Volume 9 | Issue 3

 Abstract

Ongoing advances of innovation in digital imaging and digital storage gadgets make it conceivable to effectively produce, transmit, control and store substantial number of digital images and documents. The term CBIR can be characterized as to recover the image from low level features like texture, shape spatial data or color. In early period of this developing recorded the image was retrieved by text depiction called as Text Based Image Retrieval [TBIR]. The combination of these content-based features is needed for better retrieval of image according to the application. The main test of CBIR framework is to decide the specific/surmised matching image of database to the query image. Over the most recent two decades, CBIR frameworks have been enhanced a lot. In any case, there still stay a few issues which have not been addressed agreeably. In this study we will represent the CBIR and also represent the existing techniques with results by various authors.

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 Keywords

Digital, Image, Text, Database, query.

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Surface modelling and design have embraced subdivision surfaces as a compelling representation. They solve some of the major drawbacks of classic spline-based approaches, such as the inability to accommodate arbitrary topologies and the lack of flexibility. To allow multiscale editing procedures to be supported in this article.Methods of subdivision-based modelling with a focus on interactive tools for 3D model style and embellishment.We complete and unite two sets of surface developments that utilize polynomial bits of degree (3,3) to partner a smooth surface with a lattice. The two sets complete one another in that one broadens the subdivisionmodelling worldview, the other the NURBS fix approach to freestyle displaying. Both Catmull-Clark and polar development generalize the bi-cubic spline region. Together, they structure a powerful blend for a smooth item plan: while the CatmullClark region is more appropriate where not many aspects join,polar development pleasantly models areas where numerous aspectsjoin, as while covering expelled highlights. We tell the best way toeffectively join the cross-sections of these two speculations ofbi-cubic spline development.A related however unique speculation of bi-cubic splinesis to demonstrate non-tensor-item designs by a limitedset of flawlessly associated bi-cubic patches. PCCM does as such for designs where Catmull-Clark would apply. Weshow that a solitary NURBS fix can be utilized where polar development would be applied. This spline is independentlyparametrized, yet, utilizing a clever strategy, that's what we showthe surface is C1 and has limited shapes. Modelling non-tensor-product setups using a finite collection of smoothly linked bi-cubic patches is a related but distinct generalization of bi-cubic splines. PCCMdoes so in cases when Catmull-Clark would be appropriate. Wedemonstrate that a single NURBS patch may be utilized in place of a polar subdivision. This spline is unique.parametrized, but we demonstrate using a unique method thatThe surface has restricted curvatures and is C1.

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Catmull Clark region, subdivision-based modelling, bi-cubic spline region, NURBS.

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In this paper, we suggest using a serverclient framework model that makes use of Internet of Things (IoT) technology to manage Pap smear imaging data obtained from a high-resolution magnifying lens and to categorise those images.by learning using an Artificial Neural Network (ANN)server-side computation The Internet of Things (IoT) has the potential to enhance those magnifying tools. To communicate with one another while the ANN empowers a new generationmethod for high-precision imaging grouping. We make use ofAs a contribution to our proposed plan, 917 high-goal photographs were submitted.The technique achieves a 0.8834 root mean square error and0.6643 is the connection coefficient.Cervicography, another analytic tool, provides long-term, objective documenting of both common and atypical cervical cases. To coordinate visual colposcopy amplification and aim, the projected cervicogram is nearly same. An expert can get a cervicogram and send it to a professional for evaluation. The demonstrative precision of coloscopic, cytological, and cervicographic findings was investigated in a study of 700 women. A total of 136 cases of dysplasia or cancer were investigated by coordinated biopsy in 296 women who had a typical cytologic findings. Both colposcopy and cervicographic findings were suspected in 91.1 percent of the cases; cervicography was used only in 2.9 percent of the cases, and colposcopy was used exclusively in 5.9 percent of the cases. Nine instances (2.2 percent) of dysplasia were found in 404 routinely tested individuals who had no previous odd cytologic results.

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Cervicogram, Colposcopic, Cervicography, Artificial Neural Network, Cytologic.

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A Content Management System (CMS) is an application which licenses to fitting, control, adjust, erase and change content in a compelling and dynamic manner. Content Management Software (CMS) is a program that grants you to manage the substance on your site. A Content Management Software consistently has two sections: a Content Management Application (CMA) and a Content Delivery Application (CDA). The Content Management System is a sort of web application that has actually obtained reputation (CMS). Model-Driven Engineering (MDE) techniques, of course, hope to speed up the headway connection by focusing in on models and thoughts as opposed to acquiring code.CMS structures might potentially transform into the new standard for web application designing. The main aim of this study is to assess the Web Application Development And Cms Model-Driven Transformations. We have present a couple of techniques for making demonstrating dialects, similarly as specific points that displaying language organizers should ponder. Regardless, it is crazy to expect that metamodels - and models - don't change for a really long time.

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Content, Management, Software, CMS, program, Application, data, etc.

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SPI exercises in programming affiliations are as frequently as conceivable acted considering all around portrayed reference models distinguishing issues is a crucial part in all product cycle improvement. A lot of variable accepts huge part in programming process. The rule reason for this work is to establish the framework for SPI by discussing the piece of programming, the changes in programming advancement systems and programming improvement process models, and the quality turn of events. The main aim of this study is to discuss the Successful Process Management in Organizations by Using SPI Approach. In addition, SPI experiences are analyzed. This designing exploration technique gives a practical method for managing SPI strategy headway research, as a result of its in-manufactured considered constant turn of events and change. Moreover, it slowly makes and evaluates SPI a strategy, intertwining means to achieve the recently referenced essential accomplishment factors. The investigation relies upon a couple of modern context oriented examinations.

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Management, SPI, Software, Programs, Organization etc.

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The Semantic Web is actually apt to demand knowledge and energy certain. This's pricey, and so it might well be restricted to certain domains on the internet which see a powerful edge in the use of its, though over time as the expertise gets much more commonplace it must get more inexpensive. Furthermore, the' network effect' could work as both an incentive as well as a screen. One of the primary benefits of supplying Semantic Web annotation is actually that's could be discussed and can easily get use to others, consequently when there's little details to share, then there's very little motivation to take the excess cost of sharing; however, after the ball begins to roll, there's an exponential benefit in merging the personal data of yours with others. The main aim of this study is to discuss the Semantic Web Reasoning and Answer Set Programming with Finite and Infinite Set Programming. It is concluded that the chance to exchange knowledge with outside energy sources in a completely declarative framework like ASP is particularly crucial in view of applications of the Semantic Web region.

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Semantic Web, Answer Set Programming, finite, infinite, programming, Description Logics etc.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Agent, Misuse, Mobile, Intrusion, Detection, Wireless.

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Diabetes mellitus is a rapidly growing global health challenge, affecting millions of individuals and contributing to severe complications such as cardiovascular diseases, neuropathy, and kidney failure. Conventional therapies, while effective, often pose side effects and economic burdens, emphasizing the need for safer, cost-effective alternatives. Telangana, a region rich in biodiversity, harbors numerous medicinal plants traditionally used to manage blood glucose levels. This study aims to explore the potential of these indigenous plants in developing herbal-based nutraceuticals for diabetic management. Through ethnobotanical surveys, phytochemical analyses, and preliminary efficacy evaluations, key plants such as Gymnema sylvestre, Tinospora cordifolia, Pterocarpus marsupium, and Momordica charantia were identified for their bioactive compounds with antidiabetic properties. Formulation development and in vitro studies suggest these plants can modulate glucose metabolism and enhance insulin activity, highlighting their therapeutic potential. The findings underscore the importance of integrating traditional knowledge with modern scientific validation to create safe and effective herbal nutraceuticals. Such developments can contribute to holistic diabetes management strategies, reduce dependency on synthetic drugs, and promote the sustainable use of Telangana's botanical resources. Further clinical studies are recommended to confirm efficacy, safety, and standardization for wider application.

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Diabetes mellitus, Telangana medicinal plants, herbal nutraceuticals, phytochemicals, antidiabetic therapy, ethnobotany, glucose regulation, traditional medicine.

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In agriculture, imageprocessingis becoming more and more significant as it enables farmers to make quicker, more informed, and more precise decisions. Without spending many hours in the field, farmers may assess crop health, identify diseases, track soil conditions, and calculate yields using photos from satellites, drones, and ground- based cameras. Machine learning can be used to automatically evaluate these photos and identify issues like pest assaults, nutritional shortages, or water stress early on. Productivity is increased, resources are saved, and prompt action is made possible. Additionally, image processing promotes environmentally responsible farming by spraying pesticides, fertilizers, and water only when necessary. With workforce shortages, climate change, and rising food demand, this technology provides a useful means of enhancing as well as agriculturalsustainability

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Machine learning, sustainable farming, crop health monitoring, image processing, and precision agriculture.

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The rapid integration of Artificial Intelligence (AI) into the healthcare sector has revolutionised medical diagnostics, patient care, and hospital administration. However, this digital transformation has also made the sector a prime target for cybercriminals aiming to exploit sensitive patient data. This paper investigates the dual role of AI, as both a tool for enhancing healthcare data security and a potential vulnerability exploited by malicious actors. Through a detailed analysis of recent cyberattacks, AI-driven security frameworks, and emerging threat vectors, the study reveals how AI can bolster defences through threat detection, predictive analytics, and automated incident response. The research also highlights the ethical and technical challenges in implementing AI-based cybersecurity systems, especially in resource-constrained healthcare environments. The study concludes with strategic recommendations for developing a robust AI-integrated cybersecurity ecosystem aimed at preserving the integrity, confidentiality, and availability of healthcare data. This paper serves as a call to action for policymakers, healthcare administrators, and cybersecurity professionals to recognize AI's pivotal role in countering cybercrime and safeguarding digital healthcare infrastructure.

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AI in cybersecurity, healthcare data integrity, cybercrime in healthcare, AI-powered threat detection, data protection, digital health security, patient privacy, intelligent security systems, predictive analytics, cyber defence strategies

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Data quality poses a significant challenge for organizations deploying Customer Relationship Management (CRM) systems like Salesforce. Despite progress in data cleansing techniques, existing methodologies are prone to be dependent heavily on manual intervention or rule-based systems lacking the flexibility to learn from changing data patterns. Past research has focused mainly on static algorithms and traditional machine learning models for cleansing, which lack the ability to generalize to the dynamic and complex datasets prevalent in Salesforce environments. This limitation highlights the need for intelligent, autonomous agents with continuous learning and real-time adaptation. Autonomous AI agents with reinforcement learning and adaptive feedback capabilities hold immense potential to address these limitations by cleansing data autonomously with little or no human intervention. However, utilization of such agents specifically for Salesforce datasets has remained under-explored. This paper attempts to bridge this gap by developing a new framework where AI agents systematically find, fix, and remove data anomalies leveraging contextual knowledge of Salesforce data structures and business rules. The framework employs unsupervised learning to detect inconsistencies and reinforcement learning to improve cleansing strategies based on outcomes, promoting scalability and robustness in complex CRM datasets. Experimental testing with real-world Salesforce datasets shows spectacular improvements in accuracy, efficiency, and minimized manual effort compared to existing methodologies. This research makes contributions towards the development of autonomous data management for CRM systems towards more intelligent, self-sustaining data ecosystems that fuel decision-making and operational efficiency.

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Autonomous self-learning AI agents, automated data cleaning, Salesforce data sets, reinforcement learning, data quality, CRM data handling, unsupervised anomaly detection, adaptive data correction.

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Advanced manufacturing techniques within mechanical engineering, driven by the growing need for precision, efficiency, and adaptability in modern production systems. Key research areas included Additive Manufacturing (AM), non-traditional machining, advanced material processing, and the early integration of smart manufacturing technologies. Extensive work was conducted on optimizing AM processes such as Selective Laser Sintering (SLS), Fused Deposition Modeling (FDM), and Direct Metal Laser Sintering (DMLS), with a focus on improving part accuracy, surface quality, and mechanical performance. Non-traditional machining techniques like Electrical Discharge Machining (EDM), Laser Beam Machining (LBM), and Ultrasonic Machining were studied for their effectiveness in processing hard and composite materials. Parallel efforts in advanced material processing--including powder metallurgy, cryogenic machining, and composite fabrication--enabled the development of high-strength, lightweight components suitable for aerospace, automotive, and biomedical applications. The emergence of smart manufacturing concepts, such as sensor integration, process automation, and early-stage digital twins, marked a transition toward intelligent and connected manufacturing systems aligned with Industry 4.0 principles. Sustainability also became a focus, with research exploring energy-efficient processes and environmentally friendly practices.

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Additive Manufacturing, Selective Laser Sintering, Electrical Discharge Machining, Laser Beam Machining, Ultrasonic Machining.

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This study examines the bending behavior of single-layer armature-type graphene nanoplates of varied diameters due to variable-distribution atomic vacancy defects. This study is novel because it thoroughly examines the effects of aspect ratio, defect concentration, clamped-free (CF), clamped-clamped (CC), and simply-supported (SS) boundary conditions on graphene nanoplate buckling. Novel space frame structures were employed to characterize nanoplates to preserve their discreteness. The finite element approach anticipated buckling. The simulation results for the critical buckling load (Pcr) were confirmed by comparing them to accessible literature data. Results show that reducing this factor significantly decreases Pcr at low aspect ratios, whereas the loss becomes slow at higher aspect ratios. Research indicates that atomic-scale errors considerably reduce Pcr, with a 10% concentration resulting in a 40% drop in critical buckling stress, highlighting the significance of defects in nanostructure stability.

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Defective Nanoplates; Critical Buckling Load; Boundary Conditions.

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In this work, a sectional model of the exhaust manifold was investigated with the goal of reducing the amount of heat that is lost via pipe insulation by making use of a variety of different insulating materials. The thermal resistance of the exhaust manifold was increased by using four different types of insulating materials in order to decrease the amount of heat that was lost inside the manifold. A discretization and modeling process was performed on the insulating material model. The thermal and structural features of the discretized model have been prepared for evaluation, and they have been presented to the model. An input that is provided to the analytical model is the heat flux from the exhaust gas. The difference in temperature and the amount of heat lost were both considered outputs. The present analysis allows for the conclusion that the use of these four materials will result in a reduction in the amount of heat that is lost via the exhaust manifold.

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Thermal Property, Discretized Model, Exhaust Manifold, Automobile Element.

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This paper focuses on designing and building a passive solar tracking system to improve the performance and efficiency of solar power systems. The proposed system utilizes passive techniques that eliminate the need for active control mechanisms or external power sources, thereby reducing complexity and energy consumption. The paper encompasses the conceptualization, design, prototyping, and testing of the passive tracking system to optimize the angle at which sunlight strikes the solar panels. The design process begins with an in-depth exploration of solar tracking principles, including the Sun's movement, geographical factors, and how the incident angle affects energy capture. Various passive tracking methods, such as bimetallic strips, compressed gas systems, and shape memory alloys, are examined to identify the most appropriate approach for the paper. Based on the analysis, a new passive tracking mechanism is developed, considering aspects like simplicity, reliability, cost-effectiveness, and adaptability to various solar panel configurations. The design features mechanical components like linkages, gears, and pivots that allow the system to automatically follow the Sun's path throughout the day. The expected outcome of this paper is to demonstrate the practicality and advantages of a passive solar tracking system in boosting energy production and maximizing the use of solar resources. The results could contribute to the development of more efficient and cost-effective solar power systems, especially in situations where active tracking systems may not be feasible or economically viable.

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Passive solar tracking system, solar power systems, incident angle optimization, mechanical design, manufacturing, energy efficiency.

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 Abstract

Samvargeshti Houtra Vivaranam

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Samvargeshti Houtra Vivaranam

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Studies on Earthworm biodiversity, population Composition, population diversity and seasonal fluctuation at study site covering Forest ecosystem and adjoining of forest cropland ecosystem in northern part of Gulbarga District, Karnataka State, Earthworm found in these localities are Eudichogaster indicus and Eudichogaster poonensis, high earthworm population density and persistant population for an extended period upto November in chandrampalli forest due to high rainfall occurring in this site. The earthworm population become zero in the study sites between 0-20 cm soil depth from December onwards due to declining soil moisture content.

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Population, Earthworm, Density, Seasons, Moisture

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In today's business landscape, companies often focus on a consumer-centric approach, leveraging digital and social media marketing. Advertising on social media tends to be subtle, operating at a subconscious level, with influencers serving as a prime example. When executed effectively, this type of marketing is interactive, engaging, and fosters a connection with consumers, rather than merely appealing to their rational side. The lifestyle and electronics sectors are particularly influential in shaping the buying behavior of youth in Patiala. Lifestyle brands excel in creating compelling visuals through influencers for products related to food, fitness, and organic goods, reflecting the latest trends. Electronics, meanwhile, attract tech-savvy youth and are frequently promoted by influencers across various platforms. This research paper aims to integrate these factors by utilizing both primary and secondary data. Its objective is to examine the relationship between the trust that average consumers place in influencers and the subsequent consumer behavior of Patiala's youth aged 15-25. Primary data will be gathered through a structured questionnaire, and the findings will be analyzed and presented based on these observations.

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Social media, Youth, Consumer behavior, Lifestyle, Electronics, Influencers

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Artificial Intelligence (AI) has revolutionized IT service delivery, enabling unprecedented optimization. This study examines AI-enhanced service delivery methods to boost efficiency, save costs, and improve customer satisfaction. AI in IT service management (ITSM) has enabled automation, predictive analytics, and intelligent decision-making, which are essential to high-quality IT services. AI has greatly impacted predictive maintenance and incident management. AI systems may forecast system problems by studying past data and finding trends, enabling IT teams to intervene early. Today's always-on business environment requires less downtime and constant service availability. AI-driven incident management automation categorizes, prioritizes, and resolves problems without human interaction, streamlining the resolution process. This improves reaction times and frees up IT staff to handle more difficult jobs. AI also improves customer support in IT service delivery. Chatbots and virtual assistants are growing more intelligent and can handle a variety of consumer difficulties. These technologies provide 24/7 assistance, improving client satisfaction. AI systems improve over time by learning from each encounter. More precise and individualized replies boost client satisfaction. AI helps IT teams optimize resource allocation. AI can evaluate data and allocate staff and gear to satisfy service requests using machine learning techniques. It reduces waste and operating expenses by optimizing resource consumption. AI can forecast peak periods and adapt personnel levels to suit demand, helping workforce management. The use of AI in IT service delivery is not without hurdles. Data privacy, high initial investment, and specialist AI system management skills might hinder adoption. Over-reliance on AI may weaken human supervision and distort decision-making. Therefore, firms must carefully analyze these issues and establish risk mitigation techniques. In conclusion, AI-driven service delivery optimization may alter IT companies. IT departments may increase service delivery, efficiency, cost, and customer pleasure by using AI. AI will become more important in IT service management, therefore enterprises must adapt and embrace these advances

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AI-driven optimization, IT service delivery, predictive maintenance, incident management, customer support, resource allocation, automation, ITSM, machine learning, workforce management.

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This conceptual paper explores the intricate relationships between consumer identity, advertisement, and health consciousness in shaping healthy food consumption behavior among millennials. As a generation that prioritizes health, wellness, and authenticity, millennials are particularly susceptible to the influences of digital advertising and social media. The study posits that consumer identity, defined by personal values and lifestyle choices, mediates the impact of advertising on healthy food consumption. Simultaneously, health consciousness moderates the extent to which these identities influence purchasing decisions. By understanding these dynamics, marketers, health advocates, and policymakers can develop more targeted strategies to promote healthier eating habits among millennials, thereby fostering long-term positive health outcomes.

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Creative Commons Attribution 4.0 and The Open Definition

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Consumer Identity, Advertisement, Health Consciousness, Consumer Behaviour, Healthy Food Consumption, Millennials

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Creative Commons Attribution 4.0 and The Open Definition