Volume 12 | Issue 8 |

 Abstract

Intellectual property rights (IPR) have been defined as ideas, inventions, and creative expressions based on which there is a public willingness to bestow the status of property. IPR provide certain exclusive rights to the inventors or creators of that property, in order to enable them to reap commercial benefits from their creative efforts or reputation. There are several types of intellectual property protection like patent, copyright, trademark, etc. Patent is a recognition for an invention, which satisfies the criteria of global novelty, non-obviousness, and industrial application. IPR is prerequisite for better identification, planning, commercialization, rendering, and thereby protection of invention or creativity. Each industry should evolve its own IPR policies, management style, strategies, and so on depending on its area of specialty. Pharmaceutical industry currently has an evolving IPR strategy requiring a better focus and approach in the coming era.

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A STUDY ON INTELLECTUAL PROPERTY RIGHTS

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 Abstract

Batik art is a textile technique that uses waxes and dyes to create to stunning designs on fabrics such as cotton, silk , linen, and rayon. The process of Batik fabric is in many ways similar to silk painting in that it uses wax instead of resist. The process is relatively straight forward but varies slightly by region. Asian and African artists use unique methods to apply tie-dye art to fabrics. From being worn during traditional ceremonies such as births, marriages and deaths, tie-dye prints have come a long way to become an icon of chic clothing and fashion statements around the world. Everyone loves tie-dyeing for its simple design and delicate art form. Layer them over canvas to create masterpieces, or sew them onto curtains, sofa covers, and quilts to brighten up your office or home.

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 Keywords

Wax, Dye, Textile technique, Batik art, Traditional, Fabric.

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Creative Commons Attribution 4.0 and The Open Definition

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This concept paper explores the domain of philanthropy, focusing on its evolution and the various dimensions of giving. Philanthropy, fundamentally defined as selfless giving, is driven by the principle of altruism. However, in contemporary society, the practice of giving has often taken on a more transactional nature, with expectations of return or personal benefit. While traditional philanthropy has its roots in charity, it has evolved beyond mere charitable acts to encompass a broader understanding of goodwill. Unlike charity, which often involves assessing the worthiness of recipients, philanthropy emphasizes the act of giving without judgment or expectation. However, the commodification of philanthropy has led to a shift where giving can sometimes appear as "the rich giving to the rich," creating a disparity in the distribution of resources. This paper aims to provide a nuanced understanding of philanthropy, examining its various dimensions from individual to community-based giving. By offering insights into different forms of philanthropic endeavors, the paper seeks to enhance readers' awareness of the types of giving they may engage in, encouraging a more reflective and informed approach to philanthropy.

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PHILANTHROPY: AN OVERVIEW OF ITS DIMENSIONS

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As global environmental challenges intensify, the need for innovative approaches in sustainable research has become paramount. This article examines emerging innovations in sustainability, focusing on the transformative potential of technologies and practices across various sectors. From AI-driven environmental monitoring to circular economy models and renewable energy advancements, these solutions offer promising pathways to address pressing issues like climate change, resource scarcity, and ecosystem degradation. The article highlights key case studies and emphasizes the importance of interdisciplinary collaboration and policy support in scaling these innovations for widespread impact.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Sustainable Research, Innovation, Artificial Intelligence, Circular Economy, Renewable Energy, Environmental Sustainability

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Creative Commons Attribution 4.0 and The Open Definition

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This project delves into the critical realm of combating deep fakes in online networking platforms by employing advanced deep learning techniques. Leveraging Generative Adversarial Networks (GANs) to simulate deep fake generation and utilizing the feature extraction capabilities of Inception ResNetV2, there search aims to develop a robust deep fake detection model. The proposed model undergoes comprehensive training, evaluation, and fine-tuning, with a focus on countering adversarial techniques employed in sophisticated deep fake generation. The study contributes are liable and effective means of discerning between genuine and synthetic content, offering heightened security for online networking platforms. Furthermore, insights gained into adversarial strategies and practical deployment recommendations provide a comprehensive approach to addressing the rising threat of deep fakes in the digital landscape. The proposed model undergoes comprehensive training, evaluation and fine-tuning, with focus on countering adversarial techniques employed in sophisticated deep fake generation.

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EXPOSING DEEP FAKES IN SOCIAL MEDIA PLATFORMS USING GENERATIVE ADVERSARIAL NETWORKS

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In an era where personal health monitoring is increasingly vital, wearable technology has emerged as a powerful tool for proactive wellness management. This paper presents a novel approach to wearable wellness through the integration of machine learning techniques within the Disease Sentinel system. The Disease Sentinel represents a comprehensive wearable device equipped with an array of sensors for real-time biometric data collection. Leveraging machine learning algorithms, the system pre-processes, extracts features, and trains predictive models to analyze the collected data streams. By continuously monitoring physiological parameters, the Disease Sentinel can detect anomalies, predict potential health issues, and provide personalized insights and recommendations to users. Furthermore, adaptive learning mechanisms enable the system to evolve and improve its predictive accuracy over time. Privacy and security considerations are paramount, with robust measures implemented to protect user data throughout the machine learning process. Ultimately, the Disease Sentinel represents a significant advancement in wearable wellness technology, empowering individuals to take proactive control of their health and well-being. This paper introduces "Disease Sentinel," a predictive system harnessing smart watch technology to anticipate a spectrum of diseases, including ischemic heart disease, hypertension, respiratory ailments, thyroid disorders, stroke, myocardial infarction, kidney failure, gallstones, diabetes, and dyslipidemia, employing robust machine learning algorithms."Disease Sentinel" comprises three fundamental modules: a prototype smart watch dubbed "Sense O' Clock," equipped with eleven sensors to capture vital bodily metrics; a machine learning model for data analysis and prediction; and a mobile application to present the prediction outcomes. Adhering to ethical guidelines, patient data, encompassing bodily statistics, was ethically sourced from a local hospital with the prior consent of patients and healthcare providers. The system extends constant support to users, furnishing real-time insights into their health status and recommending necessary interventions. It represents a significant advancement in early disease prediction, enabling the anticipation of multiple disease vulnerabilities before they progress to irrecoverable stages. Finally, we conducted a comparative analysis with existing methodologies to underscore the efficacy of our approach.

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WEARABLE WELLNESS THE DISEASE SENTINEL

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Population expansion, climate change, and digitization are driving up food demand faster than the country's economic growth. Perishable goods are handled more frequently by the food-related businesses, such as fast food chains, restaurants, canteens, and catering services. Organizing consumer food orders is one of the main problems faced by the food-related industry. Occasionally, inaccurate ordering estimates might result in either too much or too little food, wasting both food and raw materials and cutting into profits for the business. In order to ensure sustainable food systems and efficient resource allocation, food demand forecast is crucial. In an era of rapidly rising populations and shifting consumer tastes, accurate food demand forecasting is critical to minimizing food waste, optimizing performance, and enhancing food security. In this quest, machine learning algorithms have proven to be effective tools, with the ability to produce forecasts that are more dynamic and exact. In order to analyze the historical data and forecast the consumption for the upcoming months, we employed the decision tree algorithm. Along with internal variables, machine learning models also incorporate external variables like social events and economic indices.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Population growth, Food-related Industry, Resource allocation, Food waste, Machine learning algorithms, External variables

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Creative Commons Attribution 4.0 and The Open Definition

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In today's interconnected world, ensuring the security of cloud-based systems is paramount. Traditional intrusion detection systems (IDS) often struggle to keep pace with the evolving landscape of cyber threats. This project proposes a novel approach to enhancing cloud security through the integration of machine learning techniques into intrusion detection. By harnessing the power of machine learning algorithms, our system can adapt and learn from patterns in network traffic data, enabling it to detect anomalous behavior indicative of potential intrusions. Leveraging the scalability and flexibility of cloud computing, our solution offers real-time monitoring and analysis of network traffic across distributed cloud environments. Key features of our cloud-based intrusion detection system include automated threat detection, rapid response mechanisms, and customizable alerting capabilities. Through continuous learning and refinement, the system improves its detection accuracy over time, bolstering the resilience of cloud infra structures against emerging cyber threats. In summary, our project presents a proactive and adaptive approach to safeguarding cloud-based systems, combining the strengths of machine learning and cloud computing to create a robust Defense mechanism against intrusions. Protecting cloud-based systems from unauthorized access, data breaches, and other malicious activities is a critical concern for organizations worldwide. Traditional intrusion detection systems (IDS) often fall short in effectively identifying and mitigating these threats in dynamic cloud environments .To address this gap, our project focuses on leveraging the Random Forest algorithm to enhance cloud-based intrusion detection capabilities. High Accuracy Random Forest excels in handling complex, high- dimensional data and can effectively distinguish between normal and malicious network activities with high accuracy.

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DATA MISMATCH AND ERROR DETECTION USING CLOUD

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The "WEB RENTAL HUB BRIDGING THE GAP IN DIGITAL POSSESSION" web application is designed to provide a platform for users to rent various items and products for a specified period. The application utilizes HTML, CSS, Bootstrap, Java, and SQL to create an interactive and user-friendly interface. The primary goal of the web application is to facilitate the rental process by connecting users who have items available for rent with those seeking to borrow them. The application allows users to browse through a available items, view detailed information about each item, and make rental requests. The front-end of the application is built using HTML, CSS, and Bootstrap, which ensures a visually appealing and responsive design across different devices. The user interface enables easy navigation, search functionality, and intuitive forms for users to input rental information. The back-end of the application is developed using Java, which handles the business logic and data processing. Java provides robust functionality for managing user authentication, item availability, and rental transactions. The application integrates with a SQL database to store and retrieve user data, item details, rental history, and other relevant information. Key features of the "Things Rental" web application include user registration and login, item listing and search, item details and availability, rental request submission, payment processing, and rental history tracking. The application incorporates security measures to protect user information and ensures a seamless rental experience. Hence this application could be very helpful for people in their day-to-day life.

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WEB RENT HUB BRIDGING THE GAP IN DIGITAL POSSESSION

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Creative Commons Attribution 4.0 and The Open Definition

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The Online Parking Booking System is a web-based application developed using Java to facilitate convenient and efficient parking reservations for users. The system aims to eliminate the hassle of finding parking spaces in crowded areas and provide a user-friendly platform for reserving parking slots in advance. This abstract provides an overview of the key features and functionalities of the Java-based Online Parking Booking System. The system allows users to register and create their accounts. Registered users can log in securely using their credentials to access the parking booking functionalities. Users can browse through available parking spaces based on location, date, and time preferences. The system presents a user-friendly interface to view parking options, including details such as location, availability, and pricing. Once users select a suitable parking space, they can proceed to book it for a specific date and time. The system handles reservation requests and ensures that bookings do not overlap, preventing double bookings. The application integrates a secure payment gateway to handle online transactions for parking reservations. Users can make payments using various payment methods, ensuring a seamless booking experience.

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 Keywords

Parking Booking System, Urban Mobility, Sustainable Transportation, Copenhagen, Energy Efficiency

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Creative Commons Attribution 4.0 and The Open Definition