Ransomware attacks have emerged as a major cybersecurity threat in terms of the massive financial and data losses it has inflicted across the globe. Such attacks cannot easily be detected by traditional detection techniques, including signature-based and rule-based detection, because these are issues that rely heavily on predefined characteristics and static rules for their identification purposes.
Dermatological disorders, and especially skin cancer, are a worldwide health issue. Precise and early diagnosis is critical in order to pursue efficient treatment, and machine learning, in this case Convolutional Neural Networks (CNNs), has tremendous potential in computerizing the diagnostic process. This research suggests a Visual Geometry Group Network (VGGNet) model for dermatological disorder diagnosis, specifically for skin disorders like melanoma, psoriasis, and eczema.
Financial fraud activities are a serious threat to the security and integrity of online banking systems. Traditional fraud detection approaches, such as rule-based and simple machine learning models, are not effective in detecting changing patterns of fraud and suffer from high false positive rates and scalability. To overcome these drawbacks, this research introduces BankSafeNet, a Dual-Autoencoder and Transformer-Based Anomaly Detection System for detecting financial fraud.
Maintenance of performance, reliability, and efficiency of any system within distributed computing environments is central to QoS. Conventional QoS optimization methods confront challenges regarding dynamic resource allocation, failure, and adaptation of systems. To overcome these limitations, a hybrid artificial neural network and Ant Colony Optimization model are proposed to provide an efficient QoS optimization strategy.
Internet of Things networks are proliferating rapidly, and securing document sharing over the cloud presents a significant challenge. Traditional encryption techniques cannot create a compromise between security, efficiency, and scalability. The known encryption techniques such as AES, RSA, and ABE have high computational overheads and their inefficient key management render them unsuitable for larger-scale IoT environments.
The Apology Strategies of Malaysian and Iraqi undergraduate students represent an investigation of cultural norms which affect spoken Apology Speech Acts. The study evaluated apology methods used between Malaysian and Iraqi students while studying the effects of collectivism and hierarchy on these techniques. The research included 120 participants sorted into equal groups of 60 students from Malaysia and Iraq ranging in age from 18 to 23 who studied different academic subjects.
The present article systematically analyzes the different environmental movements in India and looks at how these movements developed, what their prime goals were, and how they shaped the political and social environment towards the protection of the environment. This article investigates how “people’s movements” have defined environmentalism in India, beginning with the 1973’s Chipko Movement, when people began to hug trees in protest against the illicit felling of forests.
Critical care is necessary to deal with life-threatening conditions, but there are major challenges in resource-poor environments, such as Cameroon, where there is insufficient infrastructure and personnel shortages. The purpose of this study was to determine ICU features, key constructs, and procedural aspects from a range of secondary sources like academic journals and government reports. The findings thus revealed that the major features of ICUs ought to be an integrated operational design, a well-trained workforce, sufficient resources, and good leadership.
La existencia de reserva probada de baritina en la Empresa Minera Barimayo -Huánuco - Perú es 3217318,45 TM, siendo la gravedad especifica de 3,95 con una pureza de 85% y con un contenido de hierro promedio del 9%; en consecuencia la comercialización tal como se extrae de la cantera es a un precio muy bajo, porque el mercado competitivo necesita una baritina con peso específico de 4,1como mínimo a malla -200 y el grado químico mínimo debe ser 94% de sulfato de bario con 1% de óxido de hierro.
Efficient resource allocation is crucial for cloud data centers performance, scalability, and cost effectiveness. Traditional load balancing systems frequently fail to adjust changing workloads resulting in poor resource use, increased latency and excessive energy consumption. Adaptive Load Balancing Algorithm that optimizes resource allocation in cloud systems is implemented. It combines heuristic and metaheuristic techniques to efficiently distribute workloads among virtual machines resulting in lower response times and balanced computing loads.