Volume 13 | Issue 4 |

 Abstract

Natural Language Processing (NLP) has emerged as a transformative field within artificial intelligence, enabling machines to understand, interpret, and generate human language. Recent advancements in NLP, driven by deep learning architectures and large language models (LLMs), have significantly enhanced its capabilities across various domains. This paper explores key trends shaping the field in 2025, including real-time language translation, fine-tuning of deep learning models for specialized tasks, semantic search optimization, reinforcement learning applications, and the integration of NLP with other AI domains such as computer vision and robotics. Additionally, the paper highlights practical applications of NLP in healthcare (e.g., medical record analysis and telemedicine), tourism (e.g., automated translation services), and customer service (e.g., chatbots and sentiment analysis). Despite its rapid progress, NLP faces challenges related to bias, explainability, and ethical considerations. Future research directions emphasize transparency, fairness, and scalability to ensure responsible development and deployment of NLP technologies.

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 Keywords

applications,architectures,scalability,analysis,nlp,optimization learning domains,robotics,technologies

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Automated data governance's role in helping cloud based financial data lakes achieve compliance and security is researched. The research addresses the way automated systems handle challenges such as data quality, privacy and regulatory compliance. The scalability of disposal systems is also authorized to help such systems adapt to more and more volumes of data and exchange in regulations. There is less human error involved in data disposal and it continues to be monitored mechanically and approach is inside limits in terms of ensuring data security. The research determines recommended practices for implementing automated disposal frameworks that prioritized maximal data shelter and regulative compliance. Maintaining the unity and credentials of the fiscal data are authorized in the ever-changing regulative environs and these are authorized practices.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

financial data lakes, automated data governance, data quality, compliance, security, scalability, governance frameworks, privacy, data protection, regulatory compliance.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The study explores the budgeting challenges and financial habits of CA Article students in Mumbai, a city known for its high cost of living. It is important to understand the budgeting habits of students undergoing CA practical training in Mumbai and face financial challenges. CA Articleship is a very vital part of any CA Student's journey and hence it is important to focus on the problems students face in this period. The purpose of the study is to examine the income, expenses & saving pattern of students and to identify challenges and strategies used for financial management. A structured questionnaire was used to collect primary data and interviewed a few students as well after which inferences have been drawn. Data from surveys and interviews reveal that while stipends are a primary income source, they are often insufficient to cover essential expenses like rent and transportation. The findings highlight the need for better financial planning tools and institutional support to alleviate these challenges, ultimately contributing to improved financial well-being among CA students.

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 Keywords

Budgeting, CA Articleship, Cost of Living, Financial Management.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Customer loss or churn is an enormous issue for most firms currently. Regarding this, the computation of the churn rates will most definitely help the level of customers retained in and also improve the level of profit generation. It explains the most often-used algorithms of machine learning algorithms: logistic regression and random forests for modeling churning from historical data. It elaborates on specific features like demographics and history of transactions, which help to increase results on this model. That too, at a machine learning platform, if proactive retention exists, outputs are even more superior compared to normal practice. Further work might be related to incorporating real-time analytics along with deep learning to create even better predictions for predictability related to churn in the future

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 Keywords

Customer Churn Prediction, Machine learning, Logistic Regression, KNN Algorithm, Random Forest Algorithm, ROC Curve.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract Cloud computing has revolutionized data storage and sharing by providing scalable and cost-effective solutions. However, ensuring the security of shared data remains a critical challenge due to threats such as unauthorized access, data breaches, and insider attacks. This paper explores various security mechanisms for secure data sharing in cloud environments, including encryption techniques, access control models, identity management, and blockchain-based solutions. Furthermore, it analyzes the role of homomorphic encryption, attribute-based encryption (ABE), and multi-factor authentication (MFA) in enhancing security. The paper also discusses emerging threats and potential future advancements in securing cloud data sharing. The findings highlight the importance of integrating robust cryptographic techniques with advanced access control policies to achieve confidentiality, integrity, and availability in cloud-based data sharing systems.

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Keywords: Cloud computing, Data security, Encryption, Access control, Blockchain, Secure data sharing.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In addition to being dangerous, skin conditions including melanoma, eczema, and impetigo are frequently communicable, so early and precise diagnosis is essential for successful treatment. However, diagnosis usually calls for specialized understanding of dermatology, and even experts sometimes misclassify disorders, which can result in improper therapy. Our project suggests a deep learning and image processing-based skin disease detection system that uses a Convolutional Neural Network (CNN) model for precise classification in order to overcome this difficulty. This system, which is a personal computer-based program, can be implemented in places with limited resources or in remote locations, improving access to first diagnostic assistance. The system evaluates and categorizes the condition by examining a user-provided photograph of the affected skin area, providing pertinent medical information along with the projected disease.

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 Keywords

AI/ML, Python, Frameworks, etc.

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Creative Commons Attribution 4.0 and The Open Definition

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Malware is an increasing threat to cybersecurity, causing significant harm to systems, data, and networks worldwide. As cyberattacks become more sophisticated, it's crucial to develop reliable and effective methods for detecting and preventing malware. This project addresses this issue by using the Random Forest algorithm, a well-established machine learning technique known for its ability to handle complex datasets and deliver high accuracy. The model is trained to analyse features extracted from executable files, such as file size, code structure, and patterns in behaviour, to distinguish between benign and malicious software. One of the key advantages of the Random Forest approach is its ability to adapt to new and evolving types of malware, ensuring it stays effective as threats change. Additionally, its resistance to overfitting and scalability make it a robust choice for detecting even the most sophisticated malware. Ultimately, this project aims to enhance cybersecurity by providing a flexible, precise, and scalable solution for malware detection.

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 Keywords

Malware Detection, Random Forest Algorithm, Machine Learning, Predictive Accuracy, Malicious Software, Malware Identification.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Himalayan regions have been most vulnerable to the impacts of climate change in recent years. In this region, environmental justice, gender parity, and climate change adaptation are intertwined challenges. Women often face greater vulnerability to climate change impacts and are less likely to have access to resources needed for adaptation, hindering their ability to build resilience. The impacts of climate change are leading to severe consequences such as scarcity of resources, food insecurity and gender inequalities. Women, often bearing the brunt of climate change-related impacts due to their roles in resource management and subsistence livelihoods and face increased vulnerability and lack access to decision-making and resources, hindering their ability to adapt to changing conditions. Addressing this requires a holistic approach that integrates gender concerns into climate change policies, empowers women, and promotes equitable resource management. In this paper, the author examines environmental justice from gender perspectives and adaptation in the prevailing climate change scenario.

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 Keywords

Climate Change, environmental justice, gender inequality, and resource management

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The system processes 3D skeletal data from sign language gestures, constructs spatio-temporal graphs, and applies graph convolutional networks (GCNs) to extract spatial and temporal features. The trained model classifies sign language gestures into predefined categories, making sign language recognition more efficient and scalable.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Sign Language Recognition, Spatiotemporal Graph Convolutional Networks (ST-GCN), 3D Skeleton Data, Gesture Recognition, Human-Computer Interaction, Deep Learning, Motion Capture, Assistive Technology

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Creative Commons Attribution 4.0 and The Open Definition

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In order to meet the consumers of a better life the traditional furniture is developing in the direction of intelligence, but the traditional furniture with single function such as courtyard sunshade has been developing in the direction of Intelligence. The researchers purpose to change the traditional sunset, where is an automatic sunset system add an environmental monitoring system of for the sun shapes and increase voice playback function which realize the data exchange between the sun shade and the user through Bluetooth direction of Smart home . This paper uses design platform and provides the designs scheme of the intelligent as well as the beginning method of the use of the end of the paper . The feasibility of the user and the device and the flexibility of the designs scheme will be proved by Arduino and open source development platform which can be used in the development of traditional home intelligence. It also aims in proceeding protection to doors, windows and especially vehicles.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Home intelligence, Sun shade

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Creative Commons Attribution 4.0 and The Open Definition