Blockchain is a modern technology that has revolutionized the way society interacts and trades. It could be defined as a chain of blocks that stores information with digital signatures in a distributed and decentralized network. This technique was first adopted for the creation of digital cryptocurrencies, such as Bitcoin and Ethereum. However, research and industrial studies have recently focused on the opportunities that blockchain provides in various other application domains to take advantage of the main features of this technology, such as: decentralization, persistency, anonymity, and auditability. This paper reviews the use of blockchain in several interesting fields, namely: finance, healthcare, information systems, wireless networks, Internet of Things, smart grids, governmental services, and military/defense. In addition, our paper identifies the challenges to overcome, to guarantee better use of this technology.

It has been claimed that artificial intelligence (AI) has transformative potential for the healthcare sector by enabling increased productivity and creative methods of delivering healthcare services. Recently, there has been a major shift to artificial intelligence by businesses, government, and private sectors in general and the health sector in particular. Many studies have proven that artificial intelligence is contributing greatly to the health sector by discovering diseases and determining the best treatments for patients. Dentistry requires new innovative methods that serve both the patient and the service provider in obtaining the best and appropriate medical services. Artificial intelligence has the ability to develop the field of dentistry through early diagnosis and prediction of dental implant cases. This research develops a set of four machine learning algorithms to predict when a patient might need dental implants. These models are the Bayesian network, random forest, AdaBoost algorithm, and improved AdaBoost algorithm. This work shows that the developed algorithms can predict when a patient needs dental implants. Also, we believe that this proposal will advise managers and decision-makers in targeting patients with particular diagnoses. Analysis of the obtained results indicates good performance of the developed machine learning. As a result of this research, we note that the proposed improved AdaBoost algorithm increases the level of prediction accuracy and gives significantly higher performance than the other studied methods with the accuracy for the improved AdaBoost algorithm reaching 91.7%.

River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient approaches to prevent them. The Artificial Intelligence of Things (AIoT) is a recent concept that combines the best of both Artificial Intelligence and Internet of Things, and has already demonstrated its capabilities in different fields. In this paper, we introduce an AIoT architecture where river flood sensors, in each region, can transmit their data via the LoRaWAN to their closest local broadcast center. The latter will relay the collected data via 4G/5G to a centralized cloud server that will analyze the data, predict the status of the rivers countrywide using an efficient Artificial Intelligence approach, and thus, help prevent eventual floods. This approach has proven its efficiency at every level. On the one hand, the LoRaWAN-based communication between sensor nodes and broadcast centers has provided a lower energy consumption and a wider range. On the other hand, the Artificial Intelligence-based data analysis has provided better river flood predictions.

The metaverse is a unified, persistent, and shared multi-user virtual environment with a fully immersive, hyper-temporal, and diverse interconnected network. When combined with healthcare, it can effectively improve medical services and has great potential for development in realizing medical training, enhanced teaching, and remote surgical treatment. The metaverse provides immersive services for users through massive and multimodal data, and its data scale and data growth rate are bound to show exponential growth. Blockchain-based distributed storage is a fundamental way to keep the metaverse running continuously; however, many blockchains, such as Ethereum and Filecoin, suffer from low transaction throughput and high latency, which seriously affect the efficiency of distributed storage services and make it difficult to apply them to the metaverse environment. To this end, this paper first proposes a network architecture for distributed storage systems based on proof of retrievability to address the problem of centralized decision making and single point of access in centralized storage. The secure data storage of the metaverse health system is ensured. Secondly, we designed two data transmission protocols through vector commitment and encoding functions to achieve the transfer of time cost from the critical path to storage nodes and improve the efficiency of data verification between nodes as well as the scalability of the metaverse health system. Finally, this paper also conducts security analysis and performance analysis of the proposed scheme, and the results show that our scheme is secure and efficient.

The democratization of access to internet and social media has given an opportunity for every individual to openly express his or her ideas and feelings. Unfortunately, this has also created room for extremist, racist, misogynist, and offensive opinions expressed either as articles, posts, or comments. While controlling offensive speech in English-, Spanish-, and French- speaking social media communities and websites has reached a mature level, it is much less the case for their counterparts in Arabic-speaking countries. This paper presents a transfer learning solution to detect hateful and offensive speech on Arabic websites and social media platforms. This paper will compare the performance of different BERT-based models trained to classify comments as either abusive or neutral. The training dataset contains comments in standard Arabic as well as four dialects. We will also use their English translations for comparative purposes. The models were evaluated based on five metrics: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix.

This work aims to create a web-based real-time monitoring system for electrical energy consumption inside a specific residence. This electrical energy is generated from a micro-CPV system lying on the roof of this residence. The micro-CPV is composed of a Fresnel lens as the main optical element, a spherical lens as the secondary optical element, and a multi-junction solar cell. A tiny photovoltaic concentrator system with a geometric concentration ratio of 100× is analyzed in the first part of this study, while the second part is designed to monitor the electricity generated by the micro-CPV system. An ESP8266 controller chipset is used to build the sensing peripheral node, which controls a relay and a PZEM-004T current sensor. As a result, the optical element used has approximately 83% optical efficiency, with an acceptance angle of 1.5°. Regarding the monitoring system, the architecture demonstrates the ability of the system to monitor current and energy consumption in real time using a computer or smartphone and a web server specially designed to continuously update the power consumption profile in a specific smart home environment. The whole electric power consumption monitoring system generally worked well. The monitoring system is configured to provide excellent accuracy for a 0.6% hit.