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Are expensive decisions impulsive? Young adults impulsive housing and real estate buying behavior in India
Purpose: The purpose of the study is to determine website quality, materialism, psychological factors, hedonic value and social media as factors that influence the young adults impulsive housing and real estate buying behavior in India. In addition, this study also measures the mediating effects of social media influence between psychological factors and hedonic value and young adults impulsive housing and real estate buying behavior. Design/methodology/approach: Related literature, quantifiable variables with a five-point Likert Scale, hypothesis testing and mediators are used to study the model. A systematic questionnaire that was divided into six sections was used. A total of 385 valid responses were collected and analyzed through a structural equation model. Findings: The results suggest that materialism, psychological factors and social media have a considerable impact on young adults impulsive housing and real estate buying behavior. The findings also ascertained that website quality and hedonic value do not have a considerable impact on young adults impulsive housing and real estate buying behavior. Research limitations/implications: This study is limited to the responses of young consumers from a limited number of brokers and regions in India. Future studies could be more widespread across the globe. Originality/value: As per the review of existing literature, this research is the first, to the best of the authors knowledge, to determine the factors affecting the impulse buying decision mainly in the housing and real estate sector with the target consumers being young. 2022, Emerald Publishing Limited. -
Are Employees Satisfied and Positive Towards Life?: From the Perspective of Subjective Wellbeing
Most of the organizations are mainly focusing on both the physical and mental wellbeing of the employees. There is not much research happening in the concept of subjective well- being. The chapter mainly focuses on the conceptual definition of the term. Also, examines the effective and cognitive aspects of subjective well- being. It also focuses on the factors where the factors focus on personal and organizational factors and outcomes that are related to the concept of subjective wellbeing. This chapter also gives some insights into managers on the importance of the subjective wellbeing as a concept in the organization and how it fosters the over wellbeing of the individual as well as organization. 2025 by IGI Global Scientific Publishing. -
Arduino based IOT platform for remote monitoring of heart attacks and patients falls
Internet of things (IoT) is a networking concept that allows connection of various smart devices. This concept plays a huge role in the healthcare industry. The developed system is a working prototype for realtime monitoring of patient falls and heart attacks. The process of developing this system included an architecture, which was built using Arduino UNO and Arduino NANO along with pulse sensors and accelerometer sensors. The main idea is to collect health-related data from time to time and the collected data is made available using a real-time interface called Thingspeak. With the help of this process, the person can be monitored from time to time without any hassle. The proposed system also makes use of delivering notifications at the time of emergency using the GSM technology, which is embedded with the Arduino architecture. This system will be of greater help to elderly people, people suffering from Frankenstein disease or people who are in a history of getting heart attacks due to genetic disorders. 2018 Manikandan Shanmugam and Monisha Singh. -
Architecture of visible-light induced Z-scheme MoS2/g-C3N4/ZnO ternary photocatalysts for malachite green dye degradation
The synthesis of bilayer heterojunctions has received considerable attention recently. Fabrication of novel bilayer composites is of significant interest to improve their photocatalytic efficiency. In this study, molybdenum disulfide (MoS2), a layered dichalcogenide material exhibiting unique properties, in combination with graphitic carbon nitride (g-C3N4), a carbon-based layered material, was fabricated with small amounts of zinc oxide (ZnO). Three composites, MoS2/g-C3N4, MoS2/ZnO, and MoS2/g-C3N4/ZnO were prepared via a simple exfoliation method and characterized by various physicochemical methods. The Z-scheme charge transfer mechanism in the prepared ternary composite improves efficiency by inhibiting the recombination rate of electron-hole pairs. It has shown excellent performance in degrading a major water contaminant, malachite green (MG) dye, under visible light irradiation. 2022 Elsevier Inc. -
Architecture of monophase InSe thin film structures for solar cell applications
Control of microstructural evolution during the crystallization of InSe thin films is an inevitable strategy to mold their fundamental properties and potential for the fabrication of solar cells. Impact of annealing as well as substrate temperature on the crystallization progress and physical characteristics of thermally evaporated InSe was examined systematically, which eventually dictates the overall performance of resulting device. Structural and compositional characterizations have been thoroughly investigated by X-ray diffraction and energy dispersive X-ray analyses. InSe films form hexagonal structure with a preferred orientation of crystallites along the (004) direction upon crystallization. The layer of InSe is formed by two concomitant processes, deposition and recrystallization. Application of heat treatment resulted in topographical modification, which was probed by an atomic force microscope. Surface roughness was enhanced due to the influence of temperature and thereby the growth of grains. Investigations of electrical and optical properties, thus provided ample evidence for the use of crystallized monophase InSe as an absorber layer in photovoltaic conversion devices. Carrier concentration and mobility of charge carriers estimated from the Hall measurements were found to be 19.43020cm?3 and 2.01cm2V?1s?1 respectively. Moreover, this research work explores power conversion efficiency of p-InSe/n-CdS heterojunction solar cells. 2017 Elsevier B.V. -
Architecture of FTO/n-CdS/p-SnSe1-xOx/Au Heterojunction Thin Film Diodes by Thermal Evaporation
In this report, FTO/n-CdS/p-SnSe1-xOx/Au heterojunction diodes were fabricated using a homemade precursor followed by dry milling with a facile thermal evaporation method under oxygen atmosphere (10? 2mbar) for the first time. The chemical purity (45.35:45.07:9.58 at.%) and microstructure of the deposited films and device were characterized by energy dispersive x-ray analysis, x-ray photoelectron spectroscopy, and scanning electron microscopy. The crystallographic parameters, a = 11.512 b = 4.163and c = 4.452 with orthorhombic crystal structure and monophase nature were analyzed by powder x-ray diffraction. Raman spectroscopy revealed the vibrational modes, and UVVis-NIR spectroscopy was used to study the direct nature of optical absorption with a band gap of 1.14eV. The currentvoltage (I-V) characteristics of the semiconductor diode were measured in room temperature (25C) and revealed rectifying properties and the cut-off voltage for the device, 0.57V. The obtained results highlight that the use of a p-SnSe1-xOx (SSO) layer as an interface between n-CdS/Au diodes exhibits excellent rectifying behavior and enhanced diode performance. Therefore, the p-SSO layer is a suitable material for heterojuction diodes and optoelectronic switches. Graphical Abstract: [Figure not available: see fulltext.]. 2022, The Minerals, Metals & Materials Society. -
ArcGAN: Generative Adversarial Networks for 3D Architectural Image Generation
Due to advancements in infrastructural modulations, architectural design is one of the most peculiar and tedious processes. As the technology evolves to the next phase, using some latest techniques like generative adversarial networks, creating a hybrid architectural design from old and new models is possible with maximum accuracy. Training the model with appropriate samples makes it evident that the designing phase will be simple for even a layman by including proper parameters such as material description, structural engineering, etc. This research paper suggests a hybrid model for an architectural design using generative adversarial networks. For example, merging Romes architectural style with Italys will accurately and precisely recover the pixel-level structure of 3D forms without needing a 2D viewpoint or 3D annotations from a real 2D-generated image. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Arabica Coffee Bean Grading into Specialty and Commodity Type Based on Quality Using Visual Inspection
Expanding potential of coffee consumers to seek out the freshest and best flavors is a cause for the rise of specialty coffee inthe market. Specialty coffee is grown and harvestedmaintaining an emphasis on quality and clarity of flavor, whereas commodity coffee is harvested for caffeine content. Within those inclusive categories, arabica and robusta are the two types of main branches of coffee that weencounter in the coffee market. Specialty coffees differ significantly from conventional coffees in that they are cultivated at higher altitudes, can be traced, and are professionally processed after being harvested. The quality is constantly examined and understood at every stage, from growth to brewing. Green arabica quality is assessed by counting the defective beans present in the sample. These defects can be primary (Category I) or secondary (Category II). If the primary defects are null and less than five secondary defects, coffee is said to be a specialty.Prior research has been done on classifying the coffee species and differentiating good beans from bad beans. Our research involves the combination of machine learning like K-NN and deep learning convolutional neural networks for classifying specialty coffee from commodity type using computer vision. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Ar-HGSO: Autoregressive-Henry Gas Sailfish Optimization enabled deep learning model for diabetic retinopathy detection and severity level classification
Diabetic Retinopathy (DR) is one the most important problems of diabetics and it directs to the main cause of blindness. When proper treatment is afforded for DR patients, almost 90% of patients are protected from visual damage. DR does not produce any symptoms at the initial phase of the disease, thus various physical assessments, namely pupil dilation, visual acuity test, and so on are needed for DR disease detection. It is more complex to detect the DR during manual testing, because of the variations and complications of DR. The early detection and appropriate treatment assist to prevent vision loss for DR patients. Thus, it is very indispensable to categorize the levels and severity of DR for recommendation of essential treatment. In this paper, Autoregressive-Henry Gas Sailfish Optimization (Ar-HGSO)-based deep learning technique is proposed for DR detection and severity level classification of DR and Macular Edema (ME) based on color fundus images. The segmentation process is more essential for proper detection and classification process, which segments the image into various subgroups. The Deep Learning approach is utilized for effective identification of DR and severity classification of DR and ME. Moreover, the deep learning technique is trained by the designed Ar-HGSO scheme for obtaining better performance. The performance of the devised technique is evaluated using the IDRID dataset and DDR dataset. The introduced Ar-HGSO-based deep learning approach obtained better performance than other existing DR detection and classification techniques with regards to testing accuracy, sensitivity, and specificity of 0.9142, 0.9254, and 0.9142 using the IDRID dataset. 2022 Elsevier Ltd -
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 -
Aqueous symmetric supercapacitor based on hydrothermally grown reduced graphene oxide wrapped cobalt oxide nanocomposites: An efficient paradigm for enhanced performance in supercapacitors
Extensive research has been carried out on the development of electrode materials for energy storage applications, especially in the field of supercapacitors. The present work is the first report on the effect of rGO concentration on the electrochemical properties of reduced graphene oxide-cobalt oxide (rGO-Co3O4) nanocomposites. A symmetric supercapacitor device is assembled by compounding two hydrothermally grown rGO-Co3O4 nanocomposite electrodes separated by a membrane dipped in a 3 M KOH aqueous electrolyte solution. The device delivered a specific capacitance of 1006 Fg?1, energy density of 357.44 W h kg?1, and a power density of 1600 W kg?1 at a current density of 2 Ag?1. It showed a cyclic stability of 80 % during 10,000 cycles at a very high current density of 5 Ag?1 and a coulombic efficiency of 100 %, which maintained a better electrochemical performance, implying that the as-synthesized electrodes are useful for portable energy storage devices. 2025 Elsevier Ltd. -
AQUAPHISH: Leveraging Metaheuristics and Automated Machine Learning for Precision Phishing Detection
Phishing is an ongoing and dynamic threat in the field of cybersecurity, targeting user trust to capture sensitive data through fraudulent websites. Conventional detection systems tend to use binary classification and static features, which make them less flexible to new attack paradigms. This paper seeks to design a solid and comprehensible phishing detection system that alleviates the drawbacks of binary labeling by proposing a regression-based risk scoring model. The aim is to improve accuracy, feature interpretability, and deployment in real-time settings. The new method combines Whale Optimization Algorithm (WOA) for feature selection and H2O AutoML for model creation and assessment. A filtered dataset of 10,000 phishing and normal websites is operated upon using 48 features, which are then reduced to 36 using WOA. The last models are optimized with H2O AutoML, encompassing ensemble learners, and tested on various regression metrics. Interpretability is achieved with SHAP analysis. The best model had an R of 0.9534, RMSE of 0.1079, and MSE of 0.0116, better than traditional classification-based phishing detectors. The system, with only 36 features, had training time decreased by 23.6% and inference latency reduced by ~18%, without any sacrifice in detection accuracy (98.3%). Regression-based scoring also supported adaptive threat ranking in real time. By posing phishing detection as a regression problem and integrating metaheuristic feature selection with AutoML, this work introduces a scalable and explainable framework ready for real-world deployment. The low-latency yet high-accuracy model is best suited for integration into browser-level phishing filters and cloud-based threat intelligence platforms. 2025, Interdisciplinary Publishing Academia. 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. -
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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 To Improve Performance of K-Means Clustering
In this research, we present an enhanced K-Means clustering approach utilizing Neural Engine processors integrated within distributed smartphone networks. Each smartphone runs the K-Means algorithm locally using its Neural Engine to compute centroids efficiently, and these local centroids are then combined to form global clusters on a cloud server. Our implementation significantly reduces computation time while maintaining high clustering accuracy. Experimental evaluation on large datasets demonstrates improved performance over traditional K-Means, proving its suitability for big data analytics in healthcare, IoT, and smart mobile applications. This approach ensures faster processing, lower energy consumption, and effective resource utilization within distributed environments. Further, the proposed method addresses challenges in data privacy by performing local computation and only sharing centroid information. The results indicate potential for scalable clustering solutions in real-time scenarios, opening new directions for edge-cloud integrated machine learning frameworks that harness device-level AI accelerators for complex data-driven tasks efficiently. 2025 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.