The research introduces an innovative wind forecasting system that combines radar imagery with a deep learning approach using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The motivation behind this research stems from the limitations of traditional wind forecasting methods, which often fail to accurately capture complex spatial and temporal patterns in radar data. The aim is to develop a more precise and reliable forecasting model that can effectively handle these challenges. To achieve this, the study set several objectives: designing a robust deep learning framework that integrates CNNs for extracting spatial features from radar images and LSTMs for analyzing temporal sequences, optimizing the model to improve prediction accuracy, and evaluating its performance against existing methods. The results achieved include significantly low Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), reflecting enhanced forecasting precision. Additionally, the system demonstrates high Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) scores, indicating effective preservation of critical details in radar imagery. The successful implementation of this system highlights its potential applications in various fields, such as renewable energy management, weather forecasting, and environmental monitoring, where accurate and reliable wind predictions are essential for operational efficiency and decision-making.

Machine Learning (MD) has experienced significant growth in research in recent decades. Anticipated is a substantial impact of this technology on many facets of human existence. ML has revolutionized our interactions with the world, impacting industries ranging from healthcare to commerce, and from entertainment to transportation. Researchers are coming up with new algorithms and techniques to deal with complex issues, which means that ML is an essential tool in today’s technological landscape. As ML develops, its applications are projected to grow further leading to innovations and improved efficiency across various domains. This paper looks into recent and relevant scientific articles on ML approaches for weather variables analysis and numerical weather prediction. As meteorological data from several decades is recorded, we can predict wind speed, pressure, precipitation and temperature that are common studied meteorological parameters by utilizing ML techniques such as Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine etc. This is crucial for improving the accuracy and dependability of weather forecast. As it has been found in literature review, the diversity and complexity of meteorology data means that there is no any single ML technique which serves all parameters hence different methods should be used to make accurate analysis and predictions. This demonstrates the agility and flexibility of ML as it tackles various problems thus making it a key tool in modern scientific studies and technological development.

The author begins this speech by examining the impact of information and communication technology on understanding the Quran, including: easy storage and retrieval of information, establishing communication in cyberspace, and preserving old manuscripts. After that, he points to solutions for optimal use of the Internet in promoting Quranic culture in two ways: one: referring to search engines. Second: referring to subject classification spaces on the Internet. Then, under the title of optimization solutions for recognizing and accessing Quranic information, he states four of these solutions. Pathology - communication of Quranic websites and blogs, including not being updated and biased, and then optimization solutions for these sites and blogs, such as audience-centered answering questions, paying attention to the global dimensions of the Quran, developing specialized and thematic (group) blogs, is another title; and finally, the author's specific suggestions conclude the speech.

During this research, the vocabulary learning strategies (VLSs) used by college students who were studying English as a foreign language (EFL) were investigated. This study used a VLSs questionnaire with a sample size of seventy-one students from a Chinese college who were majoring in English. The students belonged to both undergraduate and graduate levels of education. Based on the findings of the study, it was found that students often used cognitive and determination strategies. Using visual and textual repetition, practicing spelling and sound, taking notes with a focus on meaning, memorising fixed phrases and sentences, using bilingual dictionaries, making informed guesses about the meanings of words, and learning Chinese equivalents were all strategies that they used. Additionally, students learnt self-talk techniques to alleviate anxiety associated with language acquisition. The mechanical procedures that students used were linked to comprehension, and their primary emphasis was on learning the meanings of words. Metacognitive and social/affective tactics, on the other hand, were hardly used. Additionally, fewer people used memory techniques like associating words or remembering their grammatical forms. Vocabulary expansion and word association were more noticeable in students with greater levels of education. Hence, students' selections of VLSs were influenced by their learning maturity. More kinds of VLSs, including memory and metacognitive methods, should be used by students in these situations. It is recommended that English professors provide students with approach instructions and facilitate vocabulary learning via class discussions. Furthermore, it is advised to think about learning vocabulary on the side via both heavy and lengthy reading.

Biometrics comprises the learning of automatic approaches for individual human beings based on physical or developmental characters. The difficulty of finding good biometric features and recognition systems has been studied broadly in current ages. This research reflects the use of ears as a biometric for human recognition. In this paper, feature extraction skills are applied such as Harris Feature, FAST Feature extraction and SURF Feature Extraction. All the images are taken from standard database and every image has different angles because of any criminal examination, accident, or ATM machine room camera taken different types of images. So, using different angular images feature extraction the research goes through these techniques to give the best result to the user.in this researcher used SIFT algorithm. SIFT is an image local feature description process built on scale-space. Its strong similar capability, SIFT has numerous applications in different areas, such as image recovery, image edging, and machine idea. After SIFT was proposed, researchers have never stopped tuning it.

The increasing reliance on microservices architecture (MSA) in financial systems has led to improved scalability, modularity, and fault tolerance. However, due to the distributed nature of microservices, cascading failures pose a significant risk to financial applications, leading to service disruptions and financial losses. This research presents a resilient microservices architecture that integrates AI-driven observability tools to predict and mitigate cascading failures in financial platforms such as Guidewire Cloud. The study explores key principles of microservices design, AI-based observability mechanisms, fault mitigation strategies, and compliance requirements. By embedding AI-powered monitoring, anomaly detection, and automated incident response, financial institutions can achieve enhanced system resilience, ensuring high availability, security, and regulatory compliance.

Nowadays, in the digital era, the use of credit cards is widespread. People buy and sell goods from home. E-commerce brings a boom to the lifestyle of the people, for the working family, it is a preferable tool to purchase and pay through online mode in order to save the time and visiting hours. However, with the increasing use of credit cards, there have also been a lot of cases of credit card frauds that augmented much in the corona period. Fake credit card transactions significantly affect financial institutions, banks, and clients, whereas fraudsters try to develop new ways of committing fraud daily. To avoid credit card fraud and maintain the credibility of our clients, we need to build a model for credit card detection. There are the various machine learning tools that can be applied in detection of such frauds, these tools can be used to design an enhanced model to detect credit card frauds efficiently. In this view, the study presents a feature selection technique approach of machine learning with different classifiers. The study proposed an optimization algorithm using ‘Firefly’ for feature selection with four different classifiers: neural network, k-nearest neighbors, Support Vector Machine, and decision tree. The performance of all these four classifiers is compared on the basis of accuracy parameter.

The integration of renewable energy sources in DC microgrids presents significant challenges in maintaining stable voltage regulation and efficient power management. Existing control approaches struggle to handle the inherent variability of renewable sources while maintaining optimal system performance. This paper presents an Adaptive Sinh Cosh Optimizer (ASCHO) integrated with cascade Fractional Order Proportional-Integral (FOPI) control to address these challenges. ASCHO enhances the original SCHO algorithm through dynamic parameter updating and improved exploration-exploitation mechanisms. The proposed control system demonstrates significant performance improvements, with ASCHO achieving a fitness value of superior to conventional approaches' values. In practical implementation, the system maintains voltage regulation within ±5V of the reference value and achieves a 70\% reduction in steady-state error (3.15V vs 10.39V) compared to SCHO. The method was extensively validated through 23 benchmark functions comprising unimodal, multimodal, and fixed-dimension multimodal functions, with ASCHO outperforming other algorithms in 17 functions. Comprehensive testing validates the system's robustness under varying solar irradiance, wind speed, and load demands. The results demonstrate faster rise times (0.00208s vs 0.00209s) and improved power management capabilities, while maintaining battery state of charge variations within 0.003%. The successful implementation establishes ASCHO as an effective solution to optimize DC microgrid control systems with high penetration of renewable energy.

Due to resource scarcity, efficient and accurate Intrusion Detection Systems (IDS) are crucial for safeguarding Internet of Things (IoT) settings from Distributed Denial of Service (DDoS) attacks. This study introduces an optimised ensemble (OE-IDS) framework that is both efficient and lightweight. It employs Recursive Feature Elimination (RFE) for feature selection and Principal Component Analysis (PCA) for feature extraction. The ensemble-based eXtreme Gradient Boosting (XGB) classifier outperforms conventional ensemble algorithms in detection rates when supplied with the appropriate information. The proposed model has been evaluated on many DDoS attack detection datasets, and experimental findings indicate that it outperforms contemporary strategies in recall, precision, F1-score, and false positive rate.The model has an efficacy of over 99% accuracy and a 99% true positive rate, making it a suitable option for DDoS attack detection.

Abstract: - This research is highly significant for several reasons. Firstly, it addresses the urgent need for modernization in compliance processes. With the increasing volume and complexity of financial transactions, coupled with the rising number of regulations, the traditional manual methods of compliance are becoming increasingly insufficient. AI technologies offer a promising solution to these challenges, enabling financial institutions to manage compliance obligations more effectively, with higher precision and speed. The significance of this work also lies in the growing importance of Regulatory Technology (Reg Tech) in the context of AI's role in compliance. By examining how AI is being used to reshape traditional compliance frameworks, this research provides insight into the practical applications of AI in regulatory reporting and compliance across industries. The ability to analyze emerging trends in AI-based compliance and Reg Tech solutions can help organizations and regulators identify future technological advancements and prepare for the next wave of regulatory challenges. Additionally, this research explores how AI can improve the accuracy, efficiency, and effectiveness of compliance processes. By focusing on the specific areas where AI has the potential to make the most impact such as risk detection, fraud prevention, automated reporting, and real-time monitoring, the research highlights AI’s ability to address persistent challenges in the financial sector. These benefits are essential for financial institutions to stay competitive, reduce operational costs, and mitigate risks in an environment of increasingly complex regulations. At the same time, the research delves into the risks and challenges associated with AI in compliance. These concerns are significant because AI, while offering numerous benefits, also raises important questions about data privacy, algorithmic bias, and regulatory compliance. As financial institutions continue to integrate AI technologies into their operations, it is crucial to understand how these technologies can be implemented responsibly to ensure they do not inadvertently introduce new forms of discrimination or inequity. Lastly, this work is significant because it contributes to the broader conversation on AI ethics, data governance, and the role of technology in regulation. By critically analyzing both the opportunities and risks, this research provides essential insights for regulators, policymakers, and financial institutions aiming to implement AI in ways that are ethically sound, fair, and compliant with regulatory standards