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Do Tourists' Motives Alter Based on What They See on E-Sources of Information? An SEM Approach to Determine the Impact
With growing competition in the tourism industry and changing tourists' expectations, motives and behavior, marketers are at the outset to promote, position and brand their tourism destinations lucratively. Though there are many allied themes on destination branding and marketing arena, tourists' motives have been widely looked up as research emphasis the dynamic revamps being seen with the intervention of digital sources of information. However, research at this aspect is at gradual phase. This paper focuses on such a theme and tries to understand the significant impact of tourists' behavior towards esources of information on their motives. Literary sources have been analyzed and hypothesis has been formulated. To test the assumption, the research location has been chosen - a district in Tamil Nadu state which serves wide range of tourists' motives and bestowed with distinct tourism attractions. A Structured questionnaire containing the necessary items measuring the study factors has been floated to 422 tourists through convenient sampling technique. However, 327 have been rounded as the final sample after excluding the illegible responses. Three Stage Analysis has been performed where Exploratory Factor Analysis (EFA) has been sued for data reduction and exploring the new factors, Confirmatory Factor Analysis (CFA) has been used for identifying and confirming the individual models and Structural Equation Modelling (SEM) has been used for understanding the structural relationship between the factors. Model fit has been found for CFA and SEM. Significant impact has been found by tourists' behavior towards the esources of information on tourists' motives. Simple Percentage Analysis (SPA) has been used to analyze the distribution of respondents based on their personal factors. Suggestions to the marketers and policy makers have been provided through managerial implications. Theoretical recommendations have also been narrated. Copyright IJHTS. -
Electrochemical investigation of neodymium doped vanadium pentoxide anchored on reduced graphene oxide nanocomposites for hybrid symmetric capacitor devices
The modern world is highly dependent on portable electronic gadgets, so high-performance energy storage devices are a major demand for human beings. Here, we construct neodymium-doped vanadium pentoxide anchored with reduced graphene oxide nanocomposite (rGO/Nd:V2O5) as the electrode material for a high-performance symmetric capacitor device. The prepared electrodes showed pseudocapacitor behaviour and double layer capacitor behaviour, indicating the hybrid nature of the rGO/Nd:V2O5 electrode. Also, the V2O5, Nd:V2O5 and rGO/Nd:V2O5 electrodes show higher capacitance behaviour of 447, 677 and 1122 F/g at 1 A/g and 89 %, 94 % and 98 % cyclic efficiency at the 1000th cycle. However, the rGO/Nd:V2O5 symmetric capacitor device exhibits a higher capacitance value of 218 F/g at 1 A/g and a cyclic efficiency of 82 % at the 10000th cycle. Also, this electrode shows a low charge transfer resistance value of 12.67 ?. This result shows the prepared rGO/Nd:V2O5 electrode as the high-performance electrode material for the supercapacitor devices. 2023 Elsevier Ltd -
Deep Q-Learning for Autonomous Vehicle Navigation in Smart Mobility
The proposed system leverages Deep Q-Learning to enhance autonomous vehicle navigation in smart mobility environments. By integrating reinforcement learning with deep neural networks, the system enables vehicles to make real-time decisions while adapting to dynamic traffic conditions. The framework employs a reward-based learning mechanism to optimize path selection, collision avoidance, and efficient maneuvering in complex urban scenarios. To improve decisionmaking accuracy, the proposed approach incorporates an experience replay mechanism, preventing overfitting and ensuring stable learning. Additionally, a target network is utilized to enhance training convergence, allowing the model to generalize effectively across varying road conditions. The system is further optimized through adaptive explorationexploitation strategies, enabling vehicles to balance learning new routes while prioritizing safe and efficient navigation. The proposed methodology demonstrates significant improvements in autonomous mobility, offering a scalable and robust solution for next-generation smart transportation systems. 2025 IEEE. -
Unveiling the Future of Business Success: Integrating Environmental and Sustainable Criteria through Business Cases
This chapter explores how environmental and sustainable criteria can enhance organizational success in rapidly changing economic and societal trends. It also focuses on future value creation. The continuing fight is leading to the obsolescence of traditional tools for management due to the increased level of competition, rapid rates of change, and time compression in society. To address urgent demands for sustainability, this research examines the blending of ecological and sustainable standards with corporate objectives, illustrating how businesses can benefit from such integration. The existing business practices are presented with evidence showing their impact on the environment and society, thereby stressing the need for sustainability measures that ensure long-term success. Through an exploration of peer-reviewed literature on this topic, a variety of ways are examined. Different companies can incorporate these criteria into their operations by an analysis of best practice followed by market leaders that involve setting clear objectives and identifying key performance indicators (KPIs) to measure progress. In addition, the chapter investigates the strategies that corporations need to implement in order to adopt environmental integrity principles within their company policies, such as renewable energy sources, intelligent buildings and circular economy models, as well as other solutions that can reduce their ecological footprints, leading to organizational excellence. 2026 selection and editorial matter, Sonal Trivedi, Balamurugan Balusamy, Krishnaraj Nagappan, Dinesh Krishnan Subramaniam and Daniel Arockiam; individual chapters, the contributors. All rights reserved. -
Advancements in Hand Gesture Technology for Enhancing Accessibility in Disability Assistance
Hand gesture recognition technology has become a crucial innovation in assistive technology, providing enhanced accessibility and independence for individuals with disabilities. In order to enhance communication, mobility, and rehabilitation support, this paper investigates the development and integration of AI-powered gesture recognition systems with wearable devices, augmented reality (AR), and the Internet of Things (IoT). The adaptability and ease of use of the current solutions, such as sign language interpretation and smart prosthetics, are severely limited. We propose a novel framework that combines cloud-based data storage, haptic feedback mechanisms, and real-time AI processing to create a highly responsive and personalized user experience to fill in these gaps. The research focuses on accuracy, responsiveness, and ease of use during its comprehensive analysis of prototype testing data and user feedback. The system is able to continually improve the accuracy of gesture recognition and adapt to the requirements of each user by making use of deep learning algorithms. The study also emphasizes the possibility of incorporating brain-computer interfaces (BCIs) for improved control and responsiveness. By providing individualized therapeutic exercises and real-time feedback, our findings suggest that incorporating gesture-controlled interfaces into rehabilitation programs can significantly benefit stroke patients and individuals with motor impairments. Gesture-based smart home control is also made possible by IoT connectivity, which makes life easier for people with limited mobility. An assessment of the system's impact, obstacles to widespread adoption, and potential future directions for improving AI models and making them more affordable are presented at the study's conclusion. The goal of this study is to help close the digital divide for people with disabilities and contribute to the ongoing development of accessible technology. 2025 IEEE. -
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. -
A study on smart device application platform
Cloud Computing is considered to be one of the hottest research areas as it provides an approach through which the data is stored and accessed over the Internet in a virtual environment. The main idea to adapt this technology is that it shares the available resources rather than having separate local servers. This technology plays a crucial role in the healthcare sector as the healthcare industries believe that by incorporating cloud services within the healthcare sector it could provide quality services to the patients. Many industrial specialists suggest ways of converting the huge amount of data collected from the healthcare into meaning information and later sharing this valuable information to the user at the right time. The smart device is an electronic rig that is efficient to answer, sympathize and interact mutually with its users and other smart devices, one of the upcoming smart devices are smart shirts. Smart shirts allow the user to share information like Facebook or LinkedIn profile details. This paper focuses on providing wearable devices to the user in order to have monitored over his/her health. Springer Nature Singapore Pte Ltd. 2019 -
A comparitive study on traditonal healthcare system and present healthcare system using cloud computing and big data
Cloud computing is one the emerging technology which provides all the necessary resources required for day to day operations of an organization in a virtual environment. It is also known as green computing as it reduces the physical existence of the hardware resources. Health is being considered as a basic right for an individual. Even though there are advancements in the healthcare sector of India when compared to earlier stages, there is still a need for betterment in this sector. In order to make progress in this field, constant learning and better economic standards are needed. This paper provides a comparative view of the progress made by India in the healthcare sector after the introduction of two major technologies such as cloud computing and big data. 2017 IEEE. -
Analysis on emotion-aware healthcare and Google cloud messaging
Cloud computing has the potential to get integrated with the healthcare sector. It provides functionality for managing data in a distributed environment. The concept of Healthcare services is becoming popular in the Healthcare sector as it helps the patients to get immediate access regarding his/her health related information whenever needed and wherever needed using cloud computing technology. The Big Data Application in Emotion-aware Healthcare system [BDAEH], gives attention to both the emotion factor and logical reasoning of the user. The basic functions of this system are collecting health-related data, transmitting the collected data, analyzing the received data, storing them and making it available to a user in order to perform diagnosis and predict medications. Mobile devices are becoming an essential tool in our day to day lives. By integrating the concept of Google Cloud messaging alongside BDAEH system, numerous tasks can be done efficiently. 2017 IEEE. -
Heavy metal ion sensing strategies using fluorophores for environmental remediation
The main aim of this review is to provide a holistic summary of the latest advances within the research area focusing on the detection of heavy metal ion pollution, particularly the sensing strategies. The review explores various heavy metal ion detection approaches, encompassing spectrometry, electrochemical methods, and optical techniques. Numerous initiatives have been undertaken in recent times in response to the increasing demand for fast, sensitive, and selective sensors. Notably, fluorescent sensors have acquired prominence owing to the numerous advantages such as good specificity, reversibility, and sensitivity. Further, this review also explores the advantages of various nanomaterials employed in sensing heavy metal ions. In this regard, exclusive emphasis is placed on fluorescent nanomaterials based on organic dyes, quantum dots, and fluorescent aptasensors for metal ion removal from aqueous systems, and to identify the fate of heavy metal ions in the natural environment. 2024 -
CHARACTERIZATION OF BOUNDS FOR ??ADJACENCY ENERGY OF A GRAPH
Recently Nikiforov et.al [9] put forward the ??adjacency energy of a graph G. In this paper, we continue the work on ??adjacency energy and obtain bounds for this new parameter in terms of order, size and the first Zagreb index. Indian Mathematical Society, 2023. -
Exploring motivations, impact, and coping mechanisms of post-dating on female dating app users
Online dating apps are a popular platform for forming new connections. Generally, a virtual acquaintance intensifies between the users, eventually converting it into an offline relationship. Despite the anonymity of users being a prime motivation to initiate a conversation, it does pose challenges, including mental health issues and sexual abuse. There are increasing reports of privacy violations, extortions, cyberstalking, and sexual assaults connected with dating apps. While people in a relationship decide to set boundaries to avoid each other, both users go through an uncomfortable phase. Unless both parties mutually share the responsibility, the relationship issues are not sorted out without going through an abusive phase. One person's separation anxiety might become a physical manifestation, including virtual and physical assaults. The current study relies on focus group discussions to understand postdating app manifestations and its impact on female dating app users. The study involves respondents mostly exposed to online dating. 2023, IGI Global. All rights reserved. -
DATING APPS, FORMS OF ABUSE AND PERSONALITY TYPE
[No abstract available] -
Ensuring cinema's success and failing audience: Exploring dominant cinematic violence
Screen violence has steadily increased in Indian cinema and has become a commercially lucrative aesthetic. The more Indian cinema portrays violence, the more success it registers, breaking the previous record set by yet another violent crime movie. The intended meaning of these successful violent productions and the screen messages perceived by the audience seem to echo each other, reinforcing specific dominant cultural values. While recording commercial success, these films completely rewire the essential Indian cinematic aesthetics cultivated over a century. This paper is a narrative commentary on the increased violence in Indian cinema in the last six years and attempts to point out the lost significance of other film genres. The arguments presented are drawn from a visual content analysis of 50 commercially successful films produced between 2018-23. The paper attempts to problematize the shrinking diverse audience and the increasing monolithic audience looking for a one-time screen experience rather than appreciating the cinema possibilities as a mass-appealing medium. 2025 by IGI Global Scientific Publishing. -
Early bruise detection, classification and prediction in strawberry using Vis-NIR hyperspectral imaging
The most frequent kind of damage to strawberries is bruising. However, most of the bruises are so barely perceptible at an early stage on the surface, that detection of them with the human eye is quite challenging. This study proposes a method for accurately detecting and classifying the damage using reflectance imaging spectroscopy. In order to carry out the study, an experiment was devised to artificially induce bruises and a dataset was generated at different bruise intervals. A model for detecting and classifying bruises at their latent stage was developed using machine learning classifiers, including support vector machines (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), to investigate the changes over time after bruise occurrence on the detection performance. Regression models for the prediction of bruising time were developed using partial least square regression (PLSR), RF, gradient boosting (GB), support vector regression (SVR), and DT. Among the compared models, both SVM and LDA could achieve 99.99 % classification accuracy. RF was regarded as being the most advisable for detection and prediction jobs due to its high performance. It achieved MSE of 0.052 and R2 of 0.989 for prediction. 2024 Elsevier Ltd -
Early bruise detection, classification and prediction in strawberry using Vis-NIR hyperspectral imaging
The most frequent kind of damage to strawberries is bruising. However, most of the bruises are so barely perceptible at an early stage on the surface, that detection of them with the human eye is quite challenging. This study proposes a method for accurately detecting and classifying the damage using reflectance imaging spectroscopy. In order to carry out the study, an experiment was devised to artificially induce bruises and a dataset was generated at different bruise intervals. A model for detecting and classifying bruises at their latent stage was developed using machine learning classifiers, including support vector machines (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), to investigate the changes over time after bruise occurrence on the detection performance. Regression models for the prediction of bruising time were developed using partial least square regression (PLSR), RF, gradient boosting (GB), support vector regression (SVR), and DT. Among the compared models, both SVM and LDA could achieve 99.99 % classification accuracy. RF was regarded as being the most advisable for detection and prediction jobs due to its high performance. It achieved MSE of 0.052 and R2 of 0.989 for prediction. 2024 Elsevier Ltd -
Analysis of the chemical properties and high-temperature rheological properties of MDI modified bio-asphalt
As an environmentally friendly material, bio-oil is employed to partially replace non-renewable petroleum asphalt, but its addition weakens the high-temperature non-deformability of petroleum asphalt. Therefore, the 4,4?-diphenylmethane diisocyanate (MDI) was employed as a chemical modifier of bio-asphalt to improve its high temperature rheological properties. The MDI with addition of 0.5%, 1%, 2%, 4% by weight, and the bio-oil with addition of 12% were used to obtain the MDI modified bio-asphalts. The chemical reaction mechanism between the MDI and bio-asphalt was analyzed by employing the Fourier-transform infrared spectroscopy (FTIR) and gel permeation chromatography (GPC) tests. Meanwhile, the rotational plate viscosity (RPV) test, the temperature sweep test, and the multiple stress creep and recovery (MSCR) test were employed to evaluate the high-temperature rheological properties of the MDI modified bio-asphalts. Moreover, the relationships between the chemical reaction mechanism and high-temperature rheological parameters of MDI modified bio-asphalt were established. Test results show that a nucleophilic addition reaction occurred between the MDI and the active hydrogen of bio-asphalt to form urethane chains, which increased the content of macromolecular polymers in the bio-asphalt. The MDI increased the G*/sin? (rutting factor) and the E(?) (visco-flow activation energy) of the bio-asphalt, but decreased its permanent strain and Jnr (non-recoverable creep compliance). Therefore, the MDI modifier effectively enhanced the permanent non-deformability of the bio-asphalt. Both IUrethane and LMS were positively correlated with the rutting factor, viscosity and 1/Jnr, and had significant correlations at a significance level of 0.05. Furthermore, the optimal ratio of MDI to bio-oil was determined to be 1:6 by mass. 2020 Elsevier Ltd -
AI-Driven Tutorial Code Learning System: Personalized Programming Education Through Adaptive Instruction and Gamification
Existing programming education faces critical challenges, such as lack of personalization, restricted feedback tools, and scalability limitations that hinder efficient learning outcomes. This paper presents an AI-powered tutorial code learning system to transform programming education through personalized and adaptive instruction. The system integrates advanced components, including learner modeling, intelligent content recommendation, error analysis, adaptive evaluation, gamification, learning analytics, integration frameworks, quality assurance, security, and scalability layers. To evaluate the system, the study employs a mixed-methods research approach, incorporating embedded case studies and a randomized controlled trial (RCT). Rigorous data collection methods, system measure validation, undergraduate program participant selection, quality assurance protocols, statistical analysis, and ethical considerations are utilized in this work. The architecture demonstrates potential for scalable and globally accessible programming education, addressing traditional challenges through personalized learning protocols. Unlike traditional platforms offering static content and limited feedback, this AI-powered system acts as a personalized tutor, providing active problem-solving and continuous learner engagement. The adaptive system delivers optimal learning paths based on individual student needs and has the potential to transform programming education delivery and outcomes. 2025 IEEE. -
Performance analysis of Clustering algorithms for dyslexia detection
Clustering algorithms plays vital role in analysing and evaluating vast number of high dimensional health care data ranging from medical data repositories, clinical data, electronic health records, body sensor networks, IoT devices, and so on. Dyslexia, a learning disorder is a common problem that is found in children during the initial stages of formal education, which is detected as mild to severe. It can also be one of the reasons of failure in the school. According to the literature this difficulty is commonly seen among Special Education Need children. There are few studies focussed on the application of classification algorithms for detecting the presence of dyslexia. This paper focusses one of SDG, goal 4:Quality Education, as dyslexic students can be given equal and quality education. Analyses of an online gamified test-based dataset is done by applying various clustering techniques such as K-means, Fuzzy c-means, and Bat K-means to assess their effectiveness in detecting the problem dyslexia. As the dataset is large, it is observed that usage of clustering methods gives us gain insight into the distribution of data to observe characteristics of each cluster. The clustering results are evaluated using root mean squared error (RMSE), mean absolute error (MAE), Xie-Beni index and it is found K Means outperforms FCM, Bat K Means algorithm for analysing different levels of the learning disorder. The Electrochemical Society -
Emotion Trajectory Analysis and Model Comparison for Hate Speech and Radicalization Detection in Code-Mixed Platforms
The growing presence of multilingual and codemixed content on social media creates major challenges for automated emotion recognition and mental health support. In this work, we introduce an emotion-aware computational framework that processes code-mixed Indian language comments and predicts user emotions with high accuracy, followed by context-aware support suggestions. Our dataset comes from the AI4Bharat IndicNLP corpus [14] and the Dravidian-CodeMix sentiment dataset [15], featuring a variety of multilingual user comments. To maintain linguistic consistency, we translate the raw texts into English using Google Translator and then preprocess them through normalization, tokenization, and stopword removal. We use three advanced transformer-based models, DistilBERT (six emotions), DistilRoBERTa (seven emotions), and RoBERTa GoEmotions (27+ emotions), to categorize the emotions in the comments. We compare predictions across the models and select the most reliable label for each text, which is further verified through manual checks with human annotators. This process results in a curated dataset labeled with emotions and enriched with model provenance. With this dataset, we train a Logistic Regression classifier using TF-IDF features to create an efficient, explainable prediction pipeline. The system classifies emotions and provides tailored suggestions based on emotional states, improving user support in online interactions. Experimental results show the robustness of the pipeline and its ability to adapt to various code-mixed inputs. This study offers an integrated dataset-model-suggestion framework that advances emotion recognition in multilingual contexts and supports the creation of practical emotion-aware digital systems. 2025 IEEE.