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An hybrid technique for optimized clustering of EHR using binary particle swarm and constrained optimization for better performance in prediction of cardiovascular diseases
The significant adoption of Electronic Health Records (EHR) in healthcare has furnished large new quantities of information for statistical machine gaining knowledge of researchers in their efforts to version and expects affected person health popularity, doubtlessly permitting novel advances in treatment. Unsupervised system learning is the project of studying styles in facts where no labels are present. In comparison to loads of optimization problems, an most beneficial clustering end result does not exist. One-of-a-kind algorithms with special parameters produce special clusters, and none can be proved to be the quality answer given that numerous good walls of the records might be found. In the previous work, a novel Two-fold clustering technique which uses the Long Short Term Memory (LSTM) technique (TFC: LSTM) for the prediction of Cardiovascular Disease (CVD) was proposed. The proposed model was fond to be experimentally efficient; however when applied to large EHR data, the model suffered from optimization issues on the number of clusters formed and time complexity. In order to overcome the drawbacks, this paper proposes a hybrid method of optimization using the Binary Particle Swarm (BPS) and Constrained Optimization (CO) for optimizing the number of clusters produced and to increase the efficiency in terms of decreasing the time complexity. 2022 The Authors -
An iconic turn in philosophy
[No abstract available] -
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 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 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 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 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 Mechanism for Optimizing Fault Detection for Big Data Analytics Environment
In the applications of fault detection, the inputs are the data reflected from health state of the observed system. A major challenge to finding errors is the nonlinear relationship between the data. Big data has other drawbacks, and the volume and speed with which it is generated are reflected in the data streams themselves. In this paper, we develop a deep learning model that aims to provide fault detection in big data analytics engine. This investigation develops an approach for fault detection in large datasets using unsupervised learning. In this research, an unsupervised method of learning is developed specifically for the task of classifying large datasets. To discover regular textual patterns in large datasets, this research use data visualization methods. In this virtual environment, we employ an unsupervised learning method of machine learning that does not require human oversight. Instead, the system should be allowed some leeway to work and find things on its own. The unsupervised learning approach utilizes data that has not been tagged. In contrast to supervised learning, this approach can handle complex tasks. 2024, Ismail Saritas. All rights reserved. -
An in-Depth Analysis on the Cumulative Effect of Co and Sintering Temperatures on the Formaldehyde Sensing Attributes of NiO
In-depth studies are availing to explore and utilize the sensing attributes of p-type NiO nanostructures. However, the surface functionalization of NiO using Co for gas sensing along with varying temperature profile is a novel attempt till date. The research succeeded in synthesizing pure and substituted NiO via co-precipitation route and assessed the sensing capability of the samples by testing with 10 different target gases. The Co doped NiO sintered at 500C exhibited promising sensing performance within a concentration range of 1100ppm, notably achieving a high response of 7817 for 100ppm HCHO at room temperature. The proposed sensor demonstrated rapid response and recovery times (9s and 8s), and it successfully passed stability tests conducted over a 30-day period and repeatability tests consisting of eight cycles. The work paved a way to the implication of the prepared sensor as a breath analyzer to detect lung cancer due to its appreciable formaldehyde sensing characteristics. Graphical Abstract: (Figure presented.) The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
An in-silico pharmacophore-based molecular docking study to evaluate the inhibitory potentials of novel fungal triterpenoid Astrakurkurone analogues against a hypothetical mutated main protease of SARS-CoV-2 virus
Background: The main protease is an important structural protein of SARS-CoV-2, essential for its survivability inside a human host. Considering current vaccines' limitations and the absence of approved therapeutic targets, Mpro may be regarded as the potential candidate drug target. Novel fungal phytocompound Astrakurkurone may be studied as the potential Mpro inhibitor, considering its medicinal properties reported elsewhere. Methods: In silico molecular docking was performed with Astrakurkurone and its twenty pharmacophore-based analogues against the native Mpro protein. A hypothetical Mpro was also constructed with seven mutations and targeted by Astrakurkurone and its analogues. Furthermore, multiple parameters such as statistical analysis (Principal Component Analysis), pharmacophore alignment, and drug likeness evaluation were performed to understand the mechanism of protein-ligand molecular interaction. Finally, molecular dynamic simulation was done for the top-ranking ligands to validate the result. Result: We identified twenty Astrakurkurone analogues through pharmacophore screening methodology. Among these twenty compounds, two analogues namely, ZINC89341287 and ZINC12128321 showed the highest inhibitory potentials against native and our hypothetical mutant Mpro, respectively (?7.7 and ?7.3 kcal mol?1) when compared with the control drug Telaprevir (?5.9 and ?6.0 kcal mol?1). Finally, we observed that functional groups of ligands namely two aromatic and one acceptor groups were responsible for the residual interaction with the target proteins. The molecular dynamic simulation further revealed that these compounds could make a stable complex with their respective protein targets in the near-native physiological condition. Conclusion: To conclude, Astrakurkurone analogues ZINC89341287 and ZINC12128321 can be potential therapeutic agents against the highly infectious SARS-CoV-2 virus. 2022 Elsevier Ltd -
An incisive framework for attention deficit hyperactivity disorder discernment /
Current Trends in Technology and Science, Vol-3 (2), pp. 65-68. ISSN-2279-0535 -
An individualised psychosocial intervention program for persons with MND/ALS and their families in low resource settings
Motor Neuron Disease (MND) leads to significant psychosocial distress for the person with the illness and caregivers. Psychosocial factors influence the management and quality of life to a significant degree. Objective: To develop individualised psychosocial intervention program for people with MND and their families in India. Methods: People with MND and healthcare staff were constructively involved in co-designing the intervention program in four phases adapted from the MRC framework: 1. A detailed need assessment phase where 30 participants shared their perceptions of psychosocial needs 2. Developing the intervention module (synthesis of narrative review, identified needs); 3. Feasibility testing of the intervention program among seven participants; 4. Feedback from participants on the feasibility (acceptance, practicality adaptation). The study adopted an exploratory research design. Results: Intervention program of nine sessions, addressing psychosocial challenges through the different stages of progression of the illness and ways to handle the challenges, specific to the low resource settings, was developed and was found to be feasible. People with MND and families who participated in the feasibility study shared the perceived benefit through feedback interviews. Conclusion: MND has changing needs and challenges. Intervention programme was found to be feasible to be implemented among larger group to establish efficacy. The Author(s) 2022. -
An Insight into Photophysical Investigation of (E)-2-Fluoro-N-(1-(4-Nitrophenyl)Ethylidene)Benzohydrazide through Solvatochromism Approaches and Computational Studies
A fluoro-based Schiff base (E)-2-fluoro-N?-(1-(4-nitrophenyl)ethylidene)benzohydrazide (FNEB) has been synthesized from condensation of 2-fluorobenzohydrazide and 4?-nitroacetophenone catalyzed by glacial acetic acid with ethanol as the solvent. The dipole moment of FNEB in both the electronic states were found using different solvatochromic approaches such as Lippert-Mataga, Bakhshiev, Kawski-Chamma-Viallet, Reichardt and Bilot-Kawski. The experimental ground state dipole moment of FNEB was calculated using Guggenheim-Debye method and theoretical ground state dipole moment using Bilot-Kawski solvatochromic approach. The solvatochromic behavior of the Schiff base in different solvents was studied using absorption and emission spectra. Catalan and Kamlet-Abboud-Taft parameters were used from the multiple linear regression (MLR) analysis in order to study the solute-solvent interaction. The dipole moments were also calculated using Time Dependent-Density Functional Theory (TD-DFT). The chemical stability of FNEB was determined using computational and Cyclic Voltammetry by the use of obtained energy gap between the frontier orbitals. Using the frontier orbitals energy gap, global reactivity parameters were computed. Further, Light Harvesting efficiency was determined to comprehend the photovoltaic property of the Schiff base. 2019, Springer Science+Business Media, LLC, part of Springer Nature. -
An integrated framework for digitalization of humanitarian supply chains in post COVID-19 era
Digital Supply Chains (DSCs) are transforming industries across various domains. Digitalization can improve coordination, increase data collection and retention capacities, enhance funding mechanisms, and improve operational performance and resource utilization. However, DSC adoption is constrained by lack of funding, operational complexities, infrastructure issues, etc. Thus, the need emerges to explore the digitalization of the Humanitarian Supply Chain (HSC) and provide solutions that can ease the adoption of DSC. In this study, a framework is created to facilitate the digitalization process of HSC in post COVID-19 era. Nineteen related drivers are identified with the potential to digitalize the HSC. The drivers are identified from the previous literature and finalized with the assistance of HSC stakeholders. A Principal Component Analysis is carried out to discover the most pertinent drivers from the identified list of drivers. A Kappa analysis is adopted to perfect the priority map of the digitalization drivers. Further, the neutrosophic DEMATEL methodology is adopted to prioritize the potential drivers and find their dependency on each other. The results from the study indicate that the most influential drivers fall under the operational and technological categories. However, the social drivers have the potential to play a significant contribution in an effort to HSC digitalization. In addition, the study presents strategies for enhancing funds collection and data management using emerging technologies. These strategies can assist HSC decision-makers in formulating relevant policies and strategic interventions. 2023 Elsevier Ltd -
An integrated model to predict students online learning behavior in emerging economies: a hybrid SEMANN approach
Purpose: The online learning environment is a function of dynamic market forces constantly restructuring the e-learning landscapes complete ecosystemcape. This study aims to propose an e-learning framework by integrating the Technology Acceptance Model (TAM) and Theory of Planned Behaviour (TPB) to predict students Online Learning Readiness and Behaviour. Design/methodology/approach: A structured questionnaire was used to collect data from 406 students through a survey. The data were analysed using two-stage structural equation modelling and artificial neural network (ANN). Findings: The studys results revealed that perceived ubiquity (PUB) positively influences perceived ease of use, usefulness and attitude. Similarly, perceived mobility significantly influences perceived ease of use and attitude. Furthermore, attitude, subjective norms, perceived behavioural control and perceived usefulness significantly influence readiness to learn online, which further influences students online learning behaviour. The root-mean-square error (RMSE) values obtained from the ANN analysis indicate the models predictive solid accuracy. Originality/value: The study contributes to the existing literature by proposing an Online Learning Behaviour Model by integrating the TAM and the TPB frameworks in association with two additional constructs, PUB and Perceived Mobility. Secondly, this study proposes a unique triangulation framework of recommendations for learners, educators and policymakers. 2024, Emerald Publishing Limited. -
An Integrated Reinforcement DQNN Algorithm to Detect Crime Anomaly Objects in Smart Cities
In olden days it is difficult to identify the unsusceptible forces happening in the society but with the advancement of smart devices, government has started constructing smart cities with the help of IoT devices, to capture the susceptible events happening in and around the surroundings to reduce the crime rate. But, unfortunately hackers or criminals are accessing these devices to protect themselves by remotely stopping these devices. So, the society need strong security environment, this can be achieved with the usage of reinforcement algorithms, which can detect the anomaly activities. The main reason for choosing the reinforcement algorithms is it efficiently handles a sequence of decisions based on the input captured from the videos. In the proposed system, the major objective is defined as minimum identification time from each frame by defining if then decision rules. It is a sort of autonomous system, where the system tries to learn from the penalties posed on it during the training phase. The proposed system has obtained an accuracy of 98.34% and the time to encrypt the attributes is also less. 2021. All Rights Reserved. -
An Integrated Segmentation Techniques for Myocardial Ischemia
Abstract: Myocardial Ischemia segmentation is a challenging task for basic and translational research on cardiovascular, as it provides ultimately realistic in heart muscle model. The main objective of the research work is to find an efficient segmentation technique for the myocardial ischemia based on the myocardial infarcted MRI data set for the accurate classification of scar volume. The paper will give an insight about the segmentation technique based on myocardial ischemia and discusses essential cellular components. The paper provides an integrated approach which comprises of fuzzy c-means and morphological operations along with median filtering enhancement technique help in detecting the myocardial ischemia. The developed model is tested with 2D and 3D enhanced myocardial ischemia MRI and also with normal heart. The purpose of segmentation in myocardial ischemia is to identify the scar region in the heart. The integrated model is evaluated based on statistical measures and validated based on manual segmentation done by clinical expert. The scar classification is done based on the myocardial ischemia segmentation which leads to better prediction of arrhythmia in heart patient. The integrated model is considered as one of the best model for segmenting myocardial ischemia. 2020, Pleiades Publishing, Ltd.