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Role of Artificial Intelligence and Robotics in Shaping the Students: A Higher Educational Perspective
An unprecedented shift in technology has begun in the modern era. Robotics and artificial intelligence (AI) advancements have created fresh positions while de-skilling or retraining many existing ones. Technical developments at higher education institutions (HEIs) protect students against potential changes in their field of study brought on by A) and prepare them for success in the workplace. This research aims to investigate how, over the past 150 years; globalization has fundamentally changed human civilization. Conventional education confronts enormous challenges as energy, the internet of things, and the cyber-physical systems they oversee diminish. One may argue that energy, the internet of things, and the cyber-physical systems that are under its jurisdiction are the foundations of all future education. The demise of these systems presents a significant threat to traditional schooling. Students' screen time is increased by this action, which has an impact on their mental health. Five-fold cross-validation with 210 students from Delhi NCR and abroad is beneficial for the classification techniques SVM, Naive Bayes, and Random Forest. The study examined the factors that contributed to an increased rate of mental health issues among undergraduate students in Delhi, India, following the introduction of the COVID-19 virus. The results have demonstrated that while technology's practical applications will likely have a positive influence on education in the future, there may be negative effects as well. This is an opportunity for educators and learners to support excellence and remove obstacles that prevent many kids and schools from achieving it. Therefore, in the future, every nation will need to create an education system that is more technologically sophisticated. 2024 IEEE. -
An Efficient Machine Learning Classification model for Credit Approval
Credit authorization is a critical step for banks as well as every bank's main source of revenue is its line of credit. Thus, banks can profit from the loan interest they approve. Profitability or lost opportunity of a bank is highly dependent on loans that are whether consumers repay the debt or refuse. Loan collection is a significant factor in a bank's economic results. Forecasting the customer's ability to repay the loan in order to determine whether it should authorize or deny loan documents is a significant undertaking and a critical method in data analytics is being utilized to investigate the problem of loan default prediction: On the premise of assessment, the Logistic-Regression Classification Model, Random-Forest Classifier and Decision Tree Classification Models are compared. The mentioned classification algorithms were created as well as subsequently various evaluation metrics were obtained. By utilizing a suitable strategy, the appropriate clients for loan providing may be simply identified by assessing their probability of non-performing loans. This indicates that a bank really shouldn't simply prioritize wealthy consumers when giving loans, but it should also consider a client's other characteristics. This approach is critical in making credit judgments and forecasting default risk. 2023 IEEE. -
Cybersecurity Threats Detection in Intelligent Networks using Predictive Analytics Approaches
The modern scenario of network vulnerabilities necessitates the adoption of sophisticated detection and mitigation strategies. Predictive analytics is surfaced to be a powerful tool in the fight against cybercrime, offering unparalleled capabilities for automating tasks, analyzing vast amounts of data, and identifying complex patterns that might elude human analysts. This paper presents a comprehensive overview of how AI is transforming the field of cybersecurity. Machine intelligence can bring revolution to cybersecurity by providing advanced defense capabilities. Addressing ethical concerns, ensuring model explainability, and fostering collaboration between researchers and developers are crucial for maximizing the positive impact of AI in this critical domain. 2024 IEEE. -
An analysis on direct authentication of data
Authentication is the procedure which permits a sender and receiver of data to validate each other. On the off chance that the sender and receiver of data can't legitimately confirm each other, there is no trust in the activity or data gave by either party. This paper talks about where and when can the service providers use the various authentication models adopted and the comparison between two authentication models. 2017 IEEE. -
'Enhancing Electricity Price Forecasting': Integrating Macro-Economic Factors And Renewable Energy Dynamics in A Machine Learning Approach
In the ever-evolving electricity market, accurate price prediction is imperative for informed decision-making. This research introduces an innovative predictive model that integrates renewable energy, macro-economic indicators, and external factors to enhance forecasting accuracy. By exploring historical trends, comparing machine learning algorithms, and employing advanced feature selection methods, the study addresses the complexities of the electricity market, emphasizing economic indicators, geopolitical events, and demand-supply dynamics. Informed by a literature review, the research underscores the necessity of dynamic models in electricity price forecasting. Utilizing machine learning models such as linear regression, random forest, SVM, AdaBoost, and ARIMA, the study aims to improve prediction accuracy. With a robust methodology and comprehensive evaluation metrics (MAE, RMSE, MAPE), the research contributes valuable insights into electricity market dynamics, providing a variable dictionary for clarity and emphasizing the strategic implications of the superior random forest model for stakeholders in the electricity sector. 2024 IEEE. -
Evaluation of Maximum Bending Stiffness of Stranded Cables with Refined Kinematic Relations
The mechanical response of a helically stranded cable depends on the effective stiffness offered by the collective assembly of its constituent wires. This can vary between two extreme conditions, namely a monolithic state, also known as the stickslip state, wherein all the wires in the cable behave as a single unit with no relative movements among themselves, offering the maximum stiffness for the cable. In the other extreme condition, all the wires are free to move among themselves, with no frictional holding among them, thus offering the minimum stiffness. This paper reviews the various mathematical models that are available for the estimation of maximum bending stiffness and brings out the need for considering a vital parameter known as the wire stretch effect that has been neglected by many authors till date. The consequent fundamental changes that occur in the basic kinematic relations are brought out and refined expressions for the internal wire forces and moments are established for the first time in the coupled axial-bending analysis. Further, the shear displacement of the wire due to the stretch has also been included in the wire normal and binormal shear forces. A single-layered cable with core-wire contact has been considered for analysis and the numerical results are evaluated with these new inclusions and are compared with the published results. It is hoped that the refined model suggested in this paper for the accurate estimation of the maximum stiffness, will pave way for more reasonable cable analysis in the subsequent slip stages. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Improvised Model for Estimation of Cable Bending Stiffness Under Various Slip Regimes
It is well known that the bending response of a stranded cable varies between two extremes, known as a monolithic stickslip state and a completely frictionless loose wire state. While the monolithic state offers the maximum stiffness for the cable, the latter loose wire assembly results in minimum stiffness. The estimation of the actual behavior of the cable under any loading scenario demands a proper modeling that accounts for the interaction of the constituent wires in the intermittent slip stages. During loading, the wires are not only subjected to forces along their axes but are considerably acted upon with radial forces that cause clenching effect. Major research works have focused on the frictional resistance of these radial forces from the Coulomb hypothesis, which contributes to the macro slip phenomenon. As the effect of these radial clenching forces are also significant in causing high contact stresses between wires at the adjacent layers, the need for considering the micro slip at these locations is also vital in the evaluation of the net cable stiffness. In this paper, a novel model is proposed that considers the slip caused by the Coulomb friction hypothesis and the micro slip caused by the Hertzian contact friction for the evaluation of bending stiffness. The variation of the bending stiffness has been evaluated for a single-layered cable as a function of bending curvature at various locations by studying their slip regimes. The predicted results are compared with the published results to establish the refined combined slip hypothesis suggested in this paper. The suggested slip model in this paper has also been accounted with the improvised kinematic relations that consider the wire stretch effect, a parameter that has been neglected in this cable research till date. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A review of buoyancy driven underwater gliders
In the past few years, several techniques and approaches have been developed by researchers for the ocean survey. An autonomous underwater vehicle primarily known as the glider is vastly used for oceanographic study and survey. With the help of these vehicles now it possible to have a study on the effects of pesticides, metal, biological toxins, or chemicals on the living organisms of the sea. Additionally, monitoring of threats such as biological weapons, radioactive leakage, and detection of mines is a very important parameter for keeping safety in check. Considering these parameters autonomous vehicles primarily known as glider are vastly used by oceanographers as they are relatively inexpensive, reusable, and have long mission durations. Such vehicle uses advanced sensors to perform automated monitoring and fast data acquisition. Since their inception in the 1980s, there have been considerable developments that have led to the augmentation of scientifically and commercially focused products. A comprehensive analysis of various underwater gliders and their working principle has been done here, emphasizing their architecture and working capabilities. 2022 Author(s). -
Scrutiny In-Utero to recognize Fetal Brain MRI Anomalies
In utero MRI distinguishes fbrain irregularities high precisely compared to ultrasonography as well as gives extra medical data during the pregnancies. fMRI is medically performed to get the knowledge of the brain in conditions where the inconsistency are perceived with the help of pre-birth sonography. These are common regularly solidify ventriculomegaly, not regular of the corpus callosum, and oddities of the back fossa. Fbrain inconsistencies can cause authentic brain hurt. Therefore, it is vital to recognize them from the get-go in their course so treatment can be managed to the mother, if conceivable. The job of imaging is to decide the presence, assuming any, and the degree of brain harm in the contaminated hatchling. Even though MRI is most generally utilized as a subordinate to sonography when clinical doubt is high in the setting of a typical ultrasound or to all the more likely characterize irregularities recognized by ultrasound, MRI is regularly utilized in toxoplasmosis seroconversion to conclusively preclude brain injuries, in any event, when the ultrasound examination is viewed as ordinary. X-ray is likewise utilized sequentially all through the pregnancy to check for the improvement of brain anomalies; clinical treatment brings about the astounding clinical result if the brain is typical. Intracranial irregularities are ordinarily speculated discoveries on antenatal US that are needed for assessment which is used by MRI. This audit portrays numerous irregularities imaged as a way to direct clinicians' inappropriate determination. 2021 IEEE. -
Comparison of Gradient Boosting and Extreme Boosting Ensemble Methods for Webpage Classification
Web page classification is an important task in various areas like web content filtering, contextual advertising and maintaining or expanding web directories etc. Machine Learning methods have been found to perform well to classify web pages, and ensemble models have been used to improve the results obtained from single classifiers. The Gradient Boosting and Extreme Boosting ensemble models are used in this work for binary classification. The dataset containing URLs of web pages have been collected manually. The comparison between the two boosting algorithms validated the improvement in accuracy and speed obtained through Extreme boosting. Extreme boosting has been found to be around ten times faster than Gradient boosting and also shows improvement in accuracy. The effect of three preprocessing techniques; lemmatization, stop words removal and regular expressions shows that these preprocessing techniques improves the accuracy of the results but not significantly. 2020 IEEE. -
Detection of faces from video files with different file formats
Face detection is the primary approach of all fundamental problems of human computer interaction system (HCIS). This paper evaluates the performance of detection system on single face from stored videos that are stored in different file formats. Stored videos contain raw homemade datasets as well as ready-made datasets. This proposed work concludes detection percentage of face detection system in different video formats. The implementation is done in two phases. The raw homemade dataset is tested on.3gp,.avi,.mov,.mp4 and a ready-made dataset is tested on.wmv,.m4v,.asf,.mpg file formats. The coding part for face detection has been done in MATLAB R2013a. The detection of faces from video file was 72.79 % for homemade dataset and 82.78% for ready-made dataset. 2016 IEEE. -
Extraction of features from video files using different image algebraic point operations
In the human-computer interaction (HCI) field, facial feature analysis and extraction are the most decisive stages which can lead to a robust and efficient classification system like facial expression recognition, emotion classification. In this paper, an approach to the problem of automatic facial feature extraction from different videos are presented using several image algebraic operations. These operations deal with pixel intensity values individually through some mathematical theory involved in image analysis and transformations. In this paper, 11 operations (point subtraction, point addition, point multiplication, point division, edge detecting, average neighborhood filtering, image stretching, log operation, exponential operation, inverse filtering, and image thresholding) are implemented and tested on the images (video frames) extracted from three different self-recorded videos named as video1, video2, video3. The videos are in .avi, .mp4 and .wmv format respectively. The work is tested on two types of data: grayscale and RGB (Red, Green, Blue). To assess the efficiency of each operation, three factors are considered: processing time, frames per second (FPS) and sharpness of edges of feature points based on image gradients. The implementation has been done in MATLAB R2017a. 2019 Association for Computing Machinery. -
Enhancing Educational Adaptability: A Review and Analysis of AI-Driven Adaptive Learning Platforms
This study explores the transformative potential of AI-powered adaptive learning platforms (ALPs) in education, specifically focusing on personalized learning paths and their impact on student engagement and outcomes. Through a comprehensive analysis of four prominent ALPs - Carnegie Learning, DreamBox Learning, Smart Sparrow, and Knewton - this study investigates their approaches to content tailoring and feedback delivery. The comparative analysis highlights each platform's strengths and limitations, providing educators with valuable insights for informed selection and implementation. This study also considers the broader landscape of ALPs, acknowledging concerns such as bias, data privacy, and the role of educators in the tech-driven educational environment. The findings contribute to our understanding of how ALPs can empower educators, personalize learning, and address achievement gaps, offering a nuanced perspective on the complex tapestry of AI in education. 2024 IEEE. -
AI Driven Finite Element Analysis on Spur Gear Assembly to Enhance the Fatigue Life and Minimized the Contact Pressure*
The major goal of the current research is to carry out mathematical and finite element analysis on spur gear assemblage to improve fatigue life as well as minimize contact pressure among contact teeth by modifying the face width of spur gear. AI automates FEA simulations and analyses, speeding up the design process. The investigation presented above was conducted using three separate 3d models of driving gear. The equivalent stress for the spur gear assembly of design-3 has decreased up to 13.45% in comparison to design-1, and the fatigue life has increased up to 81.59% at 600 N m, according to the results. Further AI models shall predict stress distribution, contact pressure, and other relevant factors in spur gear assemblies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Security Aspects for Mutation Testing in Mobile Applications
Due to the increase in the number of Android Platform Devices, there are more and more applications being developed across various domains. It is interesting to see the involvement of bugs/crashes even in the deployed applications even though it has been through various test phases. Unit tests are essential in a well-trusted testing environment; however, it does not guarantee that the range of test caries every component of the application. This writes up discusses the overview of mutation testing method concerning Android Applications. Even though mutation testing is found out to be very effective in other applications, it is not that easy to implement the same for an Android Developed Application because of additional resources it would hold. Further, various measures for mutation testing are discussed with types of mutant operators, tools etc. The current studies of mutation analysis mainly focus on testing all the functionalities irrespective of the resource usage. However, the target of the future mutation tests must be also to evaluate the efficiency of the applications under the same test cases. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Machine Learning Algorithms for Predictive Maintenance in Hybrid Renewable Energy Microgrid Systems
The rapid expansion of hybrid renewable energy microgrid systems presents new challenges in maintaining system reliability and performance. This paper explores the application of machine learning algorithms for predictive maintenance in such systems, focusing on the early detection of potential failures to optimize operational efficiency and reduce downtime. By integrating real-time data from solar, wind, and storage components, the proposed models predict the remaining useful life (RUL) of critical components. The results demonstrate significant improvements in predictive accuracy, offering a robust solution for enhancing the reliability and longevity of renewable energy microgrids. The Authors, published by EDP Sciences. -
Big Data Analytics Tools and Applications for Modern Business World
In the modern world, data is the unavoidable word. The digital environment in almost all our day to day life is linked with digital data. Effective data management is one of the important tasks. The gradual growth of technology in recent years, the generation of data has increased exponentially. Everything, ranging from sending a mail to simply browsing the internet generates data and this is collected and stored. This data has countless uses in various fields such as medicine, business, agriculture and marketing, but most of the time it goes unused. Business intelligence is a key factor in the current business world. Business growth is purely depending on technology. Technology is not only used in manufacturing it is applied to getting the customer. The data analytics is still in its earlier stages and has a long way to go before it yields favourable results. It is a good time as any to start working in this domain to utilize its prowess. This article has discussed the opportunities and growth of data analytics in the research domain. It can face soon when it reaches its advance stages. The big data is handling a larger amount of data in a conventional and non-conventional manner. Technology is playing a vital role to handle larger data from the database. This article is to discuss data analytics application in modern industry. In the technical perspective, big data Map-reduce is an advanced tool and for simulation part, R tool is used. 2020 IEEE. -
Analysis of Challenges Experienced by Students with Online Classes During the COVID-19 Pandemic
In the current context of the COVID-19 pandemic, due to restrictions in mobility and the closure of schools, people had to shift to work from home. India has the worlds second-largest pool of internet users, yet half its population lacks internet access or knowledge to use digital services. The shift to online mediums for education has exposed the stark digital divide in the education system. The digitization of education proved to be a significant challenge for students who lacked the devices, internet facility, and infrastructure to support the online mode of education or lacked the training to use these devices. These challenges raise concerns about the effectiveness of the future of education, as teachers and students find it challenging to communicate, connect, and assess meaningful learning. This study was conducted at one of the universities in India using a purposive sampling method to understand the challenges faced by the students during the online study and their satisfaction level. This paper aims to draw insight from the survey into the concerns raised by students from different backgrounds while learning from their homes and the decline in the effectiveness of education. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Movie Success Prediction from Movie Trailer Engagement and Sentiment Analysis
The diverse movie industry faces many challenges in the promotion of the product across different demographics. Movie trailer engagements provide valuable information about how the audience perceives the movie. This information can be used to predict the success of the upcoming movie before it gets released. The previous research works were mainly concentrating on Hindi language movies to predict success. The current research paper includes the success prediction of movies other than Hindi. This paper aims to analyze various Machine Learning models performance and select the best performing model to predict movie success. The developed model can efficiently classify successful and unsuccessful movies. For the current research, the data is collected from various sources through web scrapping and API calls in Sacnilk, The Movie Database (TMDB), YouTube, and Twitter. Different machine learning classification models such as Random Forest, Logistic Regression, KNN, and Gaussian Nae Bayes are tested to develop the best-performing prediction model. This research can help moviemakers to understand the popularity of the movie among the viewers and decide on an efficient promotional strategy to make the movie more successful. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Forex Analysis on USD to INR Conversion: A Comparative Analysis of Multiple Statistical and Machine Learning Algorithms
Foreign Currency Exchange (FOREX) engages a major role in world economy and the international market. It is a vast study based on determining whether or not to wait, buy or sell on a trading currency pair. The main objective is to predict the future currency prices using historical data in order to make more informed and accurate investment decisions for business traders and monetary market. This work experimented and implements ten machine learning strategies namely Random Forest, Decision Tree, Support vector regressor (SVM), Linear SVM, Linear Regression, Ridge, Lasso, K-Nearest Neighbor (KNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) to assess the historical data and help the traders to invest in foreign currency exchange. The dataset used to validate and verify the machine learning algorithms is available in public domain and it is the daily Foreign Currency Exchange price of United States Dollars (USD) to Indian Rupees (INR). The experimented result shows that the Long Short-Term Memory (LSTM) model performs a bit better than the other machine learning models for this particular case. This work straight away does not reject the other methods it rather needs more experimental analysis with other models that has changed architecture and different dataset. 2024 IEEE.