Transactions on Machine Learning and Artificial Intelligence

Skin Cancer Detection Using Support Vector Machine Learning Classification based on Particle Swarm Optimization Capabilities

2020-08-08T18:16:50+01:00

Skin cancer continues to be a common malignancy that has steadily increased each year. The need for early detection of such skin lesions is critical to preventing further medical complications. The main method for detection of skin cancer is by microscopic examination of skin lesions. Great efforts have been placed to use computer aided technologies for the analysis of skin lesions. In this study, we present a method for an algorithm design using Support Vector Machine (SVM) learning classification based on Particle swarm optimization (PSO) principles in order to improve the accuracy of skin lesion image analysis and classification for further diagnosis. Hospital Pedro Hispano (PH²) dataset with 200 images is used for this study. The method presented here incorporates 46 texture features in order to complete comprehensive image analytics and classification. The proposed method demonstrates an opportunity to explore best possible criteria in image analytics for clinical decision support.

Extracting Value from Unstructured Data – Implementing Text Analytics on the Voice of Student

2020-08-08T18:16:48+01:00

Unstructured data is chaotic and messy with little or no metadata and lacks of traditional organization structure. However, same as any structured data, unstructured data is also part of valuable business asset. Many times, it is text heavy and needs extensive preprocessing before data mining algorithm can apply for building models in order to reveal value hidden in the data. Text as a form of data is widely used in business operations as a major way of communication, generating increasing volumes of data. Text data in its raw form is relatively dirty. The embedded business value can be extracted through approaches in text mining and text analytics. This paper presents a case study in this general process of revealing value in unstructured data and applying on data collected to support online learning and student assistance.

A Block Diagram of Electromagnetoelastic Actuator for Control Systems in Nanoscience and Nanotechnology

2020-08-08T18:16:41+01:00

The block diagram and the transfer functions of the electromagnetoelastic actuator are received for control systems in nanoscience and nanotechnology. The block diagram of the electromagnetoelastic actuator is reflected the transformation of electrical energy into mechanical energy, in contrast to Cady’s and Mason’s electrical equivalent circuits of piezotransducer. The electromagnetoelasticity equation and the second order linear ordinary differential equation with boundary conditions are solved for calculations the block diagram of the electromagnetoelastic actuator. The block diagram of the piezoactuator is obtained with using the reverse and direct piezoelectric effects. The back electromotive force is determined from the direct piezoelectric effect equation. The transfer functions of the piezoactuators are obtained for control systems in nanoscience and nanotechnology.

Survey on Object Tracking using Deep Learning Paradigms

2020-08-08T18:16:45+01:00

The field of object tracking extends across different domains. It is a major key player in the field of image processing and pattern recognition. Object tracking is the process of tracking an object over a continuous sequence of image frames to determine over time the relative movements or changes. With the massive advancements in the field of deep learning, the use of deep neural networks has risen due to their impressive accomplishments in object detection and tracking. In this Survey, the objective is to give a comprehensive overview of the recent attempts in the field of object tracking with a focus on the use of deep learning techniques and algorithms. The paper is divided into four sections; at first, we will give an overview of the recent work to highlight the techniques and methods which have been used in object tracking using deep learning. The second section focus is on the object tracking that uses convolutional networks techniques. The third section focuses on some of the recurrent neural networks to tack objects. The final section is concentrated on auto-encoders object tracking.

Reviewing Sentiment Analysis at the Shallow End

2020-08-08T18:16:52+01:00

The social media space has evolved into a large labyrinth of information exchange platform and due to the growth in the adoption of different social media platforms, there has been an increasing wave of interests in sentiment analysis as a paradigm for the mining and analysis of users’ opinions and sentiments based on their posts. In this paper, we present a review of contextual sentiment analysis on social media entries with a specific focus on Twitter. The sentimental analysis consists of two broad approaches which are machine learning which uses classification techniques to classify text and is further categorized into supervised learning and unsupervised learning; and the lexicon-based approach which uses a dictionary without using any test or training data set, unlike the machine learning approach.

A Survey of Challenges Facing Streaming Data

2020-08-08T18:16:43+01:00

This survey performs a thorough enumeration and analysis of existing methods for data stream processing. It is a survey of the challenges facing streaming data. The challenges addressed are preprocessing of streaming data, detection and dealing with concept drifts in streaming data, data reduction in the face of data streams, approximate queries and blocking operations in streaming data.