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Abstract:

The educational process grows intellectual and critical thinking which helps a person to make correct or optimal decisions by using logic, calculations, and experiments. This factor helps a person to use available resources in an optimal way to maximize the outcome. Unfortunately, along with all other areas, the educational process was seized initially during COVID-19 as well. To continue the education process in lockdowns, academia has shifted from traditional learning (TL) towards the online learning (OL) process. Instructors and learners of different academies belong to different fields and backgrounds. Thus, it is not easy to smoothly adopt OL for all of them. Therefore, this study is aimed to conduct a survey to check the feasibility of the adoption of OL for both types of audiences i.e. instructors and learners. The purpose is to compare the thoughts of both audiences and find the difference between them by using different descriptive and inferential statistical techniques and to have a brief overview of OL and TL in the academies of Pakistan. This study will help academies to understand the flaws, gaps, and limitations of OL from instructors' and learners' perspectives as the gaps can be filled by improving existing approaches to make the OL system smoothly adoptable by everyone in Pakistan in the future.

Keywords:

Online Learning,Traditional Learning,COVID-19,Instructor,Learners,

Refference:

I. Adane, Fentahun, Yoseph Merkeb Alamneh, and Melaku Desta. “Computer vision syndrome and predictors among computer users in Ethiopia: a systematic review and metaanalysis.” Tropical Medicine and Health 50.1 (2022): 1-12
II. Al-adwan A. &Smedly J. Implementing E-learning in the Jordanian Higher Education System: Factors Affecting Impact. International Journal of Education and Development using Information and communication technology (IJEDICT). 2012; 8(1): 121 – 135.
III. Alias NA &ZainuddinAM. Innovation for Better Teaching and Learning: Adopting the Learning Management System.Malaysian Online Journal of Instructional Technology. 2005; 2(2): 27 – 40.
IV. Berstorff PC & Lowes SL. Student Perceptions and Opinions towards E-learning in the college environment. Academy of Educational Leadership Journal. 2007; 11(2): 13 – 30.
V. Bisht, Raj Kishor, Sanjay Jasola, and Ila Pant Bisht. “Acceptability and challenges of online higher education in the era of COVID-19: a study of students’ perspective.” Asian Education and Development Studies (2020).
VI. Blehm, Clayton, et al. “Computer vision syndrome: a review.” Survey of ophthalmology 50.3 (2005): 253-262. 15
VII. Chandra, Yamini. “Online education during COVID-19: perception of academic stress and emotional intelligence coping strategies among college students.” Asian education and development studies (2020).
VIII. Das, Pamela, and Richard Horton. “Rethinking our approach to physical activity.” Lancet (London, England) 380.9838 (2012): 189-190.
IX. Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of educational technology systems, 49(1), 5-22.
X. Elfaki, Nahid Khalil, Itedal Abdulraheem, and Rashida Abdulrahim. “Impact of e-learning vs traditional learning on student’s performance and attitude.” International Journal of Medical Research & Health Sciences 8.10 (2019): 76-82.
XI. Fariha Shaikh, Shafiq-ur-Rehman, et al. (2022) “Effects of Online Educational System on Personal Health of Students and Teachers in COVID-19 Crises.” [in press].
XII. Hannay, Maureen, and Tracy Newvine. “Perceptions of distance learning: A comparison of online and traditional learning.” Journal of online learning and teaching 2.1 (2006): 1-11.Hscodhod
XIII. Holley D. Which Room is the Virtual Seminar in Please? Educational and Training. 2002; 44(3): 112 – 121.
XIV. Kumar, Naveen, and Nageshwar Sharma. “To determine the prevalence of computer vision syndrome among computer users: a descriptive study.” European Journal of Molecular & Clinical Medicine (EJMCM) 7.10 (2020): 2020.
XV. Lee, I-Min, et al. “Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy.” The lancet 380.9838 (2012): 219-229.
XVI. Valentina A. & Nelly A. The role of E-learning, the advantages and disadvantages of its adoption in higher education.International Journal of Education and Research. 2014; 2(12): 397 -410.
XVII. Zarei-Zavaraki E &Rezael I. The impact of Using Electronic Portfolio on Attitude, Motivation and Educational Progress of Students’ Khaje Nasir Toosi University.Educational Measurement Periodical. 2011; 2(5): 67 – 96.

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Abstract:

Present-day computational capabilities allow digital assets like images, videos, text, and audio have features comparable to those in real-world entities. Location is one such aspect. Similar to real-world bodies being represented by vectors on cartesian coordinates, digital media entities (like text, as discussed in this paper) when encoded, each component of the encoding representing a feature, conceptually should have a vector representation in each such encoding. The concept is put to practice by text encodings (embedding) techniques like Bag-of-words, TF-IDF, Word2Vec, Glove, and Transformer models like BERT, AlBERT etc which create vectors out of the text. This paper aims to use a combination of features in text analogous to mass and distance and propose a new plagiarism index cloning the formula of gravitational force. Parameters like the length of documents/number of words, semantics, frequency of each word, etc, one or many of which are often missed out in prevalent algorithms of text similarity calculations, are important for detecting and measuring plagiarism. The paper aims to consider all such possible parameters in the formulation of a new plagiarism metric to be coined as Gravity Score.

Keywords:

Natural Language Processing,Text Embedding,Text token,Gravitation,

Refference:

I. Abdi, H., and L. J. Williams. 2010. “Principal component analysis.” Wiley interdisciplinary reviews: computational statistics 2 (4): 433–459.
II. Alemi, A. A., and P. Ginsparg. 2015. “Text segmentation based on semantic word embeddings.” arXiv preprint arXiv:1503.05543.
III. Cattaneo, C. 1958. “General relativity: relative standard mass, momentum, energy and gravitational field in a general system of reference.” Il Nuovo Cimento (1955-1965) 10 (2): 318–337.
IV. Chang, A. X., and C. D. Manning. 2014. “TokensRegex: Defining cascaded regular expressions over tokens.” Stanford University Computer Science Technical Reports. CSTR 2:2014.
V. Chujo, K., and M. Utiyama. 2005. “Understanding the Role of Text Length, Sample Size and Vocabulary Size in Determining Text Coverage.” Reading in a foreign language 17 (1): 1–22.
VI. Danielsson, P.-E. 1980. “Euclidean distance mapping.” Computer Graphics and image processing 14 (3): 227– 248.
VII. Edelbaum, T. N. 1962. “Theory of maxima and minima.” In Mathematics in Science and Engineering, 5:1–32. Elsevier.
VIII. Fock, V. 2015. The theory of space, time and gravitation. Elsevier.
IX. Gan, L., and J. Jiang. 1999. “A test for global maximum.” Journal of the American Statistical Association 94 (447): 847–854.
X. Grefenstette, G., and P. Tapanainen. 1994. “What is a word, what is a sentence?: problems of Tokenisation.”
XI. Kanada, Y. 1990. “A Vectorization Technique of Hashing and Its Application to Several Sorting Algorithms.” In PARBASE, 147–151.
XII. Lahitani, A. R., A. E. Permanasari, and N. A. Setiawan. 2016. “Cosine similarity to determine similarity measure: Study case in online essay assessment.” In 2016 4th International Conference on Cyber and IT Service Management, 1–6. IEEE.
XIII. Mikolov, T., K. Chen, G. Corrado, and J. Dean. 2013. “Efficient estimation of word representations in vector space.” arXiv preprint arXiv:1301.3781.

XIV. Morita, K., E.-S. Atlam, M. Fuketra, K. Tsuda, M. Oono, and J.-i. Aoe. 2004. “Word classification and hierarchy using co-occurrence word information.” Information processing & management 40 (6): 957–972.
XV. Nation, P., and R. Waring. 1997. “Vocabulary size, text coverage and word lists.” Vocabulary: Description, acquisition and pedagogy 14:6–19.
XVI. Niwattanakul, S., J. Singthongchai, E. Naenudorn, and S. Wanapu. 2013. “Using of Jaccard coefficient for keywords similarity.” In Proceedings of the international multiconference of engineers and computer scientists, 1:380–384. 6.
XVII. Pennington, J., R. Socher, and C. D. Manning. 2014. “Glove: Global vectors for word representation.” In Pro- ceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 1532– 1543.
XVIII. Ramos, J., et al. 2003. “Using tf-idf to determine word relevance in document queries.” In Proceedings of the first instructional conference on machine learning, 242:29–48. 1. New Jersey, USA.
XIX. Van der Maaten, L., and G. Hinton. 2008. “Visualizing data using t-SNE.” Journal of machine learning research 9 (11).
XX. Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. “Attention is all you need.” Advances in neural information processing systems 30.
XXI. Verlinde, E. 2011. “On the origin of gravity and the laws of Newton.” Journal of High Energy Physics 2011 (4): 1–27.
XXII. Zhang, Y., R. Jin, and Z.-H. Zhou. 2010. “Understanding bag-of-words model: a statistical framework.” Interna- tional journal of machine learning and cybernetics 1 (1): 43–52.

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Abstract:

The method of curve fitting by the principle of the Least Square (L.S) method is a relevant and well-received method of trend analysis, especially to make a project for the future time. The Least Square (L.S) method helps to fit mathematical functions to a given data set. For this research, we accumulated data from the Yearbook of Agricultural Statistics of Bangladesh for the year 2007-08 to 2019-20 with the help of the Bangladesh Bureau of Statistics (BBS) website. We arranged the data according to the proposed method and graphically represented it. This research aimed to forecast the production of rice in Bangladesh with trend line analysis by the method of Least Square (L.S) for the years 2020-21 to 2024-25. As a result, we found an upward trend line for the production of rice in Bangladesh. Therefore the production will be maximum in the year 2024-25.

Keywords:

Least Square Method,Linear Trend Line,Forecasting,Time series,Bangladesh,

Refference:

I. Abdulkabir, M., Tunde, R. S., & Edem, U. A. (2015). Trend analysis on road traffic accident in Nigeria. Science Innovation, 3(5), 52-57.
II. Adhikari, R., & Agrawal, R. K. (2013). An introductory study on time series modeling and forecasting. arXiv preprint arXiv:1302.6613.
III. Alam, M. S., Kalpoma, K., Karim, M. S., Al Sefat, A., & Kudoh, J. I. (2019, July). Boro Rice Yield Estimation Model Using Modis Ndvi Data for Bangladesh. In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 7330-7333). IEEE.
IV. Ara, J., Md. Moheuddin, M., Hossain, S., & Abdus Sattar Titu, M. (2020). A mathematical study of break-even analysis based on dairy farms in Bangladesh. International Journal of Economic Behavior and Organization, 8(2), 38. https://doi.org/10.11648/j.ijebo.20200802.13
V. Athiyarath, S., Paul, M., & Krishnaswamy, S. (2020). A comparative study and analysis of time series forecasting techniques. SN Computer Science, 1(3). https://doi.org/10.1007/s42979-020-00180-5
VI. Awal, M. A., & Siddique, M. A. B. (2011). Rice production in Bangladesh employing by ARIMA model. Bangladesh Journal of Agricultural Research, 36(1), 51-62.
VII. Bangladesh Bureau of Statistics. Bangladesh Bureau of Statistics-Government of the People’s Republic of Bangladesh. (n.d.). Retrieved December 15, 2021, from http://bbs.gov.bd/site/page/3e838eb6-30a2-4709-be85-40484b0c16c6/-
VIII. Bangladesh Rice Research Institute. Bangladesh Rice Research Institute-Government of the People’s Republic of Bangladesh. (n.d.). Retrieved December 15, 2021, from http://www.brri.gov.bd/
IX. Bangladesh. Ricepedia. (n.d.). Retrieved December 15, 2021, from https://ricepedia.org/index.php/bangladesh
X. CAI, S., ZHANG, H., CHEN, H., & SHA, J. (2007). Research of piecewise cubic curve-fitting method based on least-square principle. Science technology and Engineering, 3.
XI. Chatfield, C. (2000). Time-series forecasting. CRC press.
XII. Chen, G., Ren, Z. L., & Sun, H. Z. (2005). Curve fitting in least-square method and its realization with Matlab. Ordnance industry automation, 3, 063.
XIII. Chen, L., & Zheng, Y. (2012). Study on curve fitting based on least square method. Journal of Wuxi Institute of Technology, 11(5), 52-55.
XIV. De Gooijer, J. G., & Hyndman, R. J. (2006). 25 years of time series forecasting. International journal of forecasting, 22(3), 443-473.
XV. Finance Division, Ministry of Finance. Finance Division, Ministry of Finance-Government of the People\’s Republic of Bangladesh. (n.d.). Retrieved December 15, 2021, from https://mof.gov.bd/site/page/44e399b3-d378-41aa-86ff-8c4277eb0990/BangladeshEconomicReview
XVI. Hossain, M. A., Uddin, M. N., Hossain, M. A., & Jang, Y. M. (2017, October). Predicting rice yield for Bangladesh by exploiting weather conditions. In 2017 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 589-594). IEEE.
XVII. Hossain, M. M., & Abdulla, F. (2015). Jute production in Bangladesh: A time series analysis. Journal of Mathematics and Statistics, 11(3), 93–98. https://doi.org/10.3844/jmssp.2015.93.98
XVIII. Kabir, M. S., Salam, M. U., Chowdhury, A., Rahman, N. M. F., Iftekharuddaula, K. M., Rahman, M. S., … & Biswas, J. K. (2015).Rice vision for Bangladesh:2050 and beyond. Bangladesh Rice Journal, 19(2),1-18.
XIX. Kabir, M. S., Salam, M. U., Islam, A. K. M. S., Sarkar, M. A. R., Mamun, M. A. A., Rahman, M. C., … & Rahman, N. M. F. (2020). Doubling rice productivity in Bangladesh: A way to achieving SDG 2 and moving forward. Bangladesh Rice Journal, 24(2), 1-47.
XX. Karmaker, C. L., Halder, P. K., & Sarker, E. (2017). A study of time series model for predicting jute yarn demand: Case study. Journal of Industrial Engineering, 2017, 1–8. https://doi.org/10.1155/2017/2061260
XXI. Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocomputin, 55(1-2), 307-319.
XXII. Mahmood, R., Meo, M., Legates, D. R., & Morrissey, M. L. (2003). The CERES-Rice model-based estimates of potential monsoon season rainfed rice productivity in Bangladesh. The Professional Geographer, 55(2), 259-273.
XXIII. Miller, J. L., McCahon, C. S., & Bloss, B. K. (1990). Food production forecasting with simple time series models. Hospitality Research Journal, 14(3), 9–21. https://doi.org/10.1177/109634809101400303
XXIV. Paul, A., Bhowmik, R., Chowdary, V. M., Dutta, D., Sreedhar, U., & Sankar, H. R. (2017). Trend analysis of time series rainfall data using robust statistics. Journal of Water and Climate Change, 8(4), 691–700.
XXV. Salam, M. A., Ross, L. G., & Beveridge, C. M. M. (2003). A comparison of development opportunities for crab and shrimp aquaculture in southwestern Bangladesh, using GIS Modelling. Aquaculture, 220(1-4), 477–494. https://doi.org/10.1016/s0044-8486(02)00619-1
XXVI. Sanwlani, M., & Vijayalakshmi, M. (2013). Forecasting sales through time series clustering. International Journal of Data Mining & Knowledge Management Process, 3(1), 39.
XXVII. Sarker, M. A. R., Alam, K., & Gow, J. (2012). Exploring the relationship between climate change and rice yield in Bangladesh: An analysis of time series data. Agricultural Systems, 112, 11-16.
XXVIII. Seber, G. A., & Lee, A. J. (2012). Linear regression analysis (Vol. 329). John Wiley & Sons.
XXIX. Shil, R. N., & Debnath S. C. (2001). An introduction to the theory of statistics. M. Shil & A. Debnath.
XXX. Weatherford, L. R., & Kimes, S. E. (2003). A comparison of forecasting methods for Hotel Revenue Management. International Journal of Forecasting, 19(3), 401–415. https://doi.org/10.1016/s0169-2070(02)00011-0

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Abstract:

  Mathematical research in the ancient world was especially interesting when put in the context of philosophical ideas. No country has ever thrived without investing heavily in its children's education. It is crucial to achieving this requirement in order to be classified as a “Developed Nation” within a certain time limit. Allocating sufficient funds to Math and Computer Science programs at all educational levels is essential. In contrast to the study of mathematics for practical purposes, pure mathematics focuses only on the study of mathematical ideas themselves. Although the inspiration for these ideas sometimes comes from real-world problems, and the solutions often have practical applications, pure mathematicians are not typically driven by the potential utility of their work. Mathematics has been essential in the IT revolution. There are many examples of how computer science has contributed to modern life, including the information technology sector, the manufacturing sector, satellites, electronic banking and commerce, the communication revolution, the global positioning system (GPS), the geographic information system (GIS), remote sensing, and many more.

Keywords:

Mathematics,Education,Computer Science,Pure mathematics,Applied Mathematics,Real-world Applications,Practical Applications,Information Technology,Satellites,E-Banking,E-Commerce,Communication Technology,Remote Sensing,

Refference:

I. A. Ali, S. Talpur and S. Narejo, “Detecting Faulty Sensors by Analyzing the Uncertain Data Using Probabilistic Database,” 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2020, pp. 1-7, doi: 10.1109/iCoMET48670.2020.9074069.
II. B. Musil, S. Gartner, I. Pesek and M. Krašna, “ICT competences assessment through ICT escape room,” 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2019, pp. 622-626, doi: 10.23919/MIPRO.2019.8757043.
III. C. H. Hsu, C. E. Montenegro Marin, R. Gonzalez Crespo and H. F. Mohamed El-sayed, “Guest Editorial Introduction to the Special Section on Social Computing and Social Internet of Things,” in IEEE Transactions on Network Science and Engineering, vol. 9, no. 3, pp. 947-949, 1 May-June 2022, doi: 10.1109/TNSE.2022.3167460.

IV. D. Połap, G. Srivastava, A. Jolfaei and R. M. Parizi, “Blockchain Technology and Neural Networks for the Internet of Medical Things,” IEEE INFOCOM 2020 – IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2020, pp. 508-513, doi: 10.1109/INFOCOMWKSHPS50562.2020.9162735.
V. H. Dehghani, “The effectiveness of a mobile application “Kalcal” on the learning of mathematics in students with dyscalculia,” 2019 International Serious Games Symposium (ISGS), 2019, pp. 1-6, doi: 10.1109/ISGS49501.2019.9047035.
VI. M. de Berg, H. L. Bodlaender, S. Kisfaludi-Bak and S. Kolay, “An ETH-Tight Exact Algorithm for Euclidean TSP,” 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS), 2018, pp. 450-461, doi: 10.1109/FOCS.2018.00050.
VII. M. T. Azhar, M. B. Khan and M. M. Zafar, “Architecture of an Enterprise Project Life Cycle using Hyperledger platform,” 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), 2019, pp. 1-5, doi: 10.1109/MACS48846.2019.9024764.
VIII. R. A. Canessane, R. Dhanalakshmi, B. Pavithra, B. Sasikanth and C. Sandeep, “HUSP Mining Techniques to Detect Most Weighted Disease and Most Affected Diseases for the Healthcare Industry,” 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), 2019, pp. 25-32, doi: 10.1109/ICONSTEM.2019.8918784.
IX. R. A. Canessane, R. Dhanalakshmi, B. Pavithra, B. Sasikanth and C. Sandeep, “HUSP Mining Techniques to Detect Most Weighted Disease and Most Affected Diseases for the Healthcare Industry,” 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), 2019, pp. 25-32, doi: 10.1109/ICONSTEM.2019.8918784.
X. S. Dragoumanos, L. Garcia, D. Schultz and L. Bowlby, “IEEE pre-university science, technology, engineering, and mathematics outreach: Share. Give back. Inspire,” in IEEE Potentials, vol. 41, no. 5, pp. 12-14, Sept.-Oct. 2022, doi: 10.1109/MPOT.2022.3181437.
XI. S. H. Said Abdelaziz Abdelrazek, H. B. Kutty Mammi and M. M. Din, “Privilege Escalation Focused Offensive Security Training Platform,” 2021 International Conference on Data Science and Its Applications (ICoDSA), 2021, pp. 169-174, doi: 10.1109/ICoDSA53588.2021.9617497.
XII. S. Hu, L. Shuai, Q. Yang and H. Chen, “Study on Wireless Signal Propagation in Residential Outdoor Activity Area Based on Deep Learning,” 2021 International Conference on Computer, Control and Robotics (ICCCR), 2021, pp. 225-230, doi: 10.1109/ICCCR49711.2021.9349418.
XIII. S. Mistry and L. Wang, “Efficient Prediction of Heart Disease Using Cross Machine Learning Techniques,” 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), 2022, pp. 1002-1006, doi: 10.1109/IPEC54454.2022.9777309.
XIV. S. Xiong, Q. Cao and W. Si, “Adaptive Path Tracing with Programmable Bloom Filters in Software-Defined Networks,” IEEE INFOCOM 2019 – IEEE Conference on Computer Communications, 2019, pp. 496-504, doi: 10.1109/INFOCOM.2019.8737387.
XV. T. Ostojic, “Applicability of knowledge: Motivational factor for beginning learners of programming,” 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2018, pp. 0491-0493, doi: 10.23919/MIPRO.2018.8400093.

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