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An Overview of the Workflow of Next-Generation Sequencing Data Analysis 4

An Overview of the Workflow of Next-Generation Sequencing Da...

引用

4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Lalrinmawii, C. Muppalaneni, Naresh Babu National Institute of Technology Silchar Computer Science and Engineering Silchar India

ISBN: (纸本)9798350300093

DNA sequencing data analysis has been an important area of research. However, due to the high-cost sequence production, data production is hindered. In recent years, the development of Next Generation Sequencing technology, which is a more cost-effective sequencing technology as compared to its predecessors, has lead to the increase in the availability of data. The increase in the availability of the data lead to the rise in the studies of the respective area. Machine Learning algorithms as well as Deep Learning algorithm has been applied in the NGS data analysis with the high possibility of growth in the area. In this paper, the workflow of NGS data analysis in the field of bioinformatics is presented based on the variant identification of reads. The paper provides an overview of the NGS data analysis workflow related to medical health and different tools application. © 2023 IEEE.

关键词： Genome

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Vehicle Point Cloud Segmentation Based on Deep Learning 9

Vehicle Point Cloud Segmentation Based on Deep Learning

引用

9th International Conference on computer and Communications, ICCC 2023

作者： Xu, Xiaodong Yi, Guohong Li, Jianting Wu, Bingqian Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798350317251

A vehicle point cloud segmentation model is designed to address the common issue of real-time acquisition of point cloud of vehicle body and wheels in intelligent parking systems. Firstly, a Multi resolution Feature Pruning Segmentation Network (MFPS-Net) is proposed. This method takes pure point cloud information as input, adopts PointNet++ trunk network, and integrates channel level pruning to accurately segment vehicle point clouds while improving model segmentation efficiency. Then, in order to verify the effectiveness of the model, a dataset with real labels was generated from the vehicle point cloud using bilateral LiDAR data collected from the vehicle buffer. Finally, point cloud segmentation experiments were conducted. The experimental results showed that the accuracy of vehicle point cloud segmentation reached 92.01%. © 2023 IEEE.

关键词： Deep learning

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Tracking of Food Waste in Food Supply Chain Using Machine Learning

Tracking of Food Waste in Food Supply Chain Using Machine Le...

引用

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Sriman, B. Santhosh, R. Guru, R. Santhosh Raju, V. Saras Prasad Prasad, H. Ruma Sailendiran, R. Computer Science and Engineering Rajalakshmi Institute of Technology TamilNadu Chennai India

ISBN: (纸本)9798350334357

In India, annually, 67 million tonnes of food are wasted, costing the country roughly 92000 crores. The motivation behind recycling food waste is the desire to divert waste from landfills. This study aims to manage food wastage in regional Food Supply 7hChains (FSC). In this model, we use modern generation machine learning techniques to track the amount of food waste in FSCs and redirect it for productive usage. The future recommended model is inferred to be effective across a number of areas based on the numerous studies that were conducted. © 2023 IEEE.

关键词： Land fill

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Real-time XSS Vulnerability Detection 3

Real-time XSS Vulnerability Detection

引用

3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Roy, Ritocheta Mandal, Siddharth Kavya, Saguturu Dedeepya, Seetha Sai Singh, Alpana Reddy, Elluri Lokesh Patil, Rujula Nitin Ramamoorthi, Jaya Subalakshmi Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350338607

In today's world, web application attacks have become a major concern as they can lead to unauthorized access to important information and even disruption of online services. In the context of web application attacks, one example is XSS (Cross-site Scripting) attack. These attacks are dangerous because they can steal important information like passwords and credit card details or take users to harmful websites without their knowledge. Therefore, in this paper, a plugin has been designed to classify the websites as vulnerable and alert users on not giving data to the respective page to prevent any data loss or potential cyber-attack. This method produces a true negativity of 74.49%, a true positivity of 8.16%, false negativity of 2.04%, and false positivity of 15.31%. © 2023 IEEE.

关键词： Websites

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Opinion Mining from Audio Conversation using Machine Learning tools and BART Transformer 3

Opinion Mining from Audio Conversation using Machine Learnin...

引用

3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Mathews, Ann Singh, Paras Nath CMR Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350317022

The discipline of opinion mining is expanding rapidly. It gathers subjective data from text and audio sources using Natural Language Processing and text analysis. Finding and extracting subjective data automatically from news stories, blogs, social media posts, customer evaluations, and other sources is the primary objective of opinion mining. Opinions, assessments, appraisals, sentiments, and emotions fall within this category. As consumers increase and data accumulates daily, companies rely on data collection and analytics to track sales, customer insights, and brand reputation. However, voice data, the most direct feedback, is often overlooked. This paper explains how to analyse the sentiment in audio recordings and apply the top three approaches in three different groups of opinions (positive, negative, and neutral) in order to gain a better understanding of how customers evaluate goods and services. An extensive examination of benchmarked datasets, popular feature sets, algorithms, approaches, open-source tools, difficulties, practical applications, and insights into several facets of opinion mining are provided in this research. With 99%+ accuracy for various data sets using Python's BART transformer, these results have both theoretical and practical implications, as well as demonstrating the utility of opinion mining from sound to text converted information in a holistic society. © 2023 IEEE.

关键词： Audio to text BERT and BART Opinion Mining Paraphraser Sentiment Analysis Tokenizer

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Systematic Number Plate detection using improved YOLOv5 detector 2

Systematic Number Plate detection using improved YOLOv5 dete...

引用

2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Kathirvel, A. Blesso Danny, J. Gandu, Shalem Preetham Hinn, Joe T. O. Kennedy, Roak C. Immanuel, Aldrin J. Karunya Institute of Technology and Sciences Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350347982

The count of automobiles has risen over the past decade on the road. There must be more automobiles on Indian roadways than the citizens living in. It is necessary to automate the fine-collecting procedure which minimizes vehicles from driving too fast and exceeding the posted speed limit by identifying the license plate. In this paper, a systematic number plate recognition (SNPR) methodology was proposed. A system based on YOLOv5s is used for training the model with annotated images in the dataset. The process was divided into several steps, comprising acquisition, detection, segmentation, and finally text recognition in an image. The automobile is recognised from each photograph in the first stage. The next stage is to identify the automobiles' license plates from the identified cars. After the segmentation, the license plates are cropped. The characters are recognised in the last phase from the collected number plates. YOLOv5 is used by the system for number plate detection and Keras for character recognition. The characters from a number plate are retrieved and entered into an excel spreadsheet. Images of Indian license plates are used to evaluate the model's performance. The accuracy for automobile detection, number plate identification and character recognition are 97.6%, 98.2%, and 99.1%. © 2023 IEEE.

关键词： License plates (automobile)

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Movie Recommendation System and Data Analysis

Movie Recommendation System and Data Analysis

引用

2023 International Conference on computer, Electronics and Electrical engineering and their Applications, IC2E3 2023

作者： Jain, Digha Jain, Atishay Gonge, Sudhanshu Joshi, Rahul Kothari, Sonali Kotecha, Ketan Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350338003

We are living in a digital era, nowadays everything starting from shopping to entertainment is easily accessible online to all the age groups, thanks to the user-friendly interface. This technological advancement was maximized during COVID-19 and continues to flourish till now. But this development made about 29.1 percent of the population that consists of children under the age of 18 vulnerable to movies, shows having illegal and adult content. Not only this, even for adults it can be a tiresome process to scroll through a long list of movies on sites just to find that one movie of your taste. In such cases, a recommendation system will help the user filter out the content easily. In this paper, we have done detailed data analysis over the IMDB movie dataset and created a recommendation system where users can look for movies on the basis of actor, director, genre, language etc. This system works using the concept of correlation and feature engineering. © 2023 IEEE.

关键词： Recommender systems

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Explainable AI for Leukemia Diagnosis: Modified ResNet with Hard Attention Mechanisms 1

Explainable AI for Leukemia Diagnosis: Modified ResNet with ...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ramanujam, E. Sekar, K. Bavashree, G.S. Prabakar, Shreya Sai National Institute of Technology Department of Computer Science and Engineering Silchar India Rajalakshmi Engineering College Department of Aiml Chennai India

ISBN: (纸本)9798331529246

Leukemia is a highly complex and aggressive form of cancer, necessitating accurate and timely diagnosis for effective treatment. This study investigates the use of Explainable Artificial Intelligence (XAI) techniques to enhance computer-aided diagnosis (CAD) systems for leukemia. A modified ResNet architecture, integrated with a hard attention mechanism, is proposed to improve model performance and interpretability. The approach emphasizes robust segmentation of white blood cell (WBC) nuclei, utilizing advanced image processing techniques and U-Net models for precise feature extraction. Model evaluation focuses on both accuracy and interpretability, employing Grad-CAM and clustering space analysis to assess network focus and transparency in decision-making. The results underscore the strengths and limitations of the proposed architecture, offering insights into further advancements in XAI to improve the reliability and transparency of CAD systems. This work ultimately aims to advance clinical outcomes in leukemia diagnosis by providing more reliable and interpretable diagnostic tools. The proposed model produces 99.7% of accuracy for detection and classification of leukemia. © 2025 IEEE.

关键词： Diseases

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Federated and Split Learning for Enhanced Breast Cancer Diagnosis

Federated and Split Learning for Enhanced Breast Cancer Diag...

引用

2023 IEEE Conference on engineering Informatics, EI 2023

作者： Karnati, Hemanth Baiju, B.V. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350338522

Protecting patient data is very important in healthcare sector, With the rise of digital applications and the increasing volume of patient data being generated, there is a pressing need to develop models that can handle this data responsibly while still delivering accurate results. This research explores the distributed machine learning techniques of Federated Learning (FL) and Split Learning (SL). By applying these methods to the renowned Breast Cancer Wisconsin (Diagnostic) dataset (WDBC), we observed remarkable results: 98% accuracy with FL and 99% with SL. These figures not only demonstrate the effectiveness of these models but also their edge over traditional centralized approaches. Importantly, while delivering this level of accuracy, FL and SL ensure that data remains decentralized, bolstering patient data security. Our research underscores the viability of these approaches in modern healthcare scenarios, where the balance between data confidentiality and diagnostic precision is crucial. © 2023 IEEE.

关键词： Diseases

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Cuisine Prediction from Ingredients using Hyper Parameter Tuning on Machine Learning Algorithms

Cuisine Prediction from Ingredients using Hyper Parameter Tu...

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2023 IEEE Silchar Subsection Conference, SILCON 2023

作者： Saikia, Parikshit Mangal, Anshul Jain, Hardik Singh, Aditya National Institute of Technology Department of Computer Science and Engineering Silchar India

ISBN: (纸本)9798350314144

This study focuses on analyzing the correlation between cuisines and ingredients by employing different classification techniques of machine learning. Utilizing a Kaggle dataset, we compare the accuracy of multiple classifiers by applying linear support vector classifier(Linear SVC), extreme gradient boosting (XGBoost), decision trees, random forest, logistic regression, k-nearest neighbor, and naive Bayes. The LSVC achieves an accuracy of 80.43%. Applying the hyper-parameter tuning, the accuracy is further improved, which is 81.62%. This enhancement carries significant implications, as even small accuracy improvement can provide more reliable predictions and valuable insights for understanding cuisine and ingredient patterns in a large-scale dataset. © 2023 IEEE.

关键词： Decision trees

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