Coronary Artery Disease (CAD) takes place in the category of fatal diseases resulting in death in our country and around the world. Each year about 340 thousand patients lost their lives due to CAD in Turkey. Early diagnosis is essential to reduce risk and prolong lifetime of these patients for diseases that require long-term treatment having death risk like CAD. For this reason, classification of CAD by using medical data processing and machine learning algorithms are important in order to develop assistive or expert systems for physicians. In this study, five different machine learning algorithms were applied to estimate whether patients in the Z-Alizadeh Sani data set extracted from the UCI machine learning pool are CAD. Accuracy, precision, recall, specificity and F1 score were compared as classification performance indicators to evaluate decision tree, random forest (RF), support vector machines (SVM), nearest neighborhood (k-NN) and multi-layer sensor (MLP) methods. According to the evaluation results, the MLP method gave high classification accuracy with 90%. It also appears that RF performs relatively better than other metrics. This results, show that these classification algorithms can be use for helping healthcare systems.

The concept of Big Data become extensively popular for their vast usage in emerging technologies. Despite being complex and dynamic, big data environment has been generating the colossal amount of data which is impossible to handle from traditional data processing applications. Nowadays, the Internet of things (IoT) and social media platforms like, Facebook, Instagram, Twitter, WhatsApp, LinkedIn, and YouTube generating data in various formats. Therefore, this promotes a drastic need for technology to store and process this tremendous volume of data. This research outlines the fundamental literature required to understand the concept of big data including its nature, definitions, types, and characteristics. Additionally, the primary focus of the current study is to deal with two fundamental issues; storing an enormous amount of data and fast data processing. Leading to objectives, the paper presents Hadoop as a solution to address the problem and discussed the Hadoop Distributed File System (HDFS) and MapReduce programming framework for storage and processing in Big Data efficiently. Future research directions in this field determined based on opportunities and several emerging issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal solutions to address Big Data storage and processing problems. Moreover, this study contributes to the existing body of knowledge by comprehensively addressing the opportunities and emerging issues of Big Data.

Many students are faced with the challenge of deciding on a suitable career path. This is because decisions are characterized by a number of subjective judgements. Therefore, choosing a particular career path without first determining the suitability of a student, as a fundamental step, will yield an undesirable outcome. This paper aims at developing a career path decision making model for senior secondary schools. The concept of fuzzy logic was used in developing the model. Crisp sets are converted to fuzzy sets using fuzzy K- nearest neighbour algorithm method. The model was implemented in the MATLAB environment. The performance of the model was evaluated using specificity and accuracy as performance metrics. The results obtained showed the model has accuracy value of 90.22%. This result show that the model is approximately 90% accurate. Also, it has a specificity value of 96.97%. These results show that the model provides a good support for decision making while eliminating the challenges of indecision and floundering that are characterized with choosing a career path among upper basic school students, that is, Junior Secondary School students. The model will also serve as a tool in enhancing the work of career experts.

Developing nations like India have a huge potential for agricultural business and better cultivation. Because of the large size of cultivation land, improper water supply systems and lack of technology-based agricultural practices, there is a huge gap among expected and actual quantity and quality of agricultural products. Hence there is a need for significant revival in agribusiness using emerging technologies. The article proposes an intelligent water framework device called Agribot designed for the agricultural industry to minimize the water wastage and a better supply of cultivating materials using the Internet of Things (IoT). Our proposed IOT- Agribot will energize the water framework, improve the cost-effective water usage and reduce the labor force to achieve precision agriculture. The proposed IOT- Agribot has performed well for variable weather conditions, soli type, moisture content and crops.

Key Process Areas (KPAs) for ‎Software Engineering Institute (SEI) Maturity ‎Level 4 can be described in terms of Quantitative ‎Process Management (QPM) which is the metric ‎to control the quantitative performance of a ‎software project. On the other hand, Software ‎Quality Management (SQM) monitors and ‎controls the quality of the project. The survey ‎conducted in this paper covers around 20 ‎software houses of Pakistan. The study revealed ‎that there is weakness in both KPAs, SQM and ‎QPM. Each KPA defines a set of rules that are necessary to be followed to meet the standard but many organizations fail to follow these rules defined in every KPA. If specified and ‎appropriate measures are taken, the software ‎industry will lift it up to the higher CMMI Level.‎