The rapid spread of novel coronavirus disease
(COVID19) throughout the world without available
specific treatment or vaccine necessitates alternative
options to contain the disease. Historically, children
and pregnant women were considered high-risk
population of infectious diseases but rarely have been
spotlighted nowadays in the regular COVID-19
updates, may be due to low global rates of incidence,
morbidity, and mortality. However, complications did
occur in these subjects affected by COVID-19. We
aimed to explore the latest updates of
immunotherapeutic perspectives of COVID-19
patients in general population and some added details
regarding pediatric and obstetrical practice.
Immune system boo

The rapid spread of novel coronavirus disease(COVID19) throughout the world without availablespecific treatment or vaccine necessitates alternativeoptions to contain the disease. Historically, childrenand pregnant women were considered high-riskpopulation of infectious diseases but rarely have beenspotlighted nowadays in the regular COVID-19updates, may be due to low global rates of incidence,morbidity, and mortality. However, complications didoccur in these subjects affected by COVID-19. Weaimed to explore the latest updates ofimmunotherapeutic perspectives of COVID-19patients in general population and some added detailsregarding pediatric and obstetrical practice.Immune system boosting strategy is one of therecently emerging issue

Melanoma, a highly malignant form of skin cancer, affects individuals of all genders and is associated with high mortality rates, especially in advanced stages. The use of tele-dermatology has emerged as a proficient diagnostic approach for skin lesions and is particularly beneficial in rural areas with limited access to dermatologists. However, accurately, and efficiently segmenting melanoma remains a challenging task due to the significant diversity observed in the morphology, pigmentation, and dimensions of cutaneous nevi. To address this challenge, we propose a novel approach called DenseUNet-169 with a dilated convolution encoder-decoder for automatic segmentation of RGB dermascopic images. By incorporating dilated convolution,

Software-Defined Networking (SDN) has evolved network management by detaching the control plane from the data forwarding plane, resulting in unparalleled flexibility and efficiency in network administration. However, the heterogeneity of traffic in SDN presents issues in achieving Quality of Service (QoS) demands and efficiently managing network resources. SDN traffic flows are often divided into elephant flows (EFs) and mice flows (MFs). EFs, which are distinguished by their huge packet sizes and long durations, account for a small amount of total traffic but require disproportionate network resources, thus causing congestion and delays for smaller MFs. MFs, on the other hand, have a short lifetime and are latency-sensitive, but they accou

The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre

It is believed that Organizations around the world should be prepared for the transition to IPv6 and make sure they have the " know how" to be able to succeed in choosing the right migration to start time. This paper focuses on the transition to IPv6 mechanisms. Also, this paper proposes and tests a deployment of IPv6 prototype within the intranet of the University of Baghdad (BUniv) using virtualization software. Also, it deals with security issues, improvements and extensions of IPv6 network using firewalls, Virtual Private Network ( VPN), Access list ( ACLs). Finally, the performance of the obtainable intrusion detection model is assessed and compared with three approaches.

Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr