iosr-JCE   Volume-27 ~ Issue-5 ~ Sep. - Oct. 2025

Abstract: The contemporary financial environment is marked with its rising complexity and speed due to a combination of globalization and technological inventions as well as the real-time information flow. Investors, financial institutions and risk managers need to have a way to guide them in determining price behavior in the short term; otherwise, they will not be able to develop appropriate trading strategies and avoid risks. Nevertheless, this is still one of the most challenging problems in the quantitative finance. The uncertainties of financial markets, which can be characterized by such aspects as the existence of significant volatility, non-stationarity, and non-linear relationships, are a well-known problem that makes question the effectiveness of traditional forecasting models. This has caused a continued argument and quest to stronger prediction models.

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Abstract: Background: This project presents an “virtual reality in education: enhancing learning quality and skill acquisition in university settings” Designed to automate student attendance and monitor academic performance in virtual classroom environments like Zoom. The system efficiently tracks student presence during online sessions, automatically recording attendance. If a student fails to attend or exits the session prematurely, immediate notifications.......

Key Word: Deep Learning, Computer Vision, Human Activity Recognition

[1]. Sharma Et Al. (2019) – Real-Time Engagement Via Webcam And ML Used Webcam-Based Emotion, Eye, And Head Tracking To Predict Engagement; Limited To Basic Expressions.
[2]. Nair Et Al. (2023) – CNN-Based Emotion Detection In Virtual Classrooms Achieved High Accuracy In Nuanced Emotion Recognition, But Posed Real-Time And Privacy Challenges.
[3]. Abedi & Khan (2024) – Privacy-Aware Engagement With ST-GCN Utilized Facial Landmarks And Spatio-Temporal Models With Explainability; Performance Affected By Lighting.
[4]. Sen Sharma Et Al. (2022) – Gaze-Based Attention Monitoring Tool Monitored Student Gaze In Virtual Classes; Lacked Strong Evidence Linking Tool To Academic Improvement.
[5]. Multiple Authors (2022) – Temporal Facial Analysis For Engagement Combined CNN And LSTM For Tracking Engagement Over Time; Limited By Self-Reported Data And Compute Needs.

Abstract: The growing need for comprehensive user activity monitoring in security analysis, parental control, and employee supervision has led to the evolution of advanced keylogging systems. This article presents “System-Wide Keylogger and Device Report Generation,” a Python-based solution that seamlessly captures keystrokes and gathers detailed system diagnostics in real time. Leveraging the pynput library, the system logs all keyboard activity with contextual metadata, while also generating an extensive system report using platform and psutil, covering.......

Keywords: Keylogger, System Report, Python, pynput, psutil, Keystroke Monitoring, Device Information, Real-time Logging

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Abstract: In the current digital era, fake news has become a major problem that spreads false information and damages society. Growing interest in using artificial intelligence (AI) to identify fake news has coincided with the development of AI technologies. With an emphasis on the application of machine learning and deep learning techniques, this paper offers a thorough analysis of fake news detection in AI. We look at the difficulties and possibilities.......

Keywords: Fake news, AI, detection, machine learning, deep learning, misinformation, social media, natural language processing, fact-checking

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[4]. T. Granskogen And J. A. Gulla, “Fake News Detection: Network Data From Social Media Used To Predict Fakes,” Ceur Workshop Proc., Vol. 2041, No. 1, Pp. 59–66, 2017.
[5]. R. V. L, C. Yimin, And C. N. J, “Deception Detection For News: Three Types Of Fakes,” Proc. Assoc. Inf. Sci. Technol., Vol. 52, No. 1, Pp. 1–4, 2016.

Abstract: This research study examines the intersection of artificial intelligence (AI), behavioural economics, and entrepreneurship and elucidates how this nascent intersection can transform the process of creating, managing, and growing businesses. Behavioural economics has long proven that people tend to make choices not on the basis of rational optimization but on biases such as loss aversion, anchoring, and overconfidence. Historically, business owners have been guided by limited market analysis and intuition to discover and react to these consumer trends. Yet, with the introduction of AI and machine learning technologies, it is now possible to collect, analyse.....

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Abstract: The theory of Pythagorean fuzzy sets provides important advantages for modeling vagueness and complex uncertainty. Pythagorean fuzzy information captures the ambiguity inherent in subjective judgments and allows for a more flexible assessment of fuzziness and imprecision. In this paper, we define certain properties of Pythagorean fuzzy primary ideals and Pythagorean fuzzy semiprimary ideals, and we present several results derived from these properties

Keywords: Fuzzy sets, Intuitionistic fuzzy sets, pythagorean fuzzy sets, intuitionistic fuzzy primary ideal, intuitionistic fuzzy semiprimary ideal

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[2]. Kumari, K., Adak, A. K., Agarwal, N. K. (2020). Pythagorean Q-Fuzzy Ideal Of Near-Ring. Ideal Science Review, 15(1), 61-68.
[3]. Adak, A. K., Salokolaei, D. D. (2021). Some Properties Rough Pythagorean Fuzzy Sets. Fuzzy Information And Engineering (IEEE) , 13(4), 420-435.
[4]. Adak, A. K., & Kumar, D. (2022). Some Properties Of Pythagorean Fuzzy Ideals Of Γ-Near-Rings, Palestine Journal Of Mathematics, 11(4), 336-346.
[5]. Adak, A. K. (2022). Characterization Of Pythagorean Q-Fuzzy Ideal Of Near-Ring, Handbook Of Research On Advances And Applications Of Fuzzy Sets And Logic, IGI-Global, 229-242.