scholarly journals A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7786
Author(s):  
Sharnil Pandya ◽  
Aanchal Thakur ◽  
Santosh Saxena ◽  
Nandita Jassal ◽  
Chirag Patel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Immune System ◽  
Future Research ◽  
Human Immune System ◽  
Specialized Knowledge ◽  
Recent Developments ◽  
The Face ◽  
Future Research Directions ◽  
Recent Trends

The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.

Android Malware Detection Techniques: A Literature Review

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 ◽  
Future Research Directions ◽  
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.

Deep Learning and Medical Imaging

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 ◽  
Future Research Directions ◽  
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.

Feature Selection Techniques in High Dimensional Data With Machine Learning and Deep Learning

2021 ◽  
pp. 17-37
Author(s):  
Bhanu Chander
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Deep Learning ◽  
High Dimensional Data ◽  
Complete Information ◽  
High Dimensional ◽  
Future Research ◽  
Future Research Directions ◽  
Class Labels ◽  
Feature Selection Techniques

High-dimensional data inspection is one of the major disputes for researchers plus engineers in domains of deep learning (DL), machine learning (ML), as well as data mining. Feature selection (FS) endows with proficient manner to determine these difficulties through eradicating unrelated and outdated data, which be capable of reducing calculation time, progress learns precision, and smooth the progress of an enhanced understanding of the learning representation or information. To eradicate an inappropriate feature, an FS standard was essential, which can determine the significance of every feature in the company of the output class/labels. Filter schemes employ variable status procedure as the standard criterion for variable collection by means of ordering. Ranking schemes utilized since their straightforwardness and high-quality accomplishment are detailed for handy appliances. The goal of this chapter is to produce complete information on FS approaches, its applications, and future research directions.

Service Innovation in Professional Service Firms

2015 ◽  
Author(s):  
Michael Barratt ◽  
Bob Hinings
Keyword(s):  
Technology Development ◽  
Service Innovation ◽  
Professional Service ◽  
Future Research ◽  
Service Firms ◽  
Professional Service Firms ◽  
Recent Developments ◽  
The Face ◽  
Rapid Pace ◽  
Future Research Directions

Service innovation in Professional Service Firms involves the development and use of new practices by professionals. In the face of increasing competition and the rapid pace of technology development service innovation is of increasing importance for these firms. Despite these developments, there has been little discussion of innovation in the Professional Service Firm (PSF) literature. The emphasis has been on change and knowledge management with little recognition as to how these are related to innovation. In this chapter, the authors review the PSF literature and recent developments on service innovation and propose future research directions around a practice perspective for exploring service innovation in professional service firms.

A state-of-the-art overview of job-crafting research: current trends and future research directions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maria Tims ◽  
Melissa Twemlow ◽  
Christine Yin Man Fong
Keyword(s):  
State Of The Art ◽  
Work Behavior ◽  
Job Crafting ◽  
Future Research ◽  
25Th Anniversary ◽  
Content Type ◽  
Recent Developments ◽  
Future Research Directions ◽  
Recent Trends ◽  
The Impact

PurposeIn celebration of the 25th anniversary of the founding of Career Development International, a state-of-the-art overview of recent trends in job-crafting research was conducted. Since job crafting was introduced twenty years ago as a type of proactive work behavior that employees engage in to adjust their jobs to their needs, skills, and preferences, research has evolved tremendously.Design/methodology/approachTo take stock of recent developments and to unravel the latest trends in the field, this overview encompasses job-crafting research published in the years 2016–2021. The overview portrays that recent contributions have matured the theoretical and empirical advancement of job-crafting research from three perspectives (i.e. individual, team and social).FindingsWhen looking at the job-crafting literature through these three perspectives, a total of six trends were uncovered that show that job-crafting research has moved to a more in-depth theory-testing approach; broadened its scope; examined team-level job crafting and social relationships; and focused on the impact of job crafting on others in the work environment and their evaluations and reactions to it.Originality/valueThe overview of recent trends within the job-crafting literature ends with a set of recommendations for how future research on job crafting could progress and create scientific impact for the coming years.

Deep Learning--based Text Classification

2021 ◽  
Vol 54 (3) ◽  
pp. 1-40
Author(s):  
Shervin Minaee ◽  
Nal Kalchbrenner ◽  
Erik Cambria ◽  
Narjes Nikzad ◽  
Meysam Chenaghlu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Text Classification ◽  
Question Answering ◽  
Future Research ◽  
Learning Models ◽  
Research Directions ◽  
Comprehensive Review ◽  
Future Research Directions ◽  
Classification Tasks

Deep learning--based models have surpassed classical machine learning--based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this article, we provide a comprehensive review of more than 150 deep learning--based models for text classification developed in recent years, and we discuss their technical contributions, similarities, and strengths. We also provide a summary of more than 40 popular datasets widely used for text classification. Finally, we provide a quantitative analysis of the performance of different deep learning models on popular benchmarks, and we discuss future research directions.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

scholarly journals Mechanical challenges and cytoskeletal impairments in focal segmental glomerulosclerosis

2018 ◽  
Vol 314 (5) ◽  
pp. F921-F925 ◽  
Author(s):  
Di Feng ◽  
Clark DuMontier ◽  
Martin R. Pollak
Keyword(s):  
Blood Flow ◽  
Focal Segmental Glomerulosclerosis ◽  
Kidney Injury ◽  
Future Research ◽  
Disease States ◽  
Filtration Barrier ◽  
Segmental Glomerulosclerosis ◽  
The Face ◽  
Foot Process ◽  
Future Research Directions

Focal segmental glomerulosclerosis (FSGS) is a histologically defined form of kidney injury typically mediated by podocyte dysfunction. Podocytes rely on their intricate actin-based cytoskeleton to maintain the glomerular filtration barrier in the face of mechanical challenges resulting from pulsatile blood flow and filtration of this blood flow. This review summarizes the mechanical challenges faced by podocytes in the form of stretch and shear stress, both of which may play a role in the progression of podocyte dysfunction and detachment. It also reviews how podocytes respond to these mechanical challenges in dynamic fashion through rearranging their cytoskeleton, triggering various biochemical pathways, and, in some disease states, altering their morphology in the form of foot process effacement. Furthermore, this review highlights the growing body of evidence identifying several mutations of important cytoskeleton proteins as causes of FSGS. Lastly, it synthesizes the above evidence to show that a better understanding of how these mutations leave podocytes vulnerable to the mechanical challenges they face is essential to better understanding the mechanisms by which they lead to disease. The review concludes with future research directions to fill this gap and some novel techniques with which to pursue these directions.

Frontiers in Service Science: Fintech Operations—An Overview of Recent Developments and Future Research Directions

2021 ◽  
Vol 13 (1) ◽  
pp. 19-35
Author(s):  
Baile Lu ◽  
Shuai Hao ◽  
Michael Pinedo ◽  
Yuqian Xu
Keyword(s):  
Future Research ◽  
Algorithmic Trading ◽  
Payment Systems ◽  
Research Directions ◽  
Operational Risks ◽  
Technology Industry ◽  
Recent Developments ◽  
Future Research Directions ◽  
Conclusion Section ◽  
Financial Technology

In this paper, we provide a survey of recent developments in the fintech (financial technology) industry, focusing on the operational structures, the technologies involved, and the operational risks associated with the new systems. In particular, we discuss payment systems, algorithmic trading, robo-advisory, crowdfunding, and peer-to-peer lending. In the conclusion section, we discuss various promising research directions.

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