A New Approach of Multilevel Unsupervised Clustering for Detecting Replication Level in Large Image Set

M, Gunashree; Ahmed, Syed Thouheed; M, Sindhuja; P, Bhumika; Anusha, B.; Ishwarya, B.

doi:10.1016/j.procs.2020.04.174

Cited by 8 publications

(8 citation statements)

References 12 publications

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“…A literature survey is brief summary of previous research work on a particular topic [6] [8] [9]. The work should enumerate, summarize and objectively evaluate the previous research.…”

Section: Literature Surveymentioning

confidence: 99%

“…Document classification is a procedure that involves the classifying of text documents to their respective set of classes which are predefined with labels [6,7]. It is a supervised learning approach in which training set of documents {D1, D2….…”

Section: Introductionmentioning

confidence: 99%

“…It has been variously described as a science and the art of justice. Law can be divided into two types 6 namely, public and the private law. public law concerns government, society.…”

mentioning

confidence: 99%

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An Improved Approach of Unstructured Text Document Classification Using Predetermined Text Model and Probability Technique

Ahmed¹,

Flora²,

Hemanth³

et al. 2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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Document classification is the task to split the document set into distinct highly relative classes or groups based on nature of the document contents.Here, an improved approach of document classification called keywordbased document classification (KBDC) is introduced. It focuses on splitting the unstructured text document set into K number of dissimilar classes based on K predetermined keywords text models by improved probability technique. This new system comprises of the following stages. Namely, pre-processing, classification and classifier stage respectively. Initial, the proposed system (KBDC) recognizes all the immaterial existing contents in the input text document through constructed Predetermined Irrelevant Text Pattern Model (PITPM). Next, it divides the pre-processed document set into 'K' different groups or classes by K number of Predetermined Keyword Text Pattern Models (PKTPM) through probability technique, where K denotes the number of groups or classes or models. Finally, the KBDC system classifies the trial test text document without any class label that belongs to either of the existing group based on the K different class models (PKTPs). Experimentation results show that the KBDC is appropriate to split and identifies the unstructured text document set into K distinct extremely comparative classes.

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“…A literature survey is brief summary of previous research work on a particular topic [6] [8] [9]. The work should enumerate, summarize and objectively evaluate the previous research.…”

Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved Approach of Unstructured Text Document Classification Using Predetermined Text Model and Probability Technique

Ahmed¹,

Flora²,

Hemanth³

et al. 2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

View full text Add to dashboard Cite

show abstract

“…Confirmation marks for qualification understudies is that the sum total divided by number of tests acquired of tenth and twelfth test grades passage marks. [15] On account of qualification understudies, it incorporates semester grade checks and passageway grades. The final expectation model might be utilized to build up understudy's performance for each subject.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Analysis on Student Proficiency Conjecture and Course Selection Assortment

Jovith¹,

Saveetha²,

Sharma³

2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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Conventional online instructive frameworks still have weaknesses when contrasted with a genuine study hall education, for example, absence of logical and versatile help, and absence of adaptable help of the introduction and input, absence of the agreeable help among understudies and frameworks. Likewise, they depend on the live information and anticipate the out comings dependent on that. This does exclude information of understudies in a foundation concentrating for some earlier years. This poses a problem for any learning and predictive algorithms to work on them. This work intends to assist the students in articulating their subject, club, project, internship, job preferences. In addition to student profiling, the venture additionally gives counsel to understudies with respect to how the profiles might be utilized to improve their scholastic and quantitative aptitude. In this regard, it is trusted that the profiles will give a valuable device to enable understudies to build up their employability. The profiling framework monitors the learning exercises and connection history of every individual understudy into the understudy profiling database model. In light of this model and along these lines the work demonstrates dynamic learning plans for individual understudies.Data analytic tools, classification techniques, and algorithms will be used to predict the outcomes of the student subject choices. Data (marks and interests) of the students will be classified into clusters upon which self-learning, predictive algorithms will be implemented to cater to students interests and needs. Regression techniques like map reduce will be used to segregate and classify data into definite datasets. It works on these four dimensions like input, comprehending, preparing and understanding. This paper gives the best way to use collaborative filtering strategies for understudy execution forecast. These strategies are frequently utilized in recommender frameworks like Netflix. The essential thought of such frameworks is to use the similitude of users dependent on their evaluations of the things in the system. We have chosen to utilize these procedures in the instructive condition to foresee understudy execution. We compute the comparability of understudies using their examination results, shown by the evaluations of their recently passed subjects.

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“…The training data is processed to all the four algorithms to obtain the trained model and the model is validated using Test data and to predict the most accurate Result or Attack. [8] After detecting the attack, the system will send the user attack notification via Matplot library, text message, email etc., The architectural diagram of our proposed system is shown in the figure given below.…”

Section: Proposed Modelmentioning

confidence: 99%

A Network Intrusion Detection System Using Supervised Learning Techniques

Shalini¹,

Kumar²,

Abhishek³

et al. 2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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With rapid increase in the use of computer network in the fields of industries, education, commerce, social media etc., makes the data security a need of hour. And one of the major threats for data security is the network intrusions, where the attacker intruded the network to steal confidential data's like (passwords, account details, etc.,), tries to stop the services or take control of the user devices. In order to stop this intrusion, the Network Intrusion Detection System is proposed (NIDS). The NIDS monitors the network and if any attack occurs, this system detects the attack and will alert the user about respective attack that occurred. Thus,these systemshelp in preventing intrusions in our networks. And for developing the most accurate NIDS, four different Machine Learning (ML) algorithms are used.The four algorithms used to build a high accuracy system are Random forest, SVM, Naïve Bayes, KNN are used. By identifying the algorithm with highest accuracy, the exact attack can be detected hence the required preventive measures can be taken.

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A New Approach of Multilevel Unsupervised Clustering for Detecting Replication Level in Large Image Set

Cited by 8 publications

References 12 publications

An Improved Approach of Unstructured Text Document Classification Using Predetermined Text Model and Probability Technique

An Improved Approach of Unstructured Text Document Classification Using Predetermined Text Model and Probability Technique

Data Analysis on Student Proficiency Conjecture and Course Selection Assortment

A Network Intrusion Detection System Using Supervised Learning Techniques

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