Machine Learning Approaches for Anomaly Detection in IoT: An Overview and Future Research Directions

2021 ◽  
Author(s):  
Nusaybah Alghanmi ◽  
Reem Alotaibi ◽  
Seyed M. Buhari
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Future Research ◽  
Learning Approaches ◽  
Research Directions ◽  
Future Research Directions

Inclusive Host Language Teaching

2022 ◽  
pp. 183-210
Author(s):  
Raquel Amaro ◽  
Susana Correia ◽  
Matilde Gonçalves ◽  
Chiara Barbero ◽  
Miguel Magalhães
Keyword(s):  
Machine Learning ◽  
Language Teaching ◽  
Future Research ◽  
Learning Approaches ◽  
Teaching Approaches ◽  
Research Directions ◽  
Reliable Assessment ◽  
Future Research Directions ◽  
Teaching Learning ◽  
Institutional Texts

This chapter presents research on the teaching-learning of Portuguese as a host language, based on the exploration of authentic informational and institutional texts targeting migrant and refugee people, and considering that successful host language teaching must correspond to the needs of its target audience. The chapter discusses methods of defining and identifying criteria and features to monitor official texts with regard to inclusiveness and bias. It provides insights on how to select real texts to be used in task-based language teaching approaches for inclusive host language teaching. Departing from a real corpus analysis, the potential and the limitations of existing guidelines to inclusiveness for the assessment of real texts are shown, as well as other still neglected issues. Furthermore, this chapter provides future research directions to an effective and reliable assessment of inclusive texts that can serve as inclusive host language teaching materials through NLP and machine learning approaches.

scholarly journals Machine learning approaches and databases for prediction of drug–target interaction: a survey paper

2020 ◽  
Author(s):  
Maryam Bagherian ◽  
Elyas Sabeti ◽  
Kai Wang ◽  
Maureen A Sartor ◽  
Zaneta Nikolovska-Coleska ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Future Research ◽  
Learning Approaches ◽  
Research Directions ◽  
Target Interaction ◽  
Survey Paper ◽  
Advantages And Disadvantages ◽  
Efficient Prediction ◽  
Future Research Directions

Abstract The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

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.

Main Features and Types of Educational Use of Wiki Technology

2021 ◽  
pp. 213-228
Author(s):  
Nikolaos Karipidis ◽  
Jim Prentzas
Keyword(s):  
Learning Communities ◽  
Learning Process ◽  
Future Research ◽  
Learning Approaches ◽  
Research Directions ◽  
Online Learning Communities ◽  
Wiki Technology ◽  
History Of ◽  
Future Research Directions ◽  
The Web

Wiki technology has become very popular during the last years and is used in many fields. It enables the collaborative creation and management of content retaining the history of changes. There is abundant wiki-based content on the web covering a large number of subjects. A significant contribution of wikis involves education. Under certain conditions, technology may enhance the learning process due to the unique features it encompasses. In this context, wikis may prove very helpful as they provide the infrastructure for collaborative learning approaches and the development of online learning communities. This chapter discusses main features of wikis, wiki features specifically required in education, and typical uses of wikis in education. Representative examples of successful wikis are presented. Future research directions are also outlined.

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.

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.

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 A Survey of the Application of Artifical Intellegence on COVID-19 Diagnosis and Prediction

2021 ◽  
Vol 11 (6) ◽  
pp. 7824-7835
Author(s):  
H. Alalawi ◽  
M. Alsuwat ◽  
H. Alhakami
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Future Research ◽  
Classification Algorithms ◽  
Classification Methods ◽  
Research Directions ◽  
The World ◽  
Modern Methods ◽  
Future Research Directions ◽  
Class Labels

The importance of classification algorithms has increased in recent years. Classification is a branch of supervised learning with the goal of predicting class labels categorical of new cases. Additionally, with Coronavirus (COVID-19) propagation since 2019, the world still faces a great challenge in defeating COVID-19 even with modern methods and technologies. This paper gives an overview of classification algorithms to provide the readers with an understanding of the concept of the state-of-the-art classification algorithms and their applications used in the COVID-19 diagnosis and detection. It also describes some of the research published on classification algorithms, the existing gaps in the research, and future research directions. This article encourages both academics and machine learning learners to further strengthen the basis of classification methods.

scholarly journals Automated Machine Learning on Graphs: A Survey

2021 ◽  
Author(s):  
Ziwei Zhang ◽  
Xin Wang ◽  
Wenwu Zhu
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Research Community ◽  
Future Research ◽  
Research Directions ◽  
Vast Number ◽  
Neural Architecture ◽  
Future Research Directions ◽  
Automated Machine Learning ◽  
Optimal Machine

Machine learning on graphs has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually design the optimal machine learning algorithm for different graph-related tasks. To solve this critical challenge, automated machine learning (AutoML) on graphs which combines the strength of graph machine learning and AutoML together, is gaining attention from the research community. Therefore, we comprehensively survey AutoML on graphs in this paper, primarily focusing on hyper-parameter optimization (HPO) and neural architecture search (NAS) for graph machine learning. We further overview libraries related to automated graph machine learning and in-depth discuss AutoGL, the first dedicated open-source library for AutoML on graphs. In the end, we share our insights on future research directions for automated graph machine learning. This paper is the first systematic and comprehensive review of automated machine learning on graphs to the best of our knowledge.

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