Eswar Prasad Galla - Academia.edu (original) (raw)

Papers by Eswar Prasad Galla

10.5281/zenodo.14066345, 2022

Sentiment analysis (SA) is essentially a subfield of data mining and natural language processing ... more Sentiment analysis (SA) is essentially a subfield of data mining and natural language processing that is primarily
concerned with the problem of extracting useful knowledge from comments posted by users of the web. However,
academic studies of various topics in South Africa have been underway for over than ten years. The method of
present study incorporates a machine learning and natural language processing of sentiments on data of twitter.
Synthetic Minority Over-sampling Technique (SMOTE) has been employed in the identification of samples that
are from the minority class to create synthesized samples. Bag of Words (BoW) is applied here for the extraction
of features since textual data require a good representation before further analysis. The evaluation metrics
employed are accuracy, precision, recall and F1 score which are applied on several classification models such as
MLP, LR and PSO. From the data it emerges that the LR model is the best one in this case with 88% accuracy,
83% precision, 81% recall and 82% F1-score. This paper suggests directions for further research in the subsequent
social media analytics by proving the efficiency of machine learning approaches in handling sentiment analysis.

Scientific Research Publishing, 2024

ESP Journal of Engineering & Technology Advancements, 2021

The most common of these technologies include artificial intelligence (AI) as well as machine lea... more The most common of these technologies include artificial intelligence (AI) as well as machine learning (ML), both of which are revolutionizing the healthcare system, especially in disease prediction. Given the emerging data produced from systems in healthcare, EHRs, social media, environment, and genomics, AI and ML algorithms continue to find genuine applications in anticipating disease outbreaks or patients' health futures. Disease forecasting is looked at in this paper with regard to the different methods and algorithms being used in current practice, as well as the possibility that AI/ML could do more in identifying patterns and relationships that are difficult to decipher using traditional statistical analytical tools. It also presents issues that are associated with the adoption of AI/ML in medicine for instance, data protection, prejudice in the algorithms, and the intractability of the AI/ML models. There is potential seen in the use of AI in disease prediction in that it will help in the early detection of disease outbreaks, modelling of chronic disease and progression, and development of treatment plans. These developments have already been implemented in healthcare organizations globally and have positively impacted patient satisfaction as well as the management of healthcare. However, with such prospects come steep obstacles, as there are technical and ethical constraints that need to be crossed before the full potential of AI/ML in disease prediction can be achieved. This article has the purpose of reviewing the current AI/ML developments in disease forecasting, showing how they are used in the field including epidemiology, oncology, cardiovascular diseases and rare diseases. Finally, it highlights some trends, both synergistic and adverse, the concept of ethical decision-making and other aspects of this fairly new and dynamic discipline.

Educational Administration: Theory and Practice, 2021

Moreover, authentication schemes have also evolved to be multi-modal, such as combining fingerpri... more Moreover, authentication schemes have also evolved to be multi-modal, such as combining fingerprint and power spectrum of handwriting or validating face and signature to give a better level of assurance to the biometric authentication. The big data paradigm enables storing and managing large data efficiently, and applying artificial intelligence models for these data adds a security layer to the overall system. It aims to enhance security through behavior and skill, and it enhances transaction efficiencies through the reduction of friction. Furthermore, both AI and Big Data technologies are mostly used in many digital biometric authentication processes, such as Behavioral Biometrics like Keystroke Dynamics, Mouse Dynamics, and Gait Analysis; Physiological Biometrics like Thermal imaging for FiO2 estimation; Speech Recognition; Facial Expression Technology; etc., are detailed. Big data and AI play very important roles in many areas. Biometric authentication is getting more attention for secure digital transactions while individuals and organizations tend to deploy big data and AI in the process of authentication systems to achieve secure and completely secured transactions. This paper deals with the importance of big data and AI innovations in biometric authentication for secure digital transactions. Big data and AI concepts have been effectively analyzed and reviewed in the area of biometric authentication and their importance has been effectively shown. Biometric identifiers are the preferred methods of user authentication, which have moved beyond fingerprints, iris, and facial scans.

Journal of Recent Trends in Computer Science and Engineering, 2020

Image processing, as well as acoustic signal detection, have had major enhancements over the yea... more Image processing, as well as acoustic signal detection, have had major enhancements over
the years, and this is due to AI. In the past, most algorithms involved using basic signal
processing where features needed to be extracted manually and then various rules were
applied when the data grew large. Deep learning models, for example, provide a durable
solution to ventilation by eliminating the need for manual feature engineering as well as
improving the detection rate in areas of health, surveillance and even industrial
applications. This paper offers a comprehensive analysis of the emerging innovation
driven by Advanced Intelligence in the field of image processing and the detection of
acoustic signals with regard to the substrate patterns identified by AI technologies such
as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), as well as
other sophisticated algorithms. The paper also describes how AI, when combined with
image processing and acoustic detection, can add more value to the results being
produced. Due to the large number of cases and training data, patterns can be learned and
are as follows: image classification, object detection, process anomaly detection in
industrial systems, as well as acoustic event recognition in noisy environments. The paper
aims to provide an understanding of the AI methodologies adopted in both domains and,
to this end, offers examples of specific industries and rationales for their implementation
of these technologies. An extensive discussion of the basics of neural networks and their
modifications is provided, with emphasis on the application of those structures for
automated image feature extraction and acoustic pattern recognition. We also study the issues of comparison, accuracy, computational complexity, and the ability of AI models to
function in similar conditions. This article also seeks to present how AI models can be
enhanced by integrating image processing with acoustic signal detection methods and
should produce possible research directions for increasing AI performance. Finally, the
authors recap the main findings, provide information about advanced methods in their
field, and show some possible future uses in self-driving cars, robots and drones, and
meteorological monitoring

Educational Administration: Theory and Practice, 2023

AI-powered insights are becoming increasingly essential in every industry. The cost of doing geno... more AI-powered insights are becoming increasingly essential in every industry. The cost of doing genomic science is becoming comparable to 'big data' requirements, leading to a need for data-driven insights. This essay will investigate how AIpowered insights can build and expand the data-rich and bias-free genomic insights needed in healthcare, with a particular focus on DNA collection and genomic DNA-based healthcare research. Many challenges will be discussed during this paper should the data ecosystem be expanded to include many more humans worldwide or focused on the increasingly complex data types associated with post-genomic healthcare. We will also explore the AI methods likely to make significant breakthroughs in the future and will need further investment.

Journal of Contemporary Education Theory & Artificial Intelligence, 2023

Despite attempts to reduce it, financial fraud continues to be a major problem in many industries... more Despite attempts to reduce it, financial fraud continues to be a major problem in many industries, including healthcare, banking, and insurance. Traditional fraud detection techniques, which are often manual, are inefficient, time-consuming, and costly. As a result, methods that use AI and ML have been implemented to improve fraud detection procedures. This study examines the application of ML algorithms for credit card fraud detection using a dataset consisting of 284,807 transactions made by European cardholders in 2013, out of which 492 were fraudulent. Preprocessing steps, including Label Encoding, SMOTE for handling class imbalance, and PCA for feature reduction, were applied to the dataset. On the training dataset have applied ML based classification models like DT, SVM, and ANNs were employed to evaluate their performance. The models were assessed using accuracy, precision, and recall as key metrics. The ANN model emerged as the best-performing model, achieving 98.41%precision, 98.69%accuracy, and 98.98%recall, outperforming both Decision Trees and SVM. This study highlights the effectiveness of ML models, particularly ANNs, in improving financial fraud detection.

In the rapidly evolving landscape of data engineering, the integration of Artificial Intelligence... more In the rapidly evolving landscape of data engineering, the integration of Artificial Intelligence (AI) and Machine Learning (ML) is transforming Enterprise Resource Planning (ERP) systems and supply chain management. This paper explores the profound impact of AI and ML technologies on these critical business domains. By leveraging AI and ML, organizations can enhance their ERP systems' efficiency through advanced data analytics, predictive modeling, and automation, leading to more informed decision-making and streamlined operations. Similarly, in supply chain management, these technologies enable real-time insights, improved demand forecasting, and optimized logistics, thereby reducing costs and increasing agility. This study examines current trends, practical applications, and case studies that highlight the benefits and challenges of incorporating AI and ML into ERP and supply chain processes. It also addresses the future directions of data engineering solutions and their potential to revolutionize business operations, offering valuable insights for professionals aiming to leverage these technologies for competitive advantage.

Educational Administration: Theory and Practice, 2022

Every second of every hour, billions of Internet of Things-enabled devices are creating massive s... more Every second of every hour, billions of Internet of Things-enabled devices are creating massive streams of data individually tailored to the intimate personal habits of their users. Simultaneously, sophisticated cybercriminal organizations, nation-state actors, and rapidly proliferating malware attacks ranging from hijacked personal tablets through Fortune 200 penetrated databases are impacting digital and thus physical assets across the entire political spectrum. This connectivity matrix is generating a massive and ever-expanding volume of network, system, and end-user security event data that combines with personal information from both the private sector and governments to fuel the artificial intelligence insights that we enjoy in our everyday lives. Yet, while the entire cybersecurity compliance lifecycle, including policy, network, system, enforcement, and incident response, generates and uses colossal data quantities, the proprietary, unstructured, and often classified nature of this data flow historically has limited our industry's adherence to AI-driven precepts. In this paper, we introduce the principles of Threat Hooking, a Network Theorydriven approach to detecting and selectively blocking individual components within a collective logical threat. Our data science, Network Security Characterization Model detailed in this paper quantifies a specific element of Network Theory, which provides insight into both Network Health and individualized Threat Status. To demonstrate the innovation and theoretical underpinnings of Threat Hooking, we identify and analyze the massive datasets required from the network data immune system that we developed. After distilling relevant content from current cybersecurity research, we compiled an annotated dataset of live and emulated threat data and reported how AI-identified network artifacts that lead to human interpretable threat event detection can be verified, and if necessary, acted upon by cyber professionals.

Lib Progress international, 0

Big data analytics and AI are emerging technologies that can help businesses improve their email ... more Big data analytics and AI are emerging technologies that can help businesses improve their email security. There is a wide range of research that implements big data analytics for email security, and phishing email detection is one dimension of email security. Therefore, the essay emphasizes the use of big data analytics and AI for developing real-time phishing email recognition. Our research demonstrates that a phishing email detection technique utilizing big-data technologies can be used to create a large-scale phishing email dataset, detect phishing emails, visualize additional features, and recognize phishing emails as soon as possible. Chapter 2 outlines present changes in the cybercrime landscape and the current situation of time and defense mechanisms for email security. Then, the concept of harnessing big data analytics and technologies to improve cybersecurity is discussed. In the third segment, an attempt is made to offer a comprehensive list of studies that have been conducted applying bigdata analytics to email security and scrutinizing phishing email tactics or technologies for this type of cybercrime. In the final chapter, the implementation of a big-data-based technology utilizing Enron email traffic is highlighted.The essay discusses the application of big data analytics to designing a phishing email detection system in real time. Relevant studies are included in the content as well. Email security has become a chief concern for individuals and organizations. A cybercriminal can victimize anyone after proliferating a phishing email, and millions of phishing emails are distributed to millions of email traffic. With the continual and widespread proliferation of time, cybercriminals are using more sophisticated methods of attacking and have the capability to create more feature-rich phishing emails. This situation necessitates the use of technology to protect us from these methods. The precise recognition of phishing emails reduces their utilization, leading to decreased cybercrimes.