Applying Rule Induction in Software Prediction

2011 ◽  
pp. 265-286
Author(s):  
Bhekisipho Twala ◽  
Michelle Cartwright ◽  
Martin Shepperd
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Software Engineering ◽  
Principal Components ◽  
Research Work ◽  
Rule Induction ◽  
Future Research ◽  
Human Beings ◽  
Data Application ◽  
Key Issues

Recently, the use of machine learning (ML) algorithms has proven to be of great practical value in solving a variety of software engineering problems including software prediction, for example, cost and defect processes. An important advantage of machine learning over statistical analysis as a modelling technique lies in the fact that the interpretation of production rules is more straightforward and intelligible to human beings than, say, principal components and patterns with numbers that represent their meaning. The main focus of this chapter is upon rule induction (RI): providing some background and key issues on RI and further examining how RI has been utilised to handle uncertainties in data. Application of RI in prediction and other software engineering tasks is considered. The chapter concludes by identifying future research work when applying rule induction in software prediction. Such future research work might also help solve new problems related to rule induction and prediction.

scholarly journals Recommendation Systems for Education: Systematic Review

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1611
Author(s):  
María Cora Urdaneta-Ponte ◽  
Amaia Mendez-Zorrilla ◽  
Ibon Oleagordia-Ruiz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Formal Education ◽  
Research Work ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Educational Resources ◽  
Future Research ◽  
Collaborative Approach ◽  
Developmental Approach

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

scholarly journals AI in learning

2021 ◽  
Vol 15 ◽  
pp. 183449092110381 ◽  
Author(s):  
Hannele Niemi
Keyword(s):  
Machine Learning ◽  
Ethical Issues ◽  
Cognitive Task ◽  
Future Research ◽  
Human Beings ◽  
Special Issue ◽  
Recent Discussion ◽  
Data Ownership ◽  
Digital Pedagogy ◽  
Intelligent Tools

This special issue raises two thematic questions: (1) How will AI change learning in the future and what role will human beings play in the interaction with machine learning, and (2), What can we learn from the articles in this special issue for future research? These questions are reflected in the frame of the recent discussion of human and machine learning. AI for learning provides many applications and multimodal channels for supporting people in cognitive and non-cognitive task domains. The articles in this special issue evidence that agency, engagement, self-efficacy, and collaboration are needed in learning and working with intelligent tools and environments. The importance of social elements is also clear in the articles. The articles also point out that the teacher’s role in digital pedagogy primarily involves facilitating and coaching. AI in learning has a high potential, but it also has many limitations. Many worries are linked with ethical issues, such as biases in algorithms, privacy, transparency, and data ownership. This special issue also highlights the concepts of explainability and explicability in the context of human learning. We need much more research and research-based discussion for making AI more trustworthy for users in learning environments and to prevent misconceptions.

A Short Introduction to the Ethics of Artificial Intelligence

2020 ◽  
pp. 1-42
Author(s):  
S. Matthew Liao
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intelligent Systems ◽  
Ethical Issues ◽  
Black Box ◽  
Human Beings ◽  
Key Issues ◽  
Long Term Impact ◽  
Bad Data

This introduction outlines in section I.1 some of the key issues in the study of the ethics of artificial intelligence (AI) and proposes ways to take these discussions further. Section I.2 discusses key concepts in AI, machine learning, and deep learning. Section I.3 considers ethical issues that arise because current machine learning is data hungry; is vulnerable to bad data and bad algorithms; is a black box that has problems with interpretability, explainability, and trust; and lacks a moral sense. Section I.4 discusses ethical issues that arise because current machine learning systems may be working too well and human beings can be vulnerable in the presence of these intelligent systems. Section I.5 examines ethical issues arising out of the long-term impact of superintelligence such as how the values of a superintelligent AI can be aligned with human values. Section I.6 presents an overview of the essays in this volume.

scholarly journals Estimation of similarity between functions extracted from x86 executable files

2015 ◽  
Vol 12 (2) ◽  
pp. 253-262
Author(s):  
Katarina Berta ◽  
Sasa Stojanovic ◽  
Milos Cvetanovic ◽  
Zaharije Radivojevic
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Binary Code ◽  
Source Code ◽  
Future Research ◽  
Malware Analysis ◽  
Previous Knowledge ◽  
The Third

Comparison of functions is required in various domains of software engineering. In most domains, comparison is done using source code, but in some domains, such as license violation or malware analysis, only binary code is available. The goal of this paper is to evaluate whether the existing solution meant for ARM architecture can be applied to x86 architecture. The existing solution encompasses multiple approaches, but for the purpose of this paper three representative approaches are implemented; two are based on machine learning, and the third does not require previous knowledge. Results show that the best recalls obtained for the first ten positions on both architectures are comparable and do not differ significantly. The results confirm that adaptation of all approaches of the existing solution is not only possible but also promising and represent adequate basis for future research.

scholarly journals Machine Learning Models and Neural Network Techniques for Predicting Uddanam CKD

2019 ◽  
Vol 8 (2) ◽  
pp. 2550-2563
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Statistical Analysis ◽  
Early Detection ◽  
Clinical Data ◽  
Process Model ◽  
Research Work ◽  
Process Time ◽  
Data Set ◽  
The World

Chronic kidney disease (CKD) is one of the most widely spread diseases across the world. Mysteriously some of the areas in the world like Srilanka, Nicrgua and Uddanam (India), this disease affect more and it is cause of thousands of deaths particular areas. Now days, the prevention with utilizing statistical analysis and early detection of CKD with utilizing Machine Learning (ML) and Neural Networks (NNs) are the most important topics. In this research work, we collected the data form Uddanam (costal area of srikakulam district, A.P, India) about patient’s clinical data, living styles (Habits and culture) and environmental conditions (water, land and etc.) data from 2016 to 2019. In this paper, we conduct the statistical analysis, Machine Learning (ML) and Neural Network application on clinical data set of Uddanam CKD for prevention and early detection of CKD. As per statistical analysis we can prevent the CKD in the Uddanam area. As per ML analysis Naive Bayes model is the best where the process model is constructed within 0.06 seconds and prediction accuracy is 99.9%. In the analysis of NNs, the 9 neurons hidden layer (HL) Artificial Neural Network (ANN) is very accurate than other all models where it performs 100% of accuracy for predicting CKD and it takes the 0.02 seconds process time.

scholarly journals An Empirical Review of Automated Machine Learning

Computers ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 11
Author(s):  
Lorenzo Vaccaro ◽  
Giuseppe Sansonetti ◽  
Alessandro Micarelli
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Research Work ◽  
Industrial Sector ◽  
Point Of View ◽  
Future Research ◽  
Theoretical Point ◽  
Academic Field ◽  
Automated Machine Learning ◽  
Made In

In recent years, Automated Machine Learning (AutoML) has become increasingly important in Computer Science due to the valuable potential it offers. This is testified by the high number of works published in the academic field and the significant efforts made in the industrial sector. However, some problems still need to be resolved. In this paper, we review some Machine Learning (ML) models and methods proposed in the literature to analyze their strengths and weaknesses. Then, we propose their use—alone or in combination with other approaches—to provide possible valid AutoML solutions. We analyze those solutions from a theoretical point of view and evaluate them empirically on three Atari games from the Arcade Learning Environment. Our goal is to identify what, we believe, could be some promising ways to create truly effective AutoML frameworks, therefore able to replace the human expert as much as possible, thereby making easier the process of applying ML approaches to typical problems of specific domains. We hope that the findings of our study will provide useful insights for future research work in AutoML.

scholarly journals A Review on Recent Treatment Technology for Herbicide Atrazine in Contaminated Environment

2019 ◽  
Vol 16 (24) ◽  
pp. 5129 ◽  
Author(s):  
Huijun He ◽  
Yongpan Liu ◽  
Shaohong You ◽  
Jie Liu ◽  
He Xiao ◽  
...  
Keyword(s):  
Research Work ◽  
Functional Materials ◽  
Future Research ◽  
Human Beings ◽  
Treatment Technology ◽  
Processing Technologies ◽  
Integrated Method ◽  
Advantages And Disadvantages ◽  
Microbial Remediation ◽  
Atrazine Degradation

Atrazine is a kind of triazine herbicide that is widely used for weed control due to its good weeding effect and low price. The study of atrazine removal from the environment is of great significance due to the stable structure, difficult degradation, long residence time in environment, and toxicity on the organism and human beings. Therefore, a number of processing technologies are developed and widely employed for atrazine degradation, such as adsorption, photochemical catalysis, biodegradation, etc. In this article, with our previous research work, the progresses of researches about the treatment technology of atrazine are systematically reviewed, which includes the four main aspects of physicochemical, chemical, biological, and material-microbial-integrated aspects. The advantages and disadvantages of various methods are summarized and the degradation mechanisms are also evaluated. Specially, recent advanced technologies, both plant-microbial remediation and the material-microbial-integrated method, have been highlighted on atrazine degradation. Among them, the plant-microbial remediation is based on the combined system of soil-plant-microbes, and the material-microbial-integrated method is based on the synergistic effect of materials and microorganisms. Additionally, future research needs to focus on the excellent removal effect and low environmental impact of functional materials, and the coordination processing of two or more technologies for atrazine removal is also highlighted.

Biofeedback-Based Mental Health Software and Its Statistical Analysis

2021 ◽  
pp. 136-155
Author(s):  
Rohit Rastogi ◽  
Devendra Kumar Chaturvedi ◽  
Sathiyamoorthi V.
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Statistical Analysis ◽  
Research Work ◽  
Tension Type Headache ◽  
Data Set ◽  
Closed Set ◽  
Sf 36 ◽  
Skin Response ◽  
Type Headache

Many apps and analyzers based on machine learning have been designed already to help and cure the stress issue, which is increasing. The project is based on an experimental research work that the authors have performed at Research Labs and Scientific Spirituality Centers of Dev Sanskriti VishwaVidyalaya, Haridwar and Patanjali Research Foundations, Uttarakhand. In the research work, the correctness and accuracy have been studied and compared for two biofeedback devices named as electromyography (EMG) and galvanic skin response (GSR), which can operate in three modes, audio, visual and audio-visual, with the help of data set of tension type headache (TTH) patients. The authors have realized by their research work that these days people have lot of stress in their life so they planned to make an effort for reducing the stress level of people by their technical knowledge of computer science. In the project they have a website that contains a closed set of questionnaires from SF-36, which have some weight associated with each question.

scholarly journals Reliable E-Nose System using the Improved Optimization Technique based ANN

2020 ◽  
Vol 9 (4) ◽  
pp. 1726-1731
Keyword(s):  
Machine Learning ◽  
Optimization Technique ◽  
Research Work ◽  
Error Rates ◽  
Human Beings ◽  
Training Process ◽  
Accuracy Rate ◽  
Machine Learning Model ◽  
Artificial Neural Network Ann ◽  
Pollutant Gases

(Since from last decade, there is a growing interest in a system that detects the pollutant gases and other environmental information is called Electronic Nose (E-Nose) networks. The gases such as methanol, Liquid Petroleum Gases, ammonia, etc. are harmful for human beings; therefore, such frailness required detecting automatically as well as safety alarm promoted in a specific field. The critical challenges of the E-nose system are efficient to detect with minimum error and overhead. In this paper, we targeted to design the optimized machine learning-based algorithm to detect and alert the pollutant gases, Humidity, O2 Level, and Air Temperature in the real-time datasets. We initiated E-nose design using Artificial Neural Network (ANN). Using essential ANN leads to poor accuracy and error rates, as they failed to select the best solutions during the training process. Thus, we next use the Particle Swarm Optimization (PSO) based ANN called ANN-PSO to improve the accuracy rate and error performances for E-Nose systems. Finally, the proposed Improved Optimization Technique based ANN (IOT-ANN) machine learning model designed and evaluated in current this research work. The IoT-ANN it is based on a bio-inspired algorithm to achieve reliable training during the E-Nose prediction

Impacto de la comunicación de la responsabilidad social universitaria en la selección de universidad y efecto mediador de la reputación

2020 ◽  
Author(s):  
Lucy Erazo-Coronado ◽  
Sergio Llano-Arristizábal ◽  
Miguel Garcés-Prettel ◽  
Ana-María Erazo-Coronado
Keyword(s):  
Social Responsibility ◽  
Professional Training ◽  
Research Work ◽  
Community Support ◽  
Human Beings ◽  
Cross Sectional ◽  
Public And Private ◽  
University Selection ◽  
Cross Sectional Design ◽  
Institutional Websites

The emerging concept of university social responsibility (USR) consists in the fulfillment of the mission of the university in terms of ethical standards, community support, respect for human beings, and respect for the environment. Thus, universities must not only provide professional training but also instill ethical principles in students to allow them to behave as good citizens and help solve community problems. On the other hand, competition for students requires efficient management of university reputation and communication with their stakeholders, to demonstrate the accomplishment of the university’s mission and attract potential students. The objective of this paper is thus to explain the causal relationship between communication about university social responsibility (USR) activities through institutional websites and university selection, as well as the role of reputation. Research work was carried out based on an explanatory cross-sectional design, with a sample of 356 subjects from a population of 11th-grade students from public and private schools in the City of Barranquilla (Colombia). The results revealed that USR communication through institutional websites has a significant influence on university selection, not only directly but also indirectly, through reputation as a mediating variable. These findings contribute at a theoretical level by providing empirical evidence to understand and explain the analyzed topic. Furthermore, the results provide useful information to evaluate USR disclosure and implement strategies to make progress on USR issues. Resumen El naciente concepto de responsabilidad social universitaria (RSU) consiste en el cumplimiento de la misión de la universidad dentro de unos estándares éticos, de apoyo a la comunidad, respeto al ser humano y al medio ambiente. Así, pues, la universidad no sólo debe impartir formación profesional, sino inculcar a sus estudiantes principios éticos que les permitan comportarse como buenos ciudadanos y contribuir a la solución de los problemas de la comunidad. Por otra parte, la competencia entre universidades exige una gestión eficiente de su reputación y de la comunicación con sus distintos stakeholders, para demostrar a la sociedad el cumplimiento de su misión social, y para atraer estudiantes potenciales hacia la institución. Por lo tanto, este artículo tiene como objetivo explicar la relación de causalidad entre la comunicación de las prácticas de RSU en los sitios web institucionales y la selección de universidad, y el papel mediador de la reputación en esta relación. Para lograrlo, se llevó a cabo una investigación cuantitativa de nivel explicativo experimental y diseño transversal, con una muestra conformada por 356 sujetos, de una población de estudiantes de grado 11º de colegios públicos y privados de la ciudad de Barranquilla (Colombia). Los resultados revelan que la comunicación de las prácticas de RSU en los sitios web institucionales ejerce una influencia significativa directa en la intención de selección de universidad, e indirecta, a través de la reputación como variable mediadora. La evidencia empírica aportada representa un avance teórico en la comprensión y explicación del fenómeno estudiado, e igualmente servirá para que las universidades evalúen si están comunicando adecuadamente su responsabilidad social y adopten estrategias para avanzar en este aspecto.

Sign in / Sign up

Export Citation Format

Share Document