Unmanned Aerial Vehicles (UAVs) are widely available in the current market to be used either for recreation as a hobby or to serve specific industrial requirements, such as agriculture and construction. However, illegitimate and criminal usage of UAVs is also on the rise which introduces their effective identification and detection as a research challenge. This paper proposes a novel machine learning-based for efficient identification and detection of UAVs. Specifically, an improved UAV identification and detection approach is presented using an ensemble learning based on the hierarchical concept, along with pre-processing and feature extraction stages for the Radio Frequency (RF) data. Filtering is applied on the RF signals in the detection approach to improve the output. This approach consists of four classifiers and they are working in a hierarchical way. The sample will pass the first classifier to check the availability of the UAV, and then it will specify the type of the detected UAV using the second classifier. The last two classifiers will handle the sample that is related to Bebop and AR to specify their mode. Evaluation of the proposed approach with publicly available dataset demonstrates better efficiency compared to existing detection systems in the literature. It has the ability to investigate whether a UAV is flying within the area or not, and it can directly identify the type of UAV and then the flight mode of the detected UAV with accuracy around 99%.
The world has witnessed an information explosion in the past two decades. Electronic devices are now available in many varieties such as PCs, Laptops, book readers, mobile devices and with relatively affordable prices. This and the ubiquitous use of software applications such as social media and cloud applications, and the increasing trend towards digitalization, the amount of information on the global cloud has surged to an unprecedented level. Therefore, a dire need exists in order to mine this massively large amount of data and produce meaningful information. Text Classification is one of the known and well established data mining techniques that has been used and reported in the literature. Text classification methods include statistical and machine learning algorithms such as Naive Baysian, Support Vector Machines and others have widely been used. Many works have been reported regarding text classification of various languages including English, Chinese, Russian, and many others. Arabic is the fifth most spoken language in the world. There has been many works in the literature for Arabic text classification. However, and to the best of our knowledge, there is no recent work that presents a good, critical and comprehensive survey of the Arabic text classification for the past two decades. The aim of this paper is to present a concise and yet comprehensive review of the Arabic text classification. We have covered over 50 research papers covering the past two decades (2000 -2019). The main focus of this paper is to address the following issues: 1) The techniques reported in the literature including. 2) New Techniques. 3) Most claimed efficient technique. 4) Datasets used and which ones are most popular. 5) Which feature selection techniques are used? 6) Popular classes/categories used. 7) Effect of stemming techniques on classification results.
Takaful -an Islamic alternative to conventional insurance -is fast becoming one of the most important constituents of modern Islamic financial market. The fundamental difference between the two forms of risk mitigation is entrenched from the type of contract selected. The conventional insurance work on the principle of bilateral contracts between the customer (insured) and insurance provider where the insured pay regular premium in return for payment of compensation, in case of a predefined event occurs. On the other hand, Takaful works on the principle of mutual guarantee, cooperation and indemnity where the participants in the scheme mutually insure each other. The Takaful providers are mainly responsible for managing, administering and investigating the Takaful funds according to Islamic laws. This studies provides a decentralized architecture that securely implements Takaful risk mitigation system according to its principles. Since all major banking sectors are shifting towards Blockchain technology, as it is currently the only viable solution to offers security, transparency, integrity of resources and ensure trustworthiness among customers. The proposed studies offer state-of-the-art Blockchain technology and focus provide a Takaful system that strictly follows the underlying Islamic laws for this risk mitigation system. Moreover, the proposed platform provides all Takaful transactions over Blockchain that brings confidence and transparency to the community involved in the process.
Determining hadith authenticity is vitally important in the Islamic religion because hadiths record the sayings and actions of Prophet Muhammad (PBUH), and they are the second source of Islamic teachings following the Quran. When authenticating a hadith, the reliability of the hadith narrators is a big factor that hadith scholars consider. However, many narrators share similar names, and the narrators’ full names are not usually included in the narration chains of hadiths. Thus, first, ambiguous narrators need to be identified. Then, their reliability level can be determined. There are no available datasets that could help address this problem of identifying narrators. Here, we present a new dataset that contains narration chains (sanads) with identified narrators. The AR-Sanad 280K dataset has around 280K artificial sanads and could be used to identify 18,298 narrators. After creating the AR-Sanad 280K dataset, we address the narrator disambiguation in several experimental setups. The hadith narrator disambiguation is modeled as a multiclass classification problem with 18,298 class labels. We test different representations and models in our experiments. The best results were achieved by finetuning BERT-Based deep learning model (AraBERT). We obtained a 92.9 Micro F1 score and 30.2 sanad error rate (SER) on the validation set of our artificial sanads AR-Sanad 280K dataset. Furthermore, we extracted a real test set from the sanads of the famous six books in Islamic hadith. We evaluated the best model on the real test data, and we achieved 83.5 Micro F1 score and 60.6 sanad error rate.
Healthcare record sharing among various medical roles is a critical and challenging research problem especially in today's everchanging global IT solutions. The emergence of blockchain as a new enabling technology brought radical changes to numerous business applications, including healthcare. Blockchain is a trusted distributed ledger that forms a decentralized infrastructure. There have been several proposals regarding the sharing of critical healthcare records over blockchain infrastructure without requiring prior knowledge/trust of the parties involved (patients, service providers, and insurance companies). Another yet important issue is to securely share medical records across various countries for travelling patients to ensure an integrated and ubiquitous healthcare service. In this paper, we present a globally integrated healthcare record sharing architecture based on blockchain and HL7 client. Healthcare records are stored at the hosting country and are not stored on the blockchain. This architecture avails medical records of travelling patients temporarily and after performing necessary authentication. The actual authorisation process is performed on a federated identity management system, such as, the Shibboleth. Though there are similarities with identity management systems, our system is unique as it involves the patient in the permission process and discloses to them the identities of entities accessed their health records. Our solution also improves performance and guarantees privacy and security through the use of blockchain and identity management system.
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