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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Rashmi, C. Srinivas, Dava Prabu, S. Santosh Kumar, P.N. Siddiqua, Ayesha Malla Reddy Engineering College for Women It Department Hyderabad India Jyothishmathi Institute of Technology & Science Department of Computer Science and Engineeing Karimnagar India Mahendra Institute of Technology Department of Ece Namakkal India Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The World Health Organization identified COVID-19 to be a new pandemic in March 2020. This fatal virus spread over the world, affecting many different countries. Massive volumes of useful data were provided by social media platforms like Twitter on the outbreak to help evaluate health-related decision-making. To predict disease occurrence and issue early warnings, thus implement that user's attitudes be evaluated through the use of efficient deep learning. The gathered tweets were pre-processed and divided into three categories like neutral, positive, and negative. In the second step, various features were extracted from the articles using a number of popular techniques to capture feature dataset, including TF-IDF, FastText, and Glove. The implemented TFIDF has combination of semantic features (Glove and FAsText) to accurately describe posts and enhance the classification process utilizing the deep learning method. The implemented TF-IDF-CNN method's performance is evaluated using Twitter dataset. Implemented method achieved better performance than the existing intrusion detection methods including NLP-BiLSTM, TF-IDF, SVM and CF and the metrics are 96% of accuracy, 0.89 of f-measure, 0.93 of precision, and 0.90 of recall. © 2023 IEEE.

关键词： COVID-19 Deep learning Feature extraction Sentiment analysis Twitter

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A survey on deep learning based lip-reading techniques 3

A survey on deep learning based lip-reading techniques

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3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Pujari, Sheetal Sneha, S.K. Vinusha, R. Bhuvaneshwari, P. Yashaswini, C. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9780738111834

Lip reading is a way of using skills and knowledge to understand a speaker's verbal communication by visually interpreting the lip movements of the people. This becomes a tedious task when there are obstructions or background noise in the data. Over the decade, with the buzz of Deep learning there has been an increasing demand for developing systems that can help mankind by converting speech to text. Lip reading systems are those intelligent automated systems which on looking at the face of the user try to comprehend what he/she is trying to convey and represent it visually. This process can be done by using various deep learning algorithms to detect the face, localize the lips, extract the features, train the classifier to detect the lip movement and finally convert into text. This survey paper presents a summary of various deep learning techniques, different datasets, adopted methodologies by highlighting their performance and limitations in the speech and vision applications. © 2021 IEEE.

关键词： Surveys

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Sustainable Product Choices Recommendation System using Contextual Bidirectional Encoder Representations from Transformers and Multi-Layer Perceptron 2

Sustainable Product Choices Recommendation System using Cont...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Bharattej, Rana Veer Samara Sihman Alabdeli, Haider Dubey, Tanuja V. Beula, D. Sundaram, S. meenakshi Department of Computer Science Saint Leo University St Leo United States Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Management Ashoka Business School Nashik Mumbai India Department of Physics New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9798331504960

Recent days, e-commerce platforms have booming tremendously as the people showing interest to buy the products from online based on the customer reviews and ratings. So, providing the best product recommendations to customer will increase global market and GDP growth of a country. The existing methods for recommendation of products had resulted in suggesting unnecessary products to customers. Therefore, in this research Contextual Bidirectional Encoder Representations from Transformers- Multi-Layer Perceptron (CoBERT-MLP) method is proposed for recommending sustainable product choices using a knowledge-based recommender system. The proposed method employed on Flipkart product dataset that provides details of different products. The data obtained from this dataset is pre-processed by utilising the Natural Language Toolkit (NLTK). These preprocessed data features are extracted by the employed CoBERT method that eliminates the irrelevant attributes from the data. These obtained relevant attributes are classified for categorizing the product choices of a customer. Finally, these classified choices are given to knowledge-based recommender system for providing most sustainable product choices to customers. The proposed CoBERT-MLP method achieved better results in terms of accuracy (99.14%), recall (99.38%), precision (98.56%), and F1-score (99.71%) when related with prevailing method Convolutional Neural Networks (CNN). © 2024 IEEE.

关键词： Distribution transformers

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Accurate Estimation for Stability of Slope and Partition Over Old Underground Coal Workings Using Regression-Based Algorithms 2nd

Accurate Estimation for Stability of Slope and Partition ...

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2nd International Conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2021

作者： Dorthi, Kumar Kumar, Anil Chandar, Karra Ram Department of Computer Science and Engineering Kakatiya Institute of Technology & Science Telangana Warangal India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bangalore India Department of Mining Engineering National Institute of Technology Karnataka Karnataka Mangaluru India

ISBN: (纸本)9789811902512

Numerical modeling simulation has found to be best solution for predicting slope and partition stability over old underground coal workings. But it has taken huge time to complete a single simulation model. In this regard, machine learning-based framework is used to predict the stability of old galleries. A case study is taken up in opencast mine and simulation is carried out using numerical model and machine learning-based framework. Framework has shown an overall accuracy of 94–95% for different slope and partition stability. Framework shows a speedup of 2366 × against numerical simulator. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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IoT Enabled Health Care System

IoT Enabled Health Care System

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2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Shobha Thapa, Bishesh Bhuju, Abhishek Sapkota, Dipesh Sharma, Abhaya Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore Karnataka Yelahanka India Tensor Digital Solutions Bangalore Urban Karnataka India

ISBN: (纸本)9781665489621

The core concept of the project is the implementation of a healthcare system that is able to collect data from the patients and project it to the allocated health officials with a detailed analysis. The world is going through a global pandemic right now and according to reports (Yale Studies: CNN), half of the deaths in 14 states of US comes from elderly home facilities. This has been the major factor of this report. The proposed system is designed for the weaklings and the elderly, the challenged and the less fortunate ones in Ne-pal, where the healthcare system is not managed properly. The kits that are being used in the current situation are all imported from China, and as the reports suggests 70% of them are faulty. With such situation at hand, the proposed system uses equipment that can be easily bought and implemented as well as mass produced. The idea here is to have a system that can be implemented easily in places where it is needed the most, for example;the elderly homes. Through data collection and analysis, a report can be submitted to assigned health officials and also be updated regularly at real time. This device would make the process of monitoring a person with previously known health conditions. It would provide 24/7 critical data about vital functions of the body automated, reliable and easy to implement. © 2022 IEEE.

关键词： Internet of things

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Satellite Image Classification using Convolutional Neural Network with Huber Loss Function for Land Cover Mapping in Forest Ecosystem

Satellite Image Classification using Convolutional Neural Ne...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Al-Fatlawy, Ramy Riad Raviprakash, P. Vybhavi, Gummagatta Yajaman Jagadish, Sripelli Sundaram, S meenakshi College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Cse Hyderabad India School of Computer Science & Artificial Intelligence Sr University Warangal India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India

ISBN: (纸本)9798350375442

Satellite image classification for land cover involves determining high-resolution imagery to recognize and classify various types of land covers. This process assists in monitoring forest health, managing resources, and assessing biodiversity effectively. However, accurately classifying images for land cover is difficult because of diverse and complex nature of forest environments and varying image qualities. In this research, the Convolutional Neural Network with Huber Loss Function (CNN-HLF) is proposed to classify the satellite images accurately for landcover mapping in forest ecosystem. The CNN-HLF effectively manage the variability and complexity of forest environments which improves the performance of land cover. Initially, the Eurosat dataset is used to determine the CNN-HLF performance. Resizing and image sharpening is applied to adjust the size and enhance the clarity of features. DenseNet201 is employed for extracting features effectively from pre-processed images. The proposed CNN-HLF achieves a high accuracy of 99.87% using Eurosat dataset compared to existing methods like GoogleNet with pre-processing and Lightweight Parallel CNN (LPCNN). © 2024 IEEE.

关键词： Convolutional neural networks

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Virtual Medical Assistant in English and Kannada Languages

Virtual Medical Assistant in English and Kannada Languages

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Applied Intelligence and Sustainable Computing (ICAISC), International Conference on

作者： B G Mamatha Bai Preethi Prerana Akash A Benki R Rakesh Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka India

Health care and medicine are two crucial facets of human existence. This guarantees patients the right to accurate and timely clarification of any questions they may have about their health state. A virtual assistant built with the help of Natural Language Processing [NLP] makes it simpler for consumers to consult the medical personnel in the pandemic situation from the comfort of their homes without any direct contact. This work discusses various methods for building virtual medical assistants that may interact with people to answer simple questions about health characteristics and recommend necessary actions to the user. A unique inclusion in this paper is the support to a regional language which is kannada in this case. In order to identify the ailment, the system extracts essential keywords from the chat and applies predictive analysis. The user is advised to see a doctor, take medication, or visit the hospital, depending on the severity of the symptoms. The Kannada voice-bot has a higher processing time when compared to the English voice-bot due to change in dialects. The real goal of this job is to address the user’s symptoms and offer appropriate medical advice in order to shorten the process’s time and expense requirements.

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Improving Accuracy in Object Detection Using Region-Based Convolutional Neural Network

Improving Accuracy in Object Detection Using Region-Based Co...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, T.M Kiran Aluvala, Srinivas Mandal, Preetam Saranya, K. Karunakara Rai, B. Siddaganga Institute of Technology Department of Computer Science & Engineering Tumkur India Sr University School of Computer Science & Artificial Intelligence Warangal India Siddhartha Institute of Technology and Sciences Department of Ece Hyderabad India Sona College of Technology Department of Ece Salem India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The object detection is a fundamental computer vision task that involves identifying and locating objects within a video or image. This technique employed various shape patterns for suggestion to recognize the certain objects in an input data. The existing methods have drawbacks like cannot identify the small region of objects in image. In this research, the Region-based Convolutional Neural Network (RCNN) is proposed for object detection. The dataset utilized for object detection is Caltech101 dataset and it is pre-processed by normalization and LAB color transformation to enhance the image quality. The preprocessed images are given into ResNet-50 based feature extraction and RCNN is utilized for classification. The performance of RCNN model is estimated through the performance measures of accuracy, precision, recall, specificity and f1-score. The RCNN attained high accuracy of 95.78%, precision of 94.81%, recall of 94.27%, specificity of 93.65% and f1-score of 92.49% which is comparatively superior than other existing methods like Histogram of Oriented Gradient (HOG) and Local Ternary Pattern (LTP), Hierarchical Max-poling (HMAX) and Fuzzy C-means. © 2023 IEEE.

关键词： Deep Learning LAB color transformation Object Detection Region-based Convolutional Neural Network ResNet-50

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Automatic Road Extraction using Scale Invariant Feature Transform-based Random Forest in Satellite Images 1

Automatic Road Extraction using Scale Invariant Feature Tran...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Rajesh, N. Alzubaidi, Laith H. Shivaprasad Yadav, S.G. Chembian, W.T. Prameela, Boddepalli Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India The Islamic University of Babylon Department of Computers Techniques Babylon Iraq Ramaiah Institute of Technology Department of Electronics and Telecommunication Engineering Bengaluru India Department of Computer Science and Engineering Chennai India Vignan's Institute of Information and Technology Department of Artificial Intelligence and Data Science Andhra Pradesh India

ISBN: (纸本)9798350352931

Road Extraction from High-Resolution Satellite Images (HRSI) plays a significant role in the analysis of the remote sensor image field. The extraction of road automatically labels a road's pixels in a satellite image with semantic features depending on topographical meaningful features. However, manual road extraction inaccurately extracts road networks from satellite images which has significant difficulties like complex urban environments, varying road types, and diverse image conditions. Therefore, the Scale Invariant Feature Transform-based Random Forest(SIFT-RF) is proposed for automatic road extraction in satellite images. First, the image is obtained from DeepGlobe dataset and Z-score normalization is employed to maximize model performance. Then, SIFT is established to extract the road network in satellite images. Finally, the RF is utilized to extract and classify roads as road or non-road using Machine Learning (ML). The SIFT-RF achieves a greater f1-score of 98.24% compared to existing methods like Dual-Attention Road extraction Network (DA-RoadNet), High-Resolution U-Net architecture (HRU-Net), and Global Context-aware and Batch-independent Network (GCBNet) respectively. © 2024 IEEE.

关键词： Random forests

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Enhancing Fairness and Performance: Scalable Hybrid Solutions for Class Imbalance in Big Data Analytics

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Procedia computer science 2025年 258卷 1050-1061页

作者： Puneetha B H Manoj Kumar M V Prashanth B S Ajay Kumara M.A. Department of computer science and Business Systems Bapuji Institute of Engineering and Technology Davanagere Karnataka Department of Information Science and Engg. Nitte Meenakshi Institute of Technology Bangalore-560064 Karnataka Department of Computer Science University of North Carolina Wilmington USA

Recent advancements in handling class imbalance include hybrid approaches that combine data-level and algorithmic-level techniques. However, challenges such as computational efficiency and adaptability to dynamic datasets remain to be prominently addredssed. This research paper introduces novel hybrid techniques that combine both data-level and algorithmic-level approaches to address class imbalance in large datasets. These techniques are designed to improve class distribution and processing efficiency while enhancing prediction accuracy, particularly for minority classes. The proposed model applies K-means SMOTE for over-sampling the minority class and centroid-based undersampling for the majority class, followed by Classification using XGBoost. Evaluations on multiple datasets demonstrate significant improvements in precision, recall, AUC-ROC, and F1-score compared to traditional methods. In addition to enhancing robustness and fairness, the approach effectively addresses challenges related to scalability, data diversity, and dynamic data sources in big data analytics. The hybrid technique achieved a 10-20% improvement in key performance metrics, including precision, recall, F1-score, and AUC-ROC, across various UCI datasets such as Diabetes, Hepatitis, and German Credit, confirming the model’s superiority in handling class imbalance and improving overall Classification performance.

关键词： Class imbalance Undersampling Oversampling XGBoost

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