Misbehavior-aware on-demand collaborative intrusion detection system using distributed ensemble learning for VANET

Vehicular ad hoc networks (VANETs) play an important role as enabling technology for future cooperative intelligent transportation systems (CITSs). Vehicles in VANETs share real-time information about their movement state, traffic situation, and road conditions. However, VANETs are susceptible to the cyberattacks that create life threatening situations and/or cause road congestion. Intrusion detection systems (IDSs) that rely on the cooperation between vehicles to detect intruders, were the most suggested security solutions for VANET. Unfortunately, existing cooperative IDSs (CIDSs) are vulnerable to the legitimate yet compromised collaborators that share misleading and manipulated information and disrupt the IDSs’ normal operation. As such, this paper proposes a misbehavior-aware on-demand collaborative intrusion detection system (MA-CIDS) based on the concept of distributed ensemble learning. That is, vehicles individually use the random forest algorithm to train local IDS classifiers and share their locally trained classifiers on-demand with the vehicles in their vicinity, which reduces the communication overhead. Once received, the performance of the classifiers is evaluated using the local testing dataset in the receiving vehicle. The evaluation values are used as a trustworthiness factor and used to rank the received classifiers. The classifiers that deviate much from the box-and-whisker plot lower boundary are excluded from the set of the collaborators. Then, each vehicle constructs an ensemble of weighted random forest-based classifiers that encompasses the locally and remotely trained classifiers. The outputs of the classifiers are aggregated using a robust weighted voting scheme. Extensive simulations were conducted utilizing the network security laboratory-knowledge discovery data mining (NSL-KDD) dataset to evaluate the performance of the proposed MA-CIDS model. The obtained results show that MA-CIDS performs better than the other existing models in terms of effectiveness and efficiency for VANET

SNP Based Trait Characterization Detects Genetically Important and Stable Multiple Stress Tolerance Rice Genotypes in Salt-Stress Environments

Soil salinity is a major constraint to rice production in coastal areas around the globe, and modern high-yielding rice cultivars are more sensitive to high salt stress, which limits rice productivity. Traditional breeding programs find it challenging to develop stable salt-tolerant rice cultivars with other stress-tolerant for the saline environment in Bangladesh due to large yield variations caused by excessive salinity fluctuations during the dry (boro) season. We examined trait characterization of 18 advanced breeding lines using SNP genotyping and among them, we found line G6 (BR9621-B-1-2-11) (single breeding line with multiple-stress-tolerant QTL/genes) possessed 9 useful QTLs/genes, and two lines (G4:BR9620-2-7-1-1 and G14: IR 103854-8-3-AJY1) carried 7 QTLs/genes that control the desirable traits. To evaluate yield efficiency and stability of 18 rice breeding lines, two years of field experiment data were analyzed using AMMI (additive main effect and multiplicative interaction) and GGE (Genotype, Genotype Environment) biplot analysis. The AMMI analysis of variance demonstrated significant genotype, environment, and their interaction, accounting for 14.48%, 62.38%, and 19.70% of the total variation, respectively, and revealed that among the genotypes G1, G13, G14, G17, and G18 were shown to some extent promising. Genotype G13 (IR 104002-CMU 28-CMU 1-CMU 3) was the most stable yield based on the AMMI stability value. The GGE biplot analysis indicates 76% of the total variation (PC1 48.5% and PC2 27.5%) which is performed for revealing genotype × environment interactions. In the GGE biplot analysis, genotypes were checked thoroughly in two mega-environments (ME). Genotype G14 (IR103854-8-3-AJY1) was the winning genotype in ME I, whereas G1 (BR9627-1-3-1-10) in ME II. Because of the salinity and stability factors, as well as the highest averages of grain yield, the GGE and AMMI biplot model can explain that G1 and G13 are the best genotypes. These (G1, G6, G13, G14, G17, and G18) improved multiple-stress-tolerant breeding lines with stable grain yield could be included in the variety release system in Bangladesh and be used as elite donor parents for the future breeding program as well as for commercial purposes with sustainable production

Patterns of ophthalmic emergencies presenting to a referral hospital in Medina City, Saudi Arabia

Background: Data are required on ophthalmic cases that present to the emergency eye clinics in Madinah, Saudi Arabia for proper allocation of healthcare resources. Objectives: To determine the frequency and various diagnoses of patients presenting to the A&E at Ohud Hospital, Madinah, Saudi Arabia. Methods: Data was collected prospectively for all patients who presented to the A&E ophthalmology clinic from June 2014 to September 2014. The data was analyzed and presented using frequency of incidence and percentages. Chi-square tests were used to evaluate the diagnoses based on age, sex and nationality. P â¤Â 0.05 indicated statistical significance. Results: The study sample included 868 patients. The male-to-female ratio was 1.1:1.0. The main age categories included patients â¥45 years of age (256 patients) and 251 patients between the ages of 15â30 years. Various types of Conjunctivitis was the most common diagnosis, reported in 282 patients (32.5%), and followed by dry eye syndrome in 156 (18%) patients. Nasolacrimal duct obstruction in 156 patients (18%). Eyelid infections were detected in 102 patients (12%), corneal abrasion in 102 patients (9.3%). Various eye traumas was diagnosed in 30 patients (3.5%), increased intraocular pressure (IOP) in 17 patients (2%), ruptured globe in 2 patients (0.2%) and various other non-emergency pathologies in the remaining eyes. There were no significant differences in patientâs characteristics and categories of diagnoses. Conclusion: Non-emergent ophthalmic cases were the most common reason for the ophthalmology emergency room visits. It was observed that most cases could be referred to outpatient departments and potentially be managed by primary healthcare providers. This would be more cost effective and will also allow for better management of vision threatening ocular emergencies

Role of CT Scan in Diagnosis of COVID-19 Infection-A Review

Since it was declared a worldwide pandemic, COVID-19 has ravaged almost all over the world and has overloaded several health-care systems. The pandemic also resulted in job losses as a result of lengthy shutdowns, which burdened the global economy. Even though significant clinical research progress has led to a better perceiving of the virus ( SARS-CoV-2) nature and the disease (COVID-19) management, preventing the virus's spread has become a major concern as SARSCoV-2 continues to wreak havoc around the world. Several countries suffered from the second or third wave of viral disease outbreaks, primarily caused by the mutation of SARS-CoV-2. Imaging is critical in the diagnosis and follow-up of patients with new coronavirus-infected pneumonia (NCIP). The primary imaging modality in clinically suspected cases is CT scan and it is useful for monitoring imaging changes following therapy. Therefore, CT is regarded as a useful diagnostic technique for clinically suspected cases of COVID-19. CT has the ability to detect patients who have a negative reverse transcription-polymerase chain reaction (RT-PCR) but are highly suspicious of NCIP in terms of clinical problems. In addition, the results of a CT scan may also reveal information concerning the severity of the condition. In this review article, the diagnosis of COVID-19 is discussed and CT characteristics are defined based on the newest research for the diagnosis and management of COVID-19