In this research explores the design and performance of a transformerless (TL) three-level neutral point clamped (NPC) inverter for grid-connected photovoltaic (PV) systems. In the push toward more compact, cost-effective, and efficient renewable energy systems, TL inverter topologies have emerged as a strong alternative to conventional systems that use line-frequency transformers. The proposed topology includes two separate PV sources interfaced through dedicated boost converters, which then feed into a common NPC inverter, enhancing flexibility and maximum power point tracking (MPPT) performance. Comprehensive simulations were performed using MATLAB/Simulink to evaluate grid compatibility, total harmonic distortion (THD), and transient behavior under fluctuating irradiance levels. Results validate the efficacy of the system in maintaining grid compliance, lowering leakage current, and achieving high-quality power output. Compared to traditional two-level inverters or transformer-based alternatives, the proposed system demonstrates a clear performance advantage in both efficiency and reliability.

This study provides a comprehensive synthesis of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) in ransomware defense. Using a “review of reviews” methodology based on the PRISMA, this paper gathers insights on how AI is transforming ransomware detection, prevention, and mitigation strategies in the past five years (2020-2024). The findings highlight the effectiveness of hybrid models, which combine multiple analysis techniques such as code inspection (static analysis) and behavior monitoring during execution (dynamic analysis). The study also explores anomaly detection and early warning mechanisms before encryption that tackle ransomware’s growing complexity. It also examines key challenges in ransomware defense, such as techniques designed to deceive AI driven detection, and the lack of strong and diverse datasets. It highlights AI’s role in early detection and real-time response systems, enhancing scalability and resilience. With the systematic review of reviews approach, the contributions of this study are systematically consolidating research insights from multiple review articles, identifying effective AI models, and bridging theory with practice to foster collaboration among academia, industry, and policymakers. Future research directions are anticipated and practical recommendations for cybersecurity practitioners are provided. Finally, it presents a roadmap for advancing AI-driven countermeasures, for the protection of key systems and infrastructures against evolving ransomware threats.

These days, a major concern is the security of multimedia data exchange over the internet. Compared to conventional methods, chaos-based encryption has proven increasing role and superiority in current multimedia cryptography. This research proposes an innovative multimedia encryption technique utilizing optimized chaotic systems and simple rearrangement operation. Data blocking will be used to optimize the chaotic model and to reduce the key generation time. To increase the private key length and to raise the level of data security the presented method will be implemented in multiple rounds, where each round will be treated as an independent task and the selected sequence of rounds to be executed depends on the user wish. The method can be efficiently used to cipher-decipher short messages, images, and digital speech signal, and it will use variable length of private key, variable block size and variable number of rounds. The output of simulation confirms effectiveness of proposed technique in giving strong security, and good speed up in ciphering and deciphering process comparing with other existing standard and chaotic based methods. The proposed technique shows high resistance to attacks while the quality of encrypted/decrypted digital data remains acceptable.

Underwater wireless sensor network (UWSN) is highly vulnerable to packet loss due to varied features of underwater channels, including multipath fading, high latency, and environmental interference. Accurate prediction of packet loss is critical for improving data reliability and network performance. Our research presents a new approach to forecasting using a combination of autoregressive moving average (ARMA) and long short-term memory (LSTM) networks which are statistical models. A synthetic dataset was generated to facilitate model development and evaluation, simulating realistic UWSN conditions by varying key parameters such as signal-to-noise ratio (SNR), received signal strength indicator (RSSI), depth, distance, and temperature. The ARMA model captures linear temporal trends, while the LSTM network is trained on the ARMA residuals to learn nonlinear correction patterns. The findings indicate that the hybrid ARMA-LSTM model exhibits a marked superiority over the standalone ARMA model, achieving an approximate 87.5% reduction in mean absolute error (MAE), an 84% enhancement in root mean square error (RMSE),a significant boost in predictive accuracy as reflected by the R² score, which improved from -0.45 to nearly 0. The results highlight the hybrid method a strong and precise solution for predicting packet loss in UWSN, directly impacting the improvement of reliability in underwater communication.

Crop price prediction in ASEAN markets is hindered by incomplete and inconsistent data, making data imputation essential. This study introduces the Wasserstein generative adversarial imputation network with attention (WGAIN+Att) to improve data quality for forecasting. Four configurations—GAIN, GAIN+Att, WGAIN, and WGAIN+Att—were evaluated on rice, corn, and soybean datasets (1961–2023). Results show that WGAIN+Att, particularly when attention is applied across all matrices (x, m, and z), achieved the best imputation performance, minimizing mean absolute error (MAE) and preserving statistical distributions, with optimal results at a 0.1 missing rate and 0.9 hint rate. In predictive tasks, GAIN-based imputations combined with convolutional neural networks (CNN)-long short-term memory networks (LSTM)-gated recurrent units (GRU) models consistently outperformed others in forecasting accuracy, achieving lower MAE and root mean squared error (RMSE). The findings highlight the role of attention in stabilizing imputation and ensuring realistic reconstructions, while also showing that aligning imputation with forecasting objectives improves agricultural price predictions.

The paper focuses on the persistent conflict between Israel and Palestine. of world discussion, with such sites as Reddit containing a great deal medium for public expression. This study uses natural language processing (NLP) including valence aware dictionary and sentiment reasoner (VADER) for sentimental analysis and latent Dirichlet allocation (LDA) for topic modelling of geopolitical positions in comments of 400,000 posts belonging to the topics subreddits. A sentiment analysis using VADER shows that negative sentiments observed and indicate that such sentiments prevail throughout fear of aggression, human suffering, and threat. media biases. LDA analysis for topic modelling. The process of LDA reveals such important topics as humanitarian ones. media discourses, polemizing and appeals for calm. Furthermore, the study which divides comments into geopolitical attitudes – support for or yet, Israelis, supporters of Palestine, opponents of both, and neutrals, offering a contingency comprehensive conception of public perspectives. The results emphasize the effectiveness of large-scale. Of the big data pre-processing techniques, NLP can be used to understand geopolitics and its intricate dynamics. educate policy makers, researchers, and media planners.

This paper provides a scoping review of gamified learning in virtual reality (VR) from 2010-2025, which combines trends from various disciplines, game types, and learning approaches. This paper differs from previous ones, which mainly focus on bibliometric results, by critically examining 108 publications to determine the dominant types of VR games, their correlation with learning outcomes, and the impact on the learning process. This paper follows the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) protocol. The findings suggest that simulation, role-playing, and problem-solving VR games are the most common, particularly in healthcare and science, technology, engineering, and mathematics (STEM) fields, improving engagement, retention, and skill development. The use of VR games in other fields and the needs of neurodiverse or physically disabled learners have yet to be explored. The review discusses the use of big data and cloud computing for VR deployment and adaption, and the significance of low and no-code technologies for educators developing VR without programming knowledge. Through the synthesis of patterns, research gaps, and cross-disciplinary challenges, this study provides a roadmap for VR-based gamification research, with emphasis on inclusivity, ethical considerations, long-term learning effectiveness beyond novelty effects, and sustainable educational integration.

Augmented reality (AR) has become a pivotal tool for enhancing museum engagement through interactive experiences. This study developed and evaluated an Android-based AR application for the Popa Eyato Archaeological Museum, comparing marker-based (Vuforia), and markerless (EasyAR) tracking methods. While previous studies often focus on technical development, this research provides empirical insights into usability within the Southeast Asian Museum context. Using a mixed-methods approach with 35 participants, the application was evaluated using the system usability scale (SUS) and handheld augmented reality usability scale (HARUS). Results show that the application achieved an average SUS score of 78, classified as “Good” and “Acceptable”. The comparative analysis reveals that while marker-based AR offers higher stability, markerless AR provides greater flexibility for spatial exploration. However, environmental factors like museum lighting significantly impact markerless tracking performance. These findings offer practical recommendations for museum curators and AR designers in selecting appropriate tracking technologies based on infrastructural constraints and visitor demographics. The study concludes that balancing technical stability with user mobility is essential for optimizing digital heritage preservation and educational engagement in regional museums.

The rapid expansion of internet of things (IoT) networks has intensified spectrum scarcity due to the massive growth in wireless device connectivity. Cognitive radio sensor networks (CRSNs) offer a promising solution by enabling dynamic access to underutilized spectrum bands. However, existing spectrum sensing techniques in CRSNs often suffer from high energy consumption, low adaptability, and limited prediction accuracy posing challenges in energy-constrained environments. This paper proposes an energy-efficient spectrum sensing (EESS) framework using an adaptive hybrid learning model (AHLM) that integrates wavelet transform-based signal decomposition (WT-SD), deep reinforcement learning (DRL), entropy-based hierarchical clustering (EHC), and meta-learning-based transfer learning (ML-TLM). WT-SD extracts key spectral features, while DRL with policy-gradient optimization dynamically predicts spectrum availability. The EHC mechanism clusters sensor nodes to minimize redundant sensing, and ML-TLM enhances adaptability with minimal retraining. The proposed model achieves substantial improvements over traditional methods. Experimental results show a 36% reduction in sensing time, 60% lower energy consumption than energy detection (ED) methods, and an 18.3% increase in network lifetime. The model also achieves a probability of detection of 0.998 and accuracy of 98.1%. These results confirm that the proposed EESS-AHLM framework provides a scalable and intelligent solution for energy-aware spectrum sensing in next-generation cognitive radio (CR)-IoT environments.

The caesarean section is one of the most frequently performed surgical procedures worldwide, with profound implications for maternal and neonatal health. Accurate prediction of delivery mode is essential for guiding clinical decisions, minimizing unnecessary surgical interventions, and improving patient outcomes. This study introduces a deep neural learning technique based on a temporal convolutional neural network (DNLTC) to classify delivery type—caesarean section versus normal vaginal delivery using maternal and obstetric data. The proposed model was evaluated against traditional machine learning (ML) approaches, including artificial neural networks (ANN), support vector machines (SVM), and decision trees (DT). Experimental results show that the DNLTC achieved the highest overall accuracy (85%), surpassing ANN (80%), SVM (68.8%), and DT (65%). TCNN also demonstrated strong clinical reliability, with a sensitivity of 94%, specificity of 91%, and a perfect F1-score of 100%. These findings highlight the advantages of incorporating temporal feature learning into delivery mode prediction, enabling the detection of subtle, sequential patterns that conventional models may overlook. By providing more accurate and robust predictions, the proposed framework can support obstetricians in making timely, evidence-based decisions, ultimately enhancing maternal and newborn health outcomes.