The increasing demand for real-time emergency response systems requires novel approaches to solving traffic congestion, ineffective routing, and cloud dependency delays. This paper describes MAESTRO—a multi-layered hierarchical architecture leveraging Software Defined Networking (SDN) and fog computing to improve ambulances routing in emergency response systems. The architecture integrates cloud, fog, and SDN layers and enables real-time decision making and efficient resource allocation through low-latency communication. This ensures ambulances are dispatched rapidly to accident locations and patients are admitted to hospitals in a timely manner. The fog computing layers are responsible for localized data processing, and the SDN layers dynamically control the routing of network traffic to achieve uninterrupted communication. The proposed algorithms are Ambulance Registration Protocol, Cloud-Fog-SDN Communication Algorithm, and Distributed SDN Traffic-Aware Routing Algorithm which provide continuous monitoring, ambulance-hospital coordination, and adaptive resilient routing in response to changing traffic conditions. Simulations in NS-2 and SUMO demonstrate higher response times, lower network latencies, and greater scalable improvements over traditional systems relying on cloud computing. The results validates MAESTRO’s ability to minimize the impact of congestion, optimize transit time, and deliver predictable routing to streamline emergency services.

Personalized travel sequence recommendation in urban environments poses a complex challenge due to dynamic spatial conditions, user diversity and limited resources. Traditional systems often overlook the multi-user dimension and fail to adapt to fluctuating crowd patterns. This work proposes a novel Multi-Agent Reinforcement Learning (MARL) framework that coordinates personalized itinerary planning while managing urban congestion. The framework comprises autonomous Travel Agents representing individual users and a centralized Congestion Management Authority that guides agents toward balanced spatial distributions. To enhance scalability and learning efficiency we introduce an interest-based clustering mechanism that groups users with similar preferences. Each cluster shares a policy network and experience buffer, and is trained using a Deep Q-Network (DQN) algorithm. This design significantly reduces memory consumption by 80% and training time by 67%. Compared to the baseline model our MARL framework improves interest alignment by 49.1% (0.85 vs. 0.57), PoI popularity by 50.9% (0.80 vs. 0.53) and reduces travel time by 50.5% (11.16% vs. 22.55%) resulting in more relevant, attractive and time-efficient itineraries. A real-world case study in Dubai validates the framework's ability to generate high-quality itineraries that align with user interests and mitigate overcrowding at popular sites. Additionally, a user study confirms improved satisfaction and perceived personalization compared to baseline methods. The results highlight the potential of MARL as a scalable, adaptive solution for next-generation spatial recommendation systems.

Cyberbullying is one of the serious threats on social networks particularly toward children and teenagers. Cyberbullying can cause many harmful consequences towards victims such as anxiety, depression and even suicide. This research studies cyberbullying detection in Arabic language on online social networks using deep learning techniques to automatically detect and quarantine the cyberbullying messages to safe children from getting exposed to harmful cyberbullying content. In this paper, real Arabic dataset is collected from YouTube and Twitter that is annotated manually to improve the quality of the data. All experiments went through three rounds of trials to make sure that the results are consistent. Many evaluation metrics were used to evaluate the performance of the classifiers such as macro F1 score and AUC. The best model achieves 84.58% and 85.94% in F1 score and AUC respectively.

The current Global Positioning System (GPS) has been heavily criticized by several parties (e.g., drivers and researchers) due to the delay in disseminating en-route data to drivers ahead of time. This issue resulted in poor rates of accuracy of data (i.e., traffic status), which eventually causes inefficient transportation. According to National Institute of Standards and Technology, the delay is caused mainly by the transmission medium. This paper presents a new distributed fog-based vehicles navigation system for efficient transportation. The proposed system uses closer-to-the-source data (aka drivers) nodes (i.e., fogs) as a new transmission medium for disseminate the road details to subscribed drivers who are connected to those nodes. A package delivery scenario has been implemented to show the impact of using fog nodes in reducing the navigation time and improving efficiency. Those fog nodes gather and broadcast real-time traffic data from/to drivers within the area they cover. In addition, a new road navigation simulator has been designed and developed to simulate the proposed system. The simulation results show superior performance of the new navigation system in terms of travel time and in-situ road data provisioning to drivers.

Social networks have been transforming the way people express opinions, post and react to events, and share ideas. Over the last decade, several studies on event detection from social media have been proposed, with the aim of extracting specific types of events, such as, social gatherings, natural disasters, and emergency situations, among others. However, these works do not consider the continuous processing of events over the social data streams, and therefore, cannot determine the spatial and temporal evolution of such events. This paper introduces a big data platform for event discovery, while tracking their evolution over space and time. We propose a scalable and efficient architecture that can manage and mine a huge data flow of unstructured streams, in order to detect geo-social events. The extracted clusters of events are indexed by a spatio-temporal index structure. We conduct experiments over twitter datasets to measure the effectiveness and efficiency of our system with respect to the existing major event detection techniques. An initial demonstration of our platform highlights its major advantage for detecting and tracking events spatially and temporally, thus allowing for great opportunities from application perspectives.

The tremendous rise of location-enriched microblogging has made it possible to discover social events from social media, as well as their evolution over space and time. Over the last decade, multiple studies on event detection from social media have been proposed, with the aim of extracting specific types of events, such as, social gatherings, natural disasters, and emergency situations, among others. However, existing works do not consider the incremental and continuous processing of events over the large amounts of social streams, and therefore, cannot determine the spatial and temporal evolution of such events. This work presents a big data mining platform for the incremental discovery of geo-social events based on a scalable and efficient architecture that can manage and mine a huge data flow of unstructured streams. We demonstrate our early results over twitter datasets and discuss its main advantage by incorporating advanced features for event extraction, thus allowing for great opportunities from application perspectives.

Recently, cyberbullying has grown significantly on social platforms, impacting users especially teenagers and young adults. The effects of this threat are so severe and damaging that could lead to suicide. Lately, this threat has become a significant issue in Arab countries, especially with the wide adoption of social media by the young generation. Most of existing research proposed solutions for detecting cyberbullying, mainly in English language. However, only few papers studied cyberbullying detection in Arabic Social Media Communications. This paper used machine learning for automatic detection of cyberbullying in Arabic. The proposed scheme detects cyberbullying using Naive Bayes(NB) classifier algorithm by training and testing the classifier with real data set which was collected from Youtube and Twitter.

Database outsourcing is a common cloud computing paradigm that allows data owners to take advantage of its on-demand storage and computational resources. The main challenge is maintaining data confidentiality with respect to untrusted parties i.e., cloud service provider, as well as providing relevant query results in real-time to authenticated users. Existing approaches either compromise confidentiality of the data or suffer from high communication cost between the server and the user. To overcome this problem, we propose a dual transformation and encryption scheme for spatial data, where encrypted queries are executed entirely at the service provider on the encrypted database and encrypted results are returned to the user. The user issues encrypted spatial range queries to the service provider and then uses the encryption key to decrypt the query response returned. This allows a balance between the security of data and efficient query response as the queries are processed on encrypted data at the cloud server. Moreover, we compare with existing approaches on large datasets and show that this approach reduces the average query communication cost between the authorized user and service provider, as only a single round of communication is required by the proposed approach.

Nowadays, social network sites; such as Facebook and Twitter, have tremendous number of users in their repositories. Having this huge amount of data requires analyzing them to get statistics about the users and their interests. In this paper, we propose a new algorithm that clusters the nodes in social networks into communities based on their geodesic location and the similarity between their interests. The algorithm is examined thoroughly to test its performance. The experiments show that the algorithm achieves a high community detection accuracy.

The increase of spatial data has led organizations to upload their data onto third-party service providers. Cloud computing allows data owners to outsource their databases, eliminating the need for costly storage and computational resources. The main challenge is maintaining data confidentiality with respect to untrusted parties as well as providing efficient and accurate query results to the authenticated users. We propose a dual transformation scheme on the spatial database to overcome this problem, while the service provider executes queries and returns results to the users. First, our approach utilizes the space-filling Hilbert curve to map each spatial point in the multidimensional space to a one-dimensional space. This space transformation method is easy to compute and preserves the spatial proximity. Next, the order-preserving encryption algorithm is applied to the clustered data. The user issues spatial range queries to the service provider on the encrypted Hilbert index and then uses a secret key to decrypt the query response returned. This allows data protection and reduces the query communication cost between the user and service provider.

In this work, we propose an unsupervised approach for detecting the anomalies in a crowd scene using social network model. Using a window-based approach, scene objects are first detected and tracked, and a spatio-temporal partitioning is constructed to produce a set of spatio-temporal cuboids that capture spatial and temporal features. A hierarchical social network is built to model the crowd behavior: the bottom-level models local behavior and the top level models the global. We perform anomaly detection and demonstrate the effectiveness of the proposed approach on a benchmark crowd analysis video sequences. Our results reveal that we outperform majority, if not all, the state-of-the-art methods.

Data streams and their applications appear in several fields such as physics, finance, medicine, environmental science, etc. As sensor technology improves, sensor data rates continue to increase. Consequently, analyzing data streams becomes ever more

K-nearest neighbors (KNN) search in a high-dimensional vector space is an important paradigm for a variety of applications. Despite the continuous efforts in the past years, algorithms to find the exact KNN answer set at high dimensions are outperformed by a linear scan method. In this paper, we propose a technique to find the exact KNN image objects to a given query object. First, the proposed technique clusters the images using a self-organizing map algorithm and then it projects the found clusters into points in a linear space based on the distances between each cluster and a selected reference point. These projected points are then organized in a simple, compact, and yet fast index structure called array-index. Unlike most indexes that support KNN search, the array-index requires a storage space that is linear in the number of projected points. The experiments show that the proposed technique is more efficient and robust to dimensionality as compared to other well-known techniques because of its simplicity and compactness. © 2011 Wiley Periodicals, Inc.

Recently, there is a growing need to access historical Arabic handwritten manuscripts (HAH manuscripts) that are stored in large archives; therefore, managing tools for automatic searching, indexing, classifying and retrieval of HAH manuscripts are required. The peculiar characteristics of Arabic handwriting have added an extra challenging dimension in developing such systems. This paper presents a novel holistic technique for segmenting and classifying HAH manuscripts. The classification of HAH manuscripts is performed in several steps. First, the HAH manuscript's image is segmented into words, and then each word is segmented into its connected parts. Due to the existing overlap between the adjacent connected parts of a single word, we developed a stretching algorithm to increase the gap between them and thus improve their segmentation. Second, several structural and statistical features, which are devised for Arabic text, are extracted from these connected parts and then combined to represent a word with one consolidated feature vector. Finally, a neural network is used to learn and classify the input vectors into word classes. The extraction of structural and statistical features from the individual connected parts, as compared to the extraction of these features from the whole word, improved the performance of the system significantly.

In this paper, we present a new measure of interestingness to discover interesting patterns based on the user's background knowledge, represented by a Bayesian network. The new measure (Sensitivity measure) captures the sensitivity of the Bayesian network to the patterns discovered by assessing theuncertainty-increasingpotentialof a pattern on the beliefs of the Bayesian network. Patterns that attain the highest sensitivity scores are deemed interesting. In our approach, mutual information (from information theory) came in handy as a measure of uncertainty. The Sensitivity of a pattern is computed by summing up the mutual information increases incurred by a pattern when entered as evidence/findings to the Bayesian network. We demonstrate the strength of our approach experimentally using the KSL dataset of Danish 70 year olds as a case study. The results were verified by consulting two doctors (internists).

The recent proliferation of multimedia contents led to the need to more effective and efficient content representation techniques to speed up their retrieval. MPEG-7 is a new standard that aims at describing the low-level (syntactic) and high-level (semantic) multimedia content. In MPEG-7, the relationships between moving regions (i.e. objects) in videos are represented by the directional, spatial and temporal relationships. In this paper, we propose to extend the description scheme (DS) of moving objects to include rich sets of visual-spatio-temporal (VST) relationships that support semantical descriptions of the relationships between objects. Also, we propose an XML based DSs of the VST relationships and then present the bit format representations for the VST relationships. The VST relationships are more intuitive to users, thus simplifying the formulation of user queries.

In the past few years, modeling and querying video databases have been a subject of extensive research to develop tools for effective search of videos. In this paper, we present a hierarchal approach to model videos at three levels, object level (OL), frame level (FL), and shot level (SL). The model captures the visual features of individual objects at OL, visual-spatio-temporal (VST) relationships between objects at FL, and time-varying visual features and time-varying VST relationships at SL. We call the combination of the time-varying visual features and the time-varying VST relationships a Content trajectory which is used to represent and index a shot. A novel query interface that allows users to describe the time-varying contents of complex video shots such as those of skiers, soccer players, etc., by sketch and feature specification is presented. Our experimental results prove the effectiveness of modeling and querying shots using the content trajectory approach.