Sami Azam

Bio: Sami Azam is an academic researcher from Charles Darwin University. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 12, co-authored 69 publications receiving 469 citations. Previous affiliations of Sami Azam include University College of Engineering.

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

Abstract: Cardiovascular diseases (CVD) are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).

A Comprehensive Survey for Intelligent Spam Email Detection

Abstract: The tremendously growing problem of phishing e-mail, also known as spam including spear phishing or spam borne malware, has demanded a need for reliable intelligent anti-spam e-mail filters. This survey paper describes a focused literature survey of Artificial Intelligence (AI) and Machine Learning (ML) methods for intelligent spam email detection, which we believe can help in developing appropriate countermeasures. In this paper, we considered 4 parts in the email's structure that can be used for intelligent analysis: (A) Headers Provide Routing Information, contain mail transfer agents (MTA) that provide information like email and IP address of each sender and recipient of where the email originated and what stopovers, and final destination. (B) The SMTP Envelope, containing mail exchangers' identification, originating source and destination domains\users. (C) First part of SMTP Data, containing information like from, to, date, subject - appearing in most email clients (D) Second part of SMTP Data, containing email body including text content, and attachment. Based on the number the relevance of an emerging intelligent method, papers representing each method were identified, read, and summarized. Insightful findings, challenges and research problems are disclosed in this paper. This comprehensive survey paves the way for future research endeavors addressing theoretical and empirical aspects related to intelligent spam email detection.

Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems

Abstract: With the popularity of Internet of Things (IoT) technology, the security of the IoT network has become an important issue. Traditional intrusion detection systems have their limitations when applied to the IoT network due to resource constraints and the complexity. This research focusses on the design, implementation and testing of an intrusion detection system which uses a hybrid placement strategy based on a multi-agent system, blockchain and deep learning algorithms. The system consists of the following modules: data collection, data management, analysis, and response. The National security lab–knowledge discovery and data mining NSL-KDD dataset is used to test the system. The results demonstrate the efficiency of deep learning algorithms when detecting attacks from the transport layer. The experiment indicates that deep learning algorithms are suitable for intrusion detection in IoT network environment.

Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

Abstract: Breast cancer is the most diagnosed cancer in Australia with crude incidence rates increasing drastically from 62.8 at ages 35–39 to 271.4 at ages 50–54 (cases per 100,000 women). Various researchers have proposed methods and tools based on Machine Learning and Convolutional Neural Networks for assessing mammographic images, but these methods have produced detection and interpretation errors resulting in false-positive and false-negative cases when used in the real world. We believe that this problem can potentially be resolved by implementing effective image pre-processing techniques to create training data for Deep-CNN. Therefore, the main aim of this research is to propose effective image pre-processing methods to create datasets that can save computational time for the neural network and improve accuracy and classification rates. To do so, this research proposes methods for background removal, pectoral muscle removal, adding noise to the images, and image enhancements. Adding noise without affecting the quality of details in the images makes the input images for the neural network more representative, which may improve the performance of the neural network model when used in the real world. The proposed method for background removal is the “Rolling Ball Algorithm” and “Huang’s Fuzzy Thresholding”, which succeed in removing background from 100% of the images. For pectoral muscle removal “Canny Edge Detection” and “Hough’s Line Transform” are used, which removed muscle from 99.06% of the images. “Invert”, “CTI_RAS” and “ISOCONTOUR” lookup tables (LUTs) were used for image enhancements to outline the ROIs and regions within the ROIs.

Machine Learning Based PV Power Generation Forecasting in Alice Springs

Abstract: The generation volatility of photovoltaics (PVs) has created several control and operation challenges for grid operators. For a secure and reliable day or hour-ahead electricity dispatch, the grid operators need the visibility of their synchronous and asynchronous generators’ capacity. It helps them to manage the spinning reserve, inertia and frequency response during any contingency events. This study attempts to provide a machine learning-based PV power generation forecasting for both the short and long-term. The study has chosen Alice Springs, one of the geographically solar energy-rich areas in Australia, and considered various environmental parameters. Different machine learning algorithms, including Linear Regression, Polynomial Regression, Decision Tree Regression, Support Vector Regression, Random Forest Regression, Long Short-Term Memory, and Multilayer Perceptron Regression, are considered in the study. Various comparative performance analysis is conducted for both normal and uncertain cases and found that Random Forest Regression performed better for our dataset. The impact of data normalization on forecasting performance is also analyzed using multiple performance metrics. The study may help the grid operators to choose an appropriate PV power forecasting algorithm and plan the time-ahead generation volatility.

起業と経済成長 : global entrepreneurship monitor調査報告

Abstract: 第１章 ＧＥＭ調査の概要（分析の枠組み；調査方法；起業活動の定義；起業活動率；起業活動と経済成長；起業の計画と失敗） 第２章 起業家と事業特性（起業家の背景；起業家の能力；事業特性；起業家教育） 第３章 起業の環境（社会的資源；起業家に対する評価；経済危機の影響；起業活動の投資環境） 第４章 専門家調査（資金調達；政府の方針；支援プログラム；教育システム；技術移転；コマーシャル・サービス；起業文化；事業機会；経営能力；起業家に対する評価；女性への支援；急成長への注目；イノベーションへの関心；調査結果） 第５章 政策への提

A systematic literature review on entrepreneurial intentions: citation, thematic analyses, and research agenda

Abstract: Entrepreneurial intention is a rapidly evolving field of research. A growing number of studies use entrepreneurial intention as a powerful theoretical framework. However, a substantial part of this research lacks systematization and categorization, and there seems to be a tendency to start anew with every study. Therefore, there is a need to take stock of current knowledge in this field. In this sense, this paper carries out a review of the literature on entrepreneurial intentions. A total of 409 papers addressing entrepreneurial intention, published between 2004 and 2013 (inclusive), have been analyzed. The purpose and contribution of this paper is to offer a clearer picture of the sub-fields in entrepreneurial intention research, by concentrating on two aspects. Firstly, it reviews recent research by means of a citation analysis to categorize the main areas of specialization currently attracting the attention of the academic community. Secondly, a thematic analysis is carried out to identify the specific themes being researched within each category. Despite the large number of publications and their diversity, the present study identifies five main research areas, plus an additional sixth category for a number of new research papers that cannot be easily classified into the five areas. Within those categories, up to twenty-five different themes are recognized. A number of research gaps are singled out within each of these areas of specialization, in order to induce new ways and perspectives in the entrepreneurial intention field of research that may be fruitful in filling these gaps.

Bioelectrical Signal Processing In Cardiac And Neurological Applications

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