Volume-10 , Issue-3 , Jun 2023, ISSN 2348-4519 (Online) Go Back

• Open Access   Article

  COVID-19 Tweets Sentiment Classification: A Deep Learning Model with Stacking Ensemble for Improved Performance

  Solomon Eshun, Alberta Araba Johnson, Richmond Essieku

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.10 , Issue.3 , pp.1-8, Jun-2023

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  The global impact of COVID-19 has resulted in profound consequences on society, the economy, and public health. In response to this, individuals have turned to social media platforms such as Twitter as a means to voice their sentiments and viewpoints regarding the pandemic. Sentiment analysis can be used to identify public concerns and challenges, such as misinformation and negative attitudes towards vaccinations and public health measures. However, manually analyzing such vast amounts of data is laborious and expensive. Deep learning models have demonstrated encouraging outcomes for sentiment analysis, due to their ability to learn complex patterns and relationships within datasets. As a result, this study aims to use deep learning models to predict sentiment expressed in a tweet on COVID-19 and compare their performances. The objective of the study is to gain insights into how the pandemic is affecting the population and to help policymakers and public health officials develop effective communication strategies and targeted interventions. By comparing the performances of these models, we hope to identify the most effective approach for classifying these sentiments, contributing to the broader conversation about the impacts of the pandemic on public sentiment and mental health.

  Key-Words / Index Term
  COVID-19, social media, twitter, sentiment analysis, deep learning

  References
  [1]. A. Mislove, S. Lehmann, Y. Y. Ahn, J. P. Onnela and J. N. Rosenquist, “Understanding the Demographics of Twitter Users,” In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, pp.554-557, 2010.
  [2]. A. Java, X. Song, T. Finin and B. Tseng, “Why we twitter: Understanding Microblogging Usage and Communities,” In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp.56-65, 2007.
  [3]. P. Bo and L. Lee, “Opinion Mining and Sentiment Analysis,” Foundations and Trends in Information Retrieval, vol.2, no.1-2, pp.1-135, 2008.
  [4]. B. Liu, “Sentiment Analysis and Opinion Mining,” Synthesis Lectures on Human Language Technologies, vol.5, no.1, pp.1-167, 2012.
  [5]. Y. Zhang and B. Wallace, “A sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification,” arXiv preprint arXiv:1510.03820, 2015.
  [6]. Z. Jalil, A. Abbasi, A.R. Javed, K.B. Muhammad, H. Abul, H. Mozaherul, K.M. Malik and A.K.J. Saudagar, “Covid-19 Related Sentiment Analysis using State-of-the-Art Machine Learning and Deep Learning Techniques,” Frontiers in Public Health, vol.9, 2022.
  [7]. F. Barbieri, L.E. Anke and J. Camacho-Collados, “Xlm-t: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond,” In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp.258-266, 2022.
  [8]. L. Rokach, “Ensemble-Based Classifiers,” Artificial Intelligence Review, vol.33, no.1-2, pp.1-39, 2010.
  [9]. K. Cho, B. Van Merriënboer, D. Bahdanau and Y. Bengio, “On the Properties of Neural Machine Translation: Encoder–Decoder Approaches,” arXiv preprint arXiv:1409.1259, 2014.
  [10]. U. Naseem, I. Razzak, M. Khushi, P. W. Eklund and J. Kim, “COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis,” IEEE Transactions on Computational Social Systems, vol.8, no.4, pp.1003-1015, 2021.
  [11]. A. Bandi and A. Fellah, “Socio-Analyzer: A Sentiment Analysis using Social Media Data,” In Proceedings of the 28th International Conference, vol.64, pp.61-67, 2017.
  [12]. S. Suriy and K. Sindhu Meena, “Sentimental Analysis of Twitter Using Long Short-Term Memory and Gate Recurrent Unit,” International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.1, pp.1-6, 2020.
  [13]. S.P. Akinrinwa, O. Olabode, O.C. Agbonifo and K.G. Akintola, “Breast Histopathology Images Multi-Classification using Ensemble of Deep Convolutional Neural Networks,” International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.6, pp.9-21, 2022.

  Citation
  Solomon Eshun, Alberta Araba Johnson, Richmond Essieku, "COVID-19 Tweets Sentiment Classification: A Deep Learning Model with Stacking Ensemble for Improved Performance," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.10, Issue.3, pp.1-8, 2023

• Open Access   Article

  The Concept of Basic Cipher Text for Enhancing Security in the Algorithms of Cryptography Applications Using Labelled Graphs

  Auparajita Krishnaa, Dharmendra Kumar Gurjar

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.10 , Issue.3 , pp.9-13, Jun-2023

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  This paper provides the idea of Basic Cipher Text which is a step prior to developing the final Cipher Text. This Basic Cipher Text enhances the security of the Cipher Text still further. Sometimes matrices are used in Cryptography applications which can result in the situation where there is a repetition of row/column thus rendering it unsuitable for calculation of the Inverse of such a matrix – this situation is also taken up. Lexicographic labelled graphs can be used in creation of Basic Cipher Text in a relatively simple way and results in a much hidden Cipher Text subsequently. Moreover, wherever the need for all distinct letters in creation of the Cipher Text arises, these methods can be used with any encryption and decryption techniques. As another option, the Basic Cipher Text can also be used as the final Cipher Text itself and then proceeding with decryption given in this work.

  Key-Words / Index Term
  Basic Cipher Text, Basic Plain Text, Cipher Text, Plain Text, Inverse matrix, Labelled graph, Lexicographic order, Antimagic graph labelling, Felicitous graph labelling.

  References
  [1]. Auparajita Krishnaa, “Inner Magic and Inner Anatimagic Graphs in Cryptography”, Journal of Discrete Mathematical Sciences and Cryptography, vol. 22, no. 6, pp. 1057-1066, 2019.
  [2]. Dharmendra Kumar Gurjar , Auparajita Krishnaa, “Lexicographic Labelled Graphs in Cryptography”, Advances and Applications in Discrete Mathematics, vol, 27,Issue 2, pp. 209-232, 2021,
  [3]. Dharmendra Kumar Gurjar and Auparajita Krishnaa, “Various Antimagic Labeled Graphs from Graph Theory for Cryptography Applications”, International Journal of Scientific Research in Mathematical and Statistical Sciences, ISROSET publisher, vol. 9, issue 3, pp. 11-18, 2022.
  [4]. Dharmendra Kumar Gurjar, Auparajita Krishnaa,, “ Complete Graph and Hamiltonian Cycle in Encryption and Decryption”, International Journal of Mathematics Trends and Technology, vol. 67, issue 12, pp. 42-51, 2021
  [5]. P. Mohan, . K. Rajendran ,. A. Rajesh, , “An encryption Technique Using a Complete Graph With A Self-Invertible Matrix”, Journal of Algebraic Ststistics, vol. 13, issue 3, pp. 1821-1826, 2022.
  [6]. Sachin Sharma, Jeevan Singh Bisht, “Performance Analysis of Data Encryption Algorithms”, Internal Journal of Scientific Research in Network Security and Communication, ISROSET publisher, vol. 3, issue 1, pp 1-4, 2015.
  [7]. Nandhini R., Maheswari V. ,Balaji V “A Graph Theory Approach on Cryptography”, Journal of Computational Mathematics, vol. 2 issue 1,pp. 97-104, 2018.

  Citation
  Auparajita Krishnaa, Dharmendra Kumar Gurjar, "The Concept of Basic Cipher Text for Enhancing Security in the Algorithms of Cryptography Applications Using Labelled Graphs," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.10, Issue.3, pp.9-13, 2023

• Open Access   Article

  Red Chili Price Forecasting in Indonesia Based on Data from The Strategic Food Price Information Center using the Neural Network

  Andrea Tri Rian Dani, Fachrian Bimantoro Putra, Qonita Qurrota A’yun

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.10 , Issue.3 , pp.14-20, Jun-2023

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  This study uses the Neural Network (NN) method which is applied to red chili price data in Indonesia. The NN method has the advantage of modeling fluctuations in red chili price data. The purpose of this study is to obtain the best forecasting results and forecasting accuracy values in predicting red chili price data. The data used starts from January 2018 to April 2023 which is divided into training and testing data with a proportion of 95:5. The results of this study show that the best NN is NN with the backpropagation algorithm using 2 input variables, 9 neurons in 1 hidden layer with an RMSE value of 2555,593 and a MAPE of 4,531 on the training data. In data testing, the MAPE value is <20 so that the prediction results are still quite accurate

  Key-Words / Index Term
  Neural Network, Red Chili, Backpropagation Algorithm, MAPE, RMSE, Prediction

  References
  [1] E. P. Kurniasih, The Long-Run and Short-Run Impacts of Investment, Export, Money Supply, and Inflation on Economic Growth In Indonesia, vol. 22, no. 1. STIE Perbanas Surabaya, 2019. doi: 10.14414/jebav.v22i1.1589.
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  [3] R. A. M. Mandala, “Inflation, Government Expenditure, and Economic Growth in Indonesia,” JEJ Jambura Equilibrium Journal, vol. 2, pp. 109–118, 2020, [Online]. Available: http://ejurnal.ung.ac.id/index.php/equij
  [4] S. Sumartini and A. Riswanto, “Indonesian Economic Growth Rate:,” in Proceedings of the 2nd International Conference on Economic Education and Entrepreneurship (ICEEE 2017), Scitepress, Jun. 2018, pp. 714–717. doi: 10.5220/0006892107140717.
  [5] T. C. Leasiwal, “A Longitudinal Analysis of the Effect of Wages, Inflation, Economic Growth on Unemployment Rate in Maluku Province,” 2021.
  [6] A. Chowdhury, “Does inflation affect economic growth? The relevance of the debate for Indonesia,” J Asia Pac Econ, vol. 7, no. 1, pp. 20–34, 2002, doi: 10.1080/13547860120110452.
  [7] T. Winarno, “The Dynamics Relationship Between Inflation And Economic Growth In Indonesia: A Regional Thresholds Approach.”
  [8] J. H. Stock and M. W. Watson, “Forecasting Infation,” J Monet Econ, vol. 44, pp. 293–335, 1999.
  [9] N. Mohamed, M. H. Ahmad, Z. Ismail, and S. Suhartono, “Short Term Load Forecasting Using Double Seasonal ARIMA Model,” in Proceedings of the Regional Conference on Statistical Sciences 2010, 2010, pp. 57–73.
  [10] A. F. Atiya, S. M. El-Shoura, S. I. Shaheen, and M. S. El-Sherif, “A Comparison Between Neural-Network Forecasting Techniques-Case Study: River Flow Forecasting,” 1999.
  [11] G. P. Zhang and M. Qi, “Neural network forecasting for seasonal and trend time series,” in European Journal of Operational Research, Jan. 2005, pp. 501–514. doi: 10.1016/j.ejor.2003.08.037.
  [12] N. R. Sari, W. Firdaus Mahmudy, A. Wibawa, and A. P. Wibawa, “Backpropagation on neural network method for inflation rate forecasting in Indonesia Teacher Understanding of Literacy Movement in Schools View project Capacitated Vehicle Routing Problem (CVRP) View project Backpropagation on Neural Network Method for Inflation Rate Forecasting in Indonesia,” Int. J. Advance Soft Compu. Appl, vol. 8, no. 3, 2016, [Online]. Available: https://www.researchgate.net/publication/313387413
  [13] Y. Wang, R. Zou, F. Liu, L. Zhang, and Q. Liu, “A review of wind speed and wind power forecasting with deep neural networks,” Appl Energy, vol. 304, Dec. 2021, doi: 10.1016/j.apenergy.2021.117766.
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  [15] Suhartono, D. E. Ashari, D. D. Prastyo, H. Kuswanto, and M. H. Lee, “Deep neural network for forecasting inflow and outflow in Indonesia,” Sains Malays, vol. 48, no. 8, pp. 1787–1798, 2019, doi: 10.17576/jsm-2019-4808-26.
  [16] N. H. A. Rahman, M. H. Lee, Suhartono, and M. T. Latif, “Artificial neural networks and fuzzy time series forecasting: an application to air quality,” Qual Quant, vol. 49, no. 6, pp. 2633–2647, Nov. 2015, doi: 10.1007/s11135-014-0132-6.
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  Citation
  Andrea Tri Rian Dani, Fachrian Bimantoro Putra, Qonita Qurrota A’yun, "Red Chili Price Forecasting in Indonesia Based on Data from The Strategic Food Price Information Center using the Neural Network," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.10, Issue.3, pp.14-20, 2023

• Open Access   Article

  Intercomparison of Estimators of Seven Probability Distributions for Assessment of Peak Flood Discharge

  N. Vivekanandan, C. Srishailam, R.G. Patil

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.10 , Issue.3 , pp.21-28, Jun-2023

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  Abstract
  Assessment of Peak Flood Discharge (PFD) for a given return period is considered as one of the important aspects in hydrological studies as also for planning, design and management of civil and hydraulic structures. This can be carried out by Flood Frequency Analysis (FFA) that consists of fitting probability distribution to the annual maximum series of discharge data. In this paper, seven probability distributions such as 2-parameter Log Normal, Pearson Type-3, Log Pearson Type-3, Generalized Extreme Value (GEV), Extreme Value Type-1 (EV1), Extreme Value Type-2 and Pareto are adopted in FFA for river Cauvery at Kollegal and river Sutlej at Powari. The parameters of the distributions are determined by method of moments, maximum likelihood method and method of L-Moments (LMO) wherever applicable, and used for estimation of PFD. Quantitative assessment using Goodness-of-Fit (viz., Chi-square and Kolmogorov-Smirnov) and diagnostic (viz., D-index) tests, and qualitative assessment by fitted curves of the estimated PFD are employed for checking the adequacy of fitting distributions adopted in FFA. The GoF and diagnostic tests results indicates that the EV1 (LMO) is better suited amongst seven distributions studied for estimation of PFD at Kollegal whereas GEV (LMO) for Powari. The study suggests that the estimated PFD by the selected probability distribution could be used for arriving at a design flood for designing civil and hydraulic structures, and for disaster management related activities at Kollegal and Powari sites.

  Key-Words / Index Term
  Chi-Square, D-index, Generalized Extreme Value, Kolmogorov-Smirnov, Peak Flood

  References
  [1] Isameldin A. Atiem and Nilgün B. Harmanciolu, “Assessment of Regional Floods Using L-Moments Approach: The Case of the River Nile”, Water Resources Management, Vol. 20, Issue 5, pp.723-747, 2006.
  [2] Tefaruk Haktanir and Hans B. Horlacher, “Evaluation of Various Distributions for Flood Frequency Analysis”, Hydrological Sciences Journal, Vol.38, Issue 1, pp.15-32, 1993.
  [3] Rakesh Kumar and Chandranath Chatterjee, “Regional Flood Frequency Analysis Using L-Moments for North Brahmaputra Region of India”, Journal of Hydrologic Engineering, Vol.10, Issue 1, pp.1-7, 2005.
  [4] Mohammad Ali Ghorbani, Heikki Ruskeepa AA,Vijay P. Singh and Bellie Sivakumar, “Flood Frequency Analysis Using Mathematica”, Turkish Journal of Engineering and Environmental Sciences, Vol.34, Issue 3, pp.171-188, 2010.
  [5] Neslihan Seckin, Tefaruk Haktanir and Recep Yurtal, “Flood Frequency Analysis of Turkey Using L-Moments Method”, Hydrological Process, Vol.25, No.22, pp.3499-3505, 2011.
  [6] Zhang Ziyang, Stadnyk Tricia A and Burn Donald H, “Identification of a Preferred Statistical Distribution for At-Site Flood Frequency Analysis in Canada”, Canadian Water Resources Journal, Vol.45, Issue 1, pp.43-58, 2019.
  [7] Babak Naghavi, Fang Xin Yu and Vijay P. Singh, “Comparative Evaluation of Frequency Distributions for Louisiana Extreme Rainfall”, Journal of the American Water Resources Association, Vol.29, Issue 2, pp.211-219. 1993.
  [8] Sheng Yue and Chun Yuan Wang, “Possible Regional Probability Distribution Type of Canadian Annual Stream Flow by L-Moments”, Water Resources Management, Vol.18, Issue 5, pp.425-438, 2004.
  [9] Tatiana Gubareva and Boris Gartsman, “Estimating Distribution Parameters of Extreme Hydrometeoro-Logical Characteristics by L-Moments Method”, Water Resources, Vol.37, Issue 4, pp.437-445, 2010.
  [10] Haberlandt U and Radtke I, “Hydrological Model Calibration for Derived Flood Frequency Analysis Using Stochastic Rainfall and Probability Distributions of Peak Flows”, Hydrology and Earth System Sciences, Vol.18, Issue 1, pp.353-365, 2014.
  [11] Kjeldsen TR, Smithers JC and Schulze RE, “Regional Flood Frequency Analysis in the Kwazulu-Natal Province, South Africa Using the Index-Flood Method”, Journal of Hydrology, Vol.255, Issue 1-4, pp.194-211, 2002.
  [12] Rakesh Kumar, Chatterjee C, Sanjay Kumar, Lohani AK and Singh RD, “Development of Regional Flood Frequency Relationships Using L-Moments for Middle Ganga Plains Subzone 1(f) of India”, Water Resources Management, Vol.17, Issue 4, pp.243-257, 2003.
  [13] Abida H and Ellouze M, “Probability Distribution of Flood Flows in Tunisia”, Hydrology and Earth System Sciences, Vol.12, Issue 3, pp.703-714, 2008.
  [14] Betül Saf, “Regional Flood Frequency Analysis Using L-Moments for the West Mediterranean Region of Turkey”, Water Resources Management, Vol.23, Issue 3, pp.531-551, 2009.
  [15] Malekinezhad H, Nachtnebel HP and Klik A, “Regionalization Approach for Extreme Flood Analysis Using L-Moments”, Agricultural Science and Technology (Iran), Vol.13, Supplementary Issue, pp.1183–1196, 2011.
  [16] Kossi Komi, Barnabas A Amisigo, Bernd Diekkrüger and Fabien CC Hountondji, “Regional Flood Frequency Analysis in the Volta River Basin, West Africa”, Hydrology, Vol.3, Issue 1, pp.1-15, 2016.
  [17] Ery Suhartanto, Lily Montarcih Limantara, Dina Noviadriana, Febri Iman Harta and Dwi Aryani K “Estimation of Design Flood with Four Frequency Analysis Distributions”, Asian Journal of Applied Science and Technology, Vol.2, Issue 1, pp.13-27, 2018.
  [18] Mahmood Ul Hassan, Omar Hayat and Zahra Noreen, “Selecting The Best Probability Distribution for At-Site Flood Frequency Analysis; A Study of Torne River”, SN Applied Science, Vol.1, Issue 12, Article ID: 1629, 2019.
  [19] Vivekanandan N, “Comparison of estimators of five probability distributions for estimation of peak flood”, International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.7, Issue 5, pp.53-59, 2020.
  [20] Van derSpuy D and Du Plessis JA, “Flood Frequency Analysis - Part 1: Review of the Statistical Approach in South Africa”, Water SA, Vol.48, Issue 2, pp.110-119, 2022.
  [21] Hosking JRM, “L-Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics”, Royal Statistical Society (Series B: Statistical Methodology), Vol.52, Issue 1, pp.105-124, 1990.
  [22] Hosking JRM and Wallis JR, “Some Statistics Useful in Regional Frequency Analysis”, Water Resources Research, Vol.29, Issue 2, pp.271-281, 1993.
  [23] Rao AR and Hamed KH, Flood frequency analysis, CRC Press, Florida, USA, 2000.
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  Citation
  N. Vivekanandan, C. Srishailam, R.G. Patil, "Intercomparison of Estimators of Seven Probability Distributions for Assessment of Peak Flood Discharge," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.10, Issue.3, pp.21-28, 2023

• Open Access   Article

  4 – Roman Coloring Number for Some Classes of Standard Graphs

  Chitra Ramaprakash, Joseph Paul R.

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.10 , Issue.3 , pp.29-34, Jun-2023

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  Abstract
  A Roman coloring of a graph G is an assignment of four colors, {0, 1, 2, 3}, to the vertices of G such that every vertex with the color, 0 must be adjacent to some vertex of degree 2 or 3. And the color 1 can be adjacent to color 2 or color 3. In this paper, we extend the concept of Roman coloring to 4-coloring of graphs. We study the 4-Roman coloring of bull graphs and its related graphs for some standard class of graphs.

  Key-Words / Index Term
  Roman Coloring, 4-Roman coloring, 4-Roman Chromatic Number, Triangular snakes, double triangular snakes, eccentricity, diameter, periphery

  References
  [1]. J.R.Lewis, “Vertex-Edge and Edge-Vertex Parameters in Graphs”, Ph.D.Thesis, submitted to the Graduate School of Clemson University.
  [2]. D. Ochmanek, “Time to restructure U.S defense force”, Issues in Science & Technology, Winter 1996.
  [3]. Pagourtzis, P. Penna, K. Schlude, K. Steinhofel, D. S. Tailor, P.Windmayer, “Server placements, Roman domination and other dominating set variants”, 2nd IF1P International conference on theoretical Computer Science, Montreal, pp. 280 – 291, 2002.
  [4]. Windmayer, Server placements, “Roman domination and other dominating set variants”, 2nd IFIP International conference on theoretical Computer Science, Montreal,pp. 280 -291, 2002.
  [5]. Petersen, “Defending Roman empire”, MathTreck, September 11 (2000),www.maa.org.
  [6]. Suresh Kumar J, Satheesh E.N, “Roman labeling of graphs and Application to Military Strategy”, International Journal of Mathematics Trends and Technology,– Vol 52 Number 2 December 2017.
  [7]. Suresh Kumar J, “Graph Colouring Parameters-A Survey”, International Journal for Research in Applied Science & Engineering Technology, Vol 7 Issue IV, Apr 2019.
  [8]. Suresh Kumar J, “Pseudo-Complete Color Critical Graphs”, International Journal for Research in Applied Science & Engineering Technology , Volume 6 Issue I, January 2018.
  [9]. Suresh Kumar J, “Diameter and Travers ability of PAN Critical Graphs”, International Journal of Mathematics Trends and Technology – Vol 52 Number 7 December 2017
  [10]. Suresh Kumar J, “Degrees and Degree Sequences of PAN Critical Graphs”, Int. J. Math. And Appl., 6(1{B}), pp.1025-1028, 2018
  [11]. Suresh Kumar J, “Low and High Vertices in Edge Critical Graphs”, International Journal of Mathematics Trends and Technology, Vol 65 Issue 4, April 2019
  [12]. Suresh Kumar J, “Roman colouring of Graphs and Application to Military Strategy”, International Journal for Research in Applied Science & Engineering Technology , Vol 8, Issue III, March 2020.
  [13]. Suresh Kumar J, Preethi K Pillai, “Proper Roman coloring of graphs”, International Journal of Creative Research Thoughts, March, 2020.
  [14]. Frank Harary, “Graph Theory”, Reading mass, 1969.
  [15]. Suresh Kumar J, Preethi K Pillai, “4-Roman Coloring of graphs”, International Journal of Mathematics Trends and Technology, Vol 66 Issue 3- March 2020
  [16]. Suresh Kumar J, Preethi K Pillai, “Roman Coloring of Bull Graphs and related Graphs”, International Journal for Research in Applied Science & Engineering Technology, Vol 8 Issue IV Apr 2020.
  [17]. D. B. West, "Introduction to Graph Theory", Pearson Education, 2002.
  [18]. Chitra R, M I Huilgol, "New Results on Edge Rotation Distance Graphs", International Journal of Mathematics and Soft Computing Vol.6, No.1, pp.81 – 91,2016
  [19]. R. Joseph Paul, U. Mary, "A Study on Geo Chromatic Number of Some Graph Families", International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.9, Issue.5, pp.1 – 6, Oct – 2022.

  Citation
  Chitra Ramaprakash, Joseph Paul R., "4 – Roman Coloring Number for Some Classes of Standard Graphs," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.10, Issue.3, pp.29-34, 2023

• Open Access   Article

  The Use of Math Puzzle to Improve the Grade 7 Students’ Multiplication Skills

  Jennifer R. Bulawit

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.10 , Issue.3 , pp.35-40, Jun-2023

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  Acquiring a strong foundational understanding of Mathematics is crucial for comprehending complex topics. Among these foundational concepts, mastery of multiplication plays a significant role in dealing with related Mathematical concepts. This study aimed to investigate the effectiveness of utilizing math puzzles as a means to enhance students` multiplication skills. Through the analysis of obtained findings, it was observed that Math Puzzle worksheets garnered the highest overall weighted mean among the strategies employed, while Test or Quiz obtained the lowest overall weighted mean. Students` experiences highlighted the challenging, exciting, and enjoyable nature of Math puzzles. Such engaging activities are crucial for sustaining students` interest in the subject. Furthermore, the intervention employed in this study proved to be effective in enhancing students` performance in multiplication skills. As a result, the proposed Math puzzle strategy may serve as a valuable tool for Mathematics teachers seeking to motivate their students and foster an enjoyable learning experience in mastering multiplication as a fundamental Mathematical concept.

  Key-Words / Index Term
  Foundational understanding of Mathematics, Mastery of multiplication, Math puzzle worksheets, Multiplication skills enhancement, Student engagement, Effective intervention, Motivation in Mathematics education

  References
  [1]. Monica Wong, David Evans, “Improving Basic Multiplication Fact Recall for Primary School Students,” Mathematics Education Research Journal, Vol 19, Issue. 1, 2007.
  [2]. Alvin Odon Insorio and Analisa Rosa Padua Librada, “Enhancing Mathematical Critical Thinking and Problem-Solving Skills through Emergenetics® as a Grouping Mechanism,” Contemporary Mathematics and Science Education, Vol. 2, Issue. 1, 2020.
  [3]. Austin T. Baker and Josh Cuevas, “The Importance of Automaticity Development in Mathematics,” Georgia Educational Researcher, Vol. 14, Issue. 2, 2018.
  [4]. Seppo Pohjolainen, Ossi Nykänen, Janne Venho, Jussi Kangas, “Analysing and improving students’ mathematics skills using ICT-tools,” Eurasia Journal of Mathematics, Science and Technology Education, Vol. 14, Issue. 4, pp. 1221–1227, 2018, doi: 10.29333/ejmste/81869.

  Citation
  Jennifer R. Bulawit, "The Use of Math Puzzle to Improve the Grade 7 Students’ Multiplication Skills," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.10, Issue.3, pp.35-40, 2023

• Open Access   Article

  Analyzing Bangladesh`s Present Patterns in Population Growth and Prediction By ARIMA and Exponential Smoothing Model

  Sofi Mahmud Parvez, Nur Hosain Md. Ariful Azim

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.10 , Issue.3 , pp.41-48, Jun-2023

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  Abstract
  Bangladesh has the highest population density in the entire world. It ranks as the eighth largest population nation in the world in South Asia with a population of about 170 million. Bangladesh`s rapid population growth is one of the most significant problems. Currently one of the most populous nations and the country`s major concern is its enormous population. An exponential smoothing and ARIMA (1,1,1) model is used to forecast Bangladesh`s population from 2022 to 2070 using actual data from 1950 to 2021. The population in 2070 is predicted to be between 277 million and 306 million according to our analysis, which used the ARIMA (1,1,1) models and exponential smoothing. We used ARIMA and exponential smoothing models to analyse our time series data, which were chosen based on Bayesian information criterion (BIC) with P-values less than 5% and graphical representation of ACF and PACF plots. These models are used to draw numerical pictures of future population growth and forecast.

  Key-Words / Index Term
  ARIMA, Exponential Smoothing model, Population Growth, ACF and PACF

  References
  [1] H. Mondol, U. K. Mallick, and M. H. A. Biswas, "Mathematical modeling and predicting the current trends of human population growth in Bangladesh," Modelling, Measurement & Control. D: Manufacturing, Management, Human & Socio-Economic Problems, vol. 39, no. 1, 2018.
  [2] M. R. Islam, "Modeling and predicting cumulative population of Bangladesh," American Journal of Computational and Applied Mathematics, vol. 1, no. 2, pp. 98-100, 2011.
  [3] World Bank, "The World Bank Population Report.," Available: http://data.worldbank.org/indicator/SP.POP.TOTL.
  [4] M. H. A. Biswas, M. Ara, M. N. Haque, and M. A. Rahman, "Application of control theory in the efficient and sustainable forest management," International Journal of Scientific and Engineering Research, vol. 3, p. 2, 2011.
  [5] A. N. Wali, E. Kagoyire, and P. Icyingeneye, "Mathematical modeling of Uganda population growth," 2012.
  [6] M. H. A. Biswas, L. T. Paiva, and M. D. R. de Pinho, "A SEIR model for control of infectious diseases with constraints," Mathematical Biosciences and Engineering, vol. 11, no. 4, pp. 761-784, 2014.
  [7] M. Biswas, "Model and control strategy of the deadly Nipah virus (NiV) infections in Bangladesh," Research & Reviews in Biosciences, vol. 6, no. 12, pp. 370-377, 2012.
  [8] T. Islam, D. G. Fiebig, and N. Meade, "Modelling multinational telecommunications demand with limited data," International Journal of Forecasting, vol. 18, no. 4, pp. 605-624, 2002.
  [9] J. E. Cohen, "Population growth and earth`s human carrying capacity," Science, vol. 269, no. 5222, pp. 341-346, 1995.
  [10] A. Rahman and M. M. Hasan, "Modeling and forecasting of carbon dioxide emissions in Bangladesh using Autoregressive Integrated Moving Average (ARIMA) models," Open Journal of Statistics, vol. 7, no. 4, pp. 560-566, 2017.
  [11] G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control. John Wiley & Sons, 2015.
  [12] G. G. Judge, W. E. Griffiths, R. C. Hill, H. Lütkepohl, and T.-C. Lee, The theory and practice of econometrics. John wiley & sons, 1991.
  [13] C. C. Holt, "Forecasting seasonals and trends by exponentially weighted moving averages," International journal of forecasting, vol. 20, no. 1, pp. 5-10, 2004.
  [14] P. R. Winters, "Forecasting sales by exponentially weighted moving averages," Management science, vol. 6, no. 3, pp. 324-342, 1960.

  Citation
  Sofi Mahmud Parvez, Nur Hosain Md. Ariful Azim, "Analyzing Bangladesh`s Present Patterns in Population Growth and Prediction By ARIMA and Exponential Smoothing Model," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.10, Issue.3, pp.41-48, 2023

• Open Access   Article

  Article on the applications of the Jensen’s Inequality in Alternative proofs and Problems Part-2

  Toyesh Prakash Sharma, Etisha Sharma

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.10 , Issue.3 , pp.49-56, Jun-2023

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  As it is mentioned in the title that through this article there, we will deal with various problems of the classic Jensen’s Inequality more clearly Weighted Jensen’s Inequality of both kinds of convex function and concave function. Previously we have done the work on applications of Jensen inequality and their application. Under the title applications, we will mainly discuss two aspects of it which are the application of Jensen inequality in proving well-known inequalities such as Köber`s inequality, Jordan’s Inequality and Ste?kin`s inequality and another aspect is the application of Jensen’s Inequality in solving and simplifying problems proposals published in different journals on the basis of it there are example problems for readers.

  Key-Words / Index Term
  Convex Function, Concave Function, AM-GM Inequality, Inequality, Jensen’s Inequality

  References
  [1]. Toyesh Prakash Sharma, "Article on the applications of the Jensen’s Inequality in Alternative proofs and Problems", International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.9, Issue.6, pp.36-42, 2022
  [2]. Mircea Becheanu “Junior Problems” J548, Mathematical Reflection, Issue 2, p.1, 2021.
  [3]. Daniel Sitaru and Drobeta Turnu, “Problem Solution Corner” Prob. 849, Pentagon, Vol. 79 issue 1 p.29, Spring 2019.
  [4]. Iuliana Trasca, Olt, Romania, “Problem Solution Corner” Prob. 752, Pentagon, Vol. 75 issue 1 p.51, Spring 2015.
  [5]. Dorin Marghidanu, “Problem Solution Corner” Prob. 885, Pentagon, Vol. 81 issue 2 p.71-72, Spring 2022.
  [6]. Angel Plaza “Problems” Problem 74.2, Irish Math. Soc. Bulletin, Number 74, p.105–108, Winter 2014.
  [7]. Daniel Sitaru, Problems for Junior” JP 500, Romanian Mathematical Magazine, Number 34, p.1, Autumn 2024.
  [8]. Toyesh Prakash Sharma “Elementary Problems” Prob. E-108, Arhimede Mathematical Journal, Vol. 9 No.2 p.209-210, Autumn 2022.
  [9]. Toyesh Prakash Sharma “Elementary Problem Solution Corner” Prob. B-1223. Fibonacci Quarterly, Vol. 61, issue 1, p. 84, August 2023.
  [10]. Toyesh Prakash Sharma “Elementary Problem Solution Corner” Prob. B-1228. Fibonacci Quarterly, Vol. 61, issue 2, p. 179, May 2023.
  [11]. Toyesh Prakash Sharma “Problem Section” MS 91 (3-4) 2022: Problem 8. Student, Indian Mathematical Society, Vol. 91, Nos. 3-4, p. 219, July-December (2022).
  [12]. Nguyen viet Hung “Junior Problems” J463, Mathematical Reflection, Issue 5, p.1, 2018.
  [13]. M. Mehrabi “Problem Section” Prob. 12149, American Mathematical Monthly, Vol. 126, December 2019.
  [14]. Goran Conar, “Problems and Solutions” Prob. 5738, School Science and Mathematics Journal, Issue 4, p.2, April 2023.

  Citation
  Toyesh Prakash Sharma, Etisha Sharma, "Article on the applications of the Jensen’s Inequality in Alternative proofs and Problems Part-2," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.10, Issue.3, pp.49-56, 2023

• Open Access   Article

  Fixed Point Theorems For Set- Valued Mappings in Banach Spaces

  Barnita Bharati, Kalishankar Tiwary

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.10 , Issue.3 , pp.57-60, Jun-2023

  Abstract

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  Citation

  Abstract
  In this paper, we have proved some common fixed point theorems for set-valued mappings which extend some results proved in (Ciric 1965 [4], Pai and Veeramani 1982 [15], Rhoades 1977 [16], and Singh and Tiwary 1992 [18]).

  Key-Words / Index Term
  Set-valued mappings, Banach space, Common fixed point, Weakly compact subset, Closed convex subset.

  References
  [1] D.E. Anderson, K.L. Singh and J.H.M. Whitfield, Common fixed point in metric space, Jnanabha, 16, 23, 1986.
  [2] J.S. Bae, Reflexivity of a Banach space with a uniformly normal structure, Proc. Amer. Math. Soc., 90, 269. 1984
  [3] F.E. Browder, Non-expansive non-linear operator in a Banach space, Proc. Nat. Acad. Sci., 54, 1041, 1965.
  [4] L.B. Ciric, On fixed point theorem in Banach spaces, Iubl. Inst. Math., 19 (33), 43, 1975.
  [5] M. Edelstein, On fixed and periodic points under contractive mappings, Jr. London Math. Soc., 37, 74, 1962.
  [6] B. Fisher, Common fixed points of mappings and set-valued mappings, Rostock, Math. Kolloq. 18, 69, 1981.
  17] Tarun Gain, Kalishankar Tiwary, "Some Common Fixed Point Theorems in 2-Metric Spaces for Contractive Type Conditions," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.4, pp.58-61, 2019
  [8] A.A. Gillespie, B.B. William, Fixed point theorem for a non-expansive mapping of a Banach space with uniformly normal structure, Applicable Analysis, 9, 121, 1979.
  [9] A.A. Gillespie, B.B. William, Some theorems on fixed points in Lipschitz and Kannan type mappings, J. Math. Analysis and Application, 74, 382, 1980.
  [10] D. Gohde, Zum prinzip der Kontraktivon abbildung, Math. Nach., 30, 1965.
  [11] R. Kannan, Construction of fixed points of a class of non-linear mapping, J. Math. Analysis and Application, 41, 2, 430, 1973.
  [12] W.A. Kirik, A fixed point theorem for mappings which do not increase distances, Amer. Math. Monthly, 72, 1004, 1965.
  [13] Mainak Mitra, Jigmi Dorjee Bhutia, Kalishankar Tiwary, "On the Existence of Common Fixed Point in a Complete Metric Space Using Integral Type Contraction Conditions," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.9, Issue.6, pp.25-35, 2022.
  [14] S B Nadler, Multi Valued Contraction Mappings, Pac. J. Math, 30,475- 488, 1969.
  [15] D.V. Pai, P. Veeramani, On some fixed point theorems in Banach spaces, Inter, J. Math. and Math. Sci., 5, 113, 1982.
  [16] B.E. Rhoades, Some fixed point theorems in Banach spaces, Math. Sem., Notes, 5, 69, 1977.
  [17] Krishnadhan Sarkar, Dinanath Barman, Mithun Paul, Kalishankar Tiwary, "Probabilistic Metric Space, Menger Space and Some Common Fixed Point Theorem," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.5, pp.86-90, 2019
  [18] G.N. Singh, Kalishankar Tiwary, Fixed point theorems in Banach Spaces, Bull. Allahabad Math. Soc., 7, 23, 1992.
  [19] Kalishankar Tiwary, Biplab Kumar Bag, Chandan Kamelia, "Common Fixed-Point Theorems in Complete Hausdörff Uniform Spaces," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.4, pp.84-88, 2019
  [20] Kalishankar Tiwary, Chandan Kamelia, Biplab Kumar Bag, "On Common Fixed Points of Mappings in Hilbert Spaces," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.4, pp.100-103, 2019
  [21] Kalishankar Tiwary, S.C. Debnath, Fixed point theorems for set-valued map- pings, Bull. Cal. Math. Soc., 97, (3) 275-282, 2005.

  Citation
  Barnita Bharati, Kalishankar Tiwary, "Fixed Point Theorems For Set- Valued Mappings in Banach Spaces," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.10, Issue.3, pp.57-60, 2023

• Open Access   Article

  Allocation of Available Funds for some Portfolios with Mixed Investment Purpose Using Quadratic Programming Approach

  Olawale Kolapo Steve Emiola, Musibau Abayomi Omoloye

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.10 , Issue.3 , pp.61-67, Jun-2023

  Abstract

  Full-Text HTML

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  Citation

  Abstract
  This study investigates portfolio management of investible fund using quadratic programming. The data used is secondary in nature which is abstraction from annual report of earning per share of eight different organization namely: Dangote sugar, Dangote flour mills, Dangote Cement, Dangote salt, Zenith Bank, First bank, Fidelity bank, and Access bank was collected and a model was fixed. These data were analyzed using quadratic programming in conjunction with LINDO software. The result of the analyzed data revealed that 13% to be allocated to Zenith Bank, 9% to be allocated to Dangote cement, 13% to be allocated to Dangote Sugar, 13% to be allocated to Dangote flour, 13% to be allocated to Dangote salt, 13% to be allocated to Fidelity bank, 13% to be allocated to First bank, 13% to be allocated to Access bank. This will in turn gives an average increase of 90% return on capital invested. The research has answered the question of how much an investor should allocate to each investment to minimize risk and maximize return

  Key-Words / Index Term
  Quadratic programming, portfolio management, data analysis, shares and hypothesis

  References
  [1] Bazaraa, M.S., ‘’Non-linear Programming Theory and Algorithm,’’, London, Second Edition, pp.67-69, 1979.
  [2] Emiola, O.K. S., Omoloye, M. A., Arinze, C. U., ‘’Portfolio Selection on Some Insurance Companies (Aiico, Linkage, Niger, Mutual Benefit and Lasaco) Using Current Ratio’’, International Journal of Advances in Engineering and Management (IJAEM), Vol. 2, Issue.8, Pp. 373-378, 2020.
  [3] Emiola, O. K. S., Omoloye, M. A., Umar, A. M., Taiwo, M., ‘’Application of Non Linear Programming to Portfolio Management on Some Insurance Companies Using Cash Ratio” Published in International Research Journal of Innovations in Engineering and Technology,Vol.5, Issue 9, Pp.86-91, 2021.
  [4] Emiola, O.K.S, Adeoye A.O., ‘’Analysis of Bonus on share as a Determinant for Portfolio Selection of Bank Share’’ International Journal of Advanced Research in Computer Science, Vol. 5, Issue 8, Pp. 1-4, 2014.
  [5] Emiola, O.K.S, Adeoye A.O., ‘’Return on Invested Capital as a Determinant for Future Investment’’ International Journal of Advanced Research in Computer Science. Vol.5, Issue 8, Pp. 54-57, 2014.
  [6] Emiola O.K.S., Alayemi S.A., ‘’Dividend on Share as a Policy for Portfolio Selection of Bank Share/pas’’, International Journal of Computer Science, Vol. 3, Issue 5, Pp.18-24, 2015.
  [7] Emiola O.K.S., Alayemi S.A., ‘’Using Dividend Payout Ratio as a Determinant for Portfolio Selection of Bank Share’’, International Journal of Management, Vol.5, Issue 2, Pp. 20-25, 2015.
  [8] Etukudo I. A, Effanga E. O, Onwukwe C. E., Umoren M. U., ‘’Application of portfolio selection model for optimal allocation of investible funds in a portfolio Mix’’ Scientia African Faculty of science,University of Portharcourt, Vol. 8, Issue 1, Pp. 96-102, 2009.
  [9] Etukudo I. A., Umoren M. U., ‘’A comparison of a modified super convergent line series algorithm and modified simplex method for solving quadratic programming’’ Icastor journal of mathematical sciences Kolkata India, Vol. 3, Issue 1, Pp. 41-61, 2009.
  [10] Francis J. C., ‘’Investment analysis and management’’, Third Edition McGraw - Hill. Inc. U.S.A, Pp. 76, 1980.
  [11] Hillier F. C., Liebemram G. J., ‘’Introduction to operation research’’ Eight Edition, Tata McGraw - Hill New DEllin, Pp.34, 2006.
  [12] Olawale Kolapo Steve Emiola, Musibau Abayomi Omoloye, Christiana Uchechukwu Arinze, ‘’Application of Non Linear Programming to Portfolio Management on some Insurance Companies (AIICO, LINKAGE, NIGER, Mutual benefit and LASACO) using Return on Asset’’, International Journal of Engineering Technology and Management Sciences, Vol. 4. Issue 7, Pp. 1-7, 2020.
  [13] Kothari C. R, ‘’Research methodology method and techniques’’, 2nd Edition NEW AGE International (P) Limited India, Pp.23, 2004.
  [14] Sharma J. K., ‘’Operation Research theory and application’’, 5th Edition Macmillan Publishers India Limited, 90, 2013.
  [15] Umoren M. U., Etukudo I.A., ‘’A modified super convergent line series algorithm for solving quadratic programming problems’’, Journal of mathematical sciences Kolkata India Vol. 20, Issue l, Pp. 55-66, 2009.
  [16] S.A. Mohamed, S.M. Mohamed, ‘’Estimation Methods of Kumaraswamy Lindley Regression Model with Applications on Egyptian Stock Exchange Data’’, International Journal of Scientific Research in Research Paper. Mathematical and Statistical Sciences, Vol.9, Issue1, Pp.19-23, 2022

  Citation
  Olawale Kolapo Steve Emiola, Musibau Abayomi Omoloye, "Allocation of Available Funds for some Portfolios with Mixed Investment Purpose Using Quadratic Programming Approach," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.10, Issue.3, pp.61-67, 2023