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.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

FARMER E_MARKET PLACE

  License

Creative Commons Attribution 4.0 and The Open Definition

 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.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

  License

Creative Commons Attribution 4.0 and The Open Definition

 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.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

  License

Creative Commons Attribution 4.0 and The Open Definition

 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

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

  License

Creative Commons Attribution 4.0 and The Open Definition

 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.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

  License

Creative Commons Attribution 4.0 and The Open Definition

 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

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Natural Language Processing, Chatbot

  License

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

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

  License

Creative Commons Attribution 4.0 and The Open Definition

 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.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

DATA COMPRESSION SECURING DATA IN CLOUD

  License

Creative Commons Attribution 4.0 and The Open Definition

 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.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

intensive care unit, critical care, MIMIC, machine learning

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Detecting and notifying users of suspicious activities in real-time is imperative for maintaining security in various domains. This abstract presents a robust framework for achieving this goal, leveraging advanced algorithms and real-time monitoring techniques. By continuously analyzing user behavior and system interactions, our system can swiftly identify anomalies indicative of potential security breaches or unauthorized access attempts. Once detected, immediate notifications are triggered,alerting relevant stakeholders and enabling prompt response measures. Through the integration of machine learning modelsand anomaly detection algorithms, our solution ensures adaptive and proactive threat mitigation, enhancing overall system security and user trust. Detecting and notifying users of suspicious activities in real-time is a critical aspect of modern security frameworks, particularly in digital environments where threats can evolve rapidly. Our framework builds upon cutting-edge technologies, seamlessly integrating advanced algorithms with real-time monitoring capabilities to provide comprehensive protection. By continuously scrutinizing user behavior and system interactions, our system swiftly identifies deviations from established norms, flagging potential security breaches or unauthorized access attempts. These anomalies trigger immediate notifications, alerting relevant stakeholders and facilitating swift response actions. Through the fusion of machine learning models and anomaly detection algorithms, our solution not only adapts to emerging threats but also proactively anticipates them, bolstering system resilience and fostering user confidence in an ever-evolving security landscape. Safe-guarding digital ecosystems against a myriad of threats, ranging from cyber-attacks to insider threats. Our innovative framework leverages a multifaceted approach, seamlessly weaving together state-of-the-art algorithms and dynamic monitoring mechanisms to fortify defenses. By meticulously analyzing user interactions and system behavior in real-time, our system swiftly discerns deviations from normal patterns, swiftly flagging potential security breaches or illicit activities. These alerts are not only triggered instantaneously but are also accompanied by contextual insights, empowering stakeholders to make informed decisions and execute timely response strategies.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

DETECTING AND NOTIFYING USERS OF SUSPICIOUS ACTIVITIES IN REAL TIME

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Irrigreat currently supports 22 crops. Moreover in the future, fertilizers can also be added accordingly The training was done on 10 pests and with this pesticides are suggested. In future, training can be done on more pests and more pesticides can also be added according to the pests. In Crop Recommendation, values are manually entered by user of temperature, humidity, rainfall. Admin can also use some weather API to fetch the real time parameters by the city and state. In Pesticide Recommendation, the uploaded image should be clear for correct results, otherwise with a blur image, the system sometimes gives wrong results so, further filters can be used to obtain better results. Also the system can use better DL models. In future pesticide code can be integrated with drone code so that it can take live pictures of pests and by email or by mobile the farmers would be notified about the pest along with the pesticides.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Portable, convenient, efficient, wireless, innovative

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Myers-Briggs Personality Indicator has long been regarded as a valuable tool for gaining insights into individual personality preferences. Developed with the aim of fostering a deeper understanding of diverse personality traits, the MBTI provides a framework that allows individuals to explore their unique strengths, limitations, and differences. This study leverages logistic regression to delve into the relationships between MBTI indicators and demographic factors, shedding light on the nuances of personality preferences. The study involves the collection of data from a diverse sample of individuals, including their MBTI types and relevant demographic information. Logistic regression models are constructed to assess the probability of an individual having a specific MBTI indicator based on demographic variables. These models are trained and validated to determine the significance of each demographic factor in predicting personality preferences. This research showcases the utility of logistic regression as a tool for gaining a deeper understanding of MBTI personality types in the context of demographic diversity.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Myers-Briggs Type Indicator(MBTI),Personality indicators, Demographic factors ,Logistic regression analysis, Personality preferences, Interdisciplinary research, Psychological frameworks, statistical techniques, predictive modeling, sociological implications

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Social mediahasopenedupaplatformforpeopletoexpresstheirviewsandcommunicatewitha larger audience. However, with this freedom of expression comes a darker side. Social media has become a breeding ground for hateful behavior, abusive language, cyber-bullying, and personal attacks. These types of posts can have a significant impact on others, leading to cyber bullying and harassment. Cyber bullying is the reason for the spread of rumors or threatening messages. Harassment, on the other hand, is unwanted behavior that is intended to intimidate or harm someone. The challenge for social media platforms is to identify and moderate abusive content efficiently to ensure user safety and improve online discussions. Automating this process would help us to identify abusive comments and save time, ultimately making social media a safer place for everyone. Our social media applications that use on-device machine learning to restrict abusive or vulnerable content are becoming increasingly popular. Social media platforms such as Twitter, Face book and YouTube are using machine learning technology to help the match ads to users that will be of highest interest to them. In addition, it is helping to identify violent extremism and fake-news. Amnesty International used machine-learning to quantify the scale of abuse against women on Twitte. Outsourcing this work to machine learning can help reduce the risk of suffering from PTSD as a result of repeated exposure to such distressing content. The application is built using Tensor Flow framework, Firebase database and Flutter. The machine learning algorithms are trained to detect abusive or vulnerable content in real-time and restrict the user from posting such content in offline mode. The application designed to work on multiple platforms including Android and iOS. This one discusses the design and implementation of the application along with the challenges faced during development. The results of the project are presented along with future work that can be done to improve the application.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AN ENHANCED SOCIAL MEDIA APPLICATION (INTELLIPOST)

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Lack of parking places contributes to an increase in parking, and its major issue is leading to traffic congestion as drivers looking for available spaces. As drivers slow down or stop in search of parking, the problem of insufficient parking must be tackled in a comprehensive way involving city planning, technology integration as well as sustainable transport solutions. In our project, Web and mobile application that provides availability of parking lots in a real time. The web application enables pre-booking of parking spaces. The application allows the user to choose the parking lot according to their convenience. The payment of booking is done by either fast- tag or by QR code scanner. Once the payment is made, pre-booking of slots is confirmed by a message from the server side.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

parking, traffic congestion, pre-booking, payments

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid development of machine learning (ML) techniques has paved the way for innovative applications in agriculture, including the detection of leaf diseases. Leaf diseases can significantly impact crop yield and quality, making early detection crucial for effective disease management. This study presents a comprehensive approach to detect leaf diseases using ML algorithms. The methodology involves the collection of high-resolution images of diseased and healthy leaves, followed by data preprocessing, feature extraction, and the training of ML models. The results demonstrate the potential of ML in accurately identifying and classifying leaf diseases, enabling farmers to take timely preventive measures. The developed system provides a non-invasive and cost-effective solution, contributing to sustainable agriculture and food security. This research contributes to the advancement of precision agriculture and holds promise for real-time disease monitoring and management, ultimately leading to increased crop productivity and reduced environmental impact.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine Learning, Leaf disease detection, Image processing

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Without the aid of technology-driven solutions like the multi-purpose charitable donation platform, individuals face several challenges in efficiently addressing urgent needs and contributing to charitable causes. Traditional methods often lack immediacy and precision, making it difficult to swiftly connect blood donors with emergency cases or efficiently distribute surplus food to those in need. The primary motivation is to address urgent and critical needs within communities. The focus on immediate blood donations for emergencies, surplus food distribution, and verified monetary contributions supports timely responses to various societal challenges. The multi-purpose charitable donation platform presents a significant boon to individuals in various ways. In times of urgency, the app proves invaluable by facilitating swift and targeted blood donations during emergencies, potentially saving lives. Leveraging technologies like Flutter, the motivation is to harness the power of innovation to create a seamless and efficient app. This reflects a commitment to staying at the forefront of technological advancements in the realm of charitable giving. The Flutter framework is employed to achieve a single codebase, allowing seamless deployment on both iOS and Android platforms. The application's intuitive user interface promotes ease of use, while comprehensive testing ensures data security and the app's overall reliability.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

MULTI-PURPOSE CHARITABLE DONATION PLATFORM, IMMEDIATE BLOOD DONATION

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Urban Sight represents a groundbreaking shift in urban living, seamlessly blending cutting-edge technology with a human-centric approach to governance. At its core, the project introduces an innovative smart public lighting system, driven by the Internet of Things (IoT). This system dynamically adapts street lighting in real-time, optimizing energy consumption and aligning with broader sustainability goals in urban development. Complementing the smart lighting, it integrates advanced aerial inspection through drones equipped with high-resolution cameras and machine learning. With seamless communication using Long Range (LoRa) technology, Urban Sight ensures robust connectivity for real-time data exchange among its components. The fusion of smart lighting, aerial surveillance, and proactive fault detection underscores a dedication to creating urban environments in harmony with the rhythm of human life. The smart street lighting system with drone integration enhances urban safety and efficiency by dynamically adjusting lighting levels and providing aerial surveillance. Safety measures include collision avoidance systems for drones, encrypted communication protocols, and adherence to privacy regulations. Regular maintenance and software updates ensure optimal functionality and mitigate potential risks, contributing to a safer and more sustainable urban environment.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Urban Sight ,Smart public lighting system , Internet of Things (IoT) , Dynamic lighting Sustainability

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Efficient storage structures are crucial in minimizing global grain losses, aggravated by population growth, increased consumption, and natural disasters. Mechanized practices and engineering interventions are vital to curtail post-harvest losses, with 10-15% attributed to inadequate storage facilities, exacerbating grain deficits. To mitigate this, a solution integrates a Mobile App for remote grain monitoring and real-time assessment, alongside an automated inspection system to preemptively address pest or mold issues. This holistic approach, driven by advanced technologies (IOT), enhances storage efficiency, aligning with the demand for technologically-driven agricultural solutions. It becomes pivotal in establishing resilient and sustainable grain storage infrastructure, crucial for meeting rising global food demands and promoting hygienic, economical, and scientifically designed storage structures. In the future, we aim to fully automate operations with Machine Learning (ML), developing AI-driven inventory management systems to dynamically update warehouse status and optimize storage, facilitating efficient rentals between warehouse owners and farmers.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Inventory management, Grain Monitoring, Temperature and Humidity Sensors, Real-time Alerts

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

An important goal in our world today is to eliminate food waste by reutilizing available food sources within local communities: leftover food items in restaurants, stores and food distribution centers that may be approaching expiration; and any perishable items not used in entirety within their desired period. This is highly significant, particularly during crises such as the COVID-19 pandemic. Our project focuses on creating an interesting mobile web application that provides a ubiquitous platform wherein users can visualize available food resources in their local area and consequently gain access to food, thereby tackling two major issues, i.e. hunger and food waste. This app is pertinent to the UN SDGs (United Nations Sustainable Development Goals)

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

EMPOWERING HUMANITY THROUGH FOOD DONATIONS TO COMBAT HUNGER

  License

Creative Commons Attribution 4.0 and The Open Definition