Avian Based Intelligent Algorithm to Provide Zero Tolerance Load Balancer for Cloud Based Computing Platforms

2019 ◽  
Vol 11 (4) ◽  
pp. 42-67
Author(s):  
Sivashanmugam G. ◽  
SP Shantharajah ◽  
N.Ch.Sriman Narayana Iyengar
Keyword(s):  
Artificial Intelligence ◽  
Performance Metrics ◽  
Zero Tolerance ◽  
Intelligent Algorithm ◽  
Artificial Intelligence Techniques ◽  
Investigation Area ◽  
Characteristic Behavior ◽  
Computing Platforms ◽  
Load Balancer

Artificial intelligence changes the art of solving the computational problems from defined computing structures into disorganized computing structures with the interference of naturally-inspired activities. On this basis, many algorithms were proposed and applied successfully, those results give complete as well as partial solutions for their applications. In this article, the authors consider the same phenomena and the investigation area is a load balancer. The legitimate aim is to bring a zero-tolerance load balancer by applying artificial intelligence techniques. For this, the authors introduced an algorithm named the Eagle Fly algorithm, which is a natural inspired algorithm, completely based on eagle characteristic behavior. From this, the authors examine how tasks are fetched, computed, and server on-demands are supported. This article proves performance metrics received from eagle fly algorithm is good and the results were compared with other existing natural inspired algorithms.

A Survey on Artificial Intelligence Techniques to Prevent Cyber Crime

2018 ◽  
Vol 8 (5) ◽  
pp. 80
Author(s):  
Juveriya Afreen
Keyword(s):  
Artificial Intelligence ◽  
Cyber Security ◽  
Data Security ◽  
Human Reasoning ◽  
Cyber Crime ◽  
Artificial Intelligence Techniques

Abstract-- With increase in complexity of data, security, it is difficult for the individuals to prevent the offence. Thus, by using any automation or software it’s not possible by only using huge fixed algorithms to overcome this. Thus, we need to look for something which is robust and feasible enough. Hence AI plays an epitome role to defense such violations. In this paper we basically look how human reasoning along with AI can be applied to uplift cyber security.

SISTEM PAKAR MENDIAGNOSA VIRUS MAYORA DENGAN METODE VARIABLE CENTERED INTELLIGENT RULE SYSTEM (VCIRS)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Siti Nurhena ◽  
Nelly Astuti Hasibuan ◽  
Kurnia Ulfa
Keyword(s):  
Artificial Intelligence ◽  
Immune System ◽  
Expert System ◽  
Expert Systems ◽  
Dengue Hemorrhagic Fever ◽  
Hemorrhagic Fever ◽  
Visual Basic ◽  
Artificial Intelligence Techniques ◽  
Individual Needs ◽  
Rule System

The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms. Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms. With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms.Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms.With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)

Use of Artificial Intelligence Techniques in the Prediction of the Pre and Asymptomatic Patient to COVID-19

2020 ◽  
Author(s):  
Efain Pantaleón-Matamoros ◽  
Isabelle Ribeiro Barbosa Mirabal ◽  
Orivaldo Vieira de Santana Júnior ◽  
Douglas de Souza Carvalho ◽  
Efrain Marcelo Pulgar Pantaleón ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Asymptomatic Patient ◽  
Artificial Intelligence Techniques

Artificial Intelligence for Skin Cancer Detection: A Scoping Review (Preprint)

2020 ◽  
Author(s):  
Abdulrahman Takiddin ◽  
Jens Schneider ◽  
Yin Yang ◽  
Alaa Abd-Alrazaq ◽  
Mowafa Househ
Keyword(s):  
Artificial Intelligence ◽  
Skin Cancer ◽  
Scoping Review ◽  
Performance Metrics ◽  
Data Extraction ◽  
Characteristic Curve ◽  
Evaluation Metrics ◽  
Cancer Type ◽  
Computer Algorithms ◽  
Scoping Reviews

BACKGROUND Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, Artificial Intelligence (AI) tools are being used, including shallow and deep machine learning-based techniques that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. OBJECTIVE The aim of this study is to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examines the reliability of the selected papers by studying the correlation between the dataset size and number of diagnostic classes with the performance metrics used to evaluate the models. METHODS We conducted a systematic search for articles using IEEE Xplore, ACM DL, and Ovid MEDLINE databases following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. The study included in this scoping review had to fulfill several selection criteria; to be specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were conducted by two reviewers independently. Extracted data were synthesized narratively, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. RESULTS We retrieved 906 papers from the 3 databases, but 53 studies were eligible for this review. While shallow techniques were used in 14 studies, deep techniques were utilized in 39 studies. The studies used accuracy (n=43/53), the area under receiver operating characteristic curve (n=5/53), sensitivity (n=3/53), and F1-score (n=2/53) to assess the proposed models. Studies that use smaller datasets and fewer diagnostic classes tend to have higher reported accuracy scores. CONCLUSIONS The adaptation of AI in the medical field facilitates the diagnosis process of skin cancer. However, the reliability of most AI tools is questionable since small datasets or low numbers of diagnostic classes are used. In addition, a direct comparison between methods is hindered by a varied use of different evaluation metrics and image types.

scholarly journals Using artificial intelligence techniques for COVID-19 genome analysis

2021 ◽  
Author(s):  
M. Saqib Nawaz ◽  
Philippe Fournier-Viger ◽  
Abbas Shojaee ◽  
Hamido Fujita
Keyword(s):  
Artificial Intelligence ◽  
Genome Analysis ◽  
Artificial Intelligence Techniques

Rainfall forecasting in upper Indus basin using various artificial intelligence techniques

2021 ◽  
Author(s):  
Muhammad Hammad ◽  
Muhammad Shoaib ◽  
Hamza Salahudin ◽  
Muhammad Azhar Inam Baig ◽  
Mudasser Muneer Khan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Indus Basin ◽  
Rainfall Forecasting ◽  
Artificial Intelligence Techniques ◽  
Upper Indus Basin
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