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Automated Diagnosis of Anemia Signs using Machine Learning 11

Automated Diagnosis of Anemia Signs using Machine Learning

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Sherin, K. Alphius Victoria, Ashlee P Harini, S. Jensen, Jose C. St. Joseph's Institute of Technology Computer Science and Engineering TamilNadu Chennai India

ISBN: (纸本)9798350350357

Anemia, is acommon medical condition distinguished by a shortage of red blood corpusclesposes significant health concerns globally. This should be detected at an early stage for effectual intervention and management. This study presents a multi-faceted approach to anemia detection using machine learning techniques. The dataset employed in this research comprises relevant features associated with anemia, including demographic information and clinical indicators. Initial data exploration involves loading the dataset, exploring its structure, and preprocessing steps such as label encoding to prepare the data for analysis. Machine learning (ML) algorithms, including Logistic Regression(LR), Random Forest(RF), K- Nearest Neighbors(KNN), Support Vector Machine(SVM), Decision Tree, XGBoost, Convolutional Neural Networks, are utilized for predictive modeling and several boosting algorithms such as XGBoost, gradient boosting, Adaboost are also used. These algorithms are chosen for their ability to handle both categorical and numerical data, providing a robust framework for anemia prediction. The evaluation of the models involves cross-validation techniques to ensure their generalizability. Various key performance indicators are employed to assess the accuracy and reliability of the predictive analytics. Moreover, an exploratoryanalysis of data has been undertaken to understand the distribution of anemia cases within the dataset. The findings suggest favorable accuracy and efficacy of the machine learning models in identifying instances of anemia. The system's ability to leverage diverse algorithms enhances its robustness, allowing for reliable predictions across different patient profiles. This research contributes to the development of an effective and scalable anemia detection system, laying the foundation for future work in the integration of additional algorithms and optimization strategies for enhanced accuracy and clinical applicability. © 2024 IEEE.

关键词： Decision trees

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Enhancing Product Classification and Providing Recommendation Using Hybrid Algorithms 1

Enhancing Product Classification and Providing Recommendatio...

引用

1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Muthu Kumar, S. Sivasangaran, T. Sriraghul, M. St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365337

Recommendation engines are extremely important in the constantly changing world of online business. This paper describes a deep learning-based product recommendation system that skillfully combines text and image categorization methods. With millions of products available on eCommerce platforms, providing users with accurate and personalized suggestions is a challenge. The hybrid strategy that has been suggested makes use of deep learning algorithms to effectively classify products by leveraging the advantages of both text and image data. Combining these methods allows the recommendation system to offer a variety of customized options, improving the user experience. By tackling the intricacies of product data and providing a hybrid solution for accurate and personalized product suggestions, this research adds to the rapidly evolving field of online commerce. © 2024 IEEE.

关键词： Recommender systems

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Crowd Counting Method Based on Dual-Path Multi-Feature Fusion with Transformer 6

Crowd Counting Method Based on Dual-Path Multi-Feature Fusio...

引用

6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Li, Lei Wu, Yuntao Yao, Xiaotong Zhang, Zhuoyu School of Computer Science and Engineering School of Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

In dense crowd scenes, the scale of human heads varies greatly, and current visual transformer models lack learning of different scales, resulting in the model being unable to perceive the target scale changes of human heads in crowd density images, leading to a decrease in crowd counting accuracy. To address this issue, we added a Multi Path Computing Network (MPCNet) in the feature extraction stage, applying attention mechanisms at different sampling rates and levels to capture hierarchical features of the image, and fitting proximity and multi-level dependencies to effectively perceive changes in the head scale. Adding a bidirectional feature fusion module (BIFFM) to the backend network captures more complex feature dependencies by simultaneously considering two different spatial positions, effectively enhancing the interaction between feature maps of different scales and enabling the model to perform counting efficiently. Finally, this chapter conducted extensive experiments on three mainstream population counting benchmark datasets to validate the effectiveness of the method. © 2024 IEEE.

关键词： Feature extraction

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Book Genre Classification System Through Supervised Learning Technique 1

引用

3rd International Conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Sethy, Abhisek Rout, Ajit Kumar Nayak, Soumya Ranjan Kumar, Raghvendra Department of Computer Science and Engineering Silicon Institute of Technology Odisha Bhubaneswar India Department of Information Technology GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering GIET University Odisha Gunupur India

ISBN: (数字)9789819705733

ISBN: (纸本)9789819705726

Machine learning is used in this digitizing era so there is an ever-increasing desire for computers to execute human-like jobs. Text classification is rapidly becoming one of machine learning’s most significant tasks. Manually reading the entire book and classifying it based on its genre is, however, a time-consuming operation. As a result, machine learning methods are crucial for classification and in this paper, a book description-based text classification is depicted, where with wide range of wealth of information about the book’s contents. In the proposed system, a variety of classifiers including Multinomial Naive Bayes (MNB), Gradient Boosting (GB), and Random Forest (RF) are employed to categorize the book’s genre. According to the findings, the Naive Bayes Classifier surpasses other two techniques in terms of accuracy, while the other two classifiers provide comparable results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Supervised learning

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An Efficient Detection of Phishing Website using Machine Learning 5

An Efficient Detection of Phishing Website using Machine Lea...

引用

5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Menaga, D. Vijay, S. Vignesh, V. Ramalakshmi, S. St.Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331504403

Phishing is the practice of creating fake websites to track and steal sensitive information of online users. It is a type of identity theft where thieves create fake accounts on targeted websites and tricking victims into revealing their personal information like passwords, PINs and more. Phishing attackers send phishing links via email, text messages and emails, social media. They use social engineering techniques to lure customers to phishing websites and provide sensitive personal information. Finally, stolen personal information is used to establish trust between legitimate websites or financial institutions for illegitimate gains. Enormous amount of information is simultaneously downloaded and published on the web. This makes it possible for criminals to seize sensitive personal information. Use of machine learning to solve these problems by developing an intelligent, efficient and flexible system. Phishing websites can be detected based on important characteristics like URL and registration number. Many ecommerce businesses can use this system to simplify the business process. Thanks to this system, in which users can purchase online without any hassle. Information is presented and reviewed. A website can be classified as legitimate or phishing using supervised classification algorithms such as logistic regression, gradient boosting, decision trees, and support vector machines. The proposed model achieved 97.4% accuracy in gradient enhancing classifiers among the other algorithms compared. © 2024 IEEE.

关键词： Phishing

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Performance Comparison Between Docker Container and Virtual Machine Microservices 13

Performance Comparison Between Docker Container and Virtual ...

引用

13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Verma, Ashok Kumar Patel, Ruchi Rai, Preeti Patel, Sharda Gyan Ganga Institute of Technology and Sciences Department of Computer Science & Engineering MP Jabalpur India

ISBN: (纸本)9798350305463

Currently, the microservice architecture is extensively employed in the creation and implementation of numerous time-sensitive, crucial, and expansive web services. Usually, these services are implemented on cloud platforms by utilizing Docker containers. Container technology offers superior portability, scalability, and performance in comparison to virtual machine-based services. Container-based services are recognized for their superior performance compared to virtual machine-based services. Nevertheless, our research demonstrates that services implemented through Amazon AWS ECS (EC2 Container Service) exhibit notably inferior performance compared to those implemented through Amazon EC2 VMs. We conducted a comprehensive analysis and measurement of the disparities in throughput, reaction time, and CPU use across different deployment situations. © 2024 IEEE.

关键词： Containers

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Effect of Material type and Process Parameters on Tensile Strength of 3D Printed Specimen

Effect of Material type and Process Parameters on Tensile St...

引用

2024 International Conference on Materials Innovation and Sustainable Infrastructure, ICMISI 2024

作者： Singaravel, B. Devaraj, S. Niranjan, T. Chakradhar, B. Chaitanya, P. Department of Mechanical Engineering Vignan Institute of Technology and Science Deshmukhi Telangana Hyderabad India Department of Aeronatical Engineering Institute of Aeronautical Engineering Dundigal Road Dundigal Telangana Hyderabad500043 India Department of Mechanical Engineering Mahatma Gandhi Institute of Technology Telangana Hyderabad500075 India

Fused deposition Modelling (FDM) is a solid based 3D printing process. In this work, FDM based 3D printing process with three different raw materials and its tensile strength is investigated. Nylon, Acrylonitrile butadiene styrene (ABS) and Polyethylene Terephthalate Glycol (PETG) are used as three raw materials in this work. Tensile specimen is printed using FDM and conducted tensile strength using Universal Testing Machine (UTM). Process parameters considered raw material type, layer thickness and filament extrusion speed. Response parameter considered is tensile strength. Experiments are designed on basis of Taguchi L9 orthogonal array for analysing the performance. The result of experiment is revealed that nylon is most significant parameter affecting the tensile strength. Also, it is noticed that 3D printed tensile specimen strength is influenced with lower value of layer thickness (0.1 mm) and extrusions speed (50 mm/sec). Analysis of Variance (ANOVA) is performed and the result indicated that layer thickness and material type are shown higher contribution. © Published under licence by IOP Publishing Ltd.

关键词： Rayon

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LSTM Based Approach to Detect Fake News 2

LSTM Based Approach to Detect Fake News

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Khare, Sakhi Singh, Pushpa Kumar, Pankaj Gl Bajaj Institute of Technology Dept. of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

In the age of technology, every day, we ingest a variety of news data, either willingly or accidentally. People will always use various social media apps and keep looking for information. The information passed through these social media apps is only sometimes valid. Many organizations or individuals spread the news to influence the mass public. This type of information is called fake news. Fake news is a common problem that naturally comes with social media. Detection of counterfeit news with ubiquitous data from social media is almost impossible. Fake news kills real journalism and spreads false information in society. Researchers are still looking for better solutions. In this paper, we find a better solution with Natural Language Processing (NLP). We used fake news challenge datasets for training and testing purposes. We aim to classify the news item into four stance labels: agree, disagree, discuss, and unrelated. We developed a Long Short-Term Memory (LSTM) based solution to organize the news article in the given stance label. We find that our approach classifies data effectively with 99% of accuracy © 2024 IEEE.

关键词： Fake News LSTM NLP Word Embedding

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A Novel Smartphone-Based Human Activity Recognition Using Deep Learning in Health care 3rd

A Novel Smartphone-Based Human Activity Recognition Using De...

引用

3rd International Conference on Machine Learning, Image Processing, Network Security and Data sciences, MIND 2021

作者： Soni, Vaibhav Yadav, Himanshu Semwal, Vijay Bhaskar Roy, Bholanath Choubey, Dilip Kumar Mallick, Dheeresh K. Department of Computer Science and Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal India Department of Computer Science and Engineering Indian Institute of Information Technology Bihar Bhagalpur India Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India

ISBN: (纸本)9789811958670

Nowadays, most smartphones feature a number of strong sensors, such as direction, network, location, and motion sensors. Motion and inertial sensors (i.e., accelerometer, gyroscope, etc.) are particularly popular in human activity recognition (HAR) for detecting human physical activity which helps in many research areas of the Internet of healthcare things (IoHT) including several patient’s diseases such as Parkinson, obesity, cardiovascular, and diabetes. Deep learning (DL) techniques have been increasingly popular over the years as a result of their significant outcomes. The use of deep learning techniques to recognize human physical activity in wearable and mobile sensor situations has also gotten a lot of attention from around the world. In this paper, a deep neural network (DNN) combining bidirectional long short-term memory (Bi-LSTM) and convolutional neural network (CNN) is proposed. The performance of the model has been evaluated on two publicly available datasets: WISDM and UCI-HAR. The model has achieved 97.96 and 97.15% accuracy for the WISDM and UCI-HAR, respectively. Moreover, the simulation results show the effectiveness of the proposed work compared to other state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Hyperspectral Image Classification based on Cycle GAN and EfficientNet 4

Hyperspectral Image Classification based on Cycle GAN and Ef...

引用

4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Reddy, Tatireddy Subba Anusha, Sheguri Reddy, Enugu Vishnu Vardhan Shivamani, Vuppala Venkata Mrudula, V. B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Medak India

ISBN: (纸本)9798350343670

Because of the existence of narrow contiguous bands, Hyperspectral Remote Sensing is a sophisticated remote sensing technique that improves the capacity of precise categorization. Given the availability of duplicate information, the massive quantity of continuous bands observed in hyperspectral photos raises the processing complexity. The primary data sets have a limited number of labeled pixels;however, we need additional pixels for improved hyperspectral picture categorization. As a result, the main hurdles are dimensionality reduction and a restricted number of labelled samples. To address these issues, we introduced the Kernel PCA approach for dimensionality reduction, the Cycle GAN technique for increasing labelled samples, and the EfficientNet technique for classification. These strategies will be more accurate than existing techniques for hyperspectral picture categorization. © 2024 IEEE.

关键词： Cycle GAN Hyperspectral image Indian Pines Kernal Principal Component Analysis (KPCA) Salinas scene semi-supervised classification University of Pavia

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