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Analysis of Online In-Destination Booking Service Processes in the Travel Industry: A Case Study
This article presents a comprehensive analysis of the online in-destination booking service processes within the dynamic landscape of the travel industry. Utilizing a case study approach, the research investigates the various stages involved in providing travel-related services, focusing on the key players. The study employs a quantitative method to assess the information quality, system quality, service quality, customer satisfaction, and purchase intention of online in-destination booking. The research highlights the investigation of the usability of online travel booking systems and identifies the purchase intention of customers towards online travel booking websites. To address the research objectives, the participants are selected using a nonprobability sampling method. The sample size of the study is 225 from in and around Coimbatore. The sampling procedure used is convenience sampling. The sampling is selected based on convenience and accessibility to the residents. The findings reveal that there exists a significant difference in respondents opinions on quality criteria: system quality and service quality. Additionally, the study finds that the loading time of online travel booking websites is positively correlated with quality criteria and features of travel apps. By examining a specific case within the travel sector, this study contributes valuable insights that can inform strategic decision-making for businesses operating in the online in-destination booking space. The results aim to guide industry players in enhancing their operational efficiency, leveraging technology advancements, and aligning their services with evolving customer expectations, ultimately fostering sustainable growth in the competitive travel market. 2024, Bentham Books imprint. -
Smart Finances: A Web-Based System for Personalized Financial Management
Smart Finances is a dynamic and user-friendly web application designed to assist users in managing and understanding their personal finances with ease. Built using HTML, CSS, JavaScript, and PHP with MySQL for backend functionality and data persistence, the platform offers a suite of tools, including an interest calculator, investment calculator, budget manager, and interactive spending analysis. The intuitive interface enhances usability and promotes financial literacy, especially among students and individuals new to personal finance. The application supports both guest and registered user modes, offering tiered access to features based on authentication. Smart Finances aims to make money management more approachable and insightful through accessible design and practical functionality. Initial user testing with 20 participants indicated a 30% improvement in budgeting accuracy and a high satisfaction rate (88%) with the usability of the interface. These results validate the systems impact on financial literacy and user engagement. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Advanced Malware Analysis and Detection Using Deep Neural Networks
Malware is malicious software that is used to cause harm to the computer systems, networks or users across several operating systems, such as Windows, macOS, iOS, Android and Linux. The identification and categorisation of malware is a difficult subject with no one-size-fits-all solution due to the constant evolution of the malware and lack of standardised detection frameworks. The use of deep learning models in cybersecurity encourages the growth of Explainable Artificial Intelligence (XAI) and Interpretable Machine Learning (IML) techniques. The goal of this research is to automate the way of analysing malware using a simple framework without the need of complex software. This article focuses on the application of Neural Networks, a deep learning model in detecting and analysing the behavior of malware. The model performed better when compared to other techniques by achieving an accuracy of 99.43%, precision of 99.05% and a F1 score of 0.99, which was trained on a large dataset containing 1,38,048 samples. 2025 IEEE. -
Advanced Malware Analysis and Detection Using Deep Neural Networks
Malware is malicious software that is used to cause harm to the computer systems, networks or users across several operating systems, such as Windows, macOS, iOS, Android and Linux. The identification and categorisation of malware is a difficult subject with no one-size-fits-all solution due to the constant evolution of the malware and lack of standardised detection frameworks. The use of deep learning models in cybersecurity encourages the growth of Explainable Artificial Intelligence (XAI) and Interpretable Machine Learning (IML) techniques. The goal of this research is to automate the way of analysing malware using a simple framework without the need of complex software. This article focuses on the application of Neural Networks, a deep learning model in detecting and analysing the behavior of malware. The model performed better when compared to other techniques by achieving an accuracy of 99.43%, precision of 99.05% and a F1 score of 0.99, which was trained on a large dataset containing 1,38,048 samples. 2025 IEEE. -
Zero forcing number of degree splitting graphs and complete degree splitting graphs
A subset Z V(G) of initially colored black vertices of a graph G is known as a zero forcing set if we can alter the color of all ver- tices in G as black by iteratively applying the subsequent color change condition. At each step, any black colored vertex has exactly one white neighbor, then change the color of this white vertex as black. The zero forcing number Z(G), is the minimum number of vertices in a zero forcing set Z of G (see [11]). In this paper, we compute the zero forcing num- ber of the degree splitting graph (DS-Graph) and the complete degree splitting graph (CDS-Graph) of a graph. We prove that for any simple graph, Z[DS(G)] k + t, where Z(G) = k and t is the number of newly introduced vertices in DS(G) to construct it. 2019 Sciendo. All rights reserved. -
3-Sequent achromatic sum of graphs
Three vertices x,y,z in a graph G are said to be 3-sequent if xy and yz are adjacent edges in G. A 3-sequent coloring (3s coloring) is a function ?: V (G) ?{1, 2,...,k} such that if x,y and z are 3-sequent vertices, then either ?(x) = ?(y) or ?(y) = ?(z) (or both). The 3-sequent achromatic number of a graph G, denoted ?3s(G), equals the maximum number of colors that can be used in a coloring of the vertices' of G such that if xy and yz are any two sequent edges in G, then either x or z is colored the same as y. The 3-sequent achromatic sum of a graph G, denoted a'3s(G), is the greatest sum of colors among all proper 3s-coloring that requires ?3s(G) colors. This research initiates the study of 3-sequent achromatic sum and finds the exact values of this parameter for some known graphs. Furthermore, we calculate the a'3s(G) of corona product, Cartesian product of the graphs and some important results have been proved and a comparative study is carried out. 2021 World Scientific Publishing Company. -
Cop-edge critical generalized Petersen and Paley graphs
Cop Robber game is a two player game played on an undirected graph. In this game, the cops try to capture a robber moving on the vertices of the graph. The cop number of a graph is the least number of cops needed to guarantee that the robber will be caught. We study cop-edge critical graphs, i.e. graphs G such that for any edge e in E(G) either c(G?e) < c(G) or c(G?e) > c(G). In this article, we study the edge criticality of generalized Petersen graphs and Paley graphs. 2023 Azarbaijan Shahid Madani University. -
Early prediction of lungs cancer by deep learning algorithms from the CT images with LBP features
The early prediction of the any type of cancer can save the lives of many especially if it is lung cancer which is one of the deadly diseases in the world. Thus the early prediction is implemented we can increase life expectancy and bring the mortality level low. Although there are various methods to detect the lung cancer cells by X-ray and CT scans, however the CT images are more preferred. The 2D images like CT scans are used to get medical results more accurate. The proposed method here will discuss how the LBP features are used to analyze the CT images with the support of Deep Learning methods. In this research work we will discuss how the image manipulation can be done to achieve better results from the CT images through various image processing methods. LBP features helps in estimating the distribution of local binary pattern of an image. A final result with 93% is achieved after the training of the processed images by LBP features. 2020 SERSC. -
Mining Heterogeneous Lung Cancer from Computer Tomography (CT) Scan with the Confusion Matrix
Early detection of any sort of cancer, particularly lung cancer, which is one of the worlds most lethal illnesses, can save many lives. Life expectancy can be improved and the degree of mortality reduced by adopting the early forecast. While there are different methods like X-ray and CT scans to detect lung cancer cells, CT images resulted as more favored. The 2D images are used for more accurate medical results, such as CT scans. The proposed approach here will address how to interpret the CT images for the Mining Heterogeneous Lung Cancer from Computer Tomography (CT) Scan with the Confusion Matrix. This research will explore how the image conversion can be achieved through different methods of image processing to obtain better results from CT images. The Confusion Matrix helps to estimate inequality in a picture pattern. After the evaluation of the processed images by Confusion Matrix, a final accuracy with a result of 93% is obtained. 2023 Scrivener Publishing LLC. -
A deep learning approach in early prediction of lungs cancer from the 2d image scan with gini index
Digital Imaging and Communication in Medicine (DiCoM) is one of the key protocols for medical imaging and related data. It is implemented in various healthcare facilities. Lung cancer is one of the leading causes of death because of air pollution. Early detection of lung cancer can save many lives. In the last 5years, the overall survival rate of lung cancer patients has increased, due to early detection. In this paper, we have proposed Zero-phase Component Analysis (ZCA) whitening and Local Binary Pattern (LBP) to enhance the quality of lung images which will be easy to detect cancer cells. Local Energy based Shape Histogram (LESH) technique is used to detect lung cancer. LESH feature extracts a suitable diagnosis of cancer from the CT scans. The Gini coefficient is used for characterizing lung nodules which will be helpful in Computed Tomography (CT) scan. We propose a Convolutional Neural Network (CNN) algorithm to integrate multilayer perceptron for image segmentation. In this process, we combined both traditional feature extraction and high-level feature extraction to classify lung images. The convolutional neural network for feature extraction will identify lung cancer cells with traditional feature extraction and high-level feature extraction to classify lung images. The experiment showed a final accuracy of about 93.27%. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Lung Cancer Diagnosis from CT Images Based on Local Energy Based Shape Histogram (LESH) Feature Extration and Pre-processing
Lung cancer as of now is one of the dreaded diseases and it is destroying humanity never before. The mechanism of detecting the lung cancer will bring the level down of mortality and increase the life expectancy accuracy 13% from the detected cancer diagnosis from 24% of all cancer deaths. Although various methods are adopted to find the cancer, still there is a scope for improvement and the CT images are still preferred to find if there is any cancer in the body. The medical images are always a better one to find with the cancer in the human body. The proposed idea is, how we can improve the quality of the diagnosis form using pre-processing methods and Local energy shape histogram to improve the quality of the images. The deep learning methods are imported to find the varied results from the training process and finally to analyse the result. Medical examination is always part of our research and this result is always verified by the technicians. Major pre-processing techniques are used in this research work and they are discussed in this paper. The LESH technique is used to get better result in this research work and we will discuss how the image manipulation can be done to achieve better results from the CT images through various image processing methods. The construction of the proposed method will include smoothing of the images with median filters, enhancement of the image and finally segmentation of the images with LESH techniques. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Mathematical Model to Explore the Details in an Image with Local Binary Pattern Distribution (LBP)
Mathematical understanding is required to prove the completeness of any research and scientific problem. This mathematical model will help to understand, explain and verify the results obtained in the experiment. The model in a way will portray the mathematical approach of the entire research process. This paper discusses the mathematical background of proposed prediction of lung cancer with all the parameters. Processes involved analyzing the 2D images, basic quantitative method, from, related equation and fundamental algorithmic understanding with slightly modified versions of prediction are represented in the below section with how the local binary pattern distribution can be modified so that we get reduced run time and better accuracy in the final result. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Procedural Justice, Perceived Organisational Support, and Organisational Citizenship Behaviour in Business School
Background/Purpose: The effectiveness of a Business School depends on the extra role behaviours or Organ-isational Citizenship Behaviour (OCB) of its committed academics. The social exchange theory postulates that employees tend to display OCB when they know how their organisation would treat them. As B-School academics' inclination towards OCB is less understood, this study examines the interaction between Procedural Justice (PJ), Perceived Organisational Support (POS) and Organisational Citizenship Behaviour (OCB) among B-School academics. Methods: A survey was carried out to collect data from B-School academics, 378 responses were collected from B-Schools from the state of Kerala, India. Data validity and reliability analyses, and direct and indirect effects of research variables were tested using Partial Least Square (PLS) path modelling. Results: The results indicate PJ positively influences POS as well as dimensions of the OCB for B-School academics. Contrary to previous OCB studies, this study finds that POS do not significantly relate to Courtesy. The findings also show that POS fully mediates PJ's relationship with Altruism, Conscientiousness and Civic Virtues of B-School academics. Conclusion: This research explains the dynamics of PJ and POS towards OCB in a B-School setting. The academic setting of this study provides more insight into the relationships and provides insights into enhancing the organisational citizenship behaviour of academics in enhancing educational outcomes. Further, it also adds to existing understanding of organisational behaviour theory. 2021 Elizabeth Dominic et al., published by Sciendo. -
Green synthesis of reduced graphene oxide using Plectranthus amboinicus leaf extract and its supercapacitive performance
A rapid, efficient, green and eco-friendly approach for the preparation of reduced graphene oxide (rGO) using Plectranthus amboinicus (Indian borage) leaves extract (PAE) is explored in this study. The improvement in the reduction process was studied by varying the concentration of graphene oxide (GO), temperature and time duration. The physical and chemical properties of rGO are studied using Raman spectroscopy, Fourier transform infrared spectroscopy, X-ray diffraction (XRD) and field emission scanning electron microscope. The result obtained from XRD analysis confirms the removal of an oxygen-containing functional group of GO significantly by PAE. Raman analysis showed a higher ID/IG ratio for rGO (1.297) than GO (1.07), which indicates a higher level of disorder in the rGO with a decrease in the average size of the sp2 domain. From the electrochemical studies, a significant specific capacitance of 92.05Fg1 (5mVs1) is obtained from the cyclic voltammetry (CV) curves and 73.20Fg1 (0.1Ag1) from the galvanostatic chargedischarge (GCD) curve. 2021, Indian Academy of Sciences. -
Quantum Algorithm: A Classical Realization in High-Performance Computing Using MPI
Volume3, Special Issue3 ISSN: 23198753 -
Role of Artificial Intelligence and Robotics in Shaping the Students: A Higher Educational Perspective
An unprecedented shift in technology has begun in the modern era. Robotics and artificial intelligence (AI) advancements have created fresh positions while de-skilling or retraining many existing ones. Technical developments at higher education institutions (HEIs) protect students against potential changes in their field of study brought on by A) and prepare them for success in the workplace. This research aims to investigate how, over the past 150 years; globalization has fundamentally changed human civilization. Conventional education confronts enormous challenges as energy, the internet of things, and the cyber-physical systems they oversee diminish. One may argue that energy, the internet of things, and the cyber-physical systems that are under its jurisdiction are the foundations of all future education. The demise of these systems presents a significant threat to traditional schooling. Students' screen time is increased by this action, which has an impact on their mental health. Five-fold cross-validation with 210 students from Delhi NCR and abroad is beneficial for the classification techniques SVM, Naive Bayes, and Random Forest. The study examined the factors that contributed to an increased rate of mental health issues among undergraduate students in Delhi, India, following the introduction of the COVID-19 virus. The results have demonstrated that while technology's practical applications will likely have a positive influence on education in the future, there may be negative effects as well. This is an opportunity for educators and learners to support excellence and remove obstacles that prevent many kids and schools from achieving it. Therefore, in the future, every nation will need to create an education system that is more technologically sophisticated. 2024 IEEE. -
India Gateway Program: Transformational learning opportunities in an international context
Internationalisation is increasingly important in the social work curriculum. With globalisation and international resettlement, social workers require competencies to work locally with diverse populations as well as overseas. Study abroad experiences are used to enhance international content, cultural sensitivity and self-awareness in curricula. This article evaluates an Australian study tour focussing on students perspectives. Indications are that it was effective in enhancing cultural sensitivity, understanding of factors contributing to inequity, the lived experience of poverty, personal growth and professional identity. For students, it was a valued and transformational learning experience. 2015, The Author(s) 2015. -
Servant Teachers and Online Learning in Higher Education: A Narrative Enquiry into Experiences of Teachers During COVID-19 Outbreak
This research examines the lived experiences of servant teachers of higher education during the COVID-19 pandemic focusing on the use of technology and challenges within online teaching frameworks. It aims to fill the gaps in literature regarding the individual practitioner-servant leadership in digital education settings. Ethical considerations were rigorously maintained. This research adapted qualitative research approaches guided by the SL-7 scale to identify servant teachers and semi-structured interviews to capture their lived experiences. Data analysis employed interpretive phenomenological analysis (IPA) through Osborn and Smith's (2006) four stages of IPA. The analysis yielded seven core themes: engagement, technology, experiences, impact, well-being, performance, and policies. The study illuminates how pedagogical approaches need to be student-centered but were technologically constrained from a servant teacher's perspective, thus shedding light on the dynamics of servant leadership on supportive and nurturing pedagogies in education during emergencies. The research contributes to the discourse on leadership in digital education by emphasizing the value of servant leadership traits in enhancing student satisfaction, retention, and overall academic well-being. 2026 by Divya Dosaya, Anupama Sadasivan, Sonia David, Athira M. and Palak Pipalia. -
The Dark Side of Internet of Things in E-Commerce: Uncovering the Misuse of Personal Information
The world today patronises e-commerce websites and the extent of ease, convenience and possibilities it has brought. E-commerce has positively rattled the world and, most importantly, the economy and gross domestic product of every nation. It has facilitated countries to be progressive and enjoy goods and services from beyond geographical borders. It made possible and common what was once regarded as impossible and far-fetched. E-commerce has not only over-stocked market shelves but also raised the world standard from a business and consumer point of view. E-commerce is defined to be a wide range of business activities in an online space for products, goods, and services (Gupta, 2014). E-commerce is the outcome of an economy that functions on the Internet. Though e-commerce has been present for over thirty years, its popularity and effect have risen enormously in the recent past. It is understood to be the business of buying, selling, and trading through electronic communications. E-commerce has two models in force, the B2B (Business to Business) and B2C (Business to Consumer), where the former is economic transactions between businesses, and the latter between a business and consumer. Though both models are popular and well-known, the former is sought after (Tian, and Stewart, 2006). Traditional commercial businesses have also adopted the mode of online trade and business for their goods. If not fully transformed, they have a dual approach of a continued brick-and-mortar structure and also an online portal. For example, H&M, a famous clothing store, has been in existence since 1947. It was in 1998 that they began their online retail store to keep in touch with the changing trends of the market needs. In 2023, there was as many as 1224 million e-commerce websites the world over. This in itself shows the extent to which the world has developed, adapted, and benefited from online businesses and transactions (Gennaro, 2023). The goal of e-commerce websites is just the same as a normal business. They intend to hit their profit margins, increase branding along with sales maximisation and risk minimisation. Apart from this, they have widened the aspect of an economy to now being a global economy where there are communications, interactions, and direct transactions buyers and sellers from various parts of the world. This advancement in business models has had a ripple effect on other aspects and tools of business functions, such as marketing and advertising. Marketing aims at making known to society the availability of certain products, goods, services, and their utility and features so that consumers can make an informed decision while shopping. Conventional marketing involves advertising and letting people know that such a product is available and has certain uses with the hope that promotion turns into a sale. Marketing has been open, standard, and objective. However, the marketing that we see today is powered by data, so it is subjective, customised and sure to catch the interest of consumers. This is happening through several online portals/agencies, and marketers who use data provided by online consumers for their monetary gain. At one point data is beneficial to the consumers in terms of availability of the products at their fingertips and without stepping out from home; on the other hand the data provided by the consumers remains stored with the websites, or in their cloud and subsequently used by such portals and also sold to others without approval from the users themselves. This raises serious concerns about the privacy rights of users and the extent of protection rendered to their data. There is constant debate in the convergence of data, Internet, and e-commerce because of the immense support in its ease and comfort versus the lesser known but great evil of infringement of rights to privacy and personal data. This chapter focuses on understanding how the personal information of consumers misused by e-commerce companies breaking down various roles of big data, the Internet of Things (IoT), artificial intelligence, and digital marketing. 2025 Taylor & Francis Group, LLC. -
The Internet of Things (IoT): A Cripple to Data Privacy and Security
The Fourth Industrial Revolution has paved the way for the intersection of technological advancements and innovation. Its widespread utility and convenience has had an overwhelming impact the world over. The Internet of Things (IoT) is one such advancement. It is an Internet-enabled system of physical sensory objects that have the ability to identify, share and analyse data. The goal of such advancement is to positively impact the quality of human life and to ease its everyday functioning. IoT functions on data, which can be general, personal and sensitive data. Law enforces and assures protection of personal data under the banner of privacy, known to be the right to data protection. It is recognized as an internationally accepted human right and, to nation-states, a fundamental right. The overall aim of such provisions in internationally recognised documents like the Universal Declaration of Human Rights (UDHR, 1948), the European Charter of Fundamental Rights (2000) and domestic laws like the Federal Trade Commission Act (1914) are to protect users and their data in light of the dynamic world of technology and innovation. IoT is user-friendly, enabling and convenient, but comes with its own set of challenges that cripples the fundamental and human right of data privacy and security. The current legislations on privacy hold data protection in highest reverence, assuring the user of absolute control. Though captivating in its definition of protection, in reality it falls short due to inefficiency in enforcement and application. The chapter offers a summary of the idea of data being ones own property and the crucial intervention of users data in the functioning of IoT. Highlighting the eagerness of embracing technological advancements, the chapter draws attention to the challenges of the IoT through the lens of data privacy and security. It traces its importance by using various case studies that reveal the loosely drafted, or rather incomprehensible, regulations of nation-states in protecting the right of privacy of users. The chapter concludes by considering a few ways of effective enforcement to keep intact the sanctity of the right to data privacy and security. 2025 Taylor & Francis Group, LLC.