Abstract In the operation and control of power systems, load frequency control (LFC) plays a critical role in ensuring the stability and reliability of interconnected power systems. Modern power systems with significant penetration of highly variable and intermittent renewable sources present new challenges that make traditional control strategies ineffective. To address these new challenges, this paper proposes a novel LFC strategy that employs a cascaded fractional-order proportional integral-fractional-order proportional integral derivative with a derivative filter (FOPI-FOPIDN) as a controller. The parameters of the FOPI-FOPIDN are optimised using a variant of the particle swarm optimization (PSO) in the literature called ADIWACO. The effectiveness and scalability of the proposed strategy are validated by extensive simulations conducted on two- and three-area test systems and performance comparisons with recent LFC control strategies in the literature. The performance metrics used for the evaluation are ITAE values, deviations in the power flows in the tie-lines, and deviations in the frequencies of the control areas with the power systems subjected to diverse load and RES generation disturbances in several experimental scenarios. Governor dead band, communication time delay, and generation rate constraints are considered in one of the scenarios for more realistic evaluation. Again, the controller’s robustness to uncertain model parameters is validated by varying the parameters of the three-area test system by ± 50%. The simulation results obtained confirm the controller’s robustness and its superiority over the comparison LFC strategies in terms of the above performance metrics. 

Optimally tuned cascaded FOPI-FOPIDN with improved PSO for load frequency control in interconnected power systems with RES

Research has shown that the use the Internet of Things (IoT) and artificial intelligence (AI) can enhance the effectiveness of telemedicine. Therefore, the objective of this study is to systematically investigate the utilization of machine learning (ML)-based IoT during the COVID-19 pandemic. A comprehensive search of PubMed, Scopus and IEEE was conducted from January 2020 to February 2023 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Out of the 1576 studies identified, 57 were included in the analysis. The use of IoT was 30 % for detection and 22 % for monitoring. Parameters considered included vital signs, diagnostic images, physical symptoms, tests, air quality, and medical history. A review of the sensors revealed that body temperature sensors were of interest in 65 % of the studies. The random forest algorithm was found to be the most common ML model, used in 14 % of the studies. Performance measures were reported in over 90 % of the studies to evaluate the models. This study provides valuable insights into the integration of IoT and ML to address challenges related to COVID-19. This systematic review can serve as a roadmap for advancing research at the intersection of healthcare, IoT, and ML. 

Investigating the role of machine learning techniques in internet of things during the COVID-19 pandemic: A systematic review

The health care sector is focusing on in-home health care services, where the patients can receive medical care in the privacy of their own home. A patient in a rural region can use a remote health monitoring system to communicate with a doctor in a city who is in a larger city. Machine learning has been used for smart health monitoring systems. They used a wearable sensor to identify a set of five parameters, including Electrocardiogram (ECG), pulse rate, pressure, temperature, and position detection. The technology uses machine learning algorithms to identify doctors for consultation and to identify and predict ailments. In the study, IoT technology and health monitoring have been coupled to give more personalized and responsive health care. The primary purpose of the system is to monitor patients' vital signs in real-time monitoring. The authorized individual can access the patient' s vital signs from their smartphone or PC using a cloud server. The Decision Tree (DT) attained the best accuracy of 99.1 percent after testing the suggested model, which is promising for their purposes. It is observed that the DT achieves best accuracy, while Random Forest is the second-best classifier for this problem. 

IoT and Cloud Based health monitoring system Using Machine learning

The integration of IoT with fuzzy logic provides innovative solutions for various medical applications. IoT enables seamless connectivity between medical devices, real-time monitoring, and data collection. Fuzzy logic has the ability to handle uncertainty and imprecision, thereby enhancing decision-making, medical diagnosis, and treatment. By combining IoT with fuzzy logic, medical providers can develop an intelligent system that improves the quality of healthcare systems. Moreover, the integration of IoT with fuzzy logic enables healthcare providers with powerful predictive analytics capabilities, enabling early detection of diseases and proactive interventions. Several research papers in the literature have investigated the use of IoT with fuzzy logic in medical applications. This review focuses on the use of IoT with fuzzy logic in medical applications. The contributions of this paper are to review the current state of the literature and the potential direction of the medical application of fuzzy system with IoT, and propose a novel scheme that combines Generative Artificial Intelligence (GAI) such as ChatGPT with a fuzzy-based IoT framework. The benefits, challenges, power management, and privacy are also discussed in this paper. The publications are classified according to the type of medical applications. In addition, they are classified according to the type of fuzzy logic used into four categories. We present this review to show the current status in this field. We used Scopus and Google Scholar databases as the basis for presenting this review. The number of publications, number of citations, and other information are discussed to show the achievements in this field. The related research is examined, compared, and classified, focusing on the advantages, challenges, and future research areas.

Internet of Things Based Fuzzy Systems for Medical Applications: A Review

The health care sector is focusing on in-home health care services, where the patients can receive medical care in the privacy of their own home. A patient in a rural region can use a remote health monitoring system to communicate with a doctor in a city who is in a larger city. Machine learning has been used for smart health monitoring systems. They used a wearable sensor to identify a set of five parameters, including Electrocardiogram (ECG), pulse rate, pressure, temperature, and position detection. The technology uses machine learning algorithms to identify doctors for consultation and to identify and predict ailments. In the study, IoT technology and health monitoring have been coupled to give more personalized and responsive health care. The primary purpose of the system is to monitor patients' vital signs in real-time monitoring. The authorized individual can access the patient' s vital signs from their smartphone or PC using a cloud server. The Decision Tree (DT) attained the best accuracy of 99.1 percent after testing the suggested model, which is promising for their purposes. It is observed that the DT achieves best accuracy, while Random Forest is the second-best classifier for this problem.

In this work, an IoT based on a vital health marker monitoring system is proposed and implemented using artificial intelligence by means of fuzzy logic, and the use of AT Mega and ESP32 microcontrollers to accomplish the proposed work and provide health monitoring. And the analysis to make a decision for the caregiver or the doctor by putting the results on the dashboard platform, we used several sensors to get three applications, two of which are directly related to human health and the third related indirectly. The first is the most important one that gives a decision about the condition of the observed person whether it is normal, abnormal or dangerous by measuring three vital signs (heartbeat, Body temperature, blood oxygen percentage) and Using fuzzy logic within the controller (Arduino) because data processing is precise and fast and results in smart health services that are closer to reality, while the second application is to detect the Covid-19 virus, which threatens the elderly in particular, through the two most important signs Except that it is the body temperature and the percentage of oxygen in the blood. In the event that they are outside of normal, the device sends an alert that the necessary analysis is necessary, and in the event that the monitored person needs oxygen, the device contains a sensor to measure the percentage of oxygen and the speed of its flow through the administration device and displays the results on the platform, the third application It is to monitor the atmosphere surrounding the patient in terms of temperature and humidity and to detect the presence of dangerous carbon monoxide. Through the experience, we found useful results for the observers, which were represented by giving an initial result of the degree of severity of the patient, as well as the alerts that the device sends on the telegram program, as well as displaying the results on the platform, which allows the possibility of monitoring easily from anywhere at any time. Finally, the proposed system can help a large segment of the elderly and those with chronic diseases, as well as patients with virus covid-19 inside their homes, as well as it may contribute to building an extensive monitoring and analysis system in the units or departments of the hospitals to help doctors make the diagnosis accurate because they receive all the necessary data for patients. © 2022 Author(s).

Smart health monitoring and self-analysis system based on internet of things with fuzzy controller

This paper presents an algorithm for load frequency control of multi-area power system. A more realistic models is used to represent the elements of power system. The frequency bias factor (B) is set optimally. The effect of generation rate constraints (GRC) and the dead band characteristics of the governor is taken into consideration. A simultaneous optimization of the fractional order (PIλ Dµ) control parameters, the speed regulation (R) of the governor and the frequency bias factor (B) is proposed for the first time. The objective function to be minimizes is a modified integral time absolute error (MITAE). The tuning of the control parameters is based on using cuckoo search algorithm (CSA) to minimize the objective function. The proposed algorithm is applied to a typical interconnected three-area power systems with a different levels of load step change. Results of simulation of the presented method are compared with those obtained by applying particle swarm optimization technique. The results obtained reveal the robustness of the suggested algorithm in terms of settling time and peak over shoot. 

Load frequency control of interconnected power system using artificial intelligent techniques based fractional order PIλ Dµ controller

For easy life in which everything is reachable through available applications and technologies related with communication systems, it is important to connect everything in our life together via new technology known as Internet of Thing (IoT). A special case of IoT is the Internet of Vehicles (IoV) which in this paper we review in briefing its architectures, applications, clustering methods, standards and protocols. Since we are in mobility always, then communicating with resources that available on internet or any system that provides any service is not easy unless we utilize things in our surrounding environment to be used as a connected network, things (like vehicles specially) where connected with each other or with road base stations will form a special network known as Vehicle Ad-hoc Networks (VANETs). For different IoV architecture types different applications are performed, some of these applications required clustering techniques to provide more managing on vehicular networks. For reliable communication many wireless communication technologies based special protocols are discussed, explaining the cases in which each technologies and related protocols are utilized, considering the utilization of a combined wireless communication technologies in some cases for more reliable communication.

Internet of Vehicles: Architectures, Protocols and Communication Technologies

As a consequence of the global increment in population, it becomes required to provide them with varieties of high-quality agricultural crops in huge quantities, which would be problematic without mechanization, thus, it is necessary to employ artificial intelligence and computer vision in the process of sorting and classifying these crops. Recently, many Technologies in the automatic sorting machine identifying most types of crops and sorting them from good to bad. The speed and accuracy of these technologies will increase the mass production and quality requirements and helps to minimize the overwork on human labor by saving cost and time. This paper has reviewed different methods used to sort and classify agricultural crops and fruits using image processing, the segmentation process is one of the most important techniques in image processing to simplify and classify the image to be able to pre-process a specific part of it by using different methods. Previous work was reviewed where the thresholding method was used to simplify the image processing by eliminating the background. The image was classified using k-mean clustering which helped in detecting the defected areas and other methods. The accuracy of the segmentation methods ranged between 80 to 99%, depending on the method used and its purpose which is a good percentage. The main goal of this paper is to introduces the information on image segmentation method, its types, and the most popular methods have been used in agriculture, by takes the advantage of Artificial intelligent.

Crop Sorting Using Image Segmentation and Artificial Intelligence: A Review

Automatic traffic violation fine recording systems play important role to decrease traffic rules violation and accidents, the traffic fine recorded according to traffic violation type and violation location. Manual traffic fine recording may cause false fine recordings. Systems developed to guarantee driver rights of such false traffic fines. Vehicle Black Box systems developed for recording variations in vehicles in order to minimize or analyze accidents. Other systems record traffic rules violations automatically and report them to traffic authority system. In this paper, a Vehicle Black Box developed for, safe driving and false traffic fine recordings issues with proofing the availability of a vehicle at traffic violation location with date and time stamps; the system based on Internet of Vehicles model with the utilization of Long Range wireless technology to transmit a vehicle acquired data to a base station. Vehicle-to-Infrastructure communication mode designed. Acquired data sent to an Internet of Things platform (ThingSpeak) to be stored, visualized and analyzed. Real time test done; Received Signal Strength Indicator value measured indicating the performance of LoRa signal propagation at 433MHz modulation in such environment. A correct location obtained and monitored on the ThingSpeak platform. We found that there is no any interference between two vehicles when getting the information from them, where each vehicle has its own code and send its data separately from the other.

Vehicle Black Box Monitoring based ThingSpeak with Long Range Technology

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