Browse Items (11858 total)
Sort by:
-
AR and Online Purchase Intention Towards Eye Glasses
Augmented reality (AR) can be a potent tool for Indian online eyewear marketers by bridging the gap between online and offline purchasing experiences and meeting the needs of social validation and sensory engagement, which are preferences of Indian consumers. The present research explores how augmented reality (AR) technology affects Indian consumers' intentions to buy glasses online. A combination of descriptive and exploratory research design was used on the sample size of 236 consumers. Data was analyzed using frequency table and Structured Equation modelling (SEM) to identify the relationship amongst the variables. The findings indicate that accessibility to product information, telepresence, and perceived ease of use are important variables impacting purchase intention. AR can bridge the gap between online and offline experiences, meet consumer preferences, and create trust and confidence. Future research should explore AR's effectiveness and personalization possibilities for Indian online eyewear retailers. Future research should explore AR's effectiveness and personalization possibilities for Indian online eyewear retailers. 2024 IEEE. -
Aquila Optimizer Based Optimal Allocation of Soft Open Points for Multi-Objective Operation in Electric Vehicles Integrated Active Distribution Networks
The appropriate position and sizing of soft open points (SOPs) for reducing the detrimental impact of electric vehicle (EV) load penetration and renewable energy (RE) variation on active distribution networks (ADNs) are provided in this study. Soft open points (SOPs) have been used to create a multi-objective framework that considers loss minimization and voltage profile enhancement. The non-linear multi-variable complicated SOP allocation problem is solved for the first time using a modern meta-heuristic Aquila optimizer (AO). The modified IEEE 33-bus benchmark and IEEE 69-bus ADNs are used in the simulations. Before SOPs, the average real power loss in IEEE 33-bus AND was 370.329 kW, but after SOPs, it was reduced to 259.356 kW (i.e., 29.96 percent reduction). Similarly, effective SOPs integration in the IEEE 69-bus resulted in a loss reduction of 81.07 percent. AO's computational efficiency is also compared to that of multiobjective particle swarm optimization (MOPSO), particle swarm optimization (PSO), and cuckoo search algorithm (CSA). The AO has produced better results in terms of lower losses, improved voltage profile despite variations in EV load penetration, and RE and load volatility in ADNs, according to the results 2022. International Journal of Intelligent Engineering and Systems.All Rights Reserved -
Approximate Binary Stacking Counters for Error Tolerant Computing Multipliers
To increase the power and efficiency of VLSI circuits, a new, creative multiplying methodology is required. Multiplication is a crucial arithmetic operation for many of these applications. As a result, the newly proposed error-tolerant computing multiplier is a crucial component in the design of approximate multipliers that are both power and gate efficient. We have created approximative multipliers for several operand lengths using this suggested method and a 45-nm library. Depending on their probability, the approximation for the accumulation of changing partial products varies. In compared to approximate multipliers that were previously given, the proposed circuit produces better results. When column-wise generate elements are added to the modified partial product matrix using an OR gate, the output is usually accurate. The amount of energy used, and its silicon area have been considerably reduced in the suggested multiplier when compared to traditional multipliers by 41.92% and 18.47%, respectively. One of the platforms that these suggested multipliers are suitable for is the image processing application. 2024 IEEE. -
-
Approaches Towards A Recommendation Engine for Life Insurance Products
Recommender engines are powerful tools in today's world to overcome the problem of over choice. As the world is moving towards information overload, the life insurance industry is no more immune than any other domain. Three broad categories of life insurance plans are namely - Endowment, Term and ULIP. This paper discusses a variety of ML models that aim to classify the right fit product category for a new customer (extendable to existing customers) on a real-time life insurance company dataset. The dataset used for the modelling were of 2 kinds. The first kind contained features of customer demographics - age, location, education and occupation. The second dataset included these customer demographics as well as the bureau information of the respective customers which included multiple features describing their credit history. By the means of clustering, collaborative filtering approaches were tried on. Also, the problem was tackled using predictive modelling techniques such as Random Forest, Decision Trees and XGBoost. 2021 IEEE. -
Approaches on redesigning entrepreneurship education
All over the world there is an emergence of a self-reliant life. This instilled a spark in entrepreneurship, especially during the wake of a pandemic world. The paradigm shift from dependency to self-reliance demands a set of skills and techniques as prerequisites to thrive in this competitive world. This chapter introduces a couple of innovative pedagogy strategies that can be inculcated in educational institutions, which will give rise to efficient entrepreneurs who can face adversaries and make an efficient contribution to society. The chapter aims to integrate realistic learning activities for fostering capability development in entrepreneurship education. Capability enhancement in entrepreneurship education includes activities that improve the knowledge, skills, and talents of potential entrepreneurs. The chapter aims to develop a model that further illustrates how the educational entrepreneurial experience could be explored. 2023, IGI Global. -
Approach Towards Web for Exploring the Suitable Job for Individuals
In light of future work challenges, true human resource management (HRM) must be rebuilt. This involves over time human resource development; it must also contain the concept of sustainability to move from consuming to generating human resources. The labor market is constantly changing, with nontraditional jobs becoming increasingly important, especially in light of the current COVID-19 legislation. A useful teaching strategy in a variety of academic fields, including career development, is experiential learning. Important elements for establishing experiential learning programs at the institutional level are also covered by researchers. Our framework may assist businesses in identifying the type of experiential learning that best fits their objectives and setting for professional training. It can also help ensure that the training is successfully designed and delivered. 2024 IEEE. -
Approach for Preprocessing in offline Optical Character Recognition (OCR)
offline optical character recognition (offline OCR) is one of the important applications of pattern recognition. To achieve a better recognition result, the input character images must have good quality. That is why the preprocessing step be-comes essential for any image identification task. Lots of research has been performed in numerous jobs towards this preprocessing in the literature. Here, an attempt has been made to summarize different procedures and aspects of preprocessing adopted in implementing these preprocessing techniques. This is done in the hope that this may help the research community towards the gaining of knowledge of different preprocessing techniques used in offline OCR. offline OCR has several applications, such as old manuscript digitization, signature authentication, bank cheque automatic clearance and postal letter sorting, etc. Finally, an overall summary in a concise way has been presented based on different preprocessing techniques used in offline OCR. 2022 IEEE. -
Approach for Collision Minimization and Enhancement of Power Allocation in WSNs
Wireless sensor networks (WSNs) have attracted much more attention in recent years. Hence, nowadays, WSN is considered one of the most popular technologies in the networking field. The reason behind its increasing rate is only for its adaptability as it works through batteries which are energy efficient, and for these characteristics, it has covered a wide market worldwide. Transmission collision is one of the key reasons for the decrease in performance in WSNs which results in excessive delay and packet loss. The collision range should be minimized in order to mitigate the risk of these packet collisions. The WSNs that contribute to minimize the collision area and the statistics show that the collision area which exceeds equivalents transmission power has been significantly reduced by this technique. This proposed paper optimally reduced the power consumption and data loss through proper routing of packets and the method of congestion detection. WSNs typically require high data reliability to preserve identification and responsiveness capacity while also improving data reliability, transmission, and redundancy. Retransmission is determined by the probability of packet arrival as well as the average energy consumption. 2021 Debabrata Singh et al. -
Appraisal of the potential of endophytic bacterium Bacillus amyloliquefaciens from Alternanthera philoxeroides: A triple approach to heavy metal bioremediation, diesel biodegradation, and biosurfactant production
Endophytic microbes have been associated with many positive traits due to their endurance mechanisms. The current study was designed at exploring the potential of the endophytic bacterium Bacillus amyloliquefaciens MEBAphL4 isolated from Alternanthera philoxeroides for biosurfactant production and bioremediation efficiency. This endophyte, isolated from the polluted Madiwala lake in Bangalore, displayed elevated resistance to Cr and Pb till 2000 mg/L. The metal removal efficiency was found to be higher for Cr (25.7 %) at pH 6 and for Pb (92.3 %) at pH 9. Further, the present study also describes biosurfactant production with good emulsification ability (E24-52 %) and stability over a range of pH (8?12), temperature (2040C) and salinity (515 %). Biosurfactant production was enhanced 1.18-fold using the Response Surface Methodology approach and characterised by Fourier Transformation Infra-red Spectroscopy and Ultra-Performance Liquid Chromatography- Mass Spectrometry showing the presence of lipopeptides, fengycin, iturin and surfactin of molecular weights 1463.65, 1043.44 and 1012.56 Da respectively. The potential application of the biosurfactant in degrading various hydrocarbons was evaluated, demonstrating its effectiveness in bioremediation of oil-contaminated sites. Specifically, diesel biodegradation was measured at 56.460.95 %. These findings underscore the potential of B. amyloliquefaciens in environmental applications such as heavy metal biosorption and the bioremediation of contaminated sites, particularly those affected by oil spills and correlates to UN SDG6 of clean water and sanitation. 2024 Elsevier Ltd -
Appraisal of prolyl 4-hydroxylase alpha subunit gene polymorphisms in Spondyloepimetaphyseal dysplasia of Handigodu type (SEMDHG)
Background: The Handigodu variant of Spondyloepimetaphyseal Dysplasia (SEMDHG) is a severe, progressive osteoarthritic disorder characterized by chronic pain and joint degeneration. Clinically, the disorder presents in three distinct phenotypic forms, each exhibiting varying degrees of stature reduction and disease severity. Urine analysis of affected individuals reveals an elevated peptide-bound proline to 4-hydroxyproline ratio relative to controls, suggesting disruptions in collagen metabolism. Given the critical role of prolyl 4-hydroxylase enzymes in stabilizing collagen structure, this study undertook a comprehensive sequence analysis of all three isoforms of prolyl 4-hydroxylase in both affected and unaffected individuals to elucidate potential molecular underpinnings of the disorder. Method: The entire exonic regions and 2000 base pairs upstream of the translation start sites of the P4HA1, P4HA2, and P4HA3 genes were sequenced in a cohort of 300 individuals, comprising 166 affected and 134 unaffected individuals. Results: Sequence analysis of the ? (I), ? (II), and ? (III) subunit genes identified three novel SNPs and a 39-bp deletion variant, in addition to ten previously reported SNPs catalogued in dbSNP. The SNP rs28384495 in P4HA1, the 39-bp deletion variant, and a novel mutation (SNP3) in P4HA3 exhibited significantly different allele frequencies between patients and controls. Genotype association analysis revealed that SNPs in P4HA1 and P4HA3 were associated with Type 2 and Type 3 HD under various genetic models. Notably, all Type 2 HD patients were heterozygous for the 39-bp deletion, whereas all Type 3 HD patients were homozygous for the variant. Haplotype analysis corroborated the findings of the genotype association analysis. Conclusion: This study is the first to account an association between the P4H gene and disease. Further research is needed to evaluate the functional implications of the identified mutations. 2024 -
Applying talent acquisition to the test: Assessing productivity in facilities organization /
Pramana Research Journal, Vol.9, Issue 2, pp.197-207, ISSN No: 2249-2976. -
Applying Ensemble Techniques for the Prediction of Alcoholic Liver Cirrhosis
More than fifty percent of all liver cognate deaths are caused by alcoholic liver disease (ALD). Excessive drinking over the time leads to alcohol-related steatohepatitis and fatty liver, this in turn can lead to alcoholic liver fibrosis (ALF) and in due course alcohol-related liver cirrhosis (ALC). Detecting ALD at an early stage will reduce the treatment cost to the patient and reduce mortality. In this research, a two-step model is developed for predicting the liver cirrhosis using different ensemble classifiers. Among 41 features recorded during data collection, only 15 features arefound to be effective determinants of the class variable. The proposed stacked ensemble technique for ALD prediction is compared with other ensemble models such as random forest, AdaBoost, and bagging. Through experimentation, it is observed that the proposed model with XGBoost and decision tree as base models and logistic regression as Meta model exhibits prediction accuracy of 93.86%. The prediction accuracy of theproposed stacked ensemble technique is 0.2% better in prediction accuracy and 0.3% reduced error rate in comparison with random forest classifier. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Applying Artificial Bee Colony Algorithm to Improve UWSNs Communication
The research in this study aims at implementing the ABC algorithm to enhance the communication within UWSNs. The ABC algorithm, motivated by the CPG approach being analogous to that of honey bees searching for food, specifies optimal values for critical parameters of the network such as energy consumption, reliability in data transfer, and scalability. From the analyses conducted in this exposition, it is apparent that the envisaged methodology outperforms other conventional routing parlances in the following ways: minimal energy usage, high data delivery ratios, low packet drops, and longest network lifetime. Therefore, from the above results it can be concluded that, the said ABC algorithm is helping in achieving a better result in terms of improved underwater communication as well as in mitigating with the difficulties of UWSNs. 2024 IEEE. -
Applications of neuroscience in education practices: A research review in cognitive neuroscience
The human brain is the most complex and mysterious organ in the body responsible for learning. Applications of neuroscience and genetics need to be comprehended to modulate teaching and learning practices in education. Considering the scope for application of advanced sciences in education practices, this book chapter simplifies and reviews ten critical research findings relevant for students and teachers for classroom applications and for modulating learning patterns for different age groups. The concept is also relevant for parents and the academic fraternity at large. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Applications of Machine Learning and Deep Learning Models in Brain Imaging Analysis
Brain imaging is an umbrella term including many non-invasive techniques that objectively monitor brain function. Such monitoring leads to understanding how the brain works by presenting selected stimuli. More importantly, brain function monitoring allows physicians to diagnose and predict brain disorders. In the last decade, several machine learning and deep learning models have been developed by researchers to process and analyse brain imaging data for the diagnosis, detection, and prediction of brain disorders, such as stroke, schizophrenia, autism, psychosis, and Alzheimers. This chapter reviews the various applications and properties of machine learning and deep learning models for brain image analysis. The chapter also highlights the deep learning models that have either understood the test of time or shown the promise to solve challenging problems involving brain imaging data. The review also discusses various open issues yet to have practical solutions or methodologies with the help of machine learning and deep learning. The research covers a wide range of imaging modalities, disorders and models to expose researchers and practitioners in neurological disorders and machine learning and deep learning to each others field, hopefully leading to fruitful collaborations and practical solutions for processing brain images. 2024 selection and editorial matter, Anitha S. Pillai and Bindu Menon; individual chapters, the contributors. -
Applications of Digital Technologies and Artificial Intelligence in Cryptocurrency - A Multi-Dimensional Perspective
The paradigm shift requires spreading the light of decentralized ledger technology, extraordinarily implementing cryptocurrencies, and being visible as a game-changer. Blockchain technology, along with cryptocurrencies like Bitcoin, Ethereum, and Litecoin, is a tool for global economic transformation that is rapidly gaining traction in the finance industry. However, these technologies have had low popularity in the consumer market. Many platforms have been misunderstood and ignored when there is an obvious hole in among them. The basic idea behind cryptocurrency is that it is a network-based, totally virtual exchange medium that utilizes cryptographic algorithms such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5) to secure the data. Transactions within the blockchain era are secure, transparent, traceable, and irreversible. Cryptocurrencies have gained a reputation in practically all sectors, including the monetary sector, due to these properties. The uncertainty and dynamism of their expenses, however, hazard investments substantially despite cryptocurrencies growing popularity amongst approval bodies. Studying cryptocurrency charge prediction is fast becoming a trending subject matter in the global research community. Several device mastering and deep mastering algorithms, like Gated Recurrence Units (GRUs), Neural nets (NNs), and nearly short-term memory, were employed by the scientists to analyze and forecast cryptocurrency prices. As a part of this chapter, we discuss numerous aspects of cryptographic protection and their related issues. Specifically, the research addresses the state-of-the-art by examining the underlying consensus mechanism, cryptocurrency, attack style, and applications of cryptocurrencies from a unique perspective. Secondly, we investigate the usability of blockchain generation by examining the behavioral factors that influence customers decision to use blockchain-based technology. To identify the best crypto mining strategy, the research employs an Analytic Hierarchy Process (AHP) and Fuzzy-TOPSIS hybrid analytics framework. Furthermore, it identifies the top-quality mining methods by evaluating providers overall performance during cryptocurrency mining. 2023 Scrivener Publishing LLC. -
Applications of Classification and Recommendation Techniques to Analyze Soil Data and Water Using IOT
As we are moving to a computerized and scientific world, data becomes an intrinsic part of our life. Agriculture sector is still unorganized with regard to automation and data analytics. This task is accomplished through sensors, data mining and analysis. In this paper, we propose real-time sensors to detect the soil features and predict the suitable crop cultivation using trained dataset. This would help the farmers to predict the type of cultivation to be done depending on the soil features. Today, the farmer can understand what type of cultivation will be prepared in the soil. Also, people of the upcoming generation will be using that sensor, different plant can be make. The cost of cultivation can be improved. Water level of the soil can be easily predicted. Which type of plant will be produced in the different soil can be predicted. So, this new type of cultivation followed by the next generation also. This paper has presented an improved by the pH sensor, water level sensor. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Applications of bioconvection for tiny particles due to two concentric cylinders when role of Lorentz force is significant
The bioconvection flow of tiny fluid conveying the nanoparticles has been investigated between two concentric cylinders. The contribution of Lorenz force is also focused to inspect the bioconvection thermal transport of tiny particles. The tiny particles are assumed to flow between two concentric cylinders of different radii. The first cylinder remains at rest while flow is induced due to second cylinder which rotates with uniform velocity. Furthermore, the movement of tiny particles follows the principle of thermophoresis and Brownian motion as a part of thermal and mass gradient. Similarly, the gyro-tactic microorganisms swim in the nanofluid as a response to the density gradient and constitute bio-convection. The problem is modeled by using the certain laws. The numerical outcomes are computed by using RKF-45 method. The graphical simulations are performed for flow parameters with specific range like 1?Re?5, 1?Ha?5, 0.5?Nt?2.5, 1?Nb?3, 0.2?Sc?1.8, 0.2?Pe?1.0 and 0.2???1.0. It is observed that the flow velocity decreases with the increase in the Hartmann number that signifies the magnetic field. This outcome indicates that the flow velocity can be controlled externally through the magnetic field. Also, the increase in the Schmidt numbers increases the nanoparticle concentration and the motile density. 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. -
Applications of artificial intelligence to neurological disorders: Current technologies and open problems
Neurological disorders are caused by structural, biochemical, and electrical abnormalities involving the central and peripheral nervous system. These disorders may be congenital, developmental, or acute onset in nature. Some of the conditions respond to surgical interventions while most require pharmacological intervention and management, and are also likely to be progressive in nature. Owing to a high global burden of the most common neurological disorders, such as dementia, stroke, epilepsy, Parkinsons disease, multiple sclerosis, migraine, and tension-type headache, there exist multiple challenges in early diagnosis, management, and prevention domains, which are further amplified in regions with inadequate medical services. In such situations, technology ought to play an inevitable role. In this chapter, we review artificial intelligence (AI) and machine learning (ML) technologies for mitigating the challenges posed by neurological disorders. To that end, we follow three steps. First, we present the taxonomy of neurological disorders, derived from well-established findings in the medical literature. Second, we identify challenges posed by each of the common disorders in the taxonomy that can be defined as computational problems. Finally, we review AI/ML algorithms that have either stood the test of time or shown the promise to solve each of these problems. We also discuss open problems that are yet to have an effective solution for the challenges posed by neurological disorders. This chapter covers a wide range of disorders and AI/ML techniques with the goal to expose researchers and practitioners in neurological disorders and AI/ML to each others field, leading to fruitful collaborations and effective solutions. 2022 Elsevier Inc. All rights reserved.