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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-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 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 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 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. -
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 of Collision Avoidance Path Planning Algorithms in Autonomous Vehicles
Path planning is a way to define the motion of an autonomous surface vehicle (ASV) in any existing obstacle environment to enable the vehicle's movement by setting directions to avoid that can react to the obstacles in the vehicle's path. A good, planned path perceives the environment to the extent of uncertainty and tries to build or adapt its change in the path of motion. Efficient path planning algorithms are needed to alleviate deficiencies, that are to be modified using the deterministic path that leads the ASV to reach a goal or a desired location while finding optimal solution has become a challenge in the field of optimization along with a collision-free path, making path planning a critical thinker. The traditional algorithms have a lot of training and computation, making it difficult in a realistic environment. This review paper explores the different techniques available in path planning and collision avoidance of ASV in a dynamic environment. The objective of good path planning and collision avoidance for a dynamic environment is compared effectively with the existing obstacles movement of different vehicles. Different path planning technical approaches are compared with their performance and collision avoidance for unmanned vehicles in marine environments by early researchers. This paper gives us a clear idea for developing an effective path planning technique to overcome marine accidents in the dynamic ocean environment while choosing the shortest, obstacle-free path for Autonomous Surface Vehicles that can reduce risk and enhance the safety of unmanned vehicle movement in a harsh ocean environment. 2024 Bentham Science Publishers. -
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-depth investigation into financial literacy levels in Indian households
In a complex financial world, lack of awareness complicates money management and savings. Emphasizing financial literacy is vital for informed decision-making. This study explores global financial illiteracy, advocating international initiatives. In India, it assesses disparities and government activities and reviews tax-saving and mobile banking. Gaps include limited studies on Indian households, necessitating gender-specific analyses and research on education's impact. The methodology outlines justification, operational definitions, and data collection techniques. With ANOVA and descriptive statistics on 285 respondents, the study reveals demographic analysis, indicating higher financial literacy with age and a gender gap. Education. positively correlates with financial literacy. Recommendations include interventions like financial seminars, collaboration with regulators, and destigmatizing money talks at home to enhance financial literacy and bridge gaps. 2024 by IGI Global. All rights reserved. -
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 Innovative Approach for Osteosarcoma Bone Cancer Detection based on Attention Embedded R-CNN Approach
The malignant bone tumor osteosarcoma. Any bone is at risk, but lengthy bones like the limbs are more vulnerable. Although the precise cause of this malignant growth is uncertain, experts concur that it is caused by changes to deoxyribonucleic acid (DNA) inside the bones. This can cause the breakdown of good tissue and the growth of aberrant, pathological bone. Osteosarcoma has a 76% cure rate if detected early and treated before it spreads to other parts of the body. An X-ray is the primary tool for detecting bone tumors. Bone X-rays and other imaging tests can help detect osteosarcoma. A biopsy should be performed for an accurate diagnosis. This is a time-consuming and tedious task that might be greatly reduced with the help of appropriate tools. Data preprocessing, segmentation, feature extraction, and model training are the four main pillars of the proposed approach. Unwanted noises can be filtered out with some preprocessing. Low-spatial-frequency and high-spatial-frequency components are separated using segmentation. The proposed approach employed Tumor Border Clarity, Joint Distance, Tumor Texture, and other features for feature extraction. Let's move on to A-Residual CNN model training. The success percentage of the proposed approach was 96.39 percent. 2023 IEEE. -
An Innovative Method for Brain Stroke Prediction based on Parallel RELM Model
Strokes occur when blood supply to the brain is suddenly cut off or severely impaired. Stroke victims may experience cell death as a result of oxygen and food shortages. The effectiveness of various predictive data mining algorithms in illness prediction has been the subject of numerous studies. The three stages that make up this suggested method are feature selection, model training, and preprocessing. Missing value management, numeric value conversion, imbalanced dataset handling, and data scaling are all components of data preparation. The chi-square and RFE methods are utilized in feature selection. The former assesses feature correlation, while the latter recursively seeks for ever-smaller feature sets to choose features. The whole time the model was being trained, a Parallel RELM was used. This new method outperforms both ELM and RELM, achieving an average accuracy of 95.84%. 2024 IEEE.