Bikaner Technical University

Semantic drift: the gradual change in word meaning over time, poses a critical challenge to natural language understanding in diachronic corpora. Existing methods detect drift via alignment or distance metrics but lack the ability to model its dynamics or forecast future semantic change. We present Koopman Spectral Dynamics, the first framework to model semantic drift as a nonlinear dynamical system in word embedding space, linearly lifted via the Koopman operator. This enables interpretable forecasting, spectral decomposition, and drift attribution using Koopman eigenfunctions and modes. Applied to the HistWords: All English dataset (1800s-1990s), our method outperforms strong baselines: PCA, LSTM, and Pro-crustes, with up to 24.5% lower RMSE and 19.2% lower MAE in word forecasting, and +7.8 % accuracy in semantic coherence classification. We analyze drift trajectories using spectral energy curves, degradation plots, and mode attributions, and introduce causal influence maps via Granger and mutual information matrices. Our model generalizes to multi-word phrases (e.g., gay marriage) and supports a real-time interactive forecasting tool. This work introduces Koopman operator theory to NLP and data mining, offering a principled, spectral foundation for modeling and interpreting meaning evolution over time.

The integration of Artificial Intelligence (AI) into financial forecasting has transformed traditional stock market prediction methods. This research paper explores the effectiveness of AI techniques in forecasting stock trends within the Indian stock market over the last few years. We examine AI methodologies, including machine learning (ML), deep learning (DL), and hybrid models applied to the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). By comparing historical data with AI-predicted trends, this study evaluates prediction accuracy and market relevance (Patel et al., 2015). Furthermore, the research outlines existing study gaps and proposes a future scope of integrating AI with behavioral finance and real-time analytics (Chen et al., 2022). The prediction of stock market trends remains a significant challenge due to the stochastic and non-linear nature of financial time series data (Zhang & Zhou, 2020). With the proliferation of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL) techniques, there has been a paradigm shift in the modeling and forecasting of stock price movements (Fischer & Krauss, 2018). This paper presents a comprehensive study on the effectiveness of AI in predicting stock market trends within the Indian financial ecosystem, focusing on a comparative analysis of AI models implemented over the last five years (2018–2023) on major indices and stocks listed on the NSE and BSE. This research evaluates the performance of multiple AI algorithms—including Support Vector Machines (SVM), Random Forests (RF), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and hybrid ensemble models—in forecasting short- and medium-term price trends using historical stock data (Krauss et al., 2017; Chen & He, 2021). Standard evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and classification accuracy are employed to assess model efficacy. The results indicate that while traditional machine learning algorithms offer moderate predictive power, advanced deep learning and hybrid models significantly outperform them, particularly during periods of high market volatility, such as the COVID-19 pandemic and post-lockdown recovery phases (Weng et al., 2018; Jain & Jain, 2021). The study identifies key research gaps, including limited sectoral diversification in datasets, inadequate integration of sentiment and behavioral data, overfitting issues in complex models, and the lack of real-time prediction systems in the Indian context (Gupta & Pathak, 2022). Furthermore, current models often ignore nonquantitative factors such as investor sentiment, macroeconomic indicators, and global events, which can critically impact prediction accuracy (Nassirtoussi et al., 2014). These limitations suggest the need for a more holistic and interdisciplinary approach to AI-driven financial forecasting. The scope for future research includes the development of real-time, adaptive AI systems using high-frequency trading data, the incorporation of behavioral finance through social media and news analytics, and the exploration of quantum computing-based AI models (Liu et al., 2023). From a practical standpoint, the findings of this study offer valuable insights for institutional investors, financial analysts, regulatory bodies, and developers of AIpowered trading platforms. The study concludes that while AI is not a definitive solution for market prediction, it provides a powerful augmentative tool that, when designed with robustness, transparency, and adaptability, can significantly enhance decision-making in India’s fastevolving financial markets.

This study investigates the impact of transesterification parameters on biodiesel yield and kinematic viscosity, using an Artificial Neural Network (ANN) with the Levenberg-Marquardt algorithm to predict these outcomes. Predictions are validated against experimental results using established metrics like RMSE, MAPE, and R2. The experiments follow a Design of Experiment (DOE) approach, enabling accurate yield and viscosity measurements for each biodiesel sample. Results show that the ANN model strongly aligns with experimental results, showing lower RMSE and MAPE and higher R², outperforming RSM. Consequently, the ANN model is recommended for reliable biodiesel yield and viscosity prediction.

Multi-factor authentication (MFA) has become a widely adopted security measure to enhance account protection against unauthorized access. However, this study aims to investigate the vulnerabilities and potential risks associated with MFA implementations by exploring the use of a man-in-the-middle (MITM) attack tool called EvilGinx2. The tool is designed to intercept login credentials and session cookies, effectively bypassing MFA protections and granting unauthorized access to user accounts. This research will delve into the capabilities of EvilGinx2 as a red team tool, highlighting its functionality and the techniques it employs to clone legitimate websites and prompt users to provide their MFA codes or push prompts. By analyzing the tool's ability to capture authentication cookies, this study focused on specific data attackers, including usernames, passwords, and authentication cookies.

Cardiovascular disease is a major cause of death worldwide, and the COVID-19 pandemic has only made the situation worse. The purpose of this work is to explore various time-frequency analysis methods that can be used to classify heart sound signals and identify multiple abnormalities in the heart, such as aortic stenosis, mitral stenosis, and mitral valve prolapse. The signal has been modified using three techniques—tunable quality wavelet transform (TQWT), discrete wavelet transform (DWT), and empirical mode decomposition—to detect heart signal abnormality. The proposed model detects heart signal abnormality at two stages, the user end and the clinical end. At the user end, binary classification of signals is performed, and if signals are abnormal then further classification is done at the clinic. The approach starts with signal preprocessing and uses the discrete wavelet transform (DWT) coefficients to train the hybrid model, which consists of one long short-term memory (LSTM) network layer and three convolutional neural network (CNN) layers. This method produced comparable results, with a 98.9% classification accuracy for signals, through the utilization of the CNN and LSTM model. Combining the CNN’s skill in feature extraction with the LSTM’s capacity to record time-dependent features improves the efficacy of the model. Identifying issues early and initiating appropriate medication can alleviate the burden associated with heart valve diseases.

The aim of this numerical study is to investigate the heat transfer characteristics of the air flowing in the triangular photovoltaic thermal (PV/T) array/system at variable velocities. The PV/T array in the present analysis is composed of seven photovoltaic (PV) panels connected in series along with a triangular duct throughout its length. The heat transfer analysis in the PV panel (solid component) and visualization of temperature distribution in the PV/T system has been simulated by the use of system coupling of steady state thermal and CFX tools in ANSYS 18.2. Experiment was initially performed at velocity of 1.5 m/s inside the duct by the use of ANSYS software to predict the glass temperature, outlet air temperature, and tedlar temperature of triangular PV/T system. Further, the velocity has been incremented by 1 m/s up to 6.5 m/s to see the overall performance of the system. The validation of simulation results showed a good agreement with the experimental results under reasonable limit. The gain in temperature with the increase in velocity becomes marginal (only 7%) after 4.5 m/s. The temperature contours from the numerical simulation also revealed that cooling up to the last panel is not significant beyond 4.5 m/s. Similar tendency is documented in the overall exergy efficiency which significantly increased to 4.5 m/s and became marginal with further increment in velocity. It can be inferred that the optimum velocity which can fulfill the requirement of cooling of all PV panels along with reasonable thermal output is 4.5 m/s. The thermal energy gain gradually increases as the day progresses and becomes maximum at 2:00 PM. The average thermal energy gain being 9.6% higher than that in the morning (10:00 AM) indicates its suitability for applications requiring moderately high temperature. The present numerical investigation focuses on improving the overall performance of the system without the involvement of any additional cost. The system can be used in various applications like crop drying, space heating, etc. Owing to different outlet air temperatures during the day, the applications can either be utilized independently or can be combined to be used at different times according to the temperature requirement.