Brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for the diagnosis, treatment planning, and monitoring of therapeutic outcomes. Thus, this research introduces a novel hybrid approach that combines handcrafted features with convolutional neural networks (CNNs) to enhance performance brain segmentation. In study, were extracted MRI included intensity-based, texture-based, shape-based features. parallel, unique CNN architecture was developed trained detect data...

Brain tumor segmentation in medical imaging is a critical task for diagnosis and treatment while preserving patient data privacy security. Traditional centralized approaches often encounter obstacles sharing due to regulations security concerns, hindering the development of advanced AI-based applications. To overcome these challenges, this study proposes utilization federated learning. The proposed framework enables collaborative learning by training model on distributed from multiple...

Gliomas are the most common malignant brain tumor and cause deaths.Manual segmentation is expensive, time-consuming, error-prone, dependent on radiologist's expertise experience.Manual outcomes by different radiologists for same patient may differ.Thus, more robust, dependable methods needed.Medical imaging researchers produced numerous semi-automatic fully automatic algorithms using ML pipelines accurate (handcrafted feature-based, etc.) or data-driven strategies.Current use CNN handmade...

The Internet of Medical Things (IoMT) has revolutionized healthcare by bringing real-time monitoring and data-driven treatments. Nevertheless, the security communication between IoMT devices servers remains a huge problem because inherent sensitivity health data susceptibility to cyber threats. Current solutions, including simple password-based authentication standard Public Key Infrastructure (PKI) approaches, typically do not achieve an appropriate balance low computational overhead,...

Brain cancer is a bad disease and affects millions of people in worldwide. Approximately 70% patients diagnosed with this do not survive. The Machine learning promising recent development area. However, very limited research performed direction. Therefore, research, we propose an evolutionary lightweight model aimed at detecting brain classification, starting from the analysis magnetic resonance images. proposed named ensemble combines (weighted average multiple XGBoost decision trees)...

Brain tumor classification plays a critical role in diagnosing and treating patients effectively. However, the limited availability of annotated data complexity images present significant challenges achieving accurate classification. In recent years, transfer learning has emerged as promising approach to leverage pre-trained models on large-scale datasets improve performance brain tasks. This research paper presents an in-depth exploration techniques context It examines associated with...

As the manufacturing industry advances towards Industry 5.0, which heavily integrates advanced technologies such as cyber-physical systems, artificial intelligence, and Internet of Things (IoT), potential for web-based attacks increases. Cybersecurity concerns remain a crucial challenge 5.0 environments, where cyber-attacks can cause devastating consequences, including production downtime, data breaches, even physical harm. To address this challenge, research proposes an innovative...

In the rapidly advancing domain of smart manufacturing, securing data integrity and preventing unauthorized access are critical challenges. This study introduces a novel approach that synergizes anomaly detection techniques with Zero-Knowledge Proofs (ZKPs) to fortify security framework manufacturing systems. Our methodology employs combination preprocessing, including statistical imputation smoothing, alongside advanced using classification methods neural networks, particularly focusing on...

In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques identify underlying patterns medical address various health-related issues. this context, automated disease detection has now become central concern science. Such approaches can reduce mortality rate through accurate and timely diagnosis. COVID-19 modern virus that spread all over world affecting millions people. Many countries are facing...

In the global fight against breast cancer, importance of early diagnosis is unparalleled. Early identification not only improves treatment options but also significantly survival rates. Our research introduces an innovative ensemble method that synergistically combines strengths four state‐of‐the‐art convolutional neural networks (CNNs): EfficientNet, AlexNet, ResNet, and DenseNet. These were chosen for their architectural advances proven efficacy in image classification tasks, particularly...

Breast cancer imaging is paramount to quickly detecting and accurately evaluating the disease. The scarcity of annotated mammogram data presents a significant obstacle when building deep learning models that can produce reliable outcomes. This paper proposes novel approach utilizes convolutional generative adversarial networks (DCGANs) effectively tackle issue limited availability. main goal synthetic mammograms reproduce intrinsic patterns observed in real data, enhancing current dataset....

Breast cancer is widespread worldwide and can be cured if diagnosed early. Mammography an irreplaceable critical technique in modern medicine, serving as a foundation for breast detection. In medical imaging, the reliability of synthetic mammogram images produced by Deep Convolutional Generative Adversarial Networks (DCGANs). The human validation assessing quality examining calculating perceptual variations between real-world counterparts difficult task. Thus, this research focused on...

Breast cancer (BC) remains a major global health problem designed for early diagnosis and requires innovative solutions. Mammography is the most common method of detecting breast abnormalities, but it difficult to interpret mammogram due complexities tissue tumor characteristics. The EfficientViewNet model overcome false predictions BC. consists two pathways analyze mass characteristics from craniocaudal (CC) mediolateral oblique (MLO) views. These comprehensively tumors each view. proposed...

Intrusion detection in Internet of Things (IoT)-based wireless sensor networks (WSNs) is essential due to their widespread use and inherent vulnerability security breaches. Traditional centralized intrusion systems (IDS) face significant challenges data privacy, computational efficiency, scalability, particularly resource-constrained IoT environments. This study aims create assess a federated learning (FL) framework that integrates with long short-term memory (LSTM) for efficient IoT-based...

Database forensics is a domain that uses database content and metadata to reveal malicious activities on systems in an Internet of Things environment. Although the concept has been around for while, investigation cybercrime cyber breaches environment would benefit from development common investigative standard unifies knowledge domain. Therefore, this paper proposes forensic processes using design science research approach. The proposed process comprises four phases, namely: 1)...

Early detection of brain tumors is critical to ensure successful treatment, and medical imaging essential in this process. However, analyzing the large amount data generated from various sources such as magnetic resonance (MRI) has been a challenging task. In research, we propose method for early tumor segmentation using big analysis patch-based convolutional neural networks (PBCNNs). We utilize BraTS 2012–2018 datasets. The preprocessed through steps profiling, cleansing, transformation,...

Ransomware (RW) is a distinctive variety of malware that encrypts the files or locks user’s system by keeping and taking their hostage, which leads to huge financial losses users. In this article, we propose new model extracts novel features from RW dataset performs classification benign files. The proposed can detect large number various families at runtime scan network, registry activities, file throughout execution. API-call series was reutilized represent behavior-based RW. technique...

Object detection in remote sensing imagery presents challenges due to low resolution, complex backgrounds, occlusions, and scale variations, which are critical disaster response, environmental monitoring, surveillance. This study proposes a robust object framework integrating super-resolution techniques with advanced feature extraction algorithms for images. The hybrid model combines Advanced StyleGAN Swin Transformer. enhances image facilitating the of small occluded objects, while...