Haider Dhia Zubaydi

Non-alcoholic fatty liver disease (NAFLD) is a growing public health challenge, underscoring the need for scalable, non-invasive tools to grade hepatic steatosis. Although B-mode ultrasound is accessible and safe, its reliability is limited by operator and scanner variability. We present the Deep Domain Adaptation Neural Network (DDANN), a deep learning system for multiclass steatosis classification (Normal, Mild, Moderate, Severe) from ultrasound that emphasizes cross-device generalizability. To mitigate distribution shifts across scanners (LOGIQ, iU22, EPIQ), DDANN combines a MobileNetV2 backbone with triplet loss, entropy-based domain adaptation, and preprocessing that includes speckle suppression, percentile normalization, and LOGIQ-specific harmonization. Trained on a biopsy-confirmed, multi-institutional cohort (primarily LOGIQ and iU22), the model was externally validated on an unseen EPIQ test set of 1,083 images from 47 patients, achieving 98.71% accuracy, 0.9872 macro F₁-score, and 0.9998 AUC-ROC, outperforming baselines. In a separate radiologist–AI comparison on 224 biopsy-confirmed images not used for training or validation, the AI reached 91.96% accuracy, significantly exceeding radiologists’ 19.64%–31.70% (McNemar’s test, p < 0.001), with strong agreement to ground truth (κ = 0.893) versus radiologists’ poor-to-slight agreement (κ = 0.006–0.194). The AI maintained balanced class-wise F₁-scores (0.90–0.94), while radiologists struggled, particularly with Mild and Moderate cases, and exhibited substantial inter-reader variability (κ = 0.068–0.648). These results demonstrate robust cross-device performance and support integrating AI as a reliable second reader or primary screening tool to reduce subjectivity in steatosis assessment.

Effective congestion control is crucial for maintaining network stability, especially in dynamic environments. Self-Clocked Rate Adaptation for Multimedia (SCReAM) is a hybrid loss and delay-based algorithm for real-time multimedia transmission. It encounters challenges with static pacing and low latency under fluctuating round-trip time (RTT) and jitter resulting in ineffective bandwidth utilization. To address these issues, we propose ALCS-PP, a novel congestion control scheme featuring dynamic rate adjustment and adaptive packet pacing. The evaluation process is divided into three phases. The first phase involves initial performance evaluation using default parameters to establish a baseline comparison. The next phase explores parameter-specific experiments, where individual parameters such as target latency, jitter thresholds, and rate scaling factors are tuned to assess their impact on performance. The last phase investigates multi-parameter combinations to optimize the overall system configuration under various network conditions. Our proposed scheme effectively reduced network queue delay (NQD) by up to 21.91% and smoothed RTT (sRTT) by 13.44%, ensuring better responsiveness in real-time multimedia transmission. These improvements are achieved through adaptive tuning using dynamic soft clamping and gradual corrections, minimizing performance oscillations. Additionally, the scheme maintains metrics such as network throughput (NThput), congestion window (CWND), and bytes in flight (BiF) close to SCReAM’s baseline values, ensuring stable performance. The experimental evaluation highlights the algorithm’s capability to balance delay reduction and network stability, making it suitable for delay-sensitive applications such as 5G, Internet of Things (IoT), and real-time multimedia streaming.

JCM is an open access journal on the science and engineering of communication.

Congestion Control (CC) is essential in networked systems, especially in environments with strict delay and throughput requirements. While CC algorithms like Self-Clocked Rate Adaptation for Multimedia (SCReAM) and Bottleneck Bandwidth and Round-trip propagation time (BBR) have shown progress, each faces limitations: BBR encounters difficulty in balancing responsiveness and fairness when rapid fluctuations in network conditions arise, which can lead to inefficient performance in shared environments. On the other hand, although SCReAM is designed for multimedia traffic, it struggles to dynamically adapt the congestion window to unpredictable or highly congested networks, leading to inefficient resource utilization. This paper proposes SACWOM, a hybrid congestion window optimization mechanism that combines our novel method with SCReAM’s rate control and BBR’s bandwidth-delay estimation to enhance throughput stability, fairness, and adaptability by dynamically adjusting the congestion window. Extensive simulations demonstrate SACWOM’s significant improvements over SCReAM in managing congestion window and bytes in flight under diverse network conditions. In Phase I, SACWOM achieved up to 11.96-13.27% increase in congestion window and bytes in flight by maintaining higher data flow. Phase II analysis shows up to 20.76-22.64% improvements with optimized configurations. Finally, Phase III, comprising 100 experiments, reveals SACWOM’s dynamic adaptability, achieving up to 50-70% improvements. These results highlight SACWOM as a robust mechanism suitable for various applications across diverse network scenarios.

The congestion problem has driven many researchers to address it, among other networking issues. In a packet-switched network, congestion is essential; it leads to a high response time to deliver packets due to heavy traffic, which eventually causes packet loss. Hence, congestion control mechanisms are utilized to prevent such cases. Several interesting algorithms are proposed to focus on this dilemma, such as the Self-Clocked Rate Adaptation for Multimedia (SCReAM) designed for interactive real-time video streaming applications. One of the main issues of SCReAM is the high design complexity due to the large size of its documentation and coding. Furthermore, there is a considerable number of parameters that can be adjusted to accomplish the desired performance. This study proposes a guided parameters’ tuning approach to assess and optimize the SCReAM algorithm in an emulated 5G environment through a detailed exploration of its parameters. The proposed approach consists of three phases, namely, the initialization phase, the standalone experimentation phase, and the hybrid experimentation phase. In the first phase, we illustrate the method of initializing and implementing the environment, followed by specifying the investigated parameters’ settings, testing, and validation. The second phase aims to investigate SCReAM parameters in isolation to identify the effect on the performance in relation to network queue delay, smoothed Round Trip Time (sRTT), and throughput. The final phase discusses the possibility of achieving the optimum performance by combining various sets to provide researchers with clear and explicit guidelines to establish an adequate SCReAM behavior for the desired application. To the best of our knowledge, this is the first study that proposes a preliminary and comprehensive analysis of the SCReAM algorithm. Based on the proposed approach, when L4S/ECN is disabled, we reduced the network queue delay by 63.36% and increased the network throughput by 48.6% as compared to the results generated by the original design. In L4S/ECN-enabled mode, the network queue delay is reduced by 16.17% while the network throughput increased by 93%.

As the Internet of Things (IoT) concept materialized worldwide in complex ecosystems, the related data security and privacy issues became apparent. While the system elements and their communication paths could be protected individually, generic, ecosystem-wide approaches were sought after as well. On a parallel timeline to IoT, the concept of distributed ledgers and blockchains came into the technological limelight. Blockchains offer many advantageous features in relation to enhanced security, anonymity, increased capacity, and peer-to-peer capabilities. Although blockchain technology can provide IoT with effective and efficient solutions, there are many challenges related to various aspects of integrating these technologies. While security, anonymity/data privacy, and smart contract-related features are apparently advantageous for blockchain technologies (BCT), there are challenges in relation to storage capacity/scalability, resource utilization, transaction rate scalability, predictability, and legal issues. This paper provides a systematic review on state-of-the-art approaches of BCT and IoT integration, specifically in order to solve certain security- and privacy-related issues. The paper first provides a brief overview of BCT and IoT’s basic principles, including their architecture, protocols and consensus algorithms, characteristics, and the challenges of integrating them. Afterwards, it describes the survey methodology, including the search strategy, eligibility criteria, selection results, and characteristics of the included articles. Later, we highlight the findings of this study which illustrates different works that addressed the integration of blockchain technology and IoT to tackle various aspects of privacy and security, which are followed by a categorization of applications that have been investigated with different characteristics, such as their primary information, objective, development level, target application, type of blockchain and platform, consensus algorithm, evaluation environment and metrics, future works or open issues (if any), and further notes for consideration. Furthermore, a detailed discussion of all articles is included from an architectural and operational perspective. Finally, we cover major gaps and future considerations that can be taken into account when integrating blockchain technology with IoT.