The Ontology Learning System (OLS) aims to automate the construction of hazard-specific ontologies, with a particular focus on floods. Using ontology learning techniques and Machine Learning algorithms, OLS undergoes a series of preprocessing steps, including text cleaning, word tokenization, and the removal of stop words and irrelevant characters. Extracting frequent and relevant terms while also classifying disaster-related keywords is a critical component of automating the construction of a flood ontology. Using Bidirectional Encoder Representations from Transformers (BERT), the model achieved 89% accuracy, precision, and recall in classifying keywords as disaster-related or not. Further, the system employs Machine Learning algorithms, namely Naive Bayes, Logistic Regression, and Linear Support Vector Classifier, for multiclass classification of disaster types. The results of the multiclass classification show that the Linear Support Vector Classifier attained the highest accuracy. The identified ontology keywords can be used to construct a flood-specific ontology, playing a crucial role in organizing flood-related information. Integrating these ontologies into broader systems can significantly enhance disaster preparedness and improve overall response capabilities.
Forest fires are among the worst natural disasters in recent years, causing many deaths, serious injuries, and significant damage to economic and environmental resources. Various methods have been proposed for the early detection of forest fire areas, including the use of meteorological data. However, none of these methods provide real-time image processing that extract significant spatial and temporal features to detect forest fire areas with high accuracy. Additionally, forest fire images exposed to unsafe public networks are vulnerable to security threats and attacks. This paper aims to predict forest fires using advanced Artificial Intelligence (AI) and blockchain based on images captured by drones. Blockchain has immense potential to reduce the time and ensure the security of sharing forest fire data. The proposed system is constructed by combining Convolutional Neural Network (CNN) and transformers to support deep feature extraction with drone-based image capture and predictive modeling. This integrated system enables early detection of forest fires, accurate prediction, and effective response to mitigate the impact of wildfires. The proposed system is applied for the detection of burned areas caused by forest fires in northeastern Algeria. The results are highly promising and are based on spatial, temporal and fire features for effective forest fire prediction, enabling timely alerts and effective firefighting planning
Recently, the Internet of Vehicles (IoV) network, a fusion of Internet of Things (IoT) and VANET, has gained prominence. It facilitates rapid communication of critical information to emergency services in the event of incident or emergencies. However, IoV networks are facing a high rate of network congestion. The primary aim of this work is to alleviate congestion by minimizing the redundancy of messages. This is accomplished through the utilization of unicast connections, enabling point-to-point communication between vehicles and nearby fog servers. Through simulations, this work demonstrates the significance of the proposed Vehicle Emergency Message Dissemination (VEMD) algorithm in enhancing safety applications. The simulation results show that the proposed VEMD algorithm outperforms an efficient work.
MRI brain tumor identification and classification is costly and time-consuming owing to the difficulty and complication of tumors. The process is highly influenced by the experience and knowledge of radiologists and neurologists. For this reason, automating this process becomes indispensable to overcome the drawbacks. In this work, we propose a new model, called DRB-BSIF (i.e., Deep Rule Based Classifier using Binarized Statistical Image Features), to enhance classification performances and reduce the complexity implicated in the diagnostic decision. Specifically, the tumor region is augmented by image dilation and used as the Region of Interest (ROI) instead of the initial tumor region. Then, features are extracted using BSIF image descriptor. Furthermore, we have constructed a Bank-BSIF, which is founded by the best parameters of BSIF filters. In the classification step, a deep-rule based classifier (DRB) has been used. The main of the BRD classifier is a self-organized set of IF and THEN fuzzy rules guided by the prototypes. These fuzzy rules are generated by DRB classifier and represented its ‘engine’. We studied classification of different kinds of brain tumors (e.g., Meningioma, Glioma, and Pituitary tumor). The proposed model is evaluated on publicly available brain CE-MRI images via different measures such as performance accuracy, sensitivity and specificity. Experimental results demonstrated that the DRB-BSIF is effective and can be used in computer aided brain tumor classification.
This paper presents a compact, four-element, Quad-band MIMO patch antenna designed for millimeter-wave (mmWave) applications in the Ka-band (26-40 GHz). The design leverages a monopole-printed antenna with a defected ground plane fabricated on a Rogers RO4003 substrate. The antenna achieves a triple-band -10 dB impedance bandwidth of 27.82-30.81 GHz, 33.8-34.6 GHz, and 35.7-36.4 GHz. Each element exhibits acceptable gain ranging from 5.2 dBi to 6.7 dBi across the operating bands (28 GHz, 30.5 GHz, 34 GHz, and 36 GHz). Following the design and analysis of a single antenna element, a four-port MIMO antenna was realized. Both antenna configurations demonstrate characteristics suitable for mmWave 5G and future wireless applications. The MIMO performance evaluation reveals a remarkably low envelope correlation (ECC) of approximately 10.4, a diversity gain of about 10 dB across the bands, and a negligible channel capacity loss (CCL) of less than 0.001 bits/s/Hz. These findings validate the proposed MIMO antenna's exceptional potential for mmWave future fifth-generation (5G) wireless communication systems deployments.
