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Improving Groundwater Forecasting Accuracy with a Hybrid ARIMA-XGBoost Approach.
In addressing the critical challenge of accurate groundwater level prediction, this study explores the comparative performance of various machine learning models. We implement a novel hybrid model combining ARIMA and Extreme Gradient Boosting (XGB) for the prediction of groundwater levels, and compare it against traditional models including ARIMA, XGBoost, LightGBM, Random Forest, and Decision Trees. Traditional approaches often rely on single models; however, our research seeks to delve into the intricacies of hybrid model architectures. Combining the strengths of ARIMA and XGB, we aim to build a highly accurate and efficient groundwater level prediction system. Comprehensive evaluations were conducted using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), The future scope of machine learning in water resource management includes integrating such models with real-time monitoring systems and expanding their applications to diverse environmental conditions and regions. 2024 IEEE. -
Improving Image Clarity with Artificial Intelligence-Powered Super-Resolution Methods
Super-resolution has advanced significantly in the last 20years, particularly with the application of deep learning methods. One of the most important image processing methods for boosting an image's resolution in computer vision is image super-resolution besides providing an extensive overview of the most recent developments in artificial intelligence and deep learning for single-image super-resolution. This study delves into the subject of image enhancement by investigating sophisticated AI-based super-resolution techniques. High-quality photographs have become more and more in demand in a variety of industries recently, including medical imaging, satellite imaging, entertainment, and surveillance. Pixilation reduction and detail preservation are two areas where traditional image enhancing techniques fall short. Artificial intelligence has demonstrated amazing promise in addressing these issues, especially with regard to Deep Learning models. The applications, benefits, and difficulties of modern super-resolution techniques are thoroughly examined in this work. We also suggest new approaches and push the limits of image enhancement by experimenting with state-of-the-art artificial intelligence algorithms. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Improving Indoor occupancy estimation using a hybrid CNN-LSTM approach
Indoor Air Quality (IAQ) monitoring has been a significant research domain in energy conservation. Many energy resources are required to maintain the IAQ using airconditioning or other ventilation systems. Currently, the research works highly optimize an on-demand driven energy usage depending on the occupant present inside the building. In the last decade, numerous research works have evolved for such an optimization by installing sensors and predicting occupants using machine learning techniques. This research fails to deploy non-intrusive sensors and appropriate machine learning algorithms to predict the occupancy count. Advancement in neural network techniques termed deep learning has made significant performance in recognition and cognitive tasks. Thus, this paper proposes a hybrid deep learning model that stacks the convolutional neural network (CNN) and long short term memory (LSTM) to improve the prediction rate of the occupancy count. Experimentation has been carried out in real-time multivariate sensor data for the occupancy estimation and evaluated the performance in terms of accuracy, RMSE, MAPE, and coefficients of determinants. 2022 IEEE. -
Improving maternal health by predicting various pregnancy-related abnormalities using machine learning algorithms
Over the past few decades, artificial intelligence has been showing its high relevance and potential in a vast number of applications, particularly in the healthcare domain. Having a healthy pregnancy is one of the best ways to promote a healthy birth. Getting early and regular prenatal care improves the chances of a healthy pregnancy. Complications involved in the individual's pregnancy need to be predicted on time accurately. AI can help clinicians to make decisions by assisting them in decision-making. In this regard, the objective of this chapter is to provide a detailed survey of various pregnancy-related abnormalities; and to explore various machine learning algorithms to classify/predict pregnancy-related abnormalities with higher accuracy. A generic framework that focuses more on classifying various features into normal and abnormal, and to be monitored patients to provide support and care during an emergency. 2023 by IGI Global. All rights reserved. -
Improving organizational environmental performance through green training
It is necessary to equip employees with green abilities as well as to develop their dedication towards green behaviour in order to improve an organization's environmental performance. The purpose of this research is to evaluate the direct impact of green training on organizational environmental performance (OEP) and the mediating effect of organizational citizenship behaviour on the environment (OCBE). The study is based on responses from 107 employees of the IT sector in India. The findings suggest that green training has a significant positive impact on the organizational environmental performance and that the impact is strengthened by organizational citizenship behaviour towards the environment. The findings are of particular importance given the growing importance of sustainability in the organizational context. 2023, IGI Global. All rights reserved. -
Improving Organizational Sustainable Performance of Organizations Through Green Training
It is necessary to equip employees with green abilities as well as to develop their dedication towards green behaviour, in order to improve an organization's environmental performance. The purpose of this research is to evaluate the direct impact of green training on organizational environmental performance (OEP) and the mediating effect of organizational citizenship behaviour on the environment (OCBE). The study is based on responses from 107 employees of the IT sector in India. The findings suggest that green training has a significant positive impact on the organizational environmental performance, and that the impact is strengthened by organizational citizenship behaviour towards the environment. The findings are of particular importance given the growing importance of sustainability in the organizational context. 2023 IGI Global. All rights reserved. -
Improving Renewable Energy Operations in Smart Grids through Machine Learning
This paper reviews the work in the areas of machine learning's role in bolstering renewable energy within smart grids. As the global shift towards eco-friendly energy sources such as wind and solar gains momentum, the challenge lies in managing these unpredictable energy sources efficiently. Innovative learning techniques are emerging as potential solutions to these challenges, optimising the use and benefits of renewable energies. Furthermore, the landscape of energy distribution is evolving, with a growing emphasis on automated decision-making software. Central to this evolution is machine learning, with its applications spanning a range of sectors. These include enhancing energy efficiency, seamlessly integrating green energy sources, making sense of vast data sets within smart grids, forecasting energy consumption patterns, and fortifying the security of power systems. Through a comprehensive review of these areas, this paper highlights the potential of machine learning in paving the way for a greener, more efficient energy future. The Authors, published by EDP Sciences, 2024. -
Improving service quality and customer engagement with marketing intelligence
To succeed, businesses must keep up with the ever-changing technological landscape and constantly introduce new advancements. The rise of digitalization has wholly transformed how companies interact with their customers, presenting both opportunities and challenges. Marketing professionals are inundated with data and need guidance on leveraging it effectively to craft successful marketing strategies. Additionally, the ethical and privacy concerns surrounding the collection and use of customer data make the marketing landscape even more complex. Improving Service Quality and Customer Engagement With Marketing Intelligence is a groundbreaking book that offers a comprehensive solution to these challenges. This book is a must-read for marketing professionals, business owners, and students, providing a practical guide to navigating the digital age. It explores the impact of digitalization on marketing practices. It offers insights into customer behavior, equipping readers with the knowledge and skills needed to thrive in today's competitive market. The book's interdisciplinary approach integrates insights from marketing, technology, data science, and ethics, giving readers a holistic understanding of marketing intelligence. With its timely and practical approach, Improving Service Quality and Customer Engagement With Marketing Intelligence is a valuable resource for anyone seeking to enhance their marketing efforts in the digital age. It features best practices, case studies, and step-by-step guides, empowering readers to make informed decisions prioritizing customer satisfaction and engagement. Reading this book will help you stay ahead of the curve and drive success in today's dynamic marketing landscape by bridging the gap between academia and industry. 2024 by IGI Global. All rights reserved. -
Improving Signal Coverage in Millimeter-Wave Massive MIMO via Efficient Predefined-Time Adaptive Neural NetworkBased Beam Training
This paper proposes an advanced deep learning framework for efficient beam training in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To overcome the limitations of conventional beam training approaches such as high overhead, slow adaptation to dynamic environments, and poor scalability, an Improving Signal Coverage in Millimeter Wave Massive MIMO via Efficient Predefined Time Adaptive Neural Network based Beam Training (ISC-MMIMO-EPTANN-BT) model is proposed. The proposed model used deep neural network (DNN) to learn complicated nonlinearities in channel power leakage (CPL) and used an efficient predefined time adaptive neural network (EPTANN) to provide real-time responsiveness and temporal synchronism in beam training. The parameters of the model are also optimized using fire hawk optimization algorithm (FHOA) to get better convergence speed and signal coverage. The proposed technique is executed in MATLAB. The proposed approach attains better performance under successful rate by significantly less beam training overhead and also increases signal coverage based on simulation results. The proposed ISC-MMIMO-EPTANN-BT method attains 26.15%, 21.08%, and 33.75% higher successful rates and 16.32%, 28.94%, and 20.24% lower normalized mean square error compared with existing methods such as deep learning for beam training in millimeter wave massive MIMO schemes (BT-MMIMO-DNN), deep learning for combined feedback and channel prediction in large-scale MIMO systems (CNN-JCS-MMIMO), and triple-refined hybrid-field beam training in mmWave extremely large-scale MIMO (TR-FBT-MIMO), respectively. The ISC-MMIMO-EPTANN-BT technique reduced beam training overhead, enhanced signal coverage, and identified a promising candidate for successful beam training in mmWave massive MIMO schemes. 2025 John Wiley & Sons Ltd. -
Improving Speaker Gender Detection by Combining Pitch and SDC
Gender detection is helpful in various applications, such as speaker and emotion recognition, which helps with online learning, telecom caller identification, etc. This process is also used in speech analysis and initiating human-machine interaction. Gender detection is a complex process but an essential part of the digital world dealing with voice. The proposed approach is to detect gender from a speech by combining acoustic features like shifted delta cepstral (SDC) and pitch. The first step is preprocessing the speech sample to retrieve valid speech data. The second step is to calculate the pitch and SDC for each frame. The multifeature fusion method combines the speech features, and the XGBoost model is applied to detect gender. This approach results in accuracy rates of 99.44 and 99.37% with the help of RAVDESS and TIMIT datasets compared to the pre-defined methods. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Improving the Accuracy of Cardiovascular Disease Classification Using CardioAugmentNet Technique
Cardiovascular disease is the leading cause of death and mortality worldwide. Thus, early diagnosis of CVDs is crucial since the disease can be managed with optimal care. In the current study, we consider CardioAugmentNet, which is a CNN model augmented with data augmentation strategies for the classification of several cardiovascular pathologies in ECG images. A proposed method was designed to provide a robust algorithm for the detection of irregular heart rhythms, myocardial infarction and other cardiac diseases. The model is trained and tested on the dataset of ECG images from individuals with various prevalent cardiovascular diseases as well as normal hearts. Therefore, the CardioAugmentNet state-of-the-art model classifies different cardiac abnormalities with high accuracy, suggesting that it can be used in clinical practice. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Improving the Security of Video Embedding Using the CFP-SPE Method
With the amount of data being transferred on a daily basis, it is becoming increasingly dangerous to save data on the Internet in the face of intruders or hackers. This study paper is one of the most effective ways to transmit information in a secure and confidential manner. The authors previously disclosed a way for embedding a secret video inside a cover video in their prior work. The writers have implemented a number of techniques to incorporate the secret video. The current work improves on the existing approach by including encryption and decryption concepts into the video embedding process. The secret data for either a large or little amount of information is put on the cover video utilising the embedding technique. Our proposed method combines compression, encryption, decryption, and secret information embedding to provide a more secure data transfer. 2022 Karthick Panneerselvam et al. -
Improving Voltage Regulation in High-Power Solar Applications
This paper presents an advanced solar-powered isolated DC-DC converter optimized for high-power applications, with a focus on precise voltage regulation at the output stage. To mitigate high-voltage stresses typically encountered in single-stage DC-DC converters, a Lossless Active Clamp Flyback circuit is integrated, offering soft switching capabilities and regenerative energy features. The proposed topology is designed using low-voltage devices, enhancing overall system efficiency. A hardware prototype rated at 2 kW has been developed to empirically validate the circuit's performance. Additionally, a novel control algorithm is introduced to further optimize the converter's operational characteristics. The proposed converter is benchmarked against existing solutions, highlighting significant improvements in terms of component count, voltage handling, and energy regeneration. The results demonstrate superior efficiency and robustness, making the system highly suitable for high-power renewable energy applications. Through this innovative approach, the converter offers substantial gains in performance and operational feasibility, especially in scenarios demanding high power density and stringent efficiency standards. 2025 IEEE. -
Improvised hand layup fabrication of alkali treated jute epoxy composites: A comparative study of positive and vacuum-assisted compaction
There lie several benefits of using fiber composites which have increased the desire for using these materials in various higher-level applications. They have been widely used in automobile sector, aerospace, sports industry, medical field, and so on. This has created a demand for better manufacturing techniques with cost-effectiveness. This work has been focused on improvising the hand layup procedure. To enhance the properties of the samples prepared by this conventional method, surface treatment was incorporated. Woven jute fiber was chemically treated with KOH under various sizing conditions. Hand layup was carried out for the samples followed by applying pressure considering two different methods; vacuum- assisted compaction and positive compaction. The jute composites prepared by the positive compaction hand layup technique were found to be better than the vacuum-assisted or negative compaction composites for the same set of sizing samples. There is a maximum increase of 32.4% in the tensile strength of treated composites prepared by positive compaction in comparison to untreated samples. On the other hand, the values of all the treated samples showed a reduction in tensile strength with a maximum decrease of 50% than the untreated sample for the negative compaction technique. The Authors, published by EDP Sciences, 2026. -
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. -
Improvising data security measures using rajan transform
Data security has always been a concern with the use of a large amount of data in our day-to-day life. There are many methods suggested and presented to secure data during the stages of its preprocessing and post-processing. However, many of them are not following the process of Homomorphism. During the study of Fast Fourier transform (FFT), Hadamard transform (HT) and Rajan transform (RT), this research work encountered a method that uses the cyclic, dyadic and graphical inverse properties of data and encrypts them which makes them homomorphic. This paper is targeting to improvise the data security measures using Homomorphism-based Rajan Transform, a method, which can help in securing data while data processing. The proposed methodology works in such a way that the encrypted data is available for processing without decrypting data into the original form. The performance of the proposed method is described by the efficiency of the algorithm, key size, Block size, and no of rounds required to complete the encryption. It has been found, if we take 512 bits of input data to get 512-bit ciphertext, it takes 9 rounds and generates a 4608-bit key. 2021 Taylor's University. All rights reserved. -
Improvized machine learning model for extracting building footprints from collapsed images using high-resolution remote sensing images
We propose the development of a robust Enhanced U-Net framework for detecting building objects in images compromised by collapse. Traditional approaches often struggle to identify smaller buildings obstructed by taller structures, trees, or cloud coverage. However, recent advancements in machine learning algorithms present promising opportunities to address these challenges and improve the accuracy of building object detection and damage assessment. The proposed method employs the Siamese U-Net framework, enhanced with novel machine learning algorithms to overcome limitations in existing methodologies and increase the accuracy and reliability of damage assessment, even in complex scenarios. By using augmented satellite images during testing and lowering the building threshold value, our model can accurately predict damaged buildings and retrieve the footprints of smaller structures. The results of this research will advance image analysis techniques, especially in scenarios where collapsed structures pose significant identification and damage assessment challenges. This will be invaluable for government disaster management agencies, insurance companies, and other related organizations. 2025 World Scientific Publishing Company. -
Impulse noise recuperation from grayscale and medical images using supervised curve fitting linear regression and mean filter
Acquisition of images from electronic devices or Transmission of the image through any medium will cause an additional commotion. This study aims to investigate a framework for eliminating impulse noise from grayscale and medical images by utilizing linear regression and a mean filter. Linear regression is a supervised machine learning algorithm that computes the value of a dependent variable based on an independent variable. The value of the recuperating pixel is measured using a curve-fitting, direction-based linear regression approach or applying a mean filter to the noise-free pixels. The efficiency of the proposed technique experiments with benchmark test images and the images of the USC-SIPI and TESTIMAGES data sets. Peak signal-to-noise ratio (PSNR) and structural similarity index metrics (SSIM) are determined to prove the performance of the proposed method. The results, when compared with the seven recent state-of-the-art techniques, show the superiority of the proposed method in terms of visual quality and accuracy. The proposed model achieves an average PSNR value of 65.21dB and an SSIM value of 0.999 for the reconstruction of medical images, proving its accuracy and efficiency. The impulse noise restoration process helps the radiologist get a clear visual clarity of the medical image for diagnosis purposes. 2022 Institute of Advanced Engineering and Science. All rights reserved. -
In search for FPI trail in blue-chip Indian bourse during a phase of rehabilitation- An investigative study /
Asian Journal of Management, Vol.8, Issue 1, pp.107-111, ISSN: 0976-495X (Print), 2321-5763 (Online). -
In search of radio emission from exoplanets: GMRT observations of the binary system HD 41004
This paper reports Giant Metrewave Radio Telescope (GMRT) observations of the binary system HD 41004 that are among the deepest images ever obtained at 150 and 400 MHz in the search for radio emission from exoplanets. The HD 41004 binary system consists of a K1 V primary star and an M2 V secondary; both stars are host to a massive planet or brown dwarf. Analogous to planets in our Solar system that emit at radio wavelengths due to their strong magnetic fields, one or both of the planet or brown dwarf in the HD 41004 binary system are also thought to be sources of radio emission. Various models predict HD 41004Bb to have one of the largest expected flux densities at 150 MHz. The observations at 150 MHz cover almost the entire orbital period of HD 41004Bb, and about 20percent of the orbit is covered at 400 MHz. We do not detect radio emission, setting 3? limits of 1.8 mJy at 150 MHz and 0.12 mJy at 400 MHz. We also discuss some of the possible reasons why no radio emission was detected from the HD 41004 binary system. 2020 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.