Study of Information Mining (DM) and Machine Learning (ML) Strategies on Digital Security

2018 ◽  
Vol 6 (6) ◽  
pp. 1412-1417
Author(s):  
Muralidhara S
Keyword(s):  
Machine Learning ◽  
Information Mining

scholarly journals Special Section Guest Editorial: Change Detection Using Multi-Source Remotely Sensed Imagery

2019 ◽  
Vol 11 (19) ◽  
pp. 2216
Author(s):  
Xin Huang ◽  
Jiayi Li ◽  
Francesca Bovolo ◽  
Qi Wang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Change Detection ◽  
Guest Editorial ◽  
Special Section ◽  
Learning Technologies ◽  
Special Issue ◽  
Information Mining ◽  
Remotely Sensed Imagery ◽  
Detection Technologies

This special issue hosts papers on change detection technologies and analysis in remote sensing, including multi-source sensors, advanced machine learning technologies for change information mining, and the utilization of these technologies in a variety of geospatial applications. The presented results showed improved results when multi-source remote sensed data was used in change detection.

A New Method of Machine Learning Based on Examples

2011 ◽  
Vol 204-210 ◽  
pp. 1078-1081
Author(s):  
De Gan Zhang ◽  
Dong Wang ◽  
Yu Xia Hu ◽  
Xue Jing Kang
Keyword(s):  
Machine Learning ◽  
Classical Method ◽  
Training Data ◽  
New Method ◽  
Information Mining ◽  
Dynamic Correlation

A new method of machine learning based on examples is given in this paper. This method improves the classical method ID3 which learns from static examples. Its limits lie on no comprehension and no memory, and no dynamic correlation. In the new method, it can learn from dynamic examples, the change of data can be learned because the training data is the initial and end process in the interval. All varieties and correlation can be understood and remembered. By experiments, the method can be used as classifier and it has special use in the field of information mining.

scholarly journals Prediction of Student Performance using Machine Learning

2019 ◽  
Vol 8 (6) ◽  
pp. 247-255 ◽  
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Information Mining ◽  
Educational Foundations ◽  
Different Parts

Educational foundations are delivering capable and shrewd understudies and specialists, yet when we think about quality and value of the student's advancement in his profession; it is as yet a challenge or an inquiry. These organizations center on quality in training. Consistently countless alumni from schools and colleges, as for the information gathered from the criticism of students, order an information mining strategy is connected to it. It is a stage to examine the elements influencing the performance of students so as to assess the present understudy execution and make proficient strides in the expectation of the no doubt happening connections between the different parts of learning and to upgrade the nature of instruction in future and help the educational organizers to design in like manner.

scholarly journals Machine learning and its algorithms: A Research

2019 ◽  
Vol 8 (12S2) ◽  
pp. 801-803
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Information Mining

The calculations which are actualized on the machines and which are additionally used to make machines clever are called as AI calculations, likewise they can make sense of how to perform significant assignments by summing up from precedents. Therefore, AI is broadly utilized in software engineering, man-made reasoning and different fields. Be that as it may, creating fruitful AI applications requires comprehension of keen frameworks and calculations required to build it. This paper totals and condenses the kinds of AI types and calculation that are required to develop a shrewd or a specialist framework. These calculations are utilized for different purposes like information mining, picture preparing, prescient examination, and so forth to give some examples. The principle bit of leeway of utilizing AI is that, when a calculation realizes how to manage information, it can do its work consequently.

scholarly journals Phishing Attack Detection using Machine Learning

2020 ◽  
Vol 9 (3) ◽  
pp. 483-487
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Database System ◽  
Attack Detection ◽  
Information Mining ◽  
Points Of Interest ◽  
Secure Information ◽  
Number Of Customers ◽  
The Web

There are number of customers who buy items on the web and make installment through different websites. There are various websites who request that client give delicate information, for example, username, secret word or master card points of interest and so on regularly for noxious reasons. This sort of websites is known as phishing site. With a specific end goal to identify and foresee phishing site, we proposed an astute, adaptable and successful framework that depends on utilizing characterization Data mining calculation. We actualized arrangement calculation and strategies to extricate the phishing informational collections criteria to order their authenticity. The phishing site can be identified in light of some imperative attributes.This application can be utilized by numerous E-trade endeavors to influence the entire exchange to process secure. Information mining calculation utilized as a part of this framework gives better execution when contrasted with other conventional orders calculations. With the assistance of this framework client can likewise buy items online with no delay. Administrator can include phishing site url or phony site url into framework where framework could access and sweep the phishing site and by utilizing calculation, it will add new suspicious watchwords to database. System utilizes machinelearning method to include new catchphrases into database.

scholarly journals Large-Scale Machine Learning on Debugging Machine Learning Systems

2019 ◽  
pp. 355-360
Author(s):  
K. Ravikumar ◽  
M. Maheswaran
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Predictive Analytics ◽  
Learning Systems ◽  
Short Article ◽  
Information Mining ◽  
Investigative Process ◽  
Working Out ◽  
Selection Of

A computation indicated applying Tensor Movement may be accomplished with minimum modify on a wide selection of heterogeneous methods, including cellular devices such as for example devices and pills around large-scale spread methods of a huge selection of products and 1000s of computational units such as for example GPU cards. Even with arrangement, it's frequent to find out restrictions of the design or improvements in the goal notion that necessitate improvements to working out information and parameters. But, by nowadays, there's number frequent knowledge by what these iterations contain, or what debugging resources are required to help the investigative process. As more information becomes accessible, more formidable issues may be tackled. Consequently, device understanding is commonly utilized in pc technology and different fields. But, establishing effective device understanding programs involves an amazing level of "dark art" that's difficult to find in textbooks. This short article summarizes a dozen critical classes that device understanding scientists and practitioners have learned. These calculations are useful for numerous applications like information mining, picture running, predictive analytics, etc. to call a few. The key benefit of applying device understanding is that, when an algorithm finds what direction to go with information, it may do their function automatically.

scholarly journals Visualization of Fire Weather Index in Aek Godang Based on the Machine Learning Approach

2021 ◽  
Vol 1 (1) ◽  
pp. 22-28
Author(s):  
Kerista Tarigan ◽  
Edison Kurniawan ◽  
Sri Wahyuni
Keyword(s):  
Machine Learning ◽  
Forest Fires ◽  
Environmental Harm ◽  
Learning Approach ◽  
Fire Weather ◽  
Weather Index ◽  
Information Mining ◽  
Fire Weather Index ◽  
Machine Learning Approach

Forest fires are a major natural issue, making temperate and environmental harm whereas dangering human lives. The examined and study for timberland fire had been worn out Aek Godang, Northern Sumatera, Indonesia. There are 26 hotspots in 2017 near Aek Godang, North Sumatera, Indonesia. In this consider, we utilize an information mining approach to prepare and test the information of woodland fire and Fire Weather Index (FWI) from meteorological information. The point of this ponders to anticipate the burned range and distinguish the woodland fire in Aek Godang ranges, North Sumatera. The result of this considers shown the Fire battling and avoidance movement may be one reason for the watched need of relationship. The reality that this dataset exists demonstrates that there's as of now a few exertions going into fire avoidance.

Student Performance Analysis Using Machine Learning Techniques

2020 ◽  
Vol 17 (8) ◽  
pp. 3366-3369
Author(s):  
K. Mohana Prasad ◽  
Naidu Reddy Roop Sumanth ◽  
Rimmalapudi Praveenkumar ◽  
S. L. Jany Shabu ◽  
J. Refonaa
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Student Performance ◽  
Machine Learning Techniques ◽  
Information Mining ◽  
Learning Techniques ◽  
Segment Information

Execution investigation of result dependent on learning is a framework which will make progress toward greatness at various levels and assorted measurements in the field of student’s inclinations. This paper proposes a total EDM structure in a type of a standard based recommender framework that isn’t created to break down and foresee the students presentation just, yet additionally to display the explanations for it. The proposed structure examines the student’s segment information, study related and mental attributes to remove all conceivable information from understudies, instructors and guardians. Looking for the most elevated conceivable exactness in scholarly execution forecast utilizing a lot of ground-breaking information mining systems. The system prevails to feature the student’s powerless focuses and give suitable suggestions. The sensible contextual investigation that has been led on 200 understudies demonstrates the extraordinary presentation of the proposed system in examination with the current ones.

scholarly journals Comprehensive Models Towards for Feature Extraction and Recognition in Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 3638-3641
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Feature Extraction ◽  
Statistical Models ◽  
Additional Data ◽  
Information Mining ◽  
Precise Information ◽  
Points Of Interest ◽  
Methods And Techniques

The scientific study of algorithms and statistical Models is often referred to as Machine learning that computer Systems use to perform specific tasks without using explicit Instructions, relaying patterns and interfaces instead. Ongoing Improvements in data frameworks just as computerization of Business forms by associations have prompted a quicker, simpler and progressively precise information investigation. Information Mining and AI procedures have been utilized progressively in the Examination of information indifferent fields extending from Medication to fund, training and vitality applications. Artificial Intelligence procedures make it conceivable to deduct important additional data from that information handled by information mining. Such important and noteworthy data causes associations to build up their future arrangements on a sounder premise, and to increase significant points of interest as far as time and cost. This investigation applies grouping calculations utilized in information mining and AI methods on that information got from people during the professional direction procedure, and attempt to decide the most suitable calculation. In this paper we study all methods and techniques used in data mining and machine learning and decide the best algorithm for machine learning

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