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Polynomial time learner for inferring subclasses of internal contextual grammars with local maximum selectors
Natural languages contain regular, context-free, and context-sensitive syntactic constructions, yet none of these classes of formal languages can be identified in the limit from positive examples. Mildly context-sensitive languages are capable to represent some context-sensitive constructions such as multiple agreement, crossed agreement, and duplication. These languages are important for natural language applications due to their expressiveness, and the fact that they are not fully context-sensitive. In this paper, we present a polynomial-time algorithm for inferring subclasses of internal contextual languages using positive examples only, namely strictly and k-uniform internal contextual languages with local maximum selectors which can contain mildly context-sensitive languages. 2017, Springer International Publishing AG. -
Popularity Prediction of Online Social Media Content: A Bibliometric Analysis
An online social network is a platform that enables individuals to interact with others who have similar backgrounds, preferences, activities, and associations. The number of features available and the format of each online social network range widely. Users of online social networks, such as Twitter, Instagram, Flicker, and Pinterest, have increased dramatically in recent years. Content sharing is the most popular feature of online social networks, used by both specific users and big enterprises. This study has used bibliometric methods to analyze the growth of the social media popularity prediction on online social network content from 2013 to 2022. The publications have been extracted from the dimensions database, and the VOS viewer software was used to visualize research patterns. The finding provides valuable information on the publication year, authors, authors country, authors organizational affiliations, publishing journals, etc. Based on the findings of this analysis, researchers will be able to design their studies better and add more insights into their empirical studies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Porous medium convection in a chemically reacting ferrofluid with lower boundary subjected to constant heat flux
The effect of exothermic chemical reaction of zero-order on Bard-Darcy ferroconvection is investigated using the technique of small perturbation. The eigenvalues associated with an adiabatic lower wall are determined by employing the Galerkin method. The Darcy-Rayleigh number is computed in terms of the parameters pertaining to chemical reaction and ferromagnetic fluid. It is established that, when chemical reaction escalates, there is a considerable shift from linearity and occurrence of asymmetry in the basic temperature profiles. It is ascertained that the threshold of Bard-Darcy ferroconvection is augmented through the stresses of both mechanisms due to chemical reaction and magnetization, and the ferroconvective instability due to nonlinearity of magnetization is rather inconsequential when chemical reaction is present. It is also shown that the destabilizing feature of magnetic forces resulting from the fluid magnetization is less pronounced when chemical reaction is present. Published under licence by IOP Publishing Ltd. -
Portfolio Optimization Using Quantum-Inspired Modified Genetic Algorithm
Optimization of portfolios has an additional level of complexity and has been an area of interest for both financial leaders and artificial intelligence experts. In this article, a quantum-inspired version of an improved genetic algorithm is proposed for the task of portfolio optimization. An effort is made to implement two different genetic versions along with their extension in the quantum-inspired space. Improvements to the popular crossover techniques, viz. (i) arithmetic and (ii) heuristic crossover are proposed to reduce computational time. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Post Covid Scenario Effective E-Mentoring System in Higher Education
During Covid-19 pandemic many people and institutions preferred online coaching instead of in person education. The problem with online is that it will be difficult to carry on interconnections between students and professors in that environment. The main constraint for conducting online session is that the people in remote areas may find a difficulty to connect to online sessions having network issues. Electronic mentoring (e-mentoring) is implemented like a website in which the mentor and mentee can communicate with each other. With the help of this mentoring the project can provide a best solution for both the mentor and mentee. They can communicate with each other with the help of online platform and even with the help of emails.This proposed method will help them to keep the track of their academic progress and achievements of students. This article mainly focus on the mentoring through physical and virtual environment in which the mentee will be interacting with the mentor to know the progress of their academics. This article discusses about the website which is developed to fulfill the needs of the student and it discusses about the various stages of development that helped in building the website. Students can share their difficulties and their achievements with the mentor who are assigned for them particularly. In future planning to implement artificial intelligence technique to online mentoring process, this is for the betterment of student's growth. 2023 IEEE. -
Potato Leaf Disease Identification using Hybrid Deep Learning Model
The potato is one of the most significant crops in the world. However, it is prone to several leaf diseases that can result in significant productivity losses, leading to economic challenges. Early and precise disease identification is essential for sensitive crops like potatoes. Deep learning approaches have demonstrated excellent potential in image-based disease classification tasks in recent years. This paper presents a hybrid strategy for classifying potato leaf image diseases by integrating Optimised Convolutional Neural Network (OCNN) and Long Short-Term Memory (LSTM) networks. The Adaptive Shark Smell Optimisation (ASSO) technique is used to optimize the weights of CNN models. The CNN component is initially used to extract pertinent characteristics from Potato leaves, capturing significant visual patterns related to various diseases. These extracted features are then fed into the LSTM model, which utilizes its sequential learning capability to model the temporal dependencies among the extracted features. The model performance is analyzed based on Accuracy, Precision, Recall, and F1-score criteria. Experimental results showed that the hybrid OCNN-LSTM model outperforms the individual CNN, LSTM, and MobileNet models. The proposed model results are compared with existing state-of-the-art work, and it was found that the OCNN-LSTM model performed better and received 99.02% accuracy. 2023 IEEE. -
Pothole Detection and Powertrain Control for Vehicular Safety
A new era of automotive technology has begun with the rapid advancement of electric vehicles (EVs), which promise efficiency and sustainability. With electric vehicles (EVs) becoming an integrated part of the traction systems, there is a growing need for novel safety and performance-enhancing features. The development of an Adaptive Cruise Control (ACC) system for autonomous powertrain control and pothole detection in electric vehicles is examined in this paper. The paper focuses on integrating an intelligent system that can detect potholes and autonomously regulate the powertrain to improve both the driving experience and safety of electric vehicles. The system makes use of Jetson Nano as the processing unit for regulation of the EV powertrain. This board enables quick and accurate reactions to changing road conditions by facilitating real-time data analysis and decision-making. The powertrain regulation will be performed by controlling the acceleration and braking signal provided to the powertrain. 2024 IEEE. -
Power Efficient e-Bike with Terrain Adaptive Intelligence
Electric bicycles or e-bikes are gaining momentum in the market as they are offering a smooth, noiseless and pollution free option for individual transportation in cities as well as in countryside. E-bikes are usually with a battery powered electric motor drive with an additional option for pedaling. In this work a low cost e-bike was designed and developed with a brushless DC hub motor with controllers. For smart control, smartphone was used a console and the e-bike can be controlled using a mobile application which was connected to the e-bike through Bluetooth. The controller will pick the gradient of the terrain and will control the power of the motor, which results in energy saving. Predicted range of the e-bike, speed, acceleration and total distance covered were displayed in the console along with the geographical position on the map and throttle control options. The bike with the proposed control tested and the results were giving a reduction in current drawn from the battery. 2019 IEEE. -
Power Line Communication Parameters in Smart Grid for Different Power Transmission Lines
In an electrical power system smart grid is a network that renewable energy sources along with smart devices. Communication capabilities of the conventional grid can be improved by the inclusion of superior sensing and computing abilities. Device control, remote management, information collection, intelligent power management is achievable by using communication networks. Wired communication technology is used because of its advantages like reliable connection, free from interference, and faster speed. In this paper, the data communication parameters have been analyzed using Power Line Communication (PLC) with various lengths of transmission lines. An orthogonal Frequency Modulation scheme is used to obtain the minimum BER.MATLAB Programming has been carried out and the results have been compared with the standards and found to be satisfactory. 2021 IEEE. -
Power quality improvement strategy for non-linear load in single phase system
Widespread use of non-linear loads in today's world scenario, increased the harmonic current injection into the grid. The harmonic current play a vital role in deteriorating the power quality of the grid. The non-linear loads may be either, single phase or a Three phase loads. In this paper, a control strategy for single phase shunt active filter is discussed, in mitigating the harmonics flowing into the grid. The extraction of reference signal of shunt active filter is designed, using instantaneous reactive power theory. Here load is considered as diode rectifier which is feeding a resistive inductive load. A complete control strategy and analysis is done in MATLAB/Simulink environment. 2016 IEEE. -
Powering Ahead: Navigating Opportunities and Challenges in the Electric Vehicle Revolution
The technology is clearing ways for buzz in the market brimming with innovative items and new prospects. The government has planned to shift to electric vehicles by 2030, whether it is for personal or commercial use. As innovative improvements are developing quickly, it is blasting the market with the EVs industry which expected to transform the future (Rajkumar S, in Indian electric vehicle conundrum: a tale of opportunities amid uncertainties, 2020). Volvo company has also announced that it will be fully electric by 2030 (https://gadgets.ndtv.com, in Volvo to go all electric by 2030, sell exclusively online, 2021). It is expected that EVs will generate more demand for electricity and help in settling the focus on resources problem. It will also help in improving the financial feasibility of power sector projects. In India, there is more dependency on renewable energy so this is a chance to be independent and provide cheap power to the people. The EVs are more economical than petrol or diesel vehicles. The government is also giving incentives to the makers of electric vehicles. GST on electric vehicles is 12% as compared to petrol and diesel vehicles with 28% GST. As per the Electricity Act, 2003, a distribution license is needed to supply power from respective state electricity regulatory commissions. Another challenge is that charging the EVs will lead to a rise in the demand of electricity which is risky for the electricity distribution companies (www.livemint.com, in Indias electric vehicle drive: challenges and opportunities, 2017). Indians are very price conscious. A recent study revealed that Indians are ready to compromise on more charging time, but they are not ready to pay higher price for EVs (Gupta NS, in Electric vehicle adoption in India: study reveals three tipping points, 2020). From Fig.1, it can be seen that in 2014 investment in EVs was $2.2 billion which has increased to $406 billion in 2019 (Shanti S, in The road to green: what makes electric vehicle adoption a challenge for India. 2020). This shows that people are shifting toward EVs. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Pre and Post Operative Brain Tumor Segmentation and Classification for Prolonged Survival
The aim of this research was to provide a detailed overview of the techniques in detecting and segmenting meningioma brain tumor in pre- and post-operative MRI images and classify for presence of meningioma thereby giving an early diagnosis to decrease the death rate. This study examines trending techniques for brain tumour segmentation and classification in Magnetic Resonance (MR) images of pre and post-surgery. For the segmentation and anomalies in the brain categorization, several approaches such as regular machine learning techniques (K-mean bunching, Fuzzy C mean grouping etc.), Deep Learning-based approaches (CNN, ResNET, Dense Net, VGG etc.), classical algorithms (Snake contour, watershed method etc.), and hybridization approaches were applied, according to the analysis. Information base, for example, BRATS, Fig-Share, EPISURG or TCIA can be taken to gather clinical pictures which principally contains of 2 classifications, pre and post pictures of Brain tumor. The multiple processes of brain tumour segmentation methodologies, such as preprocessing, feature extraction, segmentation, and classification, are also explained in this work. The task of segmenting residual and recurrent tumors differs greatly from that of segmenting tumors on baseline scans before surgery. This study shows that each approach has its own set of pros and limitations, as well as notable findings in terms of precision, sensitivity, and specificity, according to the comparison research. The use of segmentation approaches to determine success and reliability has been discovered. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Pre Packaged Insolvency - Exploring An Alternative Framework For Bankruptcy Resolution In India
This article is a review of literatures on the need for alternative bankruptcy resolution framework in India. The study explores the context & background to the recent initiation of limited Pre-Packaged Insolvency in India. The article makes a strong case for having a private & pre-negotiated mode of debt resolution along with the existing CIRP framework in India. The article provides a comparative perspective of CIRP and Pre-pack driven resolution model in India. The research paper also addresses some of the potential challenges & concerns related to initiation of pre-pack in India & accordingly discusses the relevant safeguards for the same. Lastly, the study also provide a brief view of pre-pack model currently practised in USA. The Electrochemical Society -
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. -
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. -
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 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 Employee Attrition Using Machine Learning Algorithms
Employees are considered the foundation of any organization. Due to their importance, the Human resources department implements various policies to sustain them. Yet the attrition rate in any organization is increasing yearly. The attrition rate signifies the number of employees who leaves a firm without being replaced. It is regarded as a well-known issue that requires the administration to make the best choices to retain highly competent staff. It is interesting to note that artificial intelligence is frequently used as a successful technique for foreseeing such an issue. This review paper aims to study the different machine learning approaches that predict employee attrition and factors influencing an employee to attrite from an organization. A Hybrid model comprising the various ensemble models is proposed to predict attrition at its earliest. The forecasted attrition model aids in not only taking preventive action but also in improving recruiting choices and rewarding top performers who contribute to the company's success. 2022 IEEE. -
Predicting Graduate Admissions using Ensemble Machine Learning Techniques: A Comparative Study of Classifiers and Regressors
The goal of this research is to apply machine learning techniques to forecast a student's probability of being accepted into a graduate program. Applicants' GRE and TOEFL grades, university rankings, letters of recommendation, statements of purpose, cumulative grade point averages, and prior research experience are all included in the dataset utilized for this analysis. The goal is to calculate an applicant's expected acceptance rate. This study uses a combination of Classifiers and regressors. Different prediction models are contrasted in this study: Random Forest Classifier (RFC), Decision Tree Classifier (DTC), K-Neighbors Classifier (KNC), Support Vector Classifier (SVC), Gradient Boosting Classifier (GBC), Logistic regression (LR), Support vector Regressor (SVR), Random Forest Regressor(RFR), Gradient Boosting Regressor(GBR) and Decision Tree Regressor(DTR). Using these characteristics, the models are trained and evaluated. Evaluation criteria such as accuracy, kappa value, AUC-ROC, and confusion matrix are used to find the models' effectiveness. In order to determine which model performed the best, the assessment results are compared with one another. Based on study findings, the Gradient Boosting Classifier outperforms the other models tested by a significant margin (96 per cent). This model's AUC-ROC of 0.97 indicates it does a decent job at separating the positive and negative categories. 2023 IEEE.