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Prediction Of Pulmonary Fibrosis Disease

Prediction Of Pulmonary Fibrosis Disease

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Control Instrumentation System Conference (CISCON)

作者： Disha Jain Palak Khurana Sakshi Yadav Seema Sharma Dept. of Computer Science and Engineering Amity University Uttar Pradesh India

ISBN: (数字)9798350375480

ISBN: (纸本)9798350375497

The progressive and potentially fatal lung illness pulmonary fibrosis (PF) is represented by permanent scarring in a lung tissue. A successful treatment of pulmonary function decline requires early diagnosis and close observation. For people with Pulmonary fibrosis (PF), forced vital capacity (FVC), a measurement of maximal exhaled air volume, is an essential marker of lung function. In order to improve PF diagnosis, this work investigates the viability of using deep learning and machine learning models to predict FVC. A set of three models were developed namely the Long Short Term- Memory (LSTM) network to identify sequential patterns in data from clinical trials, a Huber regression model for reliable management of outliers, and a Support Vector Regression (SVR). For model training and assessment, a dataset of 200 patients was used. In the training set, baseline Computed Tomography (CT) scan and the entire history of FVC measurements are provided and, in the testing, set baseline CT scan and only the initial FVC measurements are used. Each model's effectiveness was compared using three performance metrics- Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. The findings showed that all three of the models had potential for predicting FVC, with the SVR be the most efficient in predicting the FVC values as it has the lowest Mean squared error (MSE) which is 0.029. This work demonstrates the potential of deep learning and machine learning for non-invasive FVC prediction, which may allow for early PF detection and prompt intervention for improved clinical results.

关键词： Training Deep learning Analytical models Computed tomography Computational modeling Atmospheric modeling Lung Predictive models Metadata Long short term memory

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A Framework for Personality Prediction for E-Recruitment Using Machine Learning Algorithms

A Framework for Personality Prediction for E-Recruitment Usi...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Lakshyajit Thapa Aniket Pandey Deepanshu Gupta Ayush Deep Rakesh Garg Dept. of computer science and engineering Amity University Uttar Pradesh India

With rapid advancements in technology, the process of recruitment has evolved from traditional methods to online and automated methods known as e-recruitment. While e-recruitment streamlines the initial screening process, assessing the personality and suitability of candidates continues to remain a challenge. In this paper, we propose a framework for online personality prediction to aid the e-recruitment process. The framework utilizes machine learning algorithms to analyze user data from multiple online sources like resumes, social media profiles, and responses to psychometric tests, and predict their personality type based on established psychological theories. Four popular personality prediction models - Big Five, Myers Briggs, HEXACO, and Enneagram are discussed. State-of-the-art natural language processing and deep learning techniques like BERT are explored for feature extraction from text data. Popular supervised machine learning algorithms like logistic regression, Naive Bayes, k-nearest neighbors, support vector machines, random forest, XGBoost, and LSTM are compared to build predictive models. The proposed framework can help recruiters shortlist candidates better matched to job requirements, leading to increased hiring satisfaction and productivity.

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Hardware Implementation of Memristor-Based in-Memory Computing for Classification Tasks

Hardware Implementation of Memristor-Based in-Memory Computi...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Mohammad Reza Eslami Soheib Takhtardeshir Sarah Sharif Yaser Mike Banad Debt. of Electrical and Computer Engineering(ECE) Florida State University Tallahassee USA Dept. of Computer and Electrical Engineering (DET) Mid Sweden University Sundsvall Sweden School of Electrical and Computer Engineering(ECE) University of Oklahoma Norman USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

Recent advancements in neuromorphic computing hardware have primarily centered around the development of integrated circuits, along with the analysis and simulation of analog circuits using the SPICE program. However, a critical shortfall of SPICE is its incapacity to accommodate algorithms and circuits based on microcontrollers, posing challenges for implementing on-chip training necessary for in-memory computing. To bridge this gap, our paper explores the implementation of SPICE-compatible circuit designs in the Proteus circuit simulator. We integrate a memristor component and a neuron model featuring a sigmoid activation function into Proteus. Subsequently, we construct a neuromorphic accelerator comprising 30 memristors and 4 neurons to demonstrate on-chip training and inference processes using an Arduino microcontroller. Additionally, our study introduces an optimal learning algorithm tailored for training memristors and adjusting synaptic weights during the training phase. The designed peripheral circuits govern all memristors throughout both training and inferencing and formulate an algorithm to apply test data for evaluating network accuracy.

关键词： Training Accuracy Software algorithms Noise Memristors SPICE Hardware

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Biometric identity Authentication System Using Hand Geometry Measurements 2

Biometric identity Authentication System Using Hand Geometry...

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2nd International Conference of Modern Applications on Information and Communication Technology, ICMAICT 2020

作者： Mohammed, Hesham Hashim Baker, Shatha A. Nori, Ahmed S. Department of Electrical and Computer Engineering Northern Technical University Iraq Computer Science Dept. Mosul University Iraq

In recent years hand geometric dependent biometric system has shown to be the quite acceptable biometric trait and suitable for security applications. It has been recognized as an effective means of authenticating identity in a variety of commercial applications as a result of better hardware and improved algorithms. This paper purpose a hand recognition system that extract 21 features for the right hand to identify and authorize persons. The system has two main parts, the first contain the data collection, explains the basic pre-processing required and how hand geometry characteristics like fingers length, width, coordinates of the base of the fingers, and palm width are extracted to derive the features used for discrimination, While the second part include the training and testing of three artificial neural networks to perform the recognition. After features extraction, the system uses three kinds of artificial neural networks in performing the recognition process, which are feed forward back propagation NN, Elman NN, and the cascade forward neural network NN. The proposed system shows that the Recognition Rate RR for the neural networks after testing were 95%, 92%, 88% respectively. © Published under licence by IOP Publishing Ltd.

关键词： Geometry

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AI and Machine Learning Applications in Hepatitis B Diagnosis: An Exploration of Techniques and Outcomes

AI and Machine Learning Applications in Hepatitis B Diagnosi...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Sandeep Kumar Mohit Arora Dept. of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Hepatitis B is one of the major global health challenges, affecting millions worldwide and frequently leading to serious liver conditions like hepatocellular carcinoma and cirrhosis. Early and accurate diagnosis is very important for effective management and treatment; however, most of the traditional diagnostic approaches, including serological tests, liver function tests, liver biopsy, etc., are very often affected by limitations in resource availability and diagnostic accuracy. This review paper highlights the current development in various research works on the application of artificial intelligence (AI) and machine learning (ML) in the diagnosis of Hepatitis B. This paper reviews all the major ML computational techniques used in Hepatitis B diagnosis, underlining the use of various computational techniques: Generalized regression neural networks (GRNN), multilayer Mamdani fuzzy inference system (ML-MFIS), hybrid models combining clustering with ensemble learning, ANFIS, deep neural network (DNN), and support vector machine (SVM). These methodologies have been reported to show substantial improvement in the performance of diagnosis, where the maximum accuracy rate is up to 99.35%. This paper has also listed the datasets used for the studies and compared the performance metrics of these studies in terms of precision, accuracy, and F-score. Despite the progress, several issues persist, such as small dataset sizes, data imbalance, and the need for thorough validation across diverse clinical settings. Several research questions are also outlined to showcase the current ML diagnosis methods and their performances along with their limitations to provide the future research direction. It also covers the adoption of explainable AI (XAI) as of one the potential future aspect.

关键词： Support vector machines Fuzzy logic Accuracy Liver diseases Explainable AI Reviews Artificial neural networks Nonhomogeneous media Ensemble learning Informatics

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A CNN Based Ensemble Deep Learning Approach for Melanoma Detection

A CNN Based Ensemble Deep Learning Approach for Melanoma Det...

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IEEE Region 10 Symposium

作者： Mohammad Abu Obaida Mullick Nafi Sadman Khan Ahnaf Tahmid Fahid Mahin Mahmud Raiyan Rahman Dept. of Computer Science & Engineering United International University Dhaka Bangladesh

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

Melanoma, the most aggressive form of skin cancer, necessitates early detection for successful treatment. This paper proposes an ensemble Convolutional Neural Networks (CNNs) approach to classify dermoscopic images of Melanoma (MEL) and Melanocytic Nevi (NV), by systematically selecting and comparing various pre-trained models with different optimizers and preprocessors through trial and error, as well as optimizing loss functions. The ensemble method takes predictions from multiple pre-trained models and selects the most reliable outcome using a custom metric. The result presents an optimal CNN ensemble model built of DenseNet121 and VGG16 with cropping and scaling, contrast enhancing, and exposure adjusting as image preprocessors that achieved a notable accuracy of 97.5% on a customized and balanced version of the Kaggle HAM10000 dataset. This approach aims to improve patient outcomes by providing a valuable tool for early melanoma detection.

关键词： Measurement Deep learning Accuracy Melanoma Predictive models Convolutional neural networks Reliability Ensemble learning IEEE Regions

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Thyroid Health Forecaster: A Cutting-Edge Hybrid Deep Learning Framework for Early Disease Prediction 5

Thyroid Health Forecaster: A Cutting-Edge Hybrid Deep Learni...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Selvamuthu, C.M. Kartheek, G.L.N.V. Chandrashekhara, K.T. Thatipudi, Jayendra Gopal Vadar, Parashuram Shankar Bhattacharjee, Sandeep Tamil Nadu Chennai India Anurag Engineering college Department of Computer science and Engineering Telangana Suryapet India BMS Institute of Technology and Management Department of Information science and Engineering Bangalore India University of North Texas Dept. of Computer Science United States Yashwantrao Chavan School of Rural Development Shivaji University Maharashtra Kolhapur India Amity University Rajarhat Newtown West Bengal Kolkata700135 India

ISBN: (纸本)9798331530013

Detecting thyroid disease early is crucial for effective management and treatment, as conditions like hypothyroidism and hyperthyroidism can significantly impact a patient's health if left untreated. Traditional diagnostic methods, primarily based on hormone level testing, often fail to detect the disease in its initial stages. This study introduces a hybrid deep learning strategy to predict thyroid disease at an earlier stage, utilizing a combination of ResNet50 for feature extraction and Support Vector Machine (SVM) for classification. The methodology involves preprocessing thyroid image data, including resizing, normalization, augmentation, noise reduction, and enhancement, followed by feature extraction and classification. The hybrid model achieved an accuracy of 95.2%, precision of 94.5%, recall of 93.8%, F1 score of 94.1%, and ROC-AUC of 97%. This approach demonstrates superior performance compared to traditional machine learning models, indicating its potential for reliable early diagnosis of thyroid conditions. © 2024 IEEE.

关键词： Contrastive Learning

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Predictive Analytics in Crime: A Machine Learning Approach 22

Predictive Analytics in Crime: A Machine Learning Approach

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22nd OITS International Conference on Information Technology, OCIT 2024

作者： Rajareddy, Goluguri N.V. Lakshmeeswari, Gondi Pradhan, Prajna Paramita Dept. of Computer Science & Engineering GITAM Deemed to be University Visakhapatnam India

ISBN: (纸本)9798331510404

Traditional crime analysis methods are overwhelmed by the ever-growing volume and intricacies of crime data. This research addresses this challenge by proposing a novel system utilizing machine learning to analyze crime data in India. Focusing on "Serious Fraud - Cheating" and "Serious Fraud - Criminal Breach of Trust" across all states, the system tackles the limitations of manual analysis. By integrating k-means clustering and Decision Trees, the system efficiently processes and interprets complex data, achieving high accuracy and precision in identifying patterns. This paves the way for proactive policing strategies, empowering law enforcement to anticipate and prevent future crimes. As technology continues to reshape criminal activity, this system offers a valuable tool for law enforcement agencies to stay ahead of tech-savvy criminals by embracing cutting-edge analytical approaches. © 2024 IEEE.

关键词： K-means clustering

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Structured Implementation of ML Algorithms for Cardiovascular Disease Detection

Structured Implementation of ML Algorithms for Cardiovascula...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Drishti Arora Farhan Asif Dept. of Computer Science & Engineering Amity University Uttar Pradesh India

Cardiovascular diseases encompass a range of medical conditions that impact the heart and blood vessels The prediction of heart disease pose considerable complexity and present a formidable challenge within the medical field According to the WHO, heart diseases are the leading cause of death worldwide, claiming approximately 17.9 million lives each year, accounting for around 31% of all global deaths To address the need for accurate heart disease prediction, extensive research has employed advanced machine learning models This study conducted a comprehensive evaluation of seven distinct classification models, combining various physiological factors with well-known machine learning algorithms The model employed in this study includes Naïve Bayes, Logistic Regression, Decision Trees, Random Forest, XGBoost, CatBoost, and Voting Classifiers Using these models, a robust heart disease prediction system is designed, facilitating precise evaluation of an individual’s risk The observation shows that through meticulous evaluation and comparison, the Random Forest algorithm, which is a bagging technique, outperforms the existing state-of-the-art methods It exhibited remarkable accuracy, yielding prediction results of approximately 90.16% This exceptional accuracy establishes the random forest algorithm as the pre-eminent model for precise and reliable heart disease prediction within the scope of this study.

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“Liver Disease Risk Prediction Using Regularized Deep Learning: A Novel Approach”

“Liver Disease Risk Prediction Using Regularized Deep Learn...

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Advancements and Key Challenges in Green Energy and Computing (AKGEC), International Conference on

作者： Parvez Rahi Sandeep Singh Kang Ajay Pal Singh Inderjeet Singh Dept. of Computer Science & Engineering Chandigarh University Mohali Punjab India

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

Liver diseases are common, thereby posing a significant quality-of-life impairment. They have required accurate and prompt assessments to be managed. Typically, conventional methods of evaluation often include subjectivity and laborious amounts of work. It introduces a DNN-based model with L2 regularization to predict risk categorization of liver diseases based on five categories of categorizations: Healthy_Liver, Low_Risk, Moderate_Risk, High_Risk, and Severe_Liver_Disease. The model includes the hidden layers with rectified linear unit (ReLU) activation functions, batch normalization, and dropout for regularizing to achieve robust learning. The DNN was trained in a very comprehensive Kaggle dataset with many physiological and biochemical features and has achieved an impressive 94.07% classification accuracy. Healthcare professionals will be equipped with a significant resource to develop personalized treatment strategies facilitated by this investigation, which highlights the capabilities of deep learning methodologies in the automation of liver disease assessment. Subsequent research will focus on integrating additional data sources to improve the model’s scalability and predictive precision, thus promoting the advancement of automated liver disease evaluation in clinical settings.

关键词： Green energy Accuracy Liver diseases Computational modeling Soft sensors Scalability Medical services Predictive models Data models Physiology

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