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Go to Editorial ManagerAs a result of the tremendous development taking place in modern systems and technologies in the field of electronic monitoring. Intelligent monitoring, decision making, and automated response systems have become common subjects at this time, especially after the development of machines responsible for these processes. Traffic surveillance is a trend goal nowadays using different techniques and equipment. In this article, real-time Object detection and tracking techniques were proposed for traffic surveillance using image processing techniques. A state was specifically examined for its ability to detect and count passing motorcycles on a highway in a specific area. The results showed good reliability, with a frame processing time of approximately about (30 ms) and the achievement of real-time performance. The main contribution of this article is reaching the best result implemented by the performance the real-time process using image process technique and tracking the object by depending on the sequencing of frames and can stands with rationally not so powerful machines. Several tools have been used for different types of necessary tasks that will be part of the required application such as Python 3.7; which was used to build the basic algorithms,Visual studio code (VSC) as an Integrated Development Environment (IDE), and Anaconda navigator for downloading many useful libraries. The specifications of the used device were Intel(R) Core (TM) i7- 10750H CPU @ 2.60GHz 2.59 GHz, RAM 16.0 GB, NVIDIA GeForce GTX 1650 GPU, 64-bit operating system, x64-based processor.
This study evaluates the performance and efficiency of four deep learning models—VGG-16, ResNet-50, Inception-V3, and DenseNet-121—in detecting pneumonia from chest X-rays, addressing the critical need for balanced accuracy and computational efficiency in clinical diagnostics. Methods: A dataset of 5,234 chest X-rays (3,875 pneumonia, 1,341 normal) was augmented via rotation, flipping, and zooming to mitigate class imbalance. Models were trained on an RTX 2060 GPU for 40 epochs, with performance assessed using accuracy, F1 score, sensitivity, specificity, precision, and computational metrics (training time, memory usage). Statistical significance was validated via paired t-tests (p < 0.05). Results: DenseNet-121 achieved the highest accuracy (95.2% ± 0.8), F1 score (95.1% ± 0.7), and throughput (400 images/sec) with minimal memory usage (33MB). ResNet-50 and Inception-V3 showed moderate performance, while VGG-16 exhibited overfitting tendencies. In conclusion, DenseNet-121 showed strong performance compared to other models, both in terms of accuracy and processing speed, which is essential for use in real-time clinical settings. However, the small size of the validation set and limited population diversity are important limitations that should be addressed in future studies. Moreover, more testing on larger datasets is needed to confirm the stability of the model and see how the model will work in different settings. Future work should address ethical considerations in AI-driven diagnostics and validate findings across multi-institutional datasets.
In Urban cities, services are supported by intelligent applications and are connected to each other through ad hoc networks. Any service can be operated using a compatible of an Internet of Things (IoT) technology. This study focuses on the transportation service and finding a non-cost solution to solve the crossroads congestion that affected people time and money. The Wireless Sensor Networks (WSNs) that are planted on the roads can help in monitoring the roads situation by collecting their data and send them through wireless communication to a traffic management center. In this work two phases of time are considered for a crowded area. Low-cost components are suggested to solve the congestion at the cross roads without the need for reconstruct the roads. IoT device such as smart phone can be wirelessly connected to the Traffic Management Center (TMC), which can analyze the incoming data from WSN and send back the calculated time to the police officer to control the green light long and overcome the standard time installed for all directions. The main idea is to solve the congestion problem in real time by extending the time long of the green traffic light for the road direction with the highest vehicle density. The suggested algorithm was operated on a dataset of 6 days and for the time phase from 7:00-10:00am.
In the past few years, all over the world, crime against children has been on the rise, and parents always worry about their children whenever they are outside. For this reason, tracking and monitoring children have become a considerable necessity. This paper presents an outdoor IoT tracking system which consists of a child module and a parent module. The child module monitors the child location in real time and sends the information to a database in the cloud which forwards it to the parent module (represented as a mobile application). This information is shown in the application as a location on Google maps. The mobile application is designed for this purpose in addition to a number of extra functions. A Raspberry Pi Zero Wireless is used with a GSM/GPS module on shield to provide mobile communication, internet and to determine location. Implementation results for the suggested system are provided which shows that when the child leaves a pre-set safe area, a warring message pops up on the parent’s mobile and a path from the current parent location to the child location is shown on a map.
Medical image segmentation plays a crucial role in the realm of medical imaging. The process involves the division of an image to obtain a comprehensive view and ensure precise diagnostics. There are various methods that are employed, ranging from traditional approaches to the more advanced deep learning techniques. Both play a significant role in enhancing healthcare. With the continuous advancement in technology, there is a growing need for accurate segmentation. While traditional methods such as thresholding and region growing are effective, they may require human intervention for complex cases. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have significantly improved the process by learning intricate details and accurately segmenting the image. When these methods are combined, healthcare professionals can achieve high-quality, precise results. Furthermore, with the advancements in hardware and technology, real-time segmentation is now possible. Generally, the process of dividing medical images into segments is extremely important for the progress of healthcare with the help of artificial intelligence and the most recent advancements in the industry, such as explainable AI and multimodal learning. However, this meticulously detailed and in-depth review provides an all-encompassing and extensive analysis of the current methods utilized, their multitude of applications across various fields, and the promising emerging advancements that have the potential to pave the way for remarkable future improvements and innovations.
This study compares two different sockets, traditional and smart. It includes designs, manufacturing, and testing to evaluate the influence of the socket designs on gait symmetry. The proposed materials are locally available in the prosthetics center where traditional sockets are manufactured. and smart socket designs with the same materials as traditional additions. A simple electronic system programmed to control the movement of the stump by pneumatic pads and prevent slipping during movement is considered an advanced suspension system. A gait cycle test was carried out to evaluate the sockets. it was performed on a patient with AK amputation in two cases: the first when the patient was wearing the traditional and the second when wearing the smart. Where the difference in (gait cycle time, step velocity, heel contact, and mid-stance) between the left and right leg is equal to (0.54, 4.3, 0.19, and 0.34) respectively, when the patient uses the traditional, while these values reduce to (0.09, 0.7, 0.07, and 0.27) respectively when the patient used the smart, it improves comfort by modifying pressure distribution, relieving pressure points, and enhancing functionality through gait analysis. They adjust to the volume of the residual limb, ensuring an effective fit. Real-time monitoring and remote modifications decrease the need for in-person meetings and enhance user confidence. The smart socket, designed to fit user requirements, provides enhanced comfort, functionality, and independence. The studies will explore its long-term benefits and broader applications, focusing on its originality, practical implications, and outcome measurement.