Volume 12 | Issue 8 |

 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.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

DETECTING AND NOTIFYING USERS OF SUSPICIOUS ACTIVITIES IN REAL TIME

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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

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Portable, convenient, efficient, wireless, innovative

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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

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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

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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

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AN ENHANCED SOCIAL MEDIA APPLICATION (INTELLIPOST)

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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.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

parking, traffic congestion, pre-booking, payments

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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.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine Learning, Leaf disease detection, Image processing

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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

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

MULTI-PURPOSE CHARITABLE DONATION PLATFORM, IMMEDIATE BLOOD DONATION

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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

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

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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

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

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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

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

EMPOWERING HUMANITY THROUGH FOOD DONATIONS TO COMBAT HUNGER

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Creative Commons Attribution 4.0 and The Open Definition