iosr-JCE   Volume-27 ~ Issue-6 ~ Nov. - Dec. 2025

Abstract: In today's film industry, predicting a movie’s earnings is crucial for increasing profitability. This project aims to create a machine learning model that forecasts movie earnings based on factors like the movie name, MPAA rating, genre, year of release, IMDb rating, viewer votes, director, writer, leading cast, production country, budget, production....

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Abstract: This paper focuses on how the roles of middle management and the competencies required in those roles are changing due to generative artificial intelligence. Through a systematic literature review of sources, we examine how the use of AI automates routine managerial tasks while placing demands for new strategic and ethical capabilities. We formulate.......

Keywords: generative AI, middle management, automation, augmentation, business transformation, ethical leadership

[1] J. Koponen, S. Julkunen, A. Laajalahti, M. Turunen, And B. Spitzberg, Work Characteristics Needed By Middle Managers When Leading Ai-Integrated Service Teams, Journal Of Service Research, 28(1), 2023, 168-185.
Https://Doi.Org/10.1177/10946705231220462
[2] H. Mintzberg, The Nature Of Managerial Work (New York: Harper & Row, 1973).Https://Mintzberg.Org/Books/Nature-Managerial-Work
[3] Z. Rezvani, Who Is A Middle Manager: A Literature Review, International Journal Of Family Business And Management, 1(2), 2017, 1-9.Https://Www.Researchgate.Net/Publication/323573857_Who_Is_A_Middle_Manager_A_Literature_Review
[4] S. L. Wamba-Taguimdje, S. Fosso Wamba, J. R. Kala Kamdjoug, And C. E. Tchatchouang Wanko, Influence Of Artificial Intelligence (Ai) On Firm Performance: The Business Value Of Ai-Based Transformation Projects, Business Process Management Journal, 26(7), 2020, 1893-1924. Https://Doi.Org/10.1108/Bpmj-10-2019-0411
[5] J. Holmström And N. Carroll, How Organizations Can Innovate With Generative Ai, Business Horizons, 68(1), 2025, 559-573. Https://Doi.Org/10.1016/J.Bushor.2024.02.010

Abstract: The Pythagorean fuzzy set extends the intuitionistic fuzzy set by defining membership and nonmembership degrees such that the sum of their squares is less than or equal to one. We propose the concept of a Pythagorean fuzzy subnear-ring (PyFSNR) of a near-ring using 𝑡-norm 𝑇 and a 𝑡-conorm 𝑆. We then characterize these substructures using a special class....

Keywords:
Pythagorean fuzzy sets, Intuitionistic fuzzy sets, Pythagorean Fuzzy Subnear-Rings, Level set.

[1]. A. Abou-Zaid, On Fuzzy Subnear-Ring, Fuzzy Sets And Systems, 81 (1989) 383-393.
[2]. Adak, A. K., Salokolaei, D. D. (2019). Some Properties Of Pythagorean Fuzzy Ideal Of Near-Rings, International Journal Of Applied Operational Research , 9(3), 1-9.
[3]. Kumari, K., Adak, A. K., Agarwal, N. K. (2020). Pythagorean Q-Fuzzy Ideal Of Near-Ring. Ideal Science Review, 15(1), 61-68.
[4]. Adak, A. K., Kumar, D., Edalatpanah, S. A. (2024). Some New Operations On Pythagorean Fuzzy Sets. Uncertainty Discourse And Applications, 1 (1), 11-19

Abstract: Modern organizations manage vast amounts of data and customer interactions daily through BPO and administrative operations. These processes are often repetitive, time-consuming, and prone to human error. This paper presents a general-purpose automation framework that uses Artificial Intelligence (AI) and pre-trained Large Language Models (LLMs) enhanced with Natural Language Processing (NLP) to perform tasks traditionally handled by human....

Keywords: Artificial Intelligence, NLP, LLM, Automation, RPA, Workflow Optimization, Autonomous Agents

[1]. Brown Et Al., “Language Models Are Few-Shot Learners,” Openai, 2020. Https://Arxiv.Org/Abs/2005.14165
[2]. Meta AI, “Llama 3: Open Foundation And Fine-Tuned Chat Models,” 2024.
Https://Ai.Meta.Com/Llama
[3]. Google Cloud AI, “Enterprise AI Solutions,” 2023.
Https://Cloud.Google.Com/Ai
[4]. IBM Research, “AI Workflow Automation,” 2023.
Https://Research.Ibm.Com/Ai
[5]. Automation Anywhere, “AI And RPA Integration For Business Efficiency,” 2023.
Https://Www.Automationanywhere.Com/Resources

Abstract: The mobile nature of Internet of things nodes together with changing network topologies makes message transmission reliability a critical issue. Our current research focuses on creating delivery protocols which ensure packet delivery in dangerous settings among node and linkage breakdowns. With Cross-layer design attackers achieve the ability to attack at various layers simultaneously. Abled attackers function across multiple layers by coordinating their attack actions to meet their objectives. The research develops a secure routing method for IoT networks that....

Keywords: CH IoT Networks, De Bruijin Graph, Packet Drops, Data Aggregation, Routing Protocol, PDMDS, NS2.35

[1]. Singh, A.P., Luhach, A.K., Gao, X.Z., Kumar, S., Roy, D.S. Evolution Of Wireless Sensor Network Design From Technology Centric To User Centric: An Architectural Perspective. International Journal Of Distribution Of Sensor Network 16(8), 1550147720949138 (2020)[2].
Sarangi K, Bhattacharya I (2019) A Study On Data Aggregation Techniques In Wireless Sensor Network In Static And Dynamic Scenarios. Innovations Systsoftw Eng 15(1):3–16
[3]. Mathapati, M., Senthil Kumaran, T., Patil, K.K., Patil, S.S., Veena, H.N. (2021). A Study On Secure Data Aggregation And Routing For Wireless Sensor Networks. In: Luhach, A.K., Jat, D.S., Bin Ghazali, K.H., Gao, XZ., Lingras, P. (Eds) Advanced Informatics For Computing Research. ICAICR 2020.
[4]. Osamy W, Khedr AM, Aziza A, El-Sawya A (2018) Cluster-Tree Routing Scheme For Data Gathering In Periodic Monitoring Applications. IEEE Access 6:77372–77387

Abstract: Within developing nations such as India, business-to-business (B2B) commerce has historically been backed by personal relationships, trust, and agreements based on credit. Businesses are increasingly relying on telecallers and digital platforms in order to keep up with the ever-increasing amount of online orders, tailor promotions, and maintain connections with their customers. In order to achieve sustainable growth and competitiveness, it is necessary to derive strategic....

Keywords: Machine Learning Framework, Customer Interaction Data, Strategic Business Models, Empirical Bayesian Approach, XGBoost, Predictive Analytics, B2B E-commerce

[1]. Manyika, J., et al. (2021). The future of data-driven business: Strategic applications. McKinsey Global Institute Report.
[2]. Kshetri, N. (2018). 1 Big data’s role in expanding access to financial services in India. IT Professional, 20(2), 54–59.
[3]. Verbeke, W., Martens, D., & Baesens, B. (2012). Social network analysis for customer churn prediction. Applied Soft Computing, 14(3), 431–446.
[4]. Kumar, A., et al. (2019). Predicting consumer behavior with machine learning: Applications in e-commerce. Information Systems Frontiers, 21(5), 1125–1140.
[5]. Wamba, S. F., et al. (2020). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 131, 355–365

Abstract: The development of cloud computing has made organizations to store and analyse a lot of information. Nonetheless, information transfer among organizations presents privacy, security and regulatory issues. The cryptographic mechanism is known as Secure Multi-Party Computation (MPC) which enables more than two parties to perform common functions on their data without disclosing their personal input. In this paper, the authors discuss....

Keywords: Secure Multi-Party Computation, Cloud Computing, Data Sharing, Cryptography, Privacy Preservation

[1]. Bogdanov, D., Laur, S., & Willemson, J. (2008). Sharemind: A framework for fast privacy-preserving computations. In European Symposium on Research in Computer Security (ESORICS) (pp. 192–206). Springer, LNCS 5283.
[2]. Damgård, I., Pastro, V., Smart, N. P., & Zakarias, S. (2012). Multiparty computation from somewhat homomorphic encryption. In Advances in Cryptology – CRYPTO 2012 (pp. 643–662). Springer.
[3]. Evans, D., Kolesnikov, V., & Rosulek, M. (2018). A pragmatic introduction to secure multi-party computation. Foundations and Trends® in Privacy and Security, 2(2–3), 70–246. https://doi.org/10.1561/3300000019
[4]. Gentry, C. (2009). Fully homomorphic encryption using ideal lattices. In Proceedings of the 41st Annual ACM Symposium on Theory of Computing (pp. 169–178). ACM. https://doi.org/10.1145/1536414.1536440
[5]. Huang, Y., & Evans, D. (2020). Practical secure two-party computation. In USENIX Security Symposium (pp. 337–354). USENIX Association

Abstract: The nature of operational reality of global aviation has been a relatively secret known only to the general public through official airline information or proprietary, closed systems of communication during decades. A fundamental paradigm shift has been triggered by the ubiquitous implementation of the Automatic Dependent Surveillance-Broadcast (ADS-B) technology and the fact that the signal is unencrypted. This paper presents a detailed discussion....

[1]. ADS-B Exchange. (2023). Why We Don’t Filter. Retrieved from https://www.adsbexchange.com/why-we-dont-filter-data/
[2]. Boin, A., & 't Hart, P. (2010). Organising for effective emergency management: Lessons from research. International Journal of Public Sector Management, 23(4), 357-373.
[3]. Bruns, A., & Burgess, J. (2012). The use of Twitter during the 2011 Queensland floods. In Proceedings of the 21st International Conference on World Wide Web (WWW '12 Companion). ACM.
[4]. El-Sayed, A. (2017). Air transport management and strategy. Routledge.
[5]. Federal Aviation Administration (FAA). (2020). Automatic Dependent Surveillance-Broadcast (ADS-B). Retrieved from https://www.faa.gov/nextgen/programs/adsb/

Abstract: The keyword-based approach in sentiment analysis offers a structured and interpretable method to assess patient feedback, aiming to enhance patient satisfaction and healthcare quality. This research proposes a hybrid sentiment analysis framework for evaluating patient feedback in the healthcare sector. The study employs both keyword-based and machine-learning approaches to classify patient comments as positive, negative, or neutral. Data was collected....

Keywords: Sentiment Analysis, Patient Satisfaction, Healthcare Quality, Machine Learning, Patient Feedback.

[1] M. He, “Enhancing E-Health with natural language processing: The role of sentiment analysis in modern healthcare,” Transactions on Computer Science and Intelligent Systems Research, vol. 7, no. 1, pp. 285–290, 2024.
[2] M. R. Rashid, A. Nawaz, T. Ali, and G. Mustafa, “Enhancing healthcare services recommendation through sentiment analysis,” Acta Universitatis Sapientiae, Informatica, vol. 15, no. 2, pp. 330–344, 2023.
[3] Ramyasri, C. Niharika, K. Maneesh, and B. M. Ismail, “Sentiment analysis of patients’ opinions in healthcare using lexicon-based method,” International Journal of Engineering and Advanced Technology, vol. 9, no. 1, pp. 2249–8958, 2019.
[4] A. Hopper and M. Uriyo, “Using sentiment analysis to review patient satisfaction data located on the internet,” Journal of Health Organization and Management, vol. 29, no. 2, pp. 221–233, 2015.
[5] T. Khan and S. Khalid, “Sentiment analysis for health care,” International Journal of Privacy and Health Information Management, vol. 3, no. 2, pp. 78–91, 2015

Abstract: A network traffic monitoring system known as an Intrusion Detection System (IDS) is a system that analyses network traffic in order to detect suspicious actions and sends out notifications whenever such an activity is identified. The program is a piece of software that performs a scan of a system or network to look for malicious behaviour or violations of regulations. In contrast to signature-based intrusion detection systems (IDS), the machine learning....

Keywords: IDS Detect, TCP-SYN, Flood, Attack, Cloud Environment, Ml, Approach

[1] Akana Chandra, M. V. S., Satyanarayana, C., Divakar, C., & Katikireddy, P. S. (2021). Sentiment analysis using neural network and LSTM. IOP Conference Series: Materials Science and Engineering, 1074(1), 012007. https://www.researchgate.net/publication/349628900
[2] Chaudhary, A., & Tiwari, V. N. (2014). Analysis of fuzzy logic-based intrusion detection systems in mobile ad hoc networks. Future Generation Computer Systems, 79, 617–624. https://www.researchgate.net/publication/364198123
[3] D’hooge, L., Wauters, T., Volckaert, B., & De Turck, F. (2020). Inter-dataset generalization strength of supervised machine learning methods for intrusion detection. Journal of Information Security and Applications, 54, 135–143. https://doi.org/10.1016/j.jisa.2020.102563

Abstract: The Secure Electronic Transaction protocol, which makes use of Near Field Communication (NFC-SET), is formally verified in this study's experimental context. Privacy, authentication, integrity, non-repudiation, and replay attack protection are some of the essential security services that will be assessed. The AVISPA tool is popular for analysis because to its robust formal modeling capabilities and several performance verification methodologies....

Keywords: Security, Verification, Transactions, Communication, Electronic

[1] S. Ahamad, I. Alshourbaji, and S. Al-Janabi, “A secure NFC mobile payment protocol based on biometrics with formal verification,” Int. J. Internet Technol. Secure Trans., vol. 6, no. 2, pp. 103–128, 2016.
[2] M. Badra and R. Borghol, “A lightweight security protocol for NFC-based mobile payments,” Procedia Comput. Sci., vol. 83, no. 2, pp. 705–711, 2016.
[3] S. Bojjagani and V. Sastry, “A secure end-to-end proximity NFC-based mobile payment protocol,” Comput. Stand. Interfaces, vol. 66, no. 1, pp. 1–21, 2019.
[4] R. Abouhogail, “A new secure lightweight authentication protocol for NFC mobile payment,” Int. J. Commun. Netw. Inf. Secur., vol. 11, no. 2, pp. 283–289, 2019.
[5] C. Thammarat, “Efficient and secure NFC authentication for mobile payment ensuring fair exchange protocol,” Symmetry, vol. 12, no. 10, pp. 2–19, 2020.

Abstract: There is a lack of objective ways for assessing caption readability, despite the fact that captioning is crucial in making multimedia material accessible to Deaf and Hard-of-Hearing (DHH) users. In order to objectively and quantitatively analyze the cognitive strain of reading captions, this study suggests keystroke dynamics. The experimental setup involved presenting DHH with a numeric key-input job while simultaneously exposing them to other....

Keywords: Caption Readability, Deaf, Keystroke, Peripheral, Symbol

[1] S. Ahn, J. Kim, C. Shin, and J.-H. Hong, “Visualizing speech styles in captions for deaf and hard-of-hearing viewers,” International Journal of Human-Computer Studies, vol. 194, no. 1, pp. 1–7, 2024.
[2] O. Alonzo, L. Elliot, B. Dingman, S. Lee, A. A. Amin, and M. Huenerfauth, “Reading-assistance tools among deaf and hard-of-hearing computing professionals in the U.S.: Their reading experiences, interests and perceptions of social accessibility,” ACM Transactions on Accessible Computing, vol. 15, no. 1, pp. 1–10, 2022.
[3] S. Kafle, B. Dingman, and M. Huenerfauth, “Deaf and hard-of-hearing users evaluating designs for highlighting key words in educational lecture videos,” ACM Transactions on Accessible Computing, vol. 14, no. 4, pp. 1–24, 2021.
[4] D. Trokoz, A. Martyshkin, E. Balzannikova, and I. Sergina, “Developing of keystroke dynamics analyzing systems using a presentation based on state contexts,” Nexo Revista Científica, vol. 34, no. 4, pp. 1258–1271, 2021.
[5] A. Alsalamah, “Using captioning services with deaf and hard of hearing students in higher education: A systematic review,” American Annals of the Deaf, vol. 165, no. 1, pp. 114–127, 2020