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N. Achyutha Prasad

Bio: N. Achyutha Prasad is an academic researcher. The author has contributed to research in topics: Machine learning & Artificial intelligence. The author has an hindex of 2, co-authored 3 publications receiving 42 citations.

Papers
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Journal ArticleDOI
TL;DR:
Abstract: Traditionally, nonlinear data processing has been approached via the use of polynomial filters, which are straightforward expansions of many linear methods, or through the use of neural network techniques. In contrast to linear approaches, which often provide algorithms that are simple to apply, nonlinear learning machines such as neural networks demand more computing and are more likely to have nonlinear optimization difficulties, which are more difficult to solve. Kernel methods, a recently developed technology, are strong machine learning approaches that have a less complicated architecture and give a straightforward way to transforming nonlinear optimization issues into convex optimization problems. Typical analytical tasks in kernel-based learning include classification, regression, and clustering, all of which are compromised. For image processing applications, a semisupervised deep learning approach, which is driven by a little amount of labeled data and a large amount of unlabeled data, has shown excellent performance in recent years. For their part, today’s semisupervised learning methods operate on the assumption that both labeled and unlabeled information are distributed in a similar manner, and their performance is mostly impacted by the fact that the two data sets are in a similar state of distribution as well. When there is out-of-class data in unlabeled data, the system’s performance will be adversely affected. When used in real-world applications, the capacity to verify that unlabeled data does not include data that belongs to a different category is difficult to obtain, and this is especially true in the field of synthetic aperture radar image identification (SAR). Using threshold filtering, this work addresses the problem of unlabeled input, including out-of-class data, having a detrimental influence on the performance of the model when it is utilized to train the model in a semisupervised learning environment. When the model is being trained, unlabeled data that does not belong to a category is filtered out by the model using two different sets of data that the model selects in order to optimize its performance. A series of experiments was carried out on the MSTAR data set, and the superiority of our method was shown when it was compared against a large number of current semisupervised classification algorithms of the highest level of sophistication. This was especially true when the unlabeled data had a significant proportion of data that did not fall into any of the categories. The performance of each kernel function is tested independently using two metrics, namely, the false alarm (FA) and the target miss (TM), respectively. These factors are used to calculate the proportion of incorrect judgments made using the techniques.

39 citations

Proceedings ArticleDOI
27 Apr 2022
TL;DR: The role of active learning approaches in minimizing the number of instances that need to be manually annotated and the transferability of learned models between domains and languages are discussed in this article.
Abstract: Sentiment analysis Sentiment analysis is the process of extracting information from the text and it is considered as opinion mining. Machine learning experiments have been shown in the study that involves review, blog, for some texts that are written in different languages including Dutch, English and French. Set of example sentence have been set that are manually labeled, neutral, positive or negative. The study covers the curiosity of the consumer regarding the specific consumption products. Categorization models have been developing that has been used in the study. Number of issues that includes noisy nature of the text has been discussed in the study. With an accuracy of roughly 83 percent for English texts, we can determine positive, negative, and neutral sentiments toward the subject under investigation using unigram features augmented with linguistic information. The role of active learning approaches in minimizing the number of instances that need to be manually annotated is discussed in this article. Our studies also give data on the transferability of learned models between domains and languages. Here, Sentiment analysis of a particular person has studied using a K-Means Clustering and SVM classifier to classify the sentiment of a person from the text

3 citations

Proceedings ArticleDOI
21 Apr 2023
TL;DR: In this paper , a psi-shaped optimized antenna is proposed for the 2.4 GHz resonant frequency for applications like RF energy harvester systems (RFEHS), radio-frequency identification (RFID) and wireless local area network (WLAN).
Abstract: With the evolution of communication systems and their methodologies, the significance of designing various types of antennae has increased to suit the different necessities. While some antennas, especially the substrate-integrated antennas are hard to optimize, the microstrip patch antennas are easy to design and optimize. Besides, providing an economic advantage, it also delivers higher efficiencies in terms of radiation and gain, making it appropriate for communication-based applications. The concerned work proposes a psi-shaped optimized antenna that is capable of scanning and receiving the 2.4 GHz resonant frequency for applications like radio-frequency (RF) energy harvester systems (RFEHS), radio-frequency identification (RFID) and wireless local area network (WLAN) communications. The structure has been tested with an inset feeding technique and the substrate used is arlon with differential conductor and cover layer elements. The entire design and simulation have been carried out in advanced design system (ADS) layout and momentum microwave electromagnetic (EM) mode. The designed psi-shaped antenna is able to produce a maximum gain of 5.47 dBi with a radiation efficiency of 70.39%, making it suitable for the aforesaid applications. A software-based prototype simulation has also been carried out to find the effectiveness of the proposed antenna.

Cited by
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Journal ArticleDOI
01 Jul 2022-2
TL;DR: In this article , the authors provide guidelines for guaranteed allocation and distribution of distributed generation (DG) in distribution systems for an acceptable reliability level and voltage profile, which involves the use of GA techniques and is solved by combining systems to estimate system reliability, losses and dg impacts on the voltage profile.
Abstract: The definition of a restricted power supply area for a distribution network disqualifies this scheme as a distributed product even though it is a very common dg scheme. Power system quality is a key issue for low and medium voltage power companies and consumers, to minimize power network losses; this paper provides guidelines for guaranteed allocation and distribution of distributed generation (DG) in distribution systems for an acceptable reliability level and voltage profile. The optimization process involves the use of genetic algorithms (GA) techniques and is solved by combining systems to estimate system reliability, losses and dg impacts on the voltage profile. The fitness evaluation process leads to the determination of the ga's relationship between investment and operating costs as a benefit of setting numerical units. Estimation based on current flow method for radial networks reconciliation of scattered generator losses with profile of voltage profile

5 citations

Journal ArticleDOI
01 Jul 2022
TL;DR: In this article , a model of workplace learning is presented to examine the sequence of opportunities available to learners as they rebuild their roles as learners through relationships with their immediate social and physical contexts.
Abstract: In recent years, technology has required workplace learning, continuous professional development, and general training of employees. However, little research has been done in this area to evaluate the methods and effectiveness of these teaching styles and is welcomed by learners. Learning collection designers often use identical policy collections, targeting students or testifying to the success of younger learning students. The word used to describe the education of a very mature learner (mostly in the workplace) of androgyny (according to the knowledge of androgyny knowledge) should have a variety of experiences, while students have only a limited amount so should be basic. The concept of opportunities for learning is borrowed from classroom discourses and expands its understanding of "socially identifiable and identifiable event", including cultural and environmental aspects such as content, time and participant. In this study, teachers are used to examine the sequence of opportunities available to them as they rebuild their roles as learners through relationships with their immediate social and physical contexts. Development, environment and conditions required for workplace learning many ideas aimed at clarifying the factors The following discussion introduces. The workplace learning model is presented in fostering the need for focus change. Outlined in this paper the proposed model of workplace learning on various ideas put forward by accredited academics and incorporates elements relevant to most workplaces.

4 citations

Journal ArticleDOI
01 Jul 2022-1
TL;DR: This paper discusses next-generation wireless e-health technologies, emerging areas and the developments of the rule and their synthesis for m-health systems, and discusses modern-day and future strategies for imposing this gadget in key fitness care fields and key scientific contexts.
Abstract: Mobile communication is an application of technology. It allows you to communicate with other people in different places without using any physical connection like wires or cables. Mobile communication makes our life easier and it saves time and effort. Next-generation wireless e-health technologies are a new and emerging topic in telemedicine and tableware systems. These technologies use mobile telecommunication technologies to eliminate the major disadvantages of wires in existing systems and provide better access to healthcare workers on the go. These technologies are gaining access to medical records and specialist care. This overcomes the limitations that exist these days among various clients using such medical statistics. One of the best blessings for all users is the greener use of assets and greater area freedom. In this paper, we will discuss these emerging areas and the developments of the rule and their synthesis for m-health systems. We can even discuss modern-day and future strategies for imposing this gadget in key fitness care fields and key scientific contexts.

4 citations

Journal ArticleDOI
TL;DR: The suggested ConvLSTM network can be created by successively combining fully connected layers, long immediate memory networks, and convolutional neural networks (CNN) to work out the activities performed by someone in a picture or video.
Abstract: Human activity recognition aims to work out the activities performed by someone in a picture or video. Examples of actions are running, sitting, sleeping, and standing. Complex movement patterns and harmful occurrences like falling may be a part of these activities. The suggested ConvLSTM network can be created by successively combining fully connected layers, long immediate memory (LSTM) networks, and convolutional neural networks (CNN). The acquisition system will pre calculate skeleton coordinates using human detection and pose estimation from the image/video sequence. The ConvLSTM model builds new controlled features from the raw skeleton coordinates and their distinctive geometric and kinematic properties. Raw skeleton coordinates are utilized to generate geometric and kinematic properties supported by relative joint position values, joint differences, and their angular velocities. By utilizing a multi-player trained CNN-LSTM combination, novel spatiotemporal directed features can be obtained. The classification head with completely connected layers is then utilized. The suggested model was tested using the KinectHAR dataset, which consists of 130,000 samples with 81 attribute variables and was compiled using the Kinect (v2) sensor Experimental data is used to compare the performance of independent CNN and LSTM networks.

4 citations

Journal ArticleDOI
01 Jul 2022-1
TL;DR: Physical transmission of data through point-to-point multidisciplinary communication channels is more flexible than analog with higher energy consumption, but required more bandwidth compared to analog systems.
Abstract: Digital communication is the physical transfer of data through a point-to-point or point-to-point multidisciplinary communication channel. This is to exchange private messages. Digital communication plays an important role in today's world of electronics. The rate of data transfer in digital communication depends on its characteristics Digital communication provides a seamless experience to customers and partners direct communication and AI chat bots and automation Digital in various forms such as digital makes communication easier for customers to access companies simultaneously. It's convenient, it's easy, cheap, and fast because it can be done over long distances over the Internet and other things can be done via digital hardware processing circuits. Physical transmission of data through point-to-point multidisciplinary communication channels is more flexible than analog with higher energy consumption. It required more bandwidth compared to analog systems.

3 citations