scholarly journals Impact Tech Startups: A Conceptual Framework, Machine-Learning-Based Methodology and Future Research Directions

2021 ◽  
Vol 13 (18) ◽  
pp. 10048
Author(s):  
Benjamin Gidron ◽  
Yael Israel-Cohen ◽  
Kfir Bar ◽  
Dalia Silberstein ◽  
Michael Lustig ◽  
...  
Keyword(s):  
Machine Learning ◽  
Conceptual Framework ◽  
Impact Category ◽  
Private Investment ◽  
Social Enterprises ◽  
Future Research ◽  
Research Directions ◽  
For Profit ◽  
Future Research Directions ◽  
The Impact

The Impact Tech Startup (ITS) is a new, rapidly developing type of organizational category. Based on an entrepreneurial approach and technological foundations, ITSs adopt innovative strategies to tackle a variety of social and environmental challenges within a for-profit framework and are usually backed by private investment. This new organizational category is thus far not discussed in the academic literature. The paper first provides a conceptual framework for studying this organizational category, as a combination of aspects of social enterprises and startup businesses. It then proposes a machine learning (ML)-based algorithm to identify ITSs within startup databases. The UN’s Sustainable Development Goals (SDGs) are used as a referential framework for characterizing ITSs, with indicators relating to those 17 goals that qualify a startup for inclusion in the impact category. The paper concludes by discussing future research directions in studying ITSs as a distinct organizational category through the usage of the ML methodology.

scholarly journals Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

2021 ◽  
Vol 54 (5) ◽  
pp. 1-36
Author(s):  
Ishai Rosenberg ◽  
Asaf Shabtai ◽  
Yuval Elovici ◽  
Lior Rokach
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions ◽  
Adversarial Attack ◽  
The Impact

In recent years, machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security. However, machine learning systems are vulnerable to adversarial attacks, and this limits the application of machine learning, especially in non-stationary, adversarial environments, such as the cyber security domain, where actual adversaries (e.g., malware developers) exist. This article comprehensively summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques and illuminates the risks they pose. First, the adversarial attack methods are characterized based on their stage of occurrence, and the attacker’ s goals and capabilities. Then, we categorize the applications of adversarial attack and defense methods in the cyber security domain. Finally, we highlight some characteristics identified in recent research and discuss the impact of recent advancements in other adversarial learning domains on future research directions in the cyber security domain. To the best of our knowledge, this work is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain, map them in a unified taxonomy, and use the taxonomy to highlight future research directions.

Tourism research for the GCC region: Current status and future research directions

2021 ◽  
pp. 135481662199996
Author(s):  
Ali Salman Saleh ◽  
Charbel Bassil ◽  
Arsalan Safari
Keyword(s):  
United Arab Emirates ◽  
Current Status ◽  
Future Research ◽  
Research Directions ◽  
Regional Tourism ◽  
Research Theme ◽  
Tourism Sector ◽  
Future Research Directions ◽  
Meta Analyses ◽  
The Impact

Tourism in the Gulf Cooperation Council (GCC) countries has recently been considered by policymakers as a new avenue for economic diversification. Despite the considerable literature concerning the impact of tourism worldwide, only a limited number of studies have looked at the tourism sector in the GCC region or analyzed its economic, sociocultural, and environmental impacts. This article therefore conducts a systematic review of the state of the literature related to tourism in the GCC region. It provides effective insights about the current status, gaps, and challenges and proposes future research directions in this area for academics, practitioners, and policymakers with an interest in regional tourism development. The preferred reporting items for systematic reviews and meta-analyses approach was used to identify and select the papers. Some 23 papers were identified and analyzed. The majority of these studies focused on the United Arab Emirates, specifically the Dubai emirate. We found the most dominant research theme to be tourism planning.

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.

Machine Learning Techniques to Identify and Characterize Sleep Disorders Using Biosignals

2021 ◽  
pp. 136-160
Author(s):  
Mercedes Barrachina ◽  
Laura Valenzuela López
Keyword(s):  
Machine Learning ◽  
Sleep Disorders ◽  
Adult Population ◽  
The United States ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Research Directions ◽  
Learning Techniques ◽  
Control And Prevention ◽  
Future Research Directions

Sleep disorders are related to many different diseases, and they could have a significant impact in patients' health, causing an economic impact to the society and to the national health systems. In the United States, according to information from the Center for Disease Control and Prevention, those disorders are affecting 50-70 million in the adult population. Sleep disorders are causing annually around 40,000 deaths due to cardiovascular problems, and they cost the health system more than 16 billion. In other countries, such as in Spain, those disorders affect up to 48% of the adult population. The main objective of this chapter is to review and evaluate the different machine learning techniques utilized by researchers and medical professionals to identify, assess, and characterize sleep disorders. Moreover, some future research directions are proposed considering the evaluated area.

Play It Like Beckham!

2017 ◽  
pp. 43-61
Author(s):  
Sylvaine Castellano ◽  
Insaf Khelladi
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Academic Research ◽  
Future Research ◽  
The Internet ◽  
Research Directions ◽  
Media Usage ◽  
Future Research Directions ◽  
The Impact

New opportunities and challenges are emerging thanks to the growing Internet importance and social media usage. Although practitioners have already recognized the strategic dimension of e-reputation and the power of social media, academic research is still in its infancy when it comes to e-reputation determinants in a social networks context. A study was conducted in the sports setting to explore the impact of social networks on the sportspeople's e-reputation. Whereas the study emphasized (1) the influence of social networks' perception on the sportspeople's e-reputation, and the neutral roles of (2) the motives for following sportspeople online, and (3) the negative content on the Internet, additional insights are formulated on maintaining, restoring and managing e-reputation on social networks. Finally, future research directions are suggested on the role of image to control e-reputation.

scholarly journals The Guangzhou Twin Eye Study: 2019 Update

2019 ◽  
Vol 22 (6) ◽  
pp. 492-498
Author(s):  
Xiaohu Ding ◽  
Wei Wang ◽  
Jane Scheetz ◽  
Mingguang He
Keyword(s):  
Intraocular Pressure ◽  
Axial Length ◽  
Corneal Thickness ◽  
Environmental Influences ◽  
Eye Diseases ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions ◽  
The Impact ◽  
Fundus Photo

AbstractThe primary aim of the Guangzhou Twin Eye Study (GTES) is to explore the impact that genes and environmental influences have on common eye diseases. Since 2006, approximately 1300 pairs of twins, aged 7–15 years, were enrolled at baseline. Progressive phenotypes, such as cycloplegic refraction, axial length, height and weight, have been collected annually. Nonprogressive phenotypes such as parental refraction, corneal thickness, fundus photo, intraocular pressure and DNA were collected once at baseline. We are collaborating with fellow international twin researchers and psychologists to further explore links with general medical conditions. In this article, we review the history, major findings and future research directions for the GTES.

Future research directions in children and hoarding

2020 ◽  
Vol 45 (3) ◽  
pp. 175-181
Author(s):  
Andrew G. Guzick ◽  
Sophie C. Schneider ◽  
Eric A. Storch
Keyword(s):  
Childhood Development ◽  
Future Research ◽  
Systematic Research ◽  
Evidence Based ◽  
Research Directions ◽  
Hoarding Disorder ◽  
Cognitive Behavioural ◽  
Evidence Based Treatments ◽  
Future Research Directions ◽  
The Impact

Abstract Despite a rapidly growing understanding of hoarding disorder (HD), there has been relatively limited systematic research into the impact of hoarding on children and adolescents. The goal of this paper is to suggest future research directions, both for children with hoarding behaviours and children living in a cluttered home. Key areas reviewed in this paper include (1) the need for prospective studies of children with hoarding behaviours and those who grow up with a parent with HD; (2) downward extensions of cognitive-behavioural models of adult HD that emphasise different information processing and behavioural biases in youth HD; (3) developmental research into the presentation of emerging HD in childhood compared with adulthood presentations of the disorder, with consideration of typical childhood development and unique motivators for childhood saving behaviours; (4) developmentally sensitive screening and assessment; and (5) the development of evidence-based treatments for this population. The paper concludes with a discussion of methodological suggestions to meet these aims.

scholarly journals Machine learning for energy-water nexus: challenges and opportunities

2018 ◽  
Vol 2 (3) ◽  
pp. 228-267 ◽  
Author(s):  
Zaidi ◽  
Chandola ◽  
Allen ◽  
Sanyal ◽  
Stewart ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Systems ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Technological Advancement ◽  
Research Directions ◽  
Learning Techniques ◽  
Challenges And Opportunities ◽  
Future Research Directions ◽  
System Sustainability

Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainability. To this end, recent technological advancement has allowed the production of large volumes of data associated with functioning of these sectors. We are beginning to see that statistical and machine learning techniques can help elucidate characteristic patterns across these systems from water availability, transport, and use to energy generation, fuel supply, and customer demand, and in the interdependencies among these systems that can leave these systems vulnerable to cascading impacts from single disruptions. In this paper, we discuss ways in which data and machine learning can be applied to the challenges facing the energy-water nexus along with the potential issues associated with the machine learning techniques themselves. We then survey machine learning techniques that have found application to date in energy-water nexus problems. We conclude by outlining future research directions and opportunities for collaboration among the energy-water nexus and machine learning communities that can lead to mutual synergistic advantage.

The role of Guanxi on international business-to-business relationships: a systematic review and future directions

2019 ◽  
Vol 35 (7) ◽  
pp. 1125-1140 ◽  
Author(s):  
Birce Dobrucalı
Keyword(s):  
Systematic Review ◽  
Social Exchange ◽  
International Business ◽  
Future Research ◽  
Research Directions ◽  
Content Type ◽  
Future Research Directions ◽  
The Subject ◽  
The Impact ◽  
Business To Business

Purpose This paper aims to provide a comprehensive and systematic review of the extant empirical body of knowledge regarding the impact of Guanxi on international Business-to-Business (B-to-B) relationships. Design/methodology/approach After the collection and refinement of studies that appeared in marketing, business and management literature during 1995-2018 period, a systematic review was conducted to discover the current situation and future research directions on the subject. Findings Theoretically, vast majority of the reviewed studies lacked a theoretical foundation, with the remainder anchored primarily on the resource-based view, social network theory and social exchange theory. Methodologically, Ganqing, Xinren and Mianzi are the most frequently investigated dimensions, whereas Renqing is the least investigated dimension. Data are mostly obtained from both Chinese and Western counterparts through survey and analyzed through univariate and multivariate data analysis techniques. Empirically, extant research focused on many diverse outcomes including trust, financial performance, cooperation, satisfaction, time orientation, opportunism and liability of foreignness, while under-examining the drives of Guanxi. Research limitations/implications This study provides a synthesis of extant line of research on the subject that are published in peer-reviewed international journals, which publish research in English. A meta-analysis may be conducted for providing a further detailed framework. Originality/value This study contributes to international marketing literature by providing an in-depth and synthesized inventory of knowledge to scholars; deriving a comprehensive analysis of theoretical foundations, methodological approaches and findings addressed by scholars in the field; noticing theoretical, methodological and empirical gaps to be examined; and providing future research directions.

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