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Go to Editorial ManagerIn the period of digital transformation, oil companies have to cope with management of huge volume staff data from different parts company everything from management, maintenance, engineering and geology to drill teams at heart workers front line standpoint. This research presents a complete study of big data accuracy and classification improvement in K-means Clustering Learning (KCL) management for 20,000 employees in an oil company. Data were auto-generated according to global standards and technical specifications. The data tables for formwork of human resources bars were 90% prepared by file laziness. In fact, the test kernel used in this research is also based on this data. The study focuses on important problems of work such as raising data quality and classification of employees according to various factors including practical experience, education levels technical expertise, competence achieved in performance evaluations (which may change over time) or safety training hours. Our methodology incorporates advanced preprocessing techniques, feature engineering and hyper parameter optimization in order to achieve better classification accuracy. The experimental results show that the optimized KNN algorithm is capable of 94.2 percent accuracy for employee classification, which represents a significant bat improvement over the traditional method. This research offers practical lessons for oil companies employing machine learning techniques in human resources management and improved operational efficiency learning and operational efficiency.
The rapid development of the Internet of Things (IoT) has drawn significant attention from both industry and academia, driven by the integration of cloud computing, big data analytics, machine learning, and cyber-physical systems in manufacturing. Programmable Logic Controllers (PLCs), long central to industrial control systems, have evolved from basic feedback control devices to advanced components capable of networking and data exchange through IoT technologies. The Industrial Internet of Things (IIoT) refers to intelligent automation systems that continuously monitor critical parameters and respond to changes in real time. The integration of IoT with PLCs is transforming industrial automation by enabling remote real-time monitoring, data-driven decision-making, and predictive maintenance through advanced analytics. IIoT technologies enhance manufacturing performance and offer strategic value across sectors. Understanding their impact involves examining current research, including technology assessments and application-based case studies. This study provides an overview of PLC systems evolving into IIoT frameworks, with a focus on implementing proportional-integral (PI) control using the Siemens S7-300. Designed for precise and consistent temperature regulation, this approach enhances process efficiency and product quality, making it highly suitable for industrial and manufacturing environments.
The integration of Deep Learning (DL) techniques with the Internet of Things (IoT) has emerged as a transformative paradigm in the advancement of smart healthcare systems. Numerous recent studies have investigated the convergence of these technologies, demonstrating their potential in improving healthcare delivery, patient monitoring, and clinical decision-making. The ongoing evolution of Industry 5.0 in parallel with the deployment of 5G communication networks has further facilitated the development of intelligent, cost-effective, and highly responsive sensors. These innovations enable continuous and real-time monitoring of patients’ health conditions, a capability that was not feasible within the constraints of traditional healthcare models. Smart health monitoring systems have thus introduced significant improvements in terms of speed, affordability, reliability, and accessibility of medical services, particularly in remote or underserved regions. Moreover, the application of Deep Learning and Machine Learning algorithms in health data analysis has played a pivotal role in achieving preventive healthcare, reducing mortality risks, and enabling personalized treatment strategies. Such methods have also enhanced the early detection of chronic diseases, which previously posed considerable diagnostic challenges. To further optimize scalability and cost-efficiency, cloud computing and distributed storage solutions have been incorporated, ensuring secure and real-time data availability. This review therefore provides a comprehensive perspective on smart healthcare innovations, emphasizing the role of intelligent systems, recent advancements, and persisting challenges in the domain of digital health monitoring.
Internet of Things (IoT) technologies, particularly the Internet of Vehicles (IoV), have transformed transportation, enabling safer, more efficient, and intelligent mobility solutions. As mobile data and devices increase, cellular networks can support vehicular communication features for safety and non-safety purposes. This paper examines IoV integration with 5G communication technology in a smart city. With varying levels of vehicles numbers.5G efficiently supports internet vehicle communications with slicing technology offering a practical solution for IoV services. This research includes the description of the Internet of Vehicles with 5G system components. Covering the 5G with IoV in the smart cities framework for the development industry. Provide the simulation result for the IoV-5G proposed system. The results show that 5G-IoV outperforms IoV and LTE in every measured parameter, delivering up to 32% greater channel gain rate, about 65–70% lower network latency, and roughly 20–25% higher network transfer rate. The study examines and summarizes our simulation platform's performance. The analysis will be implemented by SUMO, Simu5G in the OMNeT++ simulation program.
This paper presents a simulation-based framework to optimize 5G/6G mmWave network deployments in enterprise environments. Using ns-3 and NYUSIM, it evaluates next-generation Node B (gNB) placement, beamforming, and handover strategies across factory, office, and campus settings. Leveraging the inherent high bandwidth and low latency capabilities of mmWave technology, this study systematically addresses critical challenges such as severe signal attenuation, dynamic blockage, and efficient beam management in complex indoor and outdoor enterprise settings, including large-scale industrial complexes, multi-floor smart offices, and expansive university campuses. Utilizing established open-source network simulators, specifically ns-3, and integrating publicly available, industry-standard channel models such as 3GPP TR 38.901 and NYUSIM, the research proposes and rigorously evaluates novel deployment strategies, advanced beamforming techniques, and intelligent handover mechanisms. The anticipated outcomes include validated guidelines for optimal base station placement, robust performance benchmarks for key enterprise applications (e.g., Ultra-Reliable Low-Latency Communication (URLLC), enhanced Mobile Broadband (eMBB), massive Machine-Type Communication (mMTC)), and a robust, extensible simulation framework. This work aims to provide critical, data-driven insights for telecommunication providers and network planners, enabling them to design and implement superior, reliable, and future-proof 5G/6G connectivity solutions, thereby accelerating digital transformation across various industrial and commercial sectors.