Journal Vol – 15 No -3, March 2020

Abstract:

There has been a significant growth in the digital word as per the documents are concerned. The classification of digital document is a big trend in the market as a revolution. However the classification of the document is a big task for the modern applications. There are various terms that are used for the extraction of information from the documents. The main concerned areas for the document classification are the noun and the verbs that broadly signify the topics and events. The use of NV (Noun Verb) techniques is a common and powerful practice for the words to be classified. The performance of the document depends on the NV technique due to the classification of the document. The main aim of the work shown in this study is to enhance the capability of the NV extraction methodology to classify the documents. Three classifiers namely, K-Nearest Neighbor (KNN), Naive Bayes (NB), and Support Vector Machine (SVM) are used for the comparison of the results. Various benchmark set are used in this study for the evaluation of the accuracy of the data sets. The data sets were taken from Reuters 8 and WebKb for this purpose. Other extraction methods were also enhanced and incorporated with the NV method extraction e.g., Nouns, Bag of Word (BOW), and Verbs. The results are studied and the conclusion follows them

Keywords:

BOW extraction,Document classification,NV extraction,KNN classifier,NB classifier,SVM classifier,

Refference:

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VI. Fodeh, S., Punch, W. & Tan, P. 2011. On ontology-driven document clustering using core semantic features. On ontology-driven document clustering using core semantic features, Journal of KnowlInfSyst, Springer-Verlag London. 28(2): 395-421.
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X. Kummer, O., Savoy, J., &Argand, E. 2012. Feature selection in sentiment analysis.
XI. Lewis, D. 1997. Reuters-21578 text categorization test collection. AT&T Labs Research.
XII. Liu, Xin&Beyrend-Dur, Delphine&Dur, Gael & Ban, Syuhei. (2014).
XIII. OumaymaOueslati, Ahmed Ibrahim S. Khalil, Habib Ounelli, Sentiment Analysis for Helpful Reviews Prediction, International Journal of Advance Trends in Computer Science and Engineering, Vol 7, No.3, May – June 2018
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XVI. Yao, H., Liu, C., Zhang, P., & Wang, L. 2017. A feature selection method based on synonym merging in text classification system. Journal on Wireless Communications and Networking. Springer. pp: 1-8.

INTERNET OF THINGS (IOT) BASED EDUCATIONAL DATA MINING (EDM) SYSTEM

Authors:

Nayyar Ahmed Khan, Rund Fareed Mahafdah, Omaia Mohammad Al-Omari, Samia Dardouri, Ahmed MasihUddinSiddiqi, Mohammad Ahmad Mohammad Nasimuddin

DOI NO:

https://doi.org/10.26782/jmcms.2020.03.00022

Abstract:

Internet of Things (IoT) is an emerging trend in the field of technology, which has derived a lot of attention in the recent years. The ability of this technology for reducing the burden and strain on the education or academic system makes it possible for deriving a potential and raising the standards of academics. This study proposes a standard model for the educational system with the help of IoT. This paper gives an IoT based modal for the student engagement till the industry institute linkage plan. It gives a design in which the monitoring of RFID based data can be done and results could be discovered using the IoT techniques for the further selection criteria of industries. The results for any student shall be updated and made available based on the student data and business intelligence can be applied to the university system for giving the industry for best students. The study tries to relate various components which are later for the model generation, including the strength, weaknesses, opportunities and threats for a wearable IoT university system. A lot of challenges are based by the field of academics and University’s as far as security and privacy is concerned. Future direction in the research can be derived from the existing proposed model in the study.

Keywords:

IoT,e-learning,computational learning,System Adaption,Security,privacy,challenges,smart devices,sensors-based devices,

Refference:

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MODELING AND PERFORMANCE EVALUATION OF PACKET SCHEDULING IN UPLINK 3GPP LTE SYSTEMS

Authors:

Samia Dardouri, Rund Fareed Mahafdah, Omaia Mohammad Al Omari, Ridha bouallegue

DOI NO:

https://doi.org/10.26782/jmcms.2020.03.00023

Abstract:

The radios must be distributed in the best way possible to provide higher quality of service (QoS) to users. A main component of Long-Term Evolution (LTE) processing is the packet scheduler, which includes all time and frequency support in active flows. We evaluate in this article three different scheduling algorithms in the uplink transmission path for the mixed forms of traffic flows for the Single Carrier Frequency Division Multiple Access (SC-FDMA). We apply metrics which allow fast evaluation of performance measures such as throughput, Packet Loss Ratio (PLR), Fairness Index (FI) and Spectral Eﬃciency (SE) by using the LTE-Sim open source simulator. The main contribution of this paper is to determine the appropriate uplink scheduling algorithm for VOIP and video traﬃcs in 3GPP LTE

Keywords:

SC-FDMA,QoS,LTE,Scheduling algorithms,Resource allocation,Uplink direction,throughput,fairness,Packet loss ratio,Spectral Eﬃciency,

Refference:

I. 3GPP TS 36.213 V10.1.0, Technical Speciﬁcation Group Radio Access Network (E-UTRA); Physical layer procedures, (2011-04).
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VII. H. Jang and Y. Lee, QoS-Constrained Resource Allocation Scheduling for LTE Network in International Symposium on Wireless and Pervasive Computing (ISWPC), 2013.
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FEATURE EXTRACTION FOR MOBILE HANDSET IN COHERENCY WITH PRICING FACTORS

Authors:

Anurag Tiwari, Vivek Kumar Singh, Praveen Kumar Shukla, Manuj Darbari

DOI NO:

https://doi.org/10.26782/jmcms.2020.03.00024

Abstract:

This paper presents a showcase of analysis of Mobile price with respect to the features it is able to analyse for the buyer. The paper gives machine learning approach in identification of the right price and its subsequent features detail. ANN with Back propagation algorithm has been chosen by developing a customized mobile selection algorithm using Kaggle database for modelling and Analysis. Various cost factors are adjusted in relation with the features to be incorporated in the Handset. The adjustment of input variables is done by the help of the machine learning technique giving the exact relationship in three main factors Requirement of the customer based on their segmentation, Price and Features.

Keywords:

Mobile Selection Criteria,MachineLearning,ANN,DSS,

Refference:

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BLENDING MULTI-OBJECTIVE OPTIMIZATIONAND QUALITY FUNCTION DEPLOYMENT FOR DETERMINING COST AND QUALITY

Authors:

Anurag Tiwari, Vivek Kumar Singh, Praveen Kumar Shukla, Manuj Darbari

DOI NO:

https://doi.org/10.26782/jmcms.2020.03.00025

Abstract:

The Blending problem is one of the oldest and best known optimization problems. It is generally formulated as a linear program and has been applied in many fields. However, the mixing problem encountered in the industry requires much more than direct linear programming formulation. Indeed, the classic blending model would almost always be impossible due to the problem of blending in the industry. Indeed, it is often not possible to combine the characteristics of the mixtures as desired, which leads decision makers to seek solutions as close as possible to specific solutions. In this article, we develop and solve a versatile optimization model for the problem of blending, in which we minimize the total cost of the raw materials to be used, as well as violations of the desired characteristic scores of the final blends. We also present a parametric model which is used as a reference point to compare the multi-objective optimization model.

Keywords:

MOO,QFD,Mobile Handsets,

Refference:

I. D Yagyasen, M Darbari, PK Shukla, VK Singh (2013), “Diversity and convergence issues in evolutionary multiobjective optimization: application to agriculture science”, IERI Procedia.

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IV. S Bansal, M Darbari(2012),”Application of Multi Objective Optimization in Prioritizing and Machine Scheduling: a Mobile Scheduler Toolkit”International Journal of Applied Information Systems 3 (2), 24-28.

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