Notice Board :

Call for Paper
Vol. 3 Issue 12

Submission Start Date:
Dec 01, 2022

Acceptence Notification Start:
Dec 10, 2022

Submission End:
Dec 25, 2022

Final MenuScript Due:
Dec 31, 2022

Publication Date:
Dec 31, 2022
                         Notice Board: Call for PaperVol. 3 Issue 12      Submission Start Date: Dec 01, 2022      Acceptence Notification Start: Dec 10, 2022      Submission End: Dec 25, 2022      Final MenuScript Due: Dec 31, 2022      Publication Date: Dec 31, 2022




Volume I Issue II

Author Name
Vivek Shukla, Kunal Kishore
Year Of Publication
2020
Volume and Issue
Volume 1 Issue 2
Abstract
Currently 27% of an average Materials life cycle cost, both for naturals and composite structures, is spent on inspection and repair which excludes opportunity cost. By the use of SHM it is observed that there is reduction in maintenance and savings up to 75%. New reliable approaches for damage detection such as SHM need to be developed to ensure that the total cost of ownership of critical structures does not become limiting factor for their use. In this thesis cross correlation technique is used to detect and locate the damage in the structure using vibration analysis. This work involves the analysis ofss composite beam which has been Modelled in UGNX and simulation analysis has been done in ANSYS APDL to validate the damage detection approach. The responses of undamaged and damaged beam are recorded and used in cross correlation approach to compare the cross correlation functions, where the cross correlation functions are determined using the impulse responses which are obtained thr
PaperID
2020/IJEASM/1/2020/1612a

Author Name
Deepika Sharma, Jitendra Sheetlani, Narendra Sharma, Harsh Pratap Singh
Year Of Publication
2020
Volume and Issue
Volume 1 Issue 2
Abstract
India is a developing country. Due to industrialization, urbanization and financial development, huge amount of municipal solid waste (MSW) has been recorded last many decades. Sustainable solid waste management is a very serious problem for governments. Improper waste management directly affected not only environment but also human beings. Our government is making the new policies and introduces many new advanced tools and techniques for improve the quality of solid waste management process. All of these efforts are improve the quality of municipal solid waste management process very effectively. Now , the collaboration with other field like computer science, artificial intelligence, machine learning will also very helpful for solving the municipal solid waste management problems. With the help of data mining or machine learning techniques, they have a large number of advanced algorithms and techniques which can be very useful for manage or handle the waste management proble
PaperID
2020/IJEASM/1/2020/1613a

Author Name
Tarun Kumar Tripathi, Deepak Mishra
Year Of Publication
2020
Volume and Issue
Volume 1 Issue 2
Abstract
The movie success factors depend on the critics, storyline, heros, music etc. To predict the movie success various data mining and machine learning techniques such as GuassianNB, MultinomialNB, BernoulliNB, KNeighnorsClassifier, Decision Tree, Logistic regression has been developed but, in this work, we use random forest classifier for the prediction of movie success with reduced cost and schedule. The random forest classifier selects the dataset randomly from the available dataset and the generate the decision tree of the selected dataset and then apply the voting on the prediction results and whose score and accuracy will be maximum that will indicates the success of movie. For the sample of IMDb dataset, we use online resource of kaggle and the experimental results is generated from the widely used machine learning programming language Python which helps in the analysis of the proposed methodology. The performance of proposed methodology is measured using the parameters such as Scor
PaperID
2020/IJEASM/11/2020/1608

Author Name
Sami Ahmad, Kailash Patidar, Narendra Sharma, Rishi Singh Kushwaha
Year Of Publication
2020
Volume and Issue
Volume 1 Issue 2
Abstract
The number of diabetic patient is growing day by day and various causes has been discover to diagnosis the diabetes but early prediction of the cause is very indispensable to get rid of from this disease. Data Mining is a technique which plays an imperative role by unhidden the important data related to diabetes. These data can be utilized for rapid and improved clinical decision making for protective and suggestive medicine. This paper proposes a hybrid approach SVM classifier using KNN and GA technique which can effectively discover the effectual data to diagnose the diabetes disease. The simulation and experimental analysis of the propose approach is done using MATLAB toolbox and measuring parameter specificity, accuracy and sensitivity. The simulation results of proposed approach give improved value than the existing approach
PaperID
2020/IJEASM/11/2020/1609