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Research Journal of Mathematics and Computer Science

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Research Journal of Mathematics and Computer Science (ISSN:2576-3989)  is a journal publishing articles in Mathematics and Computer Science.

Double Lipschitz Stability for Nonlinearly neutral Differential Systems with Multiple Delay

Phishing detection model for emails using classification algorithm, a mathematical model for analyzing the impact of the ban on foreign garbage in china, a new approach in one time pad key management.

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Annals of Mathematics and Computer Science

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Annals of Mathematics and Computer Science (ISSN: 2789-7206) is an electronic, open access journal. The journal publishes high quality original research articles that demonstrate interactions between various disciplines in mathematics and computer science. Articles published by the journal appear under the Creative Commons Attribution License . AMCS is a member of Crossref. 

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Asymptotically lacunary statistical equivalent sequences in partial metric spaces, machine learning impact of radiative blood flow over a wedge in a time-dependent mhd williamson fluid, equal-norm parseval continuous k-frames in hilbert spaces, multiplicative jordan triple higher semi-derivations on rings and standard operator algebras, fixed point results for almost z-contractions in partial b-metric spaces via simulation function, connections of green's relations of a γ-semigroup with operator semigroups, stability analysis of linear quaternion-valued differential equation using integral transform, existence of entropy solutions to nonlinear degenerate weighted elliptic p(.)-laplacian problem and l1-data, some common fixed point theorems using g-cone metric spaces with banach algebra, flow of mhd micropolar fluid through porous medium: a hodograhic approach for exact solution.

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The set of journals have been ranked according to their SJR and divided into four equal groups, four quartiles. Q1 (green) comprises the quarter of the journals with the highest values, Q2 (yellow) the second highest values, Q3 (orange) the third highest values and Q4 (red) the lowest values.

The SJR is a size-independent prestige indicator that ranks journals by their 'average prestige per article'. It is based on the idea that 'all citations are not created equal'. SJR is a measure of scientific influence of journals that accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from It measures the scientific influence of the average article in a journal, it expresses how central to the global scientific discussion an average article of the journal is.

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Research and Applications Towards Mathematics and Computer Science Vol. 7

Editor(s) Prof. Manuel Alberto M. Ferreira Lisbon University, Portugal. Short Biosketch

ISBN 978-81-968463-5-0 (Print) ISBN 978-81-968463-4-3 (eBook) DOI: 10.9734/bpi/ratmcs/v7

This book covers key areas of mathematics and computer science. The contributions by the authors include b raid groups, conjugacy classes, isomorphism problem, cryptosystems, Data compression, wireless sensor networks, Rice Golomb coding, MARGC algorithm, topology control, clustering, self-oganizing map, neural gas, Fuzzy C-Means, colour mapping, NG visualization, machine learning, Jaccard function, cosine similarity, artificial intelligence, spectral local linearization method, viscosity, Arrhenius model, Eckert number, Water-copper nanoparticles, permeable channel, nonlinear radiative heat flux, convective boundary conditions, Maxwell nanofluids, spinning system, Fulati-Suranjan formulae, Babylonian mathematicians, Probir's Constant, blast domination number, triple connected graph, bootstrap; markov chain monte carlo, Bayesian method, variance-covariance structure, Hausman test; random effects model, fixed effects model; least squares dummy variable. block design; incidence matrix; binary design; ternary designs; connected design; variance balanced design, incidence matrix; super saturated design, customer segmentation, machine learning. T his book contains various materials suitable for students, researchers and academicians in the field of mathematics and computer science.

Media Promotion:

A Study on Interactive Proteomic Data Clustering: A Comparison between Self-Organizing Map and Neural Gas

Terje Solsvik Kristensen

Research and Applications Towards Mathematics and Computer Science Vol. 7 , 21 December 2023, Page 1-16 https://doi.org/10.9734/bpi/ratmcs/v7/7924A

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The neural network algorithms Self-Organizing Map (SOM) and Neural Gas (NG) use unsupervised competitive learning. These methods have the critical virtue of preserving the topological structure of the data, which means that data that are close in the input distribution are mapped to neighboring positions in the network or output. This characteristic makes them intriguing to investigate in terms of data clustering. A crucial characteristic analyzing vast amounts of data manually can be challenging and time-consuming. As a result, technologies for analyzing and visualizing massive multidimensional data sets are required.  We introduce a method for comparing and visualizing the SOM and NG in this chapter. We describe these algorithms first, and then we make a pictorial comparison between them. The protein mass spectrometry data clustering is then interpreted using these visualization approaches.  

Feasibility and Efficacy of Modified Adaptive Rice Golomb Coding for Wireless Sensor Networks

S. Kalaivani , C. Tharini

Research and Applications Towards Mathematics and Computer Science Vol. 7 , 21 December 2023, Page 17-31 https://doi.org/10.9734/bpi/ratmcs/v7/11319F

Wireless Sensor Networks (WSNs), characterized by energy-constrained nodes powered by limited-capacity batteries, necessitate energy-efficient data compression techniques to extend their network lifetime. The communication module within each sensor node emerges as a primary energy consumer, making data compression a pivotal approach to curtail data transmission. This paper introduces the Modified Adaptive Rice Golomb Coding (MARGC) algorithm as an effective compression technique to enhance the network's lifespan. Simulation results, utilizing diverse datasets, underscore the feasibility and efficacy of the MARGC algorithm. Furthermore, the algorithm's real-time implementation on National Instruments Wireless Sensor Network (NI WSN) hardware demonstrates its practical applicability and performance. To benchmark our contribution, future work will involve a comparative analysis with existing techniques to highlight the superior aspects of the proposed MARGC algorithm.

Guiding the Learner in Selecting the Suitable Online Course Using Automated AI Tool (Course-AI)

P. V. Siva Kumar, Akhilesh Kumandan , Gautham Mallipeddi , Srinivasa Deepesh Kommineni , Naram Tapan Ganesh

Research and Applications Towards Mathematics and Computer Science Vol. 7 , 21 December 2023, Page 32-44 https://doi.org/10.9734/bpi/ratmcs/v7/6991B

The latest Pearson study conducted in 2019 revealed that a significant portion of Indians, specifically 62 percent, favored the concept of combining passive online learning with active in-person learning to achieve a higher quality of education. In recent years, the availability of courses online has significantly increased, making it more challenging for students to identify relevant courses and decide which one to enroll in. This is where CourseAI comes in as a helpful and pivotal tool for students. It can effectively streamline the course selection process and assist in identifying the top and relevant courses that align with a student's needs and goals. CourseAI is designed to utilize artificial intelligence algorithms to evaluate and filter the vast array of online courses available to students, making it easier for them to make informed decisions.

With CourseAI, students can efficiently navigate through a multitude of courses based on their interests, educational background, and career aspirations. The application provides personalized recommendations, taking into account various factors such as course difficulty, student reviews, and ratings.

In today's digital age, online learning has become a preferred mode of education for many students, especially those who cannot attend traditional in-person classes. CourseAI caters to the needs of these students by ensuring that they have access to high-quality courses that provide a learning experience that is both convenient and effective. The primary objective of CourseAI is to bridge the gap between passive online learning and active in-person learning, providing students with a hybrid learning experience that is both engaging and informative. This innovative approach to course selection can help students make the most of their learning experience and achieve their academic and career goals.

Numerical Investigation for Nonlinear Thermal Radiation in MHD CU-Water Nanofluid Flow in a Channel with Convective Boundary Conditions

Yusuf, T. A. , Adeosun, A. T. , Akinsola, V. O. , Lebelo R. S., Akinremi, O. J.

Research and Applications Towards Mathematics and Computer Science Vol. 7 , 21 December 2023, Page 45-65 https://doi.org/10.9734/bpi/ratmcs/v7/6867B

The implications of nonlinear thermal radiation on a Cu-water nanofluid flow with varying viscosity characteristics and convective boundary conditions are investigated numerically in this chapter. The nonlinear model takes the variable viscosity in the form of the Arrhenius model and dissipation into consideration. The Spectral Local Linearization Method (SLLM) was used to address the nonlinear governing model. With excellent agreement, the numerical investigation's findings were compared. In Cu-water nanofluid flows with variable viscosity and convective boundary conditions, nonlinear thermal radiation plays an important role, as this work insightfully demonstrated. This study also investigated the impressions of several significant features that include the variability factor, nanoparticle volume fraction, magnetic factor, Reynolds number, radiation factor, biot number, Eckert number, against the velocity, temperature, skin friction and Nusselt number. Results demonstrated that with improved values of the velocity gradient, the fluctuations increased while the temperature gradient decreased. The results also revealed that the Cu-water thermal distribution lessens as the nanoparticle volume fraction upsurges. The outcomes of this study have potential applications in industrial systems such as power plants, cooling systems, and climate control systems.

On Braid Group: More Sharpness for Lower Bound of the Number of Conjugacy Classes in Positive Permutation Braids

E. A. Elrifai , M. Anis

Research and Applications Towards Mathematics and Computer Science Vol. 7 , 21 December 2023, Page 66-72 https://doi.org/10.9734/bpi/ratmcs/v7/2303G

This chapter identifies the lower and upper bounds for the number of conjugacy classes in positive permutation braids. Every possible valve for crossing number, such that, minimum and maximum crossing numbers, and a more sharpened lower bound on the number of their conjugacy classes in \(\mathit{S}^+_{n}\) are provided for such braids with associated type cycle (n). Also, for positive permutation braids with associate type cycle (\(\mathit{n}_1\), \(\mathit{n}_2\), ..., \(\mathit{n}_k\)) the minimum crossing number is given.

An Introduction to Fulati-Suranjan Formulae in Calculating the Area and Perimeter of Any Circle Without Using Pi

Research and Applications Towards Mathematics and Computer Science Vol. 7 , 21 December 2023, Page 73-92 https://doi.org/10.9734/bpi/ratmcs/v7/2546G

Customer Segmentation Using R-Studio

R. Srilatha , M. Sridevi

Research and Applications Towards Mathematics and Computer Science Vol. 7 , 21 December 2023, Page 93-102 https://doi.org/10.9734/bpi/ratmcs/v7/7093B

Customer segmentation is the process of dividing customers into groups based on common characteristics. Segmentation is one of the key aspects of business decision support system. In order to grow the business intelligently in competitive market, identification of potential customer should be done timely. It can help a business to better understand its target audience. Segmentation helps marketers to be more efficient in terms of time, money and other resources. customer segmentation allows companies to learn about their customers. Regression analysis plays a key role in customer segmentation. By collecting the previous sales, products, customers information one can improve their company growth on a wider range and R studio is used for data visualization.

Construction of Balanced Incomplete Block Designs with Binary and Ternary Variance

Rajarathinam A.

Research and Applications Towards Mathematics and Computer Science Vol. 7 , 21 December 2023, Page 103-116 https://doi.org/10.9734/bpi/ratmcs/v7/7762A

This chapter proposes a new method for constructing binary and ternary variance balanced incomplete block designs based on the Kronecker product of two Hadamard matrices and unreduced symmetrical Balanced Incomplete Block Design.  Method 1 discusses the construction of binary variance balanced block designs using the Kronecker product of two Hadamard matrices, and method 2 discusses the constructions of ternary variance balanced block design using the unreduced symmetrical, balanced incomplete block design with the parameters \(\mathit{v}\) = \(\mathit{b}\), \(\mathit{r}\) = \(\mathit{k}\) = \(\mathit{v}\) - 1, \(\lambda\) = \(\mathit{v}\) - 2 . The optimality of the constructed binary and ternary designs has been studied with numerical examples, and the constructed designs were found to be universally optimal. The built design is significant because it lowers the cost and duration of the experimentation process, increases the accuracy and efficiency of the experimental results, and enables a set of experimental treatments requiring fewer observations to be compared to a full block design.

Pursuit Over Detaching Threshold on Blast Domination Number of Graphs

Research and Applications Towards Mathematics and Computer Science Vol. 7 , 21 December 2023, Page 117-124 https://doi.org/10.9734/bpi/ratmcs/v7/8322A

Provoked by scores of triple connected domination parameters, recently the concept of Blast Domination Number was introduced by A. Ahila et.al., which got its delicate applications in blasting mines, quarries, and sensor areas. A subset  of  of a non-trivial connected graph G is called a Blast dominating set (or) BD-set, if  is a connected dominating set and the induced sub graph \(\small\langle\)\(\mathit{V - S}\) \(\small\rangle\) is triple connected. The least cardinality taken over all such Blast Dominating sets is called the Blast Domination Number (BDN) of G and is denoted by \(\gamma^{tc}_c\)( G ). The investigations over this parameter pull out its wide applications in every supplementary graph theoretical concepts. Now by the collective appliances of removal of lines, the study narrowed towards higher applications. The force of the special effects of exclusion of a node or a line or more, on any graph theoretic parameter has appealing applications in the framework of network, both constructively and economically. And hence, over the extensive investigations of the Blast domination number, this article brings out the vibrant transform of the parameter by unlinking. The theoretic properties of Blast domination number and its accurate values for some customary graphs are derived by unlinking process. The relations between Blast domination number of a graph and the effects of unlinking are also pragmatic. 

Construction of Optimal Super Saturated Design

Rajarathinam, A.

Research and Applications Towards Mathematics and Computer Science Vol. 7 , 21 December 2023, Page 125-134 https://doi.org/10.9734/bpi/ratmcs/v7/8342A

This chapter discuss the new method of constructing Optimal Super Saturated design based on 3 n  -factorial design by merging some of the treatment combinations, deleting control and some other unimportant treatment combinations in 3 n -factorial design. Also the to find the efficiency of the constructed design and to find the it’s lower bound value.  

Modeling for COVID-19 Infections and Deaths in Tamil Nadu, India, Using Panel Regression Model

Rajarathinam Arunachalam

Research and Applications Towards Mathematics and Computer Science Vol. 7 , 21 December 2023, Page 135-151 https://doi.org/10.9734/bpi/ratmcs/v7/8343A

The impacts of the coronavirus disease 2019 (COVID-19) pandemic have been extremely severe, with both economic and health crises experienced worldwide. The incidence of novel COVID-19 infections dramatically increased due to the absence of antiviral medications and vaccines, resulting in enormous economic losses, panic, and many deaths. Based on the panel regression model, this study examined the trends and correlations in the number of COVID-19-related deaths and the number of COVID-19-infected cases in all 37 regions of the Tamil Nadu state in India in August 2020. The fixed effects model had the most excellent R 2 value of 78% and exhibited significant results. The slope coefficient was also highly effective, showing a considerable variation in the relationship between new COVID-19 cases and deaths. Additionally, for every unit increase in COVID-19-infected instances, the death rate increased by 0.02%. The main issue with this model is that it did not differentiate between the various districts or inform us whether the overall COVID-19 mortality response to the explanatory variable over time was consistent across all neighborhoods.

The Parametric and Nonparametric Estimations of Normal Distribution for Mean Parameter

Autcha Araveeporn , Somsri Banditvilai

Research and Applications Towards Mathematics and Computer Science Vol. 7 , 21 December 2023, Page 152-176 https://doi.org/10.9734/bpi/ratmcs/v7/8475A

This chapter's objective describes the point and interval estimations on the mean that is a part of the unknown parameter of normal distribution. The mean represents the distribution's central location or average, while the standard deviation measures the spread or variability of the data.   Point estimation involves providing a specific value to estimate a population parameter. The interval estimation provides a range or interval of values to estimate a population parameter, called a confidence interval. These estimations are estimated by the parametric method by using the maximum likelihood, Bayesian, and Markov Chain Monte Carlo methods. The nonparametric method consists of the bootstrap and the Jackknife methods. Maximum likelihood is the well-known method to approximate parameters because there are the properties of the unbiased estimator, consistency, and efficiency estimator. The Bayesian method is a statistical approach based on probability, prior, and posterior distribution. The Markov Chain Monte Carlo technique leverages Markov chains and random sampling to estimate complex probability distributions and involves the Bayesian method. The bootstrap and Jackknife methods are the resampling techniques by repeatedly drawing samples from the available data.

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• 10 April 2024

Randomness in computation wins computer-science ‘Nobel’

• Davide Castelvecchi

You can also search for this author in PubMed   Google Scholar

Avi Wigderson received the Turing Award for his foundational contributions to the theory of computation. Credit: Dan Komoda

A leader in the field of computational theory is the latest winner of the A. M. Turing Award, sometimes described as the ‘Nobel Prize’ of computer science.

Avi Wigderson at the Institute for Advanced Study (IAS) in Princeton, New Jersey, is known for work straddling several disciplines, and had already won a share of the Abel Prize , a top mathematics award, three years ago.

He receives the Turing Award “for foundational contributions to the theory of computation, including reshaping our understanding of the role of randomness in computation, and for his decades of intellectual leadership in theoretical computer science”, the Association for Computing Machinery (ACM) in New York City announced on 10 April.

“I was extremely happy, and I didn’t expect this at all,” Wigderson tells Nature . “I’m getting so much love and appreciation from my community that I don’t need prizes.”

‘A towering intellectual force’

Wigderson was born in Haifa, Israel, in 1956. He studied at Technion — Israel Institute of Technology in Haifa and later at Princeton University; he has been at the IAS since 1999. He is known for his work on computational complexity — which studies how certain problems are inherently slow to solve, even in principle — and on randomness in computation. Many practical algorithms make random choices to achieve their objectives more efficiently; in a series of groundbreaking studies in the 1990s, Wigderson and his collaborators showed that conventional, deterministic algorithms can, in principle, be roughly as efficient as ‘randomized’ ones 1 . The results helped to confirm that random algorithms can be as accurate as deterministic ones are.

“Wigderson is a towering intellectual force in theoretical computer science,” said ACM president Yannis Ioannidis in a statement. In addition to Wigderson’s academic achievements, the ACS cited his “friendliness, enthusiasm, and generosity”, which have led him to be a mentor to or collaborate with hundreds of researchers worldwide. Wigderson admits that he is a “big proselytizer” of the intellectual pleasures of his discipline — he wrote a popular book about it and made it freely available on his website . “I think this field is great, and I am happy to explain it to anybody.”

The Turing Award is named after the celebrated British mathematician and code-breaker Alan Turing (1912–54), who in the 1930s laid the conceptual foundations of modern computing. “I feel completely at home with mathematics,” says Wigderson, adding that as an intellectual endeavour, theoretical computer science is indistinguishable from maths. “We prove theorems, like mathematicians.”

doi: https://doi.org/10.1038/d41586-024-01055-y

Impagliazzo, R. & Wigderson, A. in Proc. 29th ACM Symposium on Theory of Computing 220–229 (ACM, 1997).

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Algebraic Representation of Primes by Hybrid Factorization

Authors: Ioannis N. M. Papadakis

Pages: 12-25 Published Online: 20 March 2024

DOI: 10.11648/j.mcs.20240901.12

Analytical Results of the Motion of Oscillating Dumbbell in a Viscous Fluid

Authors: Mohammed Mattar Al-Hatmi, ... Anton Purnama

Pages: 1-11 Published Online: 5 February 2024

DOI: 10.11648/mcs.20240901.11

Real-Time Object Identification Through Convolution Neural Network Based on YOLO Algorithm

Authors: Muhammad Saleem, ... Shah Jahan

Pages: 104-111 Published Online: 28 December 2023

DOI: 10.11648/j.mcs.20230805.11

On Computing the Metric Dimension of the Families of Alternate Snake Graphs

Authors: Basma Mohamed, ... Mohamed Amin

Pages: 94-103 Published Online: 30 October 2023

DOI: 10.11648/j.mcs.20230804.12

Frailty Models Under Xgamma Distribution with Application to Survival Data

Authors: Ashok Kumar Palanisamy, ... Muthukumar Madaswamy

Pages: 87-93 Published Online: 31 August 2023

DOI: 10.11648/j.mcs.20230804.11

Ubiquitous Computing with Radio Frequency IDentification Tags

Authors: Shabbir Hassan, ... Rehan Raza

Pages: 73-86 Published Online: 28 August 2023

DOI: 10.11648/j.mcs.20230803.12

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Original research articles, a framework for recognition of facial expression using hog features, hand-sketchs based parkinson's disease screening using lightweight deep-learning with two-fold training and fused optimal features, face mask detection using haar cascades classifier to reduce the risk of coved-19, a comparative analysis of machine learning algorithms for the purpose of predicting norwegian air passenger traffic, comparing several encrypted cloud storage platforms, a new 4d hidden hyperchaotic system with higher largest lyapunov exponent and ‎its synchronization, deep learning for plant disease detection, cluster analysis on longitudinal data of patients with kidney dialysis using a smoothing cubic b-spline model, stochastic response of an airfoil and its effects on lco’s behavior under stall flutter regime, enhancing wireless security and privacy: a 2-way identity authentication method for 5g networks, review articles, overview of big data analytics in modern astronomy, a comprehensive review of artificial intelligence approaches in omics data processing: evaluating progress and challenges, a comprehensive review on advancements in artificial intelligence approaches and future perspectives for early diagnosis of parkinson's disease, a dataset for recognition of norwegian sign language.

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The International Journal of Mathematics, Statistics, and Computer Science (IJMSCS) is a high-quality refereed and open access journal publish one volume per year. IJMSCS publishes original papers in the broad subjects of mathematics Statistics, and Computer Science written in English. The journal's Editorial Board consists of internationally renowned members in the three domains of the journal. Learn more >>

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Journal of Advances in Mathematics and Computer Science (ISSN: 2456-9968) aims to publish original research articles, review articles and short communications, in all areas of mathematics and computer science. Subject matters cover pure and applied mathematics, mathematical foundations, statistics and game theory, use of mathematics in natural science, engineering, medicine, and the social sciences, theoretical computer science, algorithms and data structures, computer elements and system architecture, programming languages and compilers, concurrent, parallel and distributed systems,  telecommunication and networking, software engineering, computer graphics, scientific computing, database management, computational science, Artificial Intelligence, human-computer interactions, etc. By not excluding papers based on novelty, this journal facilitates the research and wishes to publish papers as long as they are technically correct and scientifically motivated. The journal also encourages the submission of useful reports of negative results. This is a quality controlled, OPEN peer-reviewed, open-access INTERNATIONAL journal.

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On construction structures of matrix solutions of exponential diophantine equations.

Joachim Moussounda Mouanda

Generating Functions For a New Class of Recursive Polynomials

V. James, B. Sivakumar

Common Fixed Points of Dass-Gupta Rational Contraction and E-Contraction

Deepak Singh, Manoj Ughade , Sheetal Yadav , Alok Kumar , Manoj Kumar Shukla

Uniform Estimates on Length of Programs and Computing Algorithmic Complexities for Quantitative Information Measures

Rohit Kumar Verma , M. Bhagya Laxmi

© Copyright 2010-Till Date, Journal of Advances in Mathematics and Computer Science. All rights reserved.

Journal of Mathematics and Computer Science (JMCS)

Online: ISSN 2008-949X

Journal of Mathematics and Computer Science-JMCS (J MATH COMPUT SCI-JM) is an international research journal devoted to the publication of significant articles in all areas and sub-areas of mathematics and computer science with applications. High visibility: Indexed in the Emerging Sources Citation Index - ESCI (Web of Science)  and  Scopus from Vol. 18 (2018).   S ubmitted papers must be correct, original, nontrivial, a... Read more

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• Some new exact solutions for a generalized variable coefficients KdV equation Author(s): R. Rajagopalan, A. H. Abdel Kader, M. S. Abdel Latif, D. Baleanu, A. E. Sonbaty Pages: 1--11
• New fixed point theorems for \(\theta\)-\(\phi\)-contraction on \(b\)-metric spaces Author(s): M. Rossafi, A. Kari, C. Park, J. R. Lee Pages: 12--27
• New Ostrowski type inequalities pertaining to conformable fractional operators Author(s): M. Tariq, S. K. Sahoo, H. Ahmad, A. Iampan, A. A. Shaikh Pages: 28--39
• Zalcman coefficient functional for tilted starlike functions with respect to conjugate points Author(s): D. Mohamed, N. H. A. A. Wahid Pages: 40--51
• Modelling the fourth wave of Covid-19 pandemic in Egypt Author(s): I. A. Moneim, E. I. A. El-Latif Pages: 52--59
• Approximate solutions of linear time-fractional differential equations Author(s): R. A. Oderinu, J. A. Owolabi, M. Taiwo Pages: 60--72
• Applications of the differential transformation to three-point singular boundary value problems for ordinary differential equations Author(s): G. Methi, A. Kumar, J. Rebenda Pages: 73--89
• External and weak direct products of UP (BCC)-algebras Author(s): C. Chanmanee, R. Chinram, N. Phrommarat, P. Julatha, N. Lekkoksung, A. Iampan Pages: 90--105

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