Literature Review of Inflammatory Bowel Disease in South Asian Populations

Background: South Asians have been recently identified as having rapidly rising incidence of inflammatory bowel disease (IBD). There is a paucity of data regarding the phenotypic and genotypic associations of IBD among the patients of this region. Due to the rising disease prevalence, a study on South Asian population can disclose more information about the etiopathogenetic causes of the disease. Methods: Here we did a review article of IBD among South Asians. In order to get a correct sense of factors associated with the disease, we have reviewed approximately 150 articles through the PubMed search and google scholar. Results: We attempted to find temporal trends of IBD among south Asian population, compared phenotype and genotype of IBD among South Asians and western patients and looked at the patterns of IBD presentation in different countries of South Asia. We have also reviewed the differences in the incidence of IBD among South Asian immigrants and discussed the treatment challenges of IBD among this special population. Conclusion: We identified that both patients in South Asia as well as South Asian patients living in Western countries are at greater risk for all types of IBD. This geographical region provides an opportunity for revealing possible etiopathogenetic factors. Further population-based studies, comparison of studies in South Asians and immigrants from South Asian countries, and large-scale biologic treatment models need to be accelerated to control the disease burden in South Asians, as well as to achieve reduced burden globally.

An updated systematic review of Zika virus-linked complications

Objective: To determine the breadth of Zika virus(ZIKV)-associated brain anomalies in neonates and adults. Methods: Systematic review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) statement using electronic databases ScienceDirect, Pubmed, Medline, Scopus, and Global Health Library.Only those research articles, case studies, case-control studies, case-cohort studies, crosssectional studies, and organizational survey reports were included in the study that reported any fetal outcomes for pregnant women who had infected with ZIKV during the gestational period and ZIKV-related neurological complications in adults as well. Results: Out of total 72 retrieved articles, 50 met the inclusion criteria. We estimated a significant increase in incidence of neural abnormalities such as Guillain-Barre syndrome and microcephaly in the regions that are experiencing ZIKV outbreaks. Other neurological malformations found in ZIKV patients include hydrancephaly/hydrops fetalis, myasthenia gravis,meningoencephalitis and myelitis. Conclusion: Our systematic analysis provides the broad spectrum of neurological malformations in ZIKV infected patients and these data further support the causal link of ZIKV with neurological disorders.

Increasing Crop Quality and Yield with a Machine Learning-Based Crop Monitoring System

Farming is cultivating the soil,producing crops,and keeping livestock.The agricultural sector plays a crucial role in a country’s economic growth.This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield.In the first stage,machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops.The recommended crops are based on various factors such as weather conditions,soil analysis,and the amount of fertilizers and pesticides required.In the second stage,a transfer learningbased model for plant seedlings,pests,and plant leaf disease datasets is used to detect weeds,pesticides,and diseases in the crop.The proposed model achieved an average accuracy of 95%,97%,and 98% in plant seedlings,pests,and plant leaf disease detection,respectively.The system can help farmers pinpoint the precise measures required at the right time to increase yields.

Malware Detection in Android IoT Systems Using Deep Learning

The Android Operating System(AOS)has been evolving since its inception and it has become one of the most widely used operating system for the Internet of Things(IoT).Due to the high popularity and reliability ofAOS for IoT,it is a target of many cyber-attacks which can cause compromise of privacy,financial loss,data integrity,unauthorized access,denial of services and so on.The Android-based IoT(AIoT)devices are extremely vulnerable to various malwares due to the open nature and high acceptance of Android in the market.Recently,several detection preventive malwares are developed to conceal their malicious activities from analysis tools.Hence,conventional malware detection techniques could not be applied and innovative countermeasures against such anti-detection malwares are indispensable to secure the AIoT.In this paper,we proposed the novel deep learning-based real-time multiclass malware detection techniques for the AIoT using dynamic analysis.The results show that the proposed technique outperforms existing malware detection techniques and achieves detection accuracy up to 99.87%.

A Double-Branch Xception Architecture for Acute Hemorrhage Detection and Subtype Classification

This study presents a deep learning model for efficient intracranial hemorrhage(ICH)detection and subtype classification on non-contrast head computed tomography(CT)images.ICH refers to bleeding in the skull,leading to the most critical life-threatening health condition requiring rapid and accurate diagnosis.It is classified as intra-axial hemorrhage(intraventricular,intraparenchymal)and extra-axial hemorrhage(subdural,epidural,subarachnoid)based on the bleeding location inside the skull.Many computer-aided diagnoses(CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan levels.However,these approaches performonly binary classification and suffer from a large number of parameters,which increase storage costs.Further,the accuracy of brain hemorrhage detection in existing models is significantly low for medically critical applications.To overcome these problems,a fast and efficient system for the automatic detection of ICH is needed.We designed a double-branch model based on xception architecture that extracts spatial and instant features,concatenates them,and creates the 3D spatial context(common feature vectors)fed to a decision tree classifier for final predictions.The data employed for the experimentation was gathered during the 2019 Radiologist Society of North America(RSNA)brain hemorrhage detection challenge.Our model outperformed benchmark models and achieved better accuracy in intraventricular(99.49%),subarachnoid(99.49%),intraparenchymal(99.10%),and subdural(98.09%)categories,thereby justifying the performance of the proposed double-branch xception architecture for ICH detection and classification.