Browse Articles
Discover research articles across all indexed journals
Freedom to Contract in The Romanian Legal System
The freedom to contract represents a component of the right to individual freedom, this being a fundamental citizen's right. This right can be exercised, however, under the conditions and with the limits provided by law. In capitalist democratic societies, the contract represents a cell in an infinite labyrinth of legal relations that unfold between the participants in the civil and economic circuit. From this perspective, the contract must be analyzed at the micro level, but also at the macro level. In order to ensure a state of balance and equity at a general level, often the individual freedom to contract can be limited in order to ensure a collective freedom, in order to achieve a common interest, often economic.
Prenuptial Agreements in the Institution of Marriage in India: Legal Framework & Societal Perceptions
In modern culture, prenuptial agreements represent a significant shift from traditional marital customs to a more popular strategy. Such agreements are effective in navigating potential risks and enhance communication among future spouses. These agreements embody a strengthened framework of legal protection that guarantees that the financial interests of both parties are respected, while also easing the financial burdens and emotional difficulties linked to the divorce process. Current research examines prenuptial agreements in Hindu marriages in India revealing the possible pathways beyond customary laws. It also highlights the significance of customs in Hindu marriages and growing interest in prenuptial agreements young couples. It is suggested to implement a well-defined legal framework which aligns traditional marriage with modern requirement. In India, prenuptial agreements are subject to inconsistent and ambiguous legal provisions. Although there are no particular provisions for prenuptial agreements in personal laws, they may fall under the purview of contract law or the ‘Indian Contract Act, 1872.Courts have generally held them unlawful on public policy grounds. In India, marriages are governed by Hindu marital traditions.
Managerial Reform and Privatization of Public Policies: An Analysis of Social Health Organizations in the State of São Paulo, Brazil
This article analyzes managerial reform and the privatization of public policies, focusing on the role of Social Health Organizations (SHOs) in the State of São Paulo within the context of the neoliberal agenda that intensified in Brazil from the 1990s onward. The analysis is based on the macro context of the emergence of Social Organizations (SOs), particularly the SHOs in the metropolitan region of São Paulo, utilizing documentary sources, indicators, and specific literature. The results indicate that Social Organizations, by adopting business rationality derived from the managerial model, significantly hinder state oversight and social participation, promoting a dissociation between the planning and execution of public policies. The conclusions highlight the need to reassess the State's role in regulating these entities, emphasizing that managerial reform not only privatizes management but also reshapes the very nature of public policies, perpetuating neoliberal hegemony. The study suggests that to ensure the effectiveness of health policies, it is crucial to reestablish the State's capacity to act as a regulator and guarantor of the public interest, rather than merely facilitating business practices.
Substantive Legal Principles for Exploration and Exploitation of Resources in The Area
Interest in exploring and exploiting mineral resources is increasing globally due to exhausting land based mineral resources. Exploring or exploiting land or deep seabed resources leads to diverse uncertainties and several risks to the environment necessitating guiding principles for exploring or exploiting resources. Competence over mineral resources in the sea is conferred to the International Seabed Authority (ISA). This study analyzes the effectiveness of the implementation of the substantive principle for exploring or exploiting resources. Effective implementation of the substantive principles regarding the exploring of and exploiting of deep seabed resources is the main research question. Currently, the substantive prescription and principles for exploring of and exploiting of deep seabed resources is not implemented as it should be. The methodology consists of the empirical approach while content approach and institutional approach is utilized in data analysis. The commitment provided for in the 1994 Agreement and Convention on the Law of the Sea is implemented by ISA. ISA, however is facing challenge in ensuring that financial benefits and economic benefits are shared equitably necessitating the recommendation to create a commission subject to accountability to ISA with a well-defined mechanism on how financial benefits will be shared.
Augmented Reality in Smart Learning Devices: Advancing Cognitive Development and Entertainment
The integration of Augmented Reality (AR) in smart learning devices is transforming the way children engage with educational content. These devices merge physical and digital environments to create interactive, immersive learning experiences, enhancing cognitive development and providing entertainment. This paper explores the current applications of AR in educational devices for children, its impacts on cognitive growth, the role of interactivity, and its potential in future learning environments. The paper also provides a critical review of existing literature, identifying opportunities and challenges in this emerging field. Augmented Reality (AR) has revolutionized how children interact with educational content, offering an immersive, engaging learning experience. This paper explores the integration of AR into smart learning devices and its impact on cognitive development and entertainment for children. We delve into how AR enhances memory retention, problem solving skills, and spatial reasoning while making learning enjoyable. By critically analysing current literature and case studies, we highlight the potential and challenges of using AR to bridge the gap between education and entertainment.
Personality Prediction System Using Machine Learning
A personality prediction system leverages advanced data analysis techniques to assess and predict an individual's personality traits based on diverse inputs such as text, voice, behaviour, and biometric data. These systems typically utilize psychological models, like the Big Five Personality Traits or Myers-Briggs Type Indicator (MBTI), to derive insights from patterns in the data. Text-based prediction systems use natural language processing (NLP) to analyze written or spoken communication, while voice- based systems analyze vocal attributes such as tone and pitch. Behavioral and biometric data further enhance the accuracy of these systems, with applications spanning areas like human- computer interaction, marketing, recruitment, and mental health. With the aid of artificial intelligence (AI) and machine learning, these systems are increasingly capable of delivering personalized insights, offering the potential for a wide range of applications from customer engagement to psychological well-being. This abstract outline the core mechanisms, applications, and future directions of personality prediction systems, highlighting their potential to transform how individuals are understood and interacted with in both digital and real-world contexts.
IOT Based Car Parking Using Edge Computing
The promise of autonomous driving is that edge computing can deliver real time services. Using a variety of sensors and communication modules, autonomous cars may function both indoors including in parking lots and outdoors. But because the interior station's ability to communicate with the outer world is limited globe, and the present underpowered autos don't really work; they could have to handle all the data. Autonomously, hence a control mechanism must be put in place. Completely self-sufficient car in the surroundings. In this research, we suggest an edge computing based smart parking system for self driving cars. Smart parking has resolved Boundaries and traffic, and parking spaces are managed using a grid. To assess the effectiveness of the smart parking system, In the current parking system, we contrasted the journey time with the parking time. Furthermore, the resource to make sure that the service quality margin number is offered and maintained; expenses and transaction data are examined.
IoT Based Gesture Control Light System Using Python
The Gesture Control Light System is an innovative, user-friendly interface designed to enhance the interaction between humans and electronic devices. By utilizing hand gestures as input commands, this system enables users to control lighting appliances without physical contact. It integrates gesture recognition technology, primarily using infrared sensors or cameras, to interpret specific hand movements like swipes or waves, translating them into actions such as turning lights on/off, adjusting brightness, or changing colour modes. This touchless approach offers convenience and hygiene benefits, especially in settings where hands-free control is advantageous, such as in smart homes, medical facilities, or during cooking tasks. The system's intuitive design also contributes to energy efficiency, enabling automation and personalized lighting preferences. With its practical applications and potential to improve user experience, the Gesture Control Light System represents a step forward in the evolution of smart technology and human-machine interaction.
Analysis of Netflix Recommendation System
The recommendation engines utilized by Netflix provide an extensive array of content. The algorithms employed by Netflix play a vital role in assisting users in discovering the material they seek. Numerous recommender systems are available that deliver valuable suggestions for various products and services. The recommendation algorithms of Netflix are grounded in machine learning and utilize information filtering techniques to anticipate user ratings and preferences for specific Netflix offerings or products. The term recommendation systems are often used in a pejorative sense to describe recommender systems. Generally, there are four primary categories of recommendation systems: content-based, collaborative filtering, simple recommendation systems, and hybrid methods.
Automated Cardiovascular Disease Detection with Hybrid Machine Approach
Cardiovascular ailment (CVD) is a main reason of death and disability globally, posing tremendous challenges for early detection and prevention. A heart condition or the accumulation of fatty deposits inside the arteries, is frequently linked to CVD and raises the risk of blood clots and other consequences. Predicting and diagnosing CVD is critical to mitigating its effect and enhancing patient results. With the appearance of massive data in healthcare, large volumes of patient records are available that may assist in identifying early warning symptoms of coronary heart disorder. This project aims to generate a hybrid machine learning technique which leverages deep learning and traditional machine learning algorithms to automatically locate cardiovascular illnesses. By integrating more than one dataset, we intend to upgrade the model's overall performance in diagnosing coronary heart situations. Numerous machine learning classification models, consisting of Random Forest and Gradient Boosting, might be employed and in comparison, to evaluate their effectiveness in detecting CVD. Each model can be evaluated using overall performance indicators like precision, accuracy, recall, and F1-Score. In previous research, the Random Forest model did 94% accuracy in coronary heart disorder detection, at the same time as the Gradient Boosting model established a balanced overall performance through accuracy, precision, recall, and F1-score with 73% each in detecting cardiovascular diseases via this hybrid technique. We aim to enhance prediction accuracy and offer a greater reliable tool for early CVD detection, in the long run enhancing affected person care and saving lives.
IOT Based Green House Monitoring and Controlling with Arduino
Greenhouses offer a controlled environment for growing plants. For a green house system to function effectively, it needs to consistently oversee and control various environmental elements such as hotness, dampness, soil moisture, light strength, and soil p H. The main objective of the project is to create a cost-efficient design to promote optimal plant growth and yield. Arduino system that allows for constant updating and adjustment of environmental parameter values. The DHT 11,s and acidity, LDR plus dryness are the important sensors in the project, measuring moisture, temperature, water purpose, light strength, and earth wetness. Android smart phones receive environmental data both offline and online. The present situation of the surrounding circumstances is displayed in an SMS sent via a GSM modem. The user gets a text when this sensor value goes above a specific threshold. Farmers can observe and control their green houses from anywhere. SMS commands can be used to change environmental factors like the fluid pump, motor breathe, refrigeration fan, emission fan, and manufactured illumination fluids pump, engine breathe, cooling fan, exhaust fan, and false lighting. To prevent SMS charges, data parameters are sent to mobile phones using Ethernet. Using Ethernet, all environmental data is sent to the machine ad kept on the data file in this way, the Android app allows the user to monitor and manage settings.
Personality Prediction System Using Machine Learning
A personality prediction system leverages advanced data analysis techniques to assess and predict an individual's personality traits based on diverse inputs such as text, voice, behaviour, and biometric data. These systems typically utilize psychological models, like the Big Five Personality Traits or Myers-Briggs Type Indicator (MBTI), to derive insights from patterns in the data. Text-based prediction systems use natural language processing (NLP) to analyze written or spoken communication, while voice-based systems analyze vocal attributes such as tone and pitch. Behavioral and biometric data further enhance the accuracy of these systems, with applications spanning areas like human-computer interaction, marketing, recruitment, and mental health. With the aid of artificial intelligence (AI) and machine learning, these systems are increasingly capable of delivering personalized insights, offering the potential for a wide range of applications from customer engagement to psychological well-being. This abstract outline the core mechanisms, applications, and future directions of personality prediction systems, highlighting their potential to transform how individuals are understood and interacted with in both digital and real-world contexts.
End to End Leaf Detection Using Convolutional Neural Network
Global food security is seriously threatened by crop diseases, yet in many places, it is still difficult to identify them quickly because of a lack of infrastructure. In tackling this problem, recent developments in leaf-based image categorization methods have produced encouraging outcomes. This study investigates how to differentiate between healthy and unhealthy leaves using curated datasets using the Random Forest algorithm. The development of the dataset, feature extraction, classifier training, and classification are the various stages of the implementation process. To efficiently distinguish between diseased and healthy samples, the Random Forest classifier is used to train the dataset, which includes pictures of both diseased and healthy leaves. A significant obstacle to agricultural production, plant diseases have an impact on crop yield and quality. Precision farming and sustainable agriculture depend on the timely and precise identification of leaf diseases. In this work, we present a complete system that automatically detects and classifies leaf diseases using Convolutional Neural Networks (CNN), starting with picture capture. The technology offers a quicker method of identifying leaf illnesses and does away with the requirement for manual feature engineering. Our tests, carried out on several public datasets, show the model's efficacy and achieve high accuracy for a range of illness categories. These results underline the practical uses that might improve crop management and early intervention techniques. Using machine learning on massive datasets that are accessible to the public, this approach offers a scalable solution for detecting plant diseases on a large scale.
Automatic Colorizing Black and White Image System Using Deep Learning
Colorization involves transforming a grayscale (black-and-white) image into a colorized version that reflects the semantic color tones of the original input. Over recent years, this process has garnered substantial attention, with significant advancements achieved by researchers in the field. The technique has numerous applications, such as in medical imaging and the restoration of historical artifacts. Various methods have been developed to address this challenge, leveraging technologies like Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). These models differ in their architectural frameworks and are evaluated using diverse datasets. This paper explores some of these methodologies, comparing the performance of generative models with traditional deep neural networks, while also highlighting existing limitations. Additionally, it provides a concise overview of past and current progress in the domain of image colorization, drawing on contributions from numerous researchers and studies. a widely recognized deep learning algorithm. The proposed model processes grayscale images as input and predicts their corresponding colors based on training data. The Lab color space is employed in this approach, where the model receives the L channel (lightness) as input and predicts the ab channels (color information) as output. A subset of the ImageNet dataset was used for training and evaluation, consisting of 39,604 randomly selected images, divided into 80% for training and 20% for testing.
Enhancing COVID Quarantine Compliance through GeoFencing Technology
This research paper explores the development of an Android geofencing application designed to enhance compliance with COVID-19 quarantine measures [1,5,7]. As public health authorities faced unprecedented challenges during the pandemic, the need for innovative solutions became paramount.This study investigates the effectiveness of geofencing technology in monitoring quarantine adherence by utilizing location-based services to define geographic boundaries around users' homes. The app aims to provide real-time alerts and data analytics to ensure compliance while addressing privacy concerns. Through a mixed-methods approach, including user surveys and case studies, we evaluate user adoption rates, engagement levels, and overall effectiveness in maintaining quarantine protocols.Recently, numerous containment zones have been established across the nation, categorized into red, orange, and green zones[16,17]. The red zones signify areas with high infection rates, orange zones represent regions with some level of infection, and green zones indicate locations free from infection[19, 20]. This paper primarily addresses the creation of an Android application designed to inform individuals about Covid-19 containment zones and to prevent unauthorized entry into these areas. The application provides real-time updates of the containment zone locations on a Google map. Ultimately, the study contributes to the ongoing discourse on the role of technology in managing public health crises and suggests pathways for future research and development in geofencing applications for disease control.
Iris Recognition Using Artificial Neural Network
Iris recognition has emerged as one of the most widely used and effective techniques for personal verification. It leverages the unique features of the human eye, specifically the distinct patterns found in the iris or, more precisely, the pupil. Biometric systems based on iris scanning analyze these unique iris patterns to confirm and authenticate an individual's identity. They are widely used as human eye has unique features particularly the iris. Human iris have random variations and complex texture which are stable with time even visible between identical twins. It’s replication and forgery are almost impossible. For training and testing our model we used CASIA-Iris-Interval database as camera used to collect this data uses a circular NIR LED array [1].Near Infrared LED has a suitable luminous flux for imaging human iris for recognition. Our results demonstrate that the ANN model outperforms traditional machine learning algorithms, such as Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN), in terms of both accuracy and robustness [5]. The model showed high accuracy in identifying iris patterns, even under varying conditions like changes in lighting, pupil dilation, and partial occlusions. This highlights the potential of deep learning techniques to overcome the challenges faced by conventional biometric systems, which often struggle with high-dimensional data and manual feature extraction [18].
Startup AYUSH Portal
The AYUSH Portal is designed to promote the incorporation of India's traditional medicinal systems Ayurveda, Yoga, Naturopathy, Unani, Siddha, and Homeopathy into the nation's healthcare ecosystem. Recognizing the increasing global acceptance of AYUSH practices, the Government of India has initiated Centers of Excellence to support prominent institutions involved in research and education and drug development. Notable initiatives include collaborations with premier institutions such as AIIMS and IIT Jodhpur to explore integrative health research and address key public health challenges. Programs like the Ayurgyan and Ayurswasthya Schemes prioritize research in the AYUSH sector, promoting innovations in preventive and integrative health care. The establishment of the AYUSH-ICMR Advanced Center for Integrative Research seeks to create a vibrant ecosystem for scientific validation of traditional practices. During the COVID-19 pandemic, the Ministry of AYUSH demonstrated the efficacy of traditional medicine by releasing clinical management protocols and guidelines based on Ayurveda and Yoga, highlighting their role in public health emergencies. This paper discusses the methodology adopted in the AYUSH Portal, emphasizing collaborative research efforts, intellectual property rights, and the integration of AYUSH therapies in mainstream healthcare to achieve the goal of "One Nation, One Health."
Estimation of the RUL of Alkaline Batteries in EV by using Machine Learning Algorithm
The attention on battery longevity and performance has increased due to the quick uptake of electric cars (EVs) as a sustainable substitute for conventional internal combustion engine vehicles. The foundation of EV technology, batteries control the vehicle's dependability, efficiency, and range. With their distinct chemical characteristics, alkaline. Batteries are one of the various battery varieties that may be advantageous for specific EV applications. However, a variety of variables, including charge-discharge cycles, temperature fluctuations, and usage patterns, affect their life cycle and efficiency. To maximize performance, lower maintenance costs, and guarantee user safety, it is crucial to precisely calculate these batteries' Remaining Useful Life (RUL). Conventional approaches to RUL prediction frequently depend on statistical methods or empirical models, which might not take into consideration the intricate relationships between the factors influencing battery deterioration. For a potential solution to this issue machine learning (ML) algorithms, which can examine big datasets and reveal hidden patterns. This study intends to provide a reliable framework for forecasting the RUL of alkaline batteries in EVs by utilizing cutting-edge machine learning models. This will ultimately help to enhance battery management systems and encourage the wider use of EV technology.
IoT Enabled Intelligent Load Balancing System for Railway Wagons through Artificial Intelligence
This research paper presents an innovative system that integrates IoT (Internet of Things) and AI (Artificial Intelligence) technologies to address the critical issue of load balancing in railway wagons. Traditional systems focus on post-event detection of overloading or under loading, which poses safety risks and inefficiencies. In contrast, our solution leverages IoT-enabled load cells to continuously monitor wagon loads in real-time and utilizes AI-driven algorithms such as Genetic Algorithms and Reinforcement Learning—to optimize load distribution dynamically. The system ensures balanced loading across wagons, preventing overloading and underutilization, improving safety, fuel efficiency, and operational effectiveness. The IoT sensors provide real-time data that is processed by AI models to predict and balance load allocation, thus minimizing the risk of infrastructural damage and enhancing wagon capacity utilization. Initial testing shows a 95%reduction in load imbalances, a 20% decrease in loading time, and improved fuel economy by 10%. This research contributes to railway freight logistics by offering an AI- powered solution for intelligent load management, ensuring safety, efficiency, and cost-effectiveness.
Implementation of Cyber Security Architectures Based on Review Analysis
As we know that the cyber security is an evaluating field. So, there are lots of problems which was held while the using Social Media and Security related issues. Because of this, the company of a Organisation cannot secure own system, software, data, and many more from cyber crimes like threats, phishing, cyber bullying, and more. So, How we will safe from that type of threats ? Yes, It is possible, We made a Cyber Security Architectures which will performing in some steps to prevent from threats. During the Implementation of Architecture, we use some tools like Firewall, VPN’s and Intrusion Detection System(IDS). This process involves a comprehensive assessment of current security.