Students' performance analysis system using cumulative predictor algorithm

Rose, J. Dafni; Vijayakumar, K.; Sakthivel, S.

doi:10.1504/ijris.2019.099848

Cited by 20 publications

(8 citation statements)

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“…In Fig 6, the graph demonstrates the comparison of Runtime results for synthetic traces obtained by the algorithms of MAGNETIC, PABFD, and E-eco [26] [27]. Now, the suggested method of MAGNETIC extends the Runtime for all synthetic traces compared to other present strategies of PABFD and E-eco [22,23].…”

Section: Fig6runtime (Seconds) Of Different Algorithms For Synthetimentioning

confidence: 92%

A Genetic Algorithm based task scheduling procedure for Cost-Efficient Cloud Data Centers

Prabha¹,

Ramesh²,

Geetha³

2021

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

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In the current situation, Cloud computing cut itself as a developing innovation which empowers the association to use equipment, programming and applications with no forthright expense over the internet space. The main provocation for the cloud provider is the means of providing productively and adequately the hidden computing assets like virtual machines, arrange, capacity units, and transmission capacity and so forth ought to be overseen with the goal that no computing gadget is in under-usage or over-use state in a unique domain. A decent task scheduling method is constantly required for the dynamic assignment of the task to evade such a circumstance. Through this paper we are going to introduce the Genetic Algorithm based task scheduling procedure, which will disperse the heap adequately among the virtual machine so the general reaction time (QoS) ought to be insignificant. An examination of this Genetic Algorithm based task scheduling procedure is performed on CloudSim test system which shows that, this will beat the current strategies like Greedy based, First-Come first-Serve (FCFS) methods.

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Section: Fig6runtime (Seconds) Of Different Algorithms For Synthetimentioning

confidence: 92%

A Genetic Algorithm based task scheduling procedure for Cost-Efficient Cloud Data Centers

Prabha¹,

Ramesh²,

Geetha³

2021

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

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“…Foreseeing the Higher Secondary evaluation can give an appropriate thought of what an understudy will most likely accomplish throughout everyday life. [13] Understudies experience immaturity before their HSC test and amid this stage, they are increasingly susceptible to their environment [16].…”

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 patterns in the clothinggenresareclassifiedusingtheSupportVector Machine (SVM) algorithm.Hidayatiet al [1] have proposed a novel approach for automatically classifying upper wear and lower wear genres based on the different style elements present on the clothing genres. For this purpose collect full-body human images from various online websites like Amazon, Zara .Then perform body parts detection using human body part detector [15] [16]. Collar, print style, shoulder skin, front button, sleeve are some of the visual features for upper wear genres.…”

Section: Image Based Clothingclassification Techniquesmentioning

confidence: 99%

Clothing Genre Recognition System Using Image Processing Techniques- A Survey

Saranya¹,

Geetha²

2021

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

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Nowadays, Clothing business is one of the mostimportantcomponentsinthee-commerceindustry. So, there is plenty of online clothing sites are available where people can search and retrieve the most clothing items for their user query image. Clothing genre recognition is a very active topic in computer vision and multimedia research. In the textile industry, image processing techniques provide sensitive attention in the fieldoftheimage-basedclothingrecognitionsystem.The sequence of cloth images can be given as input to the recognition system. This clothing genre recognition system helps to detect the patterns and features of cloths which helps to classify them using effective feature extraction and classification algorithms. Feature extractiontechniquescanbeusedtoobtainfeaturesfrom thecloths.Classificationalgorithmsfromsoftcomputing help to automatically classify clothes genres depending on style elements and their salient visual features. Deep learning and Support Vector Machine (SVM) classifier achieved better performance in classifying both upper wear and lower wear genres. The main motivations of this paper focus on automatically classifying both upper wearandlowerweargenrefromafull-bodyinputimage. Evaluation metrics like precision, recall, F-score were used to measure the classification accuracy.This paper addresses on issues, challenges, applications, frameworks, tools, and techniques for recognition of clothing genres is carriedout.

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Students' performance analysis system using cumulative predictor algorithm

Cited by 20 publications

References 0 publications

A Genetic Algorithm based task scheduling procedure for Cost-Efficient Cloud Data Centers

A Genetic Algorithm based task scheduling procedure for Cost-Efficient Cloud Data Centers

Data Analysis on Student Proficiency Conjecture and Course Selection Assortment

Clothing Genre Recognition System Using Image Processing Techniques- A Survey

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