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Elevating Maternal Healthcare: Synergy of Cardiotocography, Machine Learning Models and Interpretive Analysis 3

Elevating Maternal Healthcare: Synergy of Cardiotocography, ...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Niveditha, G. Maheswari, B Uma De Prado, Rocío Pérez Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Telecommunication Engineering Department University of Jaén Spain

The global goal of reducing maternal mortality and improving neonatal health, in particular during labor where clinical actions must be precise and timely, is important. Abnormalities such as low or high maternal oxygen levels, inappropriate uterine contraction patterns, or abnormal fetal heart rates during delivery can endanger the well-being of the mother and child. Currently, many essential signs are primarily assessed and recorded using visual means, which are susceptible to delay or even error. However, of late, machine learning has been characterized as a cutting-edge approach that helps improve practice in clinical settings by generating more accurate forecasts based on intricate data patterns This study's objective is to formulate an appropriate machine-learning model for predicting maternal and fetal health risks at delivery for improved clinical outcomes. Within the freely accessible cardiotocography dataset consisting of the number of uterine contractions more than and the fetal heart rate of 21 features, Recursive feature elimination (RFE) folds the features down into 15. To solve the problem of the class imbalance MSMOTE (Modified Synthetic Minority Over-sampling Technique) is used for this purpose. A number of the machine learning procedures such as Random Forest, LightGBM, CatBoost, AdaBoost, Decision Tree, K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Multi-Layer Perceptron, and Gradient Boosting amongst others are also trained and subjected to evaluation. Of these, CatBoost and LightGBM models recorded the highest level of accuracy at 96.0% and 95.1% respectively. To further interpret as well as validate the model predictions, LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) used. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Support vector machines

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Cyber Security of Mobile Applications Using Artificial Intelligence 1

Cyber Security of Mobile Applications Using Artificial Intel...

引用

1st International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2022

作者： Bishtawi, Tariq Alzubi, Reem Telecommunication and Computer Networks Engineering Amman Jordan

ISBN: (纸本)9798350332742

In recent years, the number of cyber-attacks has increased dramatically. Due to the widespread use of mobile devices, as well as the increasing popularity of mobile services, there are serious challenges in the field of cybersecurity. Traditional cybersecurity systems fail to detect malware and complex unknown attacks and do not guarantee user privacy is preserved. In the field of smartphone computing, artificial intelligence (AI) methods have expanded rapidly in recent years, often enabling devices to operate in an intelligent manner. Security against cyber-attacks on a large number of important mobile applications is a necessity in today's digital age. This paper presents how employees are using AI techniques to maintain cybersecurity in mobile applications using machine learning (ML), deep learning (DL), and artificial neural network (ANN). This paper describes an ANN to simulate a neuron in a mathematical equation, in which massive amounts of data are read to reach the desired result. ANNs are very useful in intrusion detection systems (IDS), mobile application security, and privacy breaches. We will also use supervised learning techniques in cybersecurity to identify malware and protect privacy. © 2022 IEEE.

关键词： Cybersecurity

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Banking in the Digital Age: Predicting Eligible Customers through Machine Learning and AWS 1

Banking in the Digital Age: Predicting Eligible Customers th...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Ali, Asfand Irfan, Asim Raza, Ali Memon, Zainab Mehran University of Engineering and Technology Department of Telecommunication Engineering Jamshoro Pakistan Mehran University of Engineering and Technology Computer Systems Engineering Jamshoro Pakistan Quaid-e-Awam University of Engineering Science and Technology Computer Systems Engineering Nawabshah Pakistan

ISBN: (纸本)9798350373042

This research project focuses on utilizing machine learning techniques to predict loan default among applicants in the context of financial organizations. Loan approval decisions carry substantial risks, and not all applicants can be deemed trustworthy. The primary goal of this research is to determine the feasibility of accurately presenting loan defaults by analyzing a dataset sourced from *** project involves data analysis and the application of machine learning algorithms, including logistic regression, K Nearest Neighbor, and Decision tree. Implementation will be carried out using Python programming language within Visual Studio Code, with version control through GitHub, and eventual deployment on the AWS Cloud platform. The anticipated outcomes aim for high prediction accuracy and moderate training and validation data loss, contributing to more informed decision-making in the lending process. © 2024 IEEE.

关键词： Data handling

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An RNN Based Approach to Predict Next Word in Bangla Language 1st

An RNN Based Approach to Predict Next Word in Bangla Langu...

引用

1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Mahmud, Asif Rony, Md. Nazmul Hasan Bhowmik, Deba Dip Kuri, Ratnadip Rana, A. R. M. Mahmudul Hasan Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali Bangladesh

ISBN: (纸本)9783031346187

The communication paradigm has changed with time, and text messaging has incredibly increased among people of all aspects. In addition, people use email and social media sites for interactive communication. As a result, people spend a significant amount of time typing the entire content, which is repetitious and time-killing. Therefore, word prediction technologies have been introduced to make message exchange faster and easier for predicting the next most probable word, allowing participants to choose it from a list of recommended words rather than entering it. This paper suggests a technique that predicts the next most relevant and acceptable word in Bangla, the eighth most speaking language in the world. We applied two Recurrent Neural Network models named Bi-directional LSTM (Long Short-Term Memory) and Bi-directional GRU (Gated Recurrent Unit) as language development models to our data set, which is compiled from different Bangla newspapers, storybooks, and Bangla Wikipedia. We also applied different n-grams with different LSTMs, and our approach found a maximum accuracy of 99.43% for a 5-gram dataset using the Bi-LSTM model. © 2023, ICST Institute for computer Sciences, Social Informatics and telecommunications engineering.

关键词： Long short-term memory

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Optimizing Underwater Acoustic Sensor Networks: Insights from Energy-Efficient, Reliable, and Void Issue-Based Routing Protocols 7

Optimizing Underwater Acoustic Sensor Networks: Insights fro...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Khan, Muhammad Umar Aamir, Muhammad Haider, Waleej Sir Syed University of Engineering and Technology Department of Telecommunication Engineering Pakistan Sir Syed University of Engineering and Technology Department of Computer Science and Information Technology Pakistan

ISBN: (纸本)9798331534271

The Underwater Acoustic Sensor Network (UASN) is based on the sensory elements that detect the environmental conditions of submerged regions and transmit the information through acoustic waves at the sink node placed on top of the water. All these sensory elements are battery-operated just like the Terrestrial Wireless Sensor Network (TWSN). However, these nodes are not stationary, unlike the nodes in TWSN. Thus, routing with limited energy and ever-changing topology decreases the reliability of the network and increases the chances of Void existence. This research paper focuses on the comparative analysis by critically reviewing some of the state-of-the-art routing protocols which are based on the effective usability of the limited energy resource, reliability of the network, and Void occurrence issues. An analytical method is employed to examine the strengths and shortcomings of existing routing protocols, with a specific emphasis on the identified difficulties. It provides a solid foundation related to UASN routing protocols which are primarily designed with a focus on resolving issues such as limited energy, low reliability, and high chances of void occurrence. The contribution of this comparative study helps the research partners from the academy and industry which are new to this field of study and would like to contribute. © 2024 IEEE.

关键词： Sensor nodes

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A Comparative Study of Ensemble Learning Models for Accurate Solar Irradiance Forecasting 8

A Comparative Study of Ensemble Learning Models for Accurate...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Chaudhari, Tejaram Joshi, Aditya Bharti, Devkumar Chavan, Pratibha Deshmukh, M.K. Bhalgat, Pritam Trinity College of Engineering & Research Department of Electronics & Telecommunication Engineering Pune India Birla Institute of Technology & Science Pilani Department of Electrical & Electronics Engineering Goa India SCTR's Pune Institute of Computer Department of Electronics & Telecommunication Engineering Pune India

ISBN: (纸本)9798331505462

Accurate estimation of solar radiation is very important in energy planning and the integration of renewable energy infrastructures into power grids for most places where the data may be sparse. This paper investigates advanced machine learning technologies specifically, ensemble methods such as XGBoost, LightGBM, CatBoost, NGBoost, AdaBoost, Gradient Boosting, Random Forest, and SGD Boost - to improve the prediction of solar radiation. It shows the performance of the above-mentioned models to forecast solar irradiance from meteorological factors using data from the Hawaii Space Exploration Analog and Simulation (HI-SEAS) weather station. It has been shown that ensemble learning boosts the forecast accuracy considerably, especially for ensemble learners optimized for categorical data and probabilistic predictions. This shows how machine learning can be potential and support the global shift to sustainable energy with reliable forecasts of solar radiation in data-scarce regions and offering valuable insights for optimizing renewable energy deployment. © 2024 IEEE.

关键词： Solar irradiance

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'Prodata': A Python Library for Simplifying Manual Data Preprocessing 1

'Prodata': A Python Library for Simplifying Manual Data Prep...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Nande, Poorva Kelkar, Supriya MKSSS's Cummins College of Engineering for Women Department of Electronics and Telecommunication Pune India MKSSS's Cummins College of Engineering for Women Department of Computer Engineering Pune India

ISBN: (纸本)9798350355611

Data preprocessing is a crucial phase in the data science and machine learning pipeline, often demanding significant time and expertise. This step is vital for enhancing data quality by eliminating noise and inconsistencies, which in turn improves the performance and reliability of models. By streamlining the dataset, effective preprocessing facilitates more efficient analysis. In this paper, the authors discuss 'prodata', a Python library specifically developed and designed to automate and simplify common data preprocessing tasks. 'prodata' includes features for missing data imputation, outlier treatment, categorical data encoding, and data visualization. The results demonstrate that the execution time for the 'prodata' functions averages just 0.9133 seconds, significantly less than the time required for manual coding. The 'prodata' library remarkably reduces the average number of code lines needed for each preprocessing step to just one as compared to manual preprocessing which requires at least six lines of code per step. By offering a user-friendly interface, 'prodata' abstracts the complexities of data preprocessing, making it accessible to both novice and experienced data scientists alike. This work highlights the potential of 'prodata' to enhance the efficiency of data preprocessing. © 2025 IEEE.

关键词： Codes (symbols)

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Machine Learning Techniques for Brain Tumor Classification: A CNN-SVM Approach

Machine Learning Techniques for Brain Tumor Classification: ...

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2024 International Conference on Innovations in Science, engineering and Technology, ICISET 2024

作者： Hayat, Tanvir Islam, Md. Saiful Hossain, Mosrur Hasan, Nowshad Parvez, Masud Hoque, Md. Jiabul Chittagong University of Engineering and Technology Department of Electronic and Telecommunication Engineering Chittagong Bangladesh International Islamic University Chittagong Department of Computer and Communication Engineering Chittagong Bangladesh

ISBN: (纸本)9798350355499

Brain tumors are the tenth most common type of tumor affecting people of all ages and are a leading cause of death in humans. However, early detection significantly enhances treatability. Classification of brain tumors typically relies on biopsy, a procedure often deferred until definitive brain surgery. Developing image classification techniques for tumor disorders is crucial to minimize errors in manual diagnoses by radiologists and to accelerate treatment. Advancements in machine learning (ML) offer a promising approach for assisting radiologists in diagnosing tumors using non-invasive magnetic resonance imaging (MRI). This research introduces a network based on feature extraction, where features are derived using a Convolutional Neural Network (CNN) and subsequently classified with a Support Vector Machine (SVM). The process involves transforming the multidimensional feature maps into a 2 D array where each row represents the features of a single image. The proposed method was evaluated using MRI brain images of three types of tumors: pituitary, meningioma, and glioma. The CNN-SVM method attained an accuracy of 98.75%, surpassing the CNN-RF (Random Forest) method, which achieved an accuracy of 96.25%. This result surpasses the performance of many other models. The effectiveness of the proposed approaches is analysed based on different metrics and outcomes compared to various method. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Review on Predictive Model for Forecast of Renewable Energy Output: A Case Study of National Orthopedic Hospital, Igbobi 5

Review on Predictive Model for Forecast of Renewable Energy ...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Olusanya, Olamide O. Olaiya, Olarinde Hakeem Olugbenga, Folorunso A. Bells University of Technology Department of Computer Engineering Ota Nigeria Bells University of Technology Department of Electrical/Electronics and Telecommunication Engineering Ota Nigeria

ISBN: (纸本)9798331542559

Planning and managing renewable energy systems effectively depends on accurate predictions of the energy sources' production. The National Orthopedic Hospital in Igbobi is the subject of a particular case study in this review paper, which focuses on the creation and use of predictive models for predicting the output of renewable energy. The objective is to provide an overview of the existing literature on predictive models and assess their effectiveness in forecasting renewable energy generation. The review begins by highlighting the importance of accurate renewable energy forecasts and their implications for energy management. It then explores the methodologies used in developing predictive models, including statistical techniques, machine learning algorithms, and hybrid models. Various factors influencing renewable energy output forecasting, such as weather conditions, system characteristics, and historical data availability, are discussed. Predictive models for renewable energy production forecasting in comparable healthcare contexts are reviewed, along with case studies and other relevant literature. It examines how well various modeling techniques - such as support vector regression (SVR), autoregressive integrated moving averages (ARIMA), and artificial neural networks (ANN) - capture the intricacies of the production of renewable energy. It discusses the significance of model validation and verification techniques to ensure reliable and robust forecasting results. The findings of this review contribute to the understanding of predictive models for renewable energy output forecasting in healthcare facilities. It highlights the potential benefits of accurate forecasts, such as improved energy planning, enhanced renewable energy utilization, and cost reduction. In addition, the assessment highlights areas for future research and development needs in the creation and use of predictive models for renewable energy forecasting in comparable settings. © 2024 IEEE.

关键词： Power management (telecommunication)

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An efficient extraction of the maximum power in a photovoltaic system based on enhanced P&O-Fuzzy logic controller

An efficient extraction of the maximum power in a photovolta...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Elzein, I.M. Kurdi, M. Department of Telecommunication and Networking Engineering University of Doha for Science and Technology Doha Qatar Department of Computer Science City University Lebanon

ISBN: (纸本)9781032803722

The concept of solar cell is changing the world in obtaining a clean energy that is an environment user friendly source of energy. Solar arrays process and convert the irradiance which is representing the quantity of light that comes from sun to a usable energy. The maximum power point which is denoted by Maximum power (Pmax) can be efficiently extracted from solar panels through the implementation of a tracking system denoted by the maximum power point tracking algorithm (MPPT). Such a tracking system is well known and utilized in detecting the ultimate maximum power that can be taken from the PV solar arrays under the circumstances of the various changes in both the temperature and irradiance. Through this analysis study, the Perturb and Observe (P&O) algorithm will be proposed to adjust the perturbation steps that will be controlled by an enhanced fuzzy logic controller. It will utilize the fuzzy concepts to extract the maximum power. In this research paper an analysis will be conducted to find the unknown parameters of the PV module along in selecting the appropriate set of fuzzy rules/fuzzy table as well as introducing a high gain dc-dc boost converter to improve the efficiency of power, response time, and reducing the oscillation around the Pmax in the proposed photovoltaic system. © 2025 The Author(s).

关键词： Solar irradiance

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