVM is evaluated using various kernel functions to identify the best kernel for separating data points. This methodology aims to achieve accurate classification of breast cancer in ultrasound images. Findings: The study confirms the effectiveness of deep transfer learning for breast cancer detection in ultrasound images, with Inception V3 outperforming VGG-16 and VGG-19 in extracting relevant features. The combination of Inception V3 and the SVM classifier with a polynomial kernel achieved the highest classification accuracy, indicating its ability to model complex relationships. The study demonstrated an AUC of 0.944 and a classification accuracy of 87.44% using the Inception V3 + SVM polynomial. Novelty: This research demonstrates the potential of deep transfer learning and SVM classifiers for accurate breast cancer detection in ultrasound images. It integrates Inception V3, VGG-16, and VGG-19 for breast cancer detection, demonstrating improved classification accuracy. The combination of Inception V3 and SVM (polynomial) achieved a significant AUC (0.944) and classification accuracy (87.44%), outperforming other models tested. This research underscores the potential of these technologies for accurate breast cancer detection in ultrasound images.

International journal of computing and digital system/International Journal of Computing and Digital Systems, May 1, 2024

Bulletin of Electrical Engineering and Informatics, Apr 1, 2024

Feature importance strategy that substantially impacts software development effort estimation (SD... more Feature importance strategy that substantially impacts software development effort estimation (SDEE) can help lower the dimensionality of dataset size. SDEE models developed to estimate effort, time, and wealth required to accomplish a software product on a limited budget are used more frequently by project managers as decision-support tool effort estimation algorithms trained on a dataset containing essential elements to improve their estimation accuracy. Earlier research worked on creating and testing various estimation methods to get accurate. On the other hand, ensemble produces superior prediction accuracy than single approaches. Therefore, this study aims to identify, develop, and deploy an ensemble approach feasible and practical for forecasting software development activities with limited time and minimum effort. This paper proposed a collaborative system containing a multi-level ensemble approach. The first level grabs the optimal features by adopting boosting techniques that impact the decided target; this subset features forward to the second level developed by a stacked ensemble to compute the product development effort concerning lines of code (LOC) and actual. The proposed model yields high accuracy and is more accurate than distinct models.

Procedia Computer Science, Dec 31, 2022

Diagnosing chronic kidney disease (CKD) accurately and quickly is a big challenge in healthcare t... more Diagnosing chronic kidney disease (CKD) accurately and quickly is a big challenge in healthcare today. In a region called Uddanam in Andhra Pradesh, India, there's a common kidney disease called Uddanam nephropathy. In the 1990s, Andhra Pradesh was the first place to find this kind of kidney disease. In our research, we gathered data about personal and clinical of 1,055 individuals living in the Uddanam area (Srikakulam district of Andhra Pradesh, India). Among these, 656 had CKD, and 399 did not. We looked at 37 different aspects of their health. We apply for feature reduction Principal component Analysis and for classification uses MLP and Intelligent GA-MLP Hybrid model. This method combines the power of Genetic Algorithms (GA) and Multi-Layer Perceptron neural networks (MLP) to make CKD diagnosis more accurate. By using GA's ability to find the best solutions and MLP's skill in recognizing patterns, this new method can overcome the problems with older ways of diagnosing CKD. We tested our method thoroughly, comparing it to other methods. Our results showed that our approach can find CKD early and accurately. By combining GA and MLP, we're not only helping improve medical diagnosis but also giving hope for better CKD management and patient care. Our hybrid model, which uses PCA (20 dimensions) along with GA and MLP, performed the best with an accuracy of 98.34% during training and 98.54% during testing.

Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET 2024), Advances in Computer Science Research 112, 2024

bstract. Maize plant diseases can have a severe impact on agricultural productivity, making detec... more bstract. Maize plant diseases can have a severe impact on agricultural productivity, making detection and control challenging for farmers. Early identification of diseases is crucial for minimizing losses. This study proposes a new approach that integrates machine learning (ML) and deep learning (DL) algorithms to improve maize disease diagnosis and prognosis. The research employs traditional machine learning algorithms, such as Support Vector Machine (SVM) and Multilayer Perceptron (MLP), along with extracted features of Transfer Learning models, such as InceptionV3, VGG19, and Dense-Net201. The objective is to develop a robust system for early disease detection in maize leaves using image analysis. Optimization techniques, such as the Adam optimizer, and activation functions, such as tanh and sigmoid, are also explored. The results indicate that the Adam optimizer MLP achieves the highest accuracy (MLP(100,100) layers PCA(300) accuracy 0.95107) as well as SVM (RBF kernel) with PCA(100) accuracy (0.95585) exceptional other classification methods. This integrated approach promotes agricultural sustainability and crop yield by enabling prompt disease management. Keywords: Machine Learning, Deep Learning, Transfer Learner, SVM, MLP

Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET 2024), Advances in Computer Science Research 112, 2024

One of the most recent technologies is blockchain. It is used in many industries, including healt... more One of the most recent technologies is blockchain. It is used in many industries, including healthcare, finance, supply chain management, etc. A distributed technology called blockchain makes data or information available to all network nodes. It serves as an electronic database and stores information in digital form. Blockchain is widely famously employed in cryptocurrency systems to create a secure and decentralized transaction. However, a blockchain may be used to store any data. Not only is the data related to cryptocurrency but it is also used in healthcare systems. We can use blockchain to record and store patient data due to its distributed nature. But on the dot, this has become a problem as the data of the particular patient will be all over the network which increases the risk and complexity to handle the data. So, we are going to look for a solution and utilize it. Hence, the data will be authorized, authenticated, transparent, and highly secured. The patient controls access to the data by using the consortium type of the blockchain. The patient decides who can use and access their data, which can be the most beneficial aspect of using blockchain in healthcare.
Keywords: Medical record, Smart Contract, MetaMask, Decentralized system, Authentication

Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET 2024), Advances in Computer Science Research 112, 2024

Abstract. In recent times, generating understandable prompts for AI has been a significant proble... more Abstract. In recent times, generating understandable prompts for AI has been a significant problem, which in turn leads to inaccurate results. In essence, the problem is about finding ways to make AI understand and respond accurately to the prompts given to it, which is crucial for improving its overall performance and usefulness in various applications. This paper proposes a novel approach to enhance AI comprehension by generating tailored understanding prompts through prompt engineering techniques in Natural Language Processing, along with advanced Transformer-based deep learning models. Our project integrates these techniques to transform a base prompt into a set of diverse and comprehensive understanding prompts. To ensure data security, we have also implemented data encryption standard and Blowfish encryption algorithms to protect sensitive information during the transformation process. The resulting prompts will be used to train AI models, enabling them to grasp nuanced details and context when responding to user queries. The paper's significance lies in its potential to improve the quality of AI-generated responses across a range of applications, including natural language understanding, question answering, and content generation. Crucially, the developed web application, constructed using the MERN Stack, promises more reliable and insightful interactions with AI systems, effectively bridging the gap between human comprehension and AI-generated content.

Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET 2024), Advances in Computer Science Research 112, 2024

Parkinson's disease is a disorder of the nervous system that causes impairment and changes in cog... more Parkinson's disease is a disorder of the nervous system that causes impairment and changes in cognitive behavior. Voice analysis has become a crucial tool for diagnosing neurological conditions like PD, with symptoms typically appearing in people aged 50 or older. This research suggests new methods to improve early PD diagnostic methods, focusing on assessing aspects and fine-tuning hyperparameters of machine learning algorithms. The data set includes characteristics of both healthy and PD patients, aged 50 to 85. After processing, pertinent characters or traits are extracted from those voice recordings. In this research paper, we investigate Principal Component Analysis (PCA) for feature selection in conjunction with optimization techniques for training Support Vector Machine and Multilayer Perceptron models. The optimization techniques employed include the Firefly Algorithm, Particle Swarm Optimization (PSO), Grasshopper Optimizer, Grey Wolf Optimizer, and Genetic Algorithm (GA). Our study aims to assess the effectiveness of these optimization algorithms in enhancing the performance of MLP and SVM models on the dataset of Parkinson. The MLP and SVM accuracy rates of the optimization algorithms Firefly, PSO, Genetic, Grey Wolf, and Grasshopper were high; Firefly reached 97% (MLP) and 92% (SVM) accuracy, PSO 82% and 94.87% accuracy, while Genetic, Grasshopper, and Greywolf obtained 82% and 94% accuracy, respectively.

Journal of Emerging Technologies and Innovative Research (JETIR), 2024

Digital certificates serve as essential cryptographic tools in securing online transactions and c... more Digital certificates serve as essential cryptographic tools in securing online transactions and communications by verifying the identities of parties involved. The main goal of this project is to make digital certificates more secure using blockchain technology. Digital certificates are important for verifying things like education degrees, medical records, and financial transactions. However, current methods can be vulnerable to fraud and tampering. We use blockchain technology, which is a decentralized and tamper-proof system. Digital certificates will be stored on the blockchain, making them impossible to alter. Smart contracts will automate the verification process, making it faster and more reliable. By using a decentralized approach, we can eliminate single points of failure and make the whole system more trustworthy. In the future, we'll expand the system to handle different types of certificates and work on making it compatible with existing systems. We'll also research ways to make the blockchain system work for large-scale use. Continuous improvements will be made to keep up with changes in technology and security threats.

IAENG-IJCS, 2024

Rice category identification by image analysis is essential to ensure the quality and safety of r... more Rice category identification by image analysis is essential to ensure the quality and safety of rice production. In this study, we propose an intellectual approach to improve rice category identification using deep transfer learning features and machine learning classifiers. Specifically, we extracted features from three pre-trained models (Inception V3, VGG-19 and VGG-16) using transfer learning techniques. These were used as inputs to train MultiLayerPerceptron (MLP) and support vector machine (SVM) classifiers. The results of our experiments show that the proposed strategic results achieve high accuracy in identifying rice categories. The SVM (polynomial kernel) achieves the second-highest accuracy among all models and features, with an accuracy of 0.9948 using the VGG-19 and 0.9912 using Inception V3. The MLP classifier with (30 30) hidden layers achieve the first high accuracy, with an accuracy of 0.9972 (99.72%) using VGG-19 features. The results also show that the choice of deep transfer learning model and machine learning (ML) classifier can significantly affect the accuracy of rice category identification. Among the three pretrained models, VGG-19 features consistently perform the best, followed by Inception V3 and VGG-16. The choice of MLP hidden layer size also affects the accuracy, with 30 HL neurons achieving the best performance. Our proposed approach using deep TL features and ML classifiers shows promising results in improving rice category identification. Our study provides valuable insights into optimizing ML models for agricultural image analysis.

Indian Society for Education and Environment (iSee), 2024

Objectives: The research aims to enhance breast cancer detection accuracy and effectiveness using ... more Objectives: The research aims to enhance breast cancer detection accuracy
and effectiveness using deep transfer learning and pre-trained neural net-
works. It analyses breast ultrasound images and identifies important charac-
teristics using pre-trained networks. The goal is to create a more efficient and
accurate automated system for breast cancer detection. Methods: The study
uses breast ultrasound cancer image data from the Kaggle Data Repository to
extract informative features, identify cancer-related characteristics, and clas-
sify them into benign, malignant, and normal tissue. Pre-trained Deep Neural
Networks (DNNs) extract these features and feed them into a 10-fold cross-
validation SVM classifier. The SVM is evaluated using various kernel functions
to identify the best kernel for separating data points. This methodology aims
to achieve accurate classification of breast cancer in ultrasound images. Find-
ings: The study confirms the effectiveness of deep transfer learning for breast
cancer detection in ultrasound images, with Inception V3 outperforming VGG-
16 and VGG-19 in extracting relevant features. The combination of Inception
V3 and the SVM classifier with a polynomial kernel achieved the highest clas-
sification accuracy, indicating its ability to model complex relationships. The
study demonstrated an AUC of 0.944 and a classification accuracy of 87.44%
using the Inception V3 + SVM polynomial. Novelty: This research demonstrates
the potential of deep transfer learning and SVM classifiers for accurate breast
cancer detection in ultrasound images. It integrates Inception V3, VGG-16, and
VGG-19 for breast cancer detection, demonstrating improved classification
accuracy. The combination of Inception V3 and SVM (polynomial) achieved a sig-
nificant AUC (0.944) and classification accuracy (87.44%), outperforming other
models tested. This research underscores the potential of these technologies
for accurate breast cancer detection in ultrasound images.

Indian Society for Education and Environment (iSee), 2024

Objectives: The research aims to enhance breast cancer detection accuracy and effectiveness using... more Objectives: The research aims to enhance breast cancer detection accuracy and effectiveness using deep transfer learning and pre-trained neural networks. It analyses breast ultrasound images and identifies important characteristics using pre-trained networks. The goal is to create a more efficient and accurate automated system for breast cancer detection. Methods: The study uses breast ultrasound cancer image data from the Kaggle Data Repository to extract informative features, identify cancer-related characteristics, and classify them into benign, malignant, and normal tissue. Pre-trained Deep Neural Networks (DNNs) extract these features and feed them into a 10-fold crossvalidation SVM classifier. The SVM is evaluated using various kernel functions to identify the best kernel for separating data points. This methodology aims to achieve accurate classification of breast cancer in ultrasound images. Findings: The study confirms the effectiveness of deep transfer learning for breast cancer detection in ultrasound images, with Inception V3 outperforming VGG-16 and VGG-19 in extracting relevant features. The combination of Inception V3 and the SVM classifier with a polynomial kernel achieved the highest classification accuracy, indicating its ability to model complex relationships. The study demonstrated an AUC of 0.944 and a classification accuracy of 87.44% using the Inception V3 + SVM polynomial. Novelty: This research demonstrates the potential of deep transfer learning and SVM classifiers for accurate breast cancer detection in ultrasound images. It integrates Inception V3, VGG-16, and VGG-19 for breast cancer detection, demonstrating improved classification accuracy. The combination of Inception V3 and SVM (polynomial) achieved a significant AUC (0.944) and classification accuracy (87.44%), outperforming other models tested. This research underscores the potential of these technologies for accurate breast cancer detection in ultrasound images.

Bulletin of Electrical Engineering and Informatics, 2023

Feature importance strategy that substantially impacts software development effort estimation (SD... more Feature importance strategy that substantially impacts software development effort estimation (SDEE) can help lower the dimensionality of dataset size. SDEE models developed to estimate effort, time, and wealth required to accomplish a software product on a limited budget are used more frequently by project managers as decision-support tool effort estimation algorithms trained on a dataset containing essential elements to improve their estimation accuracy. Earlier research worked on creating and testing various estimation methods to get accurate. On the other hand, ensemble produces superior prediction accuracy than single approaches. Therefore, this study aims to identify, develop, and deploy an ensemble approach feasible and practical for forecasting software development activities with limited time and minimum effort. This paper proposed a collaborative system containing a multi-level ensemble approach. The first level grabs the optimal features by adopting boosting techniques that impact the decided target; this subset features forward to the second level developed by a stacked ensemble to compute the product development effort concerning lines of code (LOC) and actual. The proposed model yields high accuracy and is more accurate than distinct models.

Procedia Computer Science, 2023

Diagnosing chronic kidney disease (CKD) accurately and quickly is a big challenge in healthcare t... more Diagnosing chronic kidney disease (CKD) accurately and quickly is a big challenge in healthcare today. In a region called Uddanam in Andhra Pradesh, India, there's a common kidney disease called Uddanam nephropathy. In the 1990s, Andhra Pradesh was the first place to find this kind of kidney disease. In our research, we gathered data about personal and clinical of 1,055 individuals living in the Uddanam area (Srikakulam district of Andhra Pradesh, India). Among these, 656 had CKD, and 399 did not. We looked at 37 different aspects of their health. We apply for feature reduction Principal component Analysis and for classification uses MLP and Intelligent GA-MLP Hybrid model. This method combines the power of Genetic Algorithms (GA) and Multi-Layer Perceptron neural networks (MLP) to make CKD diagnosis more accurate. By using GA's ability to find the best solutions and MLP's skill in recognizing patterns, this new method can overcome the problems with older ways of diagnosing CKD. We tested our method thoroughly, comparing it to other methods. Our results showed that our approach can find CKD early and accurately. By combining GA and MLP, we're not only helping improve medical diagnosis but also giving hope for better CKD management and patient care. Our hybrid model, which uses PCA (20 dimensions) along with GA and MLP, performed the best with an accuracy of 98.34% during training and 98.54% during testing.

Tuijin Jishu/Journal of Propulsion Technology, 2023

Data mining serves as an essential tool for comprehending datasets related to diseases. The data ... more Data mining serves as an essential tool for comprehending datasets related to diseases. The data in question was collected within Visakhapatnam District of Andhra Pradesh, India, spanning the period from 2021 to 2022 and encompasses 1380 instances, equally divided into 690 instances of kidney disease and 690 instances of healthy subjects. This dataset leverages various health-related profiles, including age, height, weight, gender, blood pressure, blood sugar levels, water intake, and insulin levels, to forecast the likelihood of a patient developing kidney disease. Several methods, such as feed-forward neural networks, probabilistic neural networks (PNN) including confusion matrix analysis, unsupervised clustering via Self-Organizing Maps (SOM), and dynamic time series analysis for prospect prediction, were meticulously analyzed using the MATLAB platform. The outcomes indicate that each of these methods exhibits distinct strengths concerning specific data mining objectives. Notably, the dataset achieved a remarkable 100% accuracy in predicting kidney disease, underscoring its efficacy in this context.

Alcohol consumption can have impacts on the voice, and excessive consumption can lead to long-ter... more Alcohol consumption can have impacts on the voice, and excessive consumption can lead to long-term damage to the vocal cords. A new procedure to automatically detect alcohol drinkers using vowel vocalizations is an earlier and lower-cost method than other alcohol drinker-detecting models and equipment. The hidden parameters of vowel sounds (such as frequency, jitter, shimmer, harmonic ratio, etc.) are significant for recognizing individuals who drink or do not drink. In this research, we analyze 509 multiple vocalizations of the vowels (/a, /e, /i, /o, and /u) from 290 multiple records of 46 drinkers and 219 multiple records of 38 non-drinkers. The age group is 22 to 34 years. Apply the 10-fold cross-validation vowelized dataset on intelligent machine learning models and incremental hidden layer neurons of artificial neural networks (IHLN-ANNs) with backpropagation. The findings showed that experimental ML models such as Naïve Bayes (NB), Random Forest (RF), k-NN, SVM, and C4.5 (Tre...

Proceedings on Engineering SciencesVolume, 2024

Breast cancer (BC) ranks the second most prevalent cancer among women globally and is the leading... more Breast cancer (BC) ranks the second most prevalent cancer among women globally and is the leading cause of female mortality. The conventional method for BC detection primarily relies on biopsy; this might be time-consuming and error-prone. The substantial lives lost due to BC underscores its significant threat. Mitigating this threat focuses on early detection and prevention by adopting novel techniques. Many researchers have turned to Machine Learning algorithms to develop prognosis systems. We employ a combination of deep learning (DL) and machine learning (ML) algorithms for BC identification. Our approach is a hybrid Convolutional Neural Network (CNN) model, which performs better than other experimental and existing models. This model effectively categorizes histopathological images into either benign or malignant classes. We explored various methodologies, including CNN, CNN in conjunction with Support Vector Machine (SVM), CNN with Random Forest, and VGG-16 combined with XGBOOST. This research seeks to enhance the accuracy and efficiency of BC diagnosis. It contributes to more effective early detection and improved patient outcomes.

Institute of Advanced Engineering and Science (IAES), 2024

Wireless sensor networks (WSNs) are of significant importance in many applications; nevertheless,... more Wireless sensor networks (WSNs) are of significant importance in many applications; nevertheless, their operational efficiency and longevity might be impeded by energy limitations. The low energy adaptive clustering hierarchy (LEACH) protocol has been specifically developed with the objective of achieving energy consumption equilibrium and regularly rotating cluster heads (CHs). This study presents a novel technique, namely the hierarchical fuzzy logic controller (HFLC), which is integrated with the LEACH protocol to enhance the process of CH selection and effectively prolong the network's operational lifespan. The HFLC system employs fuzzy logic as a means to address the challenges posed by uncertainty and imprecision. It assesses many aspects, including residual energy, node proximity, and network density, in order to make informed decisions. The combination of HFLC with LEACH demonstrates superior performance compared to the conventional LEACH protocol in terms of energy efficiency, stability, and network durability. This study emphasizes the potential of intelligent and adaptive mechanisms in improving the performance of WSNs by improving the survivability of nodes by reducing the energy consumption of the nodes during the communication of network process. It also paves the way for future research that integrates soft computing approaches into network protocols.

Annals of data science, Jul 7, 2024

Indian journal of science and technology, May 14, 2024

Objectives: The research aims to enhance breast cancer detection accuracy and effectiveness using... more Objectives: The research aims to enhance breast cancer detection accuracy and effectiveness using deep transfer learning and pre-trained neural networks. It analyses breast ultrasound images and identifies important characteristics using pre-trained networks. The goal is to create a more efficient and accurate automated system for breast cancer detection. Methods: The study uses breast ultrasound cancer image data from the Kaggle Data Repository to extract informative features, identify cancer-related characteristics, and classify them into benign, malignant, and normal tissue. Pre-trained Deep Neural Networks (DNNs) extract these features and feed them into a 10-fold crossvalidation SVM classifier. The SVM is evaluated using various kernel functions to identify the best kernel for separating data points. This methodology aims to achieve accurate classification of breast cancer in ultrasound images. Findings: The study confirms the effectiveness of deep transfer learning for breast cancer detection in ultrasound images, with Inception V3 outperforming VGG-16 and VGG-19 in extracting relevant features. The combination of Inception V3 and the SVM classifier with a polynomial kernel achieved the highest classification accuracy, indicating its ability to model complex relationships. The study demonstrated an AUC of 0.944 and a classification accuracy of 87.44% using the Inception V3 + SVM polynomial. Novelty: This research demonstrates the potential of deep transfer learning and SVM classifiers for accurate breast cancer detection in ultrasound images. It integrates Inception V3, VGG-16, and VGG-19 for breast cancer detection, demonstrating improved classification accuracy. The combination of Inception V3 and SVM (polynomial) achieved a significant AUC (0.944) and classification accuracy (87.44%), outperforming other models tested. This research underscores the potential of these technologies for accurate breast cancer detection in ultrasound images.

International journal of computing and digital system/International Journal of Computing and Digital Systems, May 1, 2024

Bulletin of Electrical Engineering and Informatics, Apr 1, 2024

Feature importance strategy that substantially impacts software development effort estimation (SD... more Feature importance strategy that substantially impacts software development effort estimation (SDEE) can help lower the dimensionality of dataset size. SDEE models developed to estimate effort, time, and wealth required to accomplish a software product on a limited budget are used more frequently by project managers as decision-support tool effort estimation algorithms trained on a dataset containing essential elements to improve their estimation accuracy. Earlier research worked on creating and testing various estimation methods to get accurate. On the other hand, ensemble produces superior prediction accuracy than single approaches. Therefore, this study aims to identify, develop, and deploy an ensemble approach feasible and practical for forecasting software development activities with limited time and minimum effort. This paper proposed a collaborative system containing a multi-level ensemble approach. The first level grabs the optimal features by adopting boosting techniques that impact the decided target; this subset features forward to the second level developed by a stacked ensemble to compute the product development effort concerning lines of code (LOC) and actual. The proposed model yields high accuracy and is more accurate than distinct models.

Procedia Computer Science, Dec 31, 2022

Diagnosing chronic kidney disease (CKD) accurately and quickly is a big challenge in healthcare t... more Diagnosing chronic kidney disease (CKD) accurately and quickly is a big challenge in healthcare today. In a region called Uddanam in Andhra Pradesh, India, there's a common kidney disease called Uddanam nephropathy. In the 1990s, Andhra Pradesh was the first place to find this kind of kidney disease. In our research, we gathered data about personal and clinical of 1,055 individuals living in the Uddanam area (Srikakulam district of Andhra Pradesh, India). Among these, 656 had CKD, and 399 did not. We looked at 37 different aspects of their health. We apply for feature reduction Principal component Analysis and for classification uses MLP and Intelligent GA-MLP Hybrid model. This method combines the power of Genetic Algorithms (GA) and Multi-Layer Perceptron neural networks (MLP) to make CKD diagnosis more accurate. By using GA's ability to find the best solutions and MLP's skill in recognizing patterns, this new method can overcome the problems with older ways of diagnosing CKD. We tested our method thoroughly, comparing it to other methods. Our results showed that our approach can find CKD early and accurately. By combining GA and MLP, we're not only helping improve medical diagnosis but also giving hope for better CKD management and patient care. Our hybrid model, which uses PCA (20 dimensions) along with GA and MLP, performed the best with an accuracy of 98.34% during training and 98.54% during testing.

Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET 2024), Advances in Computer Science Research 112, 2024

bstract. Maize plant diseases can have a severe impact on agricultural productivity, making detec... more bstract. Maize plant diseases can have a severe impact on agricultural productivity, making detection and control challenging for farmers. Early identification of diseases is crucial for minimizing losses. This study proposes a new approach that integrates machine learning (ML) and deep learning (DL) algorithms to improve maize disease diagnosis and prognosis. The research employs traditional machine learning algorithms, such as Support Vector Machine (SVM) and Multilayer Perceptron (MLP), along with extracted features of Transfer Learning models, such as InceptionV3, VGG19, and Dense-Net201. The objective is to develop a robust system for early disease detection in maize leaves using image analysis. Optimization techniques, such as the Adam optimizer, and activation functions, such as tanh and sigmoid, are also explored. The results indicate that the Adam optimizer MLP achieves the highest accuracy (MLP(100,100) layers PCA(300) accuracy 0.95107) as well as SVM (RBF kernel) with PCA(100) accuracy (0.95585) exceptional other classification methods. This integrated approach promotes agricultural sustainability and crop yield by enabling prompt disease management. Keywords: Machine Learning, Deep Learning, Transfer Learner, SVM, MLP

Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET 2024), Advances in Computer Science Research 112, 2024

One of the most recent technologies is blockchain. It is used in many industries, including healt... more One of the most recent technologies is blockchain. It is used in many industries, including healthcare, finance, supply chain management, etc. A distributed technology called blockchain makes data or information available to all network nodes. It serves as an electronic database and stores information in digital form. Blockchain is widely famously employed in cryptocurrency systems to create a secure and decentralized transaction. However, a blockchain may be used to store any data. Not only is the data related to cryptocurrency but it is also used in healthcare systems. We can use blockchain to record and store patient data due to its distributed nature. But on the dot, this has become a problem as the data of the particular patient will be all over the network which increases the risk and complexity to handle the data. So, we are going to look for a solution and utilize it. Hence, the data will be authorized, authenticated, transparent, and highly secured. The patient controls access to the data by using the consortium type of the blockchain. The patient decides who can use and access their data, which can be the most beneficial aspect of using blockchain in healthcare.
Keywords: Medical record, Smart Contract, MetaMask, Decentralized system, Authentication

Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET 2024), Advances in Computer Science Research 112, 2024

Abstract. In recent times, generating understandable prompts for AI has been a significant proble... more Abstract. In recent times, generating understandable prompts for AI has been a significant problem, which in turn leads to inaccurate results. In essence, the problem is about finding ways to make AI understand and respond accurately to the prompts given to it, which is crucial for improving its overall performance and usefulness in various applications. This paper proposes a novel approach to enhance AI comprehension by generating tailored understanding prompts through prompt engineering techniques in Natural Language Processing, along with advanced Transformer-based deep learning models. Our project integrates these techniques to transform a base prompt into a set of diverse and comprehensive understanding prompts. To ensure data security, we have also implemented data encryption standard and Blowfish encryption algorithms to protect sensitive information during the transformation process. The resulting prompts will be used to train AI models, enabling them to grasp nuanced details and context when responding to user queries. The paper's significance lies in its potential to improve the quality of AI-generated responses across a range of applications, including natural language understanding, question answering, and content generation. Crucially, the developed web application, constructed using the MERN Stack, promises more reliable and insightful interactions with AI systems, effectively bridging the gap between human comprehension and AI-generated content.

Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET 2024), Advances in Computer Science Research 112, 2024

Parkinson's disease is a disorder of the nervous system that causes impairment and changes in cog... more Parkinson's disease is a disorder of the nervous system that causes impairment and changes in cognitive behavior. Voice analysis has become a crucial tool for diagnosing neurological conditions like PD, with symptoms typically appearing in people aged 50 or older. This research suggests new methods to improve early PD diagnostic methods, focusing on assessing aspects and fine-tuning hyperparameters of machine learning algorithms. The data set includes characteristics of both healthy and PD patients, aged 50 to 85. After processing, pertinent characters or traits are extracted from those voice recordings. In this research paper, we investigate Principal Component Analysis (PCA) for feature selection in conjunction with optimization techniques for training Support Vector Machine and Multilayer Perceptron models. The optimization techniques employed include the Firefly Algorithm, Particle Swarm Optimization (PSO), Grasshopper Optimizer, Grey Wolf Optimizer, and Genetic Algorithm (GA). Our study aims to assess the effectiveness of these optimization algorithms in enhancing the performance of MLP and SVM models on the dataset of Parkinson. The MLP and SVM accuracy rates of the optimization algorithms Firefly, PSO, Genetic, Grey Wolf, and Grasshopper were high; Firefly reached 97% (MLP) and 92% (SVM) accuracy, PSO 82% and 94.87% accuracy, while Genetic, Grasshopper, and Greywolf obtained 82% and 94% accuracy, respectively.

Journal of Emerging Technologies and Innovative Research (JETIR), 2024

Digital certificates serve as essential cryptographic tools in securing online transactions and c... more Digital certificates serve as essential cryptographic tools in securing online transactions and communications by verifying the identities of parties involved. The main goal of this project is to make digital certificates more secure using blockchain technology. Digital certificates are important for verifying things like education degrees, medical records, and financial transactions. However, current methods can be vulnerable to fraud and tampering. We use blockchain technology, which is a decentralized and tamper-proof system. Digital certificates will be stored on the blockchain, making them impossible to alter. Smart contracts will automate the verification process, making it faster and more reliable. By using a decentralized approach, we can eliminate single points of failure and make the whole system more trustworthy. In the future, we'll expand the system to handle different types of certificates and work on making it compatible with existing systems. We'll also research ways to make the blockchain system work for large-scale use. Continuous improvements will be made to keep up with changes in technology and security threats.

IAENG-IJCS, 2024

Rice category identification by image analysis is essential to ensure the quality and safety of r... more Rice category identification by image analysis is essential to ensure the quality and safety of rice production. In this study, we propose an intellectual approach to improve rice category identification using deep transfer learning features and machine learning classifiers. Specifically, we extracted features from three pre-trained models (Inception V3, VGG-19 and VGG-16) using transfer learning techniques. These were used as inputs to train MultiLayerPerceptron (MLP) and support vector machine (SVM) classifiers. The results of our experiments show that the proposed strategic results achieve high accuracy in identifying rice categories. The SVM (polynomial kernel) achieves the second-highest accuracy among all models and features, with an accuracy of 0.9948 using the VGG-19 and 0.9912 using Inception V3. The MLP classifier with (30 30) hidden layers achieve the first high accuracy, with an accuracy of 0.9972 (99.72%) using VGG-19 features. The results also show that the choice of deep transfer learning model and machine learning (ML) classifier can significantly affect the accuracy of rice category identification. Among the three pretrained models, VGG-19 features consistently perform the best, followed by Inception V3 and VGG-16. The choice of MLP hidden layer size also affects the accuracy, with 30 HL neurons achieving the best performance. Our proposed approach using deep TL features and ML classifiers shows promising results in improving rice category identification. Our study provides valuable insights into optimizing ML models for agricultural image analysis.

Indian Society for Education and Environment (iSee), 2024

Objectives: The research aims to enhance breast cancer detection accuracy and effectiveness using ... more Objectives: The research aims to enhance breast cancer detection accuracy
and effectiveness using deep transfer learning and pre-trained neural net-
works. It analyses breast ultrasound images and identifies important charac-
teristics using pre-trained networks. The goal is to create a more efficient and
accurate automated system for breast cancer detection. Methods: The study
uses breast ultrasound cancer image data from the Kaggle Data Repository to
extract informative features, identify cancer-related characteristics, and clas-
sify them into benign, malignant, and normal tissue. Pre-trained Deep Neural
Networks (DNNs) extract these features and feed them into a 10-fold cross-
validation SVM classifier. The SVM is evaluated using various kernel functions
to identify the best kernel for separating data points. This methodology aims
to achieve accurate classification of breast cancer in ultrasound images. Find-
ings: The study confirms the effectiveness of deep transfer learning for breast
cancer detection in ultrasound images, with Inception V3 outperforming VGG-
16 and VGG-19 in extracting relevant features. The combination of Inception
V3 and the SVM classifier with a polynomial kernel achieved the highest clas-
sification accuracy, indicating its ability to model complex relationships. The
study demonstrated an AUC of 0.944 and a classification accuracy of 87.44%
using the Inception V3 + SVM polynomial. Novelty: This research demonstrates
the potential of deep transfer learning and SVM classifiers for accurate breast
cancer detection in ultrasound images. It integrates Inception V3, VGG-16, and
VGG-19 for breast cancer detection, demonstrating improved classification
accuracy. The combination of Inception V3 and SVM (polynomial) achieved a sig-
nificant AUC (0.944) and classification accuracy (87.44%), outperforming other
models tested. This research underscores the potential of these technologies
for accurate breast cancer detection in ultrasound images.

Indian Society for Education and Environment (iSee), 2024

Objectives: The research aims to enhance breast cancer detection accuracy and effectiveness using... more Objectives: The research aims to enhance breast cancer detection accuracy and effectiveness using deep transfer learning and pre-trained neural networks. It analyses breast ultrasound images and identifies important characteristics using pre-trained networks. The goal is to create a more efficient and accurate automated system for breast cancer detection. Methods: The study uses breast ultrasound cancer image data from the Kaggle Data Repository to extract informative features, identify cancer-related characteristics, and classify them into benign, malignant, and normal tissue. Pre-trained Deep Neural Networks (DNNs) extract these features and feed them into a 10-fold crossvalidation SVM classifier. The SVM is evaluated using various kernel functions to identify the best kernel for separating data points. This methodology aims to achieve accurate classification of breast cancer in ultrasound images. Findings: The study confirms the effectiveness of deep transfer learning for breast cancer detection in ultrasound images, with Inception V3 outperforming VGG-16 and VGG-19 in extracting relevant features. The combination of Inception V3 and the SVM classifier with a polynomial kernel achieved the highest classification accuracy, indicating its ability to model complex relationships. The study demonstrated an AUC of 0.944 and a classification accuracy of 87.44% using the Inception V3 + SVM polynomial. Novelty: This research demonstrates the potential of deep transfer learning and SVM classifiers for accurate breast cancer detection in ultrasound images. It integrates Inception V3, VGG-16, and VGG-19 for breast cancer detection, demonstrating improved classification accuracy. The combination of Inception V3 and SVM (polynomial) achieved a significant AUC (0.944) and classification accuracy (87.44%), outperforming other models tested. This research underscores the potential of these technologies for accurate breast cancer detection in ultrasound images.

Bulletin of Electrical Engineering and Informatics, 2023

Feature importance strategy that substantially impacts software development effort estimation (SD... more Feature importance strategy that substantially impacts software development effort estimation (SDEE) can help lower the dimensionality of dataset size. SDEE models developed to estimate effort, time, and wealth required to accomplish a software product on a limited budget are used more frequently by project managers as decision-support tool effort estimation algorithms trained on a dataset containing essential elements to improve their estimation accuracy. Earlier research worked on creating and testing various estimation methods to get accurate. On the other hand, ensemble produces superior prediction accuracy than single approaches. Therefore, this study aims to identify, develop, and deploy an ensemble approach feasible and practical for forecasting software development activities with limited time and minimum effort. This paper proposed a collaborative system containing a multi-level ensemble approach. The first level grabs the optimal features by adopting boosting techniques that impact the decided target; this subset features forward to the second level developed by a stacked ensemble to compute the product development effort concerning lines of code (LOC) and actual. The proposed model yields high accuracy and is more accurate than distinct models.

Procedia Computer Science, 2023

Diagnosing chronic kidney disease (CKD) accurately and quickly is a big challenge in healthcare t... more Diagnosing chronic kidney disease (CKD) accurately and quickly is a big challenge in healthcare today. In a region called Uddanam in Andhra Pradesh, India, there's a common kidney disease called Uddanam nephropathy. In the 1990s, Andhra Pradesh was the first place to find this kind of kidney disease. In our research, we gathered data about personal and clinical of 1,055 individuals living in the Uddanam area (Srikakulam district of Andhra Pradesh, India). Among these, 656 had CKD, and 399 did not. We looked at 37 different aspects of their health. We apply for feature reduction Principal component Analysis and for classification uses MLP and Intelligent GA-MLP Hybrid model. This method combines the power of Genetic Algorithms (GA) and Multi-Layer Perceptron neural networks (MLP) to make CKD diagnosis more accurate. By using GA's ability to find the best solutions and MLP's skill in recognizing patterns, this new method can overcome the problems with older ways of diagnosing CKD. We tested our method thoroughly, comparing it to other methods. Our results showed that our approach can find CKD early and accurately. By combining GA and MLP, we're not only helping improve medical diagnosis but also giving hope for better CKD management and patient care. Our hybrid model, which uses PCA (20 dimensions) along with GA and MLP, performed the best with an accuracy of 98.34% during training and 98.54% during testing.

Tuijin Jishu/Journal of Propulsion Technology, 2023

Data mining serves as an essential tool for comprehending datasets related to diseases. The data ... more Data mining serves as an essential tool for comprehending datasets related to diseases. The data in question was collected within Visakhapatnam District of Andhra Pradesh, India, spanning the period from 2021 to 2022 and encompasses 1380 instances, equally divided into 690 instances of kidney disease and 690 instances of healthy subjects. This dataset leverages various health-related profiles, including age, height, weight, gender, blood pressure, blood sugar levels, water intake, and insulin levels, to forecast the likelihood of a patient developing kidney disease. Several methods, such as feed-forward neural networks, probabilistic neural networks (PNN) including confusion matrix analysis, unsupervised clustering via Self-Organizing Maps (SOM), and dynamic time series analysis for prospect prediction, were meticulously analyzed using the MATLAB platform. The outcomes indicate that each of these methods exhibits distinct strengths concerning specific data mining objectives. Notably, the dataset achieved a remarkable 100% accuracy in predicting kidney disease, underscoring its efficacy in this context.

Alcohol consumption can have impacts on the voice, and excessive consumption can lead to long-ter... more Alcohol consumption can have impacts on the voice, and excessive consumption can lead to long-term damage to the vocal cords. A new procedure to automatically detect alcohol drinkers using vowel vocalizations is an earlier and lower-cost method than other alcohol drinker-detecting models and equipment. The hidden parameters of vowel sounds (such as frequency, jitter, shimmer, harmonic ratio, etc.) are significant for recognizing individuals who drink or do not drink. In this research, we analyze 509 multiple vocalizations of the vowels (/a, /e, /i, /o, and /u) from 290 multiple records of 46 drinkers and 219 multiple records of 38 non-drinkers. The age group is 22 to 34 years. Apply the 10-fold cross-validation vowelized dataset on intelligent machine learning models and incremental hidden layer neurons of artificial neural networks (IHLN-ANNs) with backpropagation. The findings showed that experimental ML models such as Naïve Bayes (NB), Random Forest (RF), k-NN, SVM, and C4.5 (Tre...