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Machine Learning Algorithms: A Conceptual Review 1

Machine Learning Algorithms: A Conceptual Review

引用

1st International Conference on Intelligent Computing and Research Trends, ICRT 2023

作者： Kumar, Manish Ali Khan, Shueb Bhatia, Abhay Sharma, Vibhor Jain, Parag Roorkee Institute of Technology Department of Computer Science & Engineering Uttarakhand Roorkee India KSVCEM Department of Computer Science & Engineering Uttar Pradesh Bijnor India

ISBN: (纸本)9798350336771

The study of statistical models and algorithms that computers use to complete a particular task without external guidance or being explicitly programmed is known as machine learning. Lots of applications that we are using on regularly use ML algorithms. When we use search engine like Google to search the internet, it uses a machine learning algorithm. Teaching computers to maximize a performance criterion based on prior knowledge or sample data is known as machine learning. We have a model with certain parameters stated, and learning is the process of utilizing training data or prior knowledge to run a computer programme to improve the parameters. The model might be pictorial to learn from the data, foretelling to make forecasting, or both. ML algorithms have figured out the way to rank websites. These are used for various type of things, including data collection, data preprocessing, visualization, prediction of data and take decision. The main aid of using machine learning algorithm is that formerly it learns how to handle data, it can finish work independently. This analysis provides a rapid review and viewpoint on the vast applications of machine learning techniques. © 2023 IEEE.

关键词： Search engines

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XGB-RFE: An XGBoost Approach for Improved Playback Spoofing Detection in Automatic Speaker Verification Systems Using Recursive Feature Elimination 11

XGB-RFE: An XGBoost Approach for Improved Playback Spoofing ...

引用

11th IEEE Region 10 Humanitarian technology Conference, R10-HTC 2023

作者： Sanghvi, Harshil Mankad, Sapan H. Institute of Technology Nirma University Ahmedabad Department of Computer Science and Engineering India

ISBN: (纸本)9798350326147

Playback spoofing, the act of impersonating another speaker by playing back recordings of their voice, poses a significant threat tot he security o f Automatic Speaker Veri-fication (ASV) systems. This paper introduces anew method for detecting playback spoofing in A SV systems. O ur approach combines temporal and spectral features with Machine Learning (ML) and Deep Learning (DL) techniques to distinguish between playback spoofed and genuine speech. We employed Recursive Feature Elimination (RFE) to retain the most relevant features, improving performance. The results of our evaluation on a subset of ASV spoof 2019 dataset show that the combination of RFE and XGBoost produced outstanding results, with accuracy rising from 89.07% to 99.86% and Equal Error Rate (EER) decreasing from 48.05% to a mere 0.69%. Our study highlights the significance of RFE in enhancing the performance of speaker verification systems and the potential of XGBoost as an algorithm for detecting playback spoofing attacks. © 2023 IEEE.

关键词： Deep learning

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Pressure Ulcer Detection and Prevention Using Neural Networks 1st

Pressure Ulcer Detection and Prevention Using Neural Network...

引用

1st International Conference on Expert Clouds and Applications, ICOECA 2023

作者： Bhavani, A. Durga Likith, S. Singh, Khushwinder Nitya Dyuthi, A. Department of Computer Science and Engineering BMS Institute of Technology & Management Bangalore India

ISBN: (纸本)9789819917440

Pressure Ulcers (PU) or Decubitus Ulcers (DU) are localized injuries to the skin or underlying tissue, usually over a bony prominence resulting from unrelieved pressure. They are deep scars that can potentially reach up to the bones and put the patient under extreme pain. They affect people that do not have much ambulation and are bound to a bed all day, with minimal mobility. This study observed patients with a risk of bedsores and attempts to tackle the issue with predictive and preventive methods. This work intends to reduce the risk of developing pressure ulcers in patients with COVID and those who are bedridden. The dataset was collected from the post-surgery and paralyzed patients at a hospital. The overall performance of the system of F1 score is 98%, and the accuracy is 97.5. This system is affordable, disposable, wireless, and inconspicuous, with fabric-based pressure sensors and a hygrometer that continuously monitors tissue status in at-risk areas. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hygrometers

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Crop Recommender System Based on Ensemble Classifiers

Crop Recommender System Based on Ensemble Classifiers

引用

2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Vuyyala, Voshma Reddy Kona, Michael Sadgun Rao Pusuluri, Sai Bhargavi Variganji, Swetha Nenavathu, Bhavani PVP Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350396485

Farmers are facing problems because they are unable to manage cultivation because of bad weather conditions and uneven rainfall. Thus, to reduce the problems of farmers, the latest technologies are introduced such as machine learning to implement crop recommendation systems. A wide range of classification techniques are used, and a specific model is selected based on their accuracy levels. By using feature selection techniques, the raw data is converted into a dataset which is useful for efficiently training the model with relevant data. Reducing redundant data and utilizing just the aspects that are significantly relevant in deciding the model's final output will improve the model's accuracy. The findings show that, compared to other classifiers, the ensemble approach delivers better prediction with a 99.54% accuracy rate. document is a ''live'' template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. © 2023 IEEE.

关键词： Recommender systems

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Effective Teaching Aids for People with Dyslexia 5th

Effective Teaching Aids for People with Dyslexia

引用

5th International Conference on Information Systems and Management Science, ISMS 2022

作者： Banerjee, Jaya Chakraborty, Durbar Chakraborty, Baisakhi Basu, Anupam Department of Computer Science and Engineering National Institute of Technology Durgapur713209 India

ISBN: (纸本)9783031311529

Dyslexia refers to a learning disability that affects a wide range of individuals, specifically children worldwide. Subjects with dyslexia have different experiences with recognizing the letters and the words comprising it. While several modern technologies have been envisaged for the purpose of detecting dyslexia effectively, the means to assist this learning disorder is largely inadequate till date and technical interventions in this regard have either been quite slim or the solutions presently at hand fail to take up a pragmatic approach towards dealing with the issue at hand. In this research, we have adopted an application-based efficient approach which will assist and enhance the learning process of the people who have been medically diagnosed with dyslexia. People with dyslexia tend to have problems reading text or electronic documents in the conventional font which is largely used across various resources and this invariably slows down their learning process. Dyslexia Assistant aims to address this issue and takes up multiple feasible approaches which will help enhance the visual as well as auditory perception of the concerned person with dyslexia during the course of the learning process. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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Exploratory Data Analysis: An Analysis on Geotagged Twitter COVID Data 3

Exploratory Data Analysis: An Analysis on Geotagged Twitter ...

引用

3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Gethse Sharon, D. Juliet, Sujitha Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350322842

Exploratory data analysis is the need to analyze data in depth. It comprises of basic methods like spot anomalies, stemming, hypothesis testing, check assumptions and visualize data. The sentiment analysis is a main function of exploratory analysis of data which is carried out along with keyword extraction is mentioned in the paper. The main objective of this proposed work is carried out by analyzing the twitter COVID-19 data. The pandemic played a major role in every living thing and so did it spread through the whole world by means of social media. The spread of the information is gathered here by using exploratory data analysis in python programming. © 2023 IEEE.

关键词： COVID-19

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Deep Ear Biometrics for Gender Classification 4th

Deep Ear Biometrics for Gender Classification

引用

4th International Conference on Communication, Devices and Computing (ICCDC 2023)

作者： Singh, Ritwiz Kashyap, Keshav Mukherjee, Rajesh Bera, Asish Chakraborty, Mamata Dalui Department of Computer Science and Engineering Haldia Institute of Technology WB Haldia India Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Rajasthan Pilani Campus 333031 India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur India

ISBN: (纸本)9789819927098

Human gender classification based on biometric features is a major concern for computer vision due to its vast variety of applications. The human ear is popular among researchers as a soft biometric trait, because it is less affected by age or changing circumstances and is non-intrusive. In this study, we have developed a deep convolutional neural network (CNN) model for automatic gender classification using the samples of ear images. The performance is evaluated using four cutting-edge pre-trained CNN models. In terms of trainable parameters, the proposed technique requires significantly less computational complexity. The proposed model has achieved 93% accuracy on the EarVN1.0 ear dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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Advance Kernel Rootkit Detection: Survey 7

Advance Kernel Rootkit Detection: Survey

引用

7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Suresh Kumar, S. Sudalaimuthu, T. Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

Rootkits are a group of software tools that an attacker uses to break into a system, giving him the ability to access confidential information, hide his own existence, and install additional malicious software. A rootkit cannot be installed without administrative level access for the attacker. Due to their evasiveness, rootkits pose the greatest detection difficulty among malware. Attacks on rootkits today primarily target operating system kernel changes. In this work, a detailed investigation of rootkit detection technology is used to develop a novel typeof rootkit detection structure, a new technique (software), and a structured and comprehensive view of research on rootkit, process hiding, hook, and detection/prevention. © 2023 IEEE.

关键词： computer viruses

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Comparative Analysis of TextRank and Latent Semantic Analysis Algorithms for Extractive News Summarization 3

Comparative Analysis of TextRank and Latent Semantic Analysi...

引用

3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Kodicherla, Pranathi Sathineni, Snigdha Reddy Sai, J. Shiva Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350302288

News summarization is an essential process that condenses news stories or collections of stories while retaining key details and main ideas. It offers readers a concise overview of significant events and essential takeaways from news stories, allowing them to quickly grasp the necessary information without reading the entire article. This paper presents a comparative analysis of two widely used algorithms, TextRank, a graph-based algorithm based on the PageRank algorithm, and Latent Semantic Analysis (LSA), a statistical method that captures latent semantic relationships using singular value decomposition for news summarization. The experiment results obtained using established evaluation metrics such as ROUGE-N and ROUGE-L suggest that the TextRank algorithm performs better than the LSA algorithm for all news categories and consistently demonstrates higher Precision, Recall, and F-Score values. The research aims to contribute to a deeper understanding of the strengths and weaknesses of TextRank and LSA in summarizing news by incorporating five news categories (Business, Politics, Sports, Entertainment, and technology) and using a significantly large dataset containing 2225 news articles and their summaries. This knowledge will empower researchers and practitioners to make informed decisions when selecting an algorithm for summarization tasks based on their requirements and constraints. © 2023 IEEE.

关键词： Singular value decomposition

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Optimizing Waste Management through Intelligent Machine Learning Systems: A Comparative Study 14

Optimizing Waste Management through Intelligent Machine Lear...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Shravani, Nandikante Vigneshwari, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335095

The purpose of this research study is to examine the use of intelligent machine learning systems in the process of optimising waste management practises. Several other approaches, such as Artificial Neural Networks, K-nearest neighbours, Logistic regression, Decision Trees, Support Vector Machine, and Random Forest, are being investigated as potential ways to improve the currently utilised waste management paradigm. On the other hand, with the implementation of algorithms for machine learning, these measures saw a huge improvement. The use of these methods brought in a remarkable increase in classification accuracy, which reached 92.349%, and a recall rate of 91.04%. This significant improvement in performance demonstrates the usefulness of machine learning in the process of optimising waste management. The Random Forest classifier proved to be the strategy that offered the best results after being compared to the other approaches investigated. In particular, the use of this classifier helped to improve the accuracy of estimates for the amount of time required to empty recycling containers. In a nutshell, the purpose of this study article was to demonstrate how effective intelligent machine learning systems can be in improving existing waste management procedures. © 2023 IEEE.

关键词： Neural networks

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