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Popularity of food blogs: Exploring the rationale /
Food blogs have been introduced to the world long years back. But it gained popularity only in recent years. It was considered as a leisure medium or as a hobby. The need for food blogs came into existence when people started to write about the food they ate or which they cooked. Most of the people who follow food blogs get attracted to the facts like the content, photographs, visual appeal etc. -
A Relative Analysis on the Spotting of Cardiovascular Disease Employing Machine Learning Techniques
Heart is one of the significant segments in the human body since it powers blood to the all the pieces of the body. Blood courses through the vein. Cardiovascular sickness is corresponded with the blockage of vein. The sign of heart sickness depends whereupon condition is impacting an individual. The term coronary illness is ordinarily utilized instead of cardiovascular infection. Dilated cardiomyopathy, Heart failure, Arrhythmia, Pulmonary stenosis, Mitral regurgitation, Coronary artery disease, Myocardial infraction, Mitral valve prolapse, Hypertrophic cardiomyopathy are the sorts of coronary illness. The several machine learning techniques are analyzed to spot heart disease. This paper gives relative investigation of coronary illness expectation utilizing machine learning. 2021 IEEE. -
Implementation of hybrid machine learning approach for intrusion detection system
The Intrusion Detection System (IDS) enforces information security and is responsible to identify attacks and vulnerabilities inside a network. It does this by analyzing the packet stream throughout the network. In traditional IDS systems, the analysis is done by looking for signatures of known attacks or deviations of normal activity as described by the rules provided for the IDS system. Machine learning helps in deriving predictive knowledge and this makes it ideal to apply Machine learning in an IDS system to detect attacks. This paper focuses on creating a hybrid model that is best to implement in an IDS system. A hybrid model is implemented which combine multiple machine learning algorithms using Ensemble method. The experiments include evaluating machine learning algorithms such as Decision Tree, MLP (Multi-Layer Perceptron), Gradient Boosting etc. The algorithms with the best results are taken to construct Hybrid model. This Hybrid approach will improve the accuracy and efficiency for identifying the attacks by the IDS system. Depending on the type of attack, the IDS system can classify packets as DoS (Denial of Service), Probe, R2L (Root to Local), U2R (User to Root) or Normal. The experiments are carried using NSL-KDD Dataset. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Influence of Sinusoidal and Non-Sinusoidal Two-Frequency Gravity Modulation in Viscoelastic Fluids Driven by Triple Diffusivity
This study focuses on understanding the system's response to gravity modulation with two frequency components, characterized by both sinusoidal (sine wave) and non-sinusoidal (square, triangular, and sawtooth) waveforms, on three-component convection, considering a viscoelastic fluid modelled using an Oldroyd-B fluid. We apply the Venezian approach to evaluate the Rayleigh number, its corrected form, and the wave number by deriving a five-mode Lorenz model to investigate the onset of convection. A nonlinear analysis is conducted to investigate the dynamics of heat and mass transfer by solving an extended eight-mode Lorenz model, capturing higher order interactions. The onset of convection and the transport properties were observed to be influenced by combinations of sinusoidal and non-sinusoidal waveforms. This study optimizes convection-driven systems subjected to external periodic forcing by offering a more comprehensive understanding of convective instabilities in viscoelastic fluids. 2025 Wiley Periodicals LLC. -
Chaos in a triple diffusive system involving a viscoelastic fluid layer
This study investigates the linear and weakly nonlinear stability analysis in a Rayleigh-Bard configuration with a viscoelastic fluid layer influenced by two additional solutal components. The governing equations for both stationary and oscillatory convective regimes, and the critical point at which convection sets in is derived. The comparative analysis is performed for three different viscoelastic fluid models: Oldroyd-B, Maxwell, Rivlin-Ericksen fluid, along with the Newtonian fluid model. In weakly nonlinear stability analysis, a generalized eight-mode Lorenz model is developed that satisfies the general properties of a classical Lorenz model. From this reduced model, the critical points and Hopf-Rayleigh number, representing the initiation of chaos through Hopf bifurcation are determined. The Lyapunov exponents are used to characterize the chaotic, periodic and quasi-periodic motions of the system. The results show that the viscoelastic and triple diffusion parameters affect the initiation of convection and transition to chaos. It is also observed that the Maxwell fluid exhibits the earliest initiation of chaos and the Newtonian fluid the latest, with Oldroyd-B and Rivlin-Ericksen exhibiting intermediate behaviour. The presence of additional solutal concentrations delays the onset of chaotic motion. 2026 The Physical Society of the Republic of China (Taiwan). -
AI and IoT for universal health and well-being across generations
Over the last several years, the confluence of AI and the Internet of Things (IoT) has caused tremendous changes in many areas of our life, including the healthcare industry. Because of this cooperation, new possibilities have emerged with the aim of enhancing the health and welfare of people across all different generations. The ability to efficiently gather, analyze, and derive insights from large volumes of real-time data has revolutionized healthcare, allowing for better patient treatment and community health management. This is made feasible by combining algorithms powered by artificial intelligence with IoT-connected devices. Examining the gamechanging possibilities of AI and the IoT in the healthcare industry is the goal of this introductory piece. The function of AI and the Internet of Things in advancing health equity and wellness across diverse age groups is the primary emphasis of this study. Countless and varied uses of AI and the internet of things may be found in the medical field. Some examples of these uses include remote patient monitoring and the development of predictive analytics tools for use in illness prevention.Health outcomes and quality of life for individuals of all ages can be improved via the development of individualized therapies and treatment programs that cater to each person's specific needs. It is feasible to create these opportunities with the help of these technologies. Healthcare issues may be effectively addressed in a variety of locations, from densely populated cities to more rural places, by implementing solutions that leverage the internet of things and artificial intelligence. Because these solutions are both accessible and scalable, this is the result. It is possible for healthcare systems to overcome barriers to service delivery and access by utilizing these technologies. As a result, people of all ages and from all over the world will be able to live the kind of healthy, fulfilling lives they deserve. 2024, IGI Global. All rights reserved. -
Visiting Indian Hospitals Before, during and after COVID
The prevailing COVID-19 situation has brought in temporary and permanent changes in the attitude and lifestyle of people. Starting from Hand sanitizers and face masks, it extends to online classrooms and work from home culture. In case of visiting hospitals and medications, people with pre-existing medical conditions and minor health issues tend to delay or avoid visiting hospitals due to fear of infection, which is dangerous. Further, people or patients tend to access several alternatives and precautions. The alternatives include home remedies, ayurvedic medication, yoga and meditation. On the other hand, hospitals are trying to adapt online consulting and telemedicine. Besides, Cancellation or delay of nonemergency surgeries became inevitable in the lockdown phase. This survey conducted among the people of Erode district, Tamilnadu to study the perception of people concerning visiting hospitals for health issues. The results show that fear of infection, financial and transportation difficulties are the major factors which affected people from visiting hospital. Also, changing trends like Telemedicine and home remedies are likely to be permanently opted by people. In Brief, the outcomes reveal the changing attitude of people towards medication and hospital visiting habits. 2022 World Scientific Publishing Company. -
Let there be Light, but not too much: The Need to Legally Address Light Pollution in India
Electricity and artificial lights were synonymous with economic growth and development. Unfortunately, over usage of artificial lights has proven adverse effects. Research shows that excessive light impacts human health and endangers ecological balance, disturbs wildlife, causes decline in insect, moth, reptile pollution and depletes energy resources. Countries around the world have gradually started recognising light pollution as an emerging challenge and have brought in regulations to curb it. However, India is yet to recognise the threat of light pollution. Against this backdrop, the authors have established the need to recognise light pollution as a matter requiring dedicated and concerted focus. This was achieved through the analysis of recent and credible journal articles category with a cite score of over ten. Reliance was also placed on the light pollution map to understand the intensity of the problem, especially in India. The authors next conducted a study of legal regimes governing light pollution and artificial light, in different jurisdictions around the globe. The paper draws upon the best practices from these jurisdictions and suggests that India adopt techno-legal legislation, at the earliest, to combat light pollution. 2023- Kalpana Corporation. -
BCI Radiology Images Converting into Report Using BERT and GPT
The construction of precise radiology reports from medical images is an essential aspect of Contemporary healthcare. Medical images such as X-rays, MRIs, CT scans, or ultrasounds. Also, it can make use of medical reports. Medical report has a bunch of details about each patients medical history, diagnosis, treatment plan, lab results, and more. This paper represents a theoretical examination. The paper mainly focuses on two prominent NLP models. One is BERT (Bidirectional Encoder Representations from Transformers) and the other one is GPT (Generative Pre-trained Transformer). This paper is going to validate their applicability to transforming brain-computer interfaces (BCI). This paper will utilize these radiology images in perfectly framed medical reports. By differentiating these models based on their Architectural properties, Linguistic processing abilities, and capability for clinical integration, this papers goal is to establish the most effective method for automated medical reporting. Merging of these insights from existing studies recommends that when BERT leads in context-based precision and getting an idea of complex medical terminology, GPT offers outstanding text-generation potential. This paper proposes that an intermixture procedure taking advantage of the strengths of both models may offer the most supreme solution for automated medical reporting, balancing precision with readability and clinical applicability. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Next Generation AI-Enhanced Intelligent Imaging for Automated Knee X-ray Interpretation in Osteoarthritis: Front office Integration and Employee Training
Osteoarthritis is a long-lasting musculoskeletal ailment in which the degenerative alterations in the cartilage of the articles progressively arise. As the pathological anatomy develops to bone structure degeneration, it results in pain, stiffness and functional impairment. It results in immobility, reduced quality of life, more susceptibility to fractures, hospitalization, and mental health problems in the older adults and postmenopausal women. To address the increasing global prevalence of OA and the necessity to diagnose X-rays of the knee promptly, affordably and accessibly, this paper presents an AI-based imaging system based on the use of Convolutional Neural Networks (CNNs) and compares their results with the existing machine learning models. The framework will be structured to integrate the front-office workflows and streamline the diagnostic process of patient registration to report delivery and establish a well-organized staff training ecosystem aimed at enabling clinical staff to operate the AI-enabled workflow and adapt to it. The study offers a comprehensive, deployment ready, diagnostic platform through advanced automation, user oriented front-office functionality and the consistent upskilling of the workforce, to provide scalable, precise, cost effective OA detection, clinical efficiency, interpretative variability reduction, and workforce transition to scalable intelligent, patient centred care. 2025 IEEE. -
Growth Of ZnSnN2 Semiconductor Films For Gas Sensor Applications
ZnSnN2 is a member of class of nitride semiconductors which have the additional benefits of earth abundance and non-toxicity. For device applications, NH3 gas detecting senor, which finds use in chemical, pharmaceutical, and food process industries, are fabricated with zinc-tin-nitride (ZnSnN2) thin-films on glass substrate by making use of metal as contacts. The ZnSnN2 sensor is found extremely selective to ammonia (NH3) amongst other gases like ethanol, NO2, H2S and exhibited good detecting responses at room temperature. There are many ways to develop thin films of ZnSnN2, and hence in this work we are trying to find a cost effective, feasible and easier method of synthesis, i.e., chemical vapor deposition method. The first step was the optimization of process parameters to grow Zinc-Tin (ZnSn) thin-films. Later, optimization of the process parameters for the growth of compound ZnSnN2 was completed. The grown films are characterized by material quality using X-Ray Diffraction and UV- Vis spectroscopy. 2022 American Institute of Physics Inc.. All rights reserved. -
Does environmental reporting ofbanks affect their financial performance? Evidence from India
Purpose: The present study aims to investigate the effect of environmental reporting on the financial performance of banks in India. Design/methodology/approach: The study is based on the secondary data. The sample includes the banks listed in the NSE Nifty Bank Index from 20162017 to 20202021. The environmental reporting data was obtained through the content analysis technique. The financial data was collected from the CMIE Prowess database. Panel regression analysis was used to analyse the data. Findings: The findings indicate a negative significant influence of environmental reporting on the ROA and ROE of banks. On the other hand, environmental reporting does not significantly influence the EPS of banking institutions. Originality/value: To the best of the authors knowledge, this study is the first to contribute to the scarce literature on the influence of environmental reporting on financial performance, pertinently in the context of a developing nation's banking sector. 2023, Emerald Publishing Limited. -
Do Bank Characteristics Really Matter for its Environmental Reporting?
The last few decades have seen an increasing number of researches in the area of environmental reporting. Institutions across the globe have been extensively reporting their environmental initiatives through their annual reports. There is a dearth of research on environmental reporting in the Indian context. Thus, this study comprehensively analyzes the environmental reporting practices of scheduled banks in India. It further attempts to explore the association between environmental reporting and bank characteristics. The secondary data is collected from the annual reports, sustainability reports, and business responsibility reports for the period 2017-2022. The sample consists of ten top-rated commercial banks chosen based on market capitalization during June 2022. The content analysis technique is used to extract information on environmental practices under twelve major categories. This study employs correlation analysis to examine the association between environmental reporting and bank characteristics. The findings of this study reveal that Indian commercial banks are increasingly reporting environmental information in their bank reports and websites. Further, the results of correlation analysis revealed a significant association between environmental reporting and the banks' age, size, and profitability. Further, this study recommends policymakers and concerned professional bodies introduce additional environmental guidelines and widen the scope of reporting in the banking industry. 2024 National Institute of Science Communication and Policy Research. All rights reserved. -
The linkage between green banking practices and green loyalty: A customer perspective
The aim of this study is to explore the bank customers perceptions towards green banking practices. This study uses a convenient sampling method. Pre-tested questionnaires were employed to collect data. The data were collected conveniently from 358 bank customers. However, the final sample includes 304 responses after ignoring null responses (n = 304). The Structural equation modeling (SEM) was applied for the analyses. The significant results of the study indicate that green banking practices positively influence green image (p = 0.001) and green trust (p = 0.025), while it does not significantly affect green loyalty (p = 0.642). The mediation analysis reveals that green image mediates the relationship between green banking practices and green loyalty, while green trust does not mediate the relationship between the same. The results have practical implications for banking institutions in India to recognize the importance of environmental initiatives in influencing the decisions of bank customers. Deepthi S. Pawar, Jothi Munuswamy, 2022. -
Machine Learning Methods to Identify Aggressive Behavior in Social Media
With the more usage of Internet and online social media, platforms creep with lot of cybercrimes. Texts in the online platforms and chat rooms are aggressive. In few instances, people target and humiliate them with the text. It affects victim mental health. Therefore, there is a need of detecting the abuse words in the text. In this paper, a study of machine learning methods is done to identify the aggressive behavior. Accuracy can be improved by incorporating additional features. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Evidence Acquisition in Social Media for Cyber Crime
Social Media forensics a branch of forensics involves in collecting the evidences for the cyber crime. Investigating social media is a complex process which involves the privacy issues for accessing the users, suspects and victims information on social media. Manual processing of social media data is not feasible as it contains large volumes of data. An automated process is needed to incident specification, evidence extraction and for provenance. The need for handling heterogeneity of data as users have accounts with multiple social websites is also explained. This study briefs the existing models and the challenges faced in analyzing with those models. The research goals in this field are also addressed. A pool of tools which can contribute in guarding the solution for cyber crime is also presented. 2022 IEEE. -
An AI-Based Forensic Model for Online Social Networks
With the growth of social media usage, social media crimes are also creeping sprightly. Investigation of such crimes involves the thorough examination of data like user, activity, network, and content. Although investigating social media looks quite straight forward process, it is always challenging for the investigators due to the complex process involved in it. Due to the immense growth of social media content, manual processing of data for investigation is not possible. Most of the works from this area provide an automatic model or semi-automated, and much of the contributions lacks the logical reasoning and explainability of the evidence extracted. Searching techniques like entity-based search and explainable AI add value to the quick retrieval within appropriate scope and explain the results to the court of law. This paper provides a model by adding these new techniques to the basic forensic process. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Explainable AI Method for Cyber bullying Detection
People of all ages and genders are using social media platforms to engage themselves in all sorts of activities. People create profiles on online social networks in order to communicate with one another in this virtual environment. Hundreds or thousands of friends and followers are split across many profiles. Along with the virtual communication in this social media life, cyber-crimes also creep in many distinguished forms to grab user's information and emotionally degrade them with harassment and arrogant behavior. A set of machine learning methods are proposed and used to detect such a bullying behavior. Along with the detection of such an act, the model should also provide the logical reasoning of the evidence extracted. The explain ability of the models classification will give us a view of the way towards portraying a suspect as a bullier. This paper illustrates a machine learning model that works on a twitter data set to suggest the tweets as category bullying or non-bullying. LIME a tool to predict the interpretability of the model is used to depict the performance of model and provides explainability. 2022 IEEE. -
Environmental Reporting Practices : Evidence From Indian Commercials Banks
The last decade has witnessed increasing concern towards the environment due to the ravages inflicted on them by mankind. With the concept of sustainable development perpetually growing over the years, global institutions have been acknowledging their other duties towards the society, and have been integrating environmental practices into their strategic framework to significantly contribute to the sustainable bandwagon. However, since the financial crisis of newline2008, it has been found that banking institutions have become active participants in fostering environmental sustainability. Also, due to the increased awareness and pressure from the stakeholders, these institutions have been reporting their environmental initiatives and practices in their bank reports and websites. The extensive review of the literature reveals that there are hardly any studies that have been carried out in the Indian context, pertinently in the banking sector. Therefore, considering this as a major research gap, the present study aims to comprehensively evaluate the environmental reporting practices of selected commercial banks in India for the period from 2011 to 2022. newlineThis study follows an explorative and descriptive research design, with a deductive research approach. However, this research is based on secondary data, and adopts both qualitative and quantitative research methods. Following the judgmental sampling technique, the sample of the study consists of thirty public and private commercial banks in India. The content analysis technique has been adopted to extract environmental information from bank reports and websites using the developed environmental reporting index. The independent sample t-test is newlineused to compare the environmental reporting performance of public and private-sector newlinecommercial banks. This study analyses the relationship between environmental reporting and bank-specific characteristics using the Pearson correlation coefficient analysis. -
Analysing Twitter User Behaviour with Process Mining: A Study on Activity Patterns
Social media sites provide a platform to share the information. People share their views and interests. Social media data provides information on user, activity, network, and content. Researchers anticipate a lot of information from social media data. It covers the activities of user, people connected to them, and their likes and dislikes. If users data is processed keenly, one can easily understand a users behaviour with his actions and predicts the next action of the user. It also helps in describing the relations among the users. This study illustrated the process mining algorithms to uncover the insights of Twitter users data. The model depicts the overall process flow of Twitter user activities. Behavioural patterns like common sequences, repeated user actions, direct relations, and rare interactions are analysed. The models performance is assessed with the metrics like fitness, precision, and simplicity to choose the best model for the dataset. Inductive miner outperformed well with other algorithms. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.