Accurate detection and classification of breast cancer is a critical task in medical imaging due to the complexity of breast tissues Due to automatic feature extraction ability, deep learning methods have been successfully applied in different areas, especially in the field of medical imaging In this study, a novel patch-based deep learning method called Pa-DBN-BC is proposed to detect and classify breast cancer on histopathology images using the Deep Belief Network (DBN) Features are extracted through an unsupervised pre-training and supervised fine-tuning phase The network automatically extracts features from image patches Logistic regression is used to classify the patches from histopathology images The features extracted from the patches are fed to the model as input and the model presents the result as a probability matrix as either a positive sample (cancer) or a negative sample (background) The proposed model is trained and tested on the whole slide histopathology image dataset having images from four different data cohorts and achieved an accuracy of 86% Consequently, the proposed method is better than the traditional ones, as it automatically learns the best possible features and experimental results show that the model outperformed the previously proposed deep learning methods

/pdf/breast-cancer-classification-from-histopathological-images-2zdh4ltv4x.pdf

Breast Cancer Classification From Histopathological Images Using Patch-Based Deep Learning Modeling

Opening the book as a scene-setting chapter, this chapter covers introduction and background as well as the aim, structure and content, and organization and design purposes of the book. The main topics, concepts and theories, research issues, knowledge gaps, opportunities, and prospects pointing to a need for elaboration or investigation in relevance to the focus and scope of the book are introduced in this chapter and then will be developed further or addressed and discussed in more details in the subsequent chapters as part of the systematic exploration of the field of smart sustainable/sustainable smart urbanism and the examination of the unprecedented paradigmatic shifts and practical advances it is undergoing in light of big data science and analytics and the underlying advanced technologies.

Big Data Science and Analytics for Smart Sustainable Urbanism: Unprecedented Paradigmatic Shifts and Practical Advancements

Heart failure comes in the top causes of death worldwide. The number of deaths from heart failure exceeds the number of deaths resulting from any other causes. Recent studies have focused on the use of machine learning techniques to develop predictive models that are able to predict the incidence of heart failure. The majority of these studies have used a binary output class, in which the prediction would be either the presence or absence of heart failure. In this study, a multi-level risk assessment of developing heart failure has been proposed, in which a five risk levels of heart failure can be predicted using C4.5 decision tree classifier. On the other hand, we are boosting the early prediction of heart failure through involving three main risk factors with the heart failure data set. Our predictive model shows an improvement on existing studies with 86.5% sensitivity, 95.5% specificity, and 86.53% accuracy.

/pdf/predicting-the-likelihood-of-heart-failure-with-a-multi-31l1ru255b.pdf

Predicting the likelihood of heart failure with a multi level risk assessment using decision tree

With the ever-increasing diffusion of smart devices and Internet of Things (IoT) applications, a completely new set of challenges have been added to the Data Mining domain. Edge Mining and Cloud Mining refer to Data Mining tasks aimed at IoT scenarios and performed according to, respectively, Cloud or Edge computing principles. Given the orthogonality and interdependence among the Data Mining task goals (e.g., accuracy, support, precision), the requirements of IoT applications (mainly bandwidth, energy saving, responsiveness, privacy preserving, and security) and the features of Edge/Cloud deployments (de-centralization, reliability, and ease of management), we propose EdgeMiningSim, a simulation-driven methodology inspired by software engineering principles for enabling IoT Data Mining. Such a methodology drives the domain experts in disclosing actionable knowledge, namely descriptive or predictive models for taking effective actions in the constrained and dynamic IoT scenario. A Smart Monitoring application is instantiated as a case study, aiming to exemplify the EdgeMiningSim approach and to show its benefits in effectively facing all those multifaceted aspects that simultaneously impact on IoT Data Mining.

A Simulation-driven Methodology for IoT Data Mining Based on Edge Computing

Investigating the antecedents to the adoption of SCRM technologies by start-up companies

Tendency for data mining application in healthcare today is great, because healthcare sector is rich with information, and data mining is becoming a necessity. Healthcare organizations produce and collect large volumes of information on daily basis. Use of information technologies allows automatization of processes for extraction of data that help to get interesting knowledge and regularities, which means the elimination of manual tasks and easier extraction of data directly from electronic records, transferring onto secure electronic system of medical records which will save lives and reduce the cost of the healthcare services, as well and early discovery of contagious diseases with the advanced collection of data. Data mining can enable healthcare organizations to predict trends in the patient conditions and their behaviors, which is accomplished by data analysis from different perspectives and discovering connections and relations from seemingly unrelated information. Raw data from healthcare organizations are voluminous and heterogeneous. They need to be collected and stored in the organized forms, and their integration enables forming of hospital information system. Healthcare data mining provides countless possibilities for hidden pattern investigation from these data sets. These patterns can be used by physicians to determine diagnoses, prognoses and treatments for patients in healthcare organizations.

http://www.arabianjbmr.com/pdfs/KD_VOL_1_12/7.pdf

Prediction and Decision Making in Health Care using Data Mining

MiloviC, B. and v. RadojeviC, 2015. application of data mining in agriculture. Bulg. J. Agric. Sci., 21: 26-34 Today, agricultural organizations work with large amounts of data. Processing and retrieval of significant data in this abundance of agricultural information is necessary. Utilization of information and communications technology enables automation of extracting significant data in an effort to obtain knowledge and trends, which enables the elimination of manual tasks and easier data extraction directly from electronic sources, transfer to secure electronic system of documentation which will enable production cost reduction, higher yield and higher market price. Data mining in addition to information about crops enables agricultural enterprises to predict trends about customer’s conditions or their behavior, which is achieved by analyzing data from different perspectives and finding connections and relationships in seemingly unrelated data. Raw data of agricultural enterprises are very ample and diverse. it is necessary to collect and store them in an organized form, and their integration enables the creation of agricultural information system. Data mining in agriculture provides many opportunities for exploring hidden patterns in these collections of data. These patterns can be used to determine the condition of customers in agricultural organizations.

https://journal.agrojournal.org/page/download.php?articleID=350

Application of data mining in agriculture.

Development of information and communication technology (ICT) has enabled the development of electronic customer relations (eCRM) as a business strategy that uses web technologies for developing the capacity and enabling hotels to engage guests in the strong, personalized and mutually beneficial interactive relationship, and thus increase their profitability and sales efficiency. Internet development has enabled the rapid growth of hotel industry. Today, hotel managers are completely aware of benefits that Internet as a promotion tool offers to their business, and accordingly, they are motivated to create greater awareness of their hotels with the goal of increasing number of guests and improving hotel reputation. This article describes how eCRM strategy is used by hotel managers to promote the hotel, more specifically, how much impact websites, social media and e-mail marketing campaigns have on hotel promotion and business performance.

Social Media and eCRM as a Prerequisite for Hotel Success

Customer Relationship Management is a business strategy that is based on the philosophy of 'customer is king', ie customer is put in the central place This philosophy, with the development of information technologies in past twenty years, has experienced a change Today it can be said that customer is a modern dictator, according to whom the company always adjusts its business due to the greater availability of information, stronger market competition, and growing negotiating skills of customers in sales process The most important task of organization is satisfying a client's needs, which ultimately means their long-term value to the organization This paper describes the application of CRM strategies in several industries Large and small organizations today evaluate the quality of customer services, customer loyalty, identify customers that are less sensitive to price changes and recognize that attracting new customers is more expensive than retaining the existing ones

Application of customer relationship management strategy (CRM) in different business areas

Business decisions supported by strategy of customer relationship management CRM are interesting for: retail networks, telecom operators, banks, insurance companies, travel agencies, but also for agricultural organizations. This paper highlights the importance of IT development of customer relationship management strategy. The development of information technology in agricultural organizations and easier and cheaper access to it, enables the development of electronic relationships with customers (e-CRM) which simplifies and automates the dialogue with customers and provides an answer to their requests in real time, which leads to increase of customer loyalty and satisfaction. This business strategy helps agricultural organizations to adapt their products and services for each client.

/pdf/application-of-customer-relationship-management-crm-in-3mqvmxjeiy.pdf

Boris Milović

Papers

Prediction and Decision Making in Health Care using Data Mining

Application of data mining in agriculture.

Social Media and eCRM as a Prerequisite for Hotel Success

Application of customer relationship management strategy (CRM) in different business areas

Application of customer relationship management (CRM) in agriculture