Bio-Inspired Deep Feature-Enhanced SVM Optimisation for Automated Leaf Species Classification D. Bhanu Prakash, Arun Kumar Katkoori, Amireddy Srinish Reddy, K Bala, G Prasanna Kumar IEEE International Conference on Intelligent Systems Smart and Green Technologies Harnessing Generative AI to Foster Innovation in Energy Education and Healthcare for Achieving the Sdgs Icissgt 2026, 2026
FreeRTOS and IoT Based Smart Physiological Parameter Monitoring System Arun Kumar Katkoori, D. Bhanu Prakash, Raman Kumar M, Amireddy Srinish Reddy, G Prasanna Kumar IEEE International Conference on Intelligent Systems Smart and Green Technologies Harnessing Generative AI to Foster Innovation in Energy Education and Healthcare for Achieving the Sdgs Icissgt 2026, 2026
Stock market index prediction based on market trend using LSTM Ankireddy Yenireddy, Marimganti Srinivasa Narayana, Kalla Venkata Bangaru Ganesh, Guvvaladinne Prasanna Kumar, Madduri Venkateswarlu Indonesian Journal of Electrical Engineering and Computer Science, 2024 The stock market data analysis has received interest as a result of technological advancements and the investigation of new machine learning models, since these models provide a platform for traders and business people to choose gaining stocks. The business price prediction is a challenging and extremely complex process due to the impact of several factors on company prices. The numerous patterns that the stock market goes, they have been the focus of extensive research and analysis by numerous experts. There are several large data sets accessible, an artificial intelligence and machine learning techniques are developing quickly, and because of the machine’s improved computational power, complex stock price prediction algorithms can be developed. This paper presents stock market index prediction based on market trend using long short-term memory (LSTM). Using built-in application programmable interface (API), Yahoo Finance offers a simple method to programmatically retrieve any historical stock prices of an organization using the ticker name. The standard and poor’s 500 index (S&P 500 index) include the firms that have been taken into consideration here. Utilizing the selected input variable, single-layer and multi-layer LSTM models are implemented, and the measurement parameters of mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R) are used to compare each performance. Nearly all of the real closing price’s curve and the prediction curve’s closing price for test data overlap. A potential stock investor may benefit significantly from such a prediction by using it to make well-informed choices that would increase his earnings.
High Gain More Stable Self Biased Two Stage Differential Amplifier for Bio-signal Processing Pattem Sampath Kumar, Vijayalakshmi Ch, Swapna Thouti, G Prasanna Kumar, N. Rajeswaran 2023 9th International Conference on Advanced Computing and Communication Systems Icaccs 2023, 2023 An Op Amp's main goal is to get the maximum open loop gain feasible in order to apply feedback concepts and build a more reliable system. The majority of op amp circuit types are built based on multistage nodes to obtain high gain with stability. This work presents self-biased two stage amplifier. In this differential amplifier inverters are used as input structures and self-biasing techniques is being used. The self-bias is done through negative feedback. This differential amplifier has fully complementary design. Detailed circuit analyses such as differential-mode, common-mode, CMRR, transient and AC analysis is being carried out. Finally, the experimental results those are relevant for an integrated circuit is designed using gpdk90nm technology and 1.8V. Final Simulations and results calculations were carried out in virtuoso Cadence tool.
Plant Leaf Classification through Deep Feature Fusion with Bidirectional Long Short-Term Memory Bhanuprakash Dudi, V. Rajesh, G Prasanna Kumar Icistsd 2022 3rd International Conference on Innovations in Science and Technology for Sustainable Development, 2022 Manual plant species classification requires more time, effort, and professional knowledge and also, and they are very costly. At present days, researchers utilized deep learning techniques for the classification of plants over plant images. The deep learning models attain great success, and then, the lack of interpretability set a limit on their application. To overcome these limitations, they utilized measurable, interpretable, and computer-aided features from plant leaf images. Image processing became very complex and crucial at the time of feature extraction. There are nearly 391,000 vascular plant species present in the world widely. So, the classification and identification of plants became complex and impractical for professionals. Most of the plant species have huge similarities and it consumes a huge amount of time for classification. So, it is essential to develop a computerized system for the identification and classification of plants. A great advancement is widely developed in science and technology for the recognition and classification of plant species in biological fields. Thus, the automatic plant leaf detection models are utilized to help professionals and botanists to classify plant species rapidly. This work proposes a novel automatic plant leaf image classification method through a deep learning algorithm. At first, the input images are gathered from the standard dataset, where the deep features from the gathered images are extracted using Resnet and VGG16. The extracted features are fused and fed to the classification stage. The plant leaf image classifications are done through the Bidirectional Long Short-Term Memory (Bi-LSTM). The empirical outcomes of the developed model have achieved better performance regarding precision and accuracy.
Design and Implementation of AGU based FFT Pipeline Architecture G. Prasanna Kumar, Maturi Sarath Chandra, K Shiva Prasanna, M Mahesh Journal of Physics Conference Series, 2021 Present it is most needful task to get various applications with parallel computations by using a Fast Fourier Transform (FFT) and the derived outputs should be in regular format. This can be achieved by using an advanced technique called Multipath delay commutator (MDC) Pipelining FFT processor and this processor will be capable to perform the computation of a different data streams at a time. In this paper the design and implementation of AGU based Pipelined FFT architecture is done Caluclation of a butterfly is done within 2 cycles by the instructions proposed. A Data Processing Unit (DPU) is employed in this pipeline architecture and supports the instructions & an FFT Adress Generation Unit (FAGU) caluclates butterfly input & output data adresses automatically. The DPU proposed sysyem requires less area compared to commericial DSP chips. Futhermore, the proposed FAGU reduces the number of FFT computation cycles. The FFT design architecture will have real data paths. With various FFT sizes, different radix & various parallesim levels, the FFT can be mapped to the pipeline architecture. The most attractive feature of the pipelined FFT architecture is it consists of bit reversal operation so it requires little number of registers and better throughput.
