This research paper presents a novel approach to optimize Hindi paragraph summarization using the PageRank method. With the increasing volume of Hindi language content on the internet, there is a growing need for effective summarization techniques tailored specifically for Hindi text. The proposed method leverages the inherent structure and connectivity of Hindi paragraphs to extract the most important sentences for summarization. The PageRank algorithm is applied to construct a rank representation of the text, with sentences as nodes and their co-occurrence patterns as edges. By calculating PageRank scores iteratively, the algorithm identifies the most central and significant sentences in the paragraph. Linguistic features such as sentence length, position, and content relevance are incorporated to enhance the quality of the summaries. Additionally, the algorithm dynamically adjusts sentence importance based on their relations with other sentences rank. Experimental results on a comprehensive dataset demonstrate that the PageRank-based approach outperforms existing summarization techniques in terms of informativeness and coherence, as evidenced by higher similarity score. The proposed method offers an effective solution for optimizing Hindi paragraph summarization, facilitating the management and comprehension of Hindi text in various applications.

This study introduces a novel framework that harnesses the potential of transfer learning for MRI -based classification in prostate cancer diagnosis. The study explores the challenges of accurately classifying prostate cancer from MRI images and proposes a methodology that integrates pre-trained deep neural networks with a large, annotated dataset. The framework demonstrates superior performance compared to conventional methods, achieving higher Accuracy, Precision, Recall, Fl-Score. The approach exhibits robustness across different MRI scanners and patient populations, highlighting its potential for real-world clinical applications. Additionally, this study emphasizes the interpretability of the model through visualization techniques, aiding clinicians in understanding the underlying pathology and making informed treatment decisions. Overall, this research contributes to the revolutionization of prostate cancer diagnosis, offering improved accuracy and efficiency in clinical settings and paving the way for advanced computer-aided diagnostic systems.

In modern power systems, FACTS tools are essential for addressing voltage variation along with fault ride-through (FRT) challenges within the electrical power systems, particularly for wind generation integration. Several prominent publications emphasize that the squirrel cage induction generator (SCIG) currently comprises about 15% of operational wind turbines. This research proposes the use of FACTS devices to boost voltage stability and FRT capability. The implementation of these devices leads to improved efficiency in the electrical power system. This study considers many events, including an ideal wind profile, turbulent wind profile, symmetrical faults, and unsymmetrical faults, to support the proposed selection. Furthermore, the proposed approach is evaluated by comparison between a fixed capacitor, static synchronous compensator (STATCOM), and Static VAR Compensator (SVC) to guarantee the achievement of voltage stability, reactive power consumption, and FRT capacity under various wind speed profiles and fault conditions. An overall evaluation is conducted to compare them in all examined circumstances and to highlight their advantages and effects. The simulation findings demonstrate the efficacy and primacy of FACTS in enhancing the functioning of an integrated wind system, which is built upon a grid-connected SCIG, as well as enhancing the power system performance. The MATLAB/Simulink toolbox is used to design the models of SCIG, SVC, and STATCOM.