Cyberattacks can trigger power outages, military equipment problems, and breaches of confidential information, i.e., medical records could be stolen if they get into the wrong hands. Due to great monetary worth data it holds, banking industry is particularly at risk. As number digital footprints banks grows, so does attack surface that hackers exploit. This paper aims detect distributed denial-of-service (DDOS) attacks on financial organizations using Banking Dataset. In this research, we...

In the realm of medical imaging, early detection kidney issues, particularly renal cell hydronephrosis, holds immense importance. Traditionally, identification such conditions within ultrasound images has relied on manual analysis, a labor-intensive and error-prone process. However, in recent years, emergence deep learning-based algorithms paved way for automation this domain. This study aims to harness power learning models autonomously detect hydronephrosis taken close proximity kidneys....

Background StudyUltrasound is the most widely used medical imaging technique during pregnancy for monitoring developing fetus and assessing maternal fetal health. Conventional 2D, as well advanced 3D 4D technologies, provide valuable diagnostic insights. Recent advancements in machine learning, particularly deep learning techniques, have enhanced interpretation of ultrasound images, enabling early predictions improving healthcare. However, limited integration Transformers Convolutional...

In the published article [...]

<title>Abstract</title> Ultrasound imaging plays an important role in fetal growth and maternal-fetal health evaluation, but due to the complicated anatomy of fetus image quality fluctuation, its interpretation is quite challenging. Although deep learning include Convolution Neural Networks (CNNs) have been promising, they largely limited one task or other, such as segmentation detection structures, thus lacking integrated solution that accounts for intricate interplay between anatomical...

This article focuses on the study of cloud computing, it’s various models, and service types such as SaaS, PaaS, IaaS. It emphasizes security challenges cyber threats associated with environments, while also proposing methods solutions to protect these systems. The underlines advantages computing in offering rapid, cost-effective access technology services, but points out vulnerabilities multi-tenant architectures need for robust measures address risks. Additionally, presents a detailed...

Memory Denial of Service (M-DoS) attacks refer to a class cyber-attacks that aim exhaust the memory resources system, rendering it unavailable legitimate users. This type attack is particularly dangerous in cloud computing environments, where multiple users share same resources. Detection and mitigation M-DoS real-time challenging task, as they often involve large number low-rate requests, making difficult distinguish them from traffic. Several detection schemes have been proposed identify...

Federated Learning (FL) has rapidly emerged as a transformative machine learning approach, enabling healthcare institutions to collaboratively build predictive models without compromising patient data privacy. As increasingly adopts digital technologies, federated offers promising solutions critical issues such privacy, security, poisoning, and adversarial attacks. Despite the recognized potential of FL, significant gaps persist in existing research, particularly concerning comprehensive...

In the Internet of Things (IoT) era, mobile crowd sensing system (MCS) has become increasingly important. The Auto (IOTA) evolved rapidly in practically every technology field over last decade. IOTA-based is being developed this study using machine learning to detect and prevent users from engaging fake activities. It been determined through testing evaluation that our method effective for both quality estimation incentive allocation. Using IOTA Bottleneck dataset, multiple performance...

In the rapidly evolving landscape of modern technology, convergence blockchain innovation and machine learning advancements presents unparalleled opportunities to enhance computer forensics. This study introduces SentinelFusion, an ensemble-based framework designed bolster secrecy, privacy, data integrity within systems. By integrating cutting-edge security properties with predictive capabilities learning, SentinelFusion aims improve detection prevention breaches tampering. Utilizing a...

As criminal activity increasingly relies on digital devices, the field of forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed problem anomaly detection data. Our objective was to propose an effective approach for suspicious patterns activities that could indicate behavior. To achieve this, introduce novel method called Novel Support Vector Neural Network (NSVNN). We evaluated performance NSVNN by conducting experiments real-world dataset The...

The Internet of Railways (IoR) network is made up a variety sensors, actuators, layers, and communication systems that work together to build railway system. IoR’s success depends on effective communication. A railways uses protocols share transmit information amongst each other. Because the widespread usage wireless technology trains, entire system susceptible hacks. These hacks could lead harmful behavior if they spread sensitive data an infected or fake user. For previous few years,...

Development and use of IoT devices have grown significantly in recent years. Many departments such as smart homes, healthcare, sports analysis, different industries IoT-based devices. In devices, traffic is a very important part. device distinct from traditional various respects. this study, 41 Internet-of-Things (IoT) were used. provided 13 network attributes to construct multiclass classification model. Pre-processing techniques Normalization Scaling Dataset used pre-process the raw data...

In today’s globalized economic landscape, the assurance of stability is paramount importance, necessitating precise financial decision-making and policy formulation. This significantly augmented by innovative approaches to predicting crude oil prices, particularly in context energy stock markets denominated USD. paper delves into transformative effect accurate price prediction on stability. It underscores challenges limitations posed uncertainties emphasizes pivotal role solutions mitigating...