Advancements in Deep Learning Theory and Applications: Perspective in 2020 and beyond

Learning Machine

The aim of this chapter is to introduce newcomers to deep learning, deep learning platforms, algorithms, applications, and open-source datasets. This chapter will give you a broad overview of the term deep learning, in context to deep learning machine learning, and Artificial Intelligence (AI) is also introduced. In Introduction, there is a brief overview of the research achievements of deep learning. After Introduction, a brief history of deep learning has been also discussed. The history started from a famous scientist called Allen Turing (1951) to 2020. In the start of a chapter after Introduction, there are some commonly used terminologies, which are used in deep learning. The main focus is on the most recent applications, the most commonly used algorithms, modern platforms, and relevant open-source databases or datasets available online. While discussing the most recent applications and platforms of deep learning, their scope in future is also discussed. Future research directions are discussed in applications and platforms. The natural language processing and auto-pilot vehicles were considered the state-of-the-art application, and these applications still need a good portion of further research. Any reader from undergraduate and postgraduate students, data scientist, and researchers would be benefitted from this.

Cyber Crime Investigation: Landscape, Challenges, and Future Research Directions

Journal of Cybersecurity and Privacy ◽

10.3390/jcp1040029 ◽

2021 ◽

Vol 1 (4) ◽

pp. 580-596

Author(s):

Cecelia Horan ◽

Hossein Saiedian

Keyword(s):

Open Source ◽

Language Processing ◽

Digital Forensics ◽

New Technology ◽

Future Research ◽

Cyber Crime ◽

Research Directions ◽

Digital Forensic ◽

Open Source Intelligence ◽

As technology has become pivotal a part of life, it has also become a part of criminal life. Criminals use new technology developments to commit crimes, and investigators must adapt to these changes. Many people have, and will become, victims of cybercrime, making it even more important for investigators to understand current methods used in cyber investigations. The two general categories of cyber investigations are digital forensics and open-source intelligence. Cyber investigations are affecting more than just the investigators. They must determine what tools they need to use based on the information that the tools provide and how effectively the tools and methods work. Tools are any application or device used by investigators, while methods are the process or technique of using a tool. This survey compares the most common methods available to investigators to determine what kind of evidence the methods provide, and which of them are the most effective. To accomplish this, the survey establishes criteria for comparison and conducts an analysis of the tools in both mobile digital forensic and open-source intelligence investigations. We found that there is no single tool or method that can gather all the evidence that investigators require. Many of the tools must be combined to be most effective. However, there are some tools that are more useful than others. Out of all the methods used in mobile digital forensics, logical extraction and hex dumps are the most effective and least likely to cause damage to the data. Among those tools used in open-source intelligence, natural language processing has more applications and uses than any of the other options.

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

Deep Learning for Video Captioning: A Review

10.24963/ijcai.2019/877 ◽

2019 ◽

Cited By ~ 1

Author(s):

Shaoxiang Chen ◽

Ting Yao ◽

Yu-Gang Jiang

Keyword(s):

Deep Learning ◽

Natural Language ◽

Language Processing ◽

State Of The Art ◽

Future Research ◽

Research Directions ◽

Video Captioning ◽

Review State ◽

Vision And Language

Deep learning has achieved great successes in solving specific artificial intelligence problems recently. Substantial progresses are made on Computer Vision (CV) and Natural Language Processing (NLP). As a connection between the two worlds of vision and language, video captioning is the task of producing a natural-language utterance (usually a sentence) that describes the visual content of a video. The task is naturally decomposed into two sub-tasks. One is to encode a video via a thorough understanding and learn visual representation. The other is caption generation, which decodes the learned representation into a sequential sentence, word by word. In this survey, we first formulate the problem of video captioning, then review state-of-the-art methods categorized by their emphasis on vision or language, and followed by a summary of standard datasets and representative approaches. Finally, we highlight the challenges which are not yet fully understood in this task and present future research directions.

Android Malware Detection Techniques: A Literature Review

Recent Patents on Engineering ◽

10.2174/1872212114999200710143847 ◽

2020 ◽

Vol 14 ◽

Author(s):

Meghna Dhalaria ◽

Ekta Gandotra

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Malware Detection ◽

Future Research ◽

Android Malware ◽

Detection Techniques ◽

Android Malware Detection ◽

To Come ◽

Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

Advances in Human Resources Management and Organizational Development - Handbook of Research on Internationalization of Entrepreneurial Innovation in the Global Economy ◽

Organizational Innovation beyond Technology

10.4018/978-1-4666-8216-0.ch021 ◽

2015 ◽

pp. 416-435 ◽

Cited By ~ 1

Author(s):

M. Isabel Sánchez-Hernández

Keyword(s):

Organizational Innovation ◽

Empirical Evaluation ◽

Internal Marketing ◽

Internal Market ◽

Future Research ◽

Business Services ◽

History Of ◽

Knowledge Intensive ◽

Moments Of Truth

This chapter illustrates internal market orientation's philosophy (IMO) and the innovative Internal Marketing practices in competitive firms. The chapter begins with an explanation of the field of innovation in services going beyond technology to IMO research topics. A brief history of Internal Marketing (IM) and main literature contributions are provided. After that, the focus turns to the empirical evaluation of IMO's dimensions. The analysis is undertaken with data from a survey in Spanish and Portuguese knowledge intensive business services (KIBs). An exploratory factor analysis was performed and eight factors have been extracted from the data set via principal components analysis: Efforts to create a good place to work, Focus on competencies, Dissemination, Awareness of labour market conditions, Focus on individual training and development, Feed-Back communication, Managing the moments of truth, and Internal market research. The chapter concludes with some reflections and suggestions for managers and future research directions are also highlighted.

Ensuring Quality and Integrity in Online Learning Programs - Advances in Educational Technologies and Instructional Design ◽

The Same but Different

10.4018/978-1-5225-7844-4.ch001 ◽

2019 ◽

pp. 1-32

Author(s):

Maria Northcote

Keyword(s):

Higher Education ◽

Online Learning ◽

Online Courses ◽

Future Research ◽

Technological Innovations ◽

Research Directions ◽

Learning Programs ◽

History Of ◽

Practical Recommendations

The field of online learning, like many other technological innovations, has not burgeoned without controversy. Despite the debates about the role and value of online learning, it has continued to grow in many sectors, especially in higher education. Alongside the growth of online learning, discussions about its benefits and limitations have also flourished, and many studies have investigated the quality and integrity of online courses. This chapter offers an investigation of some of the history of online learning, concluding with a collection of practical recommendations and suggestions for future research directions to guide institutions embarking on online learning programs.

Advances in Healthcare Information Systems and Administration - Intelligent Systems for Healthcare Management and Delivery ◽

Deep Learning and Medical Imaging

10.4018/978-1-5225-7071-4.ch005 ◽

2019 ◽

pp. 101-147

Author(s):

Nourhan Mohamed Zayed ◽

Heba A. Elnemr

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Medical Imaging ◽

Medical Image Analysis ◽

Future Research ◽

Research Directions ◽

Conventional Machine ◽

Nested Hierarchy ◽

Great Potency

Deep learning (DL) is a special type of machine learning that attains great potency and flexibility by learning to represent input raw data as a nested hierarchy of essences and representations. DL consists of more layers than conventional machine learning that permit higher levels of abstractions and improved prediction from data. More abstract representations computed in terms of less abstract ones. The goal of this chapter is to present an intensive survey of existing literature on DL techniques over the last years especially in the medical imaging analysis field. All these techniques and algorithms have their points of interest and constraints. Thus, analysis of various techniques and transformations, submitted prior in writing, for plan and utilization of DL methods from medical image analysis prospective will be discussed. The authors provide future research directions in DL area and set trends and identify challenges in the medical imaging field. Furthermore, as quantity of medicinal application demands increase, an extended study and investigation in DL area becomes very significant.

Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction - Advances in Computer and Electrical Engineering ◽

Artificial Intelligence

10.4018/978-1-5225-7368-5.ch002 ◽

2019 ◽

pp. 13-22

Author(s):

Steven Walczak

Keyword(s):

Artificial Intelligence ◽

Problem Solving ◽

Future Research ◽

Human Intelligence ◽

Research Directions ◽

Intelligent Machines ◽

History Of ◽

New Research

Artificial intelligence is the science of creating intelligent machines. Human intelligence is comprised of numerous pieces of knowledge as well as processes for utilizing this knowledge to solve problems. Artificial intelligence seeks to emulate and surpass human intelligence in problem solving. Current research tends to be focused within narrow, well-defined domains, but new research is looking to expand this to create global intelligence. This chapter seeks to define the various fields that comprise artificial intelligence and look at the history of AI and suggest future research directions.

Machine Learning

Machine Learning ◽

10.4018/978-1-60960-818-7.ch102 ◽

2012 ◽

pp. 13-22 ◽

Cited By ~ 1

Author(s):

João Gama ◽

André C.P.L.F. de Carvalho

Keyword(s):

Machine Learning ◽

Language Processing ◽

Text Processing ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Background Information ◽

Future Research ◽

Personal View ◽

Learning Techniques ◽

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Machine Learning

Encyclopedia of Information Science and Technology, Second Edition ◽

10.4018/978-1-60566-026-4.ch392 ◽

2011 ◽

pp. 2462-2468 ◽

Cited By ~ 3

Author(s):

João Gama ◽

André C.P.L.F. de Carvalho

Keyword(s):

Machine Learning ◽

Language Processing ◽

Text Processing ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Background Information ◽

Future Research ◽

Personal View ◽

Learning Techniques ◽

A Classification Analysis of the Success of Open Source Health Information Technology Projects

Health Information Systems ◽

10.4018/978-1-60566-988-5.ch018 ◽

2011 ◽

pp. 256-273

Author(s):

Evangelos Katsamakas ◽

Balaji Janamanchi ◽

Wullianallur Raghupathi ◽

Wei Gao

Keyword(s):

Information Technology ◽

Open Source ◽

Health Information Technology ◽

Health Information ◽

Open Source Software ◽

Future Research ◽

Software Projects ◽

Information Technology Projects ◽

Development Status ◽

As the number of open source software (OSS) projects in healthcare grows rapidly, researchers are faced with the challenge of understanding and explaining the success of the open source phenomenon. This article proposes a research framework that examines the roles of project sponsorship, license type, development status and technological complements in the success of open source health information technology (HIT) projects and it develops a systematic method for classifying projects based on their success potential. Drawing from economic theory, a novel proposition in the authors’ framework suggests that higher project-license restrictiveness will increase OSS adoption, because organizations will be more confident that the OSS project will remain open source in the future. Applying the framework to a sample of open source software projects in healthcare, the authors find that although project sponsorship and license restrictiveness influence project metrics, they are not significant predictors of project success categorization. On the other hand, development status, operating system and programming language are significant predictors of an OSS project’s success categorization. Application implications and future research directions are discussed.