A Comprehensive Survey on Identification of Malware Types and Malware Classification Using Machine Learning Techniques

2021 ◽  
Author(s):  
Nagababu Pachhala ◽  
S. Jothilakshmi ◽  
Bhanu Prakash Battula
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Malware Classification ◽  
Learning Techniques ◽  
Comprehensive Survey

scholarly journals A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 565
Author(s):  
Satoshi Takahashi ◽  
Masamichi Takahashi ◽  
Shota Tanaka ◽  
Shunsaku Takayanagi ◽  
Hirokazu Takami ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Situation ◽  
Machine Learning Techniques ◽  
Current Status ◽  
Omics Data ◽  
Amount Of Information ◽  
Oncology Research ◽  
Omics Analysis ◽  
Learning Techniques ◽  
Comprehensive Survey

Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques.

scholarly journals A Survey on Software-Defined Wireless Sensor Networks: Current status, machine learning approaches and major challenges

2021 ◽  
Author(s):  
Fabian Fernando Jurado Lasso ◽  
Letizia Marchegiani ◽  
Jesus Fabian Jurado ◽  
Adnan Abu Mahfouz ◽  
Xenofon Fafoutis
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Management Policy ◽  
Wireless Sensor ◽  
Current Status ◽  
Policy Enforcement ◽  
Learning Approaches ◽  
Current State ◽  
Learning Techniques ◽  
Comprehensive Survey

This paper is aimed to present a comprehensive survey of relevant research over the period 2012-2021 of Software-Defined Wireless Sensor Network (SDWSN) proposals and Machine Learning (ML) techniques to perform network management, policy enforcement, and network configuration functions. This survey provides helpful information and insights to the scientific and industrial communities, and professional organisations interested in SDWSNs, mainly the current state-of-art, machine learning techniques, and open issues.

Machine Learning for Agents and Multi-Agent Systems

2011 ◽  
pp. 1-26
Author(s):  
Daniel Kudenko ◽  
Dimitar Kazakov ◽  
Eduardo Alonso
Keyword(s):  
Machine Learning ◽  
Autonomous Agents ◽  
Machine Learning Techniques ◽  
Multi Agent Systems ◽  
Learning Agents ◽  
Agent Systems ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Multi Agent ◽  
The Relationship

In order to be truly autonomous, agents need the ability to learn from and adapt to the environment and other agents. This chapter introduces key concepts of machine learning and how they apply to agent and multi-agent systems. Rather than present a comprehensive survey, we discuss a number of issues that we believe are important in the design of learning agents and multi-agent systems. Specifically, we focus on the challenges involved in adapting (originally disembodied) machine learning techniques to situated agents, the relationship between learning and communication, learning to collaborate and compete, learning of roles, evolution and natural selection, and distributed learning. In the second part of the chapter, we focus on some practicalities and present two case studies.

Machine Learning for Agents and Multi-Agent Systems

2011 ◽  
pp. 403-420
Author(s):  
Daniel Kudenko ◽  
Dimitar Kazakov ◽  
Eduardo Alonso
Keyword(s):  
Machine Learning ◽  
Autonomous Agents ◽  
Machine Learning Techniques ◽  
Multi Agent Systems ◽  
Learning Agents ◽  
Agent Systems ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Multi Agent ◽  
The Relationship

In order to be truly autonomous, agents need the ability to learn from and adapt to the environment and other agents. This chapter introduces key concepts of machine learning and how they apply to agent and multi-agent systems. Rather than present a comprehensive survey, we discuss a number of issues that we believe are important in the design of learning agents and multi-agent systems. Specifically, we focus on the challenges involved in adapting (originally disembodied) machine learning techniques to situated agents, the relationship between learning and communication, learning to collaborate and compete, learning of roles, evolution and natural selection, and distributed learning. In the second part of the chapter, we focus on some practicalities and present two case studies.

scholarly journals LITERATURE SURVEY ON MACHINE LEARNING BASED TECHNIQUES IN MEDICAL DATAANALYSIS

2019 ◽  
pp. 1-4
Author(s):  
Lavanya Vemulapalli
Keyword(s):  
Machine Learning ◽  
Disease Diagnosis ◽  
Research Area ◽  
Machine Learning Techniques ◽  
Intelligent Algorithms ◽  
The Past ◽  
Disease Prognosis ◽  
Learning Techniques ◽  
Machine Learning Applications ◽  
Comprehensive Survey

Machine Learning plays a significant role among the areas of Artificial Intelligence (AI). During recent years, Machine Learning (ML) has been attracting many researchers, and it has been successfully applied in many fields such as medical, education, forecasting etc., Right now, the diagnosis of diseases is mostly from expert's decision. Diagnosis is a major task in clinical science as it is crucial in determining if a patient is having the disease or not. This in turn decides the suitable path of treatment for disease diagnosis. Applying machine learning techniques for disease diagnosis using intelligent algorithms has been a hot research area of computer science. This paper throws a light on the comprehensive survey on the machine learning applications in the medical disease prognosis during the past decades

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