scholarly journals Ransomware Detection Using the Dynamic Analysis and Machine Learning: A Survey and Research Directions

2021 ◽  
Vol 12 (1) ◽  
pp. 172
Author(s):  
Umara Urooj ◽  
Bander Ali Saleh Al-rimy ◽  
Anazida Zainal ◽  
Fuad A. Ghaleb ◽  
Murad A. Rassam
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Dynamic Analysis ◽  
Future Research ◽  
Future Directions ◽  
Research Directions ◽  
Single Study ◽  
The Way

Ransomware is an ill-famed malware that has received recognition because of its lethal and irrevocable effects on its victims. The irreparable loss caused due to ransomware requires the timely detection of these attacks. Several studies including surveys and reviews are conducted on the evolution, taxonomy, trends, threats, and countermeasures of ransomware. Some of these studies were specifically dedicated to IoT and android platforms. However, there is not a single study in the available literature that addresses the significance of dynamic analysis for the ransomware detection studies for all the targeted platforms. This study also provides the information about the datasets collection from its sources, which were utilized in the ransomware detection studies of the diverse platforms. This study is also distinct in terms of providing a survey about the ransomware detection studies utilizing machine learning, deep learning, and blend of both techniques while capitalizing on the advantages of dynamic analysis for the ransomware detection. The presented work considers the ransomware detection studies conducted from 2019 to 2021. This study provides an ample list of future directions which will pave the way for future research.

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.

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.

scholarly journals Selective Harvesting Robotics: Current Research, Trends, and Future Directions

2021 ◽  
Author(s):  
Gert Kootstra ◽  
Xin Wang ◽  
Pieter M. Blok ◽  
Jochen Hemming ◽  
Eldert van Henten
Keyword(s):  
Review Paper ◽  
Production Systems ◽  
Future Research ◽  
Limiting Factor ◽  
Future Directions ◽  
Research Directions ◽  
Selective Harvesting ◽  
Important Solution ◽  
Future Research Directions ◽  
Current Systems

Abstract Purpose of Review The world-wide demand for agricultural products is rapidly growing. However, despite the growing population, labor shortage becomes a limiting factor for agricultural production. Further automation of agriculture is an important solution to tackle these challenges. Recent Findings Selective harvesting of high-value crops, such as apples, tomatoes, and broccoli, is currently mainly performed by humans, rendering it one of the most labor-intensive and expensive agricultural tasks. This explains the large interest in the development of selective harvesting robots. Selective harvesting, however, is a challenging task for a robot, due to the high levels of variation and incomplete information, as well as safety. Summary This review paper provides an overview of the state of the art in selective harvesting robotics in three different production systems; greenhouse, orchard, and open field. The limitations of current systems are discussed, and future research directions are proposed.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

scholarly journals “Nanomaterials of curcumin-hyaluronic acid”: their various methods of formulations, clinical and therapeutic applications, present gap, and future directions

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Tanzeel Rehman Charan ◽  
Muhammad Aqeel Bhutto ◽  
Mihr Ali Bhutto ◽  
Azhar Ali Tunio ◽  
Ghulam Murtaza Khuhro ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Hyaluronic Acid ◽  
Future Research ◽  
Main Body ◽  
Future Directions ◽  
Research Directions ◽  
Safety And Efficacy ◽  
Therapeutic Applications ◽  
Future Research Directions ◽  
Neural Disorders

Abstract Background Nanomaterials of curcumin with hyaluronic acid have gained a lot of attention for potential therapeutic applications of curcumin and hyaluronic acid with or without other additional drugs. Overall studies of curcumin and hyaluronic acid show that nanomaterials of curcumin with hyaluronic acid accelerate the efficacy of curcumin in the treatment of various disorders like arthritis, cancer, hepatic fibrosis, neural disorders, wound healing, and skin regeneration, it is largely due to the combined effect of hyaluronic acid and curcumin. However, due to limited clinical trials and experiments on humans and animals, there is a substantial gap in research for the safety and efficacy of nanomaterials of curcumin-hyaluronic acid in the treatment of curcumin and hyaluronic acid targeted diseases and disorders. Main body of the abstract In this current review, we have first described various reported synthetic nanomaterials of curcumin-hyaluronic acid, then in the next section, we have described various fields, disorders, and diseases where these are being applied and in the final section of this review, we discussed the research gap, and future research directions needed to propose the fabricated nanocurcumin-hyaluronic acid biomaterials. Short conclusion There are substantial gaps in research for the safety and efficacy of nanomaterials of curcumin with hyaluronic acid due to limited available data of clinical trials and experiments of nanocurcumin-hyaluronic acid biomaterials on humans and animals. So, it entirely requires serious and committed efforts through the well-organized system of practical and clinical trials which provide results, data, and detections that lead to the formulation of the best drug from curcumin with hyaluronic acid for the treatment of curcumin and hyaluronic acid targeted diseases and disorders.

scholarly journals Software Testing: Issues and Challenges of Artificial Intelligence & Machine Learning

2021 ◽  
Vol 12 (1) ◽  
pp. 101-112
Author(s):  
Kishore Sugali ◽  
Chris Sprunger ◽  
Venkata N Inukollu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Software Testing ◽  
Future Research ◽  
Critical Component ◽  
Development Methodology ◽  
Testing Strategies ◽  
History Of ◽  
Shed Light ◽  
The Way

The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has been an increase in popularity for applications that implement AI and ML technology. As with traditional development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development. Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For future research, this study has key implications. Each of the challenges outlined in this paper is ideal for further investigation and has great potential to shed light on the way to more productive software testing strategies and methodologies that can be applied to AI/ML applications.

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.

Potentials of Cryptocurrency Entrepreneurs in Transforming Global Cities and Regions

2021 ◽  
pp. 1165-1187
Author(s):  
Ismaila Rimi Abubakar ◽  
Abubakar U. Benna ◽  
Umar G. Benna
Keyword(s):  
Financial Markets ◽  
Development Trend ◽  
Cutting Edge ◽  
Future Research ◽  
Global Cities ◽  
Research Directions ◽  
Computing Technologies ◽  
New Development ◽  
Future Research Directions ◽  
The Way

The emergence of digital currencies is substantially influencing the growth of global financial markets and cities. Cryptocurrency entrepreneurs (CEs) are reshaping global cities and regions by transforming the way we live, work and interact. This chapter explores how the entrepreneurs use cryptocurrency assets and their underpinning computing technologies to transform the dysfunctional and evolving global cities. The CEs generate funds and create cutting-edge technologies to meet the challenges faced by cities, including unemployment, inadequate and rundown infrastructure and facilities as well as for new development to meet the needs of massive future urbanization. The chapter is organized in five parts. It first introduces the study and presents a background on the concepts of blockchain technologies and cryptocurrency, their emergence and development trend. It then discusses the rise of global cities and how technology impacts them, followed by the potentials and challenges of CEs in transforming global cities and regions. It ends with conclusion and future research directions.

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