Anti-Collision & Obstacle Notifier Using Machine Learning and Networking Technologies in VANET

<div>With the advancement in drone technology;</div><div>in just a few years; drones will be assisting humans</div><div>in every domain: But there are many challenges to</div><div>be tackled; communication being the chief one: This</div><div>paper aims at providing insights into the latest UAV</div><div>(Unmanned Aerial Vehicle ) communication technolo-</div><div>gies through investigation of suitable task modules;</div><div>antennas; resource handling platforms; and network</div><div>architectures: Additionally; we explore techniques such</div><div>as machine learning and path planning to enhance exist-</div><div>ing drone communication methods:Encryption and opti-</div><div>mization techniques for ensuring long􀀀lasting and se-</div><div>cure communications; as well as for power management;</div><div>are discussed:Moreover; applications of UAV networks</div><div>for di?erent contextual uses ranging from navigation to</div><div>surveillance; URLLC (Ultra-reliable and low􀀀latency</div><div>communications); edge computing and work related</div><div>to arti?cial intelligence are examined: In particular;</div><div>the intricate interplay between UAV; advanced cellu-</div><div>lar communication; and internet of things constitutes</div><div>one of the focal points of this paper: The survey en-</div><div>compasses lessons learned; insights; challenges; open</div><div>issues; and future directions in UAV communications:</div>

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Performance Analysis of Proposed Hybrid Machine Learning Model for Efficient Intrusion Detection

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.e3467.049620 ◽

2020 ◽

Vol 9 (6) ◽

pp. 904-911

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Detection System ◽

Hybrid Approach ◽

Attack Detection ◽

Cyber Attacks ◽

Machine Learning Algorithms ◽

The Internet ◽

Detection Techniques ◽

Networking Technologies

At present networking technologies has provided a better medium for people to communicate and exchange information on the internet. This is the reason in the last ten years the number of internet users has increased exponentially. The high-end use of network technology and the internet has also presented many security problems. Many intrusion detection techniques are proposed in combination with KDD99, NSL-KDD datasets. But there are some limitations of available datasets. Intrusion detection using machine learning algorithms makes the detection system more accurate and fast. So in this paper, a new hybrid approach of machine learning combining feature selection and classification algorithms is presented. The model is examined with the UNSW NB15 intrusion dataset. The proposed model has achieved better accuracy rate and attack detection also improved while the false attack rate is reduced. The model is also successful to accurately classify rare cyber attacks like worms, backdoor, and shellcode.

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A prototype analysis of machine learning methodologies for sentiment analysis of social networks

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.7.11436 ◽

2018 ◽

Vol 7 (2.7) ◽

pp. 963

Author(s):

Vijay Kumar Atmakur ◽

Dr P.Siva Kumar

Keyword(s):

Machine Learning ◽

Social Networks ◽

Sentiment Analysis ◽

Opinion Mining ◽

Support Vector ◽

Learning Approaches ◽

Social Network Data ◽

Networking Technologies ◽

Reference Classes ◽

Analysis Of Social Networks

In present day’s social networking technologies are increased because of different user’s communication with each others. There are different types of networks are available in present situations like face book, twitter and LinkedIn. These are the valuable resources for data mining applications because of prevalence presents of different user’s information present in outside environment. Sentiment analysis is the process that defines attitudes, views, emotions and opinions from text, database sources and tweets. Sentiment analysis involves to categorize data based on different opinions like positive and negative or neutral reference classes. In this paper, we analyze different machine learning approaches to define sentiment analysis on social networks. This paper describes comparative analysis of existing machine learning approaches to classify text and other reference classes to evaluate different metric representations. And also this paper describes different machine learning methodologies like Naïve Bayesian, Entropy max and support vector machine (SVM) research on social network data streams. And also discuss major innovations to evaluate different procedures and challenges of analysis of sentiment or opinion mining aspects in present social networks.

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Communication and Networking Technologies for UAVs: A Survey

10.36227/techrxiv.12912449 ◽

2020 ◽

Author(s):

chathuranga basnayaka

Keyword(s):

Machine Learning ◽

Power Management ◽

Lessons Learned ◽

Low Latency ◽

Focal Points ◽

Future Directions ◽

Vehicle Communication ◽

Work Related ◽

Aerial Vehicle ◽

Networking Technologies

<div>With the advancement in drone technology;</div><div>in just a few years; drones will be assisting humans</div><div>in every domain: But there are many challenges to</div><div>be tackled; communication being the chief one: This</div><div>paper aims at providing insights into the latest UAV</div><div>(Unmanned Aerial Vehicle ) communication technolo-</div><div>gies through investigation of suitable task modules;</div><div>antennas; resource handling platforms; and network</div><div>architectures: Additionally; we explore techniques such</div><div>as machine learning and path planning to enhance exist-</div><div>ing drone communication methods:Encryption and opti-</div><div>mization techniques for ensuring long􀀀lasting and se-</div><div>cure communications; as well as for power management;</div><div>are discussed:Moreover; applications of UAV networks</div><div>for di?erent contextual uses ranging from navigation to</div><div>surveillance; URLLC (Ultra-reliable and low􀀀latency</div><div>communications); edge computing and work related</div><div>to arti?cial intelligence are examined: In particular;</div><div>the intricate interplay between UAV; advanced cellu-</div><div>lar communication; and internet of things constitutes</div><div>one of the focal points of this paper: The survey en-</div><div>compasses lessons learned; insights; challenges; open</div><div>issues; and future directions in UAV communications:</div>

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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