Quantum computing functions on qubits, different from the classical bits. These qubits follow the properties of quantum physics such as superposition, interference and entanglement. Our aim is to use this quantum tech...
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This paper introduces H-MaP, a hybrid sequential manipulation planner that addresses complex tasks requiring both sequential actions and dynamic contact mode switches. Our approach reduces configuration space dimensio...
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This paper presents a genetic assessment agent and a student and machine co-learning model for high-school students' computational intelligence (CI) experience. We invited the IEEE CIS High School Outreach (HSO) s...
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The Most multimedia files, especially those containing private information are images. Since multimedia transmission takes place on public communication channels, it is more vulnerable to a wide range of threats as th...
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This paper reports a new photoacoustic (PA) excitation method for evaluating the shear viscoelasticity of soft tissues. By illuminating the target surface with an annular pulsed laser beam, circularly converging surfa...
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This review examines the applications, challenges, and prospects of Faster Region-based Convolutional Neural Networks (Faster R-CNN) in healthcare and disease detection. Through a meta-analysis of Web of Science liter...
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Parkinson’s disease is a neurodegenerative disorder that poses a significant global health challenge. Its prevalence has prompted the urgent need for fast and immediate diagnosis to enable timely intervention and tre...
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ISBN:
(数字)9798331534356
ISBN:
(纸本)9798331534363
Parkinson’s disease is a neurodegenerative disorder that poses a significant global health challenge. Its prevalence has prompted the urgent need for fast and immediate diagnosis to enable timely intervention and treatment planning. Parkinson’s disease causes both motor and non-motor symptoms necessitating a comprehensive approach to prediction. This research explores the intricacies of Parkinson’s disease prediction, highlighting the importance of multi-dimensional data analysis. Acknowledging the evolving landscape of medical research, the study advocates for a comprehensive approach to enhance the early detection of Parkinson’s disease, provides significant insights on the possibilities of machine learning techniques for improved patient outcomes, particularly focusing on the distinctive features offered by MDVP (Multidimensional Voice Program). Including MDVP features enhances the predictive capabilities of the models, offering a novel perspective for accurate early detection and applying machine learning models like AdaBoost, Multilayer Perceptron, Decision Tree, Random Forest, Logistic Regression, K-nearest Neighbors, Support Vector Machine (SVM), Gradient Boosting, & Naïve Bayes. This research explores the intricacies of Parkinson’s disease prediction. Results highlight the remarkable performance of the Support Vector Machine, achieving an accuracy of 98.98% & Gradient Boosting, 97.28% accuracy with the feature selection technique XGBoost and SelectKBest underscoring the significance of MDVP features in refining the predictive accuracy. This study leads to continuous efforts for the improvement of diagnostic accuracy and prognosis for Parkinson’s disease, emphasizing the importance of integrating MDVP features within the machine learning framework.
Maternal health during pregnancy is a severe issue, particularly in the rural areas of developing countries like Bangladesh, where a lack of access to healthcare and inadequate infrastructure increase risks. Maternal ...
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ISBN:
(数字)9798331519094
ISBN:
(纸本)9798331519100
Maternal health during pregnancy is a severe issue, particularly in the rural areas of developing countries like Bangladesh, where a lack of access to healthcare and inadequate infrastructure increase risks. Maternal healthcare has a great deal of difficulty due to the absence of reliable tools for forecasting health concerns. Negative results frequently result from the traditional method's inability to diagnose and manage pregnancy- related problems correctly. While there are several ways to monitor maternal health conditions, machine learning has the potential to increase diagnosis accuracy, efficiency, and speed. In this research, by using several machine learning classifiers, we built a model that can analyze the maternal health risk during pregnancy. The Maternal Health Risk dataset from the UCI machine learning repository was used in this study. SMOTE was utilized to address the class imbalance data and generated an additional 99000 data. We assess the model before and after using SMOTE. Accuracy, precision, recall, and F1-Score were utilized to evaluate the model's performance. Extreme Gradient Boosting (XGBoost) is our standout performer, with an accuracy of 84% and 95% before and after using SMOTE, respectively. Additionally, Explainable AI was used to increase the model's readability. This study demonstrates the power of machine learning, which could revolutionize maternal health care by identifying maternal health risks early.
There are many reasons for cloud computing to be used in the market today. It is a great alternative to traditional computing, which helps businesses stay competitive. Cloud computing is a new way of operating, storin...
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The quality estimation of fruits and vegetables plays a vital role in the field of agriculture. This paper reviews the latest improvements in estimating the quality of fruits and vegetables as well as grading them usi...
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