Recent Advances in Computer Science and Communications - Volume 18, Issue 2, 2025

User scheduling in millimeter-wave (mmWave) multi-user hybrid beam-forming Orthogonal Frequency Division Multiplexing (OFDM) systems involves the joint optimization of resource block group (RBG) allocation, beam pairing, and user selection. However, choosing the optimal scheduled User Equipment (UE), allocated RBGs, and communicating beams in practical mmWave hybrid beam-forming systems with non-ideal Channel State Information (CSI) remains challenging.

In this paper, we propose a low-complexity user scheduling framework. On one hand, two user classification methods are proposed under non-ideal CSI, assisted by beam search and RBG allocation respectively. On the other hand, in order to ensure both the throughput and fairness performance, a novel user selection scheme, called weighted user selection based on feedback threshold (WUSFT) scheme is proposed, and approximate closed-form expression of fairness index for both full feedback and feedback based on threshold are derived. Our proposed methods consist of three steps. First, RBGs are allocated based on the maximum received signal power (MRSP) of each user on each RBG, utilizing quasi-omnidirectional beams. In the second step, the communicating beams are further searched to achieve the MRSP with the allocated RBG.

Finally, the allocated RBG, or the determined beam information obtained through the beam search, is used to represent the correlation of user channels and classify the UEs into groups. Only UEs whose reported factor is not less than the feedback threshold will report received information. This simplifies the operation of user scheduling. Moreover, simulation results demonstrate that our proposed schemes can achieve more than 92.3% of the sum rate performance of exhaustive search methods while maintaining relatively low complexity.

In addition, the user feedback overhead (FO) reduces obviously, especially with large UE number.

Computer science significantly influences modern culture, especially with the rapid breakthroughs and technology in social media networking. Social media platforms have become significant channels for sharing and exchanging daily news and information on many issues in the current digital environment, which is known for its massive data collection and transmission capabilities. While there are many benefits to this environment, there are also many false reports and information that deceive readers and users into believing they are receiving correct information.

Nowadays, all users use social media to obtain news content, but sometimes some malicious users tamper with real news and then spread fake news, which may reduce the reputation of social media. Therefore, many existing models have been introduced to detect fake news, but these models are based on traditional machine learning algorithms, such as decision tree (DT), multilayer perceptron (MLP), random forest (RF), etc. These models Lack of performance, security, and authorization. Our proposed model can solve existing model problems using reinforcement learning and blockchain technology.

In this research paper, we explain a new way to identify fake news. This new approach, combined with policy-based heuristic reinforcement learning (PHRL), where the model dynamically adjusts through iterative learning, is the key innovation and gradually improves classification accuracy. The same as our smart contract authorization method, which enhances the authenticity of content posted safely by authorized users and improves the transparency and accountability of information.

Our model was tested on real-time information collected from various sources with 70% accuracy and valid authentication.

Our proposed model produced better results with a Mean Absolute Error (MAE) of 0.0811 and Root Mean Squared Error (RMSE) of 0.2847, both significantly lower values. Our proposed model performs better than multilayer perceptron (MLP), and random forest (RF).

Augmented reality (AR) technology has altered the retail business while also improving the shopping experience. Several studies have been conducted to investigate the factors that influence the relevance of perceived AR technology values, as well as how these values influence customer enjoyment and the desire to spread good word of mouth (WOM). This study explores how the perceived value of AR affects customer satisfaction, enjoyment, and recommendations.

This study aimed to examine how the usage frequency moderates the associations between human value orientation and perceived AR values, as well as the correlations between valued AR and satisfaction and positive WOM intention. It studies the relationship between perceived AR values and consumer happiness, as well as the influence of human value orientation on perceived AR values (receptivity to change, preservation of resources, self-improvement, and transcendence of oneself) (playful, social, aesthetic appeal, and usability).

This research extends the human value orientation theory by proposing a novel model for the link between AR value orientation and usage frequency. To obtain data, a survey was distributed to those who have engaged with retail AR features.

Contrary to popular belief, the data demonstrate that a certain group of perceived AR values is linked to each value orientation, emphasizing the significance of a certain perceived AR value in affecting customer satisfaction and positive WOM intention. This study shows that usage frequency moderates the associations between human value orientation and perceived AR values, as well as the correlations between valued AR and satisfaction and positive WOM intention. It also shows that consumer value orientation is the most essential aspect in choosing which AR technology advantage they value most. AR technology values also affect consumer satisfaction and word-of-mouth marketing (WOM).

The value orientation of an individual influences the value of augmented reality technology, which may vary depending on the frequency of usage. This paper concludes with suggestions for merchants regarding augmented reality technology. Moreover, the precise number of applications that can be made available to customers and retailers is determined by this study, which benefits both retailers and researchers.

Users are accessing websites for many purposes, such as obtaining information about a particular topic, buying items, accessing their accounts, etc. Cybercriminals use phishing websites to attain the sensitive information of the users, like usernames and passwords, credit card details, etc. Detecting phishing websites helps in protecting the information and the money of people. Machine learning algorithms can be applied to detect phishing websites.

In this paper, a model based on various machine learning algorithms is developed to detect phishing websites. The machine learning algorithms used in this model are Decision Tree, Random Forest, Extra Trees, K-Nearest Neighbors, Multilayer Perceptron and Support Vector Machine. The dataset of phishing websites is taken from the Kaggle website. The algorithms mentioned above of the developed model are compared together to identify which algorithm has better classification results.

The extra trees algorithm offers the best results for accuracy, precision, and F1-Score. This paper also compares the developed model with a previous model that uses the same dataset and relies upon decision tree, random forest, and support vector machine to determine which model has better classification report results. The developed model, depending on the Decision Tree and SVM, offers better classification results than those of the previous models. The developed model is compared with another preceding model relying upon Decision Tree and Random Forest algorithms to determine which model generates better results for accuracy, precision, recall/sensitivity, and F1-Score.

The developed model, depending on the Decision Tree, presents better results for accuracy, recall, and F1-Score than the results of accuracy, sensitivity, and F1-Score for the preceding model based on the Decision Tree.

In the wake of escalating cyber threats and the indispensability of robust network security mechanisms, it becomes crucial to understand the evolving landscape of cryptographic research. Recognizing the significant contributions and discerning emerging trends can guide future strategies and technological advancements. Our study endeavors to shed light on this through a bibliometric analysis of publications in the realms of Network Security and Cryptography.

To chronicle and synthesize the progression of research methodologies from their inception to the present day, we undertook a comprehensive Bibliometric Analysis of Network Security and Cryptography. Our data set was culled from the Clarivate Analytics Web of Science Database, encompassing 3,897 papers, 603 sources, and 7,886 authors from across the globe.

Our analysis revealed a marked upsurge in cryptographic research since 1992, with China standing out as a dominant contributor in terms of publications. Notably, while 'security' and 'cryptography' emerged as recurrent research themes, there's an observable downtrend in international collaborations. Our study also highlights pivotal topics shaping the network security domain, offering insights into the trajectories of research source growth, structural variabilities in research relevance, and prospective intellectual and collaborative avenues as guided by authorship patterns.

Cryptographic research is on an upward trajectory, both in volume and significance. However, the tapering of international collaborations and an evident need to concentrate on emergent challenges, such as data privacy and innovative network attacks, emerge as notable insights. This bibliometric review serves as a compass, directing researchers and academicians towards areas warranting heightened attention, thereby informing the roadmap for future investigative pursuits.