Optimal Ensemble Learning Based on Distinctive Feature Selection by Univariate ANOVA-F Statistics for IDS

Cyber-attacks are increasing day by day. The generation of data by the population of the world is immensely escalated. The advancements in technology, are intern leading to more chances of vulnerabilities to individual’s personal data. Across the world it became a very big challenge to bring down the threats to data security. These threats are not only targeting the user data and also destroying the whole network infrastructure in the local or global level, the attacks could be hardware or software. Central objective of this paper is to design an intrusion detection system using ensemble learning specifically Decision Trees with distinctive feature selection univariate ANOVA-F test. Decision Trees has been the most popular among ensemble learning methods and it also outperforms among the other classification algorithm in various aspects. With the essence of different feature selection techniques, the performance found to be increased more, and the detection outcome will be less prone to false classification. Analysis of Variance (ANOVA) with F-statistics computations could be a reasonable criterion to choose distinctives features in the given network traffic data. The mentioned technique is applied and tested on NSL KDD network dataset. Various performance measures like accuracy, precision, F-score and Cross Validation curve have drawn to justify the ability of the method

Optimal Ensemble Learning Based on Distinctive Feature Selection by Univariate ANOVA-F Statistics for IDS

Cyber-attacks are increasing day by day. The generation of data by the population of the world is immensely escalated. The advancements in technology, are intern leading to more chances of vulnerabilities to individual’s personal data. Across the world it became a very big challenge to bring down the threats to data security. These threats are not only targeting the user data and also destroying the whole network infrastructure in the local or global level, the attacks could be hardware or software. Central objective of this paper is to design an intrusion detection system using ensemble learning specifically Decision Trees with distinctive feature selection univariate ANOVA-F test. Decision Trees has been the most popular among ensemble learning methods and it also outperforms among the other classification algorithm in various aspects. With the essence of different feature selection techniques, the performance found to be increased more, and the detection outcome will be less prone to false classification. Analysis of Variance (ANOVA) with F-statistics computations could be a reasonable criterion to choose distinctives features in the given network traffic data. The mentioned technique is applied and tested on NSL KDD network dataset. Various performance measures like accuracy, precision, F-score and Cross Validation curve have drawn to justify the ability of the method

Role of Community Based Savings Groups (CBSGs) enhancing the utilization of community midwives in Chitral district of Pakistan

Background: Maternal and infant mortality rates in the district of Chitral in Pakistan are alarmingly high. One of the major reasons for this is the inability of women to access skilled care due to the high costs associated with traveling and utilizing such services. The Aga Khan Health Services, Pakistan (AKHSP) in partnership with the national and provincial Maternal, Neonatal and Child Health (MNCH) program, deployed 28 community midwives (CMWs) in remote villages of Chitral district. This program has also established Community-Based Savings Groups (CBSGs) to support and facilitate access to MNCH services, in particular those delivered by the CMWs. CBSGs are a simple yet cost-effective and sustainable means of providing basic financial services to low income, marginalized, rural populations.The link between CBSGs and utilization of MNCH services is not well understood. This study will assess the relationship between women membership of CBSGs and their utilization of MNCH services, specifically those offered by CMWs, in the community.Methods: The research question will be answered through guided interviews of women in the target population who have delivered within one month. The outcome variable will be the utilization of full continuum of skilled MNCH care (disaggregated by 1+ ANC, 1+ PNC and skilled delivery). The primary independent variable of interest will be participation in a CBSG.Focus Group Discussions (FGDs) will be conducted to generate further understanding and information about the social and financial factors that contribute to health behavior and health provider decision-making during pregnancy.Analysis will be tailored to answer how CBSGs, directly or indirectly, facilitate greater financial and/or social access to CMW services for pregnant women. Furthermore, the extent to which financial or social empowerment through a CBSG leads to greater utilization of CMW services.Discussion: The role of CBSGs and their interlink with the CMWs services to be replicated in other comparable areas in Pakistan as a viable mean to increase MNCH service utilization amongst rural, low income, and marginalized communities. Findings from this research will be disseminated through community, national, and international channels consisting of policy makers and social society groups

Identification and <i>In Silico</i> Analysis of a Homozygous Nonsense Variant in <i>TGM1</i> Gene Segregating with Congenital Ichthyosis in a Consanguineous Family

Background and Objectives: Lamellar ichthyosis is a rare skin disease characterized by large, dark brown plate-like scales on the entire body surface with minimum or no erythema. This phenotype is frequently associated with a mutation in the TGM1 gene, encoding the enzyme transglutaminase 1 which plays a catalytic role in the formation of the cornified cell envelop. The present study aimed to carry out clinical and genetic characterization of the autosomal recessive lamellar ichthyosis family from Balochistan. Materials and Methods: A consanguineous family with lamellar ichthyosis was enrolled from Balochistan, Pakistan. PCR amplification of all the exons and splice site junctions of the TGM1 gene followed by Sanger sequencing was performed on the genomic DNA. The identified variant was checked by In silico prediction tools to evaluate the effect of the variant on protein. Results: Sanger sequencing identified a homozygous nonsense variant c.131G >A (p.Trp44*) in the TGM1 gene that segregated in the autosomal recessive mode of inheritance in the family. The identified variant results in premature termination of transcribed mRNA and is predicted to cause a truncated or absent translation product transglutaminase-1 (TGase-1) accompanied by loss of catalytic activity, causing a severe clinical phenotype of lamellar ichthyosis in the patients. Conclusions: Here, we report a consanguineous lamellar ichthyosis family with a homozygous nonsense variant in the TGM1 gene. The variant is predicted as pathogenic by different In silico prediction tools