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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 Keywords

Frauds,MachineLearning,EssentialTools,DetectionTechnique

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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 Keywords

CNN, Tomato ,Deep Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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 Keywords

Key Words: Burnout, Coping strategies & Meshach burnout inventory

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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 Keywords

Taxi Fares, Dataset, MSE, Paradigm

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Creative Commons Attribution 4.0 and The Open Definition

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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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 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

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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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 Keywords

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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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 Keywords

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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Creative Commons Attribution 4.0 and The Open Definition