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Current Trends and Future of AI in Sales and Marketing
Industry 4.0 has been fueling the growth of Artificial Intelligence owing to the expanding progress of data produced by the Internet and IoT devices and the computation of this enormous quantity of data. Artificial Intelligence has revolutionized the business world. AI technology has had a striking influence in the field of finance and accounting. As the market is becoming increasingly competitive, AI-empowered finance and accounting structures are making the business organization sturdy competitors as AI technologies and tools help in saving time and extracting deeper understandings. Today, companies use AI to predict customer behavior, serve more people, better target campaigns, forecast trends, etc. It goes without question that AI could improve the productivity of marketing teams. For example, location, age, and other essential information can lead to more personalized content. Along with big data, AI can undoubtedly provide businesses with a powerful means to engage and convert potential customers. This chapter aims to study the use of artificial intelligence in sales and marketing. The chapter also studies the role of AI in the sales and marketing field. This chapter also describes the benefits and current trends of AI applications in sales and marketing operations. This chapter includes the fundamental and theoretical base. It attempts to analyze the key practical applications of AI in the very specific work of the sales and marketing field. 2025 by Apple Academic Press, Inc. -
Teacher preparation and professional development programs with technology-enhanced learning
The term Technology Enhanced Learning (TEL) refers to application of technology to teaching and learning process.Teaching profession requires incessant developing and upgrading. For this reason, Professional Development Programs are an essential requirement for the institutions and the teachers. Professional development programs are now designed to cater to this new need of the teachers aiming to enhance their digital skills and prepare them to use technology in their class preparations, class conduction, assessments, etc. This chapter aims to investigate the need for digital skills, technology enhanced learning to develop teachers. The purpose of this study is to identify key challenges and solutions to the effective integration of technology in teacher preparation. The study would also provide guiding principles on how to move the field towards effective integration of technology in teacher preparation, development programs and how to incorporate technology enhanced learning in their professional development. 2025 by IGI Global Scientific Publishing. -
Impact of artificial intelligence on consumer online purchase intention
Artificial intelligence (AI) is rapidly weaving itself into the fabric of online shopping as the digital landscape evolves, having a significant impact on consumer online purchase intention (COPI). This influence manifests itself in a variety of ways, ranging from personalized product recommendations curated by AI algorithms to seamless user experiences (UX) crafted through AI-driven optimization. Recommendation engines anticipate our desires by analyzing previous purchases and browsing behavior, relieving decision fatigue, and potentially nudging us towards that coveted purchase. Chatbots and virtual assistants, tireless digital companions, provide 24-hour support, increasing convenience and accessibility and thus increasing COPI. This AI-infused journey, however, is not without its challenges. Concerns about data privacy, algorithmic bias, and the ambiguity of AI decision-making can erode consumer trust, potentially impeding COPI. Ethical concerns about data manipulation, discrimination, and unfair data practices raise red flags, calling for transparency and responsible AI implementation. The key to unlocking a sustainable future for e-commerce is striking the right balance between harnessing the power of AI and mitigating its potential pitfalls. E-commerce companies must prioritize trust by being transparent, ensuring responsible AI practices, and confronting ethical concerns head on. Only then will they be able to harness AI's transformative power to foster a thriving online shopping environment in which COPI thrives alongside consumer trust and well-being. 2025 by IGI Global Scientific Publishing. All rights reserved. -
An Integrated Scalable Healthcare Management System Using IOT
Healthcare management is the challenging task of maintaining the patients medical-related data and images. Pervasive computing, which consists of a wireless network, is an innovative medium for medical data transmission. Here, we propose SHMS (Scalable Healthcare Management System) and interoperability, an available and user-friendly platform. It utilizes a huge amount of data and medical images that must be managed and stored for processing and further investigation. In our work, data like heartbeat, temperature, blood pressure, and ECG readings are collected using different sensors and in one gateway protocol. This design is used for transferring, managing, and accessing documents containing health-related information, which is scattered across different system and organization domains. It is scalable because cloud platforms provide communication APIs, the web service interfaces ensure interoperability, the availability makes patients, doctors, or administrators able to access medical-related data anywhere, and Android OS makes it user-friendly. The security of the data collected can be achieved by authenticating storage using a cryptographic ECC algorithm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Statistical and experimental studies of MoS2/g-C3N4/TiO2: a ternary Z-scheme hybrid composite
Abstract: A ternary photocatalyst, MoS2/g-C3N4/TiO2, was prepared using layered and exfoliated MoS2, g-C3N4, and TiO2 viahydrothermal and wet chemical method. It was characterized using various methods to evaluate the structural, morphological and optical properties. Successful incorporation of g-C3N4 and TiO2into MoS2 was confirmed by X-ray photoelectron spectroscopy, and the formation of heterojunctions among MoS2, g-C3N4 and TiO2 particles was established by transmission electron microscopy. These hybrid composites exhibited excellent efficiency in the degradation of malachite green dye. The composite can be recycled four times without loss of photoactivity. The remarkable improvement in photocatalytic efficiency was because of the synergism among the three nanoparticlesthrough the Z-scheme pathway which allows separation of electronhole pairs and makes MoS2/g-C3N4/TiO2 an outstanding material in the fields of photocatalysis and water treatment. The optimized experimental conditions for the degradation of the dye were assessed by the BoxBehnken design of the response surface methodology. Graphical abstract: [Figure not available: see fulltext.]. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature. -
Structural, Morphological and Optical Properties of MoS2-Based Materials for Photocatalytic Degradation of Organic Dye
Molybdenum disulfide (MoS2) is a transition metal dichalcogenide (TMDCs) having versatile properties and plays a great role in the photodegradation of organic dyes. MoS2 also finds applications in diverse fields such as catalysis, electronics, and nanomedicine transportation. MoS2 can be prepared by using chemical and physical methods such as hydrothermal, solvothermal, and chemical vapour deposition methods. The preparation method employed can produce subtle but significant changes in the morphology. To increase the efficiency of MoS2, it can be combined with different materials to produce composites that improve the photodegradation efficiency of MoS2. The various methods of preparation, the morphology of MoS2, and photodegradation activity of the MoS2-based nanocomposites are briefly discussed in this review. 2022 by the authors. -
An Empirical and Statistical Analysis of Regression Algorithms Used for Mental Fitness Prediction
In today's focus on mental well-being, technology's capability to predict and comprehend mental fitness holds substantial significance. This study delves into the relationship between mental health indicators and mental fitness levels through diverse machine learning algorithms. Drawing from a vast dataset spanning countries and years, the research unveils concealed patterns shaping mental well-being. Precise analysis of key mental health conditions reveals their prevalence and interactions across demographics. Enriched by insights into Disability-Adjusted Life Years (DALYs), the dataset offers a comprehensive view of mental health's broader impact. Through rigorous comparative analysis, algorithms like Linear Regression, Random Forest, Support Vector Regression, Gradient Boosting, K-nearest neighbors and Theil Sen Regression are assessed for predictive accuracy. Mean squared error (MSE), root mean squared error (RMSE), and Rsquared (R2) scores are used to assess the predictive accuracy of each algorithm. Results show that Mean Squared Error (MSE) ranged from 0.030 to 1.277, Root Mean Squared Error (RMSE) from 0.236 to 1.130, and R-squared (R2) scores ranged between 0.734 and 0.993, with Random Forest Regressor achieving the highest accuracy. This study offers precise prognostications regarding mental fitness and establishes the underpinnings for the creation of effective tracking tools. Amidst society's endeavor to tackle intricate issues surrounding mental health, our research facilitates well-informed interventions and individualized strategies. This underscores the noteworthy contribution of technology in shaping a more Invigorating trajectory for the future. 2023 IEEE. -
User Perception of Mobile Banking: Application of Sentiment Analysis and Topic Modelling Approaches to Online Reviews
The digital revolution has led to significant changes in the global as well as Indian banking sector. The introduction of mobile banking apps has provided increased convenience to customers, who can now avail various banking services remotely. Thus, it is imperative to study the customers' sentiments regarding these applications and find scope for improvement, so that customers can seamlessly operate their bank accounts without having to visit bank branches. Thus, the primary purpose of this research is to study the perceptions of customers towards mobile applications of six major banks in India. A sample of 3000 reviews left by users of these apps was scraped from Google Play Store and sentiment analysis was conducted using RoBERTa-base model from the Transformers library. This was followed by topic modeling using Latent Dirichlet Allocation to find the aspects that are most important to the users. Results revealed that user experience is majorly driven by customer support service, features and functionality of apps, and app performance. Our findings shall help banks identify key areas of improvement so that they can work on enhancing overall customer experience. Despite the growing popularity of mobile banking, this study is the first of its kind in Indian context. 2024 IEEE. -
Unpacking the Psychology of Investment Intention: The Role of Emotional Intelligence, Personality Traits, and Risk Behaviour
In the dynamic realm of wealth accumulation, investments demand a meticulous evaluation of both financial and non-financial aspects inherent in securities. Prudent decision-making surpasses a fixation on anticipated returns, requiring a nuanced assessment of an investment's potential to actualize desired earnings. This study challenges the presumption of investor rationality in traditional financial theories, emphasizing the profound impact of non-financial determinants on decision-making, including personality traits, emotional intelligence, and risk behavior. With a robust sample size of 396 respondents, the research establishes a statistically significant correlation between emotional intelligence, personality traits, risk behavior, and the intricate domain of investment decisions. For middle-class investors, a pivotal recommendation emerges: fostering a discerning comprehension of one's psychological attributes. Active collaboration with seasoned financial advisers is imperative, serving as a compass through the complexities of the modern financial milieu. This holistic approach, harmonizing financial acumen with nuanced psychological insight, proves indispensable for navigating intricacies and facilitating judicious investment decisions aligned with individual aspirations and risk thresholds. The nuanced integration of financial prudence and psychological acuity fortifies investment portfolios and establishes a resilient foundation for adeptly navigating the dynamic terrain of wealth management. 2024, Iquz Galaxy Publisher. All rights reserved. -
Sociocultural aspects of the medicalisation of infertility: a comparative reading of two illness narratives
This paper is a comparative reading of variations in the medicalisation of infertility caused by sociocultural aspects, in two illness narratives by patients: Elizabeth Katkins Conceivability (2018), a story of navigating a fertility industry with polycystic ovarian syndrome and antiphospholipid syndrome in America and Rohini Rajagopals Whats a Lemon Squeezer Doing in My Vagina (2021), a discussion from India of a growing awareness of medicalisation in treatment of unexplained infertility. For this purpose, it first charts scholarship on illness narratives and medicalisation, noting a historical association. Following this, it shows how infertility, a physiological symptom of reproductive incapacity or failure to show clinical pregnancy, is generally medicalised. This paper reads the texts as showing hitherto unaddressed sociocultural aspects of infertilitys medicalisation. At the same time, drawing from existing sociological and anthropological scholarship, it shows how a reading of sociocultural aspects in medicalised infertility nuances understanding of its medicalisation. This comparative reading attends to sociocultural values and norms within the texts, including pronatalism, fetal personhood, kinship organisation, purity/pollution, individual reliance, sacred duty and so forth. It draws from scholarship on embodiment, rhetorical strategies and the language of medicine. It also shows how a patients non-medicalised, affective history ofdeep sickness caused by the biographical disruption of infertility is not that of apoor historian. In laying out the particularisation of such sociocultural values and norms across America and India, medicalisations migration from its origins to the margins reveals subjectivised, stratified reproduction in infertility illness narratives. This paper is part of a turn in scholarship away from understanding the medicalisation of infertility as naturalised and decontextualised. Author(s) (or their employer(s)) 2024. -
Young adults experience of housing and real estate chatbots in India: effort expectancy moderated model
Purpose: This study aims to recognize the role of information system (IS) model on young adults experience of housing and real estate chatbots. This model of IS takes into account the quality of information, the quality of system and the quality of service. Design/methodology/approach: This study uses a sample frame for analysis which comprises young adult population in India, i.e. between the ages of 18 and 35. A questionnaire consisting of five components was used to collect information in a structured manner. The 386 responses thus collected were analysed using the structural equation model. Findings: It was found that there is a significant influence of the quality of information, quality of system and quality of service on young adults experience of housing and real estate chatbots. The findings also showed that there is moderation role of effort expectancy between the quality parameters and young adults user experience of housing and real estate chatbots. Research limitations/implications: This study focusses exclusively on the young adults from various parts of India. Future research can consider larger population categories across age groups and across sectors employing chatbots. Practical implications: This study will enable in-depth understanding of IS model quality dimensions relation with the user experience. In particular, housing and real estate organisations will profit from the expanded usage of artificial intelligence through chatbots for user correspondence and communication. Originality/value: To the best of the authors knowledge, this study is first of its kind, as it investigates how IS model quality dimensions affect the young adults experience of housing and real estate chatbots in India. This study also ventures into identifying the moderation role of effort expectancy between the quality dimensions as per IS model and young adults experience of housing and real estate chatbots. This study will be useful for the stakeholders of housing and real estate industry. 2023, Emerald Publishing Limited. -
Presence of red giant population in the foreground stellar substructure of the Small Magellanic Cloud
The eastern region of the Small Magellanic Cloud (SMC) is found to have a foreground stellar substructure, which is identified as a distance bimodality (?12 kpc apart) in the previous studies using red clump (RC) stars. Interestingly, studies of red giant branch (RGB) stars in the eastern SMC indicate a bimodal radial velocity (RV) distribution. In this study, we investigate the connection between these two bimodal distributions to better understand the nature and origin of the foreground stellar substructure in the eastern SMC. We use the Gaia Early Data Release 3 astrometric data and archival RV data of RGB stars for this study. We find a bimodal RV distribution of RGB stars (separated by ?35-45 km s-1) in the eastern and south-western (SW) outer regions. The observed proper motion values of the lower and higher RV RGB components in the eastern regions are similar to those of the foreground and main-body RC stars, respectively. This suggests that the two RGB populations in the eastern region are separated by a similar distance to those of the RC stars, and the RGB stars in the lower RV component are part of the foreground substructure. Based on the differences in the distance and RV of the two components, we estimate an approximate time of formation of this substructure as 307 65 Myr ago. This is comparable with the values predicted by simulations for the recent epoch of tidal interaction between the Magellanic Clouds. Comparison of the observed properties of RGB stars, in the outer SW region, with N-body simulations shows that the higher RV component in the SW region is at a farther distance than the main body, indicating the presence of a stellar counter-bridge in the SW region of the SMC. 2021 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Enhancing Stroke Prediction: Leveraging Ensemble Learning for Improved Healthcare
Stroke, a potentially deadly medical disorder, requires excellent prediction and prevention measures to minimize its impact on individuals and healthcare systems. In this study, ensemble learning techniques are employed to enhance the accuracy of stroke prediction. The method combines four different machine learning algorithms, Adaboost, CatBoost, XGBoost, and LightGBM, to produce a strong predictive model. The data was composed of a rich set of demographic, medical, and lifestyle information. The data was preprocessed and features were engineered to maximize predictive performance. Results showed that the stacked ensemble model, which is composed of Adaboost, CatBoost, XGB, LightGBM, and Logistic Regression, meta-model, outperformed other models. The model has the potential to be used as a decision support tool in an early stroke risk assessment system, enhancing clinician decision-making and improving healthcare outcomes. 2024 IEEE. -
A Comprehensive Study on Electric Vehicle Charging Infrastructure
Issues of global warming and hike in the fuel price have taken electric vehicles (EVs) to be popular among the ordinary people. But the main drawbacks are related to the vehicle price and the scarcity of charging infrastructure. In this paper, a review of various charging infrastructures of electric vehicles that are existing and emerging are discussed. The paper also gives an overview of the charging standards for EVs. The Electrochemical Society -
Drinking straw from coconut leaf: A study of its epicuticular wax content and phenol extrusion properties
Background and Objectives: Plastics are a ubiquitous part of our daily life but now posing a major threat to marine life, animal and human health. More than 50% of the manufactured plastic including straws are being disposed of after single-use. There is an increasing need to mitigate this trend so that the damage could be brought under control. The aim of this research was to develop a compostable, eco-friendly alternative to plastic straws using the leaves of Cocos nucifera L. Materials and Methods: The biochemical properties of 6 varieties of Cocos nucifera L. leaflets were studied in order to screen the most suitable material for making sustainable straws. Epicuticular wax content was analyzed to choose the best variety for preparation of hydrophobic straws. Total antioxidant activity, total tannin content, phenolic and flavonoid content were assayed to evaluate the potential functionality of the leaflets. The phenol extrusion properties of the material were also checked in acidic and normal beverages. Results: Estimation of epicuticular wax and phytochemical analysis in all 6 varieties revealed that all varieties of Cocos nucifera L. leaves provide a potent biomaterial for straw preparation. Silicon 732 was found to be a good adhesive agent for straw preparation. Phenol extrusion assays revealed that there is a negligible difference in the release of phytochemicals before and after dipping of straws in the beverages. Conclusion: The outcome of this research opens up vistas to carry out further research in a hitherto unexplored area of utilizing the leaf of Cocos nucifera in a novel way with far reaching economic and employment implications. 2019 Jyoti Jeena James et al. -
Structural, luminescence and NMR studies on Nd3+-doped sodiumcalcium-borate glasses for lasing applications
In this work, Neodymium (Nd3+) -doped borate glasses were synthesised by melt-quenching method and their structural as well as optical properties were analysed through XRD, Raman, NMR, DSC, UVVisible, luminescence and decay studies for the possible application as laser gain medium. DSC and XRD results revealed that the glasses have high transition temperature and are in amorphous nature, respectively. The vibrational characteristics of the host matrices as well as the effect of Nd3+ incorporation were analysed by using Raman spectra, which exhibit majorly borate groups as supported by NMR results. The band gap energy of the glasses decreases with an increase in Nd3+ concentration. Using Judd-Oflet theory the characteristic intensity parameters (??, ? = 2, 4 and 6) were calculated and further used for calculating the various radiative parameters from the emission spectra. The emission cross-section (?em) was estimated as high as 1.15 10?20 cm2 from the FhtbauerLandenburg (FL) equation for the dominant 4F3/2?4I11/2 (1056 nm) transition. The effect of Nd3+ concentration on the lifetime of the 4F3/2 luminescent level was analysed from the decay curve analyses. From which, the corresponding quantum efficiency (?) was estimated and found as high as 54%. The investigated result suggests the prepared glasses can be utilized as gain medium to generate laser at around 1.05 ?m. 2020 Elsevier Ltd and Techna Group S.r.l. -
Comparison of Machine Learning Algorithms for Predicting Chronic Kidney Disease
Early detection and characterization of chronic renal disease are crucial to ensure that patients receive the best possible treatment. This study uses data mining techniques to uncover hidden information about patients. The outcomes of using the Random Forest, Multilayer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree, XGBoost, LGBM Classifier, GaussianNB, KNeighbors Classifier, and XGBRF classifier have been compared. In our study, we demonstrate that Random Forest and XGBoost algorithms are more effective in classifying and predicting the severity level of chronic kidney disease 2022 IEEE. -
Integrated fuzzy AHP and TOPSIS as innovative student selection methodology at institutions of higher learning
BACKGROUND: The selection of students at academic institutions has been a challenging affair given multiple criteria that need to be considered by the institution. Additionally, multiple evaluators and decision makers are involved in the student selection process, rendering it inconsistent. The complexity and subjectiveness in such decisions making requires new and innovative approach in order to be more systematic and transparent. OBJECTIVE: This paper presents an innovative methodology for student selection for admission into an Institute of Higher Learning (IHL) using Fuzzy Analytical Hierarchy Process (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Drawing on the success of using these methods in other fields, this study applies the technique and principles on student selection process. METHOD: Fuzzy Analytical Hierarchy Process (FAHP) is used in determining the weights of the criteria by the decision makers which avoids the vagueness and inconsistencies in decision making process and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method ranks finds out the best alternative solution for student selection by calculating the relative closeness from the positive ideal solution. RESULTS AND CONCLUSION: This research finds using the hybrid method is effective in student selection for IHL and makes the process efficient and bias-free. This method can be applied to various fields and uses where multi-criteria decision making is involved. 2023 - IOS Press. All rights reserved. -
Indium oxide decorated graphitic carbon nitride/multiwalled carbon nanotubes ternary composite for supercapacitor applications
A hybrid ternary composite In2O3/g-C3N4/MWCNT (GCI) was synthesized by combining three-dimensional In2O3, two-dimensional g-C3N4, and one-dimensional MWCNTs employing a one-pot solvothermal method. The resulting In2O3/g-C3N4/MWCNTs composite leverages the combined benefits of the integration of different dimensionality materials and the synergy between its components. Integrating 1D, 2D, and 3-D materials can create hybrid structures with 3D architectures. It exhibits hierarchical porosity that provides better conductive pathways for ion transport and improves the rate performance. The distinct spatial structure of the composite with short ion diffusion paths maximizes the exposure of the active sites and enhances the conductivity, leading to superior energy storage performance. The electrochemical assessment of the In2O3/g-C3N4/MWCNTs composite exhibited a remarkable specific capacitance of 1081 F g?1 at 1 A g?1 with a commendable capacitance retention of 97.5 % at 3 A g?1 over 5000 cycles. An asymmetric supercapacitor fabricated using In2O3/g-C3N4/MWCNT//AC showcased a notable energy density of 57.5 Wh Kg?1 with an impressive power density of 2760 W Kg?1 at 1 A g?1. The outstanding electrochemical attributes of the fabricated device underscore the potential of the material for future applications in hybrid energy storage systems. 2024 Elsevier Ltd -
Symmetric Supercapacitors based on Reduced Graphene Oxide/Multi-walled Carbon Nanotubes/Cobalt Oxide Ternary Composites
Ternary nanocomposites of reduced graphene oxide/multi-walled carbon nanotubes/cobalt oxide nanoparticles (rGO/MWCNT/Co3O4) were synthesized employing a facile hydrolysis method with subsequent heat treatment. The good electrical conductivity and remarkable carrier mobility of rGO and MWCNTs make them a suitable matrix for hybrid supercapacitors, and their composites with metal oxides exhibit enhanced electrochemical properties due to the advantages of the synergistic contribution and the integration of different dimensionalities. The binary counterparts of Co3O4 with GO or MWCNTs were also produced using the same technique to gain additional insight into the characteristics of the individual components. The structural and morphological properties of the composites were analyzed using various analytical techniques. The electrochemical behaviors of the prepared composites were investigated using cyclic voltammetry (CV), galvanostatic chargedischarge (GCD), and electrochemical impedance spectroscopy (EIS) in 1 M H2SO4. A platinum electrode modified with the rGO/MWCNT/Co3O4 composite displayed a remarkable specific capacitance of 922 F g?1 at a current density of 1 A g?1 composite with a negligible capacitance drop after 2000 cycles. The symmetric supercapacitor fabricated using the rGO/MWCNT/Co3O4 composite showed an energy density of 32.2 Wh Kg?1 at 1 A g?1, and the corresponding power density was 2000 W Kg?1. The supercapacitor fabricated using the composite displayed 83% capacitance retention after 2000 cycles at 3 A g?1 composite. 2023 Taylor & Francis Group, LLC.