scholarly journals Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey

2021 ◽  
Vol 11 (21) ◽  
pp. 10064
Author(s):  
Wajid Ali ◽  
Wanli Zuo ◽  
Rahman Ali ◽  
Xianglin Zuo ◽  
Gohar Rahman
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Question Answering ◽  
Performance Enhancement ◽  
Learning Technologies ◽  
Future Research ◽  
Discourse Comprehension ◽  
Natural Languages ◽  
Significant Area ◽  
Area Of Concern

The era of big textual corpora and machine learning technologies have paved the way for researchers in numerous data mining fields. Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from researchers. Causality (cause-effect relations) serves as an essential category of relationships, which plays a significant role in question answering, future events predication, discourse comprehension, decision making, future scenario generation, medical text mining, behavior prediction, and textual prediction entailment. While, decades of development techniques for CM are still prone to performance enhancement, especially for ambiguous and implicitly expressed causalities. The ineffectiveness of the early attempts is mainly due to small, ambiguous, heterogeneous, and domain-specific datasets constructed by manually linguistic and syntactic rules. Many researchers have deployed shallow machine learning (ML) and deep learning (DL) techniques to deal with such datasets, and they achieved satisfactory performance. In this survey, an effort has been made to address a comprehensive review of some state-of-the-art shallow ML and DL approaches in CM. We present a detailed taxonomy of CM and discuss popular ML and DL approaches with their comparative weaknesses and strengths, applications, popular datasets, and frameworks. Lastly, the future research challenges are discussed with illustrations of how to transform them into productive future research directions.

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.

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.

Machine Learning in 5G Multimedia Communications

2021 ◽  
pp. 204-225
Author(s):  
Dragorad A. Milovanovic ◽  
Zoran S. Bojkovic ◽  
Dragan D. Kukolj
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Deep Learning ◽  
Complex Networks ◽  
Future Research ◽  
Fifth Generation ◽  
Data Formats ◽  
Research Challenges ◽  
Wireless Environments ◽  
Special Case

Machine learning (ML) has evolved to the point that this technique enhances communications and enables fifth-generation (5G) wireless networks. ML is great to get insights about complex networks that use large amounts of data, and for predictive and proactive adaptation to dynamic wireless environments. ML has become a crucial technology for mobile broadband communication. Special case goes to deep learning (DL) in immersive media. Through this chapter, the goal is to present open research challenges and applications of ML. An exploration of the potential of ML-based solution approaches in the context of 5G primary eMBB, mMTC, and uHSLLC services is presented, evaluating at the same time open issues for future research, including standardization activities of algorithms and data formats.

scholarly journals A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

2020 ◽  
Vol 12 (9) ◽  
pp. 3760 ◽  
Author(s):  
Manuel Woschank ◽  
Erwin Rauch ◽  
Helmut Zsifkovits
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Scientific Literature ◽  
Future Research ◽  
Industrial Enterprises ◽  
Smart Logistics ◽  
Research Findings ◽  
Ongoing Development ◽  
Research Initiatives

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

Comparison between support vector machine and deep learning, machine-learning technologies for detecting epiretinal membrane using 3D-OCT

2018 ◽  
Vol 39 (8) ◽  
pp. 1871-1877 ◽  
Author(s):  
Tomoaki Sonobe ◽  
Hitoshi Tabuchi ◽  
Hideharu Ohsugi ◽  
Hiroki Masumoto ◽  
Naohumi Ishitobi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Epiretinal Membrane ◽  
Learning Technologies ◽  
Support Vector ◽  
Learning Machine

scholarly journals A Survey on Machine Learning and Deep Learning based Quality of Service aware Protocols for Software Defined Networks

2021 ◽  
Author(s):  
Hiren Kumar Deva Sarma
Keyword(s):  
Machine Learning ◽  
Quality Of Service ◽  
Deep Learning ◽  
Service Level Agreement ◽  
Service Level ◽  
Future Research ◽  
Traffic Classification ◽  
Software Defined Networks ◽  
Research Issues

<p>Quality of Service (QoS) is one of the most important parameters to be considered in computer networking and communication. The traditional network incorporates various quality QoS frameworks to enhance the quality of services. Due to the distributed nature of the traditional networks, providing quality of service, based on service level agreement (SLA) is a complex task for the network designers and administrators. With the advent of software defined networks (SDN), the task of ensuring QoS is expected to become feasible. Since SDN has logically centralized architecture, it may be able to provide QoS, which was otherwise extremely difficult in traditional network architectures. Emergence and popularity of machine learning (ML) and deep learning (DL) have opened up even more possibilities in the line of QoS assurance. In this article, the focus has been mainly on machine learning and deep learning based QoS aware protocols that have been developed so far for SDN. The functional areas of SDN namely traffic classification, QoS aware routing, queuing, and scheduling are considered in this survey. The article presents a systematic and comprehensive study on different ML and DL based approaches designed to improve overall QoS in SDN. Different research issues & challenges, and future research directions in the area of QoS in SDN are outlined. <b></b></p>

Extraction of drug–drug interaction using neural embedding

2018 ◽  
Vol 16 (06) ◽  
pp. 1840027 ◽  
Author(s):  
Wen Juan Hou ◽  
Bamfa Ceesay
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Interaction ◽  
Relevant Information ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Learning Approach ◽  
Learning Techniques ◽  
Drug Drug Interaction

Information on changes in a drug’s effect when taken in combination with a second drug, known as drug–drug interaction (DDI), is relevant in the pharmaceutical industry. DDIs can delay, decrease, or enhance absorption of either drug and thus decrease or increase their action or cause adverse effects. Information Extraction (IE) can be of great benefit in allowing identification and extraction of relevant information on DDIs. We here propose an approach for the extraction of DDI from text using neural word embedding to train a machine learning system. Results show that our system is competitive against other systems for the task of extracting DDIs, and that significant improvements can be achieved by learning from word features and using a deep-learning approach. Our study demonstrates that machine learning techniques such as neural networks and deep learning methods can efficiently aid in IE from text. Our proposed approach is well suited to play a significant role in future research.

scholarly journals Recent advances of deep learning in psychiatric disorders

2020 ◽  
Vol 3 (3) ◽  
pp. 202-213
Author(s):  
Lu Chen ◽  
Chunchao Xia ◽  
Huaiqiang Sun
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Psychiatric Disorders ◽  
Language Processing ◽  
Visual Recognition ◽  
Learning Algorithm ◽  
Future Research ◽  
Academic World ◽  
Recent Advances ◽  
Conventional Machine

ABSTRACT Deep learning (DL) is a recently proposed subset of machine learning methods that has gained extensive attention in the academic world, breaking benchmark records in areas such as visual recognition and natural language processing. Different from conventional machine learning algorithm, DL is able to learn useful representations and features directly from raw data through hierarchical nonlinear transformations. Because of its ability to detect abstract and complex patterns, DL has been used in neuroimaging studies of psychiatric disorders, which are characterized by subtle and diffuse alterations. Here, we provide a brief review of recent advances and associated challenges in neuroimaging studies of DL applied to psychiatric disorders. The results of these studies indicate that DL could be a powerful tool in assisting the diagnosis of psychiatric diseases. We conclude our review by clarifying the main promises and challenges of DL application in psychiatric disorders, and possible directions for future research.

Internet of Things in Healthcare

2020 ◽  
pp. 23-39
Author(s):  
Rajasekaran Thangaraj ◽  
Sivaramakrishnan Rajendar ◽  
Vidhya Kandasamy
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Deep Learning ◽  
Internet Of Things ◽  
Healthcare Systems ◽  
Wearable Devices ◽  
Healthcare Research ◽  
Learning Technologies ◽  
Learning Approaches ◽  
And Performance

Healthcare motoring has become a popular research in recent years. The evolution of electronic devices brings out numerous wearable devices that can be used for a variety of healthcare motoring systems. These devices measure the patient's health parameters and send them for further processing, where the acquired data is analyzed. The analysis provides the patients or their relatives with the medical support required or predictions based on the acquired data. Cloud computing, deep learning, and machine learning technologies play a prominent role in processing and analyzing the data respectively. This chapter aims to provide a detailed study of IoT-based healthcare systems, a variety of sensors used to measure parameters of health, and various deep learning and machine learning approaches introduced for the diagnosis of different diseases. The chapter also highlights the challenges, open issues, and performance considerations for future IoT-based healthcare research.

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