Artificial intelligence (AI) has bloomed in recent years and is gradually becoming an irreplaceable asset in finance,
among other sectors. Personal finance is a subset of finance, which too is being revolutionized due to changing times
and technological advancements, much like AI. Security and proper financial guidance have never been more
important with such significant change. In this study, we use FinBERT, a modern large language model specialized
in the financial domain, for our AI-powered personal finance coach. However, FinBERT, although a cut above the
rest, still has room for growth, so we aim to improve its flaws and enhance its efficiency. We established that
FinBERT succeeded in detecting sentiments in explicit sentiments, but was not usually successful in doing so
correctly for implicit sentiments. FinBERT, despite its limitations, has a high accuracy and is the best model to use
in our study. This model can also be utilised to provide accurate results regarding the overall trend (positive or
negative) of the global stock market. Our results demonstrate that integrating AI in personal finances is feasible and
can successfully aid individuals in making decisions regarding their finances.

The integration of Advanced Artificial Intelligence (AI) technologies in Printed Circuit Board (PCB) manufacturing has revolutionized the industry, offering significant improvements in defect prevention and yield optimization. As PCB manufacturing processes become increasingly complex and intricate, traditional methods of defect detection and quality assurance are often insufficient to meet the high demand for precision and efficiency. This paper explores the application of cutting-edge AI methodologies, including machine learning, computer vision, and neural networks, to address critical challenges in PCB production. The primary focus of this research is on the role of AI in enhancing automated defect detection systems and optimizing production yield. AI-based systems such as predictive maintenance, real-time process monitoring, and intelligent decision-making have been shown to drastically reduce defects while improving overall production throughput. Machine learning algorithms can identify subtle defects in real time, often undetectable by conventional methods, while neural networks can analyze historical data to predict potential failures before they occur. Additionally, AI-driven optimization techniques help manufacturers adjust production parameters dynamically, ensuring higher yields and minimizing waste.

Through a combination of theoretical analysis and case studies, this paper highlights the effectiveness of AI-driven solutions in PCB manufacturing. Results from industry applications indicate significant improvements in both quality control and yield rates, providing a competitive edge to manufacturers adopting these technologies. Furthermore, this paper discusses future trends, including the integration of AI with the Internet of Things (IoT), edge computing, and sustainable manufacturing practices, which will further enhance the capabilities of AI systems in this field. The findings suggest that advanced AI technologies are not only capable of overcoming existing challenges in defect prevention but also hold the potential to reshape the future of PCB manufacturing by offering highly adaptive, precise, and efficient solutions. This research underscores the transformative impact of AI in modernizing manufacturing processes, making it an indispensable tool for the future of the PCB industry.

In the era of big data, the secure sharing of sensitive information across various domains such as healthcare, finance, and social networks has become increasingly vital. Traditional data anonymization techniques often struggle to balance the competing demands of preserving privacy and maintaining data utility, particularly in complex and dynamic data-sharing environments. This paper presents a novel hybrid approach to data anonymization that integrates differential privacy with adaptive anonymization algorithms, specifically designed to enhance privacy protection while retaining the analytical value of the data. The proposed methodology tailor’s anonymization strategies to the specific context of data sharing, effectively addressing the limitations of existing techniques. Extensive experiments conducted on diverse datasets, including healthcare and financial data, demonstrate the superior performance of this approach in reducing re-identification risks while maintaining high data utility. The findings suggest that these advancements in anonymization techniques provide a robust solution for secure and privacy-preserving data sharing, addressing the growing challenges posed by the increasing volume and sensitivity of data in modern digital ecosystems. The paper concludes with a discussion of the broader implications for cybersecurity and suggests future research directions to further enhance privacy-preserving technologies.

Fake news causes huge social problems such as misdirecting and provoking the masses, creating an atmosphere of chaos by spreading fear. Detecting and stopping the dissemination of fake news has been an important and priority issue due to its rapid spread, difficulty to detect, and negative effects. In our study, a new Chrome extension that detects fake news has been developed in order to detect and prevent the spread of fake news. In the study; natural language processing, data mining methods, and various machine learning techniques for instance Passive Aggressive, Random Forest, Support Vector Machine, AdaBoost, XGBoost, and Long-Short-Term Memory (LSTM) algorithms are used. The accuracy rates of the algorithms, confusion matrices, AUC rates, and ROC curves were compared. The created machine learning model has been implemented in the online internet environment by using Flask and Rest API in the Python program. Finally, the Chrome extension interface was built using Javascript, HTML, and CSS. The LSTM algorithm gave the highest prediction result with a rate of 90.72% compared to other algorithms.

The integration of the Internet of Things (IoT) and Edge Computing is revolutionizing the manufacturing industry, ushering in the era of smart manufacturing as part of Industry 4.0. This paper explores the synergy between IoT and Edge Computing, focusing on their combined architecture and the future trends driving innovation in smart factories. IoT enables the connection and communication of machines, sensors, and systems, allowing for real-time data collection and monitoring. However, traditional cloud-based approaches face challenges such as latency, bandwidth limitations, and security risks, which can hinder real-time decision-making in fast-paced manufacturing environments. Edge Computing addresses these issues by processing data closer to the source, minimizing latency and reducing dependence on cloud infrastructures. By combining IoT and edge solutions, smart manufacturing systems can make faster, data-driven decisions, leading to improved efficiency, reliability, and operational flexibility. This paper delves into the architectural design of IoT and edge computing in manufacturing, outlining how data flows from IoT devices to edge nodes and cloud services. Several real-world use cases and industry examples are analyzed to highlight the practical benefits of these technologies.

Additionally, this research identifies key challenges such as security vulnerabilities, the need for robust network infrastructures (e.g., 5G), and issues related to data standardization. The future of smart manufacturing is also explored, emphasizing trends like the adoption of artificial intelligence (AI) and machine learning (ML) at the edge, digital twins for real-time monitoring, and the role of IoT and edge computing in fostering sustainability through energy-efficient production processes.This study provides a comprehensive overview of IoT and edge computing architectures in smart manufacturing and offers insights into future technological trends that will shape the industry.