Browse Items (2150 total)
Sort by:
-
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. -
Can Artificial Intelligence Accelerate and Improve New Product Development
Today, AI have successfully set up a good foundation in a broad scope of business processes. Associations including AI for product headway processes have uncovered more huge yields on hypotheses, better viability in their cycles, and effective utilization of resources. A sensible headway framework is paramount for capable product development, especially for complex endeavours. AI thinking is in like manner improving new product development. AI is probably going to experience clients in numerous areas. New yield evolution as in collaboration utilizes its capital and capacities to make another item or work on a current one. Product development is viewed as one among the fundamental cycles for progress, endurance, and recharging of associations, especially for firms in, by the same token, quick-moving or cutthroat business sectors. AI assists people's lives by expanding connections creating and multiplying items that can work with individuals' daily exercises in quite a large number of areas. Consequently, the impact of involving Artificial Intelligence for new developments is to induce things simpler. This paper attempts to outline the acceleration of new product development with the help of artificial intelligence technology. This study addressed the tailored AI in product improvement and product development transformation. Lastly, this article points out how AI accelerates product development and future outlook. 2023 American Institute of Physics Inc.. All rights reserved. -
Mapping the Landscape of Business Intelligence Research: A Bibliometric Approach
The integration of Business Intelligence (BI) is an essential element in contemporary enterprises, facilitating the conversion of voluminous data into valuable insights to support informed decision-making. Consequently, a considerable body of literature has been devoted to investigating the utilization of Business Intelligence (BI) in enhancing company efficiency and competitiveness. The present investigation employs bibliometric methods as a means to examine the research pertaining to Business Intelligence (BI). This includes an examination of the main writers and universities, publication patterns, and the intellectual framework of the domain. This investigation centers on the timeframe spanning from 2000 to 2022 and scrutinizes a corpus of 3729 Scopus articles pertaining to business intelligence. The findings suggest that the domain of Business Intelligence (BI) has experienced a substantial expansion recently. The study's results reveal significant contributors, establishments, nations, and references in the discipline, along with developing research patterns and prospects for further investigation. In general, this research emphasizes the significance of bibliometric evaluation as a means of comprehending the present status of BI research and discovering approaches to enhance the utilization of BI in contemporary organizational decision-making procedures. This study has the potential to provide valuable insights into the present state of research within the field, pinpoint significant trends and themes, and highlight potential avenues for future research. 2023 IEEE. -
Implementation of Time-Series Analysis: Prediction of Stock Prices using Machine Learning and Deep learning models: A Hybrid Approach
Experts in the finance system have long found it difficult to estimate stock values. Despite the Efficient - market hypothesis Principle claim that it is difficult to anticipate share prices with any degree of precision, research has demonstrated that share price movements could be anticipated with the proper levels of precision provided the correct parameters are chosen and the proper predictive models are created. individuals who are adaptable. The share market is unpredictable in essence, making its forecasting a difficult undertaking. Stock prices are affected by more than economic reasons. In this project, Arima, LSTM and Prophet models are used to predict the future way of behaving share price, the datasets has been obtained from NSE, share price prediction algorithms have been created and tested. According to the empirical findings, the LSTM model would be used to anticipate share prices rather well over a substantial amount of time with exactness. 2022 IEEE. -
Leveraging Robotic Process Automation (RPA) in Business Operations and its Future Perspective
Robotic Process Automation (RPA) is used to automate the business process operations including its capabilities to mimic the routine tasks, which requires less human intervention. RPA has seen crucial take-up practically throughout the last few years because of its capacity to reduce expenses and quickly associate heritage applications. Fundamentally RPA would perform automated tasks much like as an individual to accomplish objectives productively and adequately. This article analyses the features in current business conditions to comprehend the movement of RPA and automated interaction has carried to substitute the businesses with automated tasks. RPA is an innovative technology which utilizes software programming to execute enormous capacity assignments that are routine and time-consuming in the business cycle. RPA streamlines by playing out those undertakings proficiently as it reduces cost and saves assets of an association as programming works till the finishing of the assignment. This study aligns with the descriptive approach and leveraging Robotic Process Automation into business operations. This article also addresses the different players in the RPA Technological segment. This study also discussed and suggested selecting RPA Vendors in a future perspective. 2023 American Institute of Physics Inc.. All rights reserved. -
A LSTM based model for stock price analysis and prediction
The share market in India is exceedingly unpredictable and volatile, with an infinite range of factors regulating the share market's orientations and tendencies; hence, forecasting the upswing and downturn is a difficult procedure. Because of several essential aspects, the principles of share market have always been unclear for shareholders. This study aims to significantly reduce the likelihood of analysis and forecasting with Long Short-term Memory (LSTM) model approach that is both resilient yet easy is still suggested. LSTM is a complete Learning Model that is a Predictive Method. Conversely, advancements in technology have opened the way for more efficient and precise share market forecasting in current times. Using the provided historical data sets, the results showed that the LSTM model has considerable potential for forecasting. 2023 Author(s). -
Artificial Intelligence Revolution in Supply Chain Management
Artificial Intelligence (AI) is a buzzword everywhere in every domain, as it is an emerging technology in all business sectors. It is essential for achieving productivity, business benefits, less human efforts in the required business sectors instead of a large workforce and many more artificial intelligence applications that are scaling up with large scale business sectors. AI capacity to identify the trade patterns, the study's business occurrence, and analyze the information. AI is necessary for today's life and as well as for upcoming generations. Artificial intelligence helps to resolve the most complex problems and difficult situations where humans have not achieved so far, as it is the artificial brainpower of humans. We have seen technological changes happening faster and progressively by AI. The supply chain vastly gained from interest and investments in AI. The digital supply chain initiation, a shift in manufacturing is up and running. Advantageous supply chain management is essential in business sectors, customers, and governments. A combination of Artificial intelligence and supply chain management is put together in making decisions. This article will discuss the overview of AI advancements in supply chain management end-to-end processes. We also reviewed the supply chain operations using AI. 2023 American Institute of Physics Inc.. All rights reserved. -
Comparative Analysis Study of 43-point and 27-point Buyoff Stations for Stressed Mirror Polishing (SMP) Metrology
As a collaborative effort within the Thirty Meter Telescope (TMT) project, India is committed to supplying 84 polished segments for the primary mirror, employing the innovative Stressed Mirror Polishing (SMP) technology obtained from Coherent Inc., USA. SMP allows for the efficient polishing of highly aspheric non-axisymmetrical glass blanks at an accelerated rate. India-TMT (I-TMT) successfully applied SMP to qualify three glass roundels at Coherent's facility in Richmond, CA. The study focuses on a comparative analysis of Buyoff Stations (BOS) used in the SMP process. It contrasts results from the 43-point hydraulic-based BOS at Coherent with simulated outcomes from the 27-point whiffletree-based BOS at I-TMT. This analysis assesses efficacy and performance differences between the two BOS configurations, involving a comprehensive examination of a 1520mm diameter polished glass roundel. The study integrates Finite Element Method (FEM) simulations with experimental data, providing insights into the efficiency of the respective BOS setups. 2024 SPIE. -
Design, development, and analysis of segment support system for TMT primary mirror
The Thirty Meter Telescope (TMT) adopts a recently developed technology known as Stressed Mirror Polishing for the polishing of its 492 mirror segments. In this process, first the meniscus type spherical shape glass blanks are converted in to a desired aspheric shape by the application of forces around the edges using warping arms followed by spherical polishing in the stressed condition. After that, the blank edges will be cut in to its final hexagonal shape. These warping as well as the hex cutting process generate significant stress within the glass which in later stage, will cause the propagation of micro cracks and results in blank breakage. So prior and after the hex cutting process, it is essential to ensure that the glass blanks are free from stress accumulation. Hence the glass blanks need to be stress relieved before the hex cutting process. To achieve this stress relaxation, the glass blanks need to be kept over a platform or a support system which will provide a zero gravity condition for a time period of at least 48 hours. As a part of this, we designed, developed and analyzed a whiffletree based support system which will equally distribute the entire mirror blank mass into three points which are equally separated by 120 from each other and thus balance itself as if it is in a floating condition. This support system which additionally gives optimized support for the glass blank which in turn minimizes the surface deformation due to its self weight sagging. This paper also discusses the positional sensitivity, reaction force sensitivity and alignment sensitivity analyses which are essential to obtain the tolerance values in the fabrication point of view. 2020 SPIE. -
Distance based properties of the semi splitting block graph of graph
The bounds on the radius and diameter of the semi splitting block graph (SB(G)) of graphs are investigated. The diametral paths and self-centeredness of semi splitting block graph of any connected graph are analyzed. The graphs where the diameter of G and SB(G) are the same are characterized and the number of blocks in the diametral path of such graphs is analyzed. 2023 Author(s). -
Digital Watermarking Techniques for Secure Image Distribution
In the contemporary era of digital advancements, it is of utmost importance to prioritize the establishment of robust security measures and traceability protocols for photos. This necessity arises from the inherent risk associated with the effortless diffusion of unlicensed information. Digital watermarking, which implants hidden data into digital photographs to verify their validity, is frequently used. This level emphasizes the need of safe photo distribution, digital platform problems, and unauthorized reproductions. The purpose of this research is to explain digital watermarking fundamentals. It emphasizes verification, IP protection, and digital watermarking monitoring. This research compares spatial and frequency domain watermarking approaches. Direct pixel manipulation in spatial domain techniques is vulnerable to attacks. Integrating watermarks with transform domains like Discrete Cosine Transform improves robustness in frequency domain techniques. The study also studies adaptive watermarking, which adjusts the watermark to the image's content to balance visibility and durability. The purpose of this research is to explore watermark identification methods. These methods use blind and non-blind watermarking. We discuss the security risks that might compromise watermarked photographs and the ways to reduce their likelihood. 2024 IEEE. -
Hybrid Model Using Interacted-ARIMA andANN Models forEfficient Forecasting
When two models applied to the same dataset produce two different sets of forecasts, it is a good practice to combine the forecasts rather than using the better one and discarding the other. Alternatively, the models can also be combined to have a hybrid model to obtain better forecasts than the individual forecasts. In this paper, an efficient hybrid model with interacted ARIMA (INTARIMA) and ANN models is proposed for forecasting. Whenever interactions among the lagged variables exist, the INTARIMA model performs better than the traditional ARIMA model. This is validated through simulation studies. The proposed hybrid model combines forecasts obtained through the INTARIMA model from the dataset, and those through the ANN model from the residuals of INTARIMA, and produces better forecasts than the individual models. The quality of the forecasts is evaluated using three error metrics viz., Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Empirical results from the application of the proposed model on the real dataset - lynx - suggest that the proposed hybrid model gives superior forecasts than either of the individual models when applied separately. The methodology is replicable to any dataset having interactions among the lagged variables.. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
5G Technology Empowering Wireless Technology
Wireless Communication is the means of transferring data from one point to another without the use of any wired means. With reference to wireless communication, wireless sensor Networks (WSN) have also developed in recent times. It can be referred as an infrastructure-less system of wireless devices which can gather and exchange information with the help of a wireless link. The information which is gathered is sent respectively to the base stations and sinks for further developments. Recently, the 5G generation network, the latest Wireless Communication Network operates at a higher frequency range than its predecessor. In this paper, a detailed analysis on the 5G generation cellular network, which is expected to be a key instrument of wireless technologies in the near future is outlined. Also a comparative analysis of different kinds of networks in context to wireless scenario is discussed. It was found that 5G provides the best outcome in terms of high speed and network spectrum bandwidth. 2023 IEEE. -
Load balancing with availability checker and load reporters (LB-ACLRs) for improved performance in distributed systems
Distributed system has quite a lot of servers to attain increased availability of service and for fault tolerance. Balancing the load among these servers is an important task to achieve better performance. There are various hardware and software based load balancing solutions available. However there is always an overhead on Servers and the Load Balancer while communicating with each other and sharing their availability and the current load status information. Load balancer is always busy in listening to clients' request and redirecting them. It also needs to collect the servers' availability status frequently, to keep itself up-to-date. Servers are busy in not only providing service to clients but also sharing their current load information with load balancing algorithms. In this paper we have proposed and discussed the concept and system model for software based load balancer along with Availability-Checker and Load Reporters (LB-ACLRs) which reduces the overhead on server and the load balancer. We have also described the architectural components with their roles and responsibilities. We have presented a detailed analysis to show how our proposed Availability Checker significantly increases the performance of the system. 2014 IEEE. -
A Secure Deep Q-Reinforcement Learning Framework for Network Intrusion Detection in IoT-Fog Systems
IoT-Fog system security depends on intrusion detection system (IDS) since the growing number of Internet-of-Things (IoT) devices has increased the attack surface for cyber threats. The dynamic nature of cyberattacks often makes it difficult for traditional IDS techniques to stay up to date. Because it can adapt to changing threat landscapes, deep Q-reinforcement learning (DQRL) has become a potential technique for ID in IoT-Fog situations. In this paper, an IDS system for IoT-Fog networks based on DQRL is proposed. The suggested solution makes use of fog nodes' distributed computing power to provide real-time IDS with excellent accuracy and minimal latency. With feedback from the network environment, the DQRL agent learns to recognize and categorize network traffic patterns as either normal or intrusive. Adaptive exploration techniques, effective reward functions, and deep neural networks for feature extraction are adopted by the system to improve predictive performance. The evaluation findings show that, in terms of detection accuracy, precision, recall and f-measure, the proposed DQRL provides flexibility to changing threat patterns as compared to conventional IDS techniques. A vast array of cyberattacks, such as malware infections, denial-of-service (DoS) attacks, and command-and-control communications, are successfully recognized and categorized by the system. It is possible that the suggested solution will be crucial in safeguarding IoT-Fog networks and preventing cyberattacks 2024 IEEE. -
Experimental Augmentation of Heat Transfer in a Shell and Tube Heat Exchanger using Twisted Tape with baffles and hiTrain Wire Matrix Inserts - A Comparative Study
Heat transfer, a mere process of exchange of heat due to a temperature gradient, plays a vital role in industries and domestic applications. Among all the heat exchangers, Shell and Tube Exchanger are used predominantly due to their compact and robust design. For a given design to increase the heat transfer characteristics needs a research investigation. Among all augmentation techniques, a passive method found widely used as it avoids mechanical modification of the existing heat exchanger and addresses only on flow geometry. Twisted tape inserts are extensively used to change the flow geometry of fluid on the tube side. The present research work intended on utilising twisted tape, twisted tape with baffles and hiTrain wire matrix inserts. Experimental investigation reveals that inserts efficiently disturb the tube side fluid flow, in turn, increases pressure drop which increases the fluid wall shear and hence enhances the substantial increase in tube side heat transfer rate. At lower Reynolds number twisted tape with baffles has comparatively higher heat transfer coefficient, and at higher Reynolds, number hiTrain wire has comparatively higher heat transfer coefficient. Friction factor decreases linearly from twisted tape with baffles to hiTrain wire matrix as Reynolds number increases. Published under licence by IOP Publishing Ltd. -
Cognitive outcomes prediction in children using machine learning and big data analytics
This study explores the potential of machine learning (ML) and big data analytics in predicting cognitive outcomes in children, aiming to enhance early identification and intervention strategies. Leveraging a diverse dataset comprising neurocognitive assessments, genetic markers, socio-economic factors, and environmental variables, our research employs advanced ML algorithms to develop predictive models. The interdisciplinary approach integrates neuroscience, psychology, and data science to discern patterns and correlations within the expansive dataset. The study emphasizes the importance of early cognitive assessment for optimal child development and academic success. By harnessing the power of big data, our models seek to uncover nuanced relationships that traditional methodologies may overlook. Preliminary results indicate promising accuracy in predicting cognitive outcomes, offering a valuable tool for educators, healthcare professionals, and policymakers. Additionally, the model's interpretability allows for a deeper understanding of the factors influencing cognitive development. Ethical considerations, privacy safeguards, and data governance are integral components of this research, ensuring responsible use of sensitive information. The implications of this study extend beyond academia, with the potential to inform educational policies, personalized learning strategies, and targeted interventions for at-risk populations. As technological advancements continue, the integration of ML and big data analytics in predicting cognitive outcomes heralds a new era in pediatric research, promoting proactive approaches to support children's cognitive well-being. 2024 IEEE. -
Improved File Security System Using Multiple Image Steganography
Steganography is the process of hiding a secret message within an ordinary message extracting it at its destination. Image steganography is one of the most common and secure forms of steganography available today. Traditional steganography techniques use a single cover image to embed the secret data which has few security shortcomings. Therefore, batch steganography has been adopted which stores data on multiple images. In this paper, a novel approach is proposed for slicing the secret data and storing it on multiple cover images. In addition, retrieval of this secret data from the cover images on the destination side has also been discussed. The data slicing ensures secure transmission of the vital data making it merely impossible for the intruder to decrypt the data without the encrypting details. 2019 IEEE. -
Football Player Substitution Analysis using NLP and Survival Analysis
Football player substitution is extremely significant in situations where the team is down by goals or attempting to retain a lead that can add value to the team's performance. However, substituting players based on their prior performance would not assist the squad in making good decisions. In one of the papers, they used an inverse gaussian hazard model to determine the survival rate of players. However, the main issue arises when players do not give their all due to their mental state, which plays a critical role during the game. Furthermore, most of the research papers relied solely on past performance of players and various analyses, which was insufficient. This study discovered that the player's mindset should be mentally stable and competitive which is also very crucial during the match by reading various research articles. Hence, this study proposes a framework which comprises of two models, namely Survival Analysis (Kaplan-Meier Fitter) and Natural Language Processing (Sentimental Analysis). Sentimental Analysis would hel p in determining a player's mindset before the match and Kaplan-Meier Fitter is used to find out the survival rate of player's performance based on several factors like goal scored, passing accuracy etc. which would allow the team to make better informed decisions. Comparison of these two models would yield the best results for substitute players on the bench on the basis of their past performance and their mental health which will allow them to make team management to make better judgments. 2023 IEEE. -
A Mathematical Correlation of Compressive Strength Among Silica, Alumina and Calcia Present in Composite Red Mud and Iron Ore Tailingbricks
Waste Red Mud generated from bauxite beneficiation in aluminium industry contains sodium oxide in minor amount along with silica and alumina in significant quantities. Waste iron ore tailings from beneficiation of iron ore in steel industry contain silica and alumina in significant quantities. A combination of both these materials in different amounts along with GGBS and lime addition resulted in complex alkali-activated reaction products consisting of (Si/Al), (Ca/Si) and (Ca/(Si+Al)) complexes which influence compressive strength of the test samples on curing for extended time periods at room temperature. Individual correlation coefficients of these complexes with compressive strength yielded high values with (Si/Al) and (Ca/Si+Al) ratios (0.92 and 0.96, respectively) while showing a poor correlation coefficient with (Ca/Si) ratio (0.88). A direct regression analysis between compressive strength and (Si/Al) ratio and (Ca/Si+Al) ratio indicated negative values with (Si/Al) ratios but positive values with (Ca/ (Si+Al)) ratios. It is therefore concluded that the addition of lime and GGBS (contributed from both GGBS and lime addition) resulted in Ca-Si-Al complex formations which are responsible for improved compressive strength of the samples. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.