Ahmad Azarnik

The rapid expansion of the internet has revolutionized knowledge sharing and decision-making processes, leading to the emergence of recommender systems that help users navigate vast amounts of information online. These systems are particularly valuable in the travel and tourism sector, where users face challenges in selecting suitable hotels, routes, and experiences based on complex factors like personal preferences and budget constraints. This research presents a multi-criteria recommender system designed specifically for the travel industry, leveraging a combination of filtering methods applied to TripAdvisor data. The system enhances recommendation accuracy by considering user preferences, service quality, and the experiences of similar users. To optimize performance further, the study integrates clustering algorithm with the Adaptive Neuro-Fuzzy Inference System (ANFIS) to model complex data relationships. Additionally, Particle Swarm Optimization (PSO) is employed to enhance ANFIS parameters, leading to improved accuracy and prediction outcomes. The results demonstrate significant enhancements in predictive performance, the ANFIS + PSO approach demonstrates superior accuracy and stability, making it highly recommended for future applications that require reliable predictive performance. This research highlights the importance of optimization techniques like PSO in enhancing the capabilities of adaptive and intelligent modeling systems.

The rapid expansion of the mobile phone market has driven the growth of mobile applications, making the global app industry highly competitive and lucrative. As developers strive to maintain revenue and market position, predicting the success of mobile apps before their release has become increasingly important. This research focuses on the Google Play Store, the largest app marketplace worldwide, where metrics like app installations and user ratings serve as key indicators of market performance in the absence of direct revenue data. To address this challenge, we developed a predictive model using the Adaptive Neuro-Fuzzy Inference System (ANFIS), enhanced by optimization techniques such as Differential Evolution (DE), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). The study utilized a dataset of 10,841 Android applications from the Google Play Store, key input features include Category, Size, Type, and Content Rating, while the output is defined as the product of user Rating and Installations to reflect app popularity. The predictive model demonstrated that Differential Evolution (DE) achieved the highest performance, followed closely by Particle Swarm Optimization (PSO), with the Genetic Algorithm (GA) also significantly enhancing the baseline ANFIS model. These findings highlight the importance of dataset-driven feature selection and optimization techniques in improving predictive accuracy. This research provides developers with a robust framework for anticipating app success, offering valuable insights in the competitive and rapidly evolving mobile app ecosystem.

The mobile and web application have become more popular due to easy access and be convenient to everybody, it is essential to look at their security issues from different aspects. User authentication is one of these issues that plays an important role for most applications. If a system authenticates a user, it means that person can access what are allowed in the system. One-time password is a kind of authentication that helps to increase the security through identification. Common methods in this area use extra devices such as smart card, token, and cell phone. However, these devices enforce users to carry hardware, to pay money, and to maintain. In this article, we propose a mutual one-time password which employs a formula to generate password for user, and every time user must use previous password and formula to compute new password. The communication line during authentication phase is supposed insecure; and a mutual authentication helps to increase the level of security. Security analysis shows how this method is secure; and comparison with some models shows its positive points.