Abstract The paper introduces a new path-planning robotic system methodology called Collision Avoidance and Routing based on Location Access (CARLA) for use in critical environments such as hospitals and crises where quick action and saving human lives are vital. The main focus of our framework is on accuracy and fast responses, such as delivering tools or items in a specific area while avoiding collisions with other robots and obstacles. CARLA is designed to provide quick responses during emergencies, unlike most existing algorithms that are integrated into site control units or distributed among mobile robots on-site. By being loaded onto a remote server node rather than individual robots, CARLA helps to conserve the robots' capabilities, hardware resources, and power consumption. Additionally, our system utilizes cloud computing and Fog servers technology to improve data transmission times between the cloud and smart devices, especially for applications with strict timing requirements like emergency response. The Fog platform is also leveraged to enhance on-site access to real-time interaction and location-based services by bringing processing power closer to the robots from far-off Cloud servers. CARLA has various applications, such as in factories and warehouses, where mobile robots need to be selected and directed by a central control system remotely. The proposed framework consists of three main modules: Robot Knowledge Module, Robot Selection Module, and Route Reservation Module, which will all be discussed in detail in this paper. The results of simulations using this framework show that the robots have improved flexibility and efficiency in terms of computing paths and successfully fulfiling requests without colliding, compared to traditional methods used in similar scenarios.

Abstract Numerous medical studies have shown that Alzheimer’s disease (AD) was present decades before the clinical diagnosis of dementia. As a result of the development of these studies with the discovery of many ideal biomarkers of symptoms of Alzheimer’s disease, it became clear that early diagnosis requires a high-performance computational tool to handle such large amounts of data, as early diagnosis of Alzheimer’s disease provides us with a healthy opportunity to benefit from treatment. The main objective of this paper is to establish a complete framework that is based on deep learning approaches and convolutional neural networks (CNN). Four stages of AD, such as (I) preprocessing and data preparation, (II) data augmentation, (III) cross-validation, and (IV) classification and feature extraction based on deep learning for medical image classification, are implemented. In these stages, two methods are implemented. The first method uses a simple CNN architecture. In the second method, the VGG16 model is the pre-trained model that is trained on the ImageNet dataset but applies the same model to the different datasets. We apply transfer learning, meaning, and fine-tuning to take advantage of the pre-trained models. Seven performance metrics are used to evaluate and compare the two methods. Compared to the most recent effort, the proposed method is proficient of analyzing AD, moreover, entails less labeled training samples and minimal domain prior knowledge. A significant performance gain on classification of all diagnosis groups was achieved in our experiments. The experimental findings demonstrate that the suggested designs are appropriate for basic structures with minimal computational complexity, overfitting, memory consumption, and temporal regulation. Besides, they achieve a promising accuracy, 99.95% and 99.99% for the proposed CNN model in the classification of the AD stage. The VGG16 pre-trained model is fine-tuned and achieved an accuracy of 97.44% for AD stage classifications.

Abstract The Internet of Robotic Things (IoRT) is an integration between autonomous robots and the Internet of Things (IoT) based on smart connectivity. It's critical to have intelligent connectivity and excellent communication for IoRT integration with digital platforms in order to maintain real-time engagement based on efficient consumer power in new-generation IoRT apps. The proposed model will be utilized to determine the optimal way of task offloading for IoRT devices for reducing the amount of energy consumed in IoRT environment and achieving the task deadline constraints. The approach is implemented based on fog computing to reduce the communication overhead between edge devices and the cloud. To validate the efficacy of the proposed schema, an extensive statistical simulation was conducted and compared to other related works. The proposed schema is evaluated against the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Artificial Bee Colony (ABC), Ant Lion Optimizer (ALO), Grey Wolf Optimizer (GWO), and Salp Swarm Algorithm to confirm its effectiveness. After 200 iterations, our proposed schema was found to be the most effective in reducing energy, achieving a reduction of 22.85%. This was followed closely by GA and ABC, which achieved reductions of 21.5%. ALO, WOA, PSO, and GWO were found to be less effective, achieving energy reductions of 19.94%, 17.21%, 16.35%, and 11.71%, respectively. The current analytical results prove the effectiveness of the suggested energy consumption optimization strategy. The experimental findings demonstrate that the suggested schema reduces the energy consumption of task requests more effectively than the current technological advances.

Sports video analysis has received much attention as it turned to be a hot research area in the field of image processing. This motivation offers opportunities that develop fascinating applications supported by analysis of different sports, especially soccer. Ball identification, in soccer images, is an essential task not only for goal-scoring but also for performance evaluation. However, ball detection suffers from several hurdles such as occlusions, fast-moving objects, shadows, poor lighting, color contrast, and other static background objects. Although several ball detection techniques have been introduced such as Frame Difference, Mixture of Gaussian (MoG), Optical Flow, and so forth; ball detection in soccer games is still an open research area. In this paper, a new Fuzzy Based Ball Detection (FB2D) strategy is proposed for identifying the ball through a set of image sequences extracted from a soccer match video. FB2D can accurately identify the ball even if it is attached to the white lines drawn on the playground or partially occluded behind players. FB2D is compared to recent ball detection techniques. Experimental results show that FB2D outperforms recent detection techniques as it introduces both the highest level of detection accuracy in the testing stage and the lowest possible error.

Abstract Alzheimer’s disease (AD) is a form of brain disorder that causes functions’ loss in a person’s daily activity. Due to the tremendous progress of Alzheimer’s patients and the lack of accurate diagnostic tools, early detection and classification of Alzheimer’s disease are open research areas. Accurate detection of Alzheimer’s disease in an effective way is one of the many researchers’ goals to limit or overcome the disease progression. The main objective of the current survey is to introduce a comprehensive evaluation and analysis of the most recent studies for AD early detection and classification under the state-of-the-art deep learning approach. The article provides a simplified explanation of the system stages such as imaging, preprocessing, learning, and classification. It addresses broad categories of structural, functional, and molecular imaging in AD. The included modalities are magnetic resonance imaging (MRI; both structural and functional) and positron emission tomography (PET; for assessment of both cerebral metabolism and amyloid). It reviews the process of pre-processing techniques to enhance the quality. Additionally, the most common deep learning techniques used in the classification process will be discussed. Although deep learning with preprocessing images has achieved high performance as compared to other techniques, there are some challenges. Moreover, it will also review some challenges in the classification and preprocessing image process over some articles what they introduce, and techniques used, and how they solved these problems.

Abstract Low‐light image enhancement algorithms have been introduced to improve the visual quality of low‐light images that may degrade the performance of many computer vision and multimedia systems designed for high‐quality images. However, the existing bright channel prior and maximum colour channel enhancement algorithms introduce halo artifacts and colour distortions while enhancing the images. To overcome these limitations, in this paper, an effective fusion‐based low‐light image enhancement algorithm is proposed. In the proposed algorithm, the illumination of the low‐light image is estimated from both the bright and maximum colour channels to overcome the halo artifacts and colour distortion problems. Further, an effective refinement method is utilized to improve the sharpness of the initial enhanced image representing the scene reflectance. Experiment results show that the proposed algorithm outperforms the state‐of‐the‐art algorithms qualitatively and quantitatively. Moreover, the proposed algorithm reduces the halo artifacts and colour distortion and enhances the details while preserving the naturalness.

Semantic Web Services (SWSs) represent the most recent and revolutionary technology developed for machine-to-machine interaction on the web 3.0. As for the conventional web services, the problem of discovering and selecting the most suitable web service represents a challenge for SWSs to be widely used. In this paper, we propose a mapping algorithm that facilitates the redefinition of the conventional web services annotations (i.e., WSDL) using semantic annotations (i.e., OWL-S). This algorithm will be a part of a new discovery mechanism that relies on the semantic annotations of the web services to perform its task. The “local ontology repository” and “ontology search and standardization engine” are the backbone of this algorithm. Both of them target to define any data type in the system using a standard ontology-based concept. The originality of the proposed mapping algorithm is its applicability and consideration of the standardization problem. The proposed algorithm is implemented and its components are validated using some test collections and real examples. An experimental test of the proposed techniques is reported, showing the impact of the proposed algorithm in decreasing the time and the effort of the mapping process. Moreover, the experimental results promises that the proposed algorithm will have a positive impact on the discovery process as a whole.

Large databases with uncertainty became more common in many applications. Ranking queries are essential tools to process these databases and return only the most relevant answers of a query, based on a scoring function. Many approaches were proposed to study and analyze the problem of efficiently answering such ranking queries. Managing distributed uncertain database is also an important issue. In fact ranking queries in such systems are an open challenge. The main objective of this paper is to discuss ranking in distributed uncertain database along with its issued problems. Starting with uncertain data representation, query processing and query types in such systems are discussed along with their challenges and open research area. Top-k query is presented with its properties, as a ranking technique in uncertain data environment, mentioning distributed top-k and distributed ranking problems.