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An ICT-integrated Modular Training Program Enhancing the Digital Research Skills of Research Scholars
The teaching profession in higher education demands strong research skills, and with rapid technological advancements, university teaching professionals must familiarize themselves with digital research skills. Thus, university teachers and PhD research scholars across the globe are eager to develop their digital research skills to enhance their work efficiency. Acquiring digital research skills on the job or during the PhD program has proven to be challenging. These skills assist higher education professionals in various ways, such as supervising doctoral students, conducting research, working on research projects, and publishing research articles. Thus, the present study attempted to provide ICT-integrated modular training (MT) to facilitate the higher education teaching faculty and PhD scholars with digital research skills. The study employed a repeated cross-sectional research design and measured the effectiveness of the MT through a single group pre and post-test design. Researchers conducted three modular training sessions annually on digital research skills over five consecutive years. In total, 300 scholars attended the training and participated in the pre-test, post-test, and satisfaction survey. Findings from paired sample t-tests (t-value varied between 4.117 to 7.525, p < 0.05) revealed that modular training has been significantly effective with a large effect size (d > 0.8). Furthermore, the satisfaction survey revealed a high degree of satisfaction among participants. Future research may explore ways to strengthen the technological and pedagogical content knowledge of modular training programs in developing digital research skills. Italian e-Learning Association. -
An ideal MBA syllabus model -An Indian perspective /
Sumedha Journal of Management, Vol.8, Issue 1, pp.155-173, ISSN No: 2277-6753. -
An Image Quality Selection and Effective Denoising on Retinal Images Using Hybrid Approaches
Retinal image analysis has remained an essential topic of research in the last decades. Several algorithms and techniques have been developed for the analysis of retinal images. Most of these techniques use benchmark retinal image datasets to evaluate performance without first exploring the quality of the retinal image. Hence, the performance metrics evaluated by these approaches are uncertain. In this paper, the quality of the images is selected by utilizing the hybrid naturalness image quality evaluator and the perception-based image quality evaluator (hybrid NIQE-PIQE) approach. Here, the raw input image quality score is evaluated using the Hybrid NIQE-PIQE approach. Based on the quality score value, the deep learning convolutional neural network (DCNN) categorizes the images into low quality, medium quality and high quality images. Then the selected quality images are again pre-processed to remove the noise present in the images. The individual green channel (G-channel) is extracted from the selected quality RGB images for noise filtering. Moreover, hybrid modified histogram equalization and homomorphic filtering (Hybrid G-MHE-HF) are utilized for enhanced noise filtering. The implementation of proposed scheme is implemented on MATLAB 2021a. The performance of the implemented method is compared with the other approaches to the accuracy, sensitivity, specificity, precision and F-score on DRIMDB and DRIVE datasets. The proposed schemes accuracy is 0.9774, sensitivity is 0.9562, precision is 0.99, specificity is 0.99, and F-measure is 0.9776 on the DRIMDB dataset, respectively. 2023 Baqiyatallah University of Medical Sciences. All rights reserved. -
An impact of AI and client acquisition strategies in real capital ventures
In the contemporary business environment, marked by rapid changes, client acquisition stands out as a pivotal factor for companies aiming at sustained growth, particularly in sectors such as finance and real estate. The ability to attract and retain clients is not only a measure of a company"s current success but also a fundamental driver for its future viability. This study focuses on Real Capital Ventures LLP, a company operating at the intersection of finance and real estate, aiming to unravel the intricacies of its client acquisition strategies. The overarching goal is to conduct an exhaustive examination of the current approaches employed by the firm and provide nuanced recommendations for refinement. By doing so, the study aspires to contribute to the enhancement of the effectiveness of Real Capital Ventures LLP"s client acquisition, ensuring its continued success in a fiercely competitive market. 2024 by IGI Global. All rights reserved. -
An impact of antibacterial efficacy of metal oxide nanoparticles: A promise for future
Since its advent, nanotechnology has seen applications in diverse fields including the biomedical domain. Many metal oxide nanoparticles (NP) have shown good antimicrobial properties. Their small size and ability to inhibit a broad spectrum of bacterial species have made them promising candidates in our search of antimicrobial agents. Since, they don't target a specific protein in a microbial species, the chances of the microbe gaining resistance is also less. This is indeed a great advantage over antibiotics, most of which target specific proteins of bacteria. Most of the pathogenic bacteria have gained resistance against commonly used antibiotics. In this context there is a dire need of antimicrobials with a broader spectrum of action. Metal oxide nanoparticles like: ZnO NPs and CuO NPs easily fit into this category. They can suppress microbial growth by reactive oxygen species production, thereby causing damage to biomolecules, cation release, interactions with membrane and ATP depletion. One of the challenges with metal oxide NP is their cytotoxicity. Scientists are in search of degradable and less toxic metal oxide NP. The current review focuses on the relative advantages and limitations of various metal oxides NPs in inhibiting microbial growth. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
An Improved AI-Based Low Latency Data Transmission in 5G Communication Systems
This paper devised an advanced artificial intelligence (AI) solution for ultra-low latency data transmission in 5G networks. With increasing data rates and lower latency required in 5G networks, efficient methods for transmitting the maximum amount of data are necessary. We have developed an approach that uses AI algorithms so that data transmission can be done more optimally and help reduce latency, providing better overall performance. Our approach consists of several steps, in which we predict the traffic patterns using machine learning techniques in step 1 and allocate network resources accordingly. That helps reduce network congestion and speeds up data transmission. We also introduce deep learning algorithms to adjust the transmission parameters according to network conditions, reducing latency. We simulate our algorithm in 5G network scenarios to assess its performance. The comparison of the results shows that a very low latency was achieved for this design over the earlier methods. Our developed AI-based improved solution provides a potential key to low latency data transmission in 5G communication systems. Integrating AI methods makes the system not only perform better but also be able to adapt more easily when network conditions change. The next steps are to explore the improvements of algorithms and implement them practically in 5G networks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
An improved AI-driven Data Analytics model for Modern Healthcare Environment
AI-driven statistics analytics is a swiftly advancing and impactful era that is transforming the face of healthcare. By leveraging the energy of AI computing and gadget studying, healthcare organizations can speedy gain insights from their huge datasets, offering a greater comprehensive and personalized approach to hospital therapy and populace health management. This paper explores the advantages of AI-driven statistics analytics in healthcare settings, masking key benefits along with progressed analysis and treatment, better-affected person effects, and financial savings. Moreover, this paper addresses the main challenges associated with AI-pushed analytics and offers potential solutions to enhance accuracy and relevance. In the long run, statistics analytics powered by way of AI gives powerful opportunities to improve healthcare outcomes, and its use is expected to expand within the coming years. 2024 IEEE. -
An Improved Alternative Method of Imputation for Missing Data in Survey Sampling
In the present paper, a new and improved method of ratio type imputation and corresponding point estimator to estimate the finite population mean is proposed in case of missing data problem. It has been shown that this estimator utilizes the readily available auxiliary information efficiently and gives better results than the ratio and mean methods of imputation; furthermore, its efficiency is also compared with the regression method of imputation and some other imputation methods, discussed in this article, using four real data sets. A simulation study is carried out to verify theoretical outcomes, and suitable recommendations are made. 2022 NSP Natural Sciences Publishing Cor. -
An Improved and Efficient YOLOv4 Method for Object Detection in Video Streaming
As object detection has gained popularity in recent years, there are many object detection algorithms available in today's world. Yet the algorithm with better accuracy and better speed is considered vital for critical applications. Therefore, in this article, the use of the YOLOV4 object detection algorithm is combined with improved and efficient inference methods. The YOLOV4 state-of-the-art algorithm is 12% faster compared to its previous version, YOLOV3, and twice as faster compared to the EfficientDet algorithm in the Tesla V100 GPU. However, the algorithm has lacked performance on an average machine and on single-board machines like Jetson Nano and Jetson TX2. In this research, we examine the performance of inferencing in several frameworks and propose a framework that effectively uses hardware to optimize the network while consuming less than 30% of the hardware of other frameworks. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Improved Artificial Intelligence based Service Quality to Increase Customer Satisfaction and Customer Loyalty in Banking Sector
This study clarifies and determines how service quality affects customer loyalty and reliability. The support of quality in the open and private financial sphere and understanding of its connection to customer loyalty and conduct goal Utilizing an upgraded SERVQUAL (BANQUAL) tool with 26 items, the review was conducted among 802 bank customers. The social goal battery was used to estimate the clients' expected conduct. The expert used a seven-point Likert scale to assess the standard and saw service quality (implementation), as well as the social expectations of the clients. The most reliable tool to quantify the conceptualization of the differentiation score is the BANQUAL instrument. It is used to evaluate gaps in service between assumptions and perceptions of service quality. The SERVQUAL instrument is modified to make it suitable in the banking industry. Questions on parking at the bank, the variety of things and programmes available, and the banks' genuine efforts to address customer grievances are added to the instrument (Responsiveness). The writing audit was sufficiently compiled from many sources, reflecting both an Indian and foreign environment. The postulation included several hypotheses then examined using structural equation modelling. To meet the exploration goals, the views were tested using the products AMOS and SISS. The data were analysed using corroborative and explorative element research to confirm the BANQUAL instrument's dependability and legitimacy of the financial business execution and service quality aspects. The resulting CFA model value exhibits excellent psychometric qualities. Professional businesses and clients increasingly use artificial intelligence support specialists (AISA) for management. However, no measure measuring the support quality can fully capture the essential factors affecting AISA service quality. By developing a scale for evaluating the quality of AISA service, this study seeks to solve this deficiency(AISAQUAL). 2023 IEEE. -
An improved atom search optimization algorithm based on ranking strategy and sine cosine algorithm for epileptic seizure detection
Epilepsy is a serious neurological disorder that remains difficult to detect with high accuracy. Automated seizure detection using EEG signals has gained increasing attention, and optimization algorithms are often applied to improve system performance. Atom Search Optimization (ASO) has strong global search ability but frequently suffers from premature convergence and limited local search efficiency. To address these issues, this study proposes a hybrid algorithm that combines ASO with the SineCosine Algorithm (SCA) and a ranking strategy (RSHASOSCA). ASO provides effective global exploration, SCA enhances local exploitation, and the ranking strategy stabilizes convergence, together creating a more balanced and reliable search process. The method was evaluated on the CHB-MIT scalp EEG dataset. Features were extracted using Wavelet Packet Transform (WPT) and refined with the KruskalWallis test (p ? 0.001). Comparative experiments against twelve established optimization algorithms showed that the RSHASOSCA framework achieved superior performance. When applied with an SVM classifier, it reached 99.13% accuracy and an AUC of 1. These findings highlight the value of integrating ASO, SCA, and ranking strategy, and demonstrate the potential of the proposed framework for reliable and efficient seizure detection in clinical practice. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025. -
An Improved Combined Adaptive Outline for Contrast Enhancement of Blood Vessels
Appropriate vascular segmentation is dependent on effective picture pre-processing techniques that improve the contrast of the blood vessels, reduce noise, eliminate non-uniform illumination, highlight thin vessels, and retain background texture. These techniques are necessary for accurate vessel segmentation. Here, both the edge- and texture-smoothed data from the vessel probability map are used in the derivation of the adaptive optimal q-order in the G-L mask. The smooth information is not affected, the textures are maintained, and the contrast of the blood vessels is enhanced, thanks to the proposed filter. In addition to sharpening the focus on the vessels themselves, a Gaussian curve fitting is used to contrast stretch the entire image. Retinal fundus images processed with cerebral DSA are subjected to both qualitative and quantitative assessments of contrast enhancement. Quantitative performance indicators are tabulated and compared to other approaches to show how well this technique works for improving medical images everywhere. The suggested filter is easy to implement, flexible enough to adapt to different images, and effective at increasing both vessel contrast and overall image contrast. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
An improved compocasting technique for uniformly dispersed multi-walled carbon nanotube in AA2219 Alloy Melt
Technology transfer for economic bulk production is the greatest challenge of the era. Production of high strength lightweight materials with nanocarbon reinforcement has attained its importance among the researchers. Property enhancement with multi-walled carbon nanotube (MWCNT) reinforcement is reported by all researchers. But effective utilization of its property remains a challenge even though it is the strongest material in the world. Achieving homogeneous dispersion especially in molten metal is a complex task. To address the same, a new approach was tried which could trigger de-bundling and make a uniform dispersion. Various metallurgical and mechanical characterizations were done. Grain refinement and the structure were studied with an optical microscope, MWCNT dispersion and structural damage was studied using field emission scanning microscope, Phase change and reactions during casting was done with XRD scan. The method remarkably facilitated 23.7% and 69.75% improvement in hardness and ultimate compressive strength respectively with the addition of MWCNT. Faculty of Mechanical Engineering, Belgrade. -
An Improved Deep Learning Framework for Energy Management in Low-Energy Building Integrated Photovoltaics (LE-BIPV)
The possibility of incorporating photovoltaics (PV) as part of building design has opened a new approach to energy generation from sustainable resources. An effective method to facilitate the good operation of these systems would be efficient energy-level management. The existing Energy Management of LE-BIPV employs a conventional control strategy, which is inconvenient for operation and fails to properly deal with nonlinearity in the PV system. The proposed model aims to provide a new deep-learning framework for the energy management of LE-BIPV. The proposed neural network framework can learn the intricate relationships between PV generation and battery storage and enable accurate energy management predictions. This proposed deep learning framework can substantially upgrade the global energy control of building-integrated PV systems in low-energy buildings. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
An improved frequent pattern tree: the child structured frequent pattern tree CSFP-tree
Frequent itemsets are itemsets that occur frequently in a dataset. Frequent itemset mining extracts specific itemsets with supports higher than or equal to a minimum support threshold. Many mining methods have been proposed but Apriori and FP-growth are still regarded as two prominent algorithms. The performance of the frequent itemset mining depends on many factors; one of them is searching the nodes while constructing the tree. This paper introduces a new prefix-tree structure called child structured frequent pattern tree (CSFP-tree), an FP-tree attached with a child search subtree to each node. The experimental results reveal that the CSFP-tree is superior to the FP-tree and its new variations for any kind of datasets. 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. -
An Improved Image Up-Scaling Technique using Optimize Filter and Iterative Gradient Method
In numerous realtime applications, image upscaling often relies on several polynomial techniques to reduce computational complexity. However, in high-resolution (HR) images, such polynomial interpolation can lead to blurring artifacts due to edge degradation. Similarly, various edge-directed and learning-based systems can cause similar blurring effects in high-frequency images. To mitigate these issues, directional filtering is employed post corner averaging interpolation, involving two passes to complete the corner average process. The initial step in low-resolution (LR) picture interpolation involves corner pixel refinement after averaging interpolation. A directional filter is then applied to preserve the edges of the interpolated image. This process yields two distinct outputs: the base image and the detail image. Furthermore, an additional cuckoo-optimized filter is implemented on the base image, focusing on texture features and boundary edges to recover neighboring boundary edges. Additionally, a Laplacian filter is utilized to enhance intra-region information within the detailed image. To minimize reconstruction errors, an iterative gradient approach combines the optimally filtered image with the sharpened detail image, generating an enhanced HR image. Empirical data supports the effectiveness of the proposed algorithm, indicating superior performance compared to state-of-the-art methods in terms of both visual appeal and measured parameters. The proposed method's superiority is demonstrated experimentally across multiple image datasets, with higher PSNR, SSIM, and FSIM values indicating better image degradation reduction, improved edge preservation, and superior restoration capabilities, particularly when upscaling High-Frequency regions of images. 2023 IEEE. -
An improved LSTM based thermal prediction and control algorithm for battery management system in hybrid electric vehicles
Effective thermal management of lithium-ion batteries is critical for ensuring safety, longevity, and optimal performance in Hybrid Electric Vehicles (HEV). This research proposes an improved Long Short-Term Memory (LSTM) based thermal prediction and control algorithm for Battery Management Systems (BMS) to enhance temperature regulation accuracy and computational efficiency. The proposed model integrates an optimized LSTM network with attention mechanisms to capture long-term dependencies in thermal dynamics while reducing prediction latency. A multi-physics-based thermal model is employed to generate high-fidelity training data, accounting for electrochemical-thermal coupling effects. The algorithm incorporates adaptive learning rates and dropout regularization to mitigate overfitting and improve generalization under varying load conditions. A model predictive control framework is designed to leverage real-time LSTM predictions for proactive cooling strategy optimization, minimizing energy consumption while maintaining safe operating temperatures. The proposed model reached RMSE of Heat generation rate of 1.08 W/mA3, Entropy coefficient Error of 0.024 mV/K, Thermal conductivity of 0.626 w/mK, Latency of 28 ms, Cooling energy Consumption of 314.61 kWh and Temperature deviation of 3.34 AC. The proposed solution offers a computationally efficient, scalable framework for next-generation BMS, enhancing battery reliability and vehicle efficiency. 2025 Elsevier Ltd -
An Improved Security Framework for Vulnerable Intrusions in High Dense Fog Networks
This work presents an advanced security model to prevent vulnerable invasions through highly dense fog networks. Advantages: One of the strengths that fog computing has revealed is how beneficial its native design can be, but on the other hand, one critical aspect to keep in mind is that it brings several fresh security subjects because of its extremely dynamic and decentralized nature. The framework uses anomaly detection and secure communication protocols to identify the potential intrusion into a network. This also includes clustering fog nodes and more frequent network updates to improve security across the whole network. We evaluate our framework by implementing it in simulation experiments. We show that communication among existing trusted peers can be enhanced, whereas non-trusted sources entering the network cannot conduct attacks. Generally, this framework provides an attractive approach to improve fog networks security and renders them more attack-resistant. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
An improved web caching system with locally normalized user intervals
Caching is one of the most promising areas in the field of future internet architecture like Information-centric Networking, Software Defined Networking, and IoT. In Web caching, most of the web content is readily available across the network, even if the webserver is not reachable. Several existing traditional caching methods and cache replacement strategies are evaluated based on the metrics like hit ratio and byte hit Ratio. However, these metrics have not been improved over the period because of the traditional caching policies. So, in this paper, we have used an intelligent function like locally normalized intervals of page visit, website duration, users' interest between user groups is proposed. These intervals are combined with multiple distance metrics like Manhattan, squared Euclidean, and 3-,4-,5-norm Minkowski. In order to obtain significant common user navigation patterns, the clustering relation between the users using different intervals and distances is thoroughly analyzed. These patterns are successfully coupled with greedy web cache replacement strategies to improve the efficiency of the proposed web cache system. Particularly for improving the caching metrics more, we used an AI-based intelligent approach like Random Forest classifier to boost the prefetch buffer performance and achieves the maximum hit rate of 0.89, 0.90, and byte hit rate of 0.87, 0.89 for Greedy Dual Size Frequency and Weighted Greedy Dual Size Frequency algorithms, respectively. Our experiments show good hit/byte hit rates than the frequently used algorithms like least recently used and least frequently used. 2013 IEEE. -
An improvised grid resource allocation and classfication through regression
The resource allocation is one of the important mechanisms of grid computing, which helps to assign the available resources very efficiently. The one of the issue of grid computing is fixing the target nodes during the grid job execution. In existing method, resource monitored data are collected from grid then jobs are allocated to the resources based on available data, through regression algorithm. In this method total execution time of an application and run time of jobs should be high. The proposed method mitigate running time by classify the resources in the data collected from grid based on dwell time using novel classification algorithm. It reduces the jobs run time and fit the best available resources to the jobs in the computational grid. 2017 IEEE.