Search results for: hardy cross networks accuracy

Authors: P. S. Jagadeesh Kumar, Yang Yung, Mingmin Pan, Xianpei Li, Wenli Hu

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Glaucoma is impelled by optic nerve mutilation habitually represented as cupping and visual field injury frequently with an arcuate pattern of mid-peripheral loss, subordinate to retinal ganglion cell damage and death. Glaucoma is the second foremost cause of blindness and the chief cause of permanent blindness worldwide. Consequently, all-embracing study into the analysis and empathy of glaucoma is happening to escort deep learning based neural network intrusions to deliberate this substantial optic neuropathy. This paper advances an ophthalmic hashing based supervision of glaucoma and corneal disorders preeminent on deep graphical model. Ophthalmic hashing is a newly proposed method extending the efficacy of visual hash-coding to predict glaucoma corneal disorder matching, which is the faster than the existing methods. Deep graphical model is proficient of learning interior explications of corneal disorders in satisfactory time to solve hard combinatoric incongruities using deep Boltzmann machines.

Keywords: corneal disorders, deep Boltzmann machines, deep graphical model, glaucoma, neural networks, ophthalmic hashing

Procedia PDF Downloads 247

Authors: Ling Choo Chiew, Yoke Yong Chen, Chuong Hock Ting, Raveca Ak Ridi

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Job frustration become a prevalent issue in many occupational settings and is linked to mental state, which affects workers when they face obstacles that block them from meeting professional objectives and/or the organization's mission. This study examined the relationship between job frustration and mental health among healthcare workers. A cross-sectional design using the Compassion Satisfaction and Fatigue test (CSF), Copenhagen Burnout Inventory (CBI), and Psychological Flexibility Questionnaire (PFQ) was employed to collect data from a sample of healthcare workers in Sarawak, Malaysia. The results showed that 44.3 % of the healthcare workers experienced compassion fatigue, 9.7% of the healthcare workers had personal burnt out, 3% were work-related burnt out, and 2% were client-related burnt out. On the other hand, the mean of psychological flexibility was 3.55 (SD = 0.838), which was found to be prevalent in the study sample, with varying degrees of severity. The results also indicated a significant association between compassion fatigue and psychological flexibility, F(₄, ₄₈₉) = 5.45, p<.001. Additionally, demographic factors were associated with higher levels of job frustration and burnout. The implications of these findings for developing targeted interventions and support strategies to promote mental well-being among healthcare workers are discussed.

Keywords: compassion fatigue, healthcare worker, job frustration, psychological flexibility

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Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

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Gliomas are most challenging and aggressive type of tumors which appear in different sizes, locations, and scattered boundaries. CNN is most efficient deep learning approach with outstanding capability of solving image analysis problems. A fully automatic deep learning based 2D-CNN model for brain tumor segmentation is presented in this paper. We used small convolution filters (3 x 3) to make architecture deeper. We increased convolutional layers for efficient learning of complex features from large dataset. We achieved better results by pushing convolutional layers up to 16 layers for HGG model. We achieved reliable and accurate results through fine-tuning among dataset and hyper-parameters. Pre-processing of this model includes generation of brain pipeline, intensity normalization, bias correction and data augmentation. We used the BRATS-2015, and Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.81 for complete, 0.79 for core, 0.80 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, HGG

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Authors: Najib Al-Fadhali, Huda Majid

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In this article, a substrate integrated waveguide cavity slot antenna was designed using a computer simulation technology software tool to address the specific design challenges for millimeter-wave communications posed by future satellite communications. Due to the symmetrical structure, a high-order mode is generated in SIW, which yields high gain and high efficiency with a compact feed structure. The antenna has dimensions of 20 mm x 20 mm x 1.34 mm. The proposed antenna bandwidth ranges from 45 GHz to 50 GHz, covering a Q-band application such as satellite communication. Antenna efficiency is above 80% over the operational frequency range. The gain of the antenna is above 9 dB with a peak value of 9.4 dB at 47.5 GHz. The proposed antenna is suitable for various millimeter-wave applications such as sensing, body imaging, indoor scenarios, new generations of wireless networks, and future satellite communications. The simulated results show that the SIW antenna resonates throughout the bands of 45 to 50 GHz, making this new antenna cover all applications within this range. The reflection coefficients are below 10 dB in most ranges from 45 to 50 GHz. The compactness, integrity, reliability, and performance at various operating frequencies make the proposed antenna a good candidate for future satellite communications.

Keywords: ultra-wideband, Q-band, SIW, mm-wave, satellite communications

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Authors: Samir K. Safi

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The purpose of this study was to compare the performance of two types of models, namely MIDAS and MIDAS-GARCH, in predicting the volatility of crude oil returns based on gold price returns and the COVID-19 pandemic. The study aimed to identify which model would provide more accurate short-term and long-term predictions and which model would perform better in handling the increased volatility caused by the pandemic. The findings of the study revealed that the MIDAS model performed better in predicting short-term and long-term volatility before the pandemic, while the MIDAS-GARCH model performed significantly better in handling the increased volatility caused by the pandemic. The study highlights the importance of selecting appropriate models to handle the complexities of real-world data and shows that the choice of model can significantly impact the accuracy of predictions. The practical implications of model selection and exploring potential methodological adjustments for future research will be highlighted and discussed.

Keywords: GARCH-MIDAS, MIDAS, crude oil, gold, COVID-19, volatility

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Authors: M. Pomianek, M. Piszczek, M. Maciejewski

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The binocular eye tracking technology is increasingly being used in industry, entertainment and marketing analysis. In the case of virtual reality, eye tracking systems are already the basis for user interaction with the environment. In such systems, the high accuracy of determining the user's eye fixation point is very important due to the specificity of the virtual reality head-mounted display (HMD). Often, however, there are unknown errors occurring in the used eye tracking technology, as well as those resulting from the positioning of the devices in relation to the user's eyes. However, can the virtual environment itself influence estimation errors? The paper presents mathematical analyses and empirical studies of the determination of the fixation point and errors resulting from the change in the size of the pupil in response to the intensity of the displayed scene. The article contains both static laboratory tests as well as on the real user. Based on the research results, optimization solutions were proposed that would reduce the errors of gaze estimation errors. Studies show that errors in estimating the fixation point of vision can be minimized both by improving the pupil positioning algorithm in the video image and by using more precise methods to calibrate the eye tracking system in three-dimensional space.

Keywords: eye tracking, fixation point, pupil size, virtual reality

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Authors: Saman Ghaffarian, Ilgin Gökaşar

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This paper presents a new automatic vehicle detection method from very high resolution aerial images to measure traffic density. The proposed method starts by extracting road regions from image using road vector data. Then, the road image is divided into equal sections considering resolution of the images. Gradient vectors of the road image are computed from edge map of the corresponding image. Gradient vectors on the each boundary of the sections are divided where the gradient vectors significantly change their directions. Finally, number of vehicles in each section is carried out by calculating the standard deviation of the gradient vectors in each group and accepting the group as vehicle that has standard deviation above predefined threshold value. The proposed method was tested in four very high resolution aerial images acquired from Istanbul, Turkey which illustrate roads and vehicles with diverse characteristics. The results show the reliability of the proposed method in detecting vehicles by producing 86% overall F1 accuracy value.

Keywords: aerial images, intelligent transportation systems, traffic density measurement, vehicle detection

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Authors: Sun Li-Ping, Zhu Jian-Xun, Liu Sheng-Nan

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In order to study the performance of dynamic positioning system during S-lay operations, dynamic positioning system is simulated with the hull-stinger-pipe coupling effect. The roller of stinger is simulated by the generalized elastic contact theory. The stinger is composed of Morrison members. Force on pipe is calculated by lumped mass method. Time domain of fully coupled barge model is analyzed combining with PID controller, Kalman filter and allocation of thrust using Sequential Quadratic Programming method. It is also analyzed that the effect of hull wave frequency motion on pipe-stinger coupling force and dynamic positioning system. Besides, it is studied that how S-lay operations affect the dynamic positioning accuracy. The simulation results are proved to be available by checking pipe stress with API criterion. The effect of heave and yaw motion cannot be ignored on hull-stinger-pipe coupling force and dynamic positioning system. It is important to decrease the barge’s pitch motion and lay pipe in head sea in order to improve safety of the S-lay installation and dynamic positioning.

Keywords: S-lay operation, dynamic positioning, coupling motion, time domain, allocation of thrust

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Authors: Ayobami P. Olatunji, Saadat Ibiyeye, Abdulaziz Ibiyeye, Tahir M. Khan

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Digital evidence has become a cornerstone in criminal investigations due to the vast amount of information available in digital form. Despite its prevalence, this evidence is often met with skepticism in court proceedings because of its inherently volatile nature. Traditional forensic processes, defined predominantly by technology experts, emphasize technical details in evidence collection while often neglecting legal procedures. This gap can pose significant challenges for legal practitioners in understanding and applying digital forensics. As digital evidence increasingly influences future cases, a cohesive framework integrating both technical and legal perspectives is essential. We propose a comprehensive techno-legal framework designed to bridge this gap. Our framework integrates key aspects of collection, preservation, examination, and documentation with legal components such as case building, certificate of compliance, cross-examination, and authorization. This balanced approach aims not to replace existing evidence presentation principles but to enhance the seamless integration of digital evidence into legal proceedings, addressing the common issues that lead to its dismissal.

Keywords: evidence presentation, warrant, digital-forensic, certificate of compliance, legal procedures, computer crime, violation, investigation cybercrime

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Authors: Libia Yanelli Yanez-Penunuri, Carlos Alejandro Hidalgo-Rasmussen, Cesar Armando Rey-Anacona

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Cultural and social norms have a great influence on individual behavior, including the use of violence. In this way, culture can protect against violence, but it can also support and encourage the use of violence. The aim of this study was to analyze differences in cultural acceptance and dating violence among Mexican high school students. A Cross-sectional study was carried out with 867 adolescent Mexican students of high school aged 14 to 18 years old in a dating relationship for at least a month in Guzman City, Mexico. To measure cultural acceptance and dating violence, the questionnaire abuse in dating (CMO) was applied. Informed consent to parents and students was requested. Analyses of descriptive and inferential statistics were performed. Participants were adolescent girls (61.4%) and adolescent boys (38.6%). About 63.7% of adolescents reported cultural acceptance of dating violence in their dating relationships. Associations between physical, sexual, economical dating violence and cultural acceptance were found. No association was found between psychological dating violence and cultural acceptance. The effect size in all dimensions was small. For future research, it is very important to take into consideration the change and evaluation of culture norms to prevent dating violence among adolescents.

Keywords: adolescents, culture, social norms, dating violence, students

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Authors: Che-An Lin, Wan-Ying Tsai, Ying-Shin Chen, Yu-Jen Chung

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With the number and area of contaminated sites continued to increase in Taiwan, the Government have to make a priority list of screening contaminated sites under the limited funds and information. This study investigated the announcement of Taiwan EPA land 261 contaminated sites (except the agricultural lands), after preliminary screening 211 valid data to propose a screening system, removed contaminated sites were used to check the accuracy. This system including two dimensions which can create the sequence and use the XY axis to construct four quadrants. One dimension included environmental and social priority and the other related economic. All of the evaluated items included population density, land values, traffic hub, pollutant compound, pollutant concentrations, pollutant transport pathways, land usage sites, site areas, and water conductivity. The classification results of this screening are 1. Prioritization promoting sites (10%). 2. Environmental and social priority of the sites (17%), 3. Economic priority of the sites (30%), 4. Non-priority sites (43 %). Finally, this study used three of the removed contaminated sites to check screening system verification. As the surmise each of them are in line with the priority site and Economic priority of the site.

Keywords: contaminated sites, redevelopment, environmental, economics

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Authors: Chen Wang, Chun Liang

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Microsatellite instability (MSI) is characterized by high degree of polymorphism in microsatellite (MS) length due to a deficiency in mismatch repair (MMR) system. MSI is associated with several tumor types and its status can be considered as an important indicator for tumor prognostic. Conventional clinical diagnosis of MSI examines PCR products of a panel of MS markers using electrophoresis (MSI-PCR) which is laborious, time consuming, and less reliable. MSIpred, a python 2 package for automatic classification of MSI was released by this study. It computes important somatic mutation features from files in mutation annotation format (MAF) generated from paired tumor-normal exome sequencing data, subsequently using these to predict tumor MSI status with a support vector machine (SVM) classifier trained by MAF files of 1074 tumors belonging to four types. Evaluation of MSIpred on an independent 358-tumor test set achieved overall accuracy of over 98% and area under receiver operating characteristic (ROC) curve of 0.967. These results indicated that MSIpred is a robust pan-cancer MSI classification tool and can serve as a complementary diagnostic to MSI-PCR in MSI diagnosis.

Keywords: microsatellite instability, pan-cancer classification, somatic mutation, support vector machine

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Authors: Pratham Madnur, Prathamkumar Shetty, Sneha Varur, Gouri Parashetti

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In order to overcome conventional obstacles, this research paper investigates novel approaches for cricket pitch and ball detection that make use of cutting-edge technologies. The research integrates OpenCV for pitch inspection and modifies the YOLOv8 model for cricket ball detection in order to overcome the shortcomings of manual pitch assessment and traditional ball detection techniques. To ensure flexibility in a range of pitch environments, the pitch detection method leverages OpenCV’s color space transformation, contour extraction, and accurate color range defining features. Regarding ball detection, the YOLOv8 model emphasizes the preservation of minor object details to improve accuracy and is specifically trained to the unique properties of cricket balls. The methods are more reliable because of the careful preparation of the datasets, which include novel ball and pitch information. These cutting-edge methods not only improve cricket analytics but also set the stage for flexible methods in more general sports technology applications.

Keywords: OpenCV, YOLOv8, cricket, custom dataset, computer vision, sports

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Authors: Mohit Kumar, Mayur Betharia

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Healthcare domain, with the emergence of Electronic Medical Record (EMR), collects a lot of data which have been attracting Data Mining expert’s interest. In the past, doctors have relied on their intuition while making critical clinical decisions. This paper presents the means to analyze the Medical workflows to get business insights out of huge dumped medical databases. Market Basket Analysis (MBA) which is a special data mining technique, has been widely used in marketing and e-commerce field to discover the association between products bought together by customers. It helps businesses in increasing their sales by analyzing the purchasing behavior of customers and pitching the right customer with the right product. This paper is an attempt to demonstrate Market Basket Analysis applications in healthcare. In particular, it discusses the Market Basket Analysis Algorithm ‘Apriori’ applications within healthcare in major areas such as analyzing the workflow of diagnostic procedures, Up-selling and Cross-selling of Healthcare Systems, designing healthcare systems more user-friendly. In the paper, we have demonstrated the MBA applications using Angiography Systems, but can be extrapolated to other modalities as well.

Keywords: data mining, market basket analysis, healthcare applications, knowledge discovery in healthcare databases, customer relationship management, healthcare systems

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Authors: Dhruveshi B. Rana, Nirav P. Vaghela, Jigar N. Mehta

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Objective: To assess the relationships among age, gender, anthropometrics and dynamic balance in 5 to 12 years of children in Anand city. Method: Cross-sectional study was conducted. 150 school going children of 5-12 (75-girls, 75-boys) years were recruited from the school of the Anand city-Shivam English Medium school, Veer Vithalbhai Patel school, Adarsh Primary school. Height, weight, arm length, and foot length were measured in 150 children of 5 to 12 years. Dynamic balance was assessed using Time Up and Go Test, Functional Reach Test, Pediatric Balance Scale. Results: Positive relationship (r = 0.58 and r= 0.77) were found between increasing age and FRT and PBS scores. A negative relationship (r = - 0.46) was observed between age of boys and TUG test. Significant gender by age group difference was observed in FRT. Arm length and height has the strongest influence on FRT, and age, height, foot length; and arm length has the strongest influence on PBS. Conclusions: Age and arm length have the strongest relationship with the dynamic balance (FRT, PBS). Dynamic balance ability is directly related to the age. It helps the pediatric therapists in selecting dynamic balance test according to the age.

Keywords: age, gender, anthropometric, dynamic balance

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Authors: Shreya Pare, Parvin Akhter

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Remotely sensing image classification becomes a very challenging task owing to the high dimensionality of hyperspectral images. The pixel-wise classification methods fail to take the spatial structure information of an image. Therefore, to improve the performance of classification, spatial information can be integrated into the classification process. In this paper, the multilevel thresholding algorithm based on a modified fuzzy entropy function is used to perform the segmentation of hyperspectral images. The fuzzy parameters of the MFE function have been optimized by using a new meta-heuristic algorithm based on the Tree-Search algorithm. The segmented image is classified by a large distribution machine (LDM) classifier. Experimental results are shown on a hyperspectral image dataset. The experimental outputs indicate that the proposed technique (MFE-TSA-LDM) achieves much higher classification accuracy for hyperspectral images when compared to state-of-art classification techniques. The proposed algorithm provides accurate segmentation and classification maps, thus becoming more suitable for image classification with large spatial structures.

Keywords: classification, hyperspectral images, large distribution margin, modified fuzzy entropy function, multilevel thresholding, tree search algorithm, hyperspectral image classification using tree search algorithm

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Authors: R. B. Adegbola, K. F. Oyedele, L. Adeoti

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We present a method that utilizes Multi-channel Analysis of Surface Waves, which was used to measure shear wave velocities with a view to establishing the probable causes of road failure, subsidence and weakening of structures in some Local Government Area, Lagos, Nigeria. Multi channel Analysis of Surface waves (MASW) data were acquired using 24-channel seismograph. The acquired data were processed and transformed into two-dimensional (2-D) structure reflective of depth and surface wave velocity distribution within a depth of 0–15m beneath the surface using SURFSEIS software. The shear wave velocity data were compared with other geophysical/borehole data that were acquired along the same profile. The comparison and correlation illustrates the accuracy and consistency of MASW derived-shear wave velocity profiles. Rigidity modulus and N-value were also generated. The study showed that the low velocity/very low velocity are reflective of organic clay/peat materials and thus likely responsible for the failed, subsidence/weakening of structures within the study areas.

Keywords: seismograph, road failure, rigidity modulus, N-value, subsidence

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Authors: Wendy Kramer

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How and when a donor-conceived person (DCP) learns about their conception significantly affects their experiences and choices, including whether they'd consider using a donor or donating their own gametes. Objective: We sought to identify factors that positively and negatively impact the experience of being a DCP. We sought to determine if DCP would consider utilizing donor gametes themselves, if unable to conceive spontaneously and if DCP were likely to be donors themselves. Materials and Methods: A cross-sectional survey of adult DCP was disseminated to members of the Donor Sibling Registry. The survey consisted of 31 items including whether experience as DCP was positive or negative, the willingness to use donor gametes if spontaneous conception was not an option, and questions regarding donating gametes. Results: 529 people (81.7% female) completed the survey, the median age was 28 years (range 18-77 years) and 94.7% were conceived via donor sperm. Most felt "neutral" (31.6%), "positive" (26.3%) or "very positive" (20.8%) about being a DCP regardless of donor type. While most found out about being a DCP after age 18 (63.4%), those with a positive experience were more likely to "have always known" (40.7%). Conclusions: People conceived by donor-assisted reproduction are more likely to have neutral to positive overall feelings surrounding their conception if they are told at a very young age about their donor-conceived origins by a family member. The majority of DCP are willing to adopt but would not consider using donated gametes themselves if unable to conceive spontaneously. DCP are not likely to become donors themselves despite the majority of DCP having a high positive feeling regarding being donor-conceived.

Keywords: donor conception, donor offspring, sperm donation, egg donation, donor-conceived people

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Authors: Chusak Thanawattano, Roongroj Bhidayasiri

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This paper proposes a novel feature set utilized for classifying the Parkinson tremor and essential tremor. Ten ET and ten PD subjects are asked to perform kinetic, postural and resting tests. The empirical mode decomposition (EMD) is used to decompose collected tremor signal to a set of intrinsic mode functions (IMF). The IMFs are used for reconstructing representative signals. The feature set is composed of peak frequencies of IMFs and reconstructed signals. Hypothesize that the dominant frequency components of subjects with PD and ET change in different directions for different tests, difference of peak frequencies of IMFs and reconstructed signals of pairwise based tests (kinetic-resting, kinetic-postural and postural-resting) are considered as potential features. Sets of features are used to train and test by classifier including the quadratic discriminant classifier (QLC) and the support vector machine (SVM). The best accuracy, the best sensitivity and the best specificity are 90%, 87.5%, and 92.86%, respectively.

Keywords: tremor, Parkinson, essential tremor, empirical mode decomposition, quadratic discriminant, support vector machine, peak frequency, auto-regressive, spectrum estimation

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Authors: Marcell Serra de Almeida Martins, Benedito de Souza Ribeiro Neto, Gerson Lima Serejo, Carlos Gustavo Resque Dos Santos

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Indoor positioning systems use sensors such as Bluetooth, ZigBee, and Wi-Fi, as well as cameras for image capture, which can be fixed or mobile. These computer vision-based positioning approaches are low-cost to implement, mainly when it uses a mobile camera. The present study aims to create a design of a fiducial marker for a low-cost indoor localization system. The marker is tape-shaped to perform a continuous reading employing two detection algorithms, one for greater distances and another for smaller distances. Therefore, the location service is always operational, even with variations in capture distance. A minimal localization and reading algorithm were implemented for the proposed marker design, aiming to validate it. The accuracy tests consider readings varying the capture distance between [0.5, 10] meters, comparing the proposed marker with others. The tests showed that the proposed marker has a broader capture range than the ArUco and QRCode, maintaining the same size. Therefore, reducing the visual pollution and maximizing the tracking since the ambient can be covered entirely.

Keywords: multiscale recognition, indoor localization, tape-shaped marker, fiducial marker

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Authors: Albert Alexander Stonier

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Due to the fossil fuel exhaustion and environmental pollution, renewable energy sources especially solar photovoltaic system plays a predominant role in providing energy to the consumers. It has been estimated that by 2050 the renewable energy sources will satisfy 50% of the total energy requirement of the world. In this context, the faults in the conversion process require a special attention which is considered as a major problem. A fault which remains even for a few seconds will cause undesirable effects to the system. The presentation comprises of the analysis, causes, effects and mitigation methods of various faults occurring in the entire solar photovoltaic energy conversion process. In order to overcome the faults in the system, an intelligent based artificial neural networks and fuzzy logic are proposed which can significantly mitigate the faults. Hence the presentation intends to find the problem in renewable energy and provides the possible solution to overcome it with simulation and experimental results. The work performed in a 3kWp solar photovoltaic plant whose results cites the improvement in reliability, availability, power quality and fault tolerant ability.

Keywords: solar photovoltaic, power electronics, power quality, PWM

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Authors: Krishan Kumar, Mohit Mittal, Pramod Kumar

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Wireless sensor network is one of the most promising communication networks for monitoring remote environmental areas. In this network, all the sensor nodes are communicated with each other via radio signals. The sensor nodes have capability of sensing, data storage and processing. The sensor nodes collect the information through neighboring nodes to particular node. The data collection and processing is done by data aggregation techniques. For the data aggregation in sensor network, clustering technique is implemented in the sensor network by implementing self-organizing feature map (SOFM) neural network. Some of the sensor nodes are selected as cluster head nodes. The information aggregated to cluster head nodes from non-cluster head nodes and then this information is transferred to base station (or sink nodes). The aim of this paper is to manage the huge amount of data with the help of SOM neural network. Clustered data is selected to transfer to base station instead of whole information aggregated at cluster head nodes. This reduces the battery consumption over the huge data management. The network lifetime is enhanced at a greater extent.

Keywords: artificial neural network, data clustering, self organization feature map, wireless sensor network

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Authors: Songul Cinaroglu

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Machine learning regression methods are recommended as an alternative to classical regression methods in the existence of variables which are difficult to model. Data for health expenditure is typically non-normal and have a heavily skewed distribution. This study aims to compare machine learning regression methods by hyperparameter tuning to predict health expenditure per capita. A multiple regression model was conducted and performance results of Lasso Regression, Random Forest Regression and Support Vector Machine Regression recorded when different hyperparameters are assigned. Lambda (λ) value for Lasso Regression, number of trees for Random Forest Regression, epsilon (ε) value for Support Vector Regression was determined as hyperparameters. Study results performed by using 'k' fold cross validation changed from 5 to 50, indicate the difference between machine learning regression results in terms of R², RMSE and MAE values that are statistically significant (p < 0.001). Study results reveal that Random Forest Regression (R² ˃ 0.7500, RMSE ≤ 0.6000 ve MAE ≤ 0.4000) outperforms other machine learning regression methods. It is highly advisable to use machine learning regression methods for modelling health expenditures.

Keywords: machine learning, lasso regression, random forest regression, support vector regression, hyperparameter tuning, health expenditure

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Authors: Min Jin Kwon, Seung Won Kim, Eui Yeon Kim, Haeyoung Lee

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Numerous anonymous chat apps that help people to connect with random strangers have been released in South Korea. However, they become a serious problem for young people since young people often use them for channels of prostitution or sexual violence. Although ISPs in South Korea are responsible for making inappropriate content inaccessible on their networks, they do not block traffic of random chat apps since 1) the use of random chat apps is entirely legal. 2) it is reported that they use HTTP proxy blocking so that non-HTTP traffic cannot be blocked. In this paper, we propose a service model that can block random chat apps at home routers. A service provider manages a blacklist that contains blocked apps’ information. Home routers that subscribe the service filter the traffic of the apps out using deep packet inspection. We have implemented a prototype of the proposed model, including a centralized server providing the blacklist, a Raspberry Pi-based home router that can filter traffic of the apps out, and an Android app used by the router’s administrator to locally customize the blacklist.

Keywords: deep packet inspection, internet filtering, juvenile protection, technical blocking

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Authors: Mohammad I. Hossain, Omar F. Hamim

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This paper presents a case study on a pile integrity test for assessing the integrity of piles as well as a physical dimension (e.g., cross-sectional area, length), continuity, and consistency of the pile materials. The recent boom in the socio-economic condition of Bangladesh has given rise to the building of high-rise commercial and residential infrastructures. The advantage of the pile integrity test lies in the fact that it is possible to get an approximate indication regarding the quality of the sub-structure before commencing the construction of the super-structure. This paper aims at providing a classification of cast-in-situ piles based on characteristic reflectograms obtained using the Sonic Integrity Testing program for the sub-soil condition of Narayanganj, Bangladesh. The piles have been classified as 'Pile Type-1', 'Pile Type-2', 'Pile Type-3', 'Pile type-4', 'Pile Type-5' or 'Pile Type-6' from the visual observations of reflections from the generated stress waves by striking the pile head with a handheld hammer. With respect to construction quality and integrity, piles have been further classified into three distinct categories, i.e., satisfactory, may be satisfactory, and unsatisfactory.

Keywords: cast-in-situ piles, characteristic reflectograms, pile integrity test, sonic integrity testing program

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Authors: Adriano Z. Zambom, Preethi Ravikumar

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One of the biggest challenges in nonparametric regression is the curse of dimensionality. Additive models are known to overcome this problem by estimating only the individual additive effects of each covariate. However, if the model is misspecified, the accuracy of the estimator compared to the fully nonparametric one is unknown. In this work the efficiency of completely nonparametric regression estimators such as the Loess is compared to the estimators that assume additivity in several situations, including additive and non-additive regression scenarios. The comparison is done by computing the oracle mean square error of the estimators with regards to the true nonparametric regression function. Then, a backward elimination selection procedure based on the Akaike Information Criteria is proposed, which is computed from either the additive or the nonparametric model. Simulations show that if the additive model is misspecified, the percentage of time it fails to select important variables can be higher than that of the fully nonparametric approach. A dimension reduction step is included when nonparametric estimator cannot be computed due to the curse of dimensionality. Finally, the Boston housing dataset is analyzed using the proposed backward elimination procedure and the selected variables are identified.

Keywords: additive model, nonparametric regression, variable selection, Akaike Information Criteria

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Authors: Joseph Wolff, Jeffrey Eilbott

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Autism Spectrum Disorders (ASD) are a spectrum of social disorders characterized by deficits in social communication, verbal ability, and interaction that can vary in severity. In recent years, researchers have used magnetic resonance imaging (MRI) to help detect how neural patterns in individuals with ASD differ from those of neurotypical (NT) controls for classification purposes. This study analyzed the classification of ASD within males and females using functional MRI data. Functional connectivity (FC) correlations among brain regions were used as feature inputs for machine learning algorithms. Analysis was performed on 558 cases from the Autism Brain Imaging Data Exchange (ABIDE) I dataset. When trained specifically on females, the algorithm underperformed in classifying the ASD subset of our testing population. Although the subject size was relatively smaller in the female group, the manual matching of both male and female training groups helps explain the algorithm’s bias, indicating the altered sex abnormalities in functional brain networks compared to typically developing peers. These results highlight the importance of taking sex into account when considering how generalizations of findings on males with ASD apply to females.

Keywords: autism spectrum disorder, machine learning, neuroimaging, sex differences

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Authors: Khabat Barkhordari, Ali Mohammad Arabzadeh

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Hepatitis delta virus (HDV) is a RNA virus that needs the function of hepatitis B virus (HBV) for its propagation and assembly. Infection by HDV can occur spontaneously with HBV infection and cause acute hepatitis or develop as secondary infection in HBV suffering patients. Based on genome sequence analysis, HDV has several genotypes which show broad geographic and diverse clinical features. The aim of current study is determine the prevalence of hepatitis delta virus genotype in patients with positive HBsAg in Kerman province of Iran. This cross-sectional study a total of 400 patients with HBV infection attending the clinic center of Besat from 2012 to 2014 were included. We carried out ELISA to detect anti-HDV antibodies. Those testing positive were analyzed further for HDV-RNA and for genotyping using restriction fragment length polymorphism (RFLP) and RT-nested PCR- sequencing. Among 400 patients in this study, 67 cases (16.75 %) were containing anti-HDV antibody which we found HDV RNA in just 7 (1.75%) serum samples. Analysis of these 7 positive HDV showed that all of them have genotype I. According to current study the HDV prevalence in Kerman is higher than the reported prevalence of 6.6% for Iran as a whole and clade 1 (genotype 1) is the predominant clade of HDV in Kerman.

Keywords: genotyping, hepatitis delta virus, molecular epidemiology, Kerman, Iran

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Authors: Yichao Ma, Chengsiong Chin, Wailok Woo

Abstract:

Since the last decade, there has been a rapid growth in digital multimedia, such as high-resolution media files and three-dimentional movies. Hence, there is a need for large digital storage such as Hard Disk Drive (HDD). As such, users expect to have a quieter HDD in their laptop. In this paper, a jury test has been conducted on a group of 34 people where 17 of them are students who is the potential consumer, and the remaining are engineers who know the HDD. A total 13 HDD sound samples have been selected from over hundred HDD noise recordings. These samples are selected based on an agreed subjective feeling. The samples are played to the participants using head acoustic playback system which enabled them to experience as similar as possible the same environment as have been recorded. Analysis has been conducted and the obtained results have indicated different group has different perception over the noises. Two neural network-based acoustic annoyance models are established based on back propagation neural network. Four psychoacoustic metrics, loudness, sharpness, roughness and fluctuation strength, are used as the input of the model, and the subjective evaluation results are taken as the output. The developed models are reasonably accurate in simulating both training and test samples.

Keywords: hdd noise, jury test, neural network model, psychoacoustic annoyance

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Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

Abstract:

Developing speed model is a challenging task particularly under mixed traffic condition where the traffic composition plays a significant role in determining vehicular speed. The present research has been conducted to model individual vehicular speed in the context of mixed traffic on an urban arterial. Traffic speed and volume data have been collected from three midblock arterial road sections in New Delhi. Using the field data, a volume based speed prediction model has been developed adopting the methodology of Artificial Neural Network (ANN). The model developed in this work is capable of estimating speed for individual vehicle category. Validation results show a great deal of agreement between the observed speeds and the predicted values by the model developed. Also, it has been observed that the ANN based model performs better compared to other existing models in terms of accuracy. Finally, the sensitivity analysis has been performed utilizing the model in order to examine the effects of traffic volume and its composition on individual speeds.

Keywords: speed model, artificial neural network, arterial, mixed traffic

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