Analysis of Hybrid Soft Computing Techniques for Intrusion Detection on Network

Intrusion detection is an action towards security of a network when a system or network is being used inappropriately or without authorization. The use of Soft Computing Approaches in intrusion detection is an Appealing co ncept for two reasons: firstly, the Soft Computing Approaches achieve tractability, robustness, low solution cost, and better report with reality. Secondly, current techniques used in network security from intrusion are not able to cope with the dynamic and increasingly complex nature of network and their security. It is hoped that Soft Computing inspired approaches in this area will be able to meet this challenge. Here we analyze the approaches including the examination of efforts in hybrid system of SC su ch as neuro - fuzzy, fuzzy - genetic, neuro - genetic, and neuro - fuzzy - genetic used the development of the systems and outcome their implementation. It provides an introduction and review of the key developments within this field, in addition to making suggestio ns for future research

Extension and Research Faculty Perspectives of Extension-Research Integration: Opportunities and Challenges

This study examined the perspectives of Extension and research faculty regarding integration of Extension and research (E-R) activities. Faculty with 50% or greater appointments in Extension or research at a Land-Grant University in the northeastern United States were identified as subjects for the study (N = 59). Study objectives were to determine the current status of E-R integration efforts, understanding of Extension and research faculty roles, barriers to E-R integration, and strategies for strengthening E-R integration activities. An instrument was developed by the researchers and data were collected using SurveyMonkey. Descriptive statistics were used to summarize data. Findings indicated that 1) both faculty groups strongly agreed that joint appointments are necessary for effective E-R integration; 2) Extension faculty viewed their role as the face of the university in that they are the link between campus and community; 3) barriers to E-R integration included lack of equal status in terms of research taking precedence over Extension, limited funding, and lack of recognition for Extension work; and 4) strategies suggested by both faculty groups included hiring more faculty with split appointments in Extension and research, increasing interdisciplinary research, enhancing funding for integration efforts, and providing graduate assistantships that involve E-R integration activities

Performance Monitoring of Optical Network using Bit Error Rate

With the increase in demand of broadband services, channel complexity has been increased. Therefore it has become necessary to monitor the performance of optical networks. Quality of service monitoring is very important tool in maintaining a strong, reliable and high capacity optical network. When one talks about QoS, BER is the parameter which needs to be monitored. In the proposed work, BER for 1m and 3m fiber cable is calculated through experimental setup and simulations. Simulations has been done in OPTSIM and later on behavior of network is compared with the experimental data. DOI: 10.17762/ijritcc2321-8169.15056

Assessing the Influence of Importance Prompt and Box Size on Response to Open-ended Questions in Mixed Mode Surveys: Evidence on Response Rate and Response Quality

To understand the thinking behind respondents’ answers, researchers occasionally use open-ended questions. Getting a quality response to open-ended questions can be challenging but attending to the visual design of the question and using a motivational statement in the question can increase item response and data quality. To understand the use of open-ended questions in surveys further, we designed an experiment testing the effect of an importance statement (present/absent) and box size (large/small) on item response rate and response quality in a mixed-mode (web and mail modes) survey. Data for the study came from a survey of Florida Cooperative Extension Service (FCES) clients. The results showed that item response was improved with the importance prompt, irrespective of box size. The combination of importance statement and larger answer box also resulted in more words. Web responses produced more words than those on paper and words counts were significantly improved with an importance prompt for web responses. Overall, the combination of importance prompt, larger box size and web mode was most important in producing the best item response rate and response quality in our mixed-mode survey

Self-Identity of Biracial Children: What Role Do Parents Play?

One of the fastest growing student groups today is the biracial student population. This study is part of a larger exploratory descriptive study on biracial college students with a focus on the extent to which parents socialized their biracial children in the cultures of both parents while growing up and how these students chose to self-identify. Biracial students at a large, predominantly white, research university in the northeastern United States completed an online survey. Descriptive statistics and content analysis were used to analyze the data. The results indicated that most students had adopted a “border” identity. The majority of parents had encouraged their children to have an acceptance and pride in their biracial status. However, many students would have appreciated more exposure and knowledge of their heritages, as well as preparation for dealing with their environment as a biracial person. Students offered recommendations for parental practices to strengthen their racial identity

Mapping of the multifoliate pinna (mfp) leaf-blade morphology mutation in grain pea Pisum sativum

The multifoliate pinna (mfp) mutation alters the leaf-blade architecture of pea, such that simple tendril pinnae of distal domain are replaced by compound pinna blades of tendrilled leaflets in mfp homozygotes. The MFP locus was mapped with reference to DNA markers using F2 and F2:5 RIL as mapping populations. Among 205 RAPD, 27 ISSR and 35 SSR markers that demonstrated polymorphism between the parents of mapping populations, three RAPD markers were found linked to the MFP locus by bulk segregant analyses on mfp/mfp and MFP/MFP bulks assembled from the F2:5 population. The segregational analysis of mfp and 267 DNA markers on 96 F2 plants allowed placement of 26 DNA markers with reference to MFP on a linkage group. The existence of common markers on reference genetic maps and MFP linkage group developed here showed that MFP is located on linkage group IV of the consensus genetic map of pea

A Conceptual Model for Selecting Extension Delivery Methods to Plan Better Programs

Extension educators today face several challenges in designing, delivering, and evaluating programs. Achieving successful program outcomes is becoming increasingly difficult. Educators must choose amongst a wide variety of technology and delivery methods, all while facing resource constraints such as cost, time, and materials. Educators require a tool that helps them to select delivery methods that achieve more successful program outcomes. We developed a conceptual model that connects the scholarly works of Bloom et al., Dale, and Bennett with over 30 program delivery methods. This article demonstrates the model’s utility by applying it to a past program on smoking cessation

Intelligent Controller Based on Artificial Neural Network and INC Based MPPT for Grid Integrated Solar PV System

Solar photovoltaic (PV) systems have become an integral part of today's advanced energy infrastructure due to its low kinetic energy, its abundance availability, and its freedom from human interference. Solar PV systems have the potential to greatly reduce our reliance on fossil fuels, but their intermittent nature means they cannot provide a constant source of electricity. The system's security should be well thought out, and it should be able to withstand a lot of abuse. The current energy system faces a significant difficulty in ensuring continuous supply. In this study, a three-phase, two-stage photovoltaic system that is managed by artificial neural networks (ANN). A DC-DC boost converter with maximum power point tracking (MPPT) based on the incremental conductance (INC) method is incorporated in the first stage. In the next step, an ANN-based controller optimizes the performance of a three-phase switching PWM inverter that is connected to the grid by controlling currents along the d-q axis. Comprehensive simulations were carried out using MATLAB or Simulink to evaluate the system's performance under various illumination and temperature conditions. Results show that the suggested approach outperforms the baseline in a number of areas. Better dynamic reactions, accurate tracking of reference currents within permissible bounds, and quick settling periods after startup are all displayed by it. These findings show that our method has the potential to greatly improve the efficiency and dependability of solar PV systems. The results of this study have implications for renewable energy in general and present a viable path toward enhancing the resilience and sustainability of energy infrastructure

Perceptions Regarding Drinking Water Quality and Its Effects on Human and Animal Health among Plain-Sect Community Members

The quality of drinking water can affect human and animal health, and plain-sect populations may be more susceptible than other rural populations due to their use of traditional farm management practices and their reliance on well water. Therefore, an interdisciplinary team conducted a pilot study to understand the status of existing drinking water quality, community perceptions regarding causes of water deterioration, its associated effect on human and animal health, and solutions to address such challenges. The study included water testing and a focus group discussion with plain-sect community members. The findings revealed that participants perceived the drinking water quality as potable and free from contamination which contradicted water testing reports, where 92% of water samples violated the standard drinking water quality parameters. Perceived causes of water deterioration included sulfate leaching, changes in farming practices, and commercial development. The participants also revealed human health (e.g., cancer, stomach ailments) and animal health (e.g., changes in milk production and conception rates) concerns but expressed no association of these health concerns with drinking water quality. This pilot study’s findings indicate that there exists a gap between perceptions of and actual drinking water quality and its relationship to health. More efforts are needed by health and conservation professionals to narrow the existing knowledge gaps by considering socio-cultural factors and appropriate scientific interventions related to best management practices of drinking water quality, and human and animal health, to achieve desired goals in plain-sect communities. [Abstract by authors.

Deep 3D Convolutional Neural Network for Automated Lung Cancer Diagnosis

Computer Aided Diagnosis has emerged as an indispensible technique for validating the opinion of radiologists in CT interpretation. This paper presents a deep 3D Convolutional Neural Network (CNN) architecture for automated CT scan-based lung cancer detection system. It utilizes three dimensional spatial information to learn highly discriminative 3 dimensional features instead of 2D features like texture or geometric shape whick need to be generated manually. The proposed deep learning method automatically extracts the 3D features on the basis of spatio-temporal statistics.The developed model is end-to-end and is able to predict malignancy of each voxel for given input scan. Simulation results demonstrate the effectiveness of proposed 3D CNN network for classification of lung nodule in-spite of limited computational capabilities.Comment: Initial draft of PAPER Presented at IRSCNS 2018 , Goa , India final version available at Mishra S., Chaudhary N.K., Asthana P., Kumar A. (2019) Deep 3D Convolutional Neural Network for Automated Lung Cancer Diagnosis. In: Peng SL., Dey N., Bundele M. (eds) Computing and Network Sustainability. Lecture Notes in Networks and Systems, vol 75. Springer, Singapor