scholarly journals Diagnosis of Melanoma Using Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Malik Bader Alazzam ◽  
Fawaz Alassery ◽  
Ahmed Almulihi
Keyword(s):  
Nearest Neighbor ◽  
Skin Lesions ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Attribute Selection ◽  
Network Architectures ◽  
Kappa Index ◽  
Melanoma Diagnosis ◽  
Building Models ◽  
Nearest Neighbor Rule

When compared to other types of skin cancer, melanoma is the deadliest. However, those who are diagnosed early on have a better prognosis for the purpose of providing a supplementary opinion to experts; various methods of spontaneous melanoma recognition and diagnosis have been investigated by different researchers. Because of the imbalance between classes, building models from existing information has proven difficult. Machine learning algorithms paired with imbalanced basis training approaches are being evaluated for their performance on the melanoma diagnosis challenge in this study. There were 200 dermoscopic photos in which patterns of skin lesions could be extracted using the VGG16, VGG19, Inception, and ResNet convolutional neural network architectures with the ABCD rule. After employing attribute selection with GS and training data balance using Synthetic Minority Oversampling Technique and Edited Nearest Neighbor rule, the random forest classifier had a sensitivity of nearly 93% and a kappa index ( k − index ) of 78%.

scholarly journals Evaluation of Melanoma Diagnosis using Imbalanced Learning

2018 ◽  
Author(s):  
Lucas Bezerra Maia ◽  
Alan Carlos Lima ◽  
Pedro Thiago Cutrim Santos ◽  
Nigel da Silva Lima ◽  
João Dallyson Sousa De Almeida ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Imbalanced Data ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Data Sets ◽  
Imbalanced Learning ◽  
Training Models ◽  
Melanoma Diagnosis ◽  
Learning Techniques ◽  
Detection And Diagnosis

Melanoma is the most lethal type of skin cancer when compared to others, but patients have high recovery rates if the disease is discovered in its early stages. Several approaches to automatic detection and diagnosis have been explored by different authors. Training models with the existing data sets has been a difficult task due to the problem of imbalanced data. This work aims to evaluate the performance of machine learning algorithms combined with imbalanced learning techniques, regarding the task of melanoma diagnosis. Preliminary results have shown that features extracted with ResNet Convolutional Neural Network, along with Random Forest, achieved an improvement of sensibility of approximately 21%, after balancing the training data with Synthetic Minority Oversampling TEchnique (SMOTE) and Edited Nearest Neighbor (ENN) rule.

scholarly journals Android Malware Detection using Machine Learning

2020 ◽  
Vol 8 (2S12) ◽  
pp. 65-70
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
User Interest ◽  
Android Malware ◽  
Android Malware Detection

Machine Learning is empowering many aspects of day-to-day lives from filtering the content on social networks to suggestions of products that we may be looking for. This technology focuses on taking objects as image input to find new observations or show items based on user interest. The major discussion here is the Machine Learning techniques where we use supervised learning where the computer learns by the input data/training data and predict result based on experience. We also discuss the machine learning algorithms: Naïve Bayes Classifier, K-Nearest Neighbor, Random Forest, Decision Tress, Boosted Trees, Support Vector Machine, and use these classifiers on a dataset Malgenome and Drebin which are the Android Malware Dataset. Android is an operating system that is gaining popularity these days and with a rise in demand of these devices the rise in Android Malware. The traditional techniques methods which were used to detect malware was unable to detect unknown applications. We have run this dataset on different machine learning classifiers and have recorded the results. The experiment result provides a comparative analysis that is based on performance, accuracy, and cost.

scholarly journals Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network

Diagnostics ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 104 ◽  
Author(s):  
Ahmed ◽  
Yigit ◽  
Isik ◽  
Alpkocak
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Leukemia Data

Leukemia is a fatal cancer and has two main types: Acute and chronic. Each type has two more subtypes: Lymphoid and myeloid. Hence, in total, there are four subtypes of leukemia. This study proposes a new approach for diagnosis of all subtypes of leukemia from microscopic blood cell images using convolutional neural networks (CNN), which requires a large training data set. Therefore, we also investigated the effects of data augmentation for an increasing number of training samples synthetically. We used two publicly available leukemia data sources: ALL-IDB and ASH Image Bank. Next, we applied seven different image transformation techniques as data augmentation. We designed a CNN architecture capable of recognizing all subtypes of leukemia. Besides, we also explored other well-known machine learning algorithms such as naive Bayes, support vector machine, k-nearest neighbor, and decision tree. To evaluate our approach, we set up a set of experiments and used 5-fold cross-validation. The results we obtained from experiments showed that our CNN model performance has 88.25% and 81.74% accuracy, in leukemia versus healthy and multiclass classification of all subtypes, respectively. Finally, we also showed that the CNN model has a better performance than other wellknown machine learning algorithms.

scholarly journals Editing training data for multi-label classification with the k-nearest neighbor rule

2015 ◽  
Vol 19 (1) ◽  
pp. 145-161 ◽  
Author(s):  
Sawsan Kanj ◽  
Fahed Abdallah ◽  
Thierry Denœux ◽  
Kifah Tout
Keyword(s):  
Nearest Neighbor ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Rule

Semi-Supervised Point Prototype Clustering

1998 ◽  
Vol 12 (05) ◽  
pp. 625-643 ◽  
Author(s):  
Amine M. Bensaid ◽  
James C. Bezdek
Keyword(s):  
Nearest Neighbor ◽  
Structural Information ◽  
Clustering Algorithms ◽  
Unlabeled Data ◽  
Training Data ◽  
Clustering Methods ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Rule ◽  
Small Set ◽  
Supervise Classification

This paper describes a class of models we call semi-supervised clustering. Algorithms in this category are clustering methods that use information possessed by labeled training data Xd⊂ ℜp as well as structural information that resides in the unlabeled data Xu⊂ ℜp. The labels are used in conjunction with the unlabeled data to help clustering algorithms partition Xu ⊂ ℜp which then terminate without the capability to label other points in ℜp. This is very different from supervised learning, wherein the training data subsequently endow a classifier with the ability to label every point in ℜp. The methodology is applicable in domains such as image segmentation, where users may have a small set of labeled data, and can use it to semi-supervise classification of the remaining pixels in a single image. The model can be used with many different point prototype clustering algorithms. We illustrate how to attach it to a particular algorithm (fuzzy c-means). Then we give two numerical examples to show that it overcomes the failure of many point prototype clustering schemes when confronted with data that possess overlapping and/or non uniformly distributed clusters. Finally, the new method compares favorably to the fully supervised k nearest neighbor rule when applied to the Iris data.

scholarly journals Investigating the Effects of Training Set Synthesis for Audio Segmentation of Radio Broadcast

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 827
Author(s):  
Satvik Venkatesh ◽  
David Moffat ◽  
Eduardo Reck Miranda
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Network Architectures ◽  
Machine Learning Algorithm ◽  
Promising Alternative ◽  
Speech Detection ◽  
Radio Broadcast

Music and speech detection provides us valuable information regarding the nature of content in broadcast audio. It helps detect acoustic regions that contain speech, voice over music, only music, or silence. In recent years, there have been developments in machine learning algorithms to accomplish this task. However, broadcast audio is generally well-mixed and copyrighted, which makes it challenging to share across research groups. In this study, we address the challenges encountered in automatically synthesising data that resembles a radio broadcast. Firstly, we compare state-of-the-art neural network architectures such as CNN, GRU, LSTM, TCN, and CRNN. Later, we investigate how audio ducking of background music impacts the precision and recall of the machine learning algorithm. Thirdly, we examine how the quantity of synthetic training data impacts the results. Finally, we evaluate the effectiveness of synthesised, real-world, and combined approaches for training models, to understand if the synthetic data presents any additional value. Amongst the network architectures, CRNN was the best performing network. Results also show that the minimum level of audio ducking preferred by the machine learning algorithm was similar to that of human listeners. After testing our model on in-house and public datasets, we observe that our proposed synthesis technique outperforms real-world data in some cases and serves as a promising alternative.

Music Emotion Recognition

2021 ◽  
Author(s):  
tejaswini kambaiahgari ◽  
Uma Rao K
Keyword(s):  
Deep Learning ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Ann Model ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Present World

Abstract In the present world, there are many songs over the internet. But the information retrieval on these songs can be complicated. This paper intends to classify songs based on emotions using deep learning. We propose a strategy to recognize the emotion present in a song by classifying their spectrograms, which contains both time and frequency information. According to human psychology, neurons within a sub pop- ulation of our brain did not react the same way for all the emotions.So only specific neurons need to be triggered for identifying an emotion. Dif- ferent deep learning and machine learning algorithms are implemented to build music emotion recognizer. The main objective of this study is to study about the features which are important for audio file ,to de- velop a music emotion classifier using deep learning algorithm and also to validate the model.The datasets are split into training and testing sets, models are trained with training data set. The accuracy of Artifi- cial Neural Network (ANN) model is 79.7% ,K-Nearest Neighbor (KNN) model is 78.26% and logistic regression for gender classification is 81%.

scholarly journals A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models

2021 ◽  
Vol 15 (15) ◽  
pp. 4
Author(s):  
Ijaz Muhammad Khan ◽  
Abdul Rahim Ahmad ◽  
Nafaa Jabeur ◽  
Mohammed Najah Mahdi
Keyword(s):  
Machine Learning ◽  
Conceptual Framework ◽  
Student Performance ◽  
Learning Analytics ◽  
Prediction Models ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Attribute Selection ◽  
Building Models ◽  
Dynamic Attributes

One of the important key applications of learning analytics is offering an opportunity to the institutions to track the student’s academic activities and provide them with real-time adaptive consultations if the student academic performance diverts towards the inadequate outcome. Still, numerous barriers exist while developing and implementing such kind of learning analytics applications. Machine learning algorithms emerge as useful tools to endorse learning analytics by building models capable of forecasting the final outcome of students based on their available attributes. The machine learning algorithm’s performance demotes with using the entire attributes and thus a vigilant selection of predicting attributes boosts the performance of the produced model. Though, several constructive techniques facilitate to identify the subset of productive attributes, however, the challenging task is to evaluate if the prediction attributes are meaningful, explicit, and controllable by the students. This paper reviews the existing literature to come up with the student’s attributes used in developing prediction models. We propose a conceptual framework which demonstrates the classification of attributes as either latent or dynamic. The latent attributes may appear significant but the student is not able to control these attribute, on the other hand, the student has command to restrain the dynamic attributes. Each of the major class is further categorized to present an opportunity to the researchers to pick constructive attributes for model development.

Optimization of Diabetes Training DATA using Machine Learning Algorithms

2018 ◽  
Vol 6 (2) ◽  
pp. 283-286
Author(s):  
M. Samba Siva Rao ◽  
◽  
M.Yaswanth . ◽  
K. Raghavendra Swamy ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data

scholarly journals Feasibility of Using Floor Vibration to Detect Human Falls

2020 ◽  
Vol 18 (1) ◽  
pp. 200
Author(s):  
Yu Shao ◽  
Xinyue Wang ◽  
Wenjie Song ◽  
Sobia Ilyas ◽  
Haibo Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Body ◽  
Nearest Neighbor ◽  
Body Movement ◽  
Modern Society ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Study Results ◽  
Floor Vibration

With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.

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