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Homomorphic DNA Security in IoT Edge Data
The Internet of Things (IoT) based intelligent medical system possesses sensitive and private patient data. Most data relates to the patient's medical records and highly sensitive information. For this reason, safety and confidentiality of information are crucial. The preservation of patient privacy when sharing medical data is the primary concern of this study. Due to their excellent performance, biological notations based on deoxyribonucleic acid (DNA) are becoming increasingly admired for guaranteeing encryption and image protection. This paper proposes lightweight homomorphic with DNA-based medical image encryption (HDNA_MIE) for heterogeneous IoT in edge computing. The proposed approach contains two steps: In the first step, the secure DNA keys are generated using lightweight operations such as shifting and Josephus ring-based permutation (JRP). In the second step, the lightweight homomorphic cryptographic algorithm with DNA sequence-based encryption algorithm is suggested for secure encryption. The suggested strategy is evaluated using computational time and statistical analysis with several measures to determine its efficacy. The experimental findings of the proposed strategy exhibited a high level of security and a noticeable enhancement in the Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI) and encryption processing time. The experiment outcomes demonstrate that our technique may be applied to highly confidential image encryption. 2024, Iquz Galaxy Publisher. All rights reserved. -
Stock Price Prediction using Deep Learning and FLASK
The forecasting of stock prices is one of the most explored issues, and it attracts the attention of both academics and business professionals. It is quite difficult to make predictions about the stock market, and it takes extensive research into the patterns of data. With the expansion of the internet and indeed the growth of social media, online media and opinions frequently mirror investor sentiment. The volatility and non-linear structure of the financial stock markets makes accurate forecasting difficult. One of the sophisticated analysis techniques that is being used by academics in a variety of fields is the neural network. In this paper, we proposed deep learning techniques for google stock price prediction. A dataset from Kaggle was collected and applied deep learning techniques RNN, LSTM variants. We achieved better results with Bidirectional LSTM. We also created a web app for stock prediction using Christ University python FLASK. 2022 IEEE. -
Airline Twitter Sentiment Classification using Deep Learning Fusion
Since the advent of the Internet, the way people express their ideas and beliefs has undergone significant transformation. Blogs, online forums, product review websites and social media are increasingly the primary means of distributing information about new products. Twitter, in particular, is giving people a platform to air their views and opinions about a variety of events and products. In order to continually enhance the quantity and quality of their products and services, entrepreneurs constantly need input from their customers. Businesses are always looking for ways to increase the quality of their products and services. As a result, it's tough to understand the consumer's sentiments because of the large volume of data. In this research work, a Kaggle dataset of airline tweets for sentiment analysis was used. The dataset contains 11,540 reviews. We proposed an ensemble CNN, LSTM architecture for sentiment analysis. For comparison of the proposed system, LSTM alone also tested for similar dataset. LSTM was given an accuracy of 91% and the proposed ensemble framework with LSTM and CNN was given an accuracy of 93%. The experiments showed that the proposed model achieved better accuracy when compared to conventional techniques. 2022 IEEE. -
Loan Default Prediction Using Machine Learning Techniques and Deep Learning ANN Model
Loan default prediction is a critical task in the financial sector, aimed at assessing the creditworthiness of borrowers and minimizing potential losses for lending institutions. Online loans continue to reach the public spotlight as Internet technology develops, and this trend is expected to continue in the foreseeable future. In this paper, the authors proposed loan default loan prediction system based on ML and DL models. This work makes use of the information on loan defaults provided by Lending Club. The dataset is preprocessed by applying various data preprocessing techniques and preprocessed dataset is generated. Later, we proposed four ML algorithms decision tree, random forest, logistic regression, K-NN and Feed forward neural network. The experimental results shown that proposed feed forward neural network achieved good accuracy for loan default prediction with an accuracy of 99%. 2023 IEEE. -
An Extensive Time Series Analysis of Covid-19 Data Sets on the Indian States
Pandemic influenza coronavirus is causing a great loss to mankind. It is creating a chaos on the global economy. Fight against this unseen enemy is affecting all the sectors of the global economy. Mankind is quivering with fear and scared to do something. This study gives a detailed presentation of the current position of virus escalation in India. Sentiment analytics from Twitter data is evaluated on sentiment, emotions and fear opinions are analyzed in the study. The analysis is on red, orange and green zones in several states of India and also gave a comprehensive interpretation on various phases of lockdown. Confirmed, active, recovered and deceased cases in all states are modeled to predict the increase of number of cases. Textual, geographical and graphical analytics are extensively described in the research study. Time series analysis is broadly elaborated as a case study till July 22, 2020, forecasting the impact of virus on Maharashtra, Kerala, Gujarat, Delhi and Tamil Nadu. This study will favor the administrative system to control the disease spread across the nation. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Natural convection of a binary liquid in cylindrical porous annuli/rectangular porous enclosures with cross-diffusion effects under local thermal non-equilibrium state
The present article reports an analytical study of the double diffusive natural convection (DDNC) in cylindrical porous annuli (CPA) and rectangular porous enclosures (RPE), which are handled in a unified way using the curvature parameter, saturated by a binary liquid under the assumption of local thermal non-equilibrium (LTNE) state. The buoyancy forces (thermal and solutal) driving the flow are assumed to be induced by the maintenance of constant and uniform heat and mass fluxes applied along the vertical (radial) walls and insulation of both horizontal walls of the annuli/rectangular enclosures. The Darcy-Boussinesq equations with LTNE assumption between the fluid and solid phases are employed to model the problem of DDNC in a binary liquid-saturated porous medium with cross-diffusion effects. The analytical results are obtained by employing the Oseen-linearization transformation technique in the study. The influence of various dimensionless parameters on heat and mass transports of the system are depicted using the Nusselt and Sherwood numbers and isotherms plots, and the obtained results are analysed with the physical explanation. Special attention is given to understand the effect of LTNE parameter and cross-diffusion parameters on heat and mass transports of the system. Different aspect ratio values are chosen to obtain the results of three types of CPA/RPE (shallow, square and tall). Among these CPA/RPE, maximum and minimum heat and mass transports are achieved in the cases of shallow and tall CPA/RPE, respectively. The results of the pure thermal convection problem is obtained at the zero value of buoyancy ratio and solute Rayleigh number. The increasing value of N magnifies the heat and mass transports in the system due to the augmented buoyancy effect resulted from the thermal and solutal gradients. The increase of solid inner cylinder radius, by fixing its volume, makes the annulus slender which yields to decrease the heat and mass transports in the system. The effects of LTNE parameter and cross-diffusion parameters on heat and mass transports of the system are clearly brought out. The results of LTE model are obtained at the infinite value of ratio of porosity modified thermal conductivities, ?, as a particular case of the present model. From the study, we conclude that the shallow porous annulus and tall rectangular enclosure are best suited in the design of heat removal and heat storage systems, respectively. 2021 -
A study of the natural convection of water- AA 7075 nanoliquids in low-porosity cylindrical annuli using a local thermal non-equilibrium model
Natural convection in nanoliquid-saturated porous cylindrical annuli due to uniform heat and mass influxes from the solid cylinder and effluxes from the outer hollow cylinder is investigated analytically. The Darcy model and the modified version of the Buongiorno two-phase model are used, and local thermal non-equilibrium between the phases is assumed. A nanoliquid-saturated porous medium made up of glass balls with a dilute concentration of AA7075 alloy nanoparticles well-dispersed in water is considered. Out of three types of annuli considered, shallow annuli provide the best heat transport and tall annuli show the worst performance. The presence of a dilute concentration of nanoparticles significantly enhances the heat transport in the system. Of nine nanoparticle shapes considered, lamina-shaped nanoparticles enhance heat transport the most. Heat transport is enhanced in the case of heat-and-mass-driven convection compared to the case of purely heat-driven convection. The results for a rectangular enclosure are obtained as a particular case of the present study. Two asymptotic routes that take us to the results of thermal equilibrium are shown. The vanishing limit of the concentration Rayleigh number yields the result for a single-phase model. Results for the base-liquid-saturated porous medium form a limiting case of the present study. We conclude that a shallow cylindrical annulus saturated with water-AA7075 lamina-shaped alloy nanoparticles is best suited for heat transfer due to its high effective thermal conductivity in comparison with that of other shaped nanoparticles and a tall rectangular enclosure saturated by water is best suited for heat storage applications. 2021 Author(s). -
Theoretical Prediction of the Number of Bénard Cells in Low-Porosity Cylindrical/Rectangular Enclosures Saturated by a Fast Chemically Reacting Fluid
Many applications including chemical engineering and meteorology require the study of a chemically driven convection in cylindrical, as well as rectangular enclosures. The present paper reports a unified analysis of a chemically driven convection in densely packed porous cylindrical/rectangular enclosures saturated by a chemically reactive binary fluid mixture. Employing the degeneracy technique and the single-term Galerkin method involving Bessel functions in a linear stability analysis, an analytical expression for the critical Rayleigh number, (Formula presented.), was obtained. An analytical expression for the number of cells that manifest in a given enclosure, at the onset of convection, was derived from (Formula presented.). The connection between the stabilizing and destabilizing effects of various parameters and the size or the number of Bénard cells that manifest are described in detail. The results depicted that the chemical parameters related to the heat of reaction destabilize and the parameter depending inversely on the rate of the chemical reaction stabilizes the system. In the latter case, a greater number of smaller cells were formed in the system compared to the former case. Hence, we concluded that the chemically reactive fluid advances the onset of convection compared to the chemically non-reactive fluid. The results of a similar problem in rectangular enclosures of infinite horizontal extent and chemically non-reactive liquid-saturated porous medium were recovered as limiting cases. Thus, the present model presents a unified analysis of six individual problems. 2023 by the authors. -
Linear and weakly non-linear stability analyses of Rayleigh-Bard convection in a water-saturated porous medium with different shapes of copper nanoparticles
The Rayleigh-Bard convection of a nanoliquid-saturated porous medium confined in a very shallow enclosure is investigated theoretically using the modified Buongiorno - Brinkman model. In the study, the chosen nanoliquid-saturated porous medium is assumed to be made up of water well dispersed with copper(Cu) nanoparticles of five different shapes saturating in a 30% reinforced polycarbonate glass fiber(GF) porous material of high porosity and its effective thermophysical properties are calculated using the phenomenological laws or mixture theory. Two kinds of boundary conditions, viz., stress-free and rigid, are employed and the analytical solution is obtained in both cases. On the other hand, Rayleigh-Bard convection in a very shallow domain of height 5mm and width 5cm filled with water-liquid and bounded by the rigid boundaries is simulated. The simulation results are then compared with the analytical results in the case of rigid boundaries. We found that the analytical results are in good agreement with those of the simulation results and this validates results of the present study. Linear and weakly non-linear stability analyses are performed to find the onset and the heat transport of the system. The effects of various parameters on the onset and heat transport of the system are depicted graphically and the physical explanation is provided for all observed results in the study. We found that the addition of dilute concentration of nanoparticles advances the onset and thereby enhances the heat transport in the system. Among five different shapes of copper nanoparticles, maximum and minimum heat transports are observed in the cases of blade and spherical shaped nanoparticles, respectively. The porous medium parameters: Brinkman number and porous parameter, show a stabilizing effect in the system. The existence of subcritical motions is also predicted for the system. The results of the Khanafer-Vafai-Lightstone(KVL) single-phase model, nanoliquid, base liquid and base liquid-saturated porous medium are obtained as limiting cases of the present study. Since nanoparticles and porous medium, respectively, show a destabilizing and stabilizing nature of influence in the system, the present work has possible applications in both heat removal and heat retainment systems. 2022, The Author(s), under exclusive licence to SocietItaliana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature. -
Natural convection of water-copper nanoliquids confined in low-porosity cylindrical annuli
Natural convection in cylindrical porous annuli saturated by a nanoliquid whose inner and outer vertical radial walls are respectively subjected to uniform heat and mass influxes and out fluxes is studied analytically using the modified Buongiorno-Darcy model (MBDM) and the Oseen-linearization technique. Nanoliquid-saturated porous medium made up of water as base liquid, copper nanoparticles of five different shapes, viz., spheres, bricks, cylinders, platelets and blades, and glass balls porous material is considered as working medium for investigation. The thermophysical properties of nanoliquid -saturated porous medium is modeled using phenomenological laws and mixture theory. The effect of various parameters and individual effects of five different shapes of copper nanoparticles on velocity, temperature and heat transport are found. From the study, it is clear that the addition of a dilute concentration of nanoparticles increases the effective thermal conductivity of the system and thereby increases the velocity and the heat transport, and decreases the temperature. In other words, the heat transport is more in the case of heat and mass driven convection compared to purely heat-driven convection. Among the five different shapes of nanoparticles, blade-shaped nanoparticles facilitate the transport of maximum temperature compared to all other shapes. Maximum heat transport is achieved in a shallow cylindrical annulus compared to square and tall circular annuli. The increase of the inner solid cylinder's radius is to decrease heat transport. The results of the KVL single-phase model are obtained from the present study by setting to zero the value of the nanoparticles concentration Rayleigh number. Also, neglecting the curvature effect in the present problem, we obtain the results of the rectangular enclosure problem. 2020 The Physical Society of the Republic of China (Taiwan) -
Study of rotating Bard-Brinkman convection of Newtonian liquids and nanoliquids in enclosures
Taylor-Bard convection of water and water-based nanoliquids confined in three different types of high porosity rectangular enclosures, viz., shallow, square and tall, is studied analytically using both infinitesimal and finite amplitude stability analyses. We make use of the modified-Buongiorno-Brinkman model(MBBM) for the governing equations concerning nanoliquid-saturated porous enclosures bounded by rigid-rigid boundaries and obtain analytical results. Among three types of enclosures, maximum and minimum heat transfers are observed in tall and shallow enclosures respectively. Water well dispersed with a dilute concentration of single-walled carbon nanotubes(SWCNTs) is considered as a working medium. The water-SWCNTs is able to flow in the porous medium because the medium is loosely-packed with porosity in the range 0.5 ? ? ? 1. In addition to this, the maximum volume fraction of nanoparticles considered in the system is 6% and thus this does not alter the fluidity of the system. We found from the study that the presence of low concentration(volume fraction-0.06) of SWCNTs in a water-saturated porous medium effectively improves the heat transport of the system due to its high thermal conductivity and large surface area. Due to the presence of a porous medium, however, the onset of convection gets delayed and heat transport in nanoliquids gets substantially reduced in a Bard-Brinkman configuration resulting from the weak thermal conductivity of the porous medium. Thus the porous medium acts as the heat storage system. Also, in a rotating frame of reference the heat transport gets reduced and rotation serves as an external mechanism of regulating heat transport in the system. The nonlinear dynamics of the system is studied using the 6-mode Lorenz model. Chaotic motion in the system is studied using the maximum Lyapunov exponent(MLE). The Hofp-bifurcation point of the system along with the MLE is used to investigate periodic, nearly periodic and mildly chaotic behaviors of the system. 2020 -
Achievenment motivation and self esteem among handicapped children
How the children with handicap perceive themselves and their self esteem levels are important yet not much focussed aspect in disability research. If we have a correct evaluation of their motivational level and self esteem it may help us to modify their training interventions and also would make them feel more satisfied and confident. So we planned to study achievement motivation and self esteem levels of handicapped children. The Objective of the study is that to to compare achievement motivation of physically handicapped to that of non-handicapped school children, and to compare self esteem of physically handicapped to that of non-handicapped school children. Methodology 40 physically handicapped school students and 40 age, gender and education matched non handicapped students were included in the study. Handicapped children of other categories like sensory disability, visual impairment, hearing impairment and speech impairment were excluded. Achievement motivation questionnaire was used to measure the motivational behaviour and Rosenberg self-esteem scale was applied by asking the respondents to reflect on their current feelings. Results and Conclusions Achievement motivation and self esteem were observed to be significantly lower in physically handicapped students compared to healthy controls. Significant gender difference in favour of females was observed i.e., self esteem and achievement motivation was significantly higher in females of both the groups compared to males. The study emphasizes need for interventions to improve self esteem and motivation levels of handicapped children. -
A Hybrid Approach Against Black Hole Attackers Using Dynamic Threshold Value and Node Credibility
Detecting black hole attackers is tedious in Vehicular Ad Hoc Networks due to vehicles' high mobility. The main consequence faced because of these attackers is an increase in the number of dropped packets which converts secure and fastest paths to compromised ones. Since these attackers can act individually and collaboratively as a group, early detection of these attackers must be feasible to preserve the network's performance. The majority of current methods rely on predetermined threshold and trust score values, which are ineffective in accurately identifying black hole attackers. Hence, this paper proposes a hybrid approach using dynamic threshold value and node credibility for early detection of black hole attackers. RSUs periodically compute the dynamic threshold value and categorize the vehicles into categories 1, 2, and 3. Vehicles classified as Category 1 are legitimate, whereas Category 3 vehicles are attackers. Vehicles in Category 2 are suspicious, requiring further analysis using node credibility values to identify attackers. It is protected against single, multiple, and collaborative black hole attackers. The NS2 simulation results demonstrate that the suggested method is optimal concerning PDR, Throughput, Delay, and Packet Loss Ratio compared to recent techniques. Since the proposed scheme efficiently identifies the attackers, it has 89.67% PDR, which is higher when compared to other schemes. 2013 IEEE. -
Cancer Prognosis by Using Machine Learning and Data Science: A Systematic Review
Cancer is one of the most fatal diseases in the world and the leading cause for most deaths worldwide. Diagnosing cancer early has become the need of the day for doctors and researchers as it allows them to categorize patients as high-risk and low-risk categories which will eventually help them in correct diagnosis and treatment. Machine learning is a subset of artificial intelligence that makes use of raw data to make predictions and insights. Using machine learning for cancer prognosis has been under study for a long time and several papers have been published regarding the same. Even though many papers have been published on the usage of statistical methods for cancer prognosis, it has been proved that machine learning models provide more accuracy when compared to conventional statistical methods of detection. These machines can be trained to detect abnormalities such as a tumour by looking at real-world examples. Models such as artificial neural networks, decision trees, clustering techniques, and K-Nearest-Neighbours (KNNs) are being used for cancer prediction, prognosis and also research purposes. The key aim of this article is to go through the popular key trends in using machine learning algorithms for cancer prognosis, types of input datasets to be fed, different types of cancers that can be studied and also the performance of these models. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Building Robust FinTech Applications and Reducing Strain on Strategic Data Centers using the LoTus Model
Agile is a well-known project management approach that has been used for many years. It places a strong emphasis on client satisfaction, adaptability, and teamwork. Agile was first developed as a software development approach, but it has now been modified for application in other sectors including marketing and finance. The Agile Manifesto, which was released in 2001 and explains the principles and ideals of Agile development, is the foundation of the Agile ideology. One or more of the guiding principles is to adapt to change instead of following a plan, prioritize functional software over thorough documentation, and collaborate with customers over negotiating contracts. Agile has gained popularity over time as businesses try to be adaptable and responsive to their customers' constantly changing business demands. The lack of predictability in Agile is one of its key drawbacks. Agile stresses client cooperation and adaptation, therefore the finished product could differ somewhat from what was originally planned. For businesses that depend on meticulous planning and a rigid schedule, this lack of predictability can be problematic. It faced a serious problem during the process of building a finance application called JazzFinance. This has led to build another robust and systematic software development method called as LoTus model. The proposed LoTus is an acronym for two abbreviations. Those are lean optimization TypeFace for Unified Systems (LoTus) and Locate dependencies, optimize for reusability, Test-Driven environment, Unify Design and Scalability. This article goes through the development of LoTus and how it has helped us build a stable finance application within a small amount of stipulated time. 2023 IEEE. -
Strengthening of brick masonry using biaxial polypropylene geogrid as confinement reinforcement
Recent and past earthquakes have once again reiterated the requirement of strengthening the masonry structures to withstand both in-plane and out-of-plane loads. In this experimental investigation, biaxial polypropylene geogrid was used as a confinement reinforcement on the surfaces to strengthen masonry specimens. The masonry specimens without and with geogrid have been subjected to a compression test, flexural bond strength test and diagonal tension (shear) test as per IS 1905, ASTM E518 and ASTM E519, respectively. From the results, it has been found that biaxial polypropylene geogrid significantly enhances the strength in masonry specimens with geogrid and also reduces crack propagation in all three tests. The relationship between compressive strength and flexural bond strength, compressive strength and shear strength of masonry specimens with geogrid has been established. Furthermore, based on the cost analysis of various strengthening techniques, it was concluded that the use of biaxial polypropylene geogrid is an economically feasible alternative to other reinforcing materials, such as stainless-steel wire mesh and polyester geogrid. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Digital Transformation in Higher Education: Impact of Instructor Training on Class Effectiveness During COVID-19
Digital technology is transforming society and business like never before. Digital technology has made inroads into all sections of society, especially with the pandemic restricting interaction and movement in the physical space. Education systems and institutions have witnessed a drastic change in their pedagogy. Education institutions adopting digital technologies can become drivers of growth and development for their ecosystems bringing significant changes in education, engagement, and management of class activities of educational institutions. The education system will have to adapt and evolve to take advantage of the new technologies and tools and develop strategies to play an active role in the digital transformation process. In the wake of the COVID-19 situation, higher education institutions have adopted digital platforms for teaching and learning. The study attempts to understand the instructors/academician/teachers training process adopted by selected higher education institutions in India to facilitate migration to digital platforms. Further, the study analyses the challenges faced in the new normal of education and the levels of training process initiated by institutions for teaching faculty. The authors have tried to analyse how this has enabled instructors to meet the challenges of conducting online classes and increase class effectiveness. The study unfolded the impact of high-level institutional training on class effectiveness and how individual digital preparedness is essential in engaging virtual classrooms. Further, the positive impact of training in reducing anxiety in engaging online sessions and the extent of motivation to continue online teaching as it has become inevitable with the second wave of the pandemic were examined across age and gender. An attempt is made to suggest few strategies for continued effective online class engagement as India battles through the second wave of the pandemic. 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
FOXS HEAD OR LIONS TAIL? WORK LIFE BALANCE OF WOMEN ENTREPRENEURS IN AGRICULTURE AND FARM VENTURES AND ITS ANTECEDENT EFFECT ON QUALITY OF LIFE; [CABE DE RAPOSA OU RABO DE LE? EQUILRIO DA VIDA PROFISSIONAL DAS MULHERES EMPREENDEDORAS NA AGRICULTURA E EMPREENDIMENTOS AGROLAS E SEU EFEITO ANTECEDENTE NA QUALIDADE DE VIDA]; [CABEZA DE ZORRO O COLA DE LE? LA CONCILIACI DE LA VIDA LABORAL Y FAMILIAR DE LAS MUJERES EMPRESARIAS EN LA AGRICULTURA Y LAS EXPLOTACIONES AGROLAS Y SU EFECTO ANTECEDENTE EN LA CALIDAD DE VIDA]
Purpose: The objective of this study was to identify the factors that influence work life balance of women entrepreneurs in the field of agriculture and allied products and how the family demands affect their work-life balance. Further, the paper explores the conflict between parental demand and running a business. Theoretical framework: Literature review points out that despite, an increase in the number of women entrepreneurs over the years, according to the (Global entrepreneurship monitor report, 2020), fewer women pursue entrepreneurship due to various challenges of managing personal and business responsibilities and striking the right balance. Work-life balance is frequently examined in the context of human resource management (Etienne St-Jean and Duhamel M.,2020)but not much has been explored in an entreprenurial context.Hence this study is to investigate and understand the influence of various factors affecting work life balance from an entrepreneurial standpoint. Design/methodology/approach: Triangulation method was used for the study by utilizing both quantitative and qualitative data. The researchers developed a questionnaire to measure work-life balance experienced by women entrepreneurs with 12 independent variables to measure the dependent variable work-life balance.The sample consisted of 450 women agripreneurs Findings: The findings reveal that the age of the children is a major determinant of the extent of parental demand a woman goes through in her life and family support systems are critical in reducing overlap and conflict between the life domains. A positive spillover between the domains significantly enhances quality of life of women entrepreneurs. Research, Practical & Social implications: We suggest a future research into other Personality traits and macro environmental factors which can have a bearing on work life balance of women entrepreneurs which would enable an inclusive entrepreneurial ecosystem. Originality/value: The researchers have concluded that positive spillover between the domains significantly enhances quality of life of women entrepreneurs. 2022 The authors. -
Indigenous tribes and inclusive engagement: An integrated approach for sustainable livelihood into the future
Tourism acts as a stimulant in rural poverty reduction and inclusive socioeco-nomic development. Sustainable tourism can significantly contribute to the economic diversification and local economic development of rural areas with its ability to create jobs and encourage infrastructural development focusing on preserving the environment, culture and indigenous groups. The detrimental effects of tourism on the economy, society and culture have shifted attention to sustainable travel. As a result, terms like 'tribal tourism', 'ecotourism' and 'sustainable tourism' have become popular. Inclusive engagement is a crucial agenda item in future tourism development and a major concern of many international organisations, including the United Nations. This chapter focuses on exploring the tribal communities and their involvement in sustainable tourism initiatives with an overarching focus on the role of the indigenous community and their skill sets in creating sustainable livelihoods through tribal tourism. Apart from creating direct and indirect employment opportunities, 2024 Kottamkunnath Lakshmypriya and Bindi Varghese. All rights reserved. -
IOT-BASED cyber security identification model through machine learning technique
Manual vulnerability evaluation tools produce erroneous data and lead to difficult analytical thinking. Such security concerns are exacerbated by the variety, imperfection, and redundancies of modern security repositories. These problems were common traits of producers and public vulnerability disclosures, which make it more difficult to identify security flaws through direct analysis through the Internet of Things (IoT). Recent breakthroughs in Machine Learning (ML) methods promise new solutions to each of these infamous diversification and asymmetric information problems throughout the constantly increasing vulnerability reporting databases. Due to their varied methodologies, those procedures themselves display varying levels of performance. The authors provide a method for cognitive cybersecurity that enhances human cognitive capacity in two ways. To create trustworthy data sets, initially reconcile competing vulnerability reports and then pre-process advanced embedded indicators. This proposed methodology's full potential has yet to be fulfilled, both in terms of its execution and its significance for security evaluation in application software. The study shows that the recommended mental security methodology works better when addressing the above inadequacies and the constraints of variation among cybersecurity alert mechanisms. Intriguing trade-offs are presented by the experimental analysis of our program, in particular the ensemble method that detects tendencies of computational security defects on data sources. 2023 The Authors