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In this paper, we introduce new classes of functions defined within the open unit disk by integrating the concepts of (λ , γ) -symmetrical functions, generalized Janowski functions, and quantum calculus. We derive a structural formula and a representation theorem for the class S q λ , γ (x , y , z) . Utilizing convolution techniques and quantum calculus, we investigate convolution conditions supported by examples and corollary, establishing sufficient conditions. Additionally, we derive properties related to coefficient estimates, which further elucidate the characteristics of the defined function classes. [ABSTRACT FROM AUTHOR]

During the past few decades, cotton leaf diseases have become a significant challenge for farmers, leading to substantial losses in harvests, productivity, and financial resources. Traditional observation methods are often time-consuming, costly, and prone to inaccuracies, exacerbating the plight of farmers in detecting and identifying diseases in their early stages. The consequences of late detection are dire, and both crops and farmers are under the brunt of prolonged infections. This study proposes a method to improve the detection of cotton leaf diseases by applying advanced deep transfer learning techniques. Using models such as ResNet101, Inception v2, and DenseNet121, and fine-tuning parameters utilizing the Nesterov accelerated gradient, the proposed system offers a powerful tool for farmers to swiftly and accurately diagnose leaf diseases. This system allows users to simply upload an image of a cotton leaf. After sophisticated image processing techniques, a Convolutional Neural Network (CNN) is deployed to detect the presence of cotton leaf diseases with high precision and efficiency. The experimental results demonstrated the effectiveness of transfer learning approaches, with the CNN achieving an impressive accuracy of 99%, while ResNet101, Inception v2, and DenseNet121 achieved 75.36%, 97.32%, and 97.16%, respectively. These findings underscore the potential of deep learning techniques to revolutionize disease detection in agricultural contexts, offering farmers a powerful tool to mitigate the impact of diseases on their crops. [ABSTRACT FROM AUTHOR]

The enormous increase in mobile data traffic and the heterogeneity and stringent Quality of Service (QoS) requirements of different applications have placed a substantial strain on the underlying network infrastructure and represent a challenge for Cellular Vehicle-to-Everything (Cellular V2X). V2X communication is a key enabler for the realization of smart and connected transportation systems, offering a wide range of applications, such as enhanced road safety, traffic management, and autonomous driving. In this context, the best way to provide great flexibility and address both the present and future QoS concerns is to use intelligent Radio Resource Management (RRM) in general and creative packet scheduling in particular. The diverse QoS requirements of multiple application classes under dynamic network conditions present substantial challenges for conventional scheduling algorithms given the increasing demand for bandwidth-hungry applications. This study proposes a scheduling system for V2X communications based on traffic prioritization that manages QoS provisioning for different types of traffic considering channel quality, remaining payload, and delay. Simulation results demonstrate the highly promising performance of the proposed New Scheduling V2X Communications (NSVC) algorithm that leads to significantly lower latencies, as the average delay scheme did not exceed 0.001 ms for 100 users. [ABSTRACT FROM AUTHOR]

Ransomware has become a significant threat to individuals and organizations worldwide, causing substantial financial losses and disruptions. Early detection of ransomware is crucial to mitigate its impact. The significance of early detection lies in the capture of ransomware in the act of encrypting sample files, thus thwarting its progression. A timely response to ransomware is crucial to prevent the encryption of additional files, a scenario not adequately addressed by current antivirus programs. This study evaluates the performance of six machine-learning algorithms for ransomware detection, comparing the accuracy, precision, recall, and F1-score of Logistic Regression, Decision Tree, Naive Bayes, Random Forest, AdaBoost, and XGBoost. Additionally, their computational performance is evaluated, including build time, training time, classification speed, computational time, and Kappa statistic. This analysis provides an insight into the practical feasibility of the algorithms for real-world deployment. The findings suggest that Random Forst, Decision Tree, and XGBoost are promising algorithms for ransomware detection due to their high accuracy of 99.37%, 99.42%, and 99.48%, respectively. These algorithms are also relatively efficient in terms of classification speed, which makes them suitable for real-time detection scenarios, as they can effectively identify ransomware samples even in the presence of noise and data variations. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) performs a vital function in text summarization, a task targeted at refining the crucial information from the massive quantity of textual data. NLP methods allow computers to comprehend and process human language, permitting the development of advanced summarization methods. Text summarization includes the automatic generation of a concise and coherent summary of a specified document or collection of documents. Extracting significant insights from text data is crucial as it provides advanced solutions to end-users and business organizations. Automatic text summarization (ATS) computerizes text summarization by decreasing the initial size of the text without the loss of main data features. Deep learning (DL) approaches exhibited significant performance in abstractive and extractive summarization tasks. This research designed an extractive text summarization using NLP with an optimal DL (ETS-NLPODL) model. The major goal of the ETS-NLPODL technique was to exploit feature selection with a hyperparameter-tuned DL model for summarizing the text. In the ETS-NLPODL technique, an initial step of data preprocessing was involved to convert the input text into a compatible format. Next, a feature extraction process was carried out and the optimal set of features was chosen by the hunger games search optimization (HGSO) algorithm. For text summarization, the ETS-NLPODL model used an attention-based convolutional neural network with a gated recurrent unit (ACNN-GRU) model. Finally, the mountain gazelle optimization (MGO) algorithm was employed for the optimal hyperparameter selection of the ACNNGRU model. The experimental results of the ETS-NLPODL system were examined under the benchmark dataset. The experimentation outcomes pointed out that the ETS-NLPODL technique gained better performance over other methods concerning diverse performance measures. [ABSTRACT FROM AUTHOR]

Fraud remains a pervasive challenge within the banking industry, where financial institutions and their clients grapple with substantial annual losses. The proliferation of digital transactions and online banking has created new avenues for fraudsters to exploit vulnerabilities, leading to financial harm to unsuspecting victims. Consequently, the imperative to promptly and accurately detect fraudulent transactions has grown significantly, both as a safeguard against financial crimes and as a pillar of trust between customers and the banking sector. This paper introduces an innovative fraud detection model designed for bank payment transactions using advanced ensembling techniques. This study presents a comprehensive evaluation of an ensembling model conducted on the Bank Account Fraud (BAF) dataset. Through meticulous analysis, the performance of various base models and ensembling methods was assessed and compared, employing a variety of critical metrics including accuracy, precision, recall, and F1-score. The proposed ensemble model, referred to as "Stacking," exhibited remarkable performance, attaining a commendable accuracy score of 0.98. This result reaffirmed its prowess as a comprehensive and balanced solution to the multifaceted challenges of fraud detection. This study has paramount implications for the banking industry, offering a robust and adaptable solution to deal with the increasing threats posed by financial fraud. Furthermore, it emphasizes the significance of precision-recall trade-offs in fraud detection and underscores the potential of ensemble methods, particularly the "Stacking" model, to fortify the resilience and efficacy of existing security systems. [ABSTRACT FROM AUTHOR]

In an era marked by escalating cybersecurity threats, our study addresses the challenge of malware variant detection, a significant concern for amultitude of sectors including petroleum andmining organizations. This paper presents an innovative Application Programmable Interface (API)-based hybrid model designed to enhance the detection performance of malware variants. This model integrates eXtreme Gradient Boosting (XGBoost) and an Artificial Neural Network (ANN) classifier, offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware authors. Themodel's design capitalizes on the benefits of both static and dynamic analysis to extract API-based features, providing a holistic and comprehensive view of malware behavior. From these features, we construct two XGBoost predictors, each of which contributes a valuable perspective on the malicious activities under scrutiny. The outputs of these predictors, interpreted as malicious scores, are then fed into an ANN-based classifier, which processes this data to derive a final decision. The strength of the proposed model lies in its capacity to leverage behavioral and signature-based features, and most importantly, in its ability to extract and analyze the hidden relations between these two types of features. The efficacy of our proposed APIbased hybrid model is evident in its performance metrics. It outperformed other models in our tests, achieving an impressive accuracy of 95% and an F-measure of 93%. This significantly improved the detection performance of malware variants, underscoring the value and potential of our approach in the challenging field of cybersecurity. [ABSTRACT FROM AUTHOR]

This study presents a novel Similarity-Based Hybrid API Malware Detection Model (HAPI-MDM) aiming to enhance the accuracy of malware detection by leveraging the combined strengths of static and dynamic analysis of API calls. Faced with the pervasive challenge of obfuscation techniques used by malware authors, the conventional detection models often struggle to maintain robust performance. Our proposed model addresses this issue by deploying a two-stage learning approach where the XGBoost algorithm acts as a feature extractor feeding into an Artificial Neural Network (ANN). The key innovation of HAPI-MDM is the similarity-based feature, which further enhances the detection accuracy of the dynamic analysis, ensuring reliable detection even in the presence of obfuscation. The model was evaluated using seven machine learning techniques with 10 K-fold cross-validation. Experimental results demonstrated HAPI-MDM's superior performance, achieving an overall accuracy of 97.91% and the lowest false-positive and false-negative rates compared to related works. The findings suggest that integrating dynamic and static API-based features and utilizing a similarity-based feature significantly improves malware detection performance, thereby offering an effective tool to fortify cybersecurity measures against escalating malware threats. [ABSTRACT FROM AUTHOR]

Malicious software (malware) is one of the main cyber threats that organizations and Internet users are currently facing. Malware is a software code developed by cybercriminals for damage purposes, such as corrupting the system and data as well as stealing sensitive data. The damage caused by malware is substantially increasing every day. There is a need to detect malware efficiently and automatically and remove threats quickly from the systems. Although there are various approaches to tackle malware problems, their prevalence and stealthiness necessitate an effective method for the detection and prevention of malware attacks. The deep learning-based approach is recently gaining attention as a suitable method that effectively detects malware. In this paper, a novel approach based on deep learning for detecting malware proposed. Furthermore, the proposed approach deploys novel feature selection, feature co-relation, and feature representations to significantly reduce the feature space. The proposed approach has been evaluated using a Microsoft prediction dataset with samples of 21,736 malware composed of 9 malware families. It achieved 96.01% accuracy and outperformed the existing techniques of malware detection. [ABSTRACT FROM AUTHOR]

Due to the proliferation of internet-enabled smartphones, many people, particularly young people in Arabic society, have widely adopted social media platforms as a primary means of communication, interaction and friendship making. The technological advances in smartphones and communication have enabled young people to keep in touch and form huge social networks from all over the world. However, such networks expose young people to cyberbullying and offensive content that puts their safety and emotional well-being at serious risk. Although, many solutions have been proposed to automatically detect cyberbullying, most of the existing solutions have been designed for English speaking consumers. The morphologically rich languages-such as the Arabic language-lead to data sparsity problems. Thus, render solutions developed for another language are ineffective once applied to the Arabic language content. To this end, this study focuses on improving the efficacy of the existing cyberbullying detection models for Arabic content by designing and developing a Consensus-based Ensemble Cyberbullying Detection Model. A diverse set of heterogeneous classifiers from the traditional machine and deep learning technique have been trained using Arabic cyberbullying labeled dataset collected from five different platforms. The outputs of the selected classifiers are combined using consensus-based decisionmaking in which the F1-Score of each classifier was used to rank the classifiers. Then, the Sigmoid function, which can reproduce human-like decision making, is used to infer the final decision. The outcomes show the efficacy of the proposed model comparing to the other studied classifiers. The overall improvement gained by the proposed model reaches 1.3% comparing with the best trained classifier. Besides its effectiveness for Arabic language content, the proposed model can be generalized to improve cyberbullying detection in other languages. [ABSTRACT FROM AUTHOR]

Phishing attacks are increasingly exploited by cybercriminals, they become more sophisticated and evade detection even by advanced technical countermeasures. With cybercriminals resorting to more sophisticated phishing techniques, strategies, and different channels such as social networks, phishing is becoming a hard problem to solve. Therefore, the main objective for any anti-phishing solution is to minimize phishing success and its consequences through complementary means to advanced technical countermeasures. Specifically, phishing threats cannot be controlled by technical controls alone, thus it is imperative to complement cybersecurity programs with cybersecurity awareness programs to successfully fight against phishing attacks. This paper provides a review of the delivery methods of cybersecurity training programs used to enhance personnel security awareness and behavior in terms of phishing threats. Although there are a wide variety of educational intervention methods against phishing, the differences between the cybersecurity awareness delivery methods are not always clear. To this end, we present a review of the most common methods of workforce cybersecurity training methods in order for them to be able to protect themselves from phishing threats. [ABSTRACT FROM AUTHOR]