A. Lakshmanarao

Aditya Birla (India)

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Co-Authors

T. Srinivasa Ravi Kiran

Siddhartha Medical College

A. Srisaila

Siddhartha Medical College

M. Raja Babu

Aditya Birla (India)

Chabi Gupta

Christ University

N. Supriya

Hindustan Institute of Technology and Science

Pennada Siva Satya Prasad

Centurion University of Technology and Management

Uma Ramesh

University of Kansas Medical Center

M. Rajesh

Aditya Birla (India)

G. Srinivas

Aditya Birla (India)

Raja Rao PBV

Cooperative Institutions

Aditya Birla (India)

Galgotias University

Federated Co-operatives (Canada)

SRM Institute of Science and Technology

Graphic Era University

Koneru Lakshmaiah Education Foundation

Vellore Institute of Technology University

Banaras Hindu University

Jawaharlal Nehru Technological University, Kakinada

Institute of Management Technology

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An Effecient Fake News Detection System Using Machine Learning

International Journal of Innovative Technology and Exploring Engineering (2019)

A. Lakshmanarao Y. Swathi T. Srinivasa Ravi Kiran

Social media plays a major role in several things in our life. Social media helps all of us to find some important news with low price. It also provides easy access in less time. But sometimes social media gives a chance for the fast-spreading of fake news. So there is a possibility that less quality news with false information is spread through the social media. This shows a negative impact on the number of people. Sometimes it may impact society also. So, detection of fake news has vast importance. Machine learning algorithms play a vital role in fake news detection; Especially NLP (Natural Language Processing) algorithms are very useful for detecting the fake news. In this paper, we employed machine learning classifiers SVM, K-Nearest Neighbors, Decision tree, Random forest. By using these classifiers we successfully build a model to detect fake news from the given dataset. Python language was used for experiments.

Fake News

Python

Emotion detection

10.35940/ijitee.j9453.0881019

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Citations (13)

Power Prediction in Combined Cycle Power Plant through ML and DL Regression Techniques

Saka Uma Maheswara Rao Ashish Kumar S. Neelima Chinta Venkata Murali Krishna A. Lakshmanarao

The process of forecasting future power demand is very important to the electric sector since it helps as the basis for decision-making about the operation and planning of power systems. Predicting the need for power may be done in a number of different ways by electrical businesses. These may be used for predicting the short-term, the medium-term, or the long-term. However, the consumption of power results from the intricate interactions that take place between climatological and socioeconomic elements. In such a dynamic environment, conventional methods of predicting are inadequate, and it is necessary to use more advanced approaches. In this research, a Deep Learning (DL) -based method is proposed for power plant power prediction. A power plant dataset from Kaggle is collected, and five machine learning techniques followed by a deep learning algorithm are applied. The experiments showed that deep learning models performed better for power prediction when compared to the existing models. Later, regression analysis was also done to investigate and identify features that are important for power prediction. Proposed DL-based approach significantly enhances accuracy in power prediction, marking a notable advancement in the field.

Predictive modelling

10.1109/icc-robins60238.2024.10533904

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Real-Time Sentiment Classification on Social Media Platform Using Online Machine Learning

Smart innovation, systems and technologies (2025)

Pradeep Venuthurumilli Dushyant Kumar Burri Naresh Priyankesh Kumar Anilkumar Karyamsetty

Sentiment Analysis

10.1007/978-981-97-7717-4_40

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Android Malware Detection using Multilayer Autoencoder and Random Forest

International Journal of Engineering Trends and Technology (2022)

A. Lakshmanarao M Shashi

Autoencoder

Android Malware

10.14445/22315381/ijett-v70i11p227

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Citations (2)

Fake News Detection Using ML and DL Approaches

G. Srinivas A. Lakshmanarao S. Sushma M. Vamsi Krishna S. Neelima

Fake News

10.1109/iccpct58313.2023.10245398

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A Robust Anomaly Detection Framework Utilizing Feature Engineering and Ensemble Techniques with BETH Data

K. Neeraja T. Prabhakara Rao Peluru Janardhana Rao Raja Rao PBV G. Jose Moses

Feature Engineering

Feature (linguistics)

Anomaly (physics)

Robustness

Ensemble Learning

10.1109/iscs61804.2024.10581243

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Phishing website detection using novel machine learning fusion approach

A. Lakshmanarao P.Surya Prabhakara Rao M. Murali Krishna

The Phishing is a sort of social designing assault regularly used to take client information, including login accreditations and credit card numbers. With the enhancements in internet technology, websites are the major resource for the cyber-attacks. There are several counter measures available for avoiding phishing attacks, but phishers are changing their attacking methods from time to time. One of the most widely used techniques for solving cybersecurity issues is machine learning. From last several years, Machine Learning and Deep Learning Techniques are suitable for solving security related issues. Machine Learning is most suitable for detecting phishing attacks because most of the phishing attacks have common characteristics. This paper has applied several machine learning techniques for detecting the phishing attacks. Here, two prioritybased algorithms are proposed. Based on the results of these algorithms, the final fusion classifier is decided. We used a dataset from UCI and applied a novel fusion classifier and achieved an accuracy of 97%. We used Python for implementing our experiments.

Phishing

Python

10.1109/icais50930.2021.9395810

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Citations (28)

Advanced Hybrid Transfer Learning Approaches for Autism Spectrum Disorder Detection Using Facial Features

Surya Lakshmi Kantham Vinti Bharathi Panduri Vijayshri Khedkar Mahesh Babu Ketha A. Lakshmanarao

Transfer of learning

10.1109/icdici62993.2024.10810869

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SMS Spam Detection using NLP and Deep Learning Recurrent Neural Network Variants

Vangapandu Venkata Kalyani M. V. Rama Sundari S. Neelima Pennada Siva Satya Prasad Permanand Mohan

The ubiquity of Short Messaging Services (SMS) has been accompanied by an upsurge in spam messages, necessitating effective detection mechanisms. This paper delves into the intricate task of SMS spam detection using a Kaggle dataset of 5570 samples. As initial step, the challenge of class imbalance was addressed by applying oversampling technique ADASYN. Later, TF-IDF embedding technique was employed to capture semantic meaning and contextual information within SMS messages. A conventional ML classifier, Random Forest has achieved a good accuracy of 92.3%. Later, various RNN architectures, including Simple RNN, LSTM, Bi-LSTM and Gated Recurrent Unit (GRU), applied for SMS spam detection. LSTM variants demonstrated superior performance with 93% accuracy for RNN, 98% for LSTM and 98.2%, 98.3% for BI-LSTM and GRU. To further enhance the accuracy, a hybrid architecture combining Bi-LSTM and GRU layers was explored, resulting in improved accuracy of 99%. The proposed model outperformed traditional ML approaches for SMS spam detection. This paper sheds the efficacy of LSTM variants in addressing the SMS spam detection challenge. Emphasizing the significance of handling class imbalances and employing effective embedding techniques, the achieved accuracies hold promising implications.

Oversampling

Word embedding

10.1109/icc-robins60238.2024.10533895

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An Efficient Android Malware Detection Model using Convnets and Resnet Models

A. Lakshmanarao P. Madhuri Kalyankumar Dasari K. Babu Shaik Ruhi Sulthana

Android Malware

Residual neural network

10.1109/iacis61494.2024.10721919

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