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Pre-Service and In-Service Teachers Perceptions of Using Virtual Reality Tools in Teaching
This paper explores pre-service and in-service teachers perceptions of virtual reality (VR) technology as a teaching and learning tool in the classroom in India. The study aimed to answer four research questions, including the adoption rate of VR technology among teachers, their confidence levels in teaching using VR technologies compared to digital technologies, attitudes towards using VR technology, and the usefulness of different uses of VR technology. The survey conducted among 102 teachers found limited adoption of VR technology, lower confidence levels in using it, but willingness to use it in the future. The paper recommends providing adequate training and support to increase teachers confidence in using VR technology in their teaching practices. The study also suggests that strategies to promote VR technology should consider gender differences in attitudes towards it. Overall, the research concludes that teachers view VR technology as having potential benefits for learning and teaching across various uses. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Pre-Service and In-Service Teachers Perceptions of Using Virtual Reality Tools in Teaching
This paper explores pre-service and in-service teachers perceptions of virtual reality (VR) technology as a teaching and learning tool in the classroom in India. The study aimed to answer four research questions, including the adoption rate of VR technology among teachers, their confidence levels in teaching using VR technologies compared to digital technologies, attitudes towards using VR technology, and the usefulness of different uses of VR technology. The survey conducted among 102 teachers found limited adoption of VR technology, lower confidence levels in using it, but willingness to use it in the future. The paper recommends providing adequate training and support to increase teachers confidence in using VR technology in their teaching practices. The study also suggests that strategies to promote VR technology should consider gender differences in attitudes towards it. Overall, the research concludes that teachers view VR technology as having potential benefits for learning and teaching across various uses. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Precise cervical cancer cell boundary denoising and segmentation with adaptive wavelet-spectral enhancement
Accurate segmentation of cell nuclei in cervical cytology images is crucial for automated cervical cancer screening, yet existing methods struggle with blurred boundaries, noise-induced degradation, and topologically implausible predictions. The current research proposes Cell-Seg Tool, a novel triplet-branch diffusion AI tool that synergistically integrates three innovations to address these limitations. The Wavelet-Enhanced Contour Refinement Branch employs a learnable multi-scale discrete wavelet transform with adaptive coefficient attention to dynamically enhance boundary features across horizontal, vertical, and diagonal orientations. The Adaptive Spectral Noise Suppression module performs dual-domain processing using DCT-based filtering and uncertainty-guided fusion, coupled with bidirectional anchor semantic feedback to couple cross-branch information. The Topology-Aware Hybrid Loss integrates a focal Tversky loss, a persistent homology loss, a directional boundary loss, a skeleton completeness loss, and a diffusion-noise MSE loss for multi-objective optimization. Comprehensive experiments on multiple datasets demonstrate superior performance, achieving 94.45% Dice coefficient and 19.2% reduction in boundary localization error compared to state-of-the-art methods. Unlike prior work that applies these techniques independently, this work demonstrates that their adaptive, synergistic integration within a diffusion-based framework yields substantial improvements in boundary accuracy and topological correctness. 2026 The Author(s). -
Precise surface molecular engineering of 2D-Bi2S3 enables the ultrasensitive simultaneous detection of dopamine, epinephrine, serotonin and uric acid
Multiple biomolecule detection at a single read is an emerging and highly desirable technology in point-of-care diagnostics. Thus, functional nanoscale materials with high precision and stability at an affordable cost are required to fabricate adaptable multiplex biosensing devices with exceptional performance. Herein, an ultrasensitive molecularly engineered 2D-Bi2S3 biosensor is developed via a two-step synthetic approach. Simultaneous detection of dopamine (DA), epinephrine (EP), serotonin (ST), and uric acid (UA) is achieved at the nanomolar level. The surface molecular engineered 2D-Bi2S3 by 4-mercaptobenzoic acid (MBA) exhibits a well crystalline nature and consists of 36 stacked layers with creased-paper-like morphology after an MBA molecule has been precisely linked at the basal plane of Bi2S3. Bi2S3-MBA's surface/vibrational spectroscopic and scanning tunneling microscopic studies demonstrate the Bi2S3-MBA electronic nature and the linked molecule present on the Bi2S3 surface with a comparatively large random distribution of MBA molecules at the basal plane than the edge plane. The density functional theory (DFT) calculation verifies the proposed molecular interaction mechanism. The success of this unique surface molecular engineering strategy, which effectively modified the electronic and surface configuration of the 2D-Bi2S3, offers an exciting possibility for building different variants of the versatile biosensor for real-world diagnostic device applications. 2024 -
Precision agriculture takes flight: Drone technology in crop management
[No abstract available] -
Precision Corn Price Prediction with Advanced ML Techniques
In the ever-evolving corn market, accurate price prediction is imperative for informed decision-making. This research introduces an innovative predictive model that integrates and external factors to enhance forecasting accuracy in the corn market. By exploring historical trends, comparing machine learning algorithms, and employing advanced feature selection methods, the study addresses the complexities of the corn market, emphasizing economic indicators, geopolitical events, and demand-supply dynamics. Informed by a literature review, the research underscores the necessity of dynamic models in corn price forecasting. Utilizing machine learning models such as linear regression, random forest, SVM, Adaboost, and ARIMA, coupled with the interpretability of SHAP values, the study aims to improve prediction accuracy in the corn market. With a robust methodology and comprehensive evaluation metrics (MAE, RMSE, MAPE), the research contributes valuable insights into corn market dynamics, providing a variable dictionary for clarity and emphasizing the strategic implications of the superior random forest model for stakeholders in the corn sector. 2024 IEEE. -
Precision Farming on Sugarcane: Drone-Based Disease Detection Using YOLOv8 Neural Models
Precision agriculture is being revolutionized by the use of UAVs and AI, enabling more efficient and sustainable crop monitoring. This study presents a drone-based solution for real-time detection of sugarcane diseases such as Rust, Red Rot, Mosaic, and Yellow Leaf. A custom quadcopter, outfitted with a high-resolution camera and Raspberry Pi 4, is used to capture aerial imagery. The onboard YOLOv8 model processes images in real time, with data stored locally on an SD card for further evaluation. The paper covers the complete system setup, including hardware components, neural network deployment, and the end-to-end workflowfrom image capture to decision support. This integrated approach supports early intervention, better yield outcomes, and cost-effective disease management in sugarcane farming. 2025 IEEE. -
Precision Food Crop Mapping Using Deep Neural Networks and Improved Dipper Throat Optimization Techniques
In recent times, the use of Remote Sensing (RS) data obtained from Unmanned Aerial Vehicles (UAVs) has gained significant popularity in crop classification tasks, including crop mapping, yield prediction, and soil classification. The classification of food crops utilizing RS Imageries (RSI) is a major application of RS tools in crop growing. Meeting the conditions for investigating these data requires more difficult approaches, and Artificial Intelligence (AI) technologies offer the mandatory support. Because of the variation and division of crop planting, archetypal classification methods have fewer classification outcomes. This manuscript focuses on the design and execution of a Leveraging Enhanced Dipper Throat Optimization Algorithm with Dipper-Inspired Precision Classification for Remote-sensed Optimized (DIP-CROP) Processing methodology. The drive of the DIP-CROP algorithm is to classify distinct types of crops that exist in remote sensing. At first, the DIP-CROP model applies image processing using the Sobel Filter (SF) to eliminate the noise. Next, the presented DIP-CROP technique takes place SqueezeNet model is employed for the feature extractor. To classify the food crop types, the DIP-CROP approach utilizes a Multi-Head Attention-based Bi-directional Long Short Term Memory (MHA-BiLSTM) algorithm. For hyperparameter tuning of the MHA-BiLSTM classifier, the Enhanced Dipper Throat Optimization Algorithm (EDTOA) will be applied in this work. The optimization process utilizes Levy flight distribution, which is known for its faster convergence due to efficient exploration of the search space. Levy flights can be used to take larger steps in exploration, which prevents getting stuck in local minima and accelerates convergence. The performance of the DIP CROP method is examined experimentally using a benchmark database. Experimental results affirmed the superior solution of the DIP-CROP algorithm over existing methods. 2026 Seventh Sense Research Group. -
Precursor to employee engagement AMID knowledge workers
The main objective of this study is to critically analyze the precursor to employee engagement. The research methodology used in this research is descriptive research. In primary data, responses are collected through well framed questionnaires and direct interaction with the employees to selected sample of 550 respondents of information technology organisations in Bengaluru City. The questionnaire consists of 20 questions based on employee engagement precursor. To reduce the dimension of this an exploratory factor analysis was carried out and 3 factors explaining 65.26% of the variance were derived. The 3 precursors identified as professional contentment (Cronbach's alpha 0.940) career development (Cronbach's alpha 0.836) and job enrichment (Cronbach's alpha 0.826). The current study adds to the research pointing at precursors to employee's engagement among knowledge researcher. Medwell Journals, 2017. -
Predictability and herding of bourse volatility: An econophysics analogue
Financial Reynolds number works as a proxy for volatility in stock markets. This piece of work helps to identify the predictability and herd behavior embedded in the financial Reynolds number (time series) series for both CNX Nifty Regular and CNX Nifty High Frequency Trading domains. Hurst exponent and fractal dimension have been used to carry out this work. Results confirm conclusive evidence of predictability and herd behavior for both the indices. However, it has been observed that CNX Nifty High Frequency Trading domain (represented by its corresponding financial Reynolds number) is more predictable and has traces of significant herd behavior. The pattern of the predictability has been found to follow a quadratic equation. Bikramaditya Ghosh, Krishna M.C., Shrikanth Rao, Emira Kozarevi?, Rahul Kumar Pandey, 2018. -
Predicting a Rise in Employee Attrition Rates Through the Utilization of People Analytics
Modern organizations have a multitude of technological tools at their disposal to augment decision-making processes, with artificial intelligence (AI) standing out as a pivotal and extensively embraced technology. Its application spans various domains, including business strategies, organizational management, and human resources. There's a growing emphasis on the significance of talent capital within companies, and the rapid evolution of AI has significantly reshaped the business landscape. The integration of AI into HR functions has notably streamlined the analysis, prediction, and diagnosis of organizational issues, enabling more informed decision-making concerning employees. This study primarily aims to explore the factors influencing employee attrition. It seeks to pinpoint the key contributors to an employee's decision to quit an organization and develop a futuristic data driven model to forecast the possibility of an employee leaving the organization. The study involves training a model using an employee turnover dataset from IBM analytics, including a total of thirty-five features and approximately one thousand and five hundred samples. Post-training, the model's performance is assessed using classical metrics. The Gaussian Nae Bayes classifier emerged as the algorithm delivering the most accurate results for the specified dataset. It notably achieved the best recall (0.54) indicating its ability to correctly identify positive observations and maintained false negative of merely 4.5%. 2023 IEEE. -
Predicting and Analyzing Early Onset of Stroke Using Advanced Machine Learning Classification Technique
Around the world, stroke is the leading cause of death. When blood vessels in the brain rupture, they cause damage. Alternatively, blockage in a blood vessel that supplies oxygen and other nutrients may also lead to this disease. This study uses various machine learning models to predict whether someone will have a stroke or not. Different physiological features were taken into account by this study while using Logistic Regression; Decision Tree Classification; Random Forest Classification; K-Nearest Neighbors (KNN); Support Vector Machine (SVM); Nae Bayes classifier algorithm; and XGBoost classification algorithm - these were used for six different models to ensure accurate predictions are made. We will accomplish the finest exactness with Bayes cv look which may be a hyper-tuning classifier with 92.87%. This consideration can be utilized for future work by doing the increase and include designing on the dataset. It is constrained to literary information, so it might not continuously be right for foreseeing stroke. so utilize the datasets that contain pictures and work on those datasets. 2024 IEEE. -
Predicting and improvising the performance of rocket nozzle throat using machine learning algorithms
This paper is a study of one dimensional heat conduction with thermo physical properties like K, row, Cp of a material varying with temperature. The physical problem is characterized by a cylinder of infinite length and thickness L, imposed with a net heat flux at x= 0, with the other end being insulated. The temperatures at the insulate end are measured by placing thermocouples. As the temperatures at the other end are very high, it is not possible to measure temperatures by keeping thermocouples which will burn away. So the problem is initialized with known sensor values near insulated end. By proper predicting values by ARIMA Model, the temperature distribution in Rocket Nozzle throat system (RNT) is calculated. The outcome of the work is processed with Machine Learning algorithm like Genetic algorithm in identifying the optimal location of sensor position which helps in improvising the performance of RNT. 2020, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Predicting and Optimizing Synergistic Drug Combinations for Breast Cancer Treatment Using Machine Learning
Objectives: The study aims to identify highly synergistic drug combinations for breast cancer treatment using machine learning models. The primary objective is to predict drug synergy scores accurately and rank combinations with the highest potential for therapeutic efficacy. Methods: Machine learning models, including XGBoost, Random Forest (RF), and CatBoost (CB), were employed to analyze breast cancer drug combination data. Four synergy metricsZIP, Bliss, Loewe, and HSAwere used to quantify drug interaction effects. The models were trained to predict these synergy scores, and their performance was evaluated using normalized root mean squared error (NRMSE) and Pearson correlation coefficient. Predicted top-ranking drug combinations were further validated by comparing observed versus expected dose-response curves and calculating the area under the curve (AUC) for synergy assessment. Results: XGBoost (XGB_5235) outperformed other models, achieving an NRMSE of 0.074 and a Pearson correlation of 0.90 for the Bliss synergy model. Based on average synergy scores, the top 20 drug combinations were identified, with Ixabepilone+Cladribine, SN 38 Lactone+Pazopanib, and Decitabine+Tretinoin emerging as the most promising. These combinations showed high synergy and were supported by biological insights into their mechanisms of action. Conclusions: The study demonstrates the effectiveness of machine learning in predicting synergistic drug combinations for breast cancer. By accelerating the screening process and reducing experimental burden, the approach offers a promising tool for guiding future in vitro and in vivo validation of combination therapies. Copyright 2025 Wolters Kluwer Health, Inc. All rights reserved. -
Predicting Coal Prices: A Machine Learning Approach for Informed Decision-Making
This research addresses the critical need for accurate coal price prediction in the dynamic global market, crucial for informing strategic decisions and investment choices. With coal playing a vital role in the world energy mix, its price fluctuations impact industries and economies worldwide. The study employs advanced machine learning models, including Linear Regression, Random Forest, SVM, Adaboost, and ARIMA, to enhance prediction precision. Key features such as S&P 500, Crude Oil Price, CPI, Exchange Rates, and Total Electricity Consumption are identified through feature importance analysis. The Random Forest model emerges as the most effective, emphasizing the significance of key variables. Leveraging explainable AI techniques, the study provides transparent insights into model decision-making, offering valuable information for risk management and strategic decision-making in the volatile coal market 2024 IEEE. -
Predicting Consumer's Brand Switching Behaviour for Cell Phones
The IUP Journal of Marketing Management, ICFAI, Vol. XV, Issue 4, ISSN No. 0972-6845 -
Predicting Consumers' Usage Intention Towards User-Generated Content: A Hybrid SEM-ANN Approach
With the change in the communication pattern, end-users are engaging in creating content and refer-ring to the content created by other users while making purchase decisions. This research aims at modelling factors affecting consumers' usage intention (UI) towards user-generated content (UGC) using Need for Cogni-tion (NfC) as a moderator of the proposed relationships. The factors affecting consumers' UI involve perceived usefulness (PU), source credibility (SC), information quality (IQ) and NfC. Further, a novel attempt has been made by using the neural network approach to assess the predictive accuracy of the model. A structured ques-tionnaire was used to collect data from 298 consumers through a survey. Data were analysed using two-stage structural equation modelling (SEM) and artificial neural network (ANN). All the independent variables viz., PU, SC, IQ and NfC significantly affect attitude towards UGC, which in turn affects UI. Results of multi-group anal-ysis and a series of chi-square difference tests reveal that a NfC significantly moderates the relationship be-tween PU and attitude, as well as that between SC and attitude. The root mean square error values from the neural network analysis suggest that the models show good predictive accuracy. This study provides a novel assessment of the usage of a hybrid SEM-ANN approach for understanding of UGC by incorporating NfC as a moderator in shaping consumers' attitudes and intentions to use UGC. 2024 World Scientific Publishing Co. -
Predicting Consumers' Usage Intention Towards User-Generated Content: A Hybrid SEM-ANN Approach
With the change in the communication pattern, end-users are engaging in creating content and referring to the content created by other users while making purchase decisions. This research aims at modelling factors affecting consumers' usage intention (UI) towards user-generated content (UGC) using Need for Cognition (NfC) as a moderator of the proposed relationships. The factors affecting consumers' UI involve perceived usefulness (PU), source credibility (SC), information quality (IQ) and NfC. Further, a novel attempt has been made by using the neural network approach to assess the predictive accuracy of the model. A structured questionnaire was used to collect data from 298 consumers through a survey. Data were analysed using two-stage structural equation modelling (SEM) and artificial neural network (ANN). All the independent variables viz., PU, SC, IQ and NfC significantly affect attitude towards UGC, which in turn affects UI. Results of multi-group analysis and a series of chi-square difference tests reveal that a NfC significantly moderates the relationship between PU and attitude, as well as that between SC and attitude. The root mean square error values from the neural network analysis suggest that the models show good predictive accuracy. This study provides a novel assessment of the usage of a hybrid SEM-ANN approach for understanding of UGC by incorporating NfC as a moderator in shaping consumers' attitudes and intentions to use UGC. 2025 World Scientific Publishing Co. -
Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis
Crude oil is an imperative energy source for the global economy. The future value of crude oil is challenging to anticipate due to its nonstationarity in nature. The focus of this research is to appraise the explosive behavior of crude oil during 20072022, including the most recent influential crisis COVID-19 pandemic, to forecast its prices. The crude oil price forecasts by the traditional econometric ARIMA model were compared with modern Artificial Intelligence (AI)based Long Short-Term Memory Networks (ALSTM). Root mean square error (RMSE) and mean average percent error (MAPE) values have been used to evaluate the accuracy of such approaches. The results showed that the ALSTM model performs better than the traditional econometric ARIMA forecast model while predicting crude oil opening price on the next working day. Crude oil investors can effectively use this as an intraday trading model and more accurately predict the next working day opening price. 2023 World Scientific Publishing Co. Pte Ltd. All rights reserved. -
Predicting Crude Oil Futures using Feed Forward Neural Networks and Technical Indicators: A Comparative Study on WTI and Brent
In the domains of economic management and energy analysis, forecasting the price of crude oil is increasing popularity. It is essential to the facilitating rapid and cost-effective development with improved quality. Accurate prediction of the crude oil market is essential for steady and fast economic development because of its enormous influence on the global economy and society. Moreover, precise crude oil price prediction aids the traders in making accurate decision to maximize profits. In this work, a machine learning method for forecasting future global price data for crude oil is provided based on past data. The proposed model consists of three phases: primarily, historical data of selected crude oil data are gathered and normalized using data normalization technique. Secondly, technical indicators are derived from the crude oil data. Finally, a Feed Forward Neural Network (FFNN) is designed and trained using these technical indicators to forecast the price of crude oil in the future. Daily, weekly, and monthly data from Brent crude oil and West Texas Intermediate (WTI) are used to evaluate the generated model's prediction ability. To find the most effective FFNN configuration, the model's efficacy is evaluated by adjusting hidden layer number and hidden neurons. Performance of the model is also analyzed by varying number of training and testing samples. The experimental outcomes demonstrates that the designed model exhibits excellent performance for both WTI and Brent data. Notably, the model proves to be effective in predicting crude oil prices, when technical indicators are used as input variables. 2026 IEEE.