This chapter presents a summary of the issues discussed during the one day workshop on “Support Vector Machines (SVM) Theory and Applications” organized as part of the Advanced Course on Artificial Intelligence (ACAI ’99) in Chania, Greece [19]. The goal of the chapter is twofold: to present an overview of the background theory and current understanding of SVM, and to discuss the papers presented as well as the issues that arose during the workshop.

Support vector machines: Theory and applications

Breast cancer detection using artificial intelligence techniques: A systematic literature review

At the moment, the most prevalent form of cancer diagnosed in women across the globe is breast cancer. It develops in the breast tissue and is one of the most frequent causes of women’s death. This cancer can be cured if it is diagnosed at preliminary stage. Malignant and benign are two types of tumor found in case of breast cancer. Malignant tumors are deadly as their rate of growth is much higher than benign tumors. So, early identification of tumor type is pivotal for the appropriate treatment of a patient having breast cancer. In this work, Wisconsin Breast Cancer Dataset has been used which was collected from UCI repository. Our goal is to analyze the dataset and evaluate the performance of various machine learning algorithms for predicting breast cancer. Here, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Decision Tree, Naive Bayes and Random Forest classifiers have been implemented for classifying tumors into benign and malignant. The accuracy of each algorithm is calculated and compared to find the most suitable one. Based on the analysis, Random Forest and Support Vector Machine outperform other classifiers with accuracy of 96.5%. These classifiers can be used to build an automatic diagnostic system for preliminary diagnosis of breast cancer.

Malignant and Benign Breast Cancer Classification using Machine Learning Algorithms

Machine Learning Classifiers on Breast Cancer Recurrences

Heart Disease is the leading causes of death and hospitalization in the world. With advancement of technology and the contribution of computer engineering, it is easy to detect heart disease and thus treatment is fast and effectively done. Machine learning nowadays is very popular in predicting disease in the medical field. In this paper, authors have tried to predict heart disease using seven machine learning algorithms and attempted to improve the accuracy of weak performing algorithms using ensemble methods like AdaBoost and voting ensemble method. The performance of Linear Discriminate Analysis is good among other algorithms, it's mean value is approximately 0.847 and mean absolute error is 0.185, the false acceptance rate is lowest among all i.e.; 0.33 and the false recognition rate is 0.076, accuracy is somehow coming 80% which is less if compared with Logistic Regression.

A Novel Approach for Prediction of Heart Disease using Machine Learning Algorithms

Breast cancer is type of tumor that occurs in the tissues of the breast. It is most common type of cancer found in women around the world and it is among the leading causes of deaths in women. This article presents the comparative analysis of machine learning, deep learning and data mining techniques being used for the prediction of breast cancer. Many researchers have put their efforts on breast cancer diagnoses and prognoses, every technique has different accuracy rate and it varies for different situations, tools and datasets being used. Our main focus is to comparatively analyze different existing Machine Learning and Data Mining techniques in order to find out the most appropriate method that will support the large dataset with good accuracy of prediction. The main purpose of this review is to highlight all the previous studies of machine learning algorithms that are being used for breast cancer prediction and this article provides the all necessary information to the beginners who want to analyze the machine learning algorithms to gain the base of deep learning.

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Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis

In this paper, a single-layer compound unit element is proposed for reflectarray antenna design operating in Ka-band (26.5-29.5GHz) at the center frequency of 28GHz. A systematic study on the performance of a compound unit element is examined first. The structure of the proposed unit element is a unique combination of two different shape simple patches i.e. cross dipole and square patches. The desired phase range is achieved due to the multi-resonance of both patch elements with a single layer without any air-gap. The compound unit element is simulated by computer models of CST Microwave studio based on the Floquet approach (infinite periodic approach) and it has achieved 348.589° reflection phase range. Furthermore, the analysis of the reflection phase range, S-curve gradient, reflection magnitude, fabrication tolerance, and surface current density is also simulated and demonstrated. Based on the remarkable performance, the proposed element can be considered as the best element of single-beam or multi-beam reflectarray antenna design for 5G applications.

Design and Analysis of Reflectarray Compound Unit Cell for 5G Communication

With the fast-growing research on radar technology, significant contributions have been made in the area of synthetic aperture radar (SAR) imaging. Somehow, the research on the fixed receiver bistatic SAR is still in its early stages; bistatic SAR provides a spatial division between the transmitter and receiver networks. With the addition of bistatic configurations to the SAR, its scope in terms of the applications being broadened. It has made a significant change in radar technology, especially the deformation monitoring and surveillance. However, there still exist some issues regarding image resolution and azimuth resolution. This paper suggests a bistatic synthetic aperture radar simulation, based on a fixed receiver. Our proposed model utilizes one receiving antenna 1Rx, and one transmitting antenna 1Tx. Both antennas were physically located widely from each other. The transmitting antenna is moving across its aperture while the receiving antenna is fixed. Our proposed model of bistatic SAR reduce the computational cost as well as it gives us improved resolution, and ground deformation monitoring as compared to the existing conventional monostatic SAR models. The modified version of the Range Doppler Algorithm (RDA) is used to conduct simulations.

Haroon Ahmed

Papers

Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis

Design and Analysis of Reflectarray Compound Unit Cell for 5G Communication

A Novel Bistatic-SAR Simulation-Based On Fixed Receiver