Journal Vol – 15 No -3, March 2020

Abstract:

The present study investigates the effect of strengthening the reinforced concrete beams using different methods, including CFRP reinforcement, GFRP and metal sheets. The analytical method used in this section is the finite element using the Abacus simulator. Accordingly, simple double-head beam was modeled and various scenarios were analyzed by applying the appropriate loading and boundary conditions. Results of the uniform loading in bending test in the modes in which the reinforcements are replaced with carbon reinforcements, Mode A3 showed the best behavior and in the case of using Class A GFRP laminates, G3 beam showed the best behavior in the bending test. In the use of steel sheets, it was observed that the steel sheets had more favorable behaviors than all other modes and it decreased compared to GFRP-reinforced modes. Stress and strain diagrams were plotted for the modeling.

Keywords:

Strengthening,Reinforced Concrete Beam,Abacus Software,CFRP Rebar,GFRP,Steel Sheets,

Refference:

I. ACI 440.1R-06. “Guide for the design and construction of structural concrete reinforced with FRP Bars”. Farmington Hills, USA: American Concrete Institute, pp.: 16-27, 2006.
II. A. Ferreira, “On the shear-deformation theories for the analysis of concrete shells reinforced with external composite laminates”. Strength Mater, Vol.: 35, Issue: 2, pp.: 128-135, 2010.
III. A. Houssam, S. Mohamed “Flexural behavior of concrete beams reinforced with glass ﬁber-reinforced polymer (GFRP) bars”. Struct J, Vol.: 97, Issue: 5, pp.: 712-719, 2000.
IV. A. Khalifa, G. Tumialan, A. Nanni, A. Belarbi, “Shear strengthening of continuous RC beams using externally bonded CFRP sheets. American Concrete Institute. In: Proceedings of the 4th international symposium on FRP for reinforcement of concrete structures (FRPRCS4)”. Baltimore, MD, pp.: 995-1008, 1999.
V. A. Khalifa, A. Nanni, “Rehabilitation of rectangular simply supported RC beams with shear deﬁciencies using CFRP composites”. Constr Build Mater, Vol.: 16, Issue: 4, pp.: 135-146, 2002.
VI. A. kumari, S. Patel, A. nayak, “Shearstrengthning of RC deep beam using externally bonded GFRP fabrics”. journal of the institution of engineers, pp.: 16-27, 2018.
VII. A. Lansi, “Structural performance of corroded RC beams repaired with CFRP sheets”. Compos Struct, Vol.: 92, Issue: 8, pp.: 19318-19329, 2010.
VIII. A. Shehata, E. Cerqueira, C. Pinto, “Strengthening of RC beams in ﬂexure and shear using CFRP laminate”. FiberReinfPlastReinfConcrStruct, Vol.: 9, Issue: 1, pp.: 97-106, 2001.
IX. C. Bazacu, T. Galatanu, P. Mizgan, R. Muntean, F. Tamas, “Experimental research in flexural behavior of carbon fiber polymer strengthened beam”. INTER-ENG, pp.: 98-112, 2016.
X. CEB-FIP, FIB 40 – Bulletin n. “40 – FRP reinforcement in RC structures – design and use of ﬁbre reinforced polymer reinforcement (FRP) in reinforced concrete structures”. International Federation for Structural Concrete (FIB), pp.: 27-38, 2007.
XI. D. Kachlakev, “Behavior of full-scale reinforced concrete beams retroﬁtted for shear and ﬂexural with FRP laminates”. Composites, Vol.: 31, Issue: 2, pp.: 445-452, 2000.
XII. E. David, C. Djelal, F. Buyle-Bodin, “Repair and strengthening of reinforced concrete beams using composite materials”. In: 2nd Int PhD symposium in civil engineering, Budapest, pp.: 218-230, 1998.
XIII. G. Malumbela, M. Alexander, P. Moyo, “Variation of steel loss and its effect on the ultimate ﬂexural capacity of RC beams corroded and repaired under load”. Constr Build Mater, Vol.: 24, Issue: 6, pp.: 1051-1059, 2010.
XIV. H. Hejabi, M. zamankabir, “Seismic ductility evaluation of shear deficient RC Frame strengthened by externally bonded cfrp sheet KSCE”. journal of civil engineering, pp.: 369-384, 2016.
XV. H. Toutanji, L. Zhao, Y. Zhang. “Flexural behaviour of reinforced concrete beams externally strengthened with CFRP sheets bonded with an inorganic matrix”. EngStruct, Vol.: 28, Issue: 4, pp.: 557-566, 2006.
XVI. J. Xie, R. Hu, “Experimental study on rehabilitation of corrosion-damaged reinforced concrete beams with carbon ﬁber reinforced polymer”. Constr Build Mater, Vol.: 38, Issue: 3, pp.: 708-716, 2013.
XVII. M. Badawi, K. Soudki, “CFRP repair of RC beams with shear-span and full-span corrosions”. J Compos Constr, Vol.: 14, Issue: 3, pp.: 323-335, 2010.
XVIII. M. Fakharifar, G. Chen, M. Arezoumandi, M. ElGawady, “Hybrid Jacketing for Rapid Repair of Seismically Damaged Reinforced Concrete Columns”. Transportation Research Record, Vol.: 2522, Issue: 1, pp.: 70-78, 2015.
XIX. M. Shahawy, T. Arockiasamy, T. Beitelmant, R. “Sowrirajan. Reinforced concrete rectangular beams strengthened with CFRP laminates”. Composites, Vol.: 27, Issue: 6, pp.: 225-233, 1996.
XX. P. Escórcio, P. França, “Experimental study of a rehabilitation solution that uses GFRP bars to replace the steel bars of reinforced concrete beams”. Engineering Structures, Vol.: 128, Issue: 5, pp.: 166-183, 2016.
XXI. P. Vijay, V. Hota, “Bending behavior and deformability of glass ﬁberreinforced polymer reinforced concrete members”. Struct J, Vol.: 98, Issue: 6, pp.: 834-429, 2001.
XXII. R. Maheri, A. Torabi, “Retrofitting external RC beam-column joints of an ordinary MRF through plastic hinge relocation using FRP laminates”. Structures, Vol.: 22, Issue: 3, pp.: 65-75, 2019.
XXIII. R. Quantrill, L. Hollaway. “The ﬂexural rehabilitation of reinforced concrete beams by the use of prestressed advanced composite plates”. Compos SciTechnol, Vol.: 58, Issue: 8, pp.: 1259-1275, 1998.
XXIV. W. Al Zand, W. Badaruzzaman, A. Mutalib, S. Hilo, “Rehabilitation and strengthening of high-strength rectangular CFST beams using a partial wrapping scheme of CFRP sheets: Experimental and numerical study”. Thin–Walled Structures, Vol.: 114, Issue: 2, pp.: 80-91, 2017.
XXV. W. Karunasena, P. Hardeo, G. Bosnich, “Rehabilitation of concrete beams by externally bonding ﬁber composite reinforcement. In composite systems: macrocomposites, microcomposites, nanocomposites”. In: Proceedings of the ACUN-4, international composites conference, 4th, Sydney, Australia, Vol.: 21, Issue: 5, pp.: 222-226, 2002.
XXVI. Y. TalebObaidat, S. Heyden, O. Dahlblom, G. Farsakh, Y. Abdel-Jawad, “Retroﬁtting of reinforced concrete beams using composite laminates”. Construction and Building Materials, Vol.: 25, Issue: 2, pp.: 591-597, 2011.
XXVII. Z. soric, J. galic, “Deflections of concrete beam reinforced with frp bars Materials an structures”. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, Vol.: 18, Issue: 8, pp.: 843-857, 2010.

THE MODELING OF EFFECTS OF CLIMATE CHANGE ON OPEN WATER EVAPORATION

Authors:

Hossein Bazzi, Hossein Ebrahimi, Babak Aminnejad

DOI NO:

https://doi.org/10.26782/jmcms.2020.03.00017

Abstract:

The aim of this article is modeling the effects of climate change on open water evaporation. To this end, SDSM model was used. Data related to base period for the evaporation modeling was from 1983 to 2005 and the maximum and minimum evaporation values were simulated for the next two periods of 2030-2050 and 2080-2100. The results showed that sea-level pressure, wind speed, geopotential height and surface temperature has the greatest effect on evaporation. Also, the results of evaporation modeling showed that the range of evaporation would be decreased for both time periods and under all scenarios. Increasing the concentration of greenhouse gases with its positive and negative feedbacks reduced the intensity of maximum evaporation and consequently increased the minimum amount of daily evaporation. The highest decrease in the maximum evaporation amounts and the highest increase in the minimum evaporation amounts would occur in the cold months of the year and in the warm months of the year, respectively

Keywords:

Climate Change,Wind speed,Surface temperature,Evaporation,SDSM Model,

Refference:

I. A. Ahmadabadi, “Sedighifar, Predicting the Impacts of Climate Change Hydrogeomorphological Properties of Can Catchment Basin Based on the Statistical Downscaling Model”. Applied Research of Geographical Science, Vol.: 18, Issue: 5, pp.: 103-114, 2018.
II. B. Loukas, J. Vasiliades, F. Tzabiras, “Climate change effects on drought severity”. Advances in Geosciences, Vol.: 17, Issue: 1, pp.: 23-29, 2008.
III. D. Bozkurt, O. LutfiSen, “Climate change impacts in the Euphrates–Tigris Basin based on different model and scenario simulations”. Journal of Hydrology, pp.: 149-161, 2013.
IV. D. Deryng, D. Conway, N. Ramankutty, J. Price R. “Warren Global crop yield response to extreme heat stress under multiple climate change futures Environmental”. Research Letters, Vol.: 9, Issue: 3, pp.: 34-48, 2014.
V. D. Easterling, B. Horton, P. Jones, T. Peterson, T. Karl, D. Parker, C. Folland, “Maximum and minimum temperature trends for the globe”. Science, Vol.: 227, Issue: 5, pp.: 364-367, 2010.
VI. D. Lenters, T. Kratz, C. “Bowser Effects of climate variability on lake evaporation: Results from a long-term energy budget study of Sparkling Lake”. Northern Wisconsin (USA), Vol.: 3, Issue: 8, pp.: 168-195, 2015.
VII. D. Rowell, “A demonstration of the uncertainty in projections of UK climate change resulting from regional model formulation”. Climate Change, Vol.: 79, Issue: 8, pp.: 243-257, 2006.
VIII. F. Helfer, C. Lemckert, H. Zhang, “Impacts of climate change on temperature and evaporation from a large reservoir in Australia”. Journal of Hydrology, Vol.: 12, Issue: 2, pp.: 365-378, 2012.
IX. F. Mpelasoka, K. Hennessy, R. Jones, B. Bates, “Comparison of suitable drought indices for climate change impacts assessment over Australia towards resource management”. International Journal of Climatology, Vol.: 28, Issue: 1, pp.: 1283-1292, 2008.
X. H. Fowler, R. Wilby, “Beyond the downscaling comparison study”. International Journal of Climatology, Vol.: 27, Issue: 2, pp.: 1543-1545, 2007.
XI. H. Tabari, S. Marofi, A. Aeini, P. Talaee, K. Mohammadi, “Sensitivity of evapotranspiration to climatic change in different climates”. Global and Planetary Change, Vol.: 11, Issue: 5, pp.: 16-23, 2014.
XII. J. Abedi, E. Mazaheri, “Assessing the Impacts of Climate Change on Temperature and Evaporation from Free Reservoirs in Isfahan. Second National Congress of Iran’s Irrigation and Drainage”. Isfahan University of Technology, Vol.: 16, Issue: 1, pp.: 169-180, 2016.

XIII. J. Chu, J. Xia, C. Xu, & V. Singh, “Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River”. China: Theoretical and Applied Climatology, Vol.: 99, Issue: 2, pp.: 149-161, 2010.
XIV. J. Donohue, & T. McVicar, M. Roderick, “Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate”. Journal of Hydrology, Vol.: 38, Issue: 6, pp.: 186-197, 2010.
XV. L. Song, J. Zhang, “Hydrological response to climate change in Beijiang River Basin Based on the SWAT Model”. J. Procedia Engineering, Vol.: 28, Issue: 8, pp.: 241-245, 2012.
XVI. L. Tanasijevic, M. Todorovic L. Pereira, C. Pizzigalli, P. Lionello, “Impacts of climate change on olive crop evapotranspiration and irrigation requirements in the Mediterranean region”. Agricultural Water Management, Vol.: 14, Issue: 4, pp.: 54-68, 2014.
XVII. M. Abbasnia, T. Tavousi, M. Khosravi, H. Toros, “Range Analysis of Uncertainty of Future Changes in Daily Maximum Temperature on Iran Using Geographic Information System”. Geographical Information Periodical (Ministry of Science), Vol.: 97, Issue: 3, pp.: 44-29, 2016.
XVIII. M. Benzaghta, T. Ahmed Mohammed, A. Ibrahim Ekhmaj, “Prediction of Evaporation from Algardabiya Reservoir”. Libyan Agriculture Research Center Journal International, Vol.: 5, Issue: 3, pp.: 120-128, 2012.
XIX. M. Goodarzi, M. Soltani, “Predicting the Impact of Climate Change on the Temperature and Precipitation of the 2020s”. Institute of Soil and Watershed Conservation, Vol.: 8, Issue: 4, pp.: 32-41, 2013.
XX. M. Jahantigh, F. Karandish, M. Delbari, “Investigating the Long-Term Impacts of Climate Change on Temperature and Precipitation in Zabol”. The first international conference and the third national conference of engineering and agricultural management of environment and sustainable natural resources, Vol.: 28, Issue: 1, pp.: 18-30, 2015.
XXI. M. Semenov, “Simulation of extreme weather events by a stochastic weather generator”. Climate Research, Vol.: 35, Issue: 5, pp.: 203-212, 2008.
XXII. R. Dankers, O. Christensen, “Climate change impact on snow coverage, evaporation and river discharge in the sub-arctic tana basin”. northernfennoscandia, Vol.: 69, Issue: 1, pp.: 367-392, 2005. M. Delghandi, “Investigating the effects of Risk of Climate Change on Potential Evapotranspiration (Case Study: Shahroud City)”. Iran’s Irrigation and Water Engineering, Vol.: 23, Issue: 6, pp.: 167-156, 2016.
XXIII. R. Wilby, I. Harris, “A framework for assessing uncertainties in climate change impacts: Low‐flow scenarios for the River Thames, UK”. Water Resources Research, Vol.: 42, Issue: 2, pp.: 369-374, 2006.
XXIV. R. Wilby, C. Dawson, “SDSM4.2–A Decision Support Tool for the Assessment of Regional Climate Impacts”. User Manual, Vol.: 8, Issue: 1, pp.: 35-49, 2007.
XXV. S. Gianniou, V. Antonopoulos, “Evaporation and energy budget in Lake Vegoritis”. Journal of Hydrology, Vol.: 45, Issue: 3, pp.: 212-223, 2007.
XXVI. S. Heidari, Y. Khoshboo, “Simulating and Predicting Future Changes of Reference Evapotranspiration at Seasonal and Annual Scales in Western Iran Based on RCP Emission Scenarios”. Applied Research on Geographical Science, Vol.: 53, Issue: 6, pp.: 157-176, 2019.
XXVII. S. Solomon, “Climate Change: The physical science basis”. Cambridge University Press, Cambridge, Vol.: 6, Issue: 2, pp.: 44-61, 2007.
XXVIII. T. Van Vliet, “Global river discharge and water temperature under climate change”. Global Environmental Change, Vol.: 9, Issue: 6, pp.: 450-464, 2013.
XXIX. X. Tao, H. Chena, C. Xua, Y. Houa, M. Jiea, “Analysis and prediction of reference evapotranspiration with climate change in Xiangjiang River Basin China”. Water Science and Engineering, Vol.: 8, Issue: 4, pp.: 273-281, 2015.
XXX. Y. Dinpajou, M. Foroughi, “The Impact of Climate Change on the Leading Changes of Potential Evapotranspiration (Case study: Northwestern Iran)”. Journal of Water and Soil (Agricultural Science and Technology), Vol.: 32, Issue: 3, pp.: 632-617, 2018.
XXXI. Y. Wang, L. Leung, & J. “McGregor, Regional climate modeling: progress, challenges, and prospects”. Journal of the Meteorological Society of Japan, Vol.: 82, Issue: 6, pp.: 1599-1628, 2004.

RECONSTRUCTION OF GRAYSCALE IMAGES WITH ARTIFICIAL NEURAL NETWORKS AFTER THEIR COMPRESSION BY PIXEL ELIMINATION METHOD

Authors:

Hafeez Ullah Jan, Dr. Gul Muhammad, Atif Jan, Muhammad Aamir Aman

DOI NO:

https://doi.org/10.26782/jmcms.2020.03.00018

Abstract:

Excessive use of electronic devices and image sharing applications in the modern world produce gigantic number of images. The huge image data demands to be handled properly to efficiently utilize the storage space and transmission bandwidth resources. Image compression techniques limit the storage size of the image for this purpose. With the passage of time compression techniques have enhancedto attain more compression and produce decompressed image of high quality. This study which is part of post graduate project suggests the use of neural network to reconstruct the gray scale images which are compressed by withdrawing the pixels from the image. MATLAB is used as programming tool to carry out the simulations. The results obtained are promising.

Keywords:

Artificial Neural Network (ANN),Hidden Neurons,MATLAB,Image Compression,Reconstruction,

Refference:

I. Asuni, N., & Giachetti, A. (2013). Testimages: A large data archive for display and algorithm testing. Journal of Graphics Tools, 17(4), 113-125.
II. Drew, P. J., & Monson, J. R. (2000). Artificial neural networks. Surgery, 127(1), 3-11.
III. Gardner, M. W., & Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric environment, 32(14-15), 2627-2636.
IV. Gonzales, R. C., & Woods, R. E. (2002). Digital image processing (Vol. 2). New Jersey: Prentice hall.
V. Jain, A. K. (1981). Image data compression: A review. Proceedings of the IEEE, 69(3), 349-389.
VI. Marsalli, M. (2006). Mcculloch-pitts neurons. In The 2008 Annual Meeting of the consortium on cognitive science instruction (ccsi) (pp. 172-179).
VII. Nelson, M., & Gailly, J. L. (1996). The data compression book (pp. 457-458). New York: M & t Books.
VIII. Warner, B., & Misra, M. (1996). Understanding neural networks as statistical tools. The american statistician, 50(4), 284-293.
IX. Yang, M., & Bourbakis, N. (2005, August). An overview of lossless digital image compression techniques. In 48th Midwest Symposium on Circuits and Systems, 2005. (pp. 1099-1102). IEEE.

OCEAN ENERGY THE CLEAN AND EFFICIENT METHOD TO OVERCOME ENERGY CRISIS OF PAKISTAN

Authors:

Rahat Ullah, Hamza Umar Afridi, Muhammad Aamir Aman, Muhammad Waheed

DOI NO:

https://doi.org/10.26782/jmcms.2020.03.00019

Abstract:

At present, Pakistan is stood up to with vitality emergency because of decrease in traditional wellsprings of vitality. There is an expansive hole among interest and supply of power. Consequently Growing worry over the danger of worldwide environmental change has prompted an expanded enthusiasm for innovative work of sustainable power source advances. The sea gives a tremendous wellspring of potential vitality assets, and as sustainable power source innovation creates, interest in sea vitality is probably going to develop. Research in sea warm vitality change, wave vitality, tidal vitality, and seaward wind vitality has prompted promising advancements and now and again, business organization. These sources can possibly help reduce the worldwide environmental change risk, yet the sea condition ought to be ensured while these advances are produced. Sustainable power sources from the sea might be misused without hurting the marine condition if ventures are sited and scaled suitably and ecological rules are pursued

Keywords:

Energy Crisis,Ocean Energy,Renewable Energy,Tidal Energy,Environmental Changes,Vital zones,

Refference:

I. Ashley Taylor and Tom krupenkin “Reverse electro wetting as a new approach to high power energy harvesting” Nature communication, pp 1-7August 2011.
II. Binoy Boban, Tom Jose V, Sijvo MT, “Electricty generation from footsteps; A Generative energy Resources” International journal of sciventic and research Publication 1-3,March 2013.
III. Global warming.
IV. G.R. Nagpal, “Power Plant Engineering” Khanna Publisher, Delhi
V. Muhammad Aamir Aman*1, Muhammad Zulqarnain Abbasi2, Hamza Umar Afridi3, Mehr-e-Munir4, Jehanzeb Khan5 Department of Electrical Engineering, Iqra National University, Pakistan “Photovoltaic (PV) System Feasibility for UrmarPayan a Rural Cell Sites in Pakistan” J.Mech.Cont.& Math. Sci., Vol.-13, No.-3, July-August (2018) Pages 173-179.
VI. Muhammad AamirAman*1, Muhammad Zulqarnain Abbasi2, Murad Ali3, Akhtar Khan4 Department of Electrical Engineering, Iqra National University, Pakistan.“To Negate the influences of Un-deterministic Dispersed Generation on Interconnection to the Distributed System considering Power Losses of the system” J.Mech.Cont.& Math. Sci., Vol.-13, No.-3, July-August (2018) Pages 117-132.
VII. Piezoelectric foot step power generation by sagar institute of technology.
VIII. S. Dhaley, D. English, E.Hu, P. Barbara, and A. Belcher, “Nature”, PP 665, 2000.

TRUCK LOADING PATTERN AND ITS IMPACT ON PAVEMENT DESIGN

Authors:

Kamran Aziz, Kamran Ahmad, S.M. Tariq Shah

DOI NO:

https://doi.org/10.26782/jmcms.2020.03.00020

Abstract:

Pakistan being a developing country, with many budget constraints, poor governance and legislation of commercial vehicle’s loading limits facing the dilemma of overloading of commercial vehicles from the last decade, as overloading is the main factor for pavement deteriorations. Highway authorities would be facing the serious problems of maintenance, rehabilitation and reconstruction of existing roads together with designing the future roads to meet the criteria for much higher traffic loadings. Thus, there is grim need to evaluate the impact of commercial vehicle’s overloading on pavement performance to come-up with the optimum solution. Data of three weigh in motion stations between the two major cities (Peshawar and Rawalpindi) on the main national highway N-5 of Pakistan were collected and analyzed. A comparative study of actual and design load equivalency factors (NTRC-1995) were carried to determine the impact of current loading pattern on the pavement performance. AASHTO flexible pavement design method was applied to compute the axle load equivalency factors and thicknesses required for pavement structures. Furthermore, the effect of variation in truck factor due to current loadings, on pavement design practice in Pakistan in term of performance period and economy was evaluated. It is found that, on average 90% of the commercial vehicles in Pakistan are going overloaded than the suggested limits with axle type-2 vehicle is more damaging to pavement structures having truck factors 2.65 times more than the design truck factors. Moreover, it was analyzed that the pavement structure designed on the basis of truck factors suggested by NTRC would get deteriorate in 3.5 years rather than 10 years with the economic loss of 4.5 million rupees approx.

Keywords:

Overloads,Truck Factor,Weigh in Motion,Flexible Pavement,Traffic,

Refference:

I. AASHTO Guide. (1993). AASHTO Guide for Design of Pavement structure. Washington-DC: American Association of State highway and Transportation Officials.
II. Chaudry, R., & Memon, A. B. (2013, January). Effects of Variation in Truck factor on pavement performance in Pakistan. Mehron University Research Journal of Engg. & technology, 32, 19-30.
III. Morton, B. S., Luttig, E., Horak, E., & Visser, A. T. (2004). Effect of axle load spectra and tire inflation pressure on standard pavement design methods. 8th confrence on asphalt pavements for southern africa (pp. 1-7). Sun city: Doccument transformation technologies cc.
IV. National Transport Resaerch Center. (1995). Axle Load Study on National Highways. NTRC. Islambad: National Transport Research Center.
V. NHA “National standards and specifications for Trailers and Semi-Trailers”National Highway Authority, Islamabad Pakistan.
VI. Pais, C. J., Amorim, S. I., & Minhoto, M. J. (2013). Impact of Traffic overload on pavement performance. jounnal of Transportation Engineering, 873-874.
VII. Rys, D., Judyci, J., & Jaskula, P. (2015, March). Analysis of effect of overloaded vehicles on fatigue life of flexible pavements based on weigh in motion (WIM) data. International Journal of Pavement Engineering, 1-12.
VIII. Rys, D., Judycki, J., & Jaskula, P. (2016, April). Determination of vehicles load equivalency factors for polish Catalogue of typical flexible and semi-rigid pavement structures. Elsevier, 14, 2082-2391.
IX. Salama, H. K., Chatti, K., & Lyles, R. W. (2006, October). Effect of Heavy Multiple Axle Trucks on Flexible Pavement Damage Using In-Service Pavement Performance Data. Journal of Transportation Engineering © ASCE, 1-8.
X. Turochy, R. E., Baker, S. M., & Timm, D. H. (2005). Spatial and Temporal Variations in Axle Load Spectra and Impacts on Pavement Design. journal of transportation Engineering, 1-7.

ANALYTICAL ASSESSMENT OF NOUN VERB TERM EXTRACTION FOR DOCUMENT CLASSIFICATION USING T-TEST

Authors:

Omaia Mohammad Al-Omari, Nazlia Omar

DOI NO:

https://doi.org/10.26782/jmcms.2020.03.00021

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:

I. Apoorva Deshpande, Ramnaresh Sharma, Multilevel Ensemble Classifier using Normalized Feature based Intrusion Detection System, International Journal of Advance Trends in Computer Science and Engineering, Vol 7, No.5, September -October 2018.
II. Bsoul, Q., &Salim, Z. 2016. Effect Verb Extraction on Crime Traditional Cluster, world applied science journal.
III. Cambria, E., & White, b. 2014. Jumping NLP Curves: A Review of Natural Language Processing Research. IEEE Computational Intelligence Magazine, 9(1): 48-57.
IV. Ding, X. & Tang, Y. 2013. Improved Mutual Information Method For Text Feature Selection. The 8th International Conference on Computer Science & Education. IEEE, pp: 163-166.
V. Dyer, M. 1995. Connectionist natural language processing: a status report. in Computational Architectures Integrating Neural and Symbolic Processes, Sun and L. Bookman, Eds. Dordrecht. The Netherlands: Kluwer Academic, 292(1):389–429.

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.
VII. Guru, S., Suhil, M., Raju, N., & Kumar, V., An Alternative Framework for Univariate Filter based Feature Selection for Text Categorization. Pattern Recognition Letters. 2018. https://doi.org/10.1016/j.patrec.2017.12.025
VIII. Hotho, A., Staab, S., &Stumme, G. 2003. WordNet improves text document clustering. In Proc. of the SIGIR 2003 Semantic Web Workshop, pp: 541-544.
IX. International Journal of Advanced Trends in Computer Science and Engineering, Volume 8, No.1, January – February 2019. Available Online at http://www.warse.org/IJATCSE/static/pdf/file/ijatcse15812019.pdf https://doi.org/10.30534/ijatcse/2019/15812019
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
XIV. Porter, F. 1997. An algorithm for suffix stripping in K. Sparck Jones, P. Willett (1st Eds) Readings in Information Retrieval, Morgan Kaufmann Multimedia Information and Systems Series, pp: 313–316.
XV. Rogati, Monica & Yang, Yiming. 2002. High-performing feature selection for text classification. 659. 10.1145/584902.584911.
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:

I. Ansari, A.N., et al. Automation of attendance system using RFID, biometrics, GSM Modem with. Net framework. in Multimedia Technology (ICMT), 2011 International Conference on. 2011. IEEE.
II. Baradwaj, B.K. and S. Pal, Mining educational data to analyze students’ performance. arXiv preprint arXiv:1201.3417, 2012.
III. Bevitt, D., C. Baldwin, and J. Calvert, Intervening early: Attendance and performance monitoring as a trigger for first year support in the biosciences. Bioscience Education, 2010. 15(1): p. 1-14.
IV. Bsoul, Q., & Salim, Z. 2016. Effect Verb Extraction on Crime Traditional Cluster, world applied science journal.
V. Cambria, E., & White, b. 2014. Jumping NLP Curves: A Review of Natural Language Processing Research. IEEE Computational Intelligence Magazine, 9(1): 48-57.
VI. Chawathe, S.S., et al. Managing RFID data. in Proceedings of the Thirtieth international conference on Very large data bases-Volume 30. 2004. VLDB Endowment.
VII. Cleveland, B.W., Engaging spaces: Innovative learning environments, pedagogies and student engagement in the middle years of school. 2011: University of Melbourne, Faculty of Architecture, Building and Planning.
VIII. Darcy, P., B. Stantic, and A. Sattar. Applying a neural network to recover missed RFID readings. in Proceedings of the Thirty-Third Australasian Conferenc on Computer Science-Volume 102. 2010. Australian Computer Society, Inc.
IX. Darcy, P., S. Tucker, and B. Stantic, Integrating RFID technology with intelligent classifiers for meaningful prediction knowledge. Advances in Internet of Things, 2013. 3(2): p. 27-33.
X. Ding, X. & Tang, Y. 2013. Improved Mutual Information Method For Text Feature Selection. The 8th International Conference on Computer Science & Education. IEEE, pp: 163-166.
XI. Doyle, L., et al., An evaluation of an attendance monitoring system for undergraduate nursing students. Nurse education in practice, 2008. 8(2): p. 129-139.
XII. Dyer, M. 1995. Connectionist natural language processing: a status report. in Computational Architectures Integrating Neural and Symbolic Processes, Sun and L. Bookman, Eds. Dordrecht. The Netherlands: Kluwer Academic, 292(1):389–429.
XIII. Ferreira, D.D.J.S.S.F.B.V., Knowledge and technology transfer between university — Industry — Society: A new crowdsourcing framework for Internet of Things, in Microwaves, Antennas, Communications and Electronic Systems (COMCAS), 2017 IEEE International Conference, IEEE, Editor. 2017, IEEE Explore: Tel-Aviv, Israel.
XIV. 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.
XV. Gershenfeld, N., R. Krikorian, and D. Cohen, The internet of things. Scientific American, 2004. 291(4): p. 76-81.
XVI. Halibas, A.S., I.G. Pillai, and A.C. Matthew. Utilization of RFID analytics in assessing student engagement. in Applications of Information Technology in Developing Renewable Energy Processes & Systems (IT-DREPS), 2017 2nd International Conference on the. 2017. IEEE.
XVII. Hanna, M., Data mining in the e-learning domain. Campus-wide information systems, 2004. 21(1): p. 29-34.
XVIII. Hotho, A., Staab, S., &Stumme, G. 2003. WordNet improves text document clustering. In Proc. of the SIGIR 2003 Semantic Web Workshop, pp: 541-544.
XIX. Hughes, G. and C. Dobbins, The utilization of data analysis techniques in predicting student performance in massive open online courses (MOOCs). Research and Practice in Technology Enhanced Learning, 2015. 10(1): p. 10.
XX. Jeffery, S.R., M. Garofalakis, and M.J. Franklin. Adaptive cleaning for RFID data streams. in Proceedings of the 32nd international conference on Very large data bases. 2006. VLDB Endowment.
XXI. Jindal, N. and B. Liu. Mining comparative sentences and relations. in AAAI. 2006.
XXII. Jing, B.-Z., et al. RFID access authorization by face recognition. in Machine Learning and Cybernetics, 2009 International Conference on. 2009. IEEE.
XXIII. Jones, K., J. Thomson, and K. Arnold, Questions of data ownership on campus. 2014.
XXIV. Kassim, M., et al. Web-based student attendance system using RFID technology. in Control and System Graduate Research Colloquium (ICSGRC), 2012 IEEE. 2012. IEEE.
XXV. Kummer, O., Savoy, J., & Argand, E. 2012. Feature selection in sentiment analysis.
XXVI. Lewis, D. 1997. Reuters-21578 text categorization test collection. AT&T Labs Research.Matthew, A.S.H.I.G.P.A.C., Utilization of RFID analytics in assessing student engagement, in Applications of Information Technology in Developing Renewable Energy Processes & Systems (IT-DREPS), 2017 2nd International Conference, IEEE, Editor. 2017, IEEE: Amman, Jordan.
XXVII. Li, D.-Y., et al. Design of Internet of Things System for Library Materials Management using UHF RFID. in RFID Technology and Applications (RFID-TA), 2016 IEEE International Conference on. 2016. IEEE.
XXVIII. Lim, T., S. Sim, and M. Mansor. RFID based attendance system. in Industrial Electronics & Applications, 2009. ISIEA 2009. IEEE Symposium on. 2009. IEEE.
XXIX. Liu, Xin&Beyrend-Dur, Delphine&Dur, Gael & Ban, Syuhei. (2014).
XXX. Mihăescu, C., et al. Learning analytics solution for building personalized quiz sessions. in Carpathian Control Conference (ICCC), 2017 18th International. 2017. IEEE.
XXXI. Porter, F. 1997. An algorithm for suffix stripping in K. Sparck Jones, P. Willett (1st Eds) Readings in Information Retrieval, Morgan Kaufmann Multimedia Information and Systems Series, pp: 313–316.
XXXII. Rogati, Monica & Yang, Yiming. 2002. High-performing feature selection for text classification. 659. 10.1145/584902.584911.
XXXIII. Romero, C. and S. Ventura, Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2013. 3(1): p. 12-27.
XXXIV. Romero, C., et al., Web usage mining for predicting final marks of students that use Moodle courses. Computer Applications in Engineering Education, 2013. 21(1): p. 135-146.
XXXV. Srinidhi, M. and R. Roy. A web enabled secured system for attendance monitoring and real time location tracking using Biometric and Radio Frequency Identification (RFID) technology. in Computer Communication and Informatics (ICCCI), 2015 International Conference on. 2015. IEEE.
XXXVI. Teague, D.M. and M.W. Corney, Is anybody there? Bootstrapping attendance with engagement. 2011.
XXXVII. Welbourne, E., et al. Challenges for pervasive RFID-based infrastructures. in Pervasive Computing and Communications Workshops, 2007. PerCom Workshops’ 07. Fifth Annual IEEE International Conference on. 2007. IEEE.
XXXVIII. Wu, D.-L., et al. Access control by RFID and face recognition based on neural network. in Machine Learning and Cybernetics (ICMLC), 2010 International Conference on. 2010. IEEE.
XXXIX. 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.
XL. Zhou, Q., et al. Design and Implementation of Learning Analytics System for Teachers and Learners Based on the Specified LMS. in Educational Innovation through Technology (EITT), 2014 International Conference of. 2014. IEEE

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).
II. 3GPP TS 36.213: Evolved Universal Terrestrial Radio Access (EUTRA); Physical layer procedures. Version 8.8.0 Release 8, 2009.
III. Abrignani, M., Giupponi, L., Lodi, A. et al. Scheduling M2M traffic over LTE uplink of a dense small cell network. J Wireless Com Network 2018, 193 (2018)..
IV. B. Nsiri, M. Nasreddine, M. Ammar,W. Hakimi, M. Soﬁen, Modeling and Performance Evaluation of Scheduling Algorithms For Downlink LTE cellular Network ICWMC 2014 : The Tenth International Conference on Wireless and Mobile Communications.
V. B. P. S. Sahoo, Deepak Puthal, Satyabrata Swain and Sambit Mishra, A Comparative Analysis of Packet SchedulingSchemes for Multimedia Services in LTE Networks in 2015 International Conference on Computational Intelligence Networks (CINE 2015).
VI. G. Piro, L. Alfredo Grieco, G. Boggia, F. Capozzi, Simulating LTE Cellular Systems: an Open Source Framework, Octobre 2010.
VII. H. Jang and Y. Lee, QoS-Constrained Resource Allocation Scheduling for LTE Network in International Symposium on Wireless and Pervasive Computing (ISWPC), 2013.
VIII. H. Safa , K. Tohme , Low Complexity Scheduling Algorithms for the LTE Uplink in the ISCC 2009 proceedings.
IX. http://trace.eas.asu.edu/, H.264/AVC and SVC video trace library.
X. J. Lim, H.G. Myung, and D.J. Goodman, Single Carrier FDMA for Uplink Wireless Transmission, in IEEE Vehicular Technology Magazine, Volume 1, Issue 3, pp 3039, 2007. 4. 3GPP, Tech. Specif. Group Radio Access Network – Physical Channel and Modulation (Release 8), 3GPP TS 36.211.
XI. K. Elgazzar, M. Salah, M. Abd-Elhamid Taha, H. Hassanein, Comparing Uplink Schedulers for LTE in IWCMC ’10 Proceedings of the 6th International Wireless Communications and Mobile Computing Conference 2010.
XII. Long Term Evolution (LTE): A Technical Overview. Motorola. Retrieved July 3, 2010.
XIII. M. Iturralde, S. Martin and T. Ali Yahiya, Resource Allocation by Pondering Parameters for Uplink System in IEEE 38th Conference on LTE Networks in Local Computer Networks (LCN), 2013.
XIV. M. Salah , Najah Abu Ali , Abd-ElhamidTaha , HosamHassanein, Evaluating Uplink Schedulers in LTE in Mixed Traﬃc Environments in the IEEE ICC 2011 proceedings.
XV. Mamman M, Hanapi ZM, Abdullah A, Muhammed A (2019) Quality of Service Class Identifier (QCI) radio resource allocation algorithm for LTE downlink. PLoS ONE 14(1): e0210310. https://doi.org/10.1371/journal.pone.0210310
XVI. Mandeep Singh, HarpreetKaur, 0, Performance Enhancement of Heterogeneous LTE Networks Using EXP/PF Packet Scheduling Algorithm, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 07, Issue 08 (August – 2018),
XVII. Patra, A. , Pauli, V. , Yu Lang, Packet Scheduling for Real-Time Communication over LTE Systems in Wireless Days (WD)proceedings, 2013.
XVIII. R. Jain et al. A quantitative measure of fairness and discrimination for resource allocation in shared computer systems. Dig. Eq. Corp., Lit, MA, DEC Rep-DECTR-301 , Sep. 1984.
XIX. S. Hussain, Dynamic Radio Resource Management in 3GPP LTE, Blekinge Institute of Technology, January, 2009.
XX. S. Kwon, Neung-Hyung Lee, Uplink QoS Scheduling for LTE System, in the Vehicular Technology Conference (VTC Spring), 2011 proceedings.
XXI. S. Mohamed, Comparative Performance Study of LTE Uplink Schedulers, Thesis (Master, Electrical Computer Engineering) Queen’s University, 2011.
XXII. S. Nawaz Khan Marwat, T. Weerawardane, Yasir Zaki1 , Carmelita Goerg1 , and Andreas Timm-Giel2, Performance Evaluation of Bandwidth and QoS Aware LTE Uplink Scheduler, in Wired/Wireless Internet Communication Lecture Notes in Computer Science Volume 7277, 2012, pp 298-306.
XXIII. Yıldız, Önem & Sokullu, Radosveta. (2019). Mobility and traffic-aware resource scheduling for downlink transmissions inLTE-A systems. TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES. 27. 2021-2035. 10.3906/elk-1808-56.

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:

I. A Chaudhary, S. Kolhe and Rajkamal, “Performance Evaluation of feature selection methods for Mobile devices”, ISSN: 2248-9622, Vol. 3, Issue 6, NovDec 2013, pp.587-594.

II. A Lapedes and R. Farber, “How Neural Networks Works”, in Neural Information Processing Systems(D.Z. Anderson,ed.),(Denver), American Institute of Physics,NewYork,pp. 442-456,1988.

III. Bourassa, S.C., Cantoni, E. and Hoesli, M. 2007. “Spatial dependence, housing submarkets, and house price prediction”, The Journal of Real Estate Finance and Economics, 35(2), p.143-160.

IV. GONGGI, S., 2011. New model for residual value prediction of used cars based on BP neural network and non-linear curve fit. In: Proceedings of the 3 rd IEEE International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

V. H. Liu and R. Setiono, “A probabilistic approach to feature selection – A filter solution,” the 13th International Conference on Machine Learning, pp. 319-327, 1996.

VI. H.White, “Learning in Artificial Neural Networks :A Statistical Perspective ”,Neural Computation,1(4),pp.425-464,1989.

VII. Kanwal Noor and Sadaqat Jan, “Vehicle Price Prediction System using Machine Learning Techniques” , International Journal of Computer Applications (0975 – 8887) Volume 167 – No.9, June 2017.

VIII. Khaidem, Luckyson&Saha, Snehanshu&Basak, Suryoday&Kar, Saibal&Dey, Sudeepa. (2016). Predicting the direction of stock market prices using random forest.

IX. Lei Dong, Carlo Ratti, Siqi Zheng,”Predicting neighborhoods’ socioeconomic attributes using restaurant data Proceedings of the National Academy of Sciences Jul 2019, 116 (31) 15447-15452;

X. Limsombunchai, V. 2004. “House Price Prediction: Hedonic Price Model vs. Artificial Neural Network”, New Zealand Agricultural and Resource Economics Society Conference, New Zealand, pp. 25-26. 2004

XI. M. Hall, “Feature Selection for Discrete and Numeric Class Machine Learning”, Department of Computer Science.

XII. M. Robnik and I. Kononenko, “Theoretical and Empirical Analysis of ReliefF and RReliefF”, Machine Learning Journal, 2003.

XIII. M.C. Mozer,”A Focused Back – propagation Algorithm for Temporal Pattern Recognition”,Complex Systems,3,pp.349-381,1989.

XIV. Mariana Listiani , 2009. “Support Vector Regression Analysis for Price Prediction in a Car Leasing Application”. Master Thesis. Hamburg University of Technology.

XV. Minitab Express Support. Interpret all statistics and graphs for Multiple Regression.[Online] Available

XVI. Mobile data and specifications online available from https://www.flipkart.com/ (Last Accessed on Friday, ‎ December ‎22, ‎2019, ‏ ‎ 3:14:34 PM)

XVII. NamhyoungK,Kyu J, Yong.K, A New Ensemble Model for Efficient Churn Prediction in Mobile Telecommunication, 2012 45th Hawaii international conf on system sciences.

XVIII. Nau, R. 2014. Notes on linear regression analysis, Lecture handouts, Duke University, Furqa School of Business, 26 nov 2014.

XIX. Nisha Thomas and Mercy.”Implementation of Back propagation Algorithm in Reconfigurable Hardware”.2011.

XX. R.Linsker, “From Basic Network Principles to Neural Architecture”, in Processings of the National Academy of Sciences,83,(USA),pp 7508-7512,8390-8394,8779-8783,1986.

XXI. S.E .Fahlman, “Fast Learning Variations on Backpropagation : An EmpricialStudy”,in Proc . 1998 connectionist Model Summer School (D.S. Touretzky, G.E. Hinton, and T.J. Sejnowski ,eds.), San Mateo ,CA:MorganKaufmann,pp. 38-51,1989.

XXII. S.Titri, H. Bourmeridja.”New Reuse Design Methodology for Artificial Neural Network Implementation”.1999.

XXIII. SameerchandPudaruth . “Predicting the Price of Used Cars using Machine Learning Techniques”, International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 7 (2014), pp. 753764

XXIV. Shonda Kuiper, “Introduction to Multiple Regression: How Much Is Your Car Worth? ” , Journal of Statistics Education • November 2008

XXV. Singh, Y., Bhatia, P. K., &Sangwan, O. 2007. “A review of studies on machine learning techniques”, International Journal of Computer Science and Security, 1(1), 70-84.

XXVI. SireeshaJasti ,TummalaSitaMahalakshmi”Multiple Linear Regression”,IJRTE,pp. 1919-1925,August 2019.

XXVII. Suebsing and N. Hiransakolwong, “Euclideanbased Feature Selection for Network Intrusion Detection”, International Conference on Machine Learning and Computing IPCST, 2011.

XXVIII. Sundsøy, Pål&Bjelland, Johannes &reme, bjørn-atle&jahani, eaman. (2016). Deep Learning Applied to Mobile Phone Data for Individual Income Classification. 10.2991/icaita-16.2016.24.

XXIX. TadayoshiHorita, Takuroa Murata and ItsuoTakanami.”A Multiple Weight and Neuron Fault Tolerant Digital Multilayer Neural Network”.2006.

XXX. Thu ZarPhyu, NyeinNyeinOo. Performance Comparison of Feature Selection Methods. MATEC Web of Conferences42, (2016).

XXXI. X.Yao ,”A New Evolutionary System for Evolving Artificial Neural Networks”,IEEETrans.Neural Networks,8,May 1997.

XXXII. Z. Karimi and M. Mansour and A. Harounabadi “Feature Ranking in Intrusion Detection Dataset using combination of filtering”, International Journal of Computer Applications,Vol.78,September 2013.

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.

II. D Yagyasen, M Darbari (2014),”Application of semantic web and petri calculus in changing business scenario”Modern Trends and Techniques in Computer Science.

III. R Asthana, NJ Ahuja, M Darbari (2011),”Model proving of urban traffic controls using neuro Petri nets and fuzzy logic”International Review on Computers and Software (IRECOS.

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.

V. SS Ahmad, M Darbari, H Purohit (2015),”Handling web dynamics of internet marketing supply chain using evolutionary algorithms and semantic breakdown strategy”International Business Information Management ConferenceNetherlands.

VI. SS Ahmad, H Purohit, F Alshaikhly, M Darbari (2013),”Information granules for medical infonomics”International Journal of Information and Operations Management Education.

VII. SaviturPrakash and ManujDarbari, “‘Quality & Popularity’ Prediction Modeling of TV Programme through Fuzzy QFD Approach,” Journal of Advances in Information Technology, Vol. 3, No. 2, pp. 77-90, May, 2012.doi:10.4304/jait.3.2.77-90

VIII. Sofia Angeletou, Matthew Rowe, and HarithAlani: Modelling and Analysis of User Behaviour in Online Communities, 10th International Semantic Web Conference Bonn, Germany, October 23-27, 2011, Lecture Notes in Computer Science, Springer-Verlag Berlin Heidelberg.

IX. Yang. S.Y, Hyun Ko, Seung, Wok. H, Hee. Y. Y, (2007), “Priority-Based Message Scheduling for the Multi-agent System in Ubiquitous Environment”, IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology – Workshops, pp. 395-398.

X. Yujian. Fu, Kan. W, Junwei. Y (2006), “A Multi-Agent System for Manufacturing Material Resource Planning”, Sixth International Conference on Intelligent Systems Design and Applications (ISDA’06) Volume 2, pp. 1118-1123.

XI. Zhanjie. W, Yanbo. L (2006), “A Multi-Agent Agile Scheduling System for Job-Shop Problem”, Sixth International Conference on Intelligent Systems Design and Applications (ISDA’06) Volume 2, pp. 679-683.