<p class="0abstract">Various mobile applications such as Mobile Health (mHealth) have been developed and spread across the world which has played an important role in mitigating the Coronavirus pandemic (COVID-19). As the COVID-19 pandemic spreads, several people have drawn parallels to influenza. While both viruses cause respiratory infections, they propagate in very different ways. This has a major impact on the public health measures that can be used to fight each virus. These viruses are pandemic-causing in the same way. That is, they both cause respiratory disease, and can present themselves in several ways, ranging from asymptomatic to severe and deadly. A proposal is presented in this paper that uses two algorithms to define and classify these pandemics, they are: The Back Propagation (BP) classification algorithm and the Fuzzy C-Mean (FCM) clustering algorithm. Two stages are implemented in the proposed system: in the first step, the FCM algorithm is used to find out the type of virus, and this algorithm is capable of handling ambiguous features of viruses. In the second step, a BP neural network is used as a classifier to detect the pandemic class. The proposed system was trained and tested using a well-known dataset (covid-19 vs influenza). Information Gain (IG) is used to optimize the related features that affect the classification process to improve speed and accuracy.  The proposed mobile application is developed to support users easily detecting the COVID-19 infection by inputting the medical tests as significant features to the proposed system. The proposed system's accuracy is up to (89%), the framework was created using the Matlab programming environment and an Android Studio for Mobil application designing.</p>

This paper introduces a secure communication protocol that provides secured communication pathways to manipulate drones through unsecured communication. The deployment of the proposed protocol works through providing two secured communication paths; drones to the drone’s controller path and controller to data centre path. The first secured communication path has achieved a high level of security and privacy by using a modification of SHA-1 method and an advanced encryption method. The modification of the SHA-1 is called 83SHA-1. These modifications can increase rounds in the first stage up to 83 rounds, inject each round with expansion and S-Boxes procedures that are used in DES to extend length from 160 to 240 bits then reduce it from 240 to 160 bits. After hash data from the drone then use the advanced encryption method which is called Geffe-Genetic (GG) Encryption algorithm where three types of keys will be used for deception attackers. The second accomplishment is to ensure providing secure communication between the drone’s controller and datacentre by using RNA-RADG-CBC (RRCBC) encryption algorithm where will generate an initialization vector (IV) for cipher block chaining (CBC) randomly, generate keys, and propose an encryption/decryption method. The security analysis shows a promising high security level of drones’s data.

Abstract—in today's rapidly growing information age, text summary has become a critical and important instrument for help understanding text information. it is really hard for human beings to physically summarize huge textual documents also there is an abundance of text content available online. text summarization is an active research field that works on compressing large pieces of text into smaller texts that preserve relevant information. text summary classified as extractive or abstractive. methods of extractive summarization working by deciding important text sentences and choosing them as a summary. that method based only on sentences from the source text. methods of abstractive summarization aim to paraphrase important information in a new form like that of humans. text summary can be achieved using different deep learning techniques, such as: fuzzy logic, Convolutional Neural (CNN), transformers, neural network, reinforcement learning, etc. in the past three years, the research trend in text summarization has also undergone a slight change, where new trends have appeared that are trends that lead to enhancement, how to improve the efficiency of text summarization to obtain high accuracy. we have made several attempts in this paper to discuss the various techniques used on the basis of deep learning for text summary in these years and observe the new trends in the field of deep learning.

In recent years, the vehicular ad hoc network (VANET) has received great attention, as it is involved in the design of the intelligent transportation system (ITS). The VANET network includes message flows from vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) where the network is propped by wireless communication technology, such as IEEE 1609 WAVE and IEEE 802.11P. The VANET network implementation faces challenges, and one of these challenges is the design of routing protocols that transfer reliable and efficient packets from vehicle to vehicle. In VANET, steering is a challenging task in the highway and urban environment. Therefore, this paper presents an assessment of ad hoc on-demand distance vector protocol (AODV) performance in the highway and urban environment and to study the effect of vehicle density on protocol performance. The AODV protocol was simulated by MATLAB. In this study, the performance of AODV protocol was evaluated through four measures, namely, packet delivery ratio (PDR), overhead, end-to-end (E2E) delay, and dropped packets. The study in our paper showed that the best performance of the AODV protocol is in an environment where vehicle speed and vehicle density are low.

This paper proposes an anti-phishing web site system it is carried out by the two following stages: Registration phase; the user enters username and password then (his/her) fingerprint, server site selects virtual fingerprint image. The fusion will be applied to fuse real fingerprint with virtual one, fused image will be input to visual cryptography(VC) scheme to produce two shares, one share kept with user in addition to fuse image, and other shares are kept with the server. Authentication phase; in this phase the user enters the password and is asked to enter the fingerprint. Pattern recognition is done to determine if it is hacker or authenticated user, when the server accepts the fingerprint the user will be required to input (his/her) share, so the user share is stacked with server share and generated image is displayed. The user will decide if it’s a phishing site or not depending on the displayed image (after matching it with the image that the server shared through registration phase).From many experimental works conducted on the proposal, we notice the strength is centered in image fusion. Where the fused fingerprint images have higher quality (entropy) than the single fingerprint image, that increases randomness of the VC shares which are extracted from the fused fingerprint.

An abstractive summary is a process of producing a brief and coherent summary that contains the original text's main concepts. In scientific texts, summarization has generally been restricted to extractive techniques. Abstractive methods that use deep learning have proven very effective in summarizing articles in public fields, like news documents. Because of the difficulty of the neural frameworks for learning specific domain- knowledge especially in NLP task, they haven't been more applied to documents that are related to a particular domain such as the medical domain. In this study, an abstractive summary is proposed. The proposed system is applied to the COVID-19 dataset which a collection of science documents linked to the coronavirus and associated illnesses, in this work 12000 samples from this dataset have been used. The suggested model is an abstractive summary model that can read abstracts of Covid-19 papers then create summaries in the style of a single-statement headline. A text summary model has been designed based on the LSTM method architecture. The proposed model includes using a glove model for word embedding which is converts input sequence to vector forms, then these vectors pass through LSTM layers to produce the summary. The results indicate that using an LSTM and glove model for word embedding together improves the summarization system's performance. This system was evaluated by rouge metrics and it achieved (43.6, 36.7, 43.6) for Rouge-1, Rouge-2, and Rouge-L respectively.