In smart cities, effective traffic congestion management hinges on adept pedestrian and vehicle detection. Unmanned Aerial Vehicles (UAVs) offer a solution with mobility, cost-effectiveness, wide field of view, yet, optimizing recognition models is crucial to surmounting challenges posed by small occluded objects. To address these issues, we utilize the YOLOv8s model Swin Transformer block introduce PVswin-YOLOv8s for detection based UAVs. Firstly, backbone network incorporates global...

This article presents a multi-intelligent reflecting surface (IRS)- and multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system for 5G/6G networks. In the studied system, multiple UAVs are integrated providing services to large-scale user equipment (UEs) with help of IRSs. aims minimize overall cost including energy consumption, completion time, maintenance by jointly optimizing trajectories phase shifts When solving this problem, one has count in mind deployment stop...

This paper presents a UAV-swarm-communication model using machine-learning approach for search-and-rescue applications. Firstly, regarding the communication of UAVs, receive signal strength (RSS) and power loss have been modeled random forest regression, mathematical representation channel matrix has also discussed. The second part consisted swarm control modeling UAVs; however, dataset five types triangular formations was generated, K-means clustering applied to predict cluster. In order...

This study presents an enhanced deep learning approach for the accurate detection of eczema and psoriasis skin conditions. Eczema are significant public health concerns that profoundly impact individuals' quality life. Early diagnosis play a crucial role in improving treatment outcomes reducing healthcare costs. Leveraging potential techniques, our proposed model, named "Derma Care," addresses challenges faced by previous methods, including limited datasets need simultaneous multiple...

Emergency vehicle detection plays a critical role in ensuring timely responses and reducing accidents modern urban environments. However, traditional methods that rely solely on visual cues face challenges, particularly adverse conditions. The objective of this research is to enhance emergency by leveraging the synergies between acoustic information. By incorporating advanced deep learning techniques for both data, our aim significantly improve accuracy response times. To achieve goal, we...

In this Paper, we create an augmented reality cricket broadcasting application that uses player recognition and automatic detection during play to display personal data. The system AdaBoost detect face use PAL based model recognize the faces of players on field. is trained a large dataset game footage achieves high accuracy in detecting recognizing players' even with several conditions such as occlusion, non-uniform illumination pose variation. has potential enhance viewing experience games...

Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application several fields, such as office automation document processing. However, OAHR continues face challenges, including high variability script intrinsic characteristics cursiveness, ligatures, diacritics, unlimited variation human handwriting, lack large public databases. In this paper, we introduce a novel context-aware model based on deep...

Over the past few years, much work has been done to develop machine learning models that perform Arabic sentiment analysis (ASA) tasks at various levels and in different domains. However, most of this based on shallow learning, with little attention given deep approaches. Furthermore, used for ASA have noncontextualized embedding schemes negatively impact model performances. This article proposes a novel learning-based multilevel parallel neural (MPAN) uses simple positioning binary scheme...

Abstract Cloud computing plays a vital task in our daily lives, which an enormous amount of data is stored on cloud storage. The highest priority for storage guaranteeing the security confidential data. can be realised through utilising one cryptographic mechanisms including encryption and hiding. With rapid development realization quantum computers, modern cryptosystems may cracked systems. Accordingly, it crucial achieving confidentiality before availability computers. Therefore, this...

Remote healthcare and telemedicine technology have witnessed a large rapid development in the last decade with of Internet Things (IoT) technology, where various types medical sensors are aggregated for measuring parameters transmitting them anywhere. Smart portable products can now be used to monitor different aspects track human health. Also, they prediagnosis diseases detecting abnormalities organ functionality. In this article, we design implement multifunction health monitoring system,...

Social media networks have grown exponentially over the last two decades, providing opportunity for users of internet to communicate and exchange ideas on a variety topics. The outcome is that opinion mining plays crucial role in analyzing user opinions applying these guide choices, making it one most popular areas research field natural language processing. Despite fact several languages, including English, been subjects studies, not much has conducted area Arabic language. morphological...

In the field of medical imaging, deep learning has made considerable strides, particularly in diagnosis brain tumors. The Internet Medical Things (IoMT) it possible to combine these models into advanced devices for more accurate and efficient diagnosis. Convolutional neural networks (CNNs) are a popular technique tumor detection because they can be trained on vast imaging datasets recognize cancers new images. Despite its benefits, which include greater accuracy efficiency, disadvantages,...

Cybersecurity threats are increasing rapidly as hackers use advanced techniques. As a result, cybersecurity has now significant factor in protecting organizational limits. Intrusion detection systems (IDSs) used networks to flag serious issues during network management, including identifying malicious traffic, which is challenge. It remains an open contest over how learn features IDS since current approaches deep learning methods. Hybrid learning, combines swarm intelligence and evolution,...

Social media, fake news, and different propaganda strategies have all contributed to an increase in misinformation online during the past ten years. As a result of scarcity high-quality data, present datasets cannot be used train deep-learning model, making it impossible establish identification. We natural language processing approach issue order create system that uses deep learning automatically identify news items. To assist scholarly community identifying text this study suggested texts...

Mobile edge computing (MEC), located at the networks edge, enhances distributed computing. However, its fixed position presents limitations during emergencies. Integrating unmanned aerial vehicles (UAVs) into MEC systems offers a solution but introduces challenges in managing UAV collaboration. This paper proposes Reinforcement Deep Q-Learning based multi-UAV framework to optimize quality of service (QoS) and route planning. The proposed addresses these by modeling user demand using...