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Wingman- Virtual Assistant for Visually Impaired

Wingman- Virtual Assistant for Visually Impaired

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Mysore Sub Section International Conference (MysuruCon), IEEE

作者： Jyothi Neeli M K Pushpanjali Ayushman Shivam Nikhil Raja Anand Romsha Sengar Department of Computer Science & Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of EEE Nitte Meenakshi Institute of Technology Bangalore India

It's clear to say that assistive technologies have grown more crucial in improving the quality of life of people with disabilities, especially those who are visually impaired, thanks to the wealth of information obtained from careful study of many articles. This research paper proposes the development of a virtual assistant for blind people that uses object detection, image classification, and voice assistant technologies. Primarily the assistant will act as a guiding dog for the visually impaired person. Various tech stacks and logic used to accomplish the final version of virtual assistant has been included in detail in different sections of the paper.

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Secure Clustering and Routing Based on Modified Whale Optimization Algorithm in MANET

Secure Clustering and Routing Based on Modified Whale Optimi...

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International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Ravi Kiran Varma, P. Kumar, Bura Vijay Hemalatha, K.L. Jabar, Muhammed Basim Manjula, B.M. Department of CSE Sagi Rama Krishnam Raju Engineering College Andhra Pradesh Bhimavaram India School of Computer Science and Artificial Intelligence SR University Warangal India Department of Information Science and Engineering Sri Krishna Institute of Technology Bangalore India National University of Science and Technology Dhi Qar Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9789819778751

Mobile Ad Hoc Networks (MANETs) are a self-organizing and adaptive technique comprising several mobile nodes. These networks’ dynamic and changeable architecture creates substantial issues in terms of trust and security for wireless communication lines. To solve these concerns, the Modified Whale Optimization Algorithm (MWOA) is used to provide a secure clustering and routing solution in MANET. The Modified K-means (MKM) method is used in the Cluster Head (CH) selection process to provide effective transmission to the target node without packet loss. MWOA, an optimized technique, is introduced to improve MANET routing security. Key parameters such as energy consumption (EC), packet delivery ratio (PDR), throughput, network Lifetime (NL), and end-to-end delay (EED) are used to evaluate the suggested methodology. Comparative investigation shows that the MWOA methodology outperforms present methods in terms of performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Crop Classification on Hyperspectral Images Using Residual Network and Multi-Head Self-Attention Feature Extraction 2

Crop Classification on Hyperspectral Images Using Residual N...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Saranya, N. Naga Vivekanandhan, V. Al-Farouni, Mohammed Shreyas, A.V. Kumar, Gotte Ranjith Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India Malla Reddy College of Engineering Department of Computer Science and Engineering Hyderabad Secunderabad India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India School of Computer Science & Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350373110

Nowadays, hyperspectral imaging has empowered classification and prediction in many fields such as agriculture, land usage, tourism, etc. due to advancements in deep learning. This research focuses on obtaining efficient crop classification using Residual Network, Multi-Head Self- Attention, Convolutional Neural Network (ResNet-MHSA- CNN) approach. The Adaptive Wiener Filtering (AWF) is applied in preprocessing to eliminate noise, handles blurred parts of images effectively and decreases error rate in inverse filtering. The Residual Network (ResNet) method is employed by implement residual learning to eliminate degradation issues of deep neural networks. The high-resolution depth features of hyperspectral images are extracted by residual learning ResNet network. The Multi-Head Self-Attention (MHSA) utilized to capture long-range dependencies and global context information and helps the model in learning diverse patterns. Finally, the extracted features are given to Convolutional Neural Network (CNN) with complex pattern learning ability that excels in image classification with multiple layers of filters. The proposed ResNet-MHSA-CNN approach is evaluated with Indian Pines dataset and achieved 99.74%, 99% accuracy and kappa value respectively. The proposed method outperformed state-of-art methods such as Cross-Mixing Residual Network of Convolutional Neural Network (CMR-CNN), Deep Stacked Denoising Autoencoder (DSDA). © 2024 IEEE.

关键词： Convolutional neural networks

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The People Moods Analysing Using Tweets Data on Primary Things with the Help of Advanced Techniques 2

The People Moods Analysing Using Tweets Data on Primary Thin...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Pillai, Sanjaikanth E Vadakkethil Somanathan Vallabhaneni, Rohith Pareek, Piyush Kumar Dontu, Sravanthi School of Electrical Engineering and Computer Science University of North Dakota Grand ForksND United States University of the Cumberlands United States Nitte Meenakshi Institute of Technology Department of Aiml Bengaluru India University of the Cumberlands Department of Information Technology United States

ISBN: (纸本)9798350382952

Researchers studying sentiment analysis are interested in Twitter, a major social media platform. Text mining research on Twitter Sentiment Analysis (TSA) is growing. Using computers, 'TSA' processes subjective Twitter data like opinions and sentiments. Social media platforms like Twitter have many customer reviews. Traditional customer feedback methods like focus groups, business surveys, and questionnaires take time and resources. However, Twitter reviews lack structure, making manual analysis difficult and time-consuming. Using machine learning and deep learning for sentiment analysis yields poor classification accuracy. This study uses optimized deep learning and machine learning models and clever SNS scraper-extracted tweets. Experimental results show that the proposed method can classify three emotions with 99% accuracy. A hybrid classifier (HC) with LSTM and Bi-GRU classifies. When Bi-GRU and LSTM are averaged, effective output is obtained. The proposed Hosted Cuckoo Optimisation Algorithm fine-tunes LSTM weight to improve detection accuracy. © 2024 IEEE.

关键词： Sentiment analysis

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A Gravitational Search Algorithm Study on Text Summarization Using NLP 1st

A Gravitational Search Algorithm Study on Text Summarization...

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1st International Conference on Artificial Intelligence and Data science, ICAIDS 2021

作者： Subbalakshmi, Chatti Pareek, Piyush Kumar Narayana, M.V. Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Hyderabad India Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9783031213847

Over the last decade, the amount of data available on the internet has grown exponentially. As a result, there is a need for a resolution that converts this massive atomic data into valuable information that a brain could comprehend arises. Text Summarization (TS) is the procedure of generating a synopsis of a particular document that comprises only the most critical info from the novel;the objective is to obtain a concise synopsis of the important points in the document. Text summarization is one such technique that is frequently used in research to aid in the management of massive amounts of data. Automated summarization is a well-known technique for distilling the main points of a document. It works by preserving significant information in the text by creating a condensed version of it. Extractive and Abstractive methods of text summarization are available. Extractive summarization methods alleviate some of the concern of summarization by extracting a subset of relevant sentences from the original text. The objective of abstractive method of multiple records is to create condensed form of the record while retaining essential info. While there are numerous methods available, investigators studying in NLP using Gravitational Search Algorithm are predominantly focus on extractive methods. The inferences of sentences are determined in terms of linguistic and statistical characteristics. Finally, this paper compiles the new and pertinent study in the area of TS for further research and examination. It will be important because it will provide a new path for forthcoming scholars concerned in this domain. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Natural language processing systems

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Fruit Type Classification using Stacked Bi-directional Long Short Term Memory

Fruit Type Classification using Stacked Bi-directional Long ...

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

作者： Borra, Subba Reddy Kaneti, Venkata Ramana Tejaswini, Vontela Anitha, S. Vinaykumar, R. Malla Reddy Engineering College for Women It Department Hyderabad India Vnr Vignana Jyothi Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Hyderabad India Cmr Engineering College Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

In recent years, Machine Learning (ML) and Deep Learning (DL) applications have become increasingly important in agriculture, particularly in the classification of fruits based on image data. Fruit recognition and categorization have attained a lot of attention from researchers. Because of the diversity and heterogeneity of fruits, this is a difficult challenge. To solve this, a Stacked Bi-LSTM image classification approach has been proposed to enhance the accuracy of fruit image classification. The process includes feature extraction by a Convolutional Neural Network (CNN), followed by feature selection using a Recurrent Neural Network (RNN). Following that, the proposed Stacked Bi-LSTM model is utilized to classify fruit images, resulting in enhanced accuracy. As indicated by evaluation parameters like as recall, precision, and F-measure, the acquired results outperform previous algorithms. The analyzed outcomes show that the Stacked Bi-LSTM model accomplishes precision of 85.4%, recall of 88.3%, and F1-score of 83.9%, demonstrating greater performance when compared to existing approaches. © 2023 IEEE.

关键词： Convolutional Neural Network Gaussian filter Long Short Term Memory Recurrent Neural Network Stacked Bi-LSTM

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TORA: Text Summarization Using Optical Character Recognition and Attention Neural Networks 6th

TORA: Text Summarization Using Optical Character Recognition...

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6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

作者： Sneha, H.R. Annappa, B. Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru560064 India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal575025 India

ISBN: (纸本)9789811613371

Text Summarization is the process of creating a short and coherent version of a longer document that holds the same meaning as that of the original data. This article illustrates the technique to read the text in a printed document (such as newspaper, brochure, web document, etc.) and generate a summary of text. The method proposed is named Text Summarization using Optical Character Recognition and Attention Neural Networks (TORA). TORA can perform extractive summarization of a news article with the aid of Recurrent Neural Networks, Bidirectional Long Short-Term Memory, and Bahadanu Attention Network. The experimental results of the proposed method are promising. The experimental results have shown 80% accuracy in producing the summary from the large text document. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optical character recognition

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An Effective Expectation of Diabetes Mellitus via Improved Support Vector Machine through Cloud Security

An Effective Expectation of Diabetes Mellitus via Improved S...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Lal N, Dayanand Hema, K. Spoorthi, T. Chegoni, Ramesh Madhura, K.G. Gitam School of Technology Gitam University Department of Computer Science & Engineering India Cmr Technical Campus Department of It Kandlakoya India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350398465

Nerve pain, eye difficulties, skin issues, renal problems, heart disease, depression, and a host of other illnesses are all linked to diabetes if left untreated. In an effort to lengthen the human lifespan, scientists all around the world have applied a variety of methods for early disease prediction. Diseases of the eyes, heart, and kidneys are prioritised because they can cause serious consequences and even death if left untreated. After conducting an in-depth analysis of the strategies used by several researchers, the following models were constructed and tested using the datasets most frequently favoured by leading scientists throughout the world. Diabetic retinopathy (DR) is an eye condition, and this research seeks to construct a hybrid learning model for early prediction of DR. The proposed approach combines elements of both Machine Learning and Deep Learning. Score Level Fusion is the last arbiter of the model's precision. Images from the first dataset of its sort reflecting the Indian population, the Indian Diabetic Retinopathy Imaging Dataset, were used to make the prediction. Predicting the DR and recommending the necessary treatment to patients is possible based on severity levels, which ultimately leads to the elimination of blindness. © 2023 IEEE.

关键词： Forecasting

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Automated Text Summarizer Using Google Pegasus

Automated Text Summarizer Using Google Pegasus

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Nalini N Agrim Narayan Akshay Mambakkam Sridharan Arkon Pradhan Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India

The recent growth of the Internet due to an increase in users has led to an explosion of all types of data. Text data is one of them and it is usually voluminous and contains a lot of information. Extraction of the important information would save a lot of time for all involved. An automatic text summarizer does exactly what everyone is looking for, a tool that distills the source into an abridged version while maintaining the semantics and various ideas of the source. In this paper, discuss the previous methods of summarizing text data and the approach to developing an application which does the same.

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Music Recommendation System Based on Facial Emotion Detection Using Spotify API

Music Recommendation System Based on Facial Emotion Detectio...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Neeli, Jyoti Shetty S, Vijaya Rao, Shreya Srujana, S.N. Narayan, Nidhi Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering Bengaluru India Bms Institute of Technology & Management Bengaluru India

ISBN: (纸本)9798331538538

In the world of entertainment, music holds considerable importance, especially for those who find joy in rhythmic experiences. Despite the abundance of streaming platforms that enable access to favorite songs, they often fall short in capturing the intricate emotional nuances of users. This research recognizes a spectrum of emotions, including fear, happiness, sadness, anger, and neutrality. Its goal is to enrich the user experience by developing a recommendation system that proposes songs based on the user's current emotional state. The emotion-driven recommendation engine has seamlessly integrated into Spotify, a well-known music streaming service, providing users with a smooth and individualized journey in exploring music. The system aims to simplify the user experience by eliminating the necessity for manual song searches and, instead, intuitively suggests tracks that resonate with the user's emotions. The Spotify API serves as a crucial tool for accessing curated playlists, enabling the retrieval of desired music from thoughtfully organized collections centered around specific themes or titles. © 2025 IEEE.

关键词： Visualization

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