scholarly journals The Development of a Skin Image Analysis Tool by Using Machine Learning Algorithms

Cosmetics ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 67
Author(s):  
Perry Xiao ◽  
Xu Zhang ◽  
Wei Pan ◽  
Xiang Ou ◽  
Christos Bontozoglou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Imaging Techniques ◽  
Learning Algorithms ◽  
Research Work ◽  
Software Tool ◽  
Machine Learning Algorithms ◽  
Analysis Tool ◽  
Skin Imaging ◽  
User Friendly

We present our latest research work on the development of a skin image analysis tool by using machine-learning algorithms. Skin imaging is very import in skin research. Over the years, we have used and developed different types of skin imaging techniques. As the number of skin images and the type of skin images increase, there is a need of a dedicated skin image analysis tool. In this paper, we report the development of such software tool by using the latest MATLAB App Designer. It is simple, user friendly and yet powerful. We intend to make it available on GitHub, so that others can benefit from the software. This is an ongoing project; we are reporting here what we have achieved so far, and more functions will be added to the software in the future.

scholarly journals An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Muhammad Waqar ◽  
Hassan Dawood ◽  
Hussain Dawood ◽  
Nadeem Majeed ◽  
Ameen Banjar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Heart Attack ◽  
High Reliability ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Unequal Distribution ◽  
The Given

Cardiac disease treatments are often being subjected to the acquisition and analysis of vast quantity of digital cardiac data. These data can be utilized for various beneficial purposes. These data’s utilization becomes more important when we are dealing with critical diseases like a heart attack where patient life is often at stake. Machine learning and deep learning are two famous techniques that are helping in making the raw data useful. Some of the biggest problems that arise from the usage of the aforementioned techniques are massive resource utilization, extensive data preprocessing, need for features engineering, and ensuring reliability in classification results. The proposed research work presents a cost-effective solution to predict heart attack with high accuracy and reliability. It uses a UCI dataset to predict the heart attack via various machine learning algorithms without the involvement of any feature engineering. Moreover, the given dataset has an unequal distribution of positive and negative classes which can reduce performance. The proposed work uses a synthetic minority oversampling technique (SMOTE) to handle given imbalance data. The proposed system discarded the need of feature engineering for the classification of the given dataset. This led to an efficient solution as feature engineering often proves to be a costly process. The results show that among all machine learning algorithms, SMOTE-based artificial neural network when tuned properly outperformed all other models and many existing systems. The high reliability of the proposed system ensures that it can be effectively used in the prediction of the heart attack.

scholarly journals Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Theyazn H.H Aldhyani ◽  
Ali Saleh Alshebami ◽  
Mohammed Y. Alzahrani
Keyword(s):  
Machine Learning ◽  
Chronic Disease ◽  
Chronic Diseases ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Upper Approximation ◽  
Healthcare Data ◽  
Ambiguous Objects

Chronic diseases represent a serious threat to public health across the world. It is estimated at about 60% of all deaths worldwide and approximately 43% of the global burden of chronic diseases. Thus, the analysis of the healthcare data has helped health officials, patients, and healthcare communities to perform early detection for those diseases. Extracting the patterns from healthcare data has helped the healthcare communities to obtain complete medical data for the purpose of diagnosis. The objective of the present research work is presented to improve the surveillance detection system for chronic diseases, which is used for the protection of people’s lives. For this purpose, the proposed system has been developed to enhance the detection of chronic disease by using machine learning algorithms. The standard data related to chronic diseases have been collected from various worldwide resources. In healthcare data, special chronic diseases include ambiguous objects of the class. Therefore, the presence of ambiguous objects shows the availability of traits involving two or more classes, which reduces the accuracy of the machine learning algorithms. The novelty of the current research work lies in the assumption that demonstrates the noncrisp Rough K-means (RKM) clustering for figuring out the ambiguity in chronic disease dataset to improve the performance of the system. The RKM algorithm has clustered data into two sets, namely, the upper approximation and lower approximation. The objects belonging to the upper approximation are favourable objects, whereas the ones belonging to the lower approximation are excluded and identified as ambiguous. These ambiguous objects have been excluded to improve the machine learning algorithms. The machine learning algorithms, namely, naïve Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNN), and random forest tree, are presented and compared. The chronic disease data are obtained from the machine learning repository and Kaggle to test and evaluate the proposed model. The experimental results demonstrate that the proposed system is successfully employed for the diagnosis of chronic diseases. The proposed model achieved the best results with naive Bayes with RKM for the classification of diabetic disease (80.55%), whereas SVM with RKM for the classification of kidney disease achieved 100% and SVM with RKM for the classification of cancer disease achieved 97.53 with respect to accuracy metric. The performance measures, such as accuracy, sensitivity, specificity, precision, and F-score, are employed to evaluate the performance of the proposed system. Furthermore, evaluation and comparison of the proposed system with the existing machine learning algorithms are presented. Finally, the proposed system has enhanced the performance of machine learning algorithms.

scholarly journals Predicting Alert Source Device using Machine Learning Algorithms

2020 ◽  
Vol 9 (9) ◽  
pp. 1-10
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Model Building ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Training Dataset ◽  
Imbalanced Dataset ◽  
Daunting Task

In a large distributed virtualized environment, predicting the alerting source from its text seems to be daunting task. This paper explores the option of using machine learning algorithm to solve this problem. Unfortunately, our training dataset is highly imbalanced. Where 96% of alerting data is reported by 24% of alerting sources. This is the expected dataset in any live distributed virtualized environment, where new version of device will have relatively less alert compared to older devices. Any classification effort with such imbalanced dataset present different set of challenges compared to binary classification. This type of skewed data distribution makes conventional machine learning less effective, especially while predicting the minority device type alerts. Our challenge is to build a robust model which can cope with this imbalanced dataset and achieves relative high level of prediction accuracy. This research work stared with traditional regression and classification algorithms using bag of words model. Then word2vec and doc2vec models are used to represent the words in vector formats, which preserve the sematic meaning of the sentence. With this alerting text with similar message will have same vector form representation. This vectorized alerting text is used with Logistic Regression for model building. This yields better accuracy, but the model is relatively complex and demand more computational resources. Finally, simple neural network is used for this multi-class text classification problem domain by using keras and tensorflow libraries. A simple two layered neural network yielded 99 % accuracy, even though our training dataset was not balanced. This paper goes through the qualitative evaluation of the different machine learning algorithms and their respective result. Finally, two layered deep learning algorithms is selected as final solution, since it takes relatively less resource and time with better accuracy values.

scholarly journals Prediction and Analysis of Student Performance in Secondary Education Based on Data Mining and Machine Learning Techniques

2020 ◽  
pp. 294-301
Author(s):  
Meenal Joshi ◽  
Shiv Kumar
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Secondary School ◽  
Student Performance ◽  
Learning Algorithms ◽  
Research Work ◽  
Educational Data Mining ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Student’S Performance

<p>According to modern era education is the key to achieve success in the future; it develops a human personality, thoughts, and social skills. The purpose of this research work is to focus on educational data mining (EDM) through machine learning algorithms. EDM means to discover hidden knowledge and pattern about student's performance. Machine learning can be useful to predict the learning outcomes of students. From last few years, several tools have been used to judge the student's performance from different points of view like the student's level, objectives, techniques, algorithms, and different methods. In this paper, predicting and analyzing student performance in secondary school is conducted using data mining techniques and machine learning algorithms such as Naive Bayes, Decision Tree algorithm J48, and Logistic Regression. For this the collection of dataset from "Secondary School" and then filtration is applying on desired values using WEKA, tool.</p>

Evaluation of Convolutional Neural Network Architecture for Feasibility Analysis on Fetal Abdomen and Brain Images

2021 ◽  
Vol 11 (10) ◽  
pp. 2573-2583
Author(s):  
P. Deepika ◽  
P. Pabitha
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Algorithms ◽  
False Negative ◽  
Research Work ◽  
Fetal Brain ◽  
Machine Learning Algorithms ◽  
Abdominal Circumference ◽  
Brain Images ◽  
Abdominal Image

This research aims to evaluate the possibilities of fetus ultrasound image classification using machine learning algorithms as normal or abnormal. Most of the earlier research works have produced a high percentage of false-negative classification results—recent research work aimed to reduce the rate of false-negative diagnoses. Also, the number of sonologists for analyzing prenatal ultrasound worldwide is very less and solved by developing an efficient algorithm, which reduces the percentage of false negatives in the diagnosis output. Several earlier research works focused on analyzing fetal abdominal image or fetal head images, making the medical industry use two different diagnostic modules separately. This work aims to design and implement a convolution frame-work named as two Convolution Neural Network (tCNN) model for diagnosing any fetal images. The proposed tCNN model diagnoses the fetal abdominal and fetal brain images and classify them as normal or abnormal. CNN1 of tCNN performs segmentation and classification based on the acceptance of abdomen circumference and stomach bubble, umbilical vein, and amniotic fluid measurements. CNN2 shows based on head circumference and head and abdominal circumference, femur, crown-rump, and humerus lengths measured.With clinical validation, an extensive experiment carried out and the results compared with the experts in terms of segmentation accuracy and the obstetric measurements. This paper provides a foundation for future multi-classification research works on diagnosing fetal intracranial abnormalities and differential diagnosis using machine learning algorithms.

scholarly journals Comparative Analysis of Supervised Machine Learning Algorithms to Build a Predictive Model for Evaluating Students’ Performance

2021 ◽  
Vol 17 (02) ◽  
pp. 90
Author(s):  
Inssaf El Guabassi ◽  
Zakaria Bousalem ◽  
Rim Marah ◽  
Aimad Qazdar
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Predictive Model ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
The Future ◽  
Log Linear

In recent years, the world's population is increasingly demanding to predict the future with certainty, predicting the right information in any area is becoming a necessity. One of the ways to predict the future with certainty is to determine the possible future. In this sense, machine learning is a way to analyze huge datasets to make strong predictions or decisions. The main objective of this research work is to build a predictive model for evaluating students’ performance. Hence, the contributions are threefold. The first is to apply several supervised machine learning algorithms (i.e. ANCOVA, Logistic Regression, Support Vector Regression, Log-linear Regression, Decision Tree Regression, Random Forest Regression, and Partial Least Squares Regression) on our education dataset. The second purpose is to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose is to determine the most important factors that influence the success or failure of the students. The experimental results showed that the Log-linear Regression provides a better prediction as well as the behavioral factors that influence students’ performance.

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