The construction industry faces persistent challenges from disputes and claims, leading to delays, cost overruns, and strained stakeholder relationships. This study proposes a strategic framework that integrates building information modeling (BIM) as a proactive tool for dispute avoidance. Using a causal loop diagram (CLD), the research maps the relationships among systemic factors contributing to disputes, such as poor communication, ambiguous specifications, and ineffective stakeholder engagement. The study highlights BIM’s transformative potential in enhancing visualization, improving collaboration, and fostering proactive conflict resolution. Validated through expert insights, the framework provides actionable recommendations for integrating BIM (with ISO19650 specs) into construction workflows, addressing the root causes of disputes, and driving project efficiency. This research contributes a structured roadmap for advancing construction management practices, emphasizing early BIM adoption considered with ISO19650, stakeholder alignment, and balancing systemic dynamics. The findings underscore BIM’s pivotal role in reshaping conflict prevention strategies, paving the way for sustainable and dispute-free project delivery.

Nowadays, efficient dental healthcare systems are considered significant for upholding oral health. Also, the ability to utilize artificial intelligence for evaluating complex data implies that dental X-ray image recognition is a critical mechanism to enhance dental disease detection. Consequently, integrating deep learning algorithms into dental healthcare systems is considered a promising approach for enhancing the reliability and efficiency of diagnostic processes. In this context, an integrated artificial intelligence model is proposed to enhance model performance and interpretability. The basic idea of the proposed model is to augment the deep learning approach with Ensemble methods to improve the accuracy and robustness of dental healthcare. In the proposed model, a Non-Maximum Suppression (NMS) ensembled technique is employed to improve the accuracy of predictions along with combining outputs from multiple single models (YOLO8 and RT-DETR) to make a final decision. Experimental results on real-world datasets show that the proposed model gives high accuracy in miscellaneous dental diseases. The results show that the proposed model achieves 18% time reductions as well as 30% improvements in accuracy compared with other competitive deep learning algorithms. In addition, the effectiveness of the proposed integrated model, achieved 74% mAP50 and 58% mAP50-90, outperforming existing models. Furthermore, the proposed model grants a high degree of system reliability.

Last Level Cache (LLC) partitioning has been ex-tensively examined to optimize energy and improve performance. Cache partitioning strategies proposed to reduce inter-core in-terference for the shared cache in multicore platforms. However, these approaches start focusing on performance in multicores and energy consummation. Current energy-efficient solutions in this field minimize power consumption by employing dynamic voltage and frequency scaling (DVFS). This paper has proposed an algorithm for improvements to existing models for energy consummation in multicore platforms. The proposed algorithm enhances the energy consummation resulting from DVFS by using LLC partitioning. The algorithm consists of two phases. The first phase sets the level of DVFS to the task, while the second phase gets the optimum partition factor of LLC to this task. Extensive simulations illustrate the efficiency and influence of the proposed method on the total energy consumption of multicore platforms by each stage of the suggested algorithm. Comparison by DVFS only and DVFS with cache partitioning show that the proposed algorithm achieves 15% improvements in energy-saving and reduces 25% L2 cache misses rate.

The detection of anomaly energy consumption patterns in smart grid metering systems remains a critical issue. This is due to data imbalance, privacy constraints, and the dynamic nature of consumption patterns. To address these concerns, we present a privacy-preserving and scalable anomaly detection framework named as FedTheftDetect framework. The proposed framework integrates deep learning algorithms into a federated learning (FL) architecture through the incorporation of advanced ensemble classifiers to detect behavioral anomalies in daily consumption patterns. A real-world smart meter dataset with significant class imbalance is used to assess the suggested framework. The dataset had significant preprocessing to identify consumption-related anomalies in behavior. Experimental results demonstrate that the suggested framework outperforms the competitive centralized and distributed models. It achieves significant improvements in Accuracy, Precision, Recall, and F1-score, all of which are close to 0.95, which indicates a great predictive capability and reliability.

The energy consumption is becoming a constraint on all computer devices, from smartphones to supercomputers. Consequently, the focus has moved from performance to energy and power consumption. Design metrics are not only based solely on performance, as the energy performance of application executions is becoming the main aspect of architecture. Also, Design metrics depend on, the manufacturers of semiconductor chips which, have implemented multicore processors to boost the level of energy efficiency by using verified techniques for voltage and frequency scaling. To utilize the maximum potential of such architectures, we need to make the right decisions because parameters such as core type, frequency, and utilization typically affect power dissipation and performance. This paper proposes a new algorithm to achieve energy-efficient by monitoring core energy and level utilization control such as: Increasing the number of cores to execute the task, scaling voltage, and frequency. Based on the built model, we analyze the energy efficiency variations for different platform configurations providing the same level of performance. We show that trading the number and type of core with frequency and voltage level and core utilization rate can lead to substantial energy efficiency gains.

Medical applications have become integral components of contemporary healthcare, leveraging advancements in technology to enhance patient care. Therefore, this chapter proposes that the application based on artificial intelligence (AI) using current technologies acts as an intermediary between the patient and the doctor. The proposed application is working in four phases. In the first phase, it collects comprehensive data about the patient, including test results, x-rays, and the patient's self-reported health status. This data is compiled into a file for each patient and stored in a database. In the second phase, the application then provides an initial diagnosis based on an artificial intelligence (AI) model and advises whether a doctor's consultation is necessary. In the third phase, the consultation is recommended, and the patient's file and initial diagnosis are included in the booking process. Finally, this comprehensive data is also emailed to both the patient and the doctor for their reference. This proposed medical application aims to redefine the doctor-patient relationship, streamlining communication for easier, more consistent, and highly accurate interactions.