Quality Evaluation of Bananas Using GoogLeNet M. Divya, R. Obulakonda Reddy, J. Alekhya, N. Sreevani, Ch. Srinivasulu, et al. Springer Proceedings in Mathematics and Statistics, 2025
Predictive Analysis and Application of Various Machine Learning Algorithms to Forecast used Car Prices Palayanur Srinivasan, R Obulakonda Reddy, Kaki Anirudh Sai, Jayanth Naidu International Conference on Sustainable Computing and Smart Systems Icscss 2023 Proceedings, 2023 India has been a huge market for secondhand market. The most popular second-hand items sold in India include used cars, furniture, electronic appliances, Mobile phones and etc. In general, a used car would cost lesser than a brand new car. Thus people prefer buying a used car. During the pandemic, the secondhand automobile industry in India has seen a sharp increase in popularity. Many people choose to purchase a used car rather than a brand-new one due to the difficulty of using public transit and the requirement for personal transportation. A used car was more cost-effective to purchase than a new one because of the economic uncertainty brought on by the pandemic and the increased prudence with which people approached their spending. Also, the pandemic’s effects on the supply chain and production delays made the used automobile market more alluring to customers. In this paper, the comparative analysis of multiple machine learning algorithms enabled us to identify the most effective method for determining the value of secondhand automobiles. Last but not least, providing an interactive web application to the owners of the cars where they can enter the details get an overview at the price. This study has processed the proposed dataset using popular Machine learning algorithms. Feature selection with Random Forest Regression yield the best performance with r$2_{-}$score = 0.9321 (accuracy of 93.2%) with MAE $\\sim$= 70213.9685. This study aims to increase the reliability of prediction of used car prices. In particular, this study concentrates on the approach of selecting features. The precise selection of features definitely showed us a significant edge over previous works.
A Study on Fish Classification Techniques using Convolutional Neural Networks on Highly Challenged Underwater Images Sudhakara Malla, M. Janaki Meena, Obulakonda Reddy. R, V. Mahalakshmi, Awatef Balobaid International Journal on Recent and Innovation Trends in Computing and Communication, 2022 Underwater Fish Species Recognition (UFSR) has attained significance because of evolving research in underwater life. Manual techniques to distinguish fish can be tricky and tedious. They might require enormous inspecting endeavours, but they can be costly. It results in limited data and a lack of human resources, which may cause incorrect object identification. Automating the fish species detection and recognition utilizing technology would assist sea life science to evolve further. UFSR in wild natural habitats is difficult because the images open natural habitat, complex background, and low luminance. Species Visualization can assist us with deep knowledge of the movements of the species underwater. Automation systems can help to classify the fish accurately and consistently. Image classification has been emerging research with the advancement of deep learning systems. The reason is that the convolutional neural networks (CNNs) don't require explicit feature extraction methods. The vast majority of the current object detection and recognition mechanisms are based on images in the outdoor environment. This paper mainly reviews the strategies proposed in the past years for underwater fish detection and classification. Further, the paper also presents the classification of three different underwater datasets using CNN with evaluation metrics.
Statistical Analysis and Deep Learning Associated Modeling for Early stage Detection of Carinoma K. Rangaswamy, D. Dhanya, B. Rupa Devi, Sateesh Kumar Reddy C, R. Obulakonda Reddy International Journal on Recent and Innovation Trends in Computing and Communication, 2022 The high death rate and overall complexity of the cancer epidemic is a global health crisis. Progress in cancer prediction based on gene expression has increased in light of the speedy advancement using modern high-throughput sequencing methods and a wide range of machine learning techniques, bringing insights into efficient and precise treatment decision-making. Therefore, it is of significant interest to create machine learning systems that accurately identify cancer patients and healthy people. Although several classification systems have been applied to cancer prediction, no single strategy has proven superior. This research shows how to apply deep learning to an optimization method that uses numerous machine learning models. Statistical analysis has helped us choose informative genes, and we've been feeding those to five different categorization models. The results from the five different classifiers are ensembled in the next step using a deep learning technique. The three most common types of adenocarcinoma are those of the lungs, stomach, and breasts. The suggested deep learning-based inter-ensembles model was tested with deep learning-based algorithms on Carcinoma data. The results of the tests show that relative to using only one set of classifiers or the simple consensus algorithm, it improves the precision of cancer prognosis in every analyzed carcinoma dataset. The suggested deep learning-based inter-ensemble approach is demonstrated to be reliable and efficient for cancer diagnosis by entirely using diverse classifiers.
Effects of Integrated Fuzzy Logic PID Controller on Satellite Antenna Tracking System R. Obulakonda Reddy, Sandeep Kautish, V.Padmanabha Reddy, N. Sudhakar Yadav, Meznah M. Alanazi, et al. Computational Intelligence and Neuroscience, 2022 An electrical device that transforms the electricity into the waves of radio and vice versa is termed the antenna. Its main deployment is in the transmitter and receiver of the antenna. While transmission, the transmitter of radio at the extremities of the antenna furnishes the electricity which oscillates at the frequency of radio wave and energy is released as current as em waves. Some of the voltage is formed from the em wave that is invaded at the point of receiving to amplify the receiver. This study focuses on the analysis of the satellite system to aid in mobile antenna tracking. It also examines the techniques for fuzzy control which make up traditional networks that are used. Initially, a basic idea of tracking loops with stabilized antennas was suggested in light of the requirement for the margin of phase and bandwidth. If the gain of the track is reduced due to changes in attributes and throughput, it will be reduced. In addition, fuzzy regulators and PID constituents are used to enhance the loop. The results indicate that the higher and lower antenna tracking gains within the loop were the best fit and the loop's fluctuations are reduced. A controller based on fuzzy logic can be most efficient due to its simplicity and robustness. It is also discovered that fuzzy logic controllers are evaluated by their behavior in relation. This paper presents an evaluation of the controllers in fuzzy logic, which is based on its integration with conventional controllers. There are three gains in PID's regulator PID and every gain can be used to control the variables of inputs and outcomes. The effects of the responses were analyzed and were compared. The commonality was discovered in the results according to the increase in time for II/6 and II/3 based on PID's regulator PID stability, it can be improved by this system, and there is a reduction in the duration of stability. Furthermore, the period of stability may be reduced through the fusion of PID and fuzzy. The effectiveness of the system could be enhanced by the implementation of the neural network. It is also possible to design the two types of control that could be used to control the proposed solid platform.
Internet of things and robotic applications in the industrial automation process Seeja G., Obulakonda Reddy R., Korupalli V. Rajesh Kumar, S. S. L. C. H. Mounika, Reddy Madhavi K. Innovations in the Industrial Internet of Things Iiot and Smart Factory, 2021 The recent industrial scenarios project its advancements and developments with the intervention of integrated technologies including internet of things (IoT), robotics, and artificial intelligence (AI) technologies. Industrial 4.0 revolutions have broken the barriers of all restricted industrial boundaries with the act of those interdisciplinary concepts and have taken a keen part in industrial development. Incorporation of these advancements considerably helps in improving product efficiency and in reducing the production cost. Based on categories of production, industrial automation processes may vary. In this regard, robots are playing a vital role to automate the production process at various levels of industrial operations. The combination of IoT, robotics, and AI technologies enhances the industrial productivity towards getting the success rate. This chapter focuses on how robotic technology with IoT and AI methods enhances the limitations of various industrial applications.
The comparative analysis of machine learning techniques for gestational diabetics prediction International Journal of Advanced Science and Technology, 2020
Unsupervised learning of XML documents by visualized clustering approach (VCA) Arpn Journal of Engineering and Applied Sciences, 2018
Mechanism for profit optimizations in cloud environment Journal of Advanced Research in Dynamical and Control Systems, 2018
Concealed under water mine detection and classification based on 2D image data analysis Journal of Advanced Research in Dynamical and Control Systems, 2018
Promoting business location by users recommended poi over social networks Journal of Advanced Research in Dynamical and Control Systems, 2018
An review of machine learning approaches in data sensitive real world applications Journal of Advanced Research in Dynamical and Control Systems, 2017
