Automation of Marketing Processes and the Discovery of Knowledge for CRM

The general perspective of the chapter is focused on discovering marketing knowledge based on Customer Relationship Management (CRM) systems. The question is: “How to do automate processes in the implemented CRM system to discover the knowledge that is useful for marketing?” It is a natural question because the stored data creates a large volume and it is difficult to set up a marketing with hands. This chapter focuses on finding the necessary product specifications to automate the marketing needs this CRM system must offer to be optimal in today's modern global society. The existing controversy is between IT for everyday use, real IT capabilities, human skills, and knowledge to support more complex implemented processes. Emphasis is placed on automation and intelligence. The analysis shows that CRM systems are interested in managing customer relationships in the form of a single agent or process to perform the necessary actions using implemented algorithms that utilize various intelligence, statistical methods, multi-criteria decision-making, and automated learning predictions.

Development of Automated Angiogram Labeling via AI Active Learning Pipeline

Background and Objective: As technology is integrated further into medicine, more specialties are discovering new uses for it in their clinical practice. However, the tasks that we want technology to complete are often removed from developer’s intended tasks. A field of research is growing that integrates medicine with current AI technology to bridge the gap and utilize already existing technology for medical uses. We desire to use an active learning pipeline (a form of machine learning) to automate the labeling of blood vessels on angiograms and potentially develop the ability to detect occlusions. By using machine learning, it would essentially allow the machine to teach itself with human guidance. Methods: A machine learning pipeline is in development for automation of the process. To create a baseline for the machine to start learning, the first set of angiograms are being labeled by hand using the program 3D Slicer. For the first pass, we have been quickly labeling the blood vessels by changing the color sensitivity threshold to highlight the darker blood vessels juxtaposed next to lighter tissue. For the second pass, we have erased any erroneous highlighting that was picked up in the first pass such as tools, tissue, contrast outside the injection site, and sutures. For the third pass, we have labeled and segmented the arteries into specific vessels such as femoral, common iliac, internal iliac, etc. This will then be entered into the machine for automated learning. Results: We are in the process of labeling the initial image set. Potential Impact: By creating a lab for angiogram automation, it will allow physicians to efficiently search images for specific arteries and save valuable time usually spent searching images. This would also allow for automated labeling of occlusions that a physician could then look at to verify.

Validity of Automated Learning Progress Assessment in English Written Expression for Students with Learning Difficulties

We evaluated the validity of an automated approach to learning progress assessment (aLPA) for English written expression. Participants (n = 105) were students in Grades 2–12 who had parent-identified learning difficulties and received academic tutoring through a community-based organization. Participants completed narrative writing samples in the fall and spring of one academic year, and some participants (n = 33) also completed a standardized writing assessment in the spring of the academic year. The narrative writing samples were evaluated using aLPA, four hand-scored written expression curriculum-based measures (WE-CBM), and ratings of writing quality. Results indicated (a) aLPA and WE-CBM scores were highly correlated with ratings of writing quality; (b) aLPA and more complex WE-CBM scores demonstrated acceptable correlations with the standardized writing subtest assessing spelling and grammar, but not the subtest assessing substantive quality; and (c) aLPA scores showed small, statistically significant improvements from fall to spring. These findings provide preliminary evidence that aLPA can be used to efficiently score narrative writing samples for progress monitoring, with some evidence that the aLPA scores can serve as a general indicator of writing skill. The use of automated scoring in aLPA, with performance comparable to WE-CBM hand scoring, may improve scoring feasibility and increase the likelihood that educators implement aLPA for decision making.

Learning through automated processes: negative social influence among coworkers

Purpose This paper discusses how minor counterproductive workplace behavior (CWB) scripts can be acquired or learned through automated processes from one employee to another. Design/methodology/approach This research is based on insights from social information processing and automated processing. Findings This paper helps explain the automated learning of minor CWBs from one’s coworkers. Practical implications While some employees purposefully engage in counterproductive workplace behaviors with the intent to harm their organizations, other less overt and minor behaviors are not always carried out with harmful intent, but remain counterproductive, nonetheless. By understanding how the transfer of minor CWBs occurs, employers can strive to set policies and practices in place to help reduce these occurrences. Originality/value This paper discusses how negative workplace learning can occur. We hope to contribute to the workplace learning literature by highlighting how and why the spread of minor CWBs occurs amongst coworkers and spur future research focusing on appropriate interventions.

Online Learning of Action Models for PDDL Planning

The automated learning of action models is widely recognised as a key and compelling challenge to address the difficulties of the manual specification of planning domains. Most state-of-the-art methods perform this learning offline from an input set of plan traces generated by the execution of (successful) plans. However, how to generate informative plan traces for learning action models is still an open issue. Moreover, plan traces might not be available for a new environment. In this paper, we propose an algorithm for learning action models online, incrementally during the execution of plans. Such plans are generated to achieve goals that the algorithm decides online in order to obtain informative plan traces and reach states from which useful information can be learned. We show some fundamental theoretical properties of the algorithm, and we experimentally evaluate the online learning of the action models over a large set of IPC domains.

An automated learning model for sentiment analysis and data classification of Twitter data using balanced CA-SVM

The modern society runs over the social media for their most time of every day. The web users spend their most time in social media and they share many details with their friends. Such information obtained from their chat has been used in several applications. The sentiment analysis is the one which has been applied with Twitter data set toward identifying the emotion of any user and based on those different problems can be solved. Primarily, the data as of the Twitter database is preprocessed. In this step, tokenization, stemming, stop word removal, and number removal are done. The proposed automated learning with CA-SVM based sentiment analysis model reads the Twitter data set. After that they have been processed to extract the features which yield set of terms. Using the terms, the tweets are clustered using TGS-K means clustering which measures Euclidean distance according to different features like semantic sentiment score (SSS), gazetteer and symbolic sentiment support (GSSS), and topical sentiment score (TSS). Further, the method classifies the tweets according to support vector machine (CA-SVM) which classifies the tweet according to the support value which is measured based on the above two measures. The attained results are validated utilizing k-fold cross-validation methodology. Then, the classification is performed by utilizing the Balanced CA-SVM (Deep Learning Modified Neural Network). The results are evaluated and compared with the existing works. The Proposed model achieved 92.48 % accuracy and 92.05% sentiment score contrasted with the existing works.

REVIEW OF AUTOMATED SYSTEMS SUPPORT OF EDUCATIONAL PROCESS

Distance education in Ukraine has undergone significant growth over the past two years. It has provided opportunities for millions of undergraduate and graduate students to continue learning in a variety of forms and ways, including online learning, internships, competitions, research, dissertation defenses, field experience reports, seminars and forums in quarantine. In contrast to Ukraine, according to the results of a survey of some higher educational institutions during the epidemic period, the degree of student dissatisfaction with distance learning on the Internet is generally high. In contrast to Ukraine, according to the results of a survey of some higher educational institutions in the world during the epidemic period, the degree of student dissatisfaction with distance learning on the Internet is generally high. In the realities of our country, distance learning is much more popular with students, since there is no need to be in the lecture hall, and it is also impossible to determine who completed the homework: student or someone else. On the other hand, the workload for teachers has increased due to the need to create and administer distance courses, presentations, multimedia labs and others. These factors contribute to an increase in dissatisfaction with the distance education process among university teachers. The overall response of students and teachers to distance education is related to the challenges of transition and adaptation. First, opinions regarding the negative impact of long-term use of electronic products cannot be ignored. Secondly, there is a problem associated with the lack of technical support and personal space at home for students. Any pandemic causes high levels of stress in the population. It is associated with uncertainty and loss of control over the situation. The COVID-19 pandemic has worsened the pre-existing mental health of both students and teachers. This is mainly due to the closure of educational institutions, the loss of work and study hours, limited social ties, and a heavy load of educational material. To ensure a sufficient level of quality of distance learning, it is necessary to use automated learning support systems. They provide an opportunity to objectively assess and maintain academic integrity for students. That is why the purpose of this work is to do overview of existing popular and most widely used automated learning support systems and to compare their functionality for design of a new system that will provide the required quality of learning. The overview made it possible to determine that a high-quality automated learning support system in a distance learning environment should contain at least the following parts: preparation of course elements by teachers, anonymous assessment of the course by students, attendance control, student recognition, exchange of teaching resources, exchange of professional knowledge and skills, conducting various types of control activities and homework, meetings and seminars, Web-based laboratories, Internet library and others. Also, the system must be cross-platform and supported on a computer, TV, mobile phone, tablet and other common gadgets based on the existing today operating systems.