With the advancement of deep learning, single-image super-resolution (SISR) has made significant strides. However, most current SISR methods are challenging to employ in real-world applications because they are doubtlessly employed by substantial computational and memory costs caused by complex operations. Furthermore, an efficient dataset is a key factor for bettering model training. The hybrid models of CNN and Vision Transformer can be more efficient in the SISR task. Nevertheless, they require substantial or extremely high-quality datasets for training that could be unavailable from time to time. To tackle these issues, a solution combined by applying a Lightweight Bimodal Network (LBNet) and Patch-Mosaic data augmentation method which is the enhancement of CutMix and YOCO is proposed in this research. With patch-oriented Mosaic data augmentation, an efficient Symmetric CNN is utilized for local feature extraction and coarse image restoration. Plus, a Recursive Transformer aids in fully grasping the long-term dependence of images, enabling the global information to be fully used to refine texture details. Extensive experiments have shown that LBNet with the proposed data augmentation with zero-free additional parameters method outperforms the original LBNet and other state-of-the-art techniques in which image-level data augmentation is applied.

The purpose of this paper is to discuss the methodology and results of researchers who conducted a study concerning the forecasting of tourism demand. In more detail, this study aims to examine and assess various studies about search engines, web traffic data, and social media data, specifically. Using an extensive database of indexed articles, we conducted the review with the goal of providing a solid understanding of the literature. The findings of our study revealed that few researchers integrate different data sources when forecasting tourism demand. Therefore, the authors of this paper decided to conduct a systematic review to provide researchers with a comprehensive overview of the importance of such data. This paper may inspire Omani researchers to undertake similar research based on its findings, which is currently lacking. Thus, this paper will improve understanding of how data sources affect forecasting accuracy and how modern technologies can support economic growth.

Many cloud services utilize an API gateway, which enables them to be offered to users through API platforms such as Platform as a Service (PaaS), Software as a service (SaaS), Infrastructure as a Service (IaaS) and cross-platforms APIs. APIs are designed for functionality and speed by developers who write a small portion of code, which has visibility and is secure. The code that is created from third-party software or libraries has no visibility, which makes it insecure. APIs are the most vulnerable points of attack, and many users are not aware of their insecurity. This paper reviews API security in cloud applications and discusses details of API vulnerabilities, existing security tools for API security to mitigate API attacks. The author’s study showed that most users are unaware of API insecurity, organizations lack resources and training to educate users about APIs, and organizations depend on the overall security of the network instead of the security of standalone APIs.

The trustworthiness of mobile nodes is considered as the principal parameter for ensuring significant data dissemination in a Mobile Ad hoc NETwork (MANET). However, the selfish behaviour of nodes minimizes the trust by dropping a considerable amount of data packets in the network. The significant dropping of data packets by the selfish node introduces huge data overhead with increased latency and energy consumption, thus increasing the number of retransmissions. In this paper, Selfish Node Detection Scheme based on Bates Distribution Inspired Trust Factor (SNDS-BDITF) is propounded for predominant detection of selfish behaviour by investigating multiple levels of factors that contribute towards effective selfishness detection. The proposed SNDS-BDITF approach is also potent in enhancing the detection rate of selfishness by multi-perspective analysis of each monitored node’s forwarding characteristics considering the benefits of other cooperating mobile nodes. The simulation results of the propounded SNDS-BDITF method are enhanced on an average by 16% and 14% when compared to the existing selfish node segregation mechanisms prevalent in the literature. 

Social networking services allow people to feel and remain connected with others. However, fatigue, loneliness, and anxiety are believed to increase as communication becomes increasingly intimate. In our previous study, we developed Palco, a paper-based, handcrafted, and tangible avatar. We demonstrate that abstract and passive communications are effective in maintaining relationships with communication partners. This study focuses on a mechanism to support the creation of avatars by users by adding the ability to identify multiple avatars and project information to each avatar using a single projector to the Palco system. A user study examined the effectiveness of user-generated avatars by comparing avatars with different levels of user involvement in the process from design to use. The findings indicate that user-generated avatars have the potential to function as a communication medium that strengthens emotional bonds between communication partners. Additionally, the experiences in the design and creation of the avatars allowed the users to demonstrate their creativity.

INTRODUCTION: With the development of technology, telecom fraud is appearing more and more frequently and causing more and more harm. OBJECTIVES: This paper focused on the detection, identification, and prevention of telecom fraud. METHODS: Firstly, the telecom fraud crime was analyzed, the existing legal system was explained, and some suggestions on the protection of telecom fraud were proposed at the legal level. Then, the characteristics of telecom fraud users were analyzed to point out the differences between fraud users and normal users in terms of call, message, and traffic behavior. Finally, the Boosting algorithm was used to detect and identify telecom fraud. RESULTS: The experiments found that the boosting algorithm had advantages in the detection and recognition of telecom fraud compared with the algorithms such as support vector machine and random forest algorithms. Among several boosting algorithms, the CatBoost algorithm performed the best, with an accuracy of 0.9465 and an F1 value of 0.9047. CONCLUSION: The results demonstrate the reliability of the CatBoost algorithm in detecting and recognizing telecom fraud, and it can be applied in practice.

Events related to cybersecurity failures have a high and growing financial, operational, and reputational impact, on organizations around the world. At the same time, there is a shortage of cybersecurity professionals. In addition, the specialization of professionals with the necessary skills in the area of cybersecurity is expensive and time-consuming. Taking these facts into consideration, this research has focused on the automation of cybersecurity processes, specifically those related to continuous vulnerability detection. To address this problem, a cybersecurity vulnerability scanner that is free to the community and requires no pre-expertise on the part of the operator, was developed. The artifact was tested by companies in the IT business, by systems engineers, most without cybersecurity background. The results demonstrated that the artifact was easy to install and that the reported results can be used by the operator in the context of an automatic and proactive securitization of the systems involved.

Deep clustering is an approach that uses deep learning to cluster data, since it involves training a neural network model to become familiar with a data representation that is suitable for clustering. Deep clustering has been applied to a wide range of data types, including images, texts, time series and has the advantage of being able to automatically learn features from the data, which can be more effective than using hand-crafted features. It is also able to handle high-dimensional data, such as time series with many variables, which can be challenging for traditional clustering techniques. In this paper, we introduce a novel deep neural network type to improve the performance of the auto-encoder part by ignoring the unnecessary extra-noises and labelling the input data. Our approach is helpful when just a limited amount of labelled data is available, but labelling a big amount of data would be costly or time-consuming. It also applies for the data in high-dimensional and difficult to define a good set of features for clustering.

INTRODUCTION: eHealth systems are raising both patient satisfaction and medical care. The proper workflow regulations and data exchange between primary and secondary healthcare are crucial. OBJECTIVES: Investigation of the major determinants influencing the physicians’ satisfaction while using an eHealth information system. METHODS: A survey of primary and secondary healthcare medical professionals was conducted in R.N. Macedonia. The categorical variables from the data analysis were presented and a logistic regression was carried out. RESULTS: The multiple logistic regression model was statistically significant for sufficient evidence to reject the null hypothesis in which the overall satisfaction rating of the eHealth system usage for managing patients and other healthcare services will not be affected by the other variables in favour of the alternative Ha. CONCLUSION: Various factors between primary and secondary healthcare professionals regarding system’s usage satisfaction are presented and studied. Various issues were revealed between both parties that should serve the policymakers and medical authorities for further improvements.

In cybersecurity field, identifying and dealing with threats from malicious websites (phishing, spam, and drive-by downloads, for example) is a major concern for the community. Consequently, the need for effective detection methods has become a necessity. Recent advances in Machine Learning (ML) have renewed interest in its application to a variety of cybersecurity challenges. When it comes to detecting phishing URLs, machine learning relies on specific attributes, such as lexical, host, and content based features. The main objective of our work is to propose, implement and evaluate a solution for identifying phishing URLs based on a combination of these feature sets. This paper focuses on using a new balanced dataset, extracting useful features from it, and selecting the optimal features using different feature selection techniques to build and conduct a comparative performance evaluation of four ML models (SVM, Decision Tree, Random Forest, and XGBoost). Results showed that the XGBoost model outperformed the others models, with an accuracy of 95.70% and a false negatives rate of 1.94%.