Search results for: statistical techniques

Authors: Shinoy Vengaramkode Bhaskaran

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The advancement of cloud computing led to a variety of security issues for both consumers and industries. Whereas machine learning (ML) is one approach to securing Cloud-based systems. Various methods have been employed to prevent or detect attacks and security vulnerabilities on the Cloud using ML techniques. In this paper, we present an ML perspective on the methodologies and techniques of cloud security. Initially, an investigative study on cloud computing is conducted with a primary emphasis on the gaps with two research questions that are impeding the adoption of cloud technology, as well as the challenges associated with threat solutions. Next, some ideas are generated based on machine learning methods to mitigate certain types of attacks that are frequently discussed through the application of ML techniques. Finally, we review different machine learning algorithms and their adoption in cloud computing.

Keywords: artificial intelligence, machine learning, cloud computing infrastructure as a service, support vector machine, platform as a service

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Authors: Jaskaran Singh Sidhu

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Aim: The purpose of this study is to find the relationship between wellness among sportsmen and non sportsmen. Methodology: The present study is an experimental study for 80 senior secondary volleyball players of 16-19 years of age from Ludhiana District of Punjab (India), and 80 non-sportsperson were taken from senior secondary school of Ludhiana district. The sample for this study was taken through a random sampling technique. Tools: A five point scale havinf 50 items was used to acess the wellness Statistical Analysis: To find out the relationship among the variables exists or not, a t-test was used to test the significance of the difference between the means. Statistics for each characteristic were calculated; Mean, Standard deviation, Standard error of Mean. Data were analyzed using SPSS (statistical package for the social sciences). Statistical significance was set at p < 0.05. Results: Substantial deviations were noted at p<0.5 in the totality of wellness. Sportsmen show significant differences exist at p<0.5 in three parameters of wellness i.e., physical wellness, mental wellness, and social wellness. In spiritual and emotional wellness attributes, non-sportsmen shows significant difference at p<0.5. Conclusion: From the data interpretation it reflects that overall wellness can be improved by participation in sports. It further noted in study that participation in sports promote the attributes of wellness i.e., physical wellness, mental wellness, emotional wellness and social wellness.

Keywords: physical, mental, social, emotional, wellness, spiritual

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Authors: Qasim Kriri, Harsh B. Desai

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Water supply and sanitation in Saudi Arabia is portrayed by difficulties and accomplishments. One of the fundamental difficulties is water shortage. With a specific end goal to beat water shortage, significant ventures have been attempted in sea water desalination, water circulation, sewerage, and wastewater treatment. The motivation behind Statistical Process Control (SPC) is to decide whether the execution of a procedure is keeping up an acceptable quality level [AQL]. SPC is an analytical decision-making method. A fundamental apparatus in the SPC is the Control Charts, which follow the inconstancy in the estimations of the item quality attributes. By utilizing the suitable outline, administration can decide whether changes should be made with a specific end goal to keep the procedure in charge. The two most important quality factors in the distilled water which were taken into consideration were pH (Potential of Hydrogen) and TDS (Total Dissolved Solids). There were three stages at which the quality checks were done. The stages were as follows: (1) Water at the source, (2) water after chemical treatment & (3) water which is sent for packing. The upper specification limit, central limit and lower specification limit are taken as per Saudi water standards. The procedure capacity to accomplish the particulars set for the quality attributes of Berain water Factory chose to be focused by the proposed SPC system.

Keywords: acceptable quality level, statistical quality control, control charts, process charts

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Authors: P. Priyanka, S. Shruthi, N. Guruprasad

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Health is a common theme in most cultures. In fact all communities have their concepts of health, as part of their culture. Health continues to be a neglected entity. Planning of Human diet should be done very careful by selecting the food items or groups of food items also the composition involved. Low price and good taste of foods are regarded as two major factors for optimal human nutrition. Linear programming techniques have been extensively used for human diet formulation for quiet good number of years. Through the process, we mainly apply “The Simplex Method” which is a very useful statistical tool based on the theorem of Elementary Row Operation from Linear Algebra and also incorporate some other necessary rules set by the Simplex Method to help solve the problem. The study done by us is an attempt to develop a programming model for optimal planning and best use of nutrient ingredients.

Keywords: diet formulation, linear programming, nutrient ingredients, optimization, simplex method

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Authors: M. Sai Veerendra

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Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nation. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliché of drug trafficking to financing terrorism and surely not forgetting personal gain. Most of the financial institutions internationally have been implementing anti-money laundering solutions (AML) to fight investment fraud activities. However, traditional investigative techniques consume numerous man-hours. Recently, data mining approaches have been developed and are considered as well-suited techniques for detecting ML activities. Within the scope of a collaboration project on developing a new data mining solution for AML Units in an international investment bank in Ireland, we survey recent data mining approaches for AML. In this paper, we present not only these approaches but also give an overview on the important factors in building data mining solutions for AML activities.

Keywords: data mining, clustering, money laundering, anti-money laundering solutions

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Authors: Hana Rabbouch

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In recent years, there has been considerable growth of denoising techniques mainly devoted to medical imaging. This important evolution is not only due to the progress of computing techniques, but also to the emergence of multi-resolution analysis (MRA) on both mathematical and algorithmic bases. In this paper, a comparative study is conducted between the two best-known MRA-based decomposition techniques: the Empirical Mode Decomposition (EMD) and the Discrete Wavelet Transform (DWT). The comparison is carried out in a framework of multi-scale denoising, where a Non-Local Means (NLM) filter is performed scale-by-scale to a sample of benchmark medical images. The results prove the effectiveness of the multiscaled denoising, especially when the NLM filtering is coupled with the EMD.

Keywords: medical imaging, non local means, denoising, multiscaled analysis, empirical mode decomposition, wavelets

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Authors: Hager Elsheikh

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Context: Pancreatitis is a condition characterized by inflammation in the pancreas, which can lead to serious complications if untreated. Both acute and chronic pancreatitis are associated with immune reactions and fibrosis, which further damage the pancreas. The type 2 immune response, primarily driven by alternative activated macrophages (AAMs), plays a significant role in the development of fibrosis. The IL-4/STAT6 pathway is a crucial signaling pathway for the activation of M2 macrophages. Pancreatic fibrosis is induced by dysregulated inflammatory responses and can result in the autodigestion and necrosis of pancreatic acinar cells. Research Aim: The aim of this study is to investigate the impact of STAT6, a crucial molecule in the IL-4/STAT6 pathway, on the severity and development of fibrosis during acute and chronic pancreatitis. The research also aims to understand the influence of the JAK/STAT6 signaling pathway on the balance between fibrosis and regeneration in the presence of different macrophage populations. Methodology: The research utilizes murine models of acute and chronic pancreatitis induced by cerulean injection. Animal models will be employed to study the effect of STAT6 knockout on disease severity and fibrosis. Isolation of acinar cells and cell culture techniques will be used to assess the impact of different macrophage populations on wound healing and regeneration. Various techniques such as PCR, histology, immunofluorescence, and transcriptomics will be employed to analyze the tissues and cells. Findings: The research aims to provide insights into the mechanisms underlying tissue fibrosis and wound healing during acute and chronic pancreatitis. By investigating the influence of the JAK/STAT6 signaling pathway and different macrophage populations, the study aims to understand their impact on tissue fibrosis, disease severity, and pancreatic regeneration. Theoretical Importance: This research contributes to our understanding of the role of specific signaling pathways, macrophage polarization, and the type 2 immune response in pancreatitis. It provides insights into the molecular mechanisms underlying tissue fibrosis and the potential for targeted therapies. Data Collection and Analysis Procedures: Data will be collected through the use of murine models, isolation and culture of acinar cells, and various experimental techniques such as PCR, histology, immunofluorescence, and transcriptomics. Data will be analyzed using appropriate statistical methods and techniques, and the findings will be interpreted in the context of the research objectives. Conclusion: By investigating the mechanisms of tissue fibrosis and wound healing during acute and chronic pancreatitis, this research aims to enhance our understanding of the disease progression and potential therapeutic targets. The findings have theoretical importance in expanding our knowledge of pancreatic fibrosis and the role of macrophage polarization in the context of the type 2 immune response.

Keywords: immunity in chronic diseases, pancreatitis, macrophages, immune response

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Authors: Satpal Yadav, Ashish Phulkar, Krishna K. Sahu

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Aim: The research aims at doing a 3 Dimension biomechanical analysis in the shot put techniques of International throwers to evaluate the performance. Research Method: The researcher adopted the descriptive method and the data was subjected to calculate by using Pearson’s product moment correlation for the correlation of the biomechanical parameters with the performance of shot put throw. In all the analyses, the 5% critical level (p ≤ 0.05) was considered to indicate statistical significance. Research Sample: Eight (N=08) international shot putters using rotational/glide technique in male category was selected as subjects for the study. The researcher used the following methods and tools to obtain reliable measurements the instrument which was used for the purpose of present study namely the tesscorn slow-motion camera, specialized motion analyzer software, 7.260 kg Shot Put (for a male shot-putter) and steel tape. All measurement pertaining to the biomechanical variables was taken by the principal investigator so that data collected for the present study was considered reliable. Results: The finding of the study showed that negative significant relationship between the angular velocity right shoulder, acceleration distance at pre flight (-0.70), (-0.72) respectively were obtained, the angular displacement of knee, angular velocity right shoulder and acceleration distance at flight (0.81), (0.75) and (0.71) respectively were obtained, the angular velocity right shoulder and acceleration distance at transition phase (0.77), (0.79) respectively were obtained and angular displacement of knee, angular velocity right shoulder, release velocity shot, angle of release, height of release, projected distance and measured distance as the values (0.76), (0.77), (-0.83), (-0.79), (-0.77), (0.99) and (1.00) were found higher than the tabulated value at 0.05 level of significance. On the other hand, there exists an insignificant relationship between the performance of shot put and acceleration distance [m], angular displacement shot, C.G at release and horizontal release distance on the technique of shot put.

Keywords: biomechanics, analysis, shot put, international throwers

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Authors: Lubabatu Sada Sodangu

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Malaria is a major cause of death in tropical and subtropical nations. Malaria cases are continually rising as a result of a number of factors, despite the fact that the condition is now treatable using effective methods. In this situation, quick and effective diagnostic methods are essential for the management and control of malaria. Malaria diagnosis using conventional methods is still troublesome, hence new technologies have been created and implemented to get around the drawbacks. The review describes the currently known malaria diagnostic techniques, their strengths and shortcomings.

Keywords: malaria, technique, diagnosis, Africa

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Authors: Lubabatu Sada Sodangi

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Malaria is a major cause of death in tropical and subtropical nations. Malaria cases are continually rising as a result of a number of factors, despite the fact that the condition is now treatable using effective methods. In this situation, quick and effective diagnostic methods are essential for the management and control of malaria. Malaria diagnosis using conventional methods is still troublesome; hence, new technologies have been created and implemented to get around the drawbacks. The review describes the currently known malaria diagnostic techniques, their strengths, and shortcomings.

Keywords: malaria, technique, diagnosis, Africa

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Authors: Chawarat Rotejanaprasert, Andrew Lawson

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Early detection of unusual health events depends on the ability to detect rapidly any substantial changes in disease, thus facilitating timely public health interventions. To assist public health practitioners to make decisions, statistical methods are adopted to assess unusual events in real time. We introduce a surveillance Kullback-Leibler (SKL) measure for timely detection of disease outbreaks for small area health data. The detection methods are compared with the surveillance conditional predictive ordinate (SCPO) within the framework of Bayesian hierarchical Poisson modeling and applied to a case study of a group of respiratory system diseases observed weekly in South Carolina counties. Properties of the proposed surveillance techniques including timeliness and detection precision are investigated using a simulation study.

Keywords: Bayesian, spatial, temporal, surveillance, prospective

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Authors: Mukul Anand

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Quality oriented affordable shelter for all has always been the key issue in the housing sector of our country. Homelessness is the acute form of housing need. It is a paradox that in spite of innumerable government initiated programmes for affordable housing, certain section of society is still devoid of shelter. About nineteen million (18.78 million) households grapple with housing shortage in Urban India in 2012. In Indian scenario there is major mismatch between the people for whom the houses are being built and those who need them. The prime force faced by public authorities in facilitation of quality housing for all is high cost of construction. The present paper will comprehend executable techniques for dilution of cost factor in housing the homeless. The key actors responsible for delivery of cheap housing stock such as capacity building, resource optimization, innovative low cost building material and indigenous skeleton housing system will also be incorporated in developing these techniques. Time performance, which is an important angle of above actors, will also be explored so as to increase the effectiveness of low cost housing. Along with this best practices will be taken up as case studies where both conventional techniques of housing and innovative low cost housing techniques would be cited. Transportation consists of approximately 30% of total construction budget. Thus use of alternative local solutions depending upon the region would be covered so as to highlight major components of low cost housing. Government is laid back regarding base line information on use of innovative low cost method and technique of resource optimization. Therefore, the paper would be an attempt to bring to light simpler solutions for achieving low cost housing.

Keywords: construction, cost, housing, optimization, shelter

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Authors: Guebli Abdelkader, Regiueg Madani, Sbaa Bouabdellah

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The goals of this study are to find ways to determine the value of dynamic priorities in motor learning between some basic skills in beginner’s basketball (U14), based on skills of shooting and defense against the shooter. Our role is to expose the statistical results in compare & correlation between samples of study in tests skills for the shooting and defense against the shooter. In order to achieve this objective, we have chosen 40 boys in middle school represented in four groups, two controls group’s (CS1, CS2) ,and two experimental groups (ES1: training on skill of shooting, skill of defense against the shooter, ES2: experimental group training on skill of defense against the shooter, skill of shooting). For the statistical analysis, we have chosen (F & T) tests for the statistical differences, and test (R) for the correlation analysis. Based on the analyses statistics, we confirm the importance of classifying priorities of basketball basic skills during the motor learning process. Admit that the benefits of experimental group training are to economics in the time needed for acquiring new motor kinetic skills in basketball. In the priority of ES2 as successful dynamic motor learning method to enhance the basic skills among beginner’s basketball.

Keywords: basic skills, basketball, motor learning, children

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Authors: Katherine Arquio

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Community-based Forest Management (CBFM) is one of the integrative programs that slowly turned the course of forest management from traditional corporate to community-based practice resulting to people empowerment. As such, one of its goals is to promote socio-economic welfare among the people in the community in which social equity is included. This study aims to look at the equity aspect of the program, particularly if there are equity differences between two CBFM sites- Matikiw in Pakil, Laguna and Minayutan and Bacong Sigsigan in Famy, Laguna. Equity indicators were identified first, since these will be the basis of the questions that will be asked on the survey, after this, the survey proper was conducted, and finally, the analysis. Two tailed t-test was used as statistical tool since the difference between the two sites is the focus of the study. Statistical analysis was done through the use of STATA program, a statistical software. There were 32 indicators identified and results showed that, out of these indicators, only 13 were found significantly different between the two. The 13 indicators were significantly observed only in Matikiw; the other 19 indicators were commonly observed in both areas and are conducive as equity indicators for the CBFM program.

Keywords: social equity, CBFM, social forestry, equity indicators

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Authors: Chen Xiatong

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The study aims to explore the interrelationship between corporate governance factors and firm performance in Mainland China using a mixed-method approach. To clarify the current effectiveness of corporate governance, uncover the complex interrelationships between governance factors and firm performance, and enhance understanding of corporate governance strategies in Mainland China. The research involves quantitative methods like statistical analysis of governance factors and firm performance data, as well as qualitative approaches including policy research, case studies, and interviews with staff members. The study aims to reveal the current effectiveness of corporate governance in Mainland China, identify complex interrelationships between governance factors and firm performance, and provide suggestions for companies to enhance their governance practices. The research contributes to enriching the literature on corporate governance by providing insights into the effectiveness of governance practices in Mainland China and offering suggestions for improvement. Quantitative data will be gathered through surveys and sampling methods, focusing on governance factors and firm performance indicators. Qualitative data will be collected through policy research, case studies, and interviews with staff members. Quantitative data will be analyzed using statistical, mathematical, and computational techniques. Qualitative data will be analyzed through thematic analysis and interpretation of policy documents, case study findings, and interview responses. The study addresses the effectiveness of corporate governance in Mainland China, the interrelationship between governance factors and firm performance, and staff members' perceptions of corporate governance strategies. The research aims to enhance understanding of corporate governance effectiveness, enrich the literature on governance practices, and contribute to the field of business management and human resources management in Mainland China.

Keywords: corporate governance, business management, human resources management, board of directors

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Authors: M. A. I Perera, C. R. Wijesinghe, A. R. Weerasinghe

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his study was conducted to review and identify the unsupervised techniques that can be employed to analyze gene expression data in order to identify better subtypes of tumors. Identifying subtypes of cancer help in improving the efficacy and reducing the toxicity of the treatments by identifying clues to find target therapeutics. Process of gene expression data analysis described under three steps as preprocessing, clustering, and cluster validation. Feature selection is important since the genomic data are high dimensional with a large number of features compared to samples. Hierarchical clustering and K Means are often used in the analysis of gene expression data. There are several cluster validation techniques used in validating the clusters. Heatmaps are an effective external validation method that allows comparing the identified classes with clinical variables and visual analysis of the classes.

Keywords: cancer subtypes, gene expression data analysis, clustering, cluster validation

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Authors: Tae-Uk Yun, Pullip Chung, Kee-Young Kwahk, Hyunchul Ahn

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Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection using machine learning techniques over the past years. But, there have been no prior studies proposed an automated fake news detection method for Korean news to our best knowledge. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (topic modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as logistic regression, backpropagation network, support vector machine, and deep neural network can be applied. To validate the effectiveness of the proposed method, we collected about 200 short Korean news from Seoul National University’s FactCheck. which provides with detailed analysis reports from 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

Keywords: fake news detection, Korean news, machine learning, text mining

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Authors: Adriana Postal, Silvio C. Sampaio, Marcio A. Villas Boas, Josué P. Castro

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This study deals with the estimation of reference evapotranspiration (ET₀) in an agricultural context, focusing on efficient irrigation management to meet the growing interest in the sustainable management of water resources. Given the importance of water in agriculture and its scarcity in many regions, efficient use of this resource is essential to ensure food security and environmental sustainability. The methodology used involved the application of artificial intelligence techniques, specifically Multilayer Perceptron (MLP) Artificial Neural Networks (ANNs), to predict ET₀ in the state of Paraná, Brazil. The models were trained and validated with meteorological data from the Brazilian National Institute of Meteorology (INMET), together with data obtained from a producer's weather station in the western region of Paraná. Two optimizers (SGD and Adam) and different meteorological variables, such as temperature, humidity, solar radiation, and wind speed, were explored as inputs to the models. Nineteen configurations with different input variables were tested; amidst them, configuration 9, with 8 input variables, was identified as the most efficient of all. Configuration 10, with 4 input variables, was considered the most effective, considering the smallest number of variables. The main conclusions of this study show that MLP ANNs are capable of accurately estimating ET₀, providing a valuable tool for irrigation management in agriculture. Both configurations (9 and 10) showed promising performance in predicting ET₀. The validation of the models with cultivator data underlined the practical relevance of these tools and confirmed their generalization ability for different field conditions. The results of the statistical metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²), showed excellent agreement between the model predictions and the observed data, with MAE as low as 0.01 mm/day and 0.03 mm/day, respectively. In addition, the models achieved an R² between 0.99 and 1, indicating a satisfactory fit to the real data. This agreement was also confirmed by the Kolmogorov-Smirnov test, which evaluates the agreement of the predictions with the statistical behavior of the real data and yields values between 0.02 and 0.04 for the producer data. In addition, the results of this study suggest that the developed technique can be applied to other locations by using specific data from these sites to further improve ET₀ predictions and thus contribute to sustainable irrigation management in different agricultural regions. The study has some limitations, such as the use of a single ANN architecture and two optimizers, the validation with data from only one producer, and the possible underestimation of the influence of seasonality and local climate variability. An irrigation management application using the most efficient models from this study is already under development. Future research can explore different ANN architectures and optimization techniques, validate models with data from multiple producers and regions, and investigate the model's response to different seasonal and climatic conditions.

Keywords: agricultural technology, neural networks in agriculture, water efficiency, water use optimization

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Authors: Arushi Singh, Anjana Jain, Prakash Vyavahare

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Recently, Li-Fi(light-fiedelity) has been launched based on VLC(visible light communication) technique, 100 times faster than WiFi. Now 5G mobile communication system is proposed to use VLC-OFDM as the transmission technique. The VLC system focused on visible rays, is considered for efficient spectrum use and easy intensity modulation through LEDs. The reason of high speed in VLC is LED, as they flicker incredibly fast(order of MHz). Another advantage of employing LED is-it acts as low pass filter results no out-of-band emission. The VLC system falls under the category of ‘green technology’ for utilizing LEDs. In present scenario, OFDM is used for high data-rates, interference immunity and high spectral efficiency. Inspite of the advantages OFDM suffers from large PAPR, ICI among carriers and frequency offset errors. Since, the data transmission technique used in VLC system is OFDM, the system suffers the drawbacks of OFDM as well as VLC, the non-linearity dues to non-linear characteristics of LED and PAPR of OFDM due to which the high power amplifier enters in non-linear region. The proposed paper focuses on reduction of PAPR in VLC-OFDM systems. Many techniques are applied to reduce PAPR such as-clipping-introduces distortion in the carrier; selective mapping technique-suffers wastage of bandwidth; partial transmit sequence-very complex due to exponentially increased number of sub-blocks. The paper discusses three companding techniques namely- µ-law, A-law and advance A-law companding technique. The analysis shows that the advance A-law companding techniques reduces the PAPR of the signal by adjusting the companding parameter within the range. VLC-OFDM systems are the future of the wireless communication but non-linearity in VLC-OFDM is a severe issue. The proposed paper discusses the techniques to reduce PAPR, one of the non-linearities of the system. The companding techniques mentioned in this paper provides better results without increasing the complexity of the system.

Keywords: non-linear companding techniques, peak to average power ratio (PAPR), visible light communication (VLC), VLC-OFDM

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Authors: Baladitya Swaika, Rahul Khatry

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Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

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Authors: Ji Xi, Cheng Song Chin, Ehsan Mesbahi

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Structure-borne noise is an important aspect of offshore platform sound field. It can be generated either directly by vibrating machineries induced mechanical force, indirectly by the excitation of structure or excitation by incident airborne noise. Therefore, limiting of the transmission of vibration energy throughout the offshore platform is the key to control the structure-borne noise. This is usually done by introducing damping treatment to the steel structures. Two types of damping treatment using on-board are presented. By conducting a statistical energy analysis (SEA) simulation on a jack-up rig, the noise level in the source room, the neighboring rooms, and remote living quarter cabins are compared before and after the damping treatments been applied. The results demonstrated that, in the source neighboring room and living quarter area, there is a significant noise reduction with the damping treatment applied, whereas in the source room where air-borne sound predominates that of structure-borne sound, the impact is not obvious. The subsequent optimization design of damping treatment in the offshore platform can be made which enable acoustic professionals to implement noise control during the design stage for offshore crews’ hearing protection and habitant comfortability.

Keywords: statistical energy analysis, damping treatment, noise control, offshore platform

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Authors: Abiola O. Temitayo-Oboh, Olugbenga L. Abodunrin, Wasiu O. Adebimpe, Micheal C. Asuzu

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Introduction: Many women who are sexually active would prefer to avoid becoming pregnant but are not using any method of contraception. These women are considered to have an unmet need for contraception. In an ideal situation, all women who want to space or limit their births and are exposed to the risk of conception would use some kind of conception; in practice, however, some women fail to use contraception which put them at risk of having mistimed or unwanted births, induced abortion, or maternal death. This study, therefore, aimed to assess the determinants of unmet need for contraception among currently married women in rural and urban communities of Osun State, South-West Nigeria. Methods: This was an analytical cross-sectional comparative study, which was carried out among currently married women. Three hundred and twenty respondents each were selected for the rural and urban groups from four Local Government Areas using multi-stage sampling technique. Data were collected using a pre-tested semi-structured interviewer-administered questionnaire and focus group discussion (FGD) guide; data analysis was done with Statistical Package for Social Sciences (SPSS) version 17.0 and detailed content analysis method respectively. Statistical analysis of the difference between proportions was done by the use of the Chi-square test and T-test was used to compare the means of the continuous variables. The study also utilized descriptive, bivariate and multivariate analytical techniques to examine the effect of some variables on unmet need. Level of statistical significance was set at p-value < 0.05 for all values. Results: Two hundred and ninety-six (92.5%) of the rural and 306 (95.6%) of the urban study population had heard of contraception, 365 (57.0 %) of the total respondents had good knowledge [162 (50.6 %) for rural respondents and 203 (63.4 %) for urban respondents]. This difference was statistically significant (p < 0.001). Five hundred and twenty-one (81.4%) respondents had a positive attitude towards contraception [243 (75.9%) in the rural and 278 (86.9%) in the urban area], and the difference was also statistically significant (p < 0.001). Only 47 (14.7%) and 59 (18.4%) of rural and urban women were current contraceptive users respectively. The total unmet need for contraception among rural women was 138 (43.1%) of which 82 (25.6%) was for spacing and 56 (17.5%), for limiting. While the total unmet need for contraception among urban women was 145 (45.3%) of which 96 (30.0%) was for spacing and 49 (15.3%) for limiting. Number of living children, knowledge of contraceptive methods, discussion with health workers about family planning, couples discussion about family planning and availability of family planning services were found to be predictors of women’s unmet need for contraception (p < 0.05). Conclusion: It is, therefore, recommended that there is need to intensify reproductive health education in bridging the knowledge gap, improving attitude and modifying practices regarding use of contraception in Nigeria. Hence, this will help to enhance the utilization of family planning services among Nigerian women.

Keywords: contraception, married women, Nigeria, rural, urban, unmet need

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Authors: Gayathri Nagarajan, L. D. Dhinesh Babu

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Health care is one of the prominent industries that generate voluminous data thereby finding the need of machine learning techniques with big data solutions for efficient processing and prediction. Missing data, incomplete data, real time streaming data, sensitive data, privacy, heterogeneity are few of the common challenges to be addressed for efficient processing and mining of health care data. In comparison with other applications, accuracy and fast processing are of higher importance for health care applications as they are related to the human life directly. Though there are many machine learning techniques and big data solutions used for efficient processing and prediction in health care data, different techniques and different frameworks are proved to be effective for different applications largely depending on the characteristics of the datasets. In this paper, we present a framework that uses ensemble machine learning technique gradient boosted trees for data classification in health care big data. The framework is built on Spark platform which is fast in comparison with other traditional frameworks. Unlike other works that focus on a single technique, our work presents a comparison of six different machine learning techniques along with gradient boosted trees on datasets of different characteristics. Five benchmark health care datasets are considered for experimentation, and the results of different machine learning techniques are discussed in comparison with gradient boosted trees. The metric chosen for comparison is misclassification error rate and the run time of the algorithms. The goal of this paper is to i) Compare the performance of gradient boosted trees with other machine learning techniques in Spark platform specifically for health care big data and ii) Discuss the results from the experiments conducted on datasets of different characteristics thereby drawing inference and conclusion. The experimental results show that the accuracy is largely dependent on the characteristics of the datasets for other machine learning techniques whereas gradient boosting trees yields reasonably stable results in terms of accuracy without largely depending on the dataset characteristics.

Keywords: big data analytics, ensemble machine learning, gradient boosted trees, Spark platform

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Authors: Dilip Singh Sisodia, Shrish Verma

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Spamming is the most common issue seen nowadays in the Internet especially in Online Social Networking Sites (like Facebook, Twitter, and Google+ etc.). Spam messages keep wasting Internet bandwidth and the storage space of servers. On social network sites; spammers often disguise themselves by creating fake accounts and hijacking user’s accounts for personal gains. They behave like normal user and they continue to change their spamming strategy. To prevent this, most modern spam-filtering solutions are deployed on the receiver side; they are good at filtering spam for end users. In this paper we are presenting some spamming techniques their behaviour and possible solutions. We have analyzed how Spammers enters into online social networking sites (OSNSs) and how they target it and the techniques they use for it. The five discussed techniques of spamming techniques which are clickjacking, social engineered attacks, cross site scripting, URL shortening, and drive by download. We have used elgg framework for demonstration of some of spamming threats and respective implementation of solutions.

Keywords: online social networking sites, spam, attacks, internet, clickjacking / likejacking, drive-by-download, URL shortening, networking, socially engineered attacks, elgg framework

Procedia PDF Downloads 349

Authors: Magno Enrique Mendoza Meza

Abstract:

This paper deals with the comparison of three control techniques: the hysteresis on-off control (HyOOC), the hybrid on-off control (HOOC) and the simple Structured Treatment Interruptions (sSTI). These techniques are applied to the mathematical model developed by Kirschner and Webb. To compare these techniques we use a cost functional that minimize the wild-type virus population and the mutant virus population, but the main objective is to minimize the systemic cost of treatment and maximize levels of healthy CD4+ T cells. HyOOC, HOOC, and sSTI are applied to the drug therapies using a reverse transcriptase and protease inhibitors; simulations show that these controls maintain the uninfected cells in a small, bounded neighborhood of a pre-specified level. The controller HyOOC and HOOC are designed by appropriate choice of virtual equilibrium points.

Keywords: virus dynamics, on-off control, hysteresis, multi-drug therapies

Procedia PDF Downloads 395

Authors: Amit Kamra, V. K. Jain, Pragya

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Mammography is widely used technique for breast cancer screening. There are various other techniques for breast cancer screening but mammography is the most reliable and effective technique. The images obtained through mammography are of low contrast which causes problem for the radiologists to interpret. Hence, a high quality image is mandatory for the processing of the image for extracting any kind of information from it. Many contrast enhancement algorithms have been developed over the years. In the present work, an efficient morphology based technique is proposed for contrast enhancement of masses in mammographic images. The proposed method is based on Multiscale Morphology and it takes into consideration the scale of the structuring element. The proposed method is compared with other state-of-the-art techniques. The experimental results show that the proposed method is better both qualitatively and quantitatively than the other standard contrast enhancement techniques.

Keywords: enhancement, mammography, multi-scale, mathematical morphology

Procedia PDF Downloads 427

Authors: Masoud Abbasalipour

Abstract:

In this study, the impact of family status on individuals, involving factors such as parents' literacy level, family size, individuals' blood group, and susceptibility to addiction, was investigated. Statistical tests were employed to scrutinize the relationships among these specified factors. The statistical population of the study consisted of 338 samples divided into two groups: individuals with addiction and those without addiction in the city of Amol. The addicted group was selected from individuals visiting the substance abuse treatment center in Amol, and the non-addicted group was randomly selected from individuals in urban and rural areas. The Chi-square test was used to examine the presence or absence of relationships among the variables, and Kramer's V test was employed to determine the strength of the relationship between them. Excel software facilitated the initial entry of data, and SPSS software was utilized for the desired statistical tests. The research results indicated a significant relationship between the variable of parents' education level and individuals' addiction. The analysis showed that the education level of their parents was significantly lower compared to non-addicted individuals. However, the variables of the number of family members and blood group did not significantly impact individuals' susceptibility to addiction.

Keywords: addiction, blood group, parents' literacy level, family status

Procedia PDF Downloads 69

Authors: Matthew Yeager, Christopher Willy, John Bischoff

Abstract:

The conceptualization and design phases of a system lifecycle consume a significant amount of the lifecycle budget in the form of direct tasking and capital, as well as the implicit costs associated with unforeseeable design errors that are only realized during downstream phases. Ad hoc or iterative approaches to generating system requirements oftentimes fail to consider the full array of feasible systems or product designs for a variety of reasons, including, but not limited to: initial conceptualization that oftentimes incorporates a priori or legacy features; the inability to capture, communicate and accommodate stakeholder preferences; inadequate technical designs and/or feasibility studies; and locally-, but not globally-, optimized subsystems and components. These design pitfalls can beget unanticipated developmental or system alterations with added costs, risks and support activities, heightening the risk for suboptimal system performance, premature obsolescence or forgone development. Supported by rapid advances in learning algorithms and hardware technology, sensors and sensor systems have become commonplace in both commercial and industrial products. The evolving array of hardware components (i.e. sensors, CPUs, modular / auxiliary access, etc…) as well as recognition, data fusion and communication protocols have all become increasingly complex and critical for design engineers during both concpetualization and implementation. This work seeks to develop and utilize a non-deterministic approach for sensor system design within the multi-attribute tradespace exploration (MATE) paradigm, a technique that incorporates decision theory into model-based techniques in order to explore complex design environments and discover better system designs. Developed to address the inherent design constraints in complex aerospace systems, MATE techniques enable project engineers to examine all viable system designs, assess attribute utility and system performance, and better align with stakeholder requirements. Whereas such previous work has been focused on aerospace systems and conducted in a deterministic fashion, this study addresses a wider array of system design elements by incorporating both traditional tradespace elements (e.g. hardware components) as well as popular multi-sensor data fusion models and techniques. Furthermore, statistical performance features to this model-based MATE approach will enable non-deterministic techniques for various commercial systems that range in application, complexity and system behavior, demonstrating a significant utility within the realm of formal systems decision-making.

Keywords: multi-attribute tradespace exploration, data fusion, sensors, systems engineering, system design

Procedia PDF Downloads 189

Authors: Simon Ibor Akpama

Abstract:

The study investigated the extent to which innovative teaching-learning techniques can influence and attenuate learning difficulties among adult learners participating in different literacy education programmes in Calabar Metropolis, Cross River State, Nigeria. A quasi-experimental design was adopted to collect data from a sample size of 150 participants of the programme. The sample was drawn using the simple random sampling method. As an experimental study, the 150 participants were divided into two equal groups –the first was the experimental group while the second was the control. A pre-test was administered to the two groups which were later exposed to a post-test after treatment. Two instruments were used for data collection. The first was the guide for the Literacy Learning Difficulties Inventory (LLDI). Three hypotheses were postulated and tested as .05 level of significance using Analysis of Covariance (ANOVA) test statistics. Results of the analysis firstly showed that the two groups (treatment and control) did not differ in the pre-test regarding their literacy learning difficulties. Secondly, the result showed that for each hypothesis, innovative teaching-learning techniques significantly influenced adult learners’ (participants) literacy learning difficulties. Based on these findings, the study recommends the use of innovative teaching-learning techniques in adult literacy education centres to mitigate the learning difficulties of adult learners in literacy education programmes in Calabar Metropolis.

Keywords: teaching, learning, techniques, innovative, difficulties, programme

Procedia PDF Downloads 123

Authors: Vishal V. Choudhari, Gaurao A. Dongre, S. P. Diwan

Abstract:

A number of reference current generation have been developed for analysis of shunt active power filter to mitigate the load compensation. Depending upon the type of load the technique has to be chosen. In this paper, six reference current generation techniques viz. instantaneous reactive power theory(IRP), Synchronous reference frame theory(SRF), Perfect harmonic cancellation(PHC), Unity power factor method(UPF), Self-tuning filter method(STF), Predictive filtering method(PFM) are compared for different operating conditions. The harmonics are introduced because of non-linear loads in the system. These harmonics are eliminated using above techniques. The results and performance of system simulated on MATLAB/Simulink platform. The system is experimentally implemented using DS1104 card of dSPACE system.

Keywords: SAPF, power quality, THD, IRP, SRF, dSPACE module DS1104

Procedia PDF Downloads 592