Israa Al Barazanchi

Wireless Body Area Networks (WBANs) have enhanced cornerstone technology in modern healthcare, enabling continuous monitoring of patients through interconnected wearable sensors. However, their reliability and security remain vulnerable due to untrusted or malicious sensor nodes that can degrade data quality and delay medical responses. This study introduces an integrated intelligent framework that enhances node trust classification indoors healthcare-oriented WBAN environments. The proposed approach combines Principal Component Analysis (PCA) for dimensionality reduction with the Dragonfly optimization algorithm algorithm for optimal feature selection, followed by a Hybrid Backpropagated Mask Convolutional Neural Network (HB-MCNN) for node trust classification. The model was evaluated using a real WBAN dataset under controlled simulation settings. Results demonstrated a classification rate of 99.2%, an accuracy of 98.5%, a packet delivery rate of 98.6%, and a latency of 7.2 ms, while reducing energy consumption to 55.5%. These outcomes confirm the framework’s ability to enhance the reliability and security of medical data transmission by accurately isolating untrusted nodes, reducing communication delays, and improving overall trust management in healthcare monitoring systems.

This study examines the key role of the National Data Center in Iraq’s digital transformation, focusing on the Iraqi National Data Center (INDC) as a key infrastructure for centralized data management and the digital role You trust to play. Iraq’s current digital infrastructure is severely limited, including outdated technologies, cybersecurity weaknesses, regulatory gaps and economic constraints. These obstacles prevent INDC from meeting the increasing demands for data storage, resources and security required for effective government functioning and self-reliance in the digital age f Deliver systems centralized for By implementing this approach, INDC will be able to enhance its data governance and security processes, align with international data authority standards, and forge partnerships that support long-term growth. The findings of this study suggest that targeted reforms in infrastructure, cybersecurity, and regulatory frameworks can significantly increase the efficiency of the INDC government, protect national intelligence from external threats, and help Iraq broader digital transformation objectives And of getting continued investment , emphasizes the importance of skilled labor development and system exchange.

Due to energy-intensive processes, dependence on fossil fuels and poor waste management, industry is among the largest contributors to global carbon emissions This environment this growing environmental problem highlights the urgent need for sustainable solutions to reduce carbon emissions. This study examines the role of green technologies in addressing these challenges, focusing on renewable energy systems, carbon capture and storage (CCS), circular economic practices, and digital products on the other hand. The main objective is to assess how these technologies can reduce greenhouse gas emissions and increase energy efficiency and potentially increase the industrial sustainability of resources activities in the field. Findings show that integrating green technologies can lead to a 25% reduction in energy consumption, 85% efficiency in carbon capture, and greenhouse gas emissions has decreased by 50% in a decade Furthermore, sustainable materials and energy efficiency solutions promise to reduce waste and improve the relative quality of infrastructure use Display results. Despite constraints such as high start-up costs and technical barriers, the study highlights the significant potential of green innovation to transform technology It provides a call to action for businesses , policy makers and consumers to embrace this technology as a means to achieve global sustainability goals ends.

Wireless Body Area Networks (WBANs) are crucial in continuous health monitoring, fitness tracking, and other applications where a real-time collection of physiological data is needed by sensors worn on the body. This is important in WBAN to achieve reliable data transmission, energy efficiency, and overall system performance. Still WBANs present several challenges: for example, the data that is being collected is heterogeneous since it originates from diverse IMDs measuring different bio-physiological signals; these can be also quite noisy because of motion artifacts when moving; as well high limitations when it comes to energy, bandwidth or storage push for low-complexity methods rather than standard deep learning techniques. Common Convolutional Neural Networks) CNNs( are successfully utilized for spatial information extraction but they cannot catch temporal dependencies well and also, WBAN sensor data has a noisy and multi-modal structure which acts as an additional challenge for traditional CNNs. These limitations emphasize the need of a flexible, fast and precise classification model based on the specific needs of WBAN applications. To overcome these challenges, this paper presents a novel hybrid neural network architecture consisting of combined 2D and 3D convolutions for spatial-temporal feature extraction along with masked convolution layers to provide an ability to adaptively ignore uninterested parts of the data. The model aims at achieving a high classification performance while also balancing with the system computational efficiency, perfect for its ipsi deployment on resource constrained WBAN devices. Then, we apply further backpropagation optimization measures such as adaptive learning rate scheduling and gradient clipping, to improve the stability of training speed and reduce latency which in return finds its way into supporting real-time processing capabilities of the model. By using each of these components, the model is able to deal with the multi-dimension aspects and high noise level nature of WBANs without excessive computation resources [18]. The Hybrid Masked CNN model is shown to out-perform existing approaches without such masking, yielding substantially higher performance in terms of accuracy, precision, recall and F1-score across all metrics defined for the application as compared to traditional 2DCNNs, 3DCNNs and other hybrid models. Consequently, the latency of the model is significantly decreased as well which confirms its applicability to real-time WBAN applications. The obtained results confirm the efficacy of features from hybrid architectures with masked convolutions along with optimization in training techniques for WBAN sensor node classification. The results of this paper improve WBAN technology by providing a solid and scalable solution which can be implemented when more reliability and flexibility are required from such systems in applications like healthcare, fitness or any other field.

This research aims to enhance Clinical Decision Support Systems (CDSS) within Wireless Body Area Networks (WBANs) by leveraging advanced machine learning techniques. Specifically, we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) layers and echo state cells. These models are tailored to improve diagnostic precision, particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal diseases. Traditional diagnostic methods and existing CDSS frameworks often fall short in managing complex, sequential medical data, struggling with long-term dependencies and data imbalances, resulting in suboptimal accuracy and delayed decisions. Our goal is to develop Artificial Intelligence (AI) models that address these shortcomings, offering robust, real-time diagnostic support. We propose a hybrid RNN model that integrates SimpleRNN, LSTM layers, and echo state cells to manage long-term dependencies effectively. Additionally, we introduce CG-Net, a novel Convolutional Neural Network (CNN) framework for gastrointestinal disease classification, which outperforms traditional CNN models. We further enhance model performance through data augmentation and transfer learning, improving generalization and robustness against data scarcity and imbalance. Comprehensive validation, including 5-fold cross-validation and metrics such as accuracy, precision, recall, F1-score, and Area Under the Curve (AUC), confirms the models’ reliability. Moreover, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) are employed to improve model interpretability. Our findings show that the proposed models significantly enhance diagnostic accuracy and efficiency, offering substantial advancements in WBANs and CDSS.

Traffic systems have been built as a result of recent technological advancements. In application, dependable communication technology is essential to link any system needs. VANET technology is used to communicate data about intelligent traffic lights, which are focused on infrastructure during traffic accidents and mechanisms to reduce traffic congestion. To ensure reliable data transfer in VANET, appropriate routing protocols must be used. This research aims to improve data transmission in VANETs implemented in intelligent traffic lights. This study investigates the capability of combining the DSDV routing protocol with the routing protocol AODV to boost AODV on an OMNET++ simulator utilizing the 802.11p wireless standard. According to the simulation results obtained by analyzing the delay parameters, network QoS, and throughput on each protocol, the DSDV-AODV routing protocol performs better in three scenarios compared to QoS, delay, and throughput parameters in every scenario that uses network topology adapted to the conditions on the road intersections. The topology with 50 fixed + 50 mobile nodes yields the best results, with 0.00062 s delay parameters, a network QoS of 640 bits/s, and a throughput of 629.437 bits/s. Aside from the poor results on the network QoS parameters, the addition of mobile nodes to the topology influences both the results of delay and throughput metrics.

The rapidly growing Internet of Things (IoT) devices pose significant security challenges due to their decentralized nature, limited computing power and reliance on centralized security models Traditional IoT security systems often face issues such as single point of failure, limited scalability and vulnerability to distributed denial of service (DDoS) attacks They also require la-doable security solutions. Blockchain technology has emerged as a potential solution due to its decentralized, immutable and transparent nature, providing advanced security for IoT environments. This study proposes a blockchain-based security strategy aimed at mitigating security risks in IoT networks. The system uses smart contracts to perform basic functions such as device authentication, data integrity verification, and access control. The main goal of the research is to develop IoT security solutions that increase device reliability speeds, reduce power consumption, reduce connection latency, and maximize network availability. The results of the survey show significant improvements over existing IoT security measures. The blockchain-based system achieves device authentication time of 0.1 to 2 seconds, 95% detection rate for unauthorized access, and supports 1000 devices, double the capacity of traditional models Also, the system reduces energy consumption 0.5–2 joules per transaction and transaction latency to 1–5 seconds , making it suitable for real-time IoT applications.