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The Impact of Digital Marketing Strategies on Customer Attitude and Purchase Intention Towards Electronic Gadgets: A Study on Indian Students
A portion of a companys long-term strategy should be devoted to digital marketing transformation. It is a challenging task to select the effective marketing strategy when conducting business in modern digital world. This study seeks to elucidate the influences of digital marketing strategy forms on customer attitude and purchase intention of students towards electronic gadgets. The relationship between four digital marketing strategies such as search engine advertising, social media, content marketing and email marketing towards customer attitudes and purchase intention was investigated in accordance with hypotheses, 225 students from Bangalore city, India, who had experience in online purchase of electronic gadgets comprised as a research sample. The relationship among the selected variables are tested with help of Correlation, ANOVA and regression analysis. The study conclude that there is an impact of various forms of digital marketing strategies on customers attitude and the purchase intention of young (Students) customer. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
The Impact of Digital Marketing Strategies on Customer Attitude and Purchase Intention Towards Electronic Gadgets: A Study on Indian Students
A portion of a companys long-term strategy should be devoted to digital marketing transformation. It is a challenging task to select the effective marketing strategy when conducting business in modern digital world. This study seeks to elucidate the influences of digital marketing strategy forms on customer attitude and purchase intention of students towards electronic gadgets. The relationship between four digital marketing strategies such as search engine advertising, social media, content marketing and email marketing towards customer attitudes and purchase intention was investigated in accordance with hypotheses, 225 students from Bangalore city, India, who had experience in online purchase of electronic gadgets comprised as a research sample. The relationship among the selected variables are tested with help of Correlation, ANOVA and regression analysis. The study conclude that there is an impact of various forms of digital marketing strategies on customers attitude and the purchase intention of young (Students) customer. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Using Document Similarity Algorithms for Suicidal Detection in Social Media: A Case Study of User Tweets
Suicidal detection and treatment from the clinical and public health perspective are reactive. For an action whose consequences are irreversible, a reactive approach to the problem cannot be the answer. A proactive approach is needed to solve and detect suicidal intent. Social media has become the television and diary of millennials and Gen z alike; hence, it is imperative to create techniques and approaches to study their actions in this particular space. This research involved creating document similarity algorithms from Corpora mined from the Twitter Developer API. Making the data unique to this platform, a methodology design involving validating data at various spectrum and selecting an appropriate threshold to classify the similarity levels were created as well as a lexicon unique to the Twitter Dataset. With an accuracy score of 84%, the Jaccard document similarity algorithm was able to spot suicidal intent from users tweets, and with an accuracy of 93%, it was also able to spot non-suicidal intent. The Jaccard model seemed to be the most durable and computationally efficient for the problem and was chosen as the algorithm for detecting suicidal tendencies in users tweets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
GrapheneLiquid Crystal Synergy: Advancing Sensor Technologies across Multiple Domains
This review explores the integration of graphene and liquid crystals to advance sensor technologies across multiple domains, with a focus on recent developments in thermal and infrared sensing, flexible actuators, chemical and biological detection, and environmental monitoring systems. The synergy between graphenes exceptional electrical, optical, and thermal properties and the dynamic behavior of liquid crystals leads to sensors with significantly enhanced sensitivity, selectivity, and versatility. Notable contributions of this review include highlighting key advancements such as graphene-doped liquid crystal IR detectors, shape-memory polymers for flexible actuators, and composite hydrogels for environmental pollutant detection. Additionally, this review addresses ongoing challenges in scalability and integration, providing insights into current research efforts aimed at overcoming these obstacles. The potential for multi-modal sensing, self-powered devices, and AI integration is discussed, suggesting a transformative impact of these composite sensors on various sectors, including health, environmental monitoring, and technology. This review demonstrates how the fusion of graphene and liquid crystals is pushing the boundaries of sensor technology, offering more sensitive, adaptable, and innovative solutions to global challenges. 2024 by the authors. -
Predicting Job Risk from Artificial Intelligence in London Using Supervised Machine Learning Models
This study investigates the risk of job automation in London due to artificial intelligence (AI), applying supervised machine learning techniques to identify occupations most at risk. Leveraging a dataset encompassing job-specific features such as primary tasks, industry domains, and associated AI models, the research develops two predictive models. A Random Forest Classifier is used to categorize jobs as low, medium, or high automation risk, while a Linear Regression model estimates the proportion of each occupation's workload likely to be automated. The Random Forest model achieved a high accuracy rate of 97% in classifying job risk, indicating strong predictive capability. Meanwhile, the regression model explained 85% of the variance in the AI workload ratio, highlighting a significant relationship between job attributes and automation potential. These results suggest that job characteristics are reliable indicators of AI impact, particularly in routine, repetitive, and low-skilled roles that are more easily codified and replicated by algorithms. The findings align with broader economic theories such as creative destruction and technological waves, suggesting that AI not only displaces certain roles but also drives structural transformation within the labor market. By focusing on London, this study provides a localized understanding of how AI is reshaping employment patterns. It underscores the growing urgency for strategic workforce re-skilling and adaptive policy frameworks to mitigate negative outcomes and maximize opportunities presented by AI. Ultimately, this research contributes valuable insights into the interaction between AI technologies and employment, helping policymakers, employers, and educators anticipate change and prepare for a more resilient, inclusive labor market. 2025 IEEE. -
Prevention of Data Breach by Machine Learning Techniques
In today's data communication environment, network and system security is vital. Hackers and intruders can gain unauthorized access to networks and online services, resulting in some successful attempts to knock down networks and web services. With the progress of security systems, new threats and countermeasures to these assaults emerge. Intrusion Detection Systems are one of these choices (IDS). An Intrusion Detection System's primary goal is to protect resources from attacks. It analyses and anticipates user behavior before determining if it is an assault or a common occurrence. We use Rough Set Theory (RST) and Gradient Boosting to identify network breaches (using the boost library). When packets are intercepted from the network, RST is used to pre-process the data and reduce the dimensions. A gradient boosting model will be used to learn and evaluate the features chosen by RST. RST-Gradient boost model provides the greatest results and accuracy when compared to other scale-down strategies like regular scaler. 2022 IEEE. -
Advancing Credit Card Fraud Detection Through Explainable Machine Learning Methods
The world of finance has experienced a significant shift in the way money flows, due to the advancements in technologies such as online banking, card payments, and QR-based payment systems. These innovative banking payment facilities are offered by ensuring the safety of the transaction and ensuring that only the authorized customer can access and utilize these banking services. Credit card fraud is innovative way to cheat the user of the card. Government all over the word encouraging to the people for the uses of digital money. This research work focuses on analyzing the machine learning database by using a labelled dataset to classify legitimate and fraudulent business transactions with explainable AI. This study is based on decision tree, logistic regression, support vector machine and random forest machine learning techniques. 2024 IEEE. -
Customer Lifetime Value Prediction: An In-Depth Exploration with Regression, Regularization and Hyperparameter Tuning
In today's dynamic business environment, companies have been strategically shifting towards a customer-centric approach from their traditional product-centric focus. The main goal of this paper is to estimate customer lifetime value of 5,000 customers in the retail industry. This research follows a step-by-step approach to construct a multiple regression machine learning model. The model used in the study is based on the nine features to predict the customer life time value. First basic train-test split model is developed, which predicted 74% of variation in the customer lifetime value. This necessitates to improve the model performance, hence to address the multicollinearity problem lasso regularization is used. After lasso regularization , final model is trained with hyperparameter turning for further model performance improvement. The results show significant improvements in predicting customer lifetime value with the final model. This study suggests that the machine learning regression models can help to businesses to better understand how much value they can generate from individual customer.This deep understanding about customers helps retail businesses to align their customer engagement strategies to create a positive impact on the profitability and maximizing overall value offered to the customers. 2024 IEEE. -
Decoding Customer Lifetime Value to Unlock Business Success with Predictive Machine Learning Approach
This study highlights how crucial customers are for a company's success who directly impacts revenue and overall business value. This study focuses on analysis of customer lifetime value, the research uses data from 5000 customers with 8 important features with the main goal of predicting customer lifetime value. Business leaders often face choices about where to invest in marketing, like loyalty programs, incentives and ads or nothing. The study suggests that customer lifetime value is a key metric for making smart decisions, which measures how much a customer spends over their time with a company. To predict this value, the research explored different machine learning models - linear regression, decision tree regressor, random forest, and AutoML regressor. Each model is checked for how well it predicts customer spending habits. The results show that AutoML regression stands out for its accuracy without overcomplicating things. This study offers insights for businesses looking to improve their customer-focused strategies and long-term success. 2024 IEEE. -
Modalities in data: understanding text, images, and audio
Data modalities, encompassing diverse forms such as text, audio, image, and video, play a pivotal role in shaping modern data analysis and machine learning applications. Each modality represents information in a unique format, requiring specific processing and interpretation methods. The integration of multiple modalities, known as multimodal data, enhances decision-making and predictive accuracy, particularly in complex systems like sentiment analysis, speech recognition, and medical diagnostics. Deep learning techniques have facilitated the seamless fusion of multimodal data, enabling a more comprehensive understanding across various fields, from healthcare to social media analytics. For example, combining text with images improves sentiment analysis, while integrating audio and video aids in more accurate speech recognition. However, the incorporation of multimodal data presents challenges, including data heterogeneity, synchronization issues, and dimensionality concerns. Data formats differ across modalities, and aligning them for cohesive analysis requires sophisticated algorithms and computational power. Despite these obstacles, multimodal data offers significant benefits, such as enhanced customer experience in business and increased diagnostic accuracy in health care. Furthermore, the rise of large datasets and artificial intelligence (AI) technologies has fueled innovation, enabling the development of more efficient models capable of uncovering intricate relationships within data. This chapter discusses various modalities, their applications, and the technological advancements driving their integration. It also highlights the challenges in multimodal data processing and the solutions being developed to address these complexities, offering valuable insights for businesses, researchers, and AI practitioners. 2026 Elsevier Inc. All rights reserved. -
The Effect of Short-Term Training of Vipassanas Body-Scan on Select Cognitive Functions
This experiment examined the effect of a short-term body-scan meditation technique of vipassana practice on select cognitive functions. Participants (n = 77) were randomly divided into an experimental group (n = 37) and an active control group (n = 40). The average age of participants in the experimental group and the active control group was 21.67 1.16 and 21.40 3.14years, respectively. The experimental group practiced body-scan mindfulness, one session per day for 6 days with each session lasting for 25min. Participants in the active control group spent an equal amount of time reading fiction of their choice and listening to soothing music. Variables that were studied included five cognitive functions, namely reaction time, attention, learning, working memory, and social-emotional cognition. Results showed that short-term mindfulness meditation decreased reaction time and increased attention, with mild effect size. It may be concluded that short-term mindfulness practice might be an alternative for individuals who, due to various reasons, cannot practice long-term courses. 2018, National Academy of Psychology (NAOP) India. -
Concept Mapping of Issues of Students Life in University
The undergraduate student body forms around 85.9% of the total number of students enrolled in India, which is a significant population. It has become imperative to understand the issues that these students face during their undergraduate years as a precursor to developing mechanisms and strategies to enable student progress, both academically and developmentally. This study aimed at developing a concept map to outline the various aspects and issues of the undergraduate students life in India utilizing the concept mapping method. Data from participants (n = 141) at different phases was analysed resulting in 49 unique life issues and aspects and 8 clusters. The emerging issues have relevance and implications for teachers, parents, administrators and other stakeholders in structuring and developing services targeted towards undergraduate students in India. 2015, National Academy of Psychology (NAOP) India. -
Study of Effect of Vipassana on Anxiety and Depression
International Journal of Psychology and Behavioral Sciences, Vol-2 (6), pp. 274-276. -
The Search for Universal Values
IOSR Journal of Humanities and Social Science Vol. 2, Issue 1, pp 69-72, ISSN No. 2279-0837 -
A turn-on bis-hydrazone fluorescent chemosensor for selective Cd2+ detection: synthesis, structural insights, and theoretical validation
Heavy metal pollution, particularly from cadmium(ii) ions (Cd2+), causes severe environmental and health risks due to its acute toxicity, carcinogenicity, and bioaccumulation, leading to kidney damage, neurological disorders, and other physiological issues. Herein, we report the one-pot synthesis of a bis-hydrazone-based fluorescent probe L2H2O (1) for selective detection of Cd2+. Probe 1 was derived from isophthalaldehyde and 3-pyridylcarbonyl hydrazine and single-crystal X-ray diffraction discloses a well-defined binding pocket with pyridyl, imine, and carbonyl donor sites suitable for Cd2+ coordination. Probe 1 exhibits weak emission in CH3CN/HEPES buffer (9?:?1, v/v, pH 7.4) due to photoinduced electron transfer (PET) and unrestricted intramolecular rotations. Upon selective binding to Cd2+, 1 displays a pronounced turn-on fluorescence response with intensity enhancements of at ?324 nm and at 420 nm, accompanied by bathochromic shifts to 327 nm (?? = 3 nm) and 445 nm (?? = 25 nm) (?ex = 295 nm). The limit of detection (LOD) for probe 1 with metal Cd2+ is 3.39 M, with a binding constant of 5 103 M?1. 1H NMR titration, DFT-optimized geometries (B3LYP/6-31+G(d)/LANL2DZ), and simulated UV-Vis spectra further confirm binding of Cd2+, blocking PET and rigidifying the structure via chelation-enhanced fluorescence (CHEF). This work presents a modular hydrazone scaffold for developing selective Cd2+ sensors with potential application in environmental and biological monitoring. The Royal Society of Chemistry and the Centre National de la Recherche Scientifique, 2026. -
The Effect of short format body-scan mindfulness meditation on cognitive function and affect
Studies of mindfulness are consistent in their finding indicating that mindfulness can serve as a therapy model to deal with many psychological and physical problems, and improve wellbeing. Further, mindfulness is also found to enhance the moment to moment experience of individuals with better clarity of phenomenon. Interestingly, most of these studies have been conducted among seasoned practitioners or as long-format courses. Although long-format can be an ideal practice, it often proves to be expensive for many. Also, some people may not be able to practice in long-format due to several reasons. Thereby, it is imperative to investigate the benefits of short-format mindfulness exploring its utility and scalability. This experimental study examined the effect of short format mindfulness especially on affect and cognitive function. Participants (N=72; F=40, M=32) were randomly divided into an experimental group (N=35; M=15, F=20) and an active control group (N=37; M=17, F=20). The average age of participants in the experimental group and the active control group was 21.79 and 21.59 respectively. The experimental group practiced body-scan mindfulness, one session per day for six days, each session lasting for 25 minutes. Participants in the active control group spent an equal amount of time while reading fiction of their choice and listening to soothing music. Variables that were considered include positive and negative affects, and five cognitive functions namely psychomotor function, attention, learning, working memory-simple, and social-emotional cognition. Results showed an increase in positive affect and a decrease in negative affect, and an increase in performance in all five cognitive functions in the experimental group with an effect size ranging from mild to moderate, in comparison to active control. The study concluded that short format mindfulness practice, although may not be ideal, might be an alternative for individuals who due to various reasons cannot practice long format courses. -
An Intelligent Method for Fraud Detection in Digital Payments based on SVR with GC-RF Approach
The use of automated algorithms to detect fraud on electronic payment networks is challenging. Digital payment systems and their users are vulnerable to cybercriminals who take advantage of security holes or users' negligence to steal passwords, perpetrate fraud, launder money, and carry out other malicious acts. Conventional methods of fraud detection are challenging to execute because of the difficulties of acquiring massive volumes of manually annotated data. It is tough to notice new trends because fraudsters are often changing their techniques. Feature extraction, model training, and data preprocessing were the main areas of emphasis in this systematic research. Data pretreatment encompassed tasks such as acquiring training sample data, cleaning, converting, integrating, and altering the data. Feature extraction is the backbone of SVR-GC-RF model training; it takes all the data in a dataset and turns it into features. The suggested method outperformed SVR and RF in terms of accuracy by 95.23 percent. The importance of hierarchical fraud detection in online payment systems is highlighted in this paper. Through the use of effective feature extraction and model training, the study enhances fraud detection. Methods for detecting fraud need to change if they are to keep up with the criminals. 2025 IEEE. -
Enhanced dielectric and supercapacitive properties of spherical like Sr doped Sm2O3@CoO triple oxide nanostructures
Integrating the hybrid nanostructures exhibiting enhanced storage and electrical properties requires tuning of composition of constituents. To address this issue, we prepared Sr2+ nanoparticles (NPs) decorated over Sm2O3@CoO nanostructures (NS) by chemical precipitation. The structure integrity of the composite was determined by analytical tools. Based on the strongest peak of X-ray diffraction (XRD), crystallite size of the nanoparticles was determined to be 26.14 nm, indicating a mixed phase of monoclinic and tetragonal crystal formation. FESEM revealed a spherical-like morphology with a homogeneous distribution of microstructures with average sizes ranging from 68 nm to 60 nm. The optical absorptivity revealed a redshift in absorption bands centred at 337.0 nm, 343.9 nm, and 353.0 nm in UV-region. The optical band gap of NS was found to be in the range of 3.38 eV to 3.15 eV, and the BET surface area of Sr15%:Sm2O3@CoO was found to be 458469 cm2/g with a corresponding pore size of 13.17 nm. All Sr-doped Sm2O3@CoO NS exhibited higher ionic conductivity and dielectric constant than undoped material. In an aqueous KOH electrolyte, the NS showed a specific capacity of 234.2C/g (65.1mAh/g) demonstrating the material as potential candidate in energy storage and dielectrics. 2022 Elsevier Ltd -
Enhanced electrical properties of CuO:CoO decorated with Sm2O3 nanostructure for high-performance supercapacitor
In the present investigation, we have synthesized samarium (Sm) nanoparticles (NPs) and anchored them onto the surface of CuO:CoO nanostructure (NS) by utilizing a simple chemical precipitation method. Nanostructures (NS) were characterized utilizing powdered X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), scanning electron spectroscopy (SEM), transmission electron spectroscopy (TEM), UVvisible spectroscopy (UVVis), and BrunauerEmmettTeller (BET) studies. Resulting Smx CuO: CoO (x = 1%, 5%, 10%, and 12%) NS were investigated for their anomalous electrical and supercapacitive behavior. NS energy storage performance was experimentally determined using cyclic voltammetry (CV), galvanostatic chargedischarge (GCD), and electrochemical impedance spectroscopy (EIS). Sm10%CuO:CoO exhibited better electrochemical response than other samples and showed a maximum specific capacitance of 283.6F/g at 0.25A/g in KOH electrolyte. However, contrary to our expectation, NS displayed rectifying nature in I-V, intercalative nature in C-V, and polaronic permittivity in all concentrations of Sm2O3 doping as compared with undoped CuO:CoO NS. The outstanding properties of Smx CuO:CoO NS are attributed to the synergy of high charge mobility of Sm NPs, leading to significant variation in dielectric permittivity, currentvoltage (I-V) response, capacitancevoltage (C-V) behavior, with the formation of Sm3+ ionic cluster. The clusters lead to a change in dipole moment creating a strong local electric field. Additionally, a CR2032 type symmetric supercapacitor cell was fabricated using Sm10%CuO:CoO, which exhibited a maximum specific capacitance of 67.4F/g at 0.1A/g. The cell was also subjected to 5000 GCD cycles where it retained 96.3% Coulombic efficiency. Graphical Abstract: [Figure not available: see fulltext.] 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Facile synthesis of novel SrO 0.5:MnO 0.5 bimetallic oxide nanostructure as a high-performance electrode material for supercapacitors
Perovskite bimetallic oxides as electrode material blends can be an appropriate method to enhance the supercapacitor properties. In the present research, SrO 0.5:MnO 0.5 nanostructures (NS) were synthesized by a facile co-precipitation method and calcinated at 750800C. Crystal structure of SrO 0.5:MnO 0.5 NS were characterized by X-ray diffraction, surface chemical composition and chemical bond analysis, and dispersion of SrO into MnO was confirmed by X-ray photoelectron spectral studies. Structural morphology was analyzed from scanning electron microscopy. Optical properties of SrO 0.5:MnO 0.5 NS were studied using UV-Visible spectrophotometer and SrO 0.5 and MnO 0.5 NS showed ?75nm grain, ? 64nm grain boundary distance, with two maxima at 261nm and 345nm as intensity of absorption patterns, respectively. The synthesized SrO 0.5:MnO 0.5 NS exhibited high specific capacitance of 392.8F/g at a current density of 0.1A/g. Electrochemical impedance spectroscopy results indicated low resistance and very low time constant of 0.2s ?73% of the capacitance was retained after 1000 galvanostatic charge-discharge (GCD) cycles. These findings indicate that SrO 0.5:MnO 0.5 bimetallic oxide material could be a promising electrode material for electrochemical energy storage systems. The Author(s) 2022.