Volume 13 | Issue 1

 Abstract

The usage of financial cards has increased dramatically as a result of the technology for onlinetransactions developing so quickly. Since credit cards are the most widely used way of payment, there arean increasing number of fraud incidents related to them. The use of digital payments in every manner is growing quickly worldwide. Thenumberoftransactionsprocessedbypaymentcompaniesisrisingquickly.There are many credit card issues in the modern world, so a strong system that can accurately identifyfraudulent activity is required to detect credit card frauds or to stop them. Such a system will be developed.this paper presents a comprehensive framework for credit card fraud detection using machine learning,addressing the inherent challenges associated with fraud detection in real-world financial transactions. Theproposed approach offers a promising avenue for financial institutions to mitigate the risks posed byfraudulent activities and safeguard the interests of both merchants and consumers. This paper describes several platforms and machine learning technologies, as well as the notion of credit card fraud, an introduction to fraud and workflow of the proposed model.

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Frauds,MachineLearning,EssentialTools,DetectionTechnique

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Navigating around a big campus can be rather intimidating, more so to newcomers and visitors. A Campus Navigation System is proposed for this paper with the help of QR code and web technology as an interactive way of user-friendly navigation. With the system, one can scan the QR code set at specific places on the campus to enter into a web application to choose his source and destination and get optimized routes. This project aims to create an efficient and intuitive navigation on campus using the technologies of HTML, CSS, and JavaScript. The outcome would be a scalable, responsive, and user-friendly system that would reduce confusion and improve accessibility across the campus.

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QR Code, Campus Navigation, Web Technology, Route Optimization, Interactive User Interface.

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India is the second largest producer of Tomato crops globally. Tomato crops are also cash crops for farmers in India and are extremely water intensive to grow and product. For most farmers many can't afford to let their current crops fall victim to diseases rendering such an intensive investment in failure. Timely detection and accurate diagnosis are essential to control the spread of these diseases and ensure optimal yields. In recent years, deep learning, especially Convolutional Neural Networks (CNNs), has emerged as a powerful tool for plant disease detection from images. This survey provides a detailed overview of CNN-based approaches for tomato crop disease detection, highlighting state-of-the-art techniques, datasets, and challenges. Additionally, it discusses the integration of a prescription system that aides farmers with timely interventions. The paper concludes by exploring the challenges and limitations.

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CNN, Tomato ,Deep Learning

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ABSTRACT Background: prevalence of burnout among healthcare professionals poses a serious health concern. Recent studies focus on prevalence and predictors of burnout among healthcare providers, emphasizing the need for well-being of Health care professionals. Aim and Objectives:- The present study aims to assess the Burn out and Coping strategies in Critical care Nurses. Objectives of the study were to assess and find out association of level of Burn out and Coping strategies among Critical care Nurses with selected sample characteristics. Methodology: A non - experimental research with descriptive survey research design was carried with 125 patients by purposive sampling technique. Data was collected by Maslach burnout inventory and coping strategies Questionnaire through face to face interview technique. Result: The significant finding of the study was that majority of the patients (52%) had severe burnout followed by moderate burnout (38.4%) due to which the coping strategies were also affected as majority of critical care nurses (48.8%) were having poor coping strategies. Coping strategies was having positive correlation as computed r value (0.25) in burnout was significant (0.005) and computed r value was (-0.28) between burnout and coping strategies that was statistically significant at 0.05 level of significance i.e. (0.001). Conclusion: The study inferred that overall patients were having poor coping strategies and having burnout.

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Key Words: Burnout, Coping strategies & Meshach burnout inventory

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Estimating taxi fares has been an important research field within the transport industry since it homeworks pricing trends and transparency of the system both for service providers and customers. The project aims to find machine learning models that predict taxi trip fares using numerous variables, such as distance travelled during trips, time of the day, traffic conditions, number of passengers, and weather. Fare estimate will be addressed with robust data preprocessing, optimal feature engineering, and advanced model training using a synthetic dataset "constructed for practical regression tasks. The dataset is a rich source of over a 1000 data points with value-key trips varying the trips duration and fare amount, as well as contextual parameters like traffic and weather. This dataset will provide you with real world problems like missing values, outliers, correlation of features all together in one bundle. Applying and comparing ML models like Random Forest and Logistic regression and decision tree on this dataset based on Realization above had proven that Random forest Model proved the best with lower values in MSE, and was capable of fitting even non-linear relationship between the features. Along with its machine-learning train and evaluation functionality, it also provides a lightweight mechanism for predicting fares from input at application runtime. It also aims to collect the data on traffic, updates from weather and real-life datasets which could to be placed into next architecture to enhance the data feeding and to improve the model adaptability in future attempts. The predictive analytics embodied in this work speaks to power as it pertains to the taxi domain in such a way where it burns stronger in the empirical sense given the scope of paradigm machine in the usage to enhance fare predictions and decision making in transportation's dynamic environment.

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Taxi Fares, Dataset, MSE, Paradigm

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This project aims to design and implement a bridge between AHB and APB protocols using Verilog. It delves into the AMBA bus architecture, emphasizing the high-performance capabilities of AHB and the energy-efficient design of APB. The project addresses the need for seamless communication between these buses in embedded systems. Key objectives include ensuring efficient data transfer, optimizing resource usage, and complying with AMBA standards.

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AHB-APB Bridge, Design using verilog, AMBA Protocol.

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Freshness is a key factor in determining a fruit or vegetable's quality, and it directly influences the physical health and coping provocation of consumers. It ascertains the nutritional value of the specified fruit or vegetable. This paper proposes a well-organized and precise fruit and vegetable classification and freshness detection method. The proposed method employs state-of-the-art deep learning models, specifically convolutional neural networks (CNNs), to analyze images of fruits and vegetables captured through highresolution cameras. The dataset used for training and evaluation is extensive and diverse, encompassing a wide variety of fruits and vegetables in various conditions. The freshness of a fruit or vegetable can be ascertained by looking at a variety of features, including color, texture, shape, and size. Fresh produce, for instance, is colorful and free of mold or brown spots. Traditional methods for assessing the quality of fruits and vegetables are both time-consuming and error-prone. These methods include inspection and sorting. It is possible to reduce these issues by utilizing automatic detection techniques. In light of this, we proposed an automated fruit-vegetable freshness detection approach that first recognizes whether the image is of a fruit or vegetable, after which it classifies it into one of three freshness categories: rotten, fresh, or mixed. To identify and categorize fruits and vegetables, two deep learning models are employed: You Only Look Once (YOLO) and Visual Geometry Group (VGG-16). The suggested method's qualitative analysis indicates superior performance on the fruit dataset.

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fruit-vegetable freshness, VGG-16, YOLO, Machine Learning, Deep Learning, CNN.

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With the integration of Artificial Intelligence (AI), including Generative AI (Gen AI), gaining momentum across sectors, it has become essential to reassess ethical standards and security protocols to meet the evolving demands of the industry landscape. This article explores the complexities of developing Gen AI systems by examining three key factors: ethics, quality, and security. Ethical considerations--such as fairness, transparency, and accountability--are crucial to ensuring AI systems align with societal norms and values. Additionally, transparency and dependability are vital aspects of quality assurance, ensuring AI systems operate reliably across diverse environments. The importance of robust security measures is also highlighted, focusing on protecting AI systems from attacks and safeguarding sensitive information. The article argues that integrating ethics, cybersecurity, and quality into AI development is vital for creating reliable and effective systems. Establishing clear guidelines for transparency and performance can further encourage the development of ethical AI technologies. This comprehensive approach provides a roadmap for the future of ethical AI, balancing rapid innovation with essential safeguards to address potential challenges and threats.

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Generative AI (Gen AI), Ethical Standards, Quality Assurance, Transparency, Cybersecurity

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Thar Desert Climate,Vegetation,Wildlife Species, Human Resources, Natural Minerals

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Th? most r?volution?ry t??hnology is dron?s, ?lso known ?s unm?nn?d ??ri?l v?hi?l?s (UAVs), whi?h h?v? ? wid? r?ng? of us?s in ?ubli? s?f?ty, ?gri?ultur?, logisti?s, milit?ry us?, ?nd ?nvironm?nt?l monitoring. This t??hnology w?s initi?lly ?r??t?d for milit?ry r??onn?iss?n??, but it h?s sin?? d?v?lo??d into ? wid? r?ng? of mor? ?d??t?bl? ???li??tions, in?luding deliv?ry of goods, ?nvironm?nt?l monitoring, s??r?h ?nd r?s?u?, ?nd mu?h mor?. Th? t??hnologi??l ?dv?n?em?nts of UAVs from th?ir ?urr?nt st?t? of th? ?rt will b? ???min?d in this ????r, with ?n em?h?sis on their ?d??t?bility ?nd ?ot?nti?l int?gr?tion into ? r?ng? of industri?s. Th? most signifi??nt t??hnologi??l ?dv?n?em?nts in ?utonomy, sw?rm int?llig?n??, ?nd obst??l?s ?r? highlight?d in this w?ll-org?niz?d ?n?lysis of som? of th? most r???nt d?v?lo?m?nts in UAV d?signs, navig?tion str?t?gi?s, ?nd d?t? ?ro??ssing m?thodologi?s. Th? ov?rvi?w of th? r?gul?tory fr?m?works ?ontrolling dron? us?, with ? fo?us on ?riv??y, s?f?ty st?nd?rds, ?nd o??r?tion?l limit?tions, is ?noth?r ?ru?i?l ?om?on?nt of this book. B?for? dron?s ??n fully r??liz? th?ir ?normous ?ot?nti?l, th?r? ?r? signifi??nt obst??l?s to ov?r?om?. M?n?ging d?t?--?ro??ssing it in r??l-tim? wh?r? it is ???tur?d ?nd off?ring s?f? stor?g? whil? o??r?ting f?r from hom? b?s?--is th? main ?h?ll?ng?. B???us? ?thi?s ?nd l?gisl?tion ?r? still in th?ir inf?n?y, ?riv??y is ? m?jor ?on??rn in surv?ill?n?? ???li??tions. R?li?bility is im???t?d by ? numb?r of f??tors, in?luding ? short b?tt?ry lif?, sus???tibility to w??th?r, ?nd ?yb?r s??urity thr??ts. Th? ?v?r-?h?nging r?gul?tions ?nd ?irs???? ?ontrol m?k? it diffi?ult for dron?s to g?in tr??tion. Dron?s h?v? s?v?r?l ?dv?nt?g?s, in?luding low?r o??r?ting ?osts, ??sy ????ss to d?ng?rous ?r??s, ?nd ?v?n r??id d?t? ?oll??tion. N?v?rth?l?ss, th?r? ?r? limit?tions lik? flight tim? r?stri?tions, m??h?ni??l issu?s, ?nd ?bus?. Our ?n?lysis indi??t?s th?t dron? t??hnology ??n str??mline num?rous industry ?ro??ss?s; how?v?r, in ord?r to ?d?qu?t?ly ?v?lu?t? s?f?ty, ?riv??y, ?nd s??urity ?on??rns, d?v?lo?m?nt must ?o??ist with r?gul?tion. As dron? t??hnology d?v?lo?s, it will b? us?d ??t?nsiv?ly in both s???i?liz?d industri?s ?nd d?ily t?sks. To ?romot? r?s?onsibl? us?, th? ?roblems n???ssit?t? ?ross-s??tor?l ?oo??r?tion. Th? ?dv?n?em?nt of ?utonomous navig?tion, b?tt?ry ?ffi?i?n?y, ?nd s??ur? ?ommuni??tion ?roto?ols must b? th? main ?r??s of futur? r?s??r?h in ord?r for UAVs to r???h th?ir full ?ot?nti?l. Dron?s ??n im???t ?r??ision ?gri?ultur?, urb?n logisti?s, ?nd ?nvironm?nt?l ?r?s?rv?tion to tr?nsform industri?s ?nd im?rov? so?i?t?l r?sili?n?? if th?s? ?roblems ?r? su???ssfully r?solv?d.

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Drones, Data Processing, Privacy Concerns, Technology Development, Public Safety, Cyber Security, Regulation frameworks, Operational constraints, Reconnaissance, Airspace control, Secure storage, Environmental monitoring, Swarm intelligence.

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Natural products have long been a rich source of bioactive compounds, contributing significantly to the development of modern drugs. In the face of emerging global health challenges, these compounds remain a cornerstone in drug discovery, offering unique molecular scaffolds and complex structures that are often difficult to synthesize artificially. Historically, natural products have led to the discovery of numerous therapeutic agents, including antibiotics, anticancer drugs, and immune-suppressants. Their ability to interact with biological targets in diverse and often highly specific ways makes them invaluable in the design of novel drugs.Recent advancements in biotechnology, genomics, and high-throughput screening have enhanced our ability to identify and characterize potential drug candidates from natural sources. In particular, natural products derived from plants, microorganisms, and marine organisms continue to provide new leads for combating diseases such as cancer, infectious diseases, and neurodegenerative disorders. Additionally, the growing understanding of natural product biosynthesis pathways has opened new avenues for optimizing yields and creating novel derivatives with improved potency and selectivity.Despite the tremendous promise of natural products, challenges remain in terms of sustainable sourcing, scalability, and regulatory hurdles. However, with continued research and innovation, natural products are poised to remain a fundamental part of modern drug development. This review highlights the enduring importance of natural products in pharmaceutical discovery, emphasizing their role in shaping the future of medicine and their continued relevance in addressing unmet medical needs.

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Nature, modern drugs, biological activity, natural products

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Background: Teenage is the turning point of human development where individuality starts from parenting. The ability of self-expressive skills and IQ get diverted easily in teenage. Hence there is a need for natural remedy to maintain self- expressive skills and IQ at teenage. Present study focusses on Yogic practices to maintain such psycho-physiological health. Aim: Balancing asana can improve self-expressive creativity and IQ in adolescents. Methods: Pre-Post study. Sampling: The study population total of 15 adolescents (12-17 YR.) Convincing sampling. Parameters: The following instruments were used: Rosenberg Self-Esteem Scale (RSE), the Creativity personality test (25). Intervention: Yoga routine practice time 45 mints for 26 days. (D. 01.10.2021 - 30.10.2021) Result: The data will be analyzed in the MS-excel and the average and Standard deviations also calculated by the MS-excel. Conclusion: The visual ability, voice recognition as an indicator of IQ, self-expressive creativity and static balance is improve by inhibit the extra curriculum of the brain with the yogic practices.

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self-expressive creative, IQ, Adolescents, cognitive development, self- esteem, standing-yoga asana,

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State of charge (SOC) estimation is important for efficient and safe lithium-ion battery operation, especially in applications such as electric vehicles, alternative energy systems, and portable electronics. This report develops and analyzes SOC estimation methods with Kalman filtering and Deep Neural Network ( DNN) algorithm. It is centralized. The Kalman filter, a model-based method, is known to be robust in estimating SOC under linear approximation conditions. On the other hand, DNN algorithm, which is a data-driven approach, leverages its powerful nonlinear battery behavior from big data sets, for enhanced accuracy in dynamic environments Through comparative analysis the report explores the performance of these methods for accurate, computational efficiency, and adaptability to different operating conditions. Experimental results show that although each method has distinct advantages, combining Kalman filter and Deep neural network(DNN) models provides a synergistic approach to improve SOC estimation The report concludes by exploring the strengths and limitations of both approaches and suggestions for future research are provided, including real-time adaptive SOC There are also hybrid systems of will be combined to form a theory.

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State of Charge (SOC), Lithium-ion batteries, Kalman filter, Deep learning, SOC estimation, Battery management systems, Nonlinear systems, Energy storage

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Ayurveda is the science of life that is based on the basic concepts of Dosha, Dhatu and Mala. Rasa, Rakta, Mamsa, Meda, Asthi, Majja, and Shukra are the seven Dhatus that make up a human constitution. In terms of modern science, Rakta Dhatu is comparable to blood However, the text demonstrate that the concept of Rakta Dhatu is far vaster than blood. Doctors frequently encounter haemorrhages in trauma units, operating rooms, and intensive care units. Doctors routinely face haemorrhage, a common medical emergency. Significant intravascular volume loss may trigger a chain of events that leads to hemodynamic instability, decreased tissue perfusion, cellular hypoxia, organ damage and death. Sushruta Samhita provides numerous references that demonstrate the effective therapy of emergency and life-threatening conditions, including haemorrhage. In Ayurveda the relevance and specificity of the classics of Acharya Sushruta listed four fundamental haemostatic techniques Skandana, Sandhana, Pachana and Dahana which will be evaluated proving the relevance in present era.

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Haemorrhage, Ayurveda, Sushruta, Dosha, dhatus, mala, Rakta

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In countries like India, the agricultural sector faces challenges such as limited land and water resources. Automated hydroponics offers a compelling solution by allowing plants to grow in nutrient-rich water with minimal space. By integrating sensors into these systems, we can monitor and adjust critical factors like temperature, humidity, and nutrient levels in real-time. This automation optimizes resource usage, conserving up to 90% of water while ensuring that plants receive ideal growing conditions. Additionally, automated systems reduce labor demands and enhance efficiency, making it easier for farmers to manage their crops. This project aims to demonstrate how automated hydroponic systems can effectively address current agricultural challenges, promoting sustainability and improving food security for communities.

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Automated Hydroponics, Sensors, IoT (Internet of Things),Arduino Mega ,pH Monitoring, Temperature Regulation ,Water Level Management

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Corneal disease, such as endothelial dysfunction and other dystrophies, are the leading causes of visual impairment and blindness worldwide. The mainstay of treatment for corneal disease is corneal transplantation. Unfortunately, the success of this approach is limited by restrictions in tissue availability and immune rejection as well as difficulties managing postoperative recovery. The advances in the diagnosis, treatment, and management of corneal disease have burgeoned due to the introduction of machine learning (ML) techniques. Although they have promising potential, existing ML models are encumbered by several challenges, including limited and imbalanced datasets, inconsistent data quality, lack of interpretability, and poor integration of multimodal data. These limit the accuracy and clinical utility of existing solutions. In this paper we review the current status of ML for corneal disease research, highlight challenges that are common in this space, and identify future directions that should be explored in order to address these challenges. Proposed approaches include data sharing and collaboration between research groups, improved data augmentation, advanced techniques such as transfer learning and few-shot learning, and interpretable models. Encouragement and consideration of such issues will motivate future ML studies to help establish a more reliable, generalizable, and clinically useful application of ML in corneal disease management.

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Corneal diseases, machine learning, endothelial dysfunction, data augmentation, clinical validation

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In this recent world, urbanization has increased tremendously. At the same phase, there is increasing amount of in waste production. Waste management has been a crucial issue to be considered.This report is a different way to achieve this good cause. In this report, smart bin is built on a microcontroller based platform Arduino - Uno board, which is interfaced with Ultrasonic sensor. It will stop overflowing of dustbins along roadsides and localities as smart Dustbins are managed in real time.Once these smart bins are implemented on a large scale by replacing the traditional bins, the waste can be quickly managed to its efficient level as it avoids unnecessary lumping of wastes on roadside. Foul smell from these rotten wastes that remain untreated for a long time, due to negligence of authorities and carelessness of public may lead to long term problems. Breeding of insects and mosquitoes can create nuisance around promoting unclean environment. This may even cause dreadful diseases. The goal of this project is to keep our environment clean. It also aims at creating a clean as well as green environment.

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91

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Insects have played important roles in human culture, economy, and health, and human progress has been based on knowledge, In ancient India, contemporary people had a close relationship with nature. Throughout the history, especially in Indus Valley period, Vedic period even during Mauryan and Gupta civilization, the scenario remains quite same. Insects were used for medicine and health purposes mostly, whereas the religious of importance or symbolization of insects were very much prevalent. The main focus of the article will be discovering the growth and genesis of insects during ancient period. The medicinal or nutritional value of insects will also remain in focus in this article, but the spiritual value in Hinduism, Buddhism and Jainism will be discussed briefly.

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Insect, Knowledge, medicine, religious, literature

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A physicochemical study of soil is based on various parameters like soil pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (N), phosphorus (P), potassium (K), and micronutrients (Fe, Mn, Cu and Zn). Four representative samples were obtained and analyzed for its alkalinity content, pH, electrical conductivity, organic carbon, sodium, potassium. A five soil samples were collected at a depth of 0-20 cm and analyzed for soils were neutral to slightly alkaline. The value of soil pH found to be 7.60 to 8.81, conductivity was ranging from 0.50 to 0.73 dSm-1, organic carbon was found to be 0.52 to 0.72%, range of sodium was 0.52 to 0.97meq% and potassium 125.31 to 630.15 kg/ha. Among the nutrients, available Nitrogen was found to be 140.01 to 252.68 kg/ha, Phosphorous was ranging from 15.11 to 54.13 kg/ha. This information will help the farmers to know amount of fertilizers to be added in soil to make production.

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soil analysis,physicochemical,micronutrients,fertility,sustainable, contamination, Fluctuations, laboratory, chemical manifestations

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Artificial Intelligence (AI) is the field dedicated to the science and engineering of creating intelligent systems, particularly those in the form of computer programs. While it overlaps with the task of using computers to simulate human cognition, AI is not bound by biological constraints. There is no universally agreed-upon definition of AI, but it is generally understood as the study of algorithms and computations that enable machines to perceive, reason, and act. In today's world, the volume of data produced by both humans and machines exceeds our capacity to process, interpret, and make decisions from it. AI stands as the foundational technology behind all machine learning and the driving force for advancing complex decision-making processes. This Research paper explores the key features, definitions, historical evolution, applications, growth, and accomplishments of Artificial Intelligence.

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Machine learning, Deep learning, Neural networks, Natural Language Processing and Knowledge Base System.

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