In recent years, telehealth and telerehabilitation have been on the rise due to lock down and quarantine placing restrictions on in-person healthcare during the 2020 Covid Pandemic. However, there are some limitations to the service that physicians are able to do it remotely. This paper proposes a contemporary way of collecting more data from the remote patient to help during their telerehabilitation appointments. Surface electrodes are placed on the participants' forearm flexor muscles and samples were collected for each exercise. The data were then analyzed and the features, RMS, IEMG, and VAR were used. The exertion of the strength of the muscle during an exercise could be seen from plotting the RMS of the exercise to the IEMG time domain graph, and the classification of the exercise could be interpreted from the IEMG data from one exercise, that has been normalized, which was plotted as a box plot diagram to be compared with the other exercises. The findings from this paper could be used in helping to build a model to ensure that the exercises are done correctly and the muscles are not being strained too hard during an exercise by the physician during a telerehabilitation session.
<strong><em>This study delves into the practical application and fine-tuning of YOLOv8 models for real-time tomato recognition using drone imagery within greenhouse environments. We evaluated YOLO&rsquo;s speed, robustness, and adaptability, finding that varying batch sizes and epochs had minimal impact on performance. Notably, YOLOv8n performed on par with the extra-large YOLOv8x model, offering a significant advantage: training time was up to 60 times shorter. Further tuning and innovative training strategies revealed that the Final Learning Rate (lrf) and dataset annotation quality were the most influential factors for model performance. Fine-tuning the lrf and re-annotating datasets markedly improved accuracy, underscoring the importance of optimizing learning rates and maintaining high-quality annotations for effective YOLO models. Our results also demonstrated the superiority of YOLOv8 over YOLOv5. The optimized YOLOv8n model is well-prepared for deployment in upcoming tomato recognition tasks, paving the way for more efficient agricultural monitoring. This work also provides valuable insights into the broader field of object recognition and offers practical guidance for researchers tackling similar challenges.</em></strong>
<div style="text-align:justify">Vehicular Ad hoc networks (VANETs) become a most trending topic nowadays hence it is attracted by the industrial environment and the academic sector. The vehicles which are present in the network communicate with the adjacent vehicles and the roadside units through the routing model using the standard of IEEE 802.11p. To make the routing more flexible the vehicles undergo certain challenges hence the mobility of the vehicles is so high and that result in the failure in communication link. Additionally due to irregular spectrum allocation the emergency data transmission is disturbed and that greatly reduces the communication quality. In order to overcome these drawbacks in this article effective spectrum allocation with priority function and multipoint relay based efficient routing model (ESPMRR) is developed in the vehicular communication. The core modules are network construction, channel model creation, priority-based data transmission and multipoint relay-based routing. Through this process the earlier drawbacks are rectified and that leads to attend an effective communication among the vehicles. The implementation of ESPMRR is constructed in the software NS3 and the matrix like energy efficiency, routing overhead, packet delivery ratio, network throughput and average delay are measured. The simulation results prove that ESPMRR attends better results when compared with the previous works.</div>
Firewalls are network security systems positioned between internal and external networks to isolate them. Their fundamental functions include zone isolation, access control, attack protection, and redundancy design. However, firewalls also face numerous security challenges, with Distributed Denial of Service (DDoS) attacks being a major concern, particularly the Denial of Firewalling (DoF) attacks targeting firewalls. Despite extensive research on DDoS attacks against traditional networks, relatively fewer studies focus on DoF attacks. To comprehensively understand the latest research progress and inspire the development of new solutions to counter DoF attacks, this paper conducts an extensive survey of existing research progress and forms a review. Firstly, we analyze the principles of DDoS attacks against firewalls, as well as the security risks of new firewall technologies, and classify them based on attack rates and target components of firewalls. Secondly, we analyze and evaluate existing DoF attack de_x0002_tection technologies. Next, we summarize existing DoF attack mitigation techniques. Finally, we discuss current challenges and open issues. It is hoped that this research work will assist relevant researchers in effectively addressing DoF attacks.
This article investigates the offloading performance of an unmanned aerial vehicle (UAV)-aided nonorthogonal multiple access (NOMA) incorporating mobile-edge computing (MEC) with the wireless power transfer (WPT)-enabled in Internet of Things (IoT) networks. To assess the system efficacy, we derive the closed-formed expressions of outage successful computation probability (OSCP) under Nakagami-m fading channel. Subsequently, we formulate a system optimization problem of maximizing OSCP by utilizing particle swarm optimization (PSO) algorithm. Numerical findings are implemented with a variety of parameters, thereby validating the precision of our work.
<em>The results emphasize HR professionals&#39; importance in driving HRA&#39;s adoption, highlighting its performance gains, and leveraging social influence. Organizations should adopt a supportive culture to improve the transition from intention to actual usage. </em>
Wireless technology enters into the recent advancement by incorporating the embedded systems to develop vehicular communication with high performance. In vehicular ad hoc networks (VANETs) due to high-speed vehicles several challenges occurred like vehicle integrity management, speed management and effective data collection with road traffic event management. To monitor the huge number of vehicles in an efficient way in a large-scale coverage area in recent times unmanned aerial vehicles (UAVs) are developed. This technology provides effective communication among the ground level and the air medium. To attend high quality communication of the vehicles in this article dynamic mobility based effective load balancing and QoS aware network selection (DELQNU) among the UAVs are constructed. The core modules of this proposed DELQNU are UAV network model, mobility model and QoS model. Using these ideas monitoring a huge number of vehicles even at the densely populated and high-speed technology becomes possible and highly efficient. The implementation of this network is constructed in NS3 software and SUMO and the parameters which are calculated for this analysis are packet delivery ratio, throughput, delay, routing overhead and energy efficiency. From the final outcome it is proven that DELQNU attends maximum results in terms of delivery ratio and throughout when compared with the earlier baseline methodologies.
Vehicular ad hoc networks (VANETs) is a kind of mobile communication in which the vehicles are considered as notes and it includes certain wireless routers. In recent times an increased number of vehicles create a traffic congestion problem in urban enrollment. In recent scientific research to normalize the communication issues of the vehicles, unmanned aerial vehicles (UAVs) are embedded with the vehicular communication mainly to control the traffic congestion and insufficient bandwidth utilization of the vehicles. The aerial vehicles are highly flexible and efficient so that they are able to control the vehicles in an efficient manner. But still certain drawbacks are present in the aerial vehicles such as improper localization and ineffective data transmission. To solve these flaws, in this article reliable data transmission and efficient vehicle path planning in the cooperative communication model (RDTEVP) is developed. The core modules of this model are reliability base network modeling and path planning based routing. Through this process the efficiency of the network is maximized and the data transmission quality is improvised. This model is structured in the software called NS3 parameters which are taken into consideration to analyses the device performance are packet delivery ratio, network throughput, energy efficiency, average delay and routing overhead. The term delay and routing overhead are greatly minimized in the RDTEVP when compared with the earlier schemes.
The Internet of things (IoT) is one of the most trending technologies which is used to monitor a huge number of devices worldwide. Device localization and optimal path selection is very essential in this technology to maintain the communication standard of the devices. To reduce the delay and power utilization of the devices and to attend high efficiency these parameters are needed to get concentrated. For that in this article distributed self-localization with an improved optimization model is developed using machine learning (DSLIOM) algorithms. The core modules of this article are efficient data processing analysis and improved optimization algorithm. This network structure with the huge number of devices is simulated in the software called NS3 where a large number of devices are effectively monitored and properly localized. The parameters which are calculated to analyses the performance are data success rate, network throughput, routing overhead, data loss rate and delay. From the result it is proven that this DSLIOM attends better performance than earlier works in terms of data success rate and the network throughput
<div style="text-align:justify">In recent decades, vehicular ad hoc networks (VANETs) have emerged the big way mainly to perform high quality and intelligent communication. The mode of transmission includes the vehicles and the roadside units and due to high speed in rapidly changing mobility several link failures and delay occurs in the network. Several earlier models are developed to improve the mobility characteristics of the dynamic vehicles but still this topic is under an open research area. Mainly to expand the efficiency and confidentiality of the devices in this article trust based relay node selection and efficient multi hop clustering model (TRSEMC) is developed. The core modules of this model are efficient network construction, multi hop clustering and vertical trust management. Even through this process the efficiency is greatly increased and that leads to expanding the life span of the devices. The proposed concept is constructed in the simulator called NS3 and the matrix which are taken to analyses the performance of the network are throughput, network delay, routing overhead, transmission accuracy and energy efficiency. This TRSEMC greatly fulfills the identified research gap and through an effective clustering mechanism the efficiency of the vehicles is increased than the earlier models.</div>
A Vehicular Ad hoc Network (VANETs) is the technology which is developed to provide intelligent communication between the vehicles and the roadside units and pedestrians in terms of high network safety and accuracy. Due to certain characteristics of vehicles such as high speed and variable mobility the performance of the network is diminished. In order to provide high efficiency to the vehicles at the time of high speed data transmission in this article certain models are developed. A novel energy consumption model and grey wolf optimization model (ECGWO) is developed in the vehicular network to attend high efficiency among the vehicles. Reduction of power utilization for each transmission among the vehicles from one place to another, helps to improvise the efficiency of the network. Providing optimization at the time of data transmission the information gets travelled in optimal path so that the network delay and overhead is greatly reduced. As a whole, this model provides a better communication standard for the vehicles in the network. This way network is simulated in the software NS3 and the parameters which are considered for analyze the effectiveness of the proposed model are data success rate, data loss rate, average delay, network throughput and routing overhead. The simulation result shows that the ECGWO has better success rate and throughput than other related methods.
<strong>The integration of vehicular communication and unmanned aerial vehicles (UAVs) technology has become a most trending topic and it occupies maximum of the attention of both the industrial and academic sectors. To achieve high quality communication with the ground and the air medium the aerial vehicles are connected to the cellular network so the year energy constraints are normalized. At the time of high speed data transmission the vehicles underwent certain drawbacks like delay during data uplink and high power consumption. To overcome these drawbacks in this article resource management and Generic Algorithm (GA) based scheduling (RMGAS-UAV) is developed for aerial networks based environments. The core modules of RMGAS-UAV are efficient system model and GA based drone scheduling. This models the data transmission quality of the aerial vehicles are highly improved. This network model is designed in the software called NS3 and the parameters which are taken for result calculation are data delivery ratio, network throughput, routing overhead, energy efficiency and energy consumption. From the calculated results it is shown that the RMGAS-UAV obtained better results in terms of energy efficiency and data delivery when compared with the earlier methods.</strong>
