This research presents a hybrid intrusion detection approach that integrates Generative Adversarial Networks (GANs) for synthetic data generation with Random Forest (RF) as the primary classifier. The study aims to improve detection performance in cybersecurity applications by enhancing dataset diversity and addressing challenges in traditional models, particularly in detecting minority attack classes often underrepresented in real-world datasets. The proposed method employs GANs to generate synthetic attack samples that mimic real-world intrusions, which are then combined with real data from the UNSW-NB15 dataset to create a more balanced training set. By leveraging synthetic data augmentation, our approach mitigates issues related to class imbalance and enhances the generalization capability of the classifier. Extensive experiments demonstrate that RF trained on the combined dataset of real and synthetic data achieves superior detection performance compared to models trained exclusively on real data. Specifically, RF trained solely on the original dataset achieves an accuracy of 97.58%, whereas integrating GAN-generated synthetic data improves accuracy to 98.27%. The proposed methodology is further evaluated through comparative analysis against alternative classifiers, including Support Vector Machine (SVM), XGBoost, Gated Recurrent Unit (GRU), and related studies in the field. Our findings indicate that GAN-augmented training significantly enhances detection rates, particularly for rare attack types, while maintaining computational efficiency. Furthermore, RF outperforms other classifiers, including deep learning models, demonstrating its effectiveness as a lightweight yet robust classification method. Integrating GANs with RF offers a scalable and adaptable framework for intrusion detection, ensuring improved resilience against evolving cyber threats.

Forecasting the risk factor of the financial frontier markets has always been a very challenging task. Unlike an emerging market, a frontier market has a missing parameter named “volatility”, which indicates the market’s risk and as a result of the absence of this missing parameter and the lack of proper prediction, it has almost become difficult for direct customers to invest money in frontier markets. However, the noises, seasonality, random spikes and trends of the time-series datasets make it even more complicated to predict stock prices with high accuracy. In this work, we have developed a novel stacking ensemble of the neural network model that performs best on multiple data patterns. We have compared our model’s performance with the performance results obtained by using some traditional machine learning ensemble models such as Random Forest, AdaBoost, Gradient Boosting Machine and Stacking Ensemble, along with some traditional deep learning models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term (BiLSTM). We have calculated the missing parameter named “volatility” using stock price (Close price) for 20 different companies of the frontier market and then made predictions using the aforementioned machine learning ensemble models, deep learning models and our proposed stacking ensemble of the neural network model. The statistical evaluation metrics RMSE and MAE have been used to evaluate the performance of the models. It has been found that our proposed stacking ensemble neural network model outperforms all other traditional machine learning and deep learning models which have been used for comparison in this paper. The lowest RMSE and MAE values we have received using our proposed model are 0.3626 and 0.3682 percent, respectively, and the highest RMSE and MAE values are 2.5696 and 2.444 percent, respectively. The traditional ensemble learning models give the highest RMSE and MAE error rate of 20.4852 and 20.4260 percent, while the deep learning models give 15.2332 and 15.1668 percent, respectively, which clearly states that our proposed model provides a very low error value compared with the traditional models.

Background: Diabetes mellitus is a severe disease characterized by high blood glucose levels resulting from dysregulation of the hormone insulin. Diabetes is managed through physical activity and dietary modification and requires careful monitoring of blood glucose concentration. Blood glucose concentration is typically monitored throughout the day by analyzing a sample of blood drawn from a finger prick using a commercially available glucometer. However, this process is invasive and painful, and leads to a risk of infection. Therefore, there is an urgent need for noninvasive, inexpensive, novel platforms for continuous blood sugar monitoring. Objective: Our study aimed to describe a pilot test to test the accuracy of a noninvasive glucose monitoring prototype that uses laser technology based on near-infrared spectroscopy. Methods: Our system is based on Raspberry Pi, a portable camera (Raspberry Pi camera), and a visible light laser. The Raspberry Pi camera captures a set of images when a visible light laser passes through skin tissue. The glucose concentration is estimated by an artificial neural network model using the absorption and scattering of light in the skin tissue. This prototype was developed using TensorFlow, Keras, and Python code. A pilot study was run with 8 volunteers that used the prototype on their fingers and ears. Blood glucose values obtained by the prototype were compared with commercially available glucometers to estimate accuracy. Results: When using images from the finger, the accuracy of the prototype is 79%. Taken from the ear, the accuracy is attenuated to 62%. Though the current data set is limited, these results are encouraging. However, three main limitations need to be addressed in future studies of the prototype: (1) increase the size of the database to improve the robustness of the artificial neural network model; (2) analyze the impact of external factors such as skin color, skin thickness, and ambient temperature in the current prototype; and (3) improve the prototype enclosure to make it suitable for easy finger and ear placement. Conclusions: Our pilot study demonstrates that blood glucose concentration can be estimated using a small hardware prototype that uses infrared images of human tissue. Although more studies need to be conducted to overcome limitations, this pilot study shows that an affordable device can be used to avoid the use of blood and multiple finger pricks for blood glucose monitoring in the diabetic population.

Background: Mobile health (mHealth) smartphone apps have shown promise in the self-management of chronic disease. In today’s oversaturated health app market, selection criteria that consumers are employing to choose mHealth apps for disease self-management are of paramount importance. App quality is critical in monitoring disease controls but is often linked to consumer popularity rather than clinical recommendations of effectiveness in disease management. Management of key disease variances can be performed through these apps to increase patient engagement in disease self-management. This paper provides a comprehensive review of features found in mHealth apps frequently used in the self- management of diabetes. Objective: The purpose of this study was to review features of frequently used and high consumer-rated mHealth apps used in the self-management of diabetes. This study aimed to highlight key features of consumer-favored mHealth apps used in the self-management of diabetes. Methods: A 2-fold approach was adopted involving the Apple iOS store and the Google search engine. The primary search was conducted on the Apple iOS store using the term “diabetes apps” (device used: Apple iPad). The top 5 most frequently used mHealth apps were identified and rated by the number of consumer reviews, app ratings, and the presence of key diabetes management features, such as dietary blood glucose, A1C, insulin, physical activity, and prescription medication. A subsequent Google search was conducted using the search term “best Apple diabetes apps.” The top 3 search results—“Healthline,” “Everyday Health,” and “Diabetes Apps–American Diabetes Association”—were explored. Results: In total, 12 mHealth apps were reviewed due to their appearing across 4 evaluated sources. Only 1 health app—Glucose Buddy Diabetes Tracker—appeared as the most frequently used within the Apple iOS store and across the other 3 sources. The OneTouch Reveal app ranked first on the list in the iOS store with 39,000 consumer reviews and a rating of 4.7 out of 5.0 stars but only appeared in 1 of the other 3 sources. Blood glucose tracking was present across all apps, but other disease management features varied in type with at least 3 of the 5 key features being present across the 12 reviewed apps. Subscription cost and integration needs were present in the apps which could impact consumers’ decision to select apps. Although mobile app preference was assessed and defined by the number of consumer reviews and star ratings, there were no scientific standards used in the selection and ranking of the health apps within this study. Conclusions: mHealth apps have shown promise in chronic disease management, but a surge in development of these nonregulated health solutions points to a need for regulation, standardization, and quality control. A governing body of health IT professionals, clinicians, policymakers, payors, and patients could be beneficial in defining health app standards for effective chronic disease management. Variabilities in features, cost, and other aspects of management could be reduced by regulatory uniformity, which would increase patient engagement and improve disease outcomes.

Blockchain technology has emerged as a transformative innovation, providing a transparent, immutable, and decentralized platform that underpins critical applications across industries such as cryptocurrencies, supply chain management, healthcare, and finance. Despite their promise of enhanced security and trust, the increasing sophistication of cyberattacks has exposed vulnerabilities within blockchain ecosystems, posing severe threats to their integrity, reliability, and adoption. This study presents a comprehensive and systematic review of blockchain vulnerabilities by categorizing and analyzing potential threats, including network-level attacks, consensus-based exploits, smart contract vulnerabilities, and user-centric risks. Furthermore, the research evaluates existing countermeasures and mitigation strategies by examining their effectiveness, scalability, and adaptability to diverse blockchain architectures and use cases. The study highlights the critical need for context-aware security solutions that address the unique requirements of various blockchain applications and proposes a framework for advancing proactive and resilient security designs. By bridging gaps in the existing literature, this research offers valuable insights for academics, industry practitioners, and policymakers, contributing to the ongoing development of robust and secure decentralized ecosystems.