iosr-Jm   Volume-22 ~ Issue-1 ~ Jan. – Feb. 2026

Abstract : In this study, data of 113,549 songs is used to examine the factors that can influence the popularity of the track in the presence of Spotify. The study question was whether the intrinsic sound characteristic of a work is less likely to predict the extrinsic metadata such as genre. The correlation diagnostic and research statistics obtained after the preprocessing phase of the research demonstrated a weak correlation of popularity with auditory features and instrumentalness showed the strongest negative correlation (r = -0.127). Higher popularity ranges were confirmed....

Keywords: Spotify, Track Popularity, Audio Features, Metadata, Genre Classification, Predictive Modeling

[1]. Merritt, S. H., Gaffuri, K., & Zak, P. J. (2023). Accurately Predicting Hit Songs Using Neurophysiology And Machine Learning. Frontiers In Artificial Intelligence, 6. Retrieved From
Https://Www.Frontiersin.Org/Journals/Artificial-Intelligence/Articles/10.3389/Frai.2023.1154663/Full
[2]. Yap, K. Y., & Raheem, M. (2024). Hit Songs Prediction: A Review On Machine Learning Perspective. AIP Conference Proceedings, 2802(1), 120027. Retrieved From Https://Pubs.Aip.Org/Aip/Acp/Article/2802/1/120027/3127381/Hit-Songs-Prediction-A-Review-On-Machine-Learning
[3]. Dimolitsas, I., Kantarelis, S., & Fouka, A. (2023). Spothitpy: A Study For ML-Based Song Hit Prediction Using Spotify. Arxiv Preprint. Retrieved From Https://Arxiv.Org/Pdf/2301.07978
[4]. Flexer, A., Dörfler, M., Schluter, J., & Grill, T. (2019). Hubness As A Case Of Technical Algorithmic Bias In Music Recommendation. Proceedings Of The IEEE International Conference On Data Mining Workshops. Retrieved From
Https://Www.Researchgate.Net/Publication/331039510_Hubness_As_A_Case_Of_Technical_Algorithmic_Bias_In_Music_Recommendation
[5]. Ekstrand, M. D., Tian, M., Azpiazu, J., & Pera, M. S. (2022). Fairness In Music Recommender Systems: A Stakeholder Perspective. Frontiers In Big Data, 5. Retrieved From Https://Www.Frontiersin.Org/Journals/Big-Data/Articles/10.3389/Fdata.2022.913608/Full

Abstract : Whether a polynomial with rational coefficients is irreducible over the field rational numbers, Q \mathbb{Q}, is one of the important problems posed in algebra which has a wide application in number theory, field theory, and computational aspects of mathematics. Eisenstein’s Criterion is a classical result in number theory. It is a nice and powerful result for irreducibility of integers. Many polynomials do not respect the strict assumptions required by these classical criteria. Therefore, they are not directly applicable. To address this limitation, various families of extended versions of Eisenstein’s Criterion have been derived. Essentially, appropriate transformations of polynomials, such as translations, scalings, compositions, etc., will ruin the polynomial but probably leave its irreducible properties intact....

Keywords: Polynomial Irreducibility, Irreducibility Criteria, Rational Field Q, Gauss’s Lemma, Eisenstein’s Criterion.

[1]. Wikipedia. (2025). Irreducible Polynomial. In Wikipedia. Retrieved December 21, 2025, From Https://En.Wikipedia.Org/Wiki/Irreducible_Polynomial. [2].
Fraleigh, J. B. (2013). A First Course In Abstract Algebra (7th Ed.). Boston, MA: Pearson.. [3].
Lang, S. (2002). Algebra (Revised 3rd Ed.). New York, NY: Springer. [4].
Wikipedia. (2025). Eisenstein’s Criterion. In Wikipedia. Retrieved December 22, 2025, From Https://En.Wikipedia.Org/Wiki/Eisenstein%27s_Criterion

Abstract : Blood supply chain management is important and effective in providing timely availability of blood and blood products there is an emergency and critical healthcare delivery. The problem of hospitals is that they have to deal with demand fluctuations, the perishability of blood products and logistical inefficiencies which cause shortages, wastage or delays and, inevitably, affect patient outcomes. Transformative solutions to these problems arise in data driven strategies using tools like predictive analytics, real time monitoring and inventory management systems. Demand forecasting is made more accurate through predictive analytics so as to help hospitals plan ahead of fluctuations and minimize shortages.......

Keywords: Blood Supply Chain, Hospitals, Data-Driven Strategies, Inventory Management.

[1]. Nayeri, S., Khoei, M. A., Rouhani-Tazangi, M. R., Ghanavatinejad, M., Rahmani, M., & Tirkolaee, E. B. (2023). A Data-Driven Model For Sustainable And Resilient Supplier Selection And Order Allocation Problem In A Responsive Supply Chain: A Case Study Of Healthcare System. Engineering Applications Of Artificial Intelligence, 124, 106511.
[2]. Bhatia, A., & Mittal, P. (2019, October). Big Data Driven Healthcare Supply Chain: Understanding Potentials And Capabilities. In Proceedings Of International Conference On Advancements In Computing & Management (ICACM).
[3]. Delen, D., Erraguntla, M., Mayer, R. J., & Wu, C. N. (2011). Better Management Of Blood Supply-Chain With GIS-Based Analytics. Annals Of Operations Research, 185, 181-193.
[4]. Lotfi, R., Kargar, B., Rajabzadeh, M., Hesabi, F., & Özceylan, E. (2022). Hybrid Fuzzy And Data-Driven Robust Optimization For Resilience And Sustainable Health Care Supply Chain With Vendor-Managed Inventory Approach. International Journal Of Fuzzy Systems, 24(2), 1216-1231.
[5]. Abbasi, B., Babaei, T., Hosseinifard, Z., Smith-Miles, K., & Dehghani, M. (2020). Predicting Solutions Of Large-Scale Optimization Problems Via Machine Learning: A Case Study In Blood Supply Chain Management. Computers & Operations Research, 119, 104941.

Abstract : In this paper, we extend and unify various well-known fixed point results by introducing the concept of 𝛼−𝜓 generalized Chatterjea–Kannan type mappings in the framework of ordered 𝑏−metric spaces.
Our approach generalizes the classical Banach, Kannan, and Chatterjea contractions as well as the recent three-point contraction of Păcurar and Popescu (2024)........

Keywords: Ordered 𝑏− metric spaces, 𝛼−𝜓 generalized Chatterjea–Kannan type mappings, Fixed point results 𝛼− admissibility 𝜓−control functions, Banach, Kannan, and Chatterjea contractions, Integral and fractional differential equations.

[1]. Banach, S. (1922). Sur Les Opérations Dans Les Ensembles Abstraits Et Leur Application Aux Équations Intégrales. Fundamenta Mathematicae, 3, 133–181.
[2]. Kannan, R. (1968). Some Results On Fixed Points. Bull. Calcutta Math. Soc., 60, 71–76.
[3]. Chatterjea, S.K. (1972). Fixed Point Theorems. C. R. Acad. Bulgare Sci., 25, 727–730.
[4]. Czerwik, S.C. (1993). Contraction Mappings In B–Metric Spaces. Acta Math. Univ. Ostrav., 1, 5–11.
[5]. Samet, B., Vetro, C., & Vetro, P. (2012). Fixed Point Theorems For Α–Ψ Contractive Type Mappings. Nonlinear Analysis, 75, 2154–2165.

Abstract : Understanding the seasonal dynamics of food consumption is essential for effective dietary assessment, agricultural planning, and food supply chain management. This study proposes a Hidden Markov Model (HMM) framework to analyse the relationship between food consumption types and seasons, treating seasons as latent (hidden) states and food types as observable states. Transition probability matrices are constructed to capture seasonal progression, while emission probabilities quantify the likelihood of observing specific food types within each season........

Keywords: Hidden Markov Model, Food Consumption, Seasonal Dynamics, Transition Probability Matrix, Stochastic Modelling

[1]. E. H. Ip Et Al., 2013, “Multi-Profile Hidden Markov Model For Mood, Dietary Intake, And Physical Activity In An Intervention Study Of Childhood Obesity”, Stat Med. 2013 August 30; 32(19): 3314–3331. Doi:10.1002/Sim.5719.
[2]. Luís Costa Et Al., 2016, “Automatic Meal Intake Monitoring Using Hidden Markov Models”, Procedia Computer Science 100, 110 – 117.
[3]. Sanjay Dorairaj, 2018, “Hidden Markov Models Simplified” Https://Medium.Com/@Postsanjay/Hidden-Markov-Models-Simplified-C3f58728caab.
[4]. Taoping Liu, Wentian Zhang, Mitchell Yuwono, Miao Zhang, Maiken Ueland, Shari L. Forbes, Steven W. Su, 2020, “A Data-Driven Meat Freshness Monitoring And Evaluation Method Using Rapid Centroid Estimation And Hidden Markov Models”, Sensors And Actuators B: Chemical, Volume 311, 2020, 127868, Issn 0925-4005, Https://Doi.Org/10.1016/J.Snb.2020.127868
[5]. Xinyue Pan Et Al., 2022, “Simulating Personal Food Consumption Patterns Using A Modified Markov Chain”, Madima ’22, October 10, 2022, Lisboa, Portugal Arxiv:2208.06709v1 [Cs.Cv] 13 Aug 2022