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Financial Literacy and Financial Capability among the Urban Street Vendors
Employment in the informal sector has grown rapidly over the years as it requires a limited skill set, limited educational background, and least initial investment. One such vulnerable sector which forms the major portion of the informal workforce is the street vendors. They lack basic facilities to newlinehave a good standard of living. It is observed that they are usually denied newlinevarious opportunities though their contribution to the economic growth and newlinedevelopment is immense. Their unstable income has left them vulnerable to many financial situations and led them into a financial debt trap. This research is carried out entirely from the view of the street vendors. newlineFinancial literacy has gained importance over the years as it enhances and empowers one s financial ability. Financial literacy is promoted through financial inclusion where all the sections of the society come under one roof to avail finance at ease. Basic financial literacy is will aid the users to newlinemake better utilization of financial schemes under financial inclusion. This in turn leads to better financial capability for individuals. It is observed that there is a gap that needs to be bridged between the street vendors and financial accessibility as they lack basic financial knowledge as are from a low educational background in this study. Better financial knowledge will newlinelead to better usage and accessibility of financial inclusion schemes which will result in better financial capability; this concept is being examined in the current study. The identified relationship impact of financial inclusion on financial literacy and financial capability forms an integral part of the study. It is useful in bringing out the gap between the street vendors and their financial distress. The research was designed to develop an instrument. A research instrument to measure variables was built based on previous studies and the expert s newlineconsultation. -
fluorescence diffuse optical tomography : Synthesis characterization and imaging of a novel target specific near infra-red contrast agent for breast cancer detection
Contrast agents are finding profound application in optical imaging of breast cancer for an early detection. In the present work, a novel estrogen receptor (ER) targeted near infra-red fluorescent dye conjugate was synthesized, referred to as Novel Dye Conjugate (nDC) hereafter. nDC is a conjugate of 17and#946;-estradiol with a derivative of indocyanine green dye, bis-1,1-(4-sulfobutyl) indotricarbocyanine-5-carboxylic acid, sodium salt. Structural composition of nDC was validated using Liquid Chromatography Mass Spectrometry (LC-MS) and Hydrogen-1 Nuclear Magnetic Resonance (1H-NMR) technique. MCF-7 and MDA MB 231 Cell lines studies proved the special biding ability of nDC with estrogen receptor positive breast cancer cell lines and its photophysical properties were verified to be in near infrared region (NIR). Similar studies were conducted on ER expressing cancerous tissues like Non-Invasive Ductal Carcinoma, Non-Invasive Lobular Carcinoma, Non-Invasive Adenocarcinoma and Non-Invasive Medullary Carcinoma. In all the above tissues, nuclear level ER binding of nDC was observed leading to the validations of the unique binding properties of the novel dye. Mathematical modeling for tumor to background mapping using nDC was carried out through Fluorescence Diffuse Optical Tomography (FDOT) simulations. Simulation results were also validated using silicone phantom experiments. An array of 8*8 boundary data was collected using frequency domain-FDOT system which was setup indigenously. Commercially available fluorescent dye Indocyanine Green (ICG) was used in the present study for comparative analysis with nDC. When compared to ICG, proposed dye had 1.5-fold higher target to background contrast with respect to fluorescent lifetime in both simulation and phantom studies. Similarly proposed novel dye had a two-fold higher target to background contrast with respect to fluorophore absorption. Above results proved the superiority of nDC compared to ICG on target(tumor) to background ratio enhancement. -
Forcing Parameters and Propagation Time of Certain Graph Classes
A branch of mathematics that treats vertices and edges of a graph is called graph theory. This theory is used to replicate many real-life situations and physical problems. Graph coloring problem is one of the prominent studies in extremal graph theory. Suppose information has to be communicated in a network or some product has to be marketed to all the people in a cluster then there are two types of cost that needs to be encountered, one the cost of selecting the initial set of vertices in a network and the second is, time it takes to propagate the information through the entire network. The sum of these two parameters is known as the total cost. Optimizing the total cost, which is the sum of vertices and the time it takes to propagate information through the entire network, is a challenging problem for any newlinegraph. Such an interesting and well-studied problem is called the dynamic coloring newlineproblem. The forcing problem also known as infecting or spreading problem is one newlinesuch dynamic coloring problem where two colors- white and black are used. Assume that a fxed set of vertices in a graph G are initially black and that the remaining vertices are considered white vertices. The aim of the forcing process is to obtain, fully black-colored vertices of the graph G by progressively applying the color change law, making sure that at least one white vertex is forced black in every discrete time interval. The forcing index is the minimum cardinality of the forcing sets. Diand#64256;erent types of forcing, such as one forcing, connected one forcing, k-forcing connected k-forcing etc., are defned based on the color change law. The one forcing is the basic form of forcing. A generalised form of one forcing is known as k-forcing where k lt V (G). The time taken by a forcing set to force the entire vertices of the graph G black is the propagation time or iteration index. The subject of study aims to fnd the one forcing number and k-forcing number of diand#64256;erent types of graph classes and derived graph classes. -
Forecasting volatility evidence from the futures market in India
This thesis focuses on modelling and forecasting of select products in the Indian futures market using econometric time series models and artificial neural network based models. These models have been compared for their forecasting accuracy to determine the best forecasting model for a particular futures series. This study applies GARCH, EGARCH, PARCH, TARCH, and Artificial Neural Networks (ANN) to assess the best predicting model for exchange rate futures, commodity index futures and stock index futures. After testing for stationarity of data series, GARCH, EGARCH, PARCH and TARCH models are developed. In addition to in-sample forecasts, 1-day, 5-day, 10-day, 15-day and 30-day out-of-sample forecasts have been carried out. For ANN, data is scaled using the minmax scaling methodology to ensure that newlinethe data series is normalised and in the range of 0 to 1. ANN is developed using the feedforward methodology. While the basic neural network architecture has one input layer, one hidden layer and one output layer, the number of neurons in the input and hidden layers vary from 1 to 20. The optimum number of input and hidden neurons in their respective layers are then selected based on the combination which gives the least error. These network combinations are used for out-of-sample forecasting and errors are compared with the forecast output of the GARCH models. RMSE, MAE, MAPE, Theil s-U statistic and Correlation coefficient is computed for error newlinecomparison. Results indicate that for currency futures and commodity index futures, ANN provides better forecast accuracy. For stock index futures, GARCH family models work better in some cases. -
Foreign Policy of China a Under Deng Xiaoping and its Contemporary Relevance
Political leadership plays an important role in foreign policy decision making in general. Studying leadership traits, styles, beliefs and world view is one of the common methods to comprehend political leaders and their influence on foreign policy. When it comes to authoritarian countries like China, its foreign policy decision making has several layers of which political leaders play all the more crucial role. Irrespective of the period Imperial, Nationalist or Communist the political leaders of China are guided by its history, philosophy and the then existing domestic and global circumstances, in formulating and implementing the country s foreign policy. Political leadership plays an important role in foreign policy decision making in general. Studying leadership traits, styles, beliefs and world view is one of the common methods to comprehend political leaders and their influence on foreign policy. When it comes to authoritarian countries like China, its foreign policy decision making has several layers of which political leaders play all the more crucial role. Irrespective of the period Imperial, Nationalist or Communist the political leaders of China are guided by its history, philosophy and the then existing domestic and global circumstances, in formulating and implementing the country s foreign policy. The central enquiry of the study is to assess contemporary relevance of Deng s foreign newlinepolicy paradigm. Through field visits and rigorous analysis of primary sources, the newlinestudy establishes that relevance of Deng s policy continues in the present context except on China s pro-activeness towards issues pertaining to its territorial integrity and sovereignty. Using China s case, the study advances the framework of understanding pertaining to the role of political leadership in foreign policy making. The study also makes certain broad policy recommendations to various stakeholders for consideration. -
Friction Stir Welding Process Parameters Optimization By Taguchi Analysis and Validating The Mathematical Model Using RSM For AA6101-C11000 Alloy Joints
Friction Stir Welding (FSW) is a well-established joining method that offers newlinesignificant advantages over traditional methods, including improved mechanical newlinecharacteristics, less distortion, and environmental friendliness. Due to its solid-state nature, the heat produced through the welding mainly influences the features of joints in FSW. In this investigation, 2D and 3D models of the base metals and welding tool newlinewith different pin profiles were designed using SOLIDWORKS. Fixture was designed and manufactured in accordance with the specifications of the welding machine. ANSYS software was used to investigate the temperature distributions near the weld newlinezones. The base metals AA6101 and C11000 of each 5 mm thickness with butt weld newlinepositioned, were joined by FSW mechanism with the help of OHNS steel tool with circular pin profile. Taguchi analysis was employed to optimize the FSW welding input process parameters, including tool rotational speed (rpm), feed rate (mm/min), and tool offset (mm), to determine their respective contribution (%) to the output response, namely ultimate tensile strength (UTS), hardness, impact load, and electrical newlineconductivity to achieve the high joint strength. During experimental work using the newlineTaguchi s design matrix, the maximum output response values were obtained when the input parameters were set to 1000 rpm, 50 mm/min and -1 mm. Taguchi analysis revealed that the tool rotational speed encounters high significance effects, followed by feed rate and least tool offset upon output response. The X-ray diffractometer (XRD) test was employed to specifically determine the existence of Al-Cu intermetallic compounds (IMCs) generated within the FSW of Al (AA6101) and Cu (C11000) joints. At medium 40 mm/min, 1000 rpm, and -1.68 mm, the IMCs newlinedeveloped were Al4Cu9 and Al1Cu3 giving a high UTS value of about 142.69 MPa. newlineMathematical model was developed utilizing the Response Surface Method (RSM) to newlinepredict the output response. -
Fuzzy Rule-Based Multimodal Health Monitoring System Leveraging Machine Learning Techniques Using Eeg Datasets For Human Emotion And Psychological Disorders
In recent decades, machine learning and data analysis have become increasingly important in mental health for diagnosing and treating psychological disorders. One area of particular interest is the use of electroencephalography (EEG) brainwave data to classify emotional states and predict psychological disorders. This study proposed a data fusion to enhance the precision of emotion recognition. A feature selection strategy using data fusion techniques was implemented, along with a multi-layer Stacking Classifier combining various algorithms such as support vector classifier, Random Forest, multilayer perceptron, and Nu-support vector classifiers. Features were selected based on Linear Regression-based correlation coefficient scores, resulting in a dataset with 39% of the original 2548 features. This framework achieved a high precision of 98.75% in identifying emotions. The study also focused on negative emotional states for recognizing psychological disorders. A Genetic Algorithm (GA) was used for feature selection, and k-means clustering organized the data. The dataset included 707 trials and 2542 unlabeled features. Resampling techniques ensured a balanced representation of emotional states, and GASearchCV optimized Gradient Boosting classifier hyperparameters. The Elbow Method determined the optimal number of k-Means clusters, and resampling addressed class imbalance. GA parameters and gradient- boosting hyperparameters were empirically determined. ROC curves and classification reports evaluated performance, resulting in a high accuracy of 97.21% in predicting psychological disorders. The proposed system employed fuzzy logic to calculate a health score that combines the outputs of the emotional and psychological disorder monitoring models for a multimodal health monitoring system. This approach provides a more comprehensive assessment of an individual's overall mental health status. The findings suggest that the system achieved high efficiency in predicting emotions, showcasing comprehensive progress in EEG-based emotion analysis and disorder diagnosis. These advancements have potential implications for mental health monitoring and treatment, particularly with the integration of the PHQ-9 Scale and fuzzy logic. -
Graphene and graphene enhanced nanomaterials from biological precursors synthesis characterization and proliferant applications
Graphene family materials with non-photocatalytic biocidal properties are highly sought after in the field of biomedicine and nanobiotechnology. But the applications of graphene-based materials were often hampered by their high production cost, low yield, non-renewable precursors, harmful processing newlinetechniques, etc. In this context, this study presented the successful usage of biomass materials as sustainable feedstock for the production of graphene derivatives. Five raw materials of biological origin namely, coconut shell, wood, sugarcane bagasse, Colocasia esculenta leaves and Nelumbo nucifera leaves, were investigated. The graphitized forms of the above materials were newlineused as precursors for the graphene nanomaterial synthesis. They were chemically oxidized and functionalized with tin oxide nanoparticles to form the composite. Nano-systems obtained using an identical chemical route from a universal source of carbon nanomaterials, namely carbon black, were also newlinestudied for the purpose of validation and comparison. The synthesis protocols adopted for the preparation of graphene-based materials were devoid of hazardous reducing agents or byproducts. The products obtained after each stage of treatment were characterized with the help of various spectroscopic and microscopic techniques. newlineEven though structural properties of all the precursors appeared to be broadly the same, a variation in their morphology and defect density was discerned. Various analyses revealed the formation of graphene oxide domains with distinct dimensions after the oxidative treatment. An increase in defect newlinedensity was also observed due to the intercalation of oxygen groups to the carbon layers. Post composite formation, a distribution of ultrafine tin oxide newlinenanoparticles on the graphene surface was observed. The distribution of oxygen newlinefunctionalities on the carbon backbone were found to play a major role in governing the dispersal of tin oxide particles during the nanocomposite formation. -
Graphs Emerging from Finite Dimensional Vector Spaces
A vector space over a field is defined as a collection closed under finite vector addition and scalar multiplication. Over the course of time, researchers have delved into exploring the intricate relationships between existing algebraic structures and graphs. This exploration led to the emergence of a distinctive class of graphs derived from vector spaces, following investigations into graphs originating from groups and rings. This thesis undertakes a thorough examination of a well-established algebraic structure known as the non-zero component graph of a finite-dimensional vector space over finite fields. Expanding on this, the thesis introduces the concept of orthogonal component graphs over finitedimensional vector spaces with a particular emphasis on the field Zp. The non-zero component graph of a finite-dimensional vector space over a newlinefinite field is a graph where vertices represent all possible non-zero vectors in newlinethe vector space. Vertices in the graph are made adjacent if they share a common basis vector in their linear combination. The thesis explores a variety of properties relating to distances, domination, and connectivity. Furthermore, it conducts in-depth study of coloring, color connections, topological indices, and centrality-based sensitivity specifically for non-zero component graphs. The concept of orthogonality among vectors in the vector space paves the way for a novel algebraic graph structure the orthogonal component graph. In this graph, vertices represent all possible non-zero vectors in the vector space, and adjacent vertices correspond to orthogonal vectors. The study extends to determining the properties of the orthogonal component graph, particularly in the newlinecontext of the field Z p. Additionally, it characterises the relationship between newlinenon-zero component graphs and orthogonal component graphs. In the latter chapters, the concept of non-zero component signed graphs is introduced and thoroughly discussed. -
Green synthesis of modified ceria nanoparticles and their catalytic activity studies
Catalysis is a phenomenon where a reaction is taken through an alternative pathway involving lesser energy. Thus, it has an energy saving dimension implicit in its definition. This thesis involves the study of catalysts, synthesized by the solution combustion method. The fuel required for the newlinecombustion is aqueous extract obtained from the leaves of selected plants which gives added credence to the ecofriendly aspirations that dominated our work. A series of ceria based nano sized catalyst materials, pure and modified using rare earth metal oxides, transition metal oxides and a non-metallic substance have been synthesized by the above method. The catalysts have then been newlinecharacterized for their composition, crystallinity, morphology, surface properties, thermal stability etc. The prepared catalysts were subsequently evaluated for their catalytic and photocatalytic efficacy. The photocatalytic potential of the catalysts was evaluated on the degradation studies of two dyes Malachite Green (MG) and Congo Red (CR) under visible light and one antibiotic drug ciprofloxacin (CIP) under UV light. The catalysts were found to show good photocatalytic efficiency newlineon all the three substances mentioned above. The catalytic efficiency was evaluated on two chemical reactions. One, the reduction of 4-nitrophenol to 4-aminophenol and the other, the synthesis of compounds of Biginelli reaction. To achieve maximum reduction the experimental conditions for the catalyst were newlineoptimized. Biginelli reaction involves the condensation of ethyl acetoacetate, newlinebenzaldehyde and urea in presence of modified ceria catalysts to form dihydropyrimidines. The reaction was performed with different catalysts and the one which gave the best yield was selected for further optimization of other reaction conditions. Employing the optimized conditions, a set of different newlinedihydropyrimidinone derivatives were synthesized by varying the precursor newlinealdehydes and ketones. Reusability studies for the catalysts were conducted for all the reactions mentioned above. -
Green Synthesis of Nano Carbon-Infused Polymer for The Detection of Toxic Heavy Metals
The global population is marching towards greener ways of life. Green nanotechnology, newlinewhich uses carbon nanomaterials for environmental remediation, is the pioneer among the existing strategies for the production, characterization, and applications of carbon nanomaterials derived from sustainable and renewable energy resources. Additionally, easily available natural ingredients are effective carbon precursors for producing carbon dots with newlineenthralling physical and chemical properties. Compared to other approaches, plant-based newlinesynthesis of nanomaterials is more dependable because it is simple, fast, ecologically newlinefriendly, and does not require particular conditions. We report for the first time, the use of a fluorescent nanocarbon material synthesised from plant, Indigofera Tinctora (L.) (IBLH), for the detection of metal ions. This nanomaterial developed using a green synthesis method that aided hydrothermal processing from the leaf extract of IBLH. The IBLH sensor used to detect hazardous metal ions (Pb2+) was very sensitive and selective. Considering the concentration from 1 nM to 100 mM and 100 mM to 1M, developed sensor displayed broad, dual linearity. The limit of detection (LOD) for the sensor appreciable low with 14.74 nM as the detection limit, with a wide and linear response spanning from 1 nM to 1M Cd2+ concentration range. Utilising Ruta Graveolens as the carbon source, we developed ARH-CDs from agricultural waste using chemical-free, one-step hydrothermal procedures that are safe for the environment. The synthesized ARH-CDs showed nano particle size, outstanding water newlinesolubility, great biocompatibility, and appreciable optical characteristics. The FTIR and XPS findings validated the existence of functional groups. such as C-O, C-C, and O-H with various oxygen functional groups, with predominating hydroxyl group, supporting the newlineexistence of CDs. For the selective detection of Hg2+, the synthesized ARH-CDs are employed as a biocompatible fluorescence sensor. -
Green Synthesis of Nanoparticles Leading to the Biocontrol of Aedes Aegypti
Mosquitoes are the potential vectors of many diseases such as malaria, dengue, brain newlinefever, etc. There is a need to check the proliferation of the population of vector newlinemosquitoes to reduce vector-borne diseases by appropriate control methods. Nanotechnology, a promising field of research, opens up in the present decade and is expected to give major impulses to technical innovations. Over the past few decades, nanoparticles of noble metals such as silver exhibited significantly distinct physical, chemical and biological properties. Presently, there is a need for increased efforts to develop newer and more effective methods to control mosquito vectors. Due to different technical and operational reasons, the existing chemical and biological methods are not as effective as in the earlier period. Therefore, this study is designed to extract silver newlinenanoparticles from plant, fungal and bacterial species and assess their impact on the third and fourth-instar mosquito larvae and the adult mosquito (Aedes spp). The study has formulated a gel material that is composed of nanomaterials that exhibited promising properties to develop a nano gel product. The study is designed in a way to have an impact on the control of mosquito larvae using biologically synthesized nanoparticle formulations. Green synthesis is expected to show a higher yield of nano products that can be formulated in various forms to standardize the biocontrol of mosquito species. Bioinformatic studies revealed the good binding potential of the extracted bio compounds against the juvenile hormone binding proteins in A. aegypti. The study deduced meaningful outcomes that can benefit the environment by controlling the mosquito population and thereby reducing disease transmission in many developing countries. -
Growth and characterization of InBi1-xSbx InBi1-xTex and γ-In2Se3 crystals
Theory and innovating practices of crystal growth heralded cutting edge breakthroughs in the production of proficient crystals towards the advancement of science and technology. Unique characteristics and band structure provide great flexibility for structural design and band gap engineering of indium bismuthide (InBi) compounds. Substitution of antimony and tellurium elements results in the transition of InBi to a semiconducting state with narrow energy gap, making it suitable for optoelectronic devices. Need of eco-friendly sustainable processes concerning the elimination of hazardous materials bring and#947;-In2Se3 in the forefront of photovoltaic industry, due to its wide band gap as well as n-type conductivity. Thus, realizing the immense potential attributes of InBi1-xSbx, InBi1-xTex (x = 0-0.2) and and#947;-In2Se3 crystals, the present research was focussed on pioneering their growth and characterization.Horizontal directional solidification (HDS), being the versatile, inexpensive melt growth technique, was employed for obtaining InBi1-xSbx, and InBi1-xTex (x = 0-0.2) crystals. On the other hand, closed tube sublimation (CTS) was found to be most effective for deposition of and#947;-In2Se3 crystals. Platelet and spherulitic morphologies of and#947;-In2Se3 crystals have been grown by the vapor deposition for the first time, under different growth environments. Morphology, structure and quality of the as-grown crystals were studied, employing various scientific procedures such as X-ray diffraction (XRD), energy dispersive analysis by X-rays (EDAX), scanning electron microscopy (SEM), atomic force microscopy (AFM) and transmission electron microscopy (TEM). Transport parameters, melting point and phase purity have been evaluated with the aid of Hall effect measurement, four probe set up, differential scanning calorimetry (DSC) and Raman spectroscopy. Vickers indentation testing was utilized for the evaluation of microhardness and deformation characteristics. -
Growth and Characterization of Sb2Se3 and SnSe2 Crystals for Photovoltaic Applications
Tremendous development in crystal growth technology led to the production of good newlinequality samples for the design and fabrication of optoelectronic devices. As naturally available solids exhibit undesirable characteristics, the present research work deals with the artificial synthesis and characterization of defect free binary layered chalcogenide materials newline(LCMs) for photovoltaic (PV) applications. Antimony selenide (Sb2Se3) and tin diselenide newline(SnSe2) have gained special attention in the PV industry due to their eco-friendly, sustainable, and non-hazardous nature as well as the salient features such as moderate melting temperature, p-type conductivity with direct transition, optimum band gap and high newlineabsorption coefficient. Therefore, cost-effective synthesis was implemented to engineer bulk Sb2Se3 and SnSe2 crystals for the enhancement of optoelectronic parameters. Single crystal growth from melt allows the fabrication of large size samples under controlled environment. It gives rise to complexities in maintaining stable temperature for crystallization and newlineachieving chemical homogeneity, if multiple elements are present in the system. The newlinechallenges associated with Bridgman-Stockbarger and Czochralski methods for preparing bulk crystals include irregular heat flow, mechanical movement of furnace or crucible, thermal stress, etc. Moreover, reactivity of the melted material with the ampoule leads to structural irregularities. Hence, horizontal normal freezing (HNF), the facile and inexpensive melt growth technique was employed to explore the suitability of cleaved samples. Most of the vapor phase synthesis methods, especially, the chemical vapor deposition (CVD) deteriorates material quality, which adversely affects the physical properties due to the presence of contamination or foreign elements. But, the physical vapor deposition (PVD) process is favorable as it offers feasible instrumentation and yields stoichiometric specimens with supreme quality and fine-tuned characteristics. -
Growth and physical properties of sb2 te3 and related thermoelectric materials
Research on crystal growth and characterization is inevitable to meet the requirements of the technological world, as there is a great demand for good quality samples free from flaws for application in newlinevarious fields which cannot be met by natural resources. The synthesis of newlinebulk crystals of Sb2Te3 compounds has intrigued the attention of the researchers in the present work, due to their diverse properties which provide boundless scope to develop innovative approaches towards the newlinedevelopment of devices with improved thermoelectric (TE) efficiency. The green technology of conversion of waste heat to electric current by the TE phenomena offers a noise-free alternative with low mechanical newlineand conduction losses for small scale refrigeration and power generation modules. Though, thermoelectric devices offer better reliability and durability, one of the major challenges is to develop a material system newlinewith high figure of merit (ZT) in the variable temperature ranges. From the research reports it is evident that, generally for scientific studies, conventional melt methods were used to grow bulk Sb2Te3 crystals, where nonstoichiometry, polycrystallinity and multi-phase formation raise problems. Furthermore, large fluctuations in TE properties have been exhibited by single crystals synthesized from the melt, which preclude their uses in devices. The ability to control as well as engineer various newlineproperties of Sb2Te3 depends on the choice of growth method, experimental tools and processes. Even though substantial work has been published on the studies of cleaved samples of crystals grown from the melt, the growth mechanism and TE investigations on vapor deposited platelet structures of Sb2Te3-xSx and Sb2-xInxTe3 have not been investigated so far. With the prime focus on vapor deposition as an alternative to melt methods to produce defect free, good quality stoichiometric and mechanically stable crystals with improved ZT, the research was aimed at growth and characterization of Sb2Te3 and related newlinethermoelectric materials. -
Health diagnosis of mango trees using image processing techniques
A Mango disease detection artificial intelligent model needs robust and effective newlinefeature extraction methods. The machine vision system has been designed for the newlineidentification of disease in plants from color leaf images. The research done proposes newlinenovel algorithms to extract color features Pseudo Color Regions and Texture Features newlineusing Pseudo Color Co-Occurrence Matrix. A new Mango dataset has been created and newlinealgorithms tested on it. An artificial intelligence model has also been created and tested on an existing disease dataset of Apple and Tomato plants. Results were compared with existing methods in the literature. The effectiveness of each statistical function was studied in classifying the pattern using a Support Vector Machine. For textures that are newlinedifferent like smooth new leaves, dry leaves, growth a Gray Level Co-occurrence based newlinestatistics was effective but values failed to discriminate in certain diseases. The proposed and implemented novel method which uses second-order statistics on a pseudo-color-based co-occurrence matrix has resulted in better classification. Pseudo Color Region feature is created using a novel intermediate data structure and found to be more effective than hue-based color features. It identifies dots, spots, patches and regions of different colors on the leaf and uses that as a feature vector to classify plant diseases. This generic method can be applied for early disease detection for plants and help farmers take corrective measures to avoid loss of yield. -
Heat amd Mass Transfer Analyses of Nanofluid in a Multilayer Model
The study offers an in-depth exploration into the dynamics and properties of multilayered nanofluids and hybrid nanofluid flow in newlinedifferent geometries. The in-vestigation ranges from sinusoidal channels with micropolar hybrid nanoliquids to concentric cylinders that exhibit electrokinetic effects and rotating disks. Also, the DarcyForchheimer model is introduced to assess non-Newtonian and Newtonian fluid interplay, emphasizing the role of asymmetric slip conditions which reduces the fluid flow. Moreover, the study on bioconvection obtained newlineby addition of gyrotac-tic microorganisms which enhances mass and heat transfer in multilayer Newtonian fluid channels. Studies explain the importance of interfacial regions in achieving optimal system temperature. The subsequent study examines the two-layer hybrid nanofluid (HNF) with magnetohydrodynamic properties between two newlineidentical ro-tating disks. The governing equations of the mathematical models are explained using PDE and solutions are attained using numerical and semi-analytical methods such as the DTM and Range Kutta method. Further, the obtained results have been explained with the help of tables and graphs. The study reveals that the immisci-bility of the base fluids forms an interfacial layer, revealing that the addition of two different fluids restricts the fluid motion nearer to the interfacial region, maintaining an optimum temperature in the system. Collectively, these findings pave the way for advanced applications in industries like solar, nuclear, biomedical, and electronic cooling, promising enhanced newlineperformance and efficiency. -
Heat and Mass Transfer Analysis of Newtonian and Non-Newtonian Nanofluids in The Presence of Motile Microorganisms
This dissertation deals with the analysis of heat and mass transfer in Newtonian and newlinenon-Newtonian nanoand#64258;uid in the presence of motile microorganisms. The major application of the and#64258;uids in heat and mass transfer process is its capability to conduct heat. Hence, the and#64258;uids act as a source that conducts heat and cools down the temperature of the appliance. Whereas, the capacity of heat conductance is low in case of regular and#64258;uids, hence the concept of nanoand#64258;uids was introduced whose thermal conductivity is more when compared to regular and#64258;uids. The high thermal conductivity of nanoparticles helps in conducting more heat and the property of and#64258;uid to and#64258;ow helps the nanoparticles to and#64258;ow all over the desired surface and conduct heat. During the process of nanoand#64258;uid and#64258;ow, the nanoparticles undergo random motion that is termed as Brownian motion and they also experience the thermophoretic force that causes the nanoparticles to move from hotter region to colder region. Further, the presence of nanoparticles would either result in sedimentation or formation a layer of nanoparticles over the surface. This layer of nanoparticles adhered to the surface creates corrosion. Hence, it is important to prevent the nanoparticles from forming its layer over the surface and also the sedimentation of nanoparticles must be avoided to have no blockages in the system. Hence in this regards, self propelled microorganisms newlineare allowed to swim in the nanoand#64258;uid which in turn constitutes bioconvection. Considering these assumptions, problems in this dissertation are modelled such that it deals with the analysis of bioconvection caused due to the swimming of microorganisms in the and#64258;ow newlineof nanoand#64258;uid. The mathematical models of the and#64258;ow, heat and mass transfer of Newtonian and non Newtonian nanoand#64258;uids are designed using the partial differential equations with various assumptions to achieve realistic results. -
Hybrid Intrusion Detection Technique for Internet of Things
The rapid expansion and integration of Internet of Things (IoT) applications in newlinevarious aspects of daily life has significantly surprised and impacted contemporary society. The most crucial keyword concerning these applications is security, specifically, in the enormous amount of data generated every second, and how it is used. These applications are vulnerable to various attacks, which could result in an unthinkable catastrophe if not managed and controlled with sufficient foresight. Growing concerns about data security in the expanding IoT landscape are driven by factors such as increased vulnerability of devices to viruses, susceptibility to denial-of-service attacks, and heightened risk of intrusion attempts. To prevent such occurrences, stronger precautions should be taken, enabling system developers and manufacturers of IoT devices to enhance their approaches to better security mitigation. It is essential to identify all potential threats and vulnerabilities that are created explicitly for IoT infrastructures. It is believed that to lessen potential dangers, there is a need for more significant research on security attacks. Security difficulties have been found and must be dealt with, so they may be avoided. Further research must address security challenges in IoT-based environments, particularly for suppliers and consumers, to gradually raise the reliability of IoT applications. Although many conventional methods are still used, there might be superior options for devices with limited resources. Artificial intelligence plays a significant role in this issue. newlineThis research first tries to comprehend how machine learning methods relate to attack newlinedetection. The effects of different machine learning techniques are evaluated using the newlineUNSW-NB 15 dataset. Additionally, it has been found that each model performs worse overall, mainly when security issues are present. As a result, real-time datasets and Deep Learning (DL) algorithms for intrusion detection in the IoT need to be prioritized.