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Motivational Behaviour of Tourism Employees in Relation to Organisational Culture and Career Orientations
The productivity and effectiveness of any organisation depends mainly on the performance level of the employees in the organisation. Human behaviour scientists over the years have conducted various studies and have concluded that, the performance of employees in any organisation depends largely on their motivational behaviour. Reviews of related literature confirm the role of various factors in the motivational behaviour of employees including organisational culture and career orientation of employees. The title of the present study is Motivational Behaviour of Tourism Employees in Relation to Organisational Culture and Career Orientations. The major objectives included ascertaining the relationship between motivational behaviour and organisational culture and career orientations of tourism employees and finding out whether differences in demographic variables would account for significant differences in motivational behaviour. The population of the study consisted of 323 employees of public sector, private sector and multinational companies working in travel agencies, tour operations, airlines and hotels and resorts in Bangalore. The sampling technique employed was judgment sampling. For the present study three tools namely: Motivational Analysis of Organisations- Behaviour (MAO-B) by Pareek (2003), Organisational Culture Survey by Pareek (2003) and Career orientations Inventory by Schein (1990) were used to collect data. The findings of the study show that while two aspects of organisational culture namely internal and future oriented influence the motivational behaviour of employees working in the private sector, no aspect of organisational culture has any influence on the motivational behaviour of employees working in the public sector. Further, only ambiguity tolerant aspect of organisational culture influence the motivational behaviour of employees working in multinational companies. -
Motivational factors leading to the limited presence of women chefs in the hotel industry of Bengaluru /
International Journal of Innovative Studies In Sociology And Humanities, Vol.3, Issue 8, pp.108-121, ISSN No: 2456-4931. -
Movie Success Prediction from Movie Trailer Engagement and Sentiment Analysis
The diverse movie industry faces many challenges in the promotion of the product across different demographics. Movie trailer engagements provide valuable information about how the audience perceives the movie. This information can be used to predict the success of the upcoming movie before it gets released. The previous research works were mainly concentrating on Hindi language movies to predict success. The current research paper includes the success prediction of movies other than Hindi. This paper aims to analyze various Machine Learning models performance and select the best performing model to predict movie success. The developed model can efficiently classify successful and unsuccessful movies. For the current research, the data is collected from various sources through web scrapping and API calls in Sacnilk, The Movie Database (TMDB), YouTube, and Twitter. Different machine learning classification models such as Random Forest, Logistic Regression, KNN, and Gaussian Nae Bayes are tested to develop the best-performing prediction model. This research can help moviemakers to understand the popularity of the movie among the viewers and decide on an efficient promotional strategy to make the movie more successful. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Movie-Induced Tour Guiding: Concepts and Future Implications in South Asian Perspective
Movies have an extensive impact on tourism and its promotion. Movie-induced tourism has been a worldwide phenomenon for the last couple of decades, but this phenomenon is confined to the marketing and promotion of tourism destinations. Here, a new approach has been introduced for co-creating a quality destination experience through traditional tour guiding. Considering the increasing emphasis on tourists experience, satisfaction, and destination imagery over the decades, this concept of movie-induced tour guiding will produce a synergistic value in the overall process of the outdoor leisure tour packages. 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Moving Towards Responsible Consumption: The Road Ahead for Sustainable Marketing
The fundamental tenet of consumerism revolves around the belief that the burgeoning consumption of goods is favourable for the economy. Since the dawn of the Industrial Revolution, humanity has witnessed an exponential upsurge in consumerism. It has been related both to the increase in the population size as well as an increase in our demands due to constant changes in lifestyle. Multiple sources have corroborated the fact that if this consumption behaviour continues unabated, we will soon face an acute shortage of resources of all kinds. Both consumer behaviour patterns such as addictive consumption and conspicuous consumption can be attributed to this. Amongst the solutions available, 'Demarketing' is one. It is a type of marketing when a brand wants to discourage you from buying its product. The paper is descriptive in nature and is based on secondary data which has been collected from journals, blogs, websites, magazines, books, etc. The paper intends to explore the theme of demarketing vis-vis the materialistic purchase behaviour of a modern-day consumer and green demarketing strategies that companies are adopting by way of sustainable marketing. The Electrochemical Society -
MR Brain Tumor Classification and Segmentation Via Wavelets
Timely, accurate detection of magnetic resonance (MR) images of brain is most important in the medical analysis. Many methods have already explained about the tumor classification in the literature. This paper explains the method of classifying MR brain images into normal or abnormal (affected by tumor), abnormality segments present in the image. This paper proposes DWT-discrete wavelet transform in first step to extract the image features from the given input image. To reduce the dimensions of the feature image principle component Analysis (PCA) is employed. Reduced extracted feature image is given to kernel support vector machine (KSVM) for processing. The data set has 90 brain MR images (both normal and abnormal) with seven common diseases. These images are used in KSVM process. Gaussian Radial Basis (GRB) kernel is used for the classification method proposed and yields maximum accuracy of 98% compared to linear kernel (LIN). From the analysis, compared with the existing methods GRB kernel method was effective. If this classification finds abnormal MR image with tumor then the corresponding part is separated and segmented by thresholding technique. 2018 IEEE. -
MRSP-Multi Routing Systems and Parameter Explanations to Build the Path in Underwater Sensor Network
The underwater network is currently widely used to locate moving objects beneath the sea, monitor marine security, and detect changes in the sea water. A large number of sensors, as well as a precise methodology, are necessary to detect changes in sea depth. The protocol should be revised in response to environmental and chronological changes. The sensor should have been designed with multiple knowledge to route packets in order to optimise transmissions. Because the node will choose the best route based on the circumstances, especially in an underwater network, the paper MRSP - multi routing systems and parameter validations to create the path in an underwater sensor network is discussed in the multi routing knowledge sensor operations, energy saving systems, redundancy reduction, and so on. All of these measures, combined with secure transmission with trusted neighbour selection, result in safer transmissions and more accurate path selection. 2022 IEEE. -
Mucormycosis (black fungus) ensuing COVID-19 and comorbidity meets - Magnifying global pandemic grieve and catastrophe begins
Post COVID-19, mucormycosis occurred after the SARS-CoV-2 has rampaged the human population and is a scorching problem among the pandemic globally, particularly among Asian countries. Invasive mucormycosis has been extensively reported from mild to severe COVID-19 survivors. The robust predisposing factor seems to be uncontrolled diabetes mellitus, comorbidity and immunosuppression acquired through steroid therapy. The prime susceptive reason for the increase of mucormycosis cases is elevated iron levels in the serum of the COVID survivors. A panoramic understanding of the infection has been elucidated based on clinical manifestation, genetic and non- genetic mechanisms of steroid drug administration, biochemical pathways and immune modulated receptor associations. This review lime-lights and addresses the What, Why, How and When about the COVID-19 associated mucormycosis (CAM) in a comprehensive manner with a pure intention to bring about awareness to the common public as the cases are inevitably and exponentially increasing in India and global countries as well. The article also unearthed the pathogenesis of mucormycosis and its association with the COVID-19 sequela, the plausible routes of entry, diagnosis and counter remedies to keep the infection at bay. Cohorts of case reports were analysed to spotlight the link between the pandemic COVID-19 and the nightmare-mucormycosis. 2021 Elsevier B.V. -
Mudhr: Malicious URL detection using heuristic rules based approach
Technology advancement helps the people in numerous ways such as it supports business development, banking, education, entertainment etc. Especially time critical and money related activities, people are fully really on internet and web applications. It saves valuable time and money. Despite of the benefits, it also gives wide space for the attackers to focus more victims. Malicious URL based attacks are most common and more dangerous attacks now a day which steals the credentials and sensitive data from the victims and perform malicious activities in the victim's space. Phishing, Spamming, drive by download are the example of such attacks and are preformed through malicious URL. Plenty of approaches are available to detect the malicious URL. That are grouped under three categories such as Blacklist based, Heuristic based and Machine Learning based approaches. Among the three, heuristic approach is better than the blacklist approach in term of better generalizing the malicious URL and gives equally accurate prediction with machine learning approach. This paper presents recent works in the field of malicious URL detection and novel technique to detect malicious URL based on the most important features derived from URL. 2022 Author(s). -
Mudscapes of Meaning: Performative Language of Chikal Kalo
Chikal Kalo is a unique mud festival celebrated in Marcela, Goa, reflecting deep religious, ecological, and cultural significance. This ethnographic study explores the oral tradition and performative practices of Chikal Kalo, which have been transmitted across generations without written documentation. Through participant observation and interviews with local residents, the research examines the festivals origins, symbolic elements, and evolving expressions. It highlights how embodied memory and ritual performance preserve indigenous heritage. The study also underscores the festivals role in reinforcing social cohesion and cultural identity. Chikal Kalo exemplifies a living tradition rooted in ecological consciousness and communal values. The festivals vibrant blend of dance, music, and food attracts travellers while preserving its devotional roots. The findings contribute to broader discussions on intangible cultural heritage and its role in sustainable community development, while safeguarding Konkani culture amid rapid modernization. 2026, IGI Global Scientific Publishing. -
Mul-Sensis: Multilingual Sentiment Analysis Framework for Emotion Detection
Sentiment analysis is a pivotal Natural Language Processing (NLP) task that enables the extraction of actionable insights from textual data, particularly social media. With the rise of public discourse on platforms like Twitter, analyzing sentiment trends has become crucial for decision-making in domains such as policy implementation, feedback evaluation, and public opinion monitoring. Mul-Sensis employs a hybrid approach combining transformer-based models with classical machine learning algorithms to enhance sentiment classification. The system integrates advanced preprocessing techniques to address linguistic complexities like sarcasm, idiomatic expressions, and domain-specific nuances. A robust hybrid annotation approach, incorporating both human expertise and machine-assisted methods, ensures high-quality, bias-free sentiment labeling. This study contributes a scalable, interpretable, and domain-agnostic framework for sentiment analysis, offering valuable insights for policymakers, researchers, and industries relying on textual data analytics. The findings highlight the transformative potential of hybrid and ensemble-based NLP approaches for understanding public sentiment across diverse cultural and linguistic contexts. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Mulberry Leaves (Morus Rubra)-Derived Blue-Emissive Carbon Dots Fed to Silkworms to Produce Augmented Silk Applicable for the Ratiometric Detection of Dopamine
Silk fibers (SF) reeled from silkworms are constituted by natural proteins, and their characteristic structural features render them applicable as materials for textiles and packaging. Modification of SF with functional materials can facilitate their applications in additional areas. In this work, the preparation of functional SF embedded with carbon dots (CD) is reported through the direct feeding of a CD-modified diet to silkworms. Fluorescent and mechanically robust SFare obtained from silkworms (Bombyx mori) that are fed on CDs synthesized from the Morus rubra variant of mulberry leaves (MB-CDs). MB-CDs are introduced to silkworms from the third instar by spraying them on the silkworm feed, the mulberry leaves. MB-CDs are synthesized hydrothermally without adding surface passivating agents and are observed to have a quantum yield of 22%. With sizes of ?4nm, MB-CDs exhibited blue fluorescence, and they can be used as efficient fluorophores to detect Dopamine (DA) up to the limit of 4.39nM. The nanostructures and physical characteristics of SF weren't altered when the SF are infused with MB-CDs. Also, a novel DA sensing application based on fluorescence with the MB-CD incorporated SF is demonstrated. 2023 Wiley-VCH GmbH. -
MuLSA-Multi Linguistic Sentimental Analyzer for Kannada and Malayalam using Deep Learning
Natural language Processing has been always a topic of interest in artificial intelligence. Opinion mining or Sentiment Analysis is an important application of Natural language Processing. Sentiment Analysis of text is to extract the sentiments underlined in the text. In this paper, a multi-linguistic sentimental analyzer (MuLSA), is implemented, a model that would address Malayalam, Kannada and English text. This model explores two languages in three categories of the text, its original script, transliterated script, and the combination of both along with English. Deep Learning, Recurrent Neural Network with LSTM is used as the basis for this model. The model exhibits 82% of prediction accuracy. 2021 IEEE. -
Multi Disease Identification in Tomato Plant using CNN and SVM
Tomato is a major trade crop; it is among the most widely consumed crops in daily life. Crop diseases reduce not only the quality of the crops but also their amount of production, thus, detection and identification of the specific diseases is of great importance. Diseases like the Mosaic virus, Bacterial Spot, and Yellow Leaf Curl Virus infect the tomato plant. The advanced detection and classification techniques are mainly employed in the diagnosis of these diseases. This helps in informing the farmers about the types of diseases that attack their crops. In this study, independent CNN and SVM classifiers built to classify the diseases. The CNN model extracts feature such as color and leaf edges from input images- then, it proceeds to classification. For SVM, PCA is applied for feature reduction in order to enhance performance and accuracy before classification. A dataset sourced from plant village has been utilized to train the network CNN and SVM. The proposed neural network model has been applied to categorize 4 types of tomato leaf conditions: one healthy and three diseased types of tomato leaves. The results show that the SVM approach achieves a classification accuracy of 94.33%, whereas the CNN model has slightly higher accuracy of 95.17%. 2025 IEEE. -
Multi objective energy aware integrated cloud scheduling with a consensus-based security
This research presents a multi-objective, energy-aware workflow scheduling framework for heterogeneous cloudedge environments that addresses both efficiency and data integrity challenges. Conventional encryption-based security mechanisms, although effective in protecting data during task offloading, often introduce significant computational and communication overhead, leading to degraded system performance. To overcome this limitation, this work proposes the consensus security-integrity and quality-aware workflow scheduler (CSIQA-WS), which integrates energy-aware scheduling with a lightweight, consensus-driven security mechanism. The model incorporates automatic service management and an attack prevention module to detect and mitigate malicious behavior during inter-node data transmission while maintaining quality of service (QoS) constraints. A dynamic coordination between edge and cloud resources enables efficient workload distribution and robust resource utilization. Experimental evaluation using scientific workflow benchmarks demonstrates that CSIQA-WS significantly reduces processing time and energy consumption compared to existing approaches. The proposed model achieves up to 92.29% reduction in processing time and consistently improves overall QoS while preserving data integrity in dynamic execution environments. These results indicate that CSIQA-WS provides an effective and scalable solution for secure and energy-efficient workflow scheduling in modern cloudedge systems. 2026 Institute of Advanced Engineering and Science. All rights reserved. -
Multi Parameterized Modified Local Binary Pattern for Lung Cancer Detection by Deep Learning Methods
The research work is focusing on developing a classification model for Lung Cancer detection by integrating the image features with Modified Local Binary Pattern (MLBP), Modified Principal Component Analysis (MPCA), newlinesymptoms and Risk factors using Deep Learning methods and converting the image features into three dimensional (3D) images. The aim of this research is to identify the malignant and normal tumours from the Computer newlineTomography (CT) images with improved accuracy. The 2D CT images of Lung Cancer patients have been preprocessed with Median and Gabor filtering methods and watershed segmentation. The CT images are also newlineprocessed with the Zero Component Analysis (ZCA) whitening and Modified Local Binary Pattern. The processed image is used in the research for classification. The Lung Cancer dataset in the research are collected from newlinevarious medical colleges. The dataset contain CT images with Lung Cancer and without Lung Cancer. The research is conducted by integrating the selected Image features, Risk factor and symptoms of Lung Cancer of the newlinesame patients. The Integration using feature selections is carried out with Modified Principal Component Analysis. The Modified Principal Component Analysis is used in the research to reduce the time complexity. The results are evaluated with Gini coefficient, Confusion Matrix parameters and ROC newlinecurve. Two Dimensional (2D) CT images are converted into a Three Dimensional (3D) image for the clarity and the visibility of Lung Cancer nodules. The conversion from 2D to 3D has been using combining two methods, the orthogonality and visualization of 4D rotation. This enabled to find the location of the Lung Cancer from different angle and with different viewpoints. The 3D image shows the location of the Lung Cancer by Four Dimensional (4D) visualization and 3D rotation, thus giving clarity to the newlineexisting 2D images. -
MULTI REFERENCE SKIP-LOT SAMPLING PLAN
Skip-lot sampling plans have become significant in modern quality control due to rising production volumes and the demand for cost-effective inspection methods that will yield high-quality outputs. When inspecting a submitted lot, a skip-lot plan is economically favourable and guarantees high quality. Thus, this approach benefits both producers and consumers. The skip-lot sampling plan generally utilizes the same sampling plan as the reference plans for both skipping and normal inspection. However, using the same plan in both phase favours either the producer or the consumer in the most essential situations. This article introduces a novel approach, the Multi Reference Skip-Lot Sampling Plan with the provision of having two different reference plans in the normal and skipping phases of the skip-lot plan. The paper explores the efficacy of this approach by deriving performance measures using a power series approach. To evaluate the proposed plan, a comparison is made with existing skip-lot sampling plans that use single sampling plans or double sampling plans as reference plans. This comparison is based on operational characteristics and average sample number values, accompanied by graphical representations. The comparative analysis demonstrates that the new plan effectively balances the satisfaction of both producers and consumers. Additionally, the study offers a strategy for selecting the plan parameters using the unity value approach, supported by a table providing unity values. 2025, Gnedenko Forum. All rights reserved. -
Multi-Agent Deep Reinforcement Learning for Hybrid Motion Planning in Dynamic Environments
This research presents a novel approach to addressing the challenges of gesture forecasting in impenetrable and dynamic atmospheres by integrating a hybrid algorithm within a multi-agent system framework. Traditional methods such as Force-based motion planning (FMP) & deep reinforcement learning (RL) often struggle to handle complex scenarios involving multiple autonomous agents due to their inherent limitations. To overcome these challenges, we propose a hybrid algorithm that seamlessly combines the strengths of RL and FMP while leveraging the coordination capabilities of a multi-agent system. By integrating this hybrid algorithm into a multi-agent framework, we demonstrate its effectiveness in enabling multiple agents to navigate densely populated environments with dynamic obstacles. Through extensive simulation studies, we illustrate the superior performance of our approach compared to traditional methods, achieving higher success rates and improved efficiency in scenarios involving simultaneous motion planning for multiple agents. A hybrid motion planning algorithm is also introduced in this very research. Performance Comparison of Hybrid Algorithm, Deep RL, and FMP are also discussed in the result section. This research paves the way for the development of robust and scalable solutions for motion planning in real-world applications such as collaborative robotics, autonomous vehicle fleets, and intelligent transportation systems. 2024 IEEE. -
Multi-atlas Graph Convolutional Networks and Convolutional Recurrent Neural Networks-Based Ensemble Learning for Classification of Autism Spectrum Disorders
Autism spectrum disorder (ASD) has an influence on social conversation and interaction, as well as encouraging people to engage in repetitive behaviors. The complication begins in childhood and persists through adolescence and maturity. Autism spectrum disorder has become the most common kind of childhood development worldwide. ASD hinders the capacity to interact, socialize, and build connections with individuals of all ages, and thus its early intervention is critical. This paper discusses some of the most recent approaches to diagnostics using convolutional networks and multi-atlas graphs for autism spectrum disorders. Also, several pre-processing approaches are elaborated. Graph convolutional neural networks (GCNs) to diagnose autism spectrum disorder (ASD) because of their remarkable effectiveness in illness prediction using multi-site data. Convolutional neural network (CNN) and recurrent neural networks (RNN) infrastructure studies functional connection patterns between various brain regions to find particular patterns to diagnose ASD. In our research, we implemented the GCN + CRNN ensemble method and achieved 89.01% accuracy based on resting-state data from the fMRI (ABIDE-II), a novel framework for detecting early signs of autism spectrum disorders is presented and discussed. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Multi-class SVM based network intrusion detection with attribute selection using infinite feature selection technique
An intrusion detection mechanism is a software program or a device that monitors the network and provides information about any suspicious activity. This paper proposes a multi-class support vector machine (SVM) based network intrusion detection using an infinite feature selection technique for identifying suspicious activity. Single and multiple classifiers generally have high complexity. To overcome all the limitations of single and multiple classifiers, we used a multi-class classifier using an infinite feature selection technique, which performed well with multiple classes and gave better results than other classifiers in terms of accuracy, precision, recall, and f_score. Infinite feature selection is a graph-based filtering approach that analyses subsets of features as routes in a graph. We used a standard dataset, namely the UNSW_NB15 data set generated by the IXIA perfect-storm tool in the Australian Centre for Cyber Security. This dataset has a total of nine types of attacks and 49 features. The comparative analysis of the manuscript work is done against eight different techniques, namely, hybrid intrusion detection system (HIDS), C5, one-class support vector machine, and others. The proposed work gave better simulation results using the 2015a Matlab simulator. 2021 Taru Publications.