Volume 12 | Issue 8 |

 Abstract

Farming is the Prime Occupation in India in spite of this, today the people involved in farming belongs to the lower class and is in deep poverty. The Advanced techniques and the Automated machines which are leading the world to new heights, is been lagging when it is concerned to farming. Even after all the hard work and the production done by the farmers, in today's market the farmers are cheated by the Agents, leading to the poverty. Farmer's e-Market will serve as a way for the farmers to sell their products across the country just with some basic knowledge about how to use the website. The Farmer's E-Market is created to help bring together all local vendors. We want to help make each stronger individually as a collective whole by providing simple lines of communication, logistics and support within the relationship of producers to buyers and producers to producers & essentially creating an online farmers market for that offers consistent connection between all producers and buyers. For farmers, this involves listing their agricultural products, complete with essential details like product type, quantity, pricing, and location. On the client side, the platform provides a user-friendly interface for browsing available products, selecting desired items, and efficiently managing purchases. To facilitate a consolidated view of selected items before finalizing a transaction, clients can utilize the shopping cart feature. This not only streamlines the purchasing process but also enhances the overall user experience. Farmers, in turn, receive timely order notifications for the products they've sold and can efficiently manage their orders within the platform.

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FARMER E_MARKET PLACE

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 Abstract

NPRENEUR is a user-friendly web project that serves as a social media platform exclusively for entrepreneurs. It offers a space where entrepreneurs can showcase their creativity by uploading and sharing their ideas, gadgets, and valuable information. The platform aims to foster a supportive community for entrepreneurs and facilitate connections with potential investors. NPRENEUR allows entrepreneurs to post content in three formats: gadgets, ideas, or informative articles. This flexibility enables them to present their work in a way that resonates with their target audience. Moreover, the platform facilitates direct communication between entrepreneurs and interested investors, providing opportunities for collaboration and investment. The platform encourages engagement by allowing users to like and comment on entrepreneurs' posts, enabling valuable feedback and discussions. This interactive environment creates a nurturing ecosystem that promotes knowledge-sharing and growth within the entrepreneurial community. NPRENEUR specifically caters to the needs of small entrepreneurs, offering a centralized hub for content sharing, networking, and potential funding opportunities. By connecting entrepreneurs and providing avenues for collaboration, NPRENEUR empowers them to thrive in their respective industries.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Entrepreneurship, Social media, platform, Creativity, Ideas, Gadgets, Information sharing

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 Abstract

The timely availability of blood and plasma plays a crucial role in saving lives during medical emergencies. However, ensuring an adequate supply of these critical resources remains a challenge, particularly in the context of unforeseen events such as accidents, natural disasters, or sudden outbreaks. This abstract explores the significance of blood and plasma donation in emergency patient care and highlights the need for proactive measures to meet the demand for these life-saving components. Blood and plasma are essential components in various medical interventions, including trauma care, surgery, and treatment of severe medical conditions. Efforts to promote donation and strengthen the blood and plasma supply chain are impe rative for safeguarding public health and saving lives in emergencies The login and registration page will allow users to create an account and log in to the system securely. The blood donation module will enable users to search for available blood donors based on blood type, location, and other relevant criteria. It will also provide a platform for users to schedule appointments with donors, track donation history, and receive notifications. Similarly, the plasma donation module will allow users to search for plasma donors, schedule appointments, and manage donation records. This module will focus on the specific requirements and processes associated with plasma donation. The system will integrate with a database management system using SQL to store and retrieve user information, donor details, appointment records, and other relevant data. The database will ensure data integrity and provide efficient data retrieval for the system's functionalities.

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 Keywords

Blood and plasma, Database management system, Blood donation, Plasma donation.

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 Abstract

Democratic voting is a crucial and serious event in anyplace, the current election scheme in any place, beita school college, or even a country is done through ballot papers or using EVM. This process has many disadvantages such as transparency, low voter turnout, vote tampering, lack of trust in electoral authorities,delayinresults,andaboveallsecurityissues.Sothegrowingdigitaltechnologyhashelpedmany people's lives nowadays. The concept of electronic voting is introduced to combat the disadvantages of the traditional voting system. Electronic voting is essentially an electronic means of casting and counting votes. It is an efficient and cost-effective way of conducting a voting procedure that isdata-rich and real-time and requires high security. Nowadays, concerns about the security of networks and the privacy of communicationsforelectronicvotinghaveincreased.Thus,theprovisionofelectronicvotingisveryurgent and is becoming a popular topic in communication and networking. One way to solve security problems is blockchain.Thepaperproposesanewblockchain-basedelectronicvotingsystemthataddressessomeofthe limitations in existing systems and evaluates some of the popular block chain frameworks to create a blockchain-basedelectronicvotingsystem.Becausetheblockchainstoresitsdatainadecentralizedmanner, the implementation result shows that it is a practical and secure electronic voting system that solves the problemofvoteforgeryinelectronicvoting.Theblockchain-basedelectronicvotingsystemcanbedirectly applied to various network applications

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 Keywords

Distributed ledger technology(DLT),elliptic curve discrete logarithm problem(ECDLP), practical Byzantine fault tolerant (PBFT).

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 Abstract

Crowded event entrances pose a significant challenge in maintaining public safety, with the potential for pushing incidents leading to injuries or even fatalities. Early detection of such incidents is crucial for timely intervention and prevention of harm. In this project, we propose a novel approach leveraging machine learning techniques, specifically the UNet architecture combined with the VGG16 convolutional neural network, to detect pushing behaviors at crowded event entrances. The UNet architecture, known for its effectiveness in image segmentation tasks, is employed to accurately delineate individuals within crowded scenes. By segmenting individuals, the model gains an understanding of the spatial layout and movement patterns within the scene. Concurrently, the VGG16 network, renowned for its feature extraction capabilities, is utilized to extract high-level features from the segmented images, capturing both global and local contextual information relevant to identifying pushing behaviors.To train the model, a dataset of annotated images depicting various crowded event scenarios, including instances of pushing, is utilized. The UNet-VGG16 model is trained using a combination of supervised learning techniques, allowing it to learn discriminative features indicative of pushing behaviors .Experimental results demonstrate the efficacy of the proposed approach in accurately detecting instances of pushing at crowded event entrances. The model achieves high precision and recall rates, indicating its potential for real-world deployment in crowd management systems. Early detection of pushing incidents enables security personnel to swiftly intervene and mitigate potential risks, thereby enhancing public safety at crowded events.

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 Keywords

Machine Learning, UNet, VGG16, Pushing Detection, Crowded Event Entrances, Public Safety

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 Abstract

Abnormal event detection, human behavior detection, as well as object recognition plays a vital role in the creation of a smart CCTV system. These systems make it possible to detect abnormal events in an environment, abnormal behaviors by humans and the state of alert in the environment. Machine Vision property along with Machine Learning are used in these systems to detect as well as identify the particular anomalies that arise in the video feed from the CCTV. Frame by frame processing is commonly used and Supervised Learning is the commonly used training method for these systems. However, since the anomalies are of many different kinds and also because it is not feasible to pre-detect and train all types of anomalies, supervised learning is being replaced by unsupervised learning and semi - supervised learning for training the system. This system provides a means of minimising or removing the human workload that has to be put on to manually detect and create an alert on detection of an abnormality in the live feed provided by the CCTV. Also the system increases the storage efficiency by storing only the abnormal events in original quality and storing the normal scenarios in low quality for archiving. Also this system provides an extension of creating a distributed abnormality classification system, where only the abnormal events are sent on to different dedicated systems to classify the abnormality

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LIVE SURVEILLANCE - DETECTING ABNORMAL EVENTS IN REAL TIME FOR ENHANCED SECURITY

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The abstract presents the concept of a first-aid Medikit assistant chatbot enhanced with natural language processing (NLP) techniques. This chatbot serves as an innovative solution to provide immediate and context-aware first-aid guidance to users during emergencies. By integrating advanced NLP algorithms, the chatbot can understand and interpret user queries related to injuries, illnesses, and medical situations. It offers personalized recommendations for administering first- aid, suggesting appropriate actions based on the description provided by the user. The chatbot's intelligent algorithms enable it to assess the severity of situations, prioritize actions, and offer step-by-step instructions tailored to the user's situation. It also has the capability to offer reassurance and guidance while connecting users with emergency services when required. The utilization of NLP ensures that the chatbot can comprehend user inputs in natural language, making it user-friendly and accessible even in stressful situations. This abstract highlight the potential of the proposed system to save lives by providing timely and accurate first-aid guidance through an interactive and responsive interface. The integration of NLP techniques empowers the chatbot to offer an indispensable tool that can assist individuals during critical medical incidents, bridging the gap between emergency situations and professional medical help.

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 Keywords

Natural Language Processing, Chatbot

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project addresses traffic detection and classification in networks by implementing classification at end systems, overcoming challenges associated with proprietary hardware access. Utilizing supervised learning models, particularly decision tree classifiers, and achieves over 98% accuracy, enabling real-time application. Integration into Software Defined Networks (SDN) enhances network efficiency and adaptability, facilitating intelligent routing decisions and fostering more responsive infrastructures. The existing system suffers from limitations such as limited access, scalability challenges, lack of flexibility, high costs, and vendor dependency, necessitating alternative approaches. The proposed system focuses on achieving high accuracy, real-time application feasibility, enhanced network efficiency, improved management, scalability, adaptability, and cost-effectiveness. Through data collection, model selection, training, integration with SDN, testing, and maintenance, the system aims to provide network administrators with enhanced visibility and control over network traffic while leveraging existing infrastructure. Overall, this project presents a comprehensive solution for efficient traffic detection and classification, paving the way for more responsive and cost-effective the networks

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Software Defined Networks (SDN), Vendor Dependency, Traffic Detection, Classification

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 Abstract

The rapid development of information technology over the last decade means that data appears in a wide range of sensor data, tweets, photos, raw data and unstructured data formats. With such an overwhelming flood of information, current data management systems cannot scale to this enormous quantity of raw, unstructured data -- Big Data, today. We show the basic concepts and designs of big data tools, algorithms and techniques in the present study. We compare the classical data mining algorithms with the Big Data algorithms by using Hadoop / Map Reduce as the core scalable algorithm implementation of Big Data. We implemented the K-means and A-priori algorithms on a 5-node Hadoop cluster with Hadoop / Map Reduce. We use MongoDB as an example to explore NoSQL databases for semi-structured, massive data scaling. Finally, we show the performance of these two algorithms between HDFS (Hadoop Distributed File System) and MongoDB data storage.

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DATA COMPRESSION SECURING DATA IN CLOUD

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 Abstract

Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). Techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology resource allocation, and enhancing clinical decision-making in intensive care settings. By continuously monitoring and analysing patient data, it provides valuable insights that help healthcare providers intervene promptly and prevent adverse outcomes.

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 Keywords

intensive care unit, critical care, MIMIC, machine learning

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