Abstractive summarization plays a critical role in managing large volumes of textual data, yet it faces persistent challenges in consistency and evaluation. Our study compares two state-of-the-art models, PEGASUS and Flan-T5, across a diverse range of benchmark datasets using both ROUGE and BARTScore metrics. Findings reveal that PEGASUS excels in generating detailed, coherent summaries for large-scale texts evidenced by an R-1 score of 0.5874 on Gigaword while Flan-T5, enhanced by our novel T5 Dual Summary Framework, produces concise outputs that closely align with reference lengths. Although ROUGE effectively measures lexical overlap, its moderate correlation with BARTScore indicates that it may overlook deeper semantic quality. This underscores the need for hybrid evaluation approaches that integrate semantic analysis with human judgment to more accurately capture summary meaning. By introducing a robust benchmark and the pioneering T5 Dual Framework, our research advocates for task-specific optimization and more comprehensive evaluation methods. Moreover, current dataset limitations point to the necessity for broader, more inclusive training sets in future studies.

The rising frequency of data breaches and unauthorized access in healthcare systems has heightened concerns over privacy protection in Electronic Health Records (EHRs), especially when third parties, such as insurance companies, are involved. Traditional EHR solutions are often inadequate in safeguarding data confidentiality during multi-user access. This paper presents the Blockchain-based Smart Medical Privacy-preserving Contract System (BSMPCS), a novel framework designed to address these privacy challenges. By integrating Bitcoin smart contracts and homomorphic encryption, BSMPCS ensures secure management of EHRs while restricting access to only authorized entities. The decentralized and immutable characteristics of blockchain enhance data integrity, while smart contracts prevent unauthorized disclosure of sensitive health information to insurance companies. Additionally, homomorphic encryption enables insurance claim verifications without exposing the actual health records, preserving both patient identity and medical privacy. Unlike conventional systems, BSMPCS eliminates the need for third-party intermediaries, significantly reducing the risk of data leaks. Combining blockchain and advanced cryptographic methods, this framework provides a robust, privacy-preserving solution suitable for modern healthcare systems.

Micro-Electro-Mechanical Systems (MEMS) have fundamentally transformed technology by combining microelectronics with mechanical systems to create miniature devices capable of diverse functionalities. This review article provides a thorough exploration of MEMS, tracing its evolution from early developments to the latest advancements. It begins by outlining the fundamental principles behind MEMS design and fabrication, detailing processes such as lithography, deposition, and etching. The paper covers a wide array of MEMS devices, including sensors, actuators, resonators, and microfluidic systems, while focusing on essential design considerations, fabrication techniques, and performance parameters. The versatility of MEMS across sectors like healthcare, automotive, aerospace, consumer electronics, and telecommunications is highlighted, illustrating their role in advancing applications such as medical diagnostics, environmental sensing, and autonomous technologies. Unlike previous reviews, this paper provides a unique synthesis linking fabrication mechanisms with device performance metrics, offering an updated comparative analysis across MEMS subcategories (RF MEMS, microfluidics, and optical MEMS). It also integrates the latest market data (2024–2025) and contextualizes how MEMS devices underpin IoT and Industry 5.0 applications. Furthermore, it emphasizes emerging research directions such as energy harvesting MEMS, bio-inspired microsystems, and security-aware MEMS integration in connected environments. These additions make this review both comprehensive and forward-looking, serving as a reference for researchers and practitioners.

Because of the nature of the Internet and the growing number of people using digital media, copyright protection is becoming more important. One of the most common ways to protect this is by implementing digital image watermarking. This protection method safeguards the image from unauthorized access. The Gorilla Troop Optimization Algorithm (GTO), a new evolutionary algorithm, is what we propose to be a powerful watermarking technique. Initially, we applied Discrete Wavelet Transform (DWT) to the cover image, followed by Singular Value Decomposition (SVD) for enhanced security, and finally, we applied SVD to the Watermark image for its embedding into the cover image. In this process, we aim to optimize the multiple scaling factors (MSFs) by applying the GTO algorithm and testing the proposed algorithm in the MATLAB environment using some standard images. We then evaluated the experiment using performance metrics such as Normalized Cross-Correlation (NCC), the Structural Similarity Index (SSIM), and the Peak Signal-to-Noise Ratio (PSNR). These metrics proved the imperceptibility of different attacks and the proposed algorithm’s performance.

The introduction of cloud technology changes the face of data management by eliminating tedious concerns with regards to proper storage and accessibility as it can done from any location, therefore, it can be said that the emergence of this technology came with a number of challenges related to data confidentiality, integrity, and authentication as well. As a resolution to certain weaknesses presented in this case, the authors in this paper suggest a hybrid security model which integrates both quantum cryptography and blockchain technology, and improves security flaws on cloud and quantum models. There are three characteristics of data that are crucial to its safety and security; confidentiality, integrity, and availability, and cloud technology has been known to be accompanied with plenty of challenges concerning these aspects, however, with the use of Blockchain technology, data becomes immutable, decentralized, and transparent thereby reducing the risk of unauthorized access. The combination of strategies proposed in this paper, helps to eliminate a number of drawbacks like key loss, data loss, and man in the middle attacks that are common in cloud infrastructure. This study shows the structural design, data transmission and processes of the architecture for the hybrid model, looking forward to achieve better data security. The analysis of the model suggests its advantages over conventional encryption model and a purely constructed model of blockchain. Performance benchmarks are also included, demonstrating that the model is resilient to cyber threats during the quantum age. The architecture is seamless with the current cloud stature, takes cloud security a notch higher by solving the considerable challenges and is poised to be deployed on a larger scale The directional works will include improving the system’s computational efficiency and extending the model to multiple cloud infrastructures to achieve higher security in today’s complex cloud systems.

Pancreatic cancer, characterized by its high mortality rate and scarce treatment options, poses a formidable challenge in the field of oncology. Now, we live in a reality that requires immediate progress in diagnostic and prognostic methodologies to find pancreatic cancer early and understand its stage. This study deals with the pressing requirement for better diagnostic tools by evaluating and deciding the suitable machine learning (ML) algorithms for detecting pancreatic cancer at an early stage. This work uses a publicly available dataset with 590 urine samples which included control, benign hepatobiliary disease as well as Pancreatic Ductal Adenocarcinoma (PDAC) samples. The primary objectives of the research included developing a predictive model based on clinical data, examining various machine learning (ML) algorithms for their diagnostic precision, and improving the early detection rates for pancreatic cancer. The study assessed the efficacy of a broad array of ML algorithms in forecasting outcomes associated with pancreatic cancer. This analysis systematically explored Random Forest, Support Vector Machine, Decision Trees, K-Nearest Neighbours, XGBoost, ADABoost, CatBoost, and GradientBoost. The assessment focused on standard performance metrics such as accuracy, precision (also known as positive predicted value or PPV), recall (sometimes called sensitivity or true positive rate), F1-score, and support. Notably, CatBoost achieved the highest accuracy of 75%, outperforming other models such as Random Forest (74%) and XGBoost (74%), demonstrating its superior classification performance in distinguishing between pancreatic cancer, benign conditions, and non-cancerous cases. In addition to performance evaluation, this study integrates SHAP (Shapley Additive Explanations) analysis to enhance model interpretability, ensuring transparency in feature contributions. SHAP analysis revealed that Plasma CA19-9, LYVE1, and TFF1 were the most influential biomarkers across all classifications, reinforcing their diagnostic significance. This research emphasizes the critical importance of early detection, model interpretability, and clinical applicability, demonstrating that ML algorithms, particularly CatBoost, not only enhance diagnostic precision but also provide explainable predictions that support real-world medical decision-making.

This paper presents an ensemble model in the determination of manifestation of emotion intensities from audio-dataset. An emotion denotes the mental state of the human mind or/and thought processes that represents a recognizable pattern of an entity like emotion arousal having a good similarity with its manifestation of vocal, facial or/and bodily signals. In this paper, we propose a stacking, late fusion approach where the best experimental outcome from two base models build from Random Forests and Extreme Gradient Boost are combined using simple majority voting. RAVDESS audio datasets, a public gender balanced dataset built by Ryerson University of Canada for the purpose of emotion study was used. 80% of the dataset was used for training while 20% was used for testing. Two features, MFCC and Chroma were introduced to the base models in a series of experimental setups and the outcome evaluated using confusion matrix, precision, recall and F1-Score. It was then compared to two state-of-the-art works done on KBES and RAVDESS datasets. This approach yielded an overall classification accuracy of 93%. 

Blockchain technology has emerged as a pivotal innovation across multiple sectors due to its decentralized nature, secure transaction processing, and transparency. Central to blockchain operations are cryptographic hashing algorithms like SHA256 and Scrypt, which play a crucial role in ensuring transaction integrity and security. This study conducts a comprehensive benchmarking analysis of SHA256 and Scrypt, focusing on their performance in blockchain block discovery, specifically evaluating hashing speed and block discovery probability. SHA256, known for its high hashing speed, demonstrated rates reaching 101.111 kH/s during a 10-hour test, whereas Scrypt performed at a slower average speed of 9 kH/s. However, Scrypt exhibited a higher probability of block discovery, achieving up to 8.18%, significantly surpassing SHA256's near-zero probability under similar conditions. Tests across various CPUs underscore these differences: SHA256 excels in raw hashing speed, while Scrypt’s memory-intensive design offers greater ASIC resistance and a higher likelihood of block discovery, especially in environments that demand enhanced security. These findings highlight the importance of choosing an algorithm aligned with the specific requirements of a blockchain application, balancing speed, security, and resistance to specialized hardware attacks. The study suggests that hybrid approaches combining SHA256’s speed with Scrypt’s security features could maximize both efficiency and security, contributing valuable insights into the ongoing optimization of blockchain technology.

The focus of the research is on the analysis of the effectiveness of different forms of educational activities in developing youth’s information and media literacy (IML), based on the results of the Ukrainian project “MEDIA & CAPSULES”, implemented within IREX’s “Learn and Discern” initiative. The study compared the impact of webinar sessions, masterclasses, and information and media workshops on three key IML indicators: information literacy, media literacy, and digital security. An empirical pre-post design was used to assess changes in participants’ competencies before and after each type of educational intervention. Statistical analysis revealed that information and media workshops had the strongest overall impact, particularly enhancing media literacy and digital security. Masterclasses were most effective in improving information literacy, while webinars showed moderate improvements across all indicators. The findings highlight the importance of aligning instructional formats with specific educational goals and provide practical implications for educators and curriculum developers working to strengthen youth resilience against misinformation and digital threats.

The convergence of Non-Orthogonal Multiple Access (NOMA) and Cognitive Radio (CR) with Simultaneous Wireless Information and Power Transfer (SWIPT) offers a transformative approach to spectrum and energy efficiency. This paper analyzes CR–NOMA with SWIPT-enabled energy harvesting (EH), focusing on outage probability (OP) and throughput. We derive explicit models under Rayleigh fading with interference-temperature constraints and validate by simulations. Results show that proper power allocation and time-switching ratios enhance user fairness while respecting primary-network interference. These insights guide robust and energyefficient designs for next-generation systems.