Search results for: measurement accuracy

Authors: Aviv Yaniv, Ron Ben Arosh, Nadav Gasner, Michael Konviser, Arbel Yaniv

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This paper introduces two methods for the evaluation of Question Decomposition Meaning Representation (QDMR) as predicted by sequence-to-sequence model and COPYNET parser for natural language questions processing, motivated by the fact that previous evaluation metrics used for this task do not take into account some characteristics of the representation, such as partial ordering structure. To this end, several heuristics to extract such partial dependencies are formulated, followed by the hereby proposed evaluation methods denoted as Proportional Graph Matcher (PGM) and Conversion to Normal String Representation (Nor-Str), designed to better capture the accuracy level of QDMR predictions. Experiments are conducted to demonstrate the efficacy of the proposed evaluation methods and show the added value suggested by one of them- the Nor-Str, for better distinguishing between high and low-quality QDMR when predicted by models such as COPYNET. This work represents an important step forward in the development of better evaluation methods for QDMR predictions, which will be critical for improving the accuracy and reliability of natural language question-answering systems.

Keywords: NLP, question answering, question decomposition meaning representation, QDMR evaluation metrics

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Authors: Gopikrishnan, Bharathiraja, Boopalan, Jensin Joshua

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The Pitot static tube has proven their values and practicability in measuring velocity of fluids for many years. With the aim of extensive usage of such Pitot tube systems, one of the major enabling technologies is to use the design and fabricate a high sensitive pitot tube for the purpose of calibration of the subsonic wind tunnel. Calibration of wind tunnel is carried out by using different instruments to measure variety of parameters. Using too many instruments inside the tunnel may not only affect the fluid flow but also lead to drag or losses. So, it is essential to replace the different system with a single system that would give all the required information. This model of high sensitive Pitot tube has been designed to ease the calibration process. It minimizes the use of different instruments and this single system is capable of calibrating the wind tunnel test section. This Pitot static tube is completely digitalized and so that the velocity data`s can be collected directly from the instrument. Since the turbulence factors are dependent on velocity, the data’s that are collected from the pitot static tube are then processed and the level of turbulence in the fluid flow is calculated. It is also capable of measuring the pressure distribution inside the wind tunnel and the flow angularity of the fluid. Thus, the well-designed high sensitive Pitot static tube is utilized in calibrating the tunnel and also for the measurement of turbulence.

Keywords: pitot static tube, turbulence, wind tunnel, velocity

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Authors: Marianne Simons, Sinan Kurt, Marjolein Stefens, Kai Karos, Johan Lataster

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As cultural diversity within older populations in European countries increases, the role of cultural background should be taken account of in aging studies. Bonding social capital (BSC), containing someone’s socio-emotional resources, is recognised as an important ingredient for wellbeing in old age and found to be associated with someone’s cultural background. The current study examined the association between BSC, loneliness and wellbeing in a sample including older Turkish migrants with a collectivistic cultural background and native Dutch older adults, both living in the Netherlands, characterised by an individualistic culture. A sample of 119 Turkish migrants (64.7% male; age 65-87, M(SD)=71.13(5.04) and 124 native Dutch adults (32.3% male, age 65-94, M(SD)= 71.9(5.32) filled out either an online or printed questionnaire measuring BSC, psychological, social and emotional well-being, loneliness and relevant demographic covariates. Regression analysis - including confounders age, gender, level of education, physical health and relationship - showed positive associations between BSC and respectively emotional, social and psychological well-being and a negative association with loneliness in both samples. Moderation analyses showed that these associations were significantly stronger for the Turkish older migrants than for their native peers. Measurement invariance analysis indicated partial metric invariance for the measurement of BSC and loneliness and non-invariance for wellbeing, calling for caution comparing means between samples. The results stress the importance of BSC for wellbeing of older migrants from collectivistic cultures living in individualistic countries. Previous research, shows a trend of older migrants displaying lower levels of BSC as well as associated variables, such as education, physical health, and financial income. This calls for more research of the interplay between demographic and psychosocial factors restraining mental wellbeing of older migrant populations. Measurement invariance analyses further emphasize the importance of taking cultural background into account in positive aging studies.

Keywords: positive aging, cultural background, wellbeing, social capital, loneliness

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Authors: Radwa R. El-Saadany , Medhat Abu Zeid, Tarek Omar, Marwa S. Maqsoud

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Attention deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders characterized by attention deficit, hyperactivity, and impulsivity. Neurofeedback (NF) is one of the neurotherapy treatments that cause brain wave changes. Method: The current pseudo-experimental study with a pre–post-test design was conducted on a population of children with attention deficit hyperactivity disorder (ADHD).The sample size comprised of (30) children selected by random sampling method and assigned to two therapeutic groups: First therapeutic group received a neurofeedback program. Based on QEEG, it reached (10) children. The second therapeutic group received an organization skills training program, it reached (10) and the control group that did not receive programs, it reached (10) children. Results: There are significant differences between pre- and post-assessments among therapeutic groups in reducing the three core symptoms of ADHD in favor of post measurement. There are no significant differences between post-assessment and follow up measurement of the therapeutic groups.

Keywords: QEEG-based neurofeedback therapy program, organizational skills training program, attention deficit hyperactivity disorder

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Authors: Zhuang Hou, Xiaolei Cao

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The general public and medical professionals recognized the importance of accurately measuring and storing blood oxygen levels and heart rate during the COVID-19 pandemic. The demand for accurate contactless devices was motivated by the need for cross-infection reduction and the shortage of conventional oximeters, partially due to the global supply chain issue. This paper evaluated a contactless mini program HealthyPai’s heart rate (HR) and oxygen saturation (SpO2) measurements compared with other wearable devices. In the HR study of 185 samples (81 in the laboratory environment, 104 in the real-life environment), the mean absolute error (MAE) ± standard deviation was 1.4827 ± 1.7452 in the lab, 6.9231 ± 5.6426 in the real-life setting. In the SpO2 study of 24 samples, the MAE ± standard deviation of the measurement was 1.0375 ± 0.7745. Our results validated that HealthyPai utilizing the Integrated Image Deep Learning Solution (IIDLS) framework, can accurately measure HR and SpO2, providing the test quality at least comparable to other FDA-approved wearable devices in the market and surpassing the consumer-grade and research-grade wearable standards.

Keywords: remote photoplethysmography, heart rate, oxygen saturation, contactless measurement, mini program

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Authors: Behnam Shakerian, Negin Razavi

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The pathogenesis of coronary artery disease is multifactorial. The epicardial fat pad is a localized fat depot lying between the myocardium and the visceral layer of the pericardium. The mechanisms through which epicardial fat pad can cause atherosclerosis are complex. The epicardial fat pad can surround the coronary arteries and contributes to the development and progression of coronary artery disease. Methods: we selected 50 patients who underwent coronary artery angiography for the evaluation of coronary artery disease that results were positive for coronary artery disease. All patients underwent an echocardiographic examination after coronary angiography to measure epicardial fat pad thickness. The epicardial fat pad was defined as an echo-free space between the myocardium's outer wall and the pericardium's visceral layer. Results: The epicardial fat pad was measured on the right ventricle apex in 46 patients. Sixty- five percent of the studied patients were male. The most common vessel with stenosis was the left anterior descending artery. A significant correlation was observed between epicardial fat pad thickness and the severity of coronary artery disease. Discussions: The epicardial fat pad provides a horizon on the pathophysiology of cardiovascular diseases. It directly contributes to the development and progression of coronary artery disease by causing inflammation and endothelial damage. Further investigations are needed to determine whether medical treatment can reduce the mass of epicardial fat pad and can help to improve atherosclerosis. Conclusion: The epicardial fat pad measurement could be used as an indicator of coronary arteries’ atherosclerosis. Therefore, thickness measurement of the epicardial fat pad in the clinical practice could be of assistance in identifying patients at risk and if required, undergoing supplementary diagnosis with coronary angiography.

Keywords: epicardial, fat pad, coronary artery disease, echocardiography

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Authors: Moumen Abdelhafidh, Stribu Bogdan, Laboureur Delphine, Gallant Johan, Hendrick Patrick

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This study is part of an ongoing effort to improve the understanding of phenomena occurring during the intermediate ballistic phase, such as muzzle flows. A thorough comprehension of muzzle flow fields is essential for optimizing muzzle device and projectile design. This flow characterization has heretofore been almost entirely limited to local and intrusive measurement techniques such as pressure measurements using pencil probes. Consequently, the body of quantitative experimental data is limited, so is the number of numerical codes validated in this field. The objective of the work presented here is to demonstrate the applicability of the Particle Image Velocimetry (PIV) technique in the challenging environment of the propellant flow of a .300 blackout weapon to provide accurate velocity measurements. The key points of a successful PIV measurement are the selection of the particle tracer, their seeding technique, and their tracking characteristics. We have experimentally investigated the aforementioned points by evaluating the resistance, gas dispersion, laser light reflection as well as the response to a step change across the Mach disk for five different solid tracers using two seeding methods. To this end, an experimental setup has been performed and consisted of a PIV system, the combustion chamber pressure measurement, classical high-speed schlieren visualization, and an aerosol spectrometer. The latter is used to determine the particle size distribution in the muzzle flow. The experimental results demonstrated the ability of PIV to accurately resolve the salient features of the propellant flow, such as the under the expanded jet and vortex rings, as well as the instantaneous velocity field with maximum centreline velocities of more than 1000 m/s. Besides, naturally present unburned particles in the gas and solid ZrO₂ particles with a nominal size of 100 nm, when coated on the propellant powder, are suitable as tracers. However, the TiO₂ particles intended to act as a tracer, surprisingly not only melted but also functioned as a combustion accelerator and decreased the number of particles in the propellant gas.

Keywords: intermediate ballistic, muzzle flow fields, particle image velocimetry, propellant gas, particle size distribution, under expanded jet, solid particle tracers

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Authors: Elham Serkani, Hossein Gharaee Garakani, Naser Mohammadzadeh, Elaheh Vaezpour

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Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset.

Keywords: decision tree, feature selection, intrusion detection system, support vector machine

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Authors: Dong Wu, Michael Grenn

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Managing software projects effectively is crucial for meeting deadlines, ensuring quality, and managing resources well. Traditional methods often struggle with predicting project timelines accurately due to uncertain schedules and complex data. This study addresses these challenges by combining time prediction algorithms with Large Language Models (LLMs). It makes use of real-world software project data to construct and validate a model. The model takes detailed project progress data such as task completion dynamic, team Interaction and development metrics as its input and outputs predictions of project timelines. To evaluate the effectiveness of this model, a comprehensive methodology is employed, involving simulations and practical applications in a variety of real-world software project scenarios. This multifaceted evaluation strategy is designed to validate the model's significant role in enhancing forecast accuracy and elevating overall management efficiency, particularly in complex software project environments. The results indicate that the integration of time prediction algorithms with LLMs has the potential to optimize software project progress management. These quantitative results suggest the effectiveness of the method in practical applications. In conclusion, this study demonstrates that integrating time prediction algorithms with LLMs can significantly improve the predictive accuracy and efficiency of software project management. This offers an advanced project management tool for the industry, with the potential to improve operational efficiency, optimize resource allocation, and ensure timely project completion.

Keywords: software project management, time prediction algorithms, large language models (LLMS), forecast accuracy, project progress prediction

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Authors: Leah Ning

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This paper discusses the development of a machine learning algorithm that accurately detects metastatic breast cancer (cancer has spread elsewhere from its origin part) in selected images that come from pathology scans of lymph node sections. Being able to develop an accurate artificial intelligence (AI) algorithm would help significantly in breast cancer diagnosis since manual examination of lymph node scans is both tedious and oftentimes highly subjective. The usage of AI in the diagnosis process provides a much more straightforward, reliable, and efficient method for medical professionals and would enable faster diagnosis and, therefore, more immediate treatment. The overall approach used was to train a convolution neural network (CNN) based on a set of pathology scan data and use the trained model to binarily classify if a new scan were benign or malignant, outputting a 0 or a 1, respectively. The final model’s prediction accuracy is very high, with 100% for the train set and over 70% for the test set. Being able to have such high accuracy using an AI model is monumental in regard to medical pathology and cancer detection. Having AI as a new tool capable of quick detection will significantly help medical professionals and patients suffering from cancer.

Keywords: breast cancer detection, AI, machine learning, algorithm

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Authors: Chairul Fajar, Muhammad Nur Hidayat, Muksalmina

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The issue of Over Dimension Over Loading (ODOL) in Indonesia remains a significant challenge, causing traffic accidents, disrupting traffic flow, accelerating road damage, and potentially leading to bridge collapses. Klaten Regency, located on the slopes of Mount Merapi along the Woro River in Kemalang District, has potential Class C excavation materials such as sand and stone. Data from the Klaten Regency Transportation Department indicates that ODOL violations account for 72%, while non-violating vehicles make up only 28%. ODOL involves modifying factory-standard vehicles beyond the limits specified in the Type Test Registration Certificate (SRUT) to save costs and travel time. This study aims to develop a prototype ‘Sabrang’ monitoring system based on Arduino Mega to control and monitor ODOL freight transportation in the mining of Class C excavation materials in Klaten Regency. The prototype is designed to automatically measure the dimensions and weight of objects using a microcontroller. The data analysis techniques used in this study include the Normality Test and Paired T-Test, comparing sensor measurement results on scaled objects. The study results indicate differences in measurement validation under room temperature and ambient temperature conditions. Measurements at room temperature showed that the majority of H0 was accepted, meaning there was no significant difference in measurements when the prototype tool was used. Conversely, measurements at ambient temperature showed that the majority of H0 was rejected, indicating a significant difference in measurements when the prototype tool was used. In conclusion, the ‘Sabrang’ monitoring system prototype is effective for controlling ODOL, although measurement results are influenced by temperature conditions. This study is expected to assist in the monitoring and control of ODOL, thereby enhancing traffic safety and road infrastructure.

Keywords: over dimension over loading, prototype, microcontroller, Arduino, normality test, paired t-test

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Authors: Jacques Cuenca, Claudio Colangeli, Agnieszka Mroz, Karl Janssens, Gunther Riexinger, Antonio D'Antuono, Giuseppe Pandarese, Milena Martarelli, Gian Marco Revel, Carlos Barcena Martin

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This study presents an on-site acoustic inspection methodology for quality and performance evaluation of building components. The work focuses on global and detailed sound source localisation, by successively performing acoustic beamforming and sound intensity measurements. A portable experimental setup is developed, consisting of an omnidirectional broadband acoustic source and a microphone array and sound intensity probe. Three main acoustic indicators are of interest, namely the sound pressure distribution on the surface of components such as walls, windows and junctions, the three-dimensional sound intensity field in the vicinity of junctions, and the sound transmission loss of partitions. The measurement data is post-processed and converted into a three-dimensional numerical model of the acoustic indicators with the help of the simultaneously acquired geolocation information. The three-dimensional acoustic indicators are then integrated into an augmented reality platform superimposing them onto a real-time visualisation of the spatial environment. The methodology thus enables a measurement-supported inspection process of buildings and the correction of errors during construction and refurbishment. Two experimental validation cases are shown. The first consists of a laboratory measurement on a full-scale mockup of a room, featuring a prefabricated panel. The latter is installed with controlled defects such as lack of insulation and joint sealing material. It is demonstrated that the combined acoustic and augmented reality tool is capable of identifying acoustic leakages from the building defects and assist in correcting them. The second validation case is performed on a prefabricated room at a near-completion stage in the factory. With the help of the measurements and visualisation tools, the homogeneity of the partition installation is evaluated and leakages from junctions and doors are identified. Furthermore, the integration of acoustic indicators together with thermal and geometrical indicators via the augmented reality platform is shown.

Keywords: acoustic inspection, prefabricated building components, augmented reality, sound source localization

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Authors: Kehabtimer Shiferaw Kotiso, Getachew Hailemariam, Abiy Seifu Estifanos

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Introduction: Despite the highest share of low birth weight (LBW) for neonatal mortality and morbidity, predicting births with LBW for better intervention preparation is challenging. This study aims to predict LBW using a dataset encompassing 53,637 birth cohorts collected from 36 primary hospitals across seven regions in Ethiopia from February 2022 to June 2024. Methods: We identified ten explanatory variables related to maternal and neonatal characteristics, including maternal education, age, residence, history of miscarriage or abortion, history of preterm birth, type of pregnancy, number of livebirths, number of stillbirths, antenatal care frequency, and sex of the fetus to predict LBW. Using WEKA 3.8.2, we developed and compared seven machine learning algorithms. Data preprocessing included handling missing values, outlier detection, and ensuring data integrity in birth weight records. Model performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the Receiver Operating Characteristic curve (ROC AUC) using 10-fold cross-validation. Results: The results demonstrated that the decision tree, J48, logistic regression, and gradient boosted trees model achieved the highest accuracy (94.5% to 94.6%) with a precision of 93.1% to 93.3%, F1-score of 92.7% to 93.1%, and ROC AUC of 71.8% to 76.6%. Conclusion: This study demonstrates the effectiveness of machine learning models in predicting LBW. The high accuracy and recall rates achieved indicate that these models can serve as valuable tools for healthcare policymakers and providers in identifying at-risk newborns and implementing timely interventions to achieve the sustainable developmental goal (SDG) related to neonatal mortality.

Keywords: low birth weight, machine learning, classification, neonatal mortality, Ethiopia

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Authors: Mian Huang, Chi Ma, Junyu Lin, William Lu

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Introduction: Bone health attracts more attention recently and an intelligent question and answer (QA) chatbot for osteoporosis is helpful for science popularization. With Generative Pretrained Transformer (GPT) technology developing, we build an osteoporosis corpus dataset and then fine-tune LLaMA, a famous open-source GPT foundation large language model(LLM), on our self-constructed osteoporosis corpus. Evaluated by clinical orthopedic experts, our fine-tuned model outperforms vanilla LLaMA on osteoporosis QA task in Chinese. Three-dimensional quantitative computed tomography (QCT) measured bone mineral density (BMD) is considered as more accurate than DXA for BMD measurement in recent years. We develop an automatic Phantom-less QCT(PL-QCT) that is more efficient for BMD measurement since no need of an external phantom for calibration. Combined with LLM on osteoporosis, our PL-QCT provides efficient and accurate BMD measurement for our chatbot users. Material and Methods: We build an osteoporosis corpus containing about 30,000 Chinese literatures whose titles are related to osteoporosis. The whole process is done automatically, including crawling literatures in .pdf format, localizing text/figure/table region by layout segmentation algorithm and recognizing text by OCR algorithm. We train our model by continuous pre-training with Low-rank Adaptation (LoRA, rank=10) technology to adapt LLaMA-7B model to osteoporosis domain, whose basic principle is to mask the next word in the text and make the model predict that word. The loss function is defined as cross-entropy between the predicted and ground-truth word. Experiment is implemented on single NVIDIA A800 GPU for 15 days. Our automatic PL-QCT BMD measurement adopt AI-associated region-of-interest (ROI) generation algorithm for localizing vertebrae-parallel cylinder in cancellous bone. Due to no phantom for BMD calibration, we calculate ROI BMD by CT-BMD of personal muscle and fat. Results & Discussion: Clinical orthopaedic experts are invited to design 5 osteoporosis questions in Chinese, evaluating performance of vanilla LLaMA and our fine-tuned model. Our model outperforms LLaMA on over 80% of these questions, understanding ‘Expert Consensus on Osteoporosis’, ‘QCT for osteoporosis diagnosis’ and ‘Effect of age on osteoporosis’. Detailed results are shown in appendix. Future work may be done by training a larger LLM on the whole orthopaedics with more high-quality domain data, or a multi-modal GPT combining and understanding X-ray and medical text for orthopaedic computer-aided-diagnosis. However, GPT model gives unexpected outputs sometimes, such as repetitive text or seemingly normal but wrong answer (called ‘hallucination’). Even though GPT give correct answers, it cannot be considered as valid clinical diagnoses instead of clinical doctors. The PL-QCT BMD system provided by Bone’s QCT(Bone’s Technology(Shenzhen) Limited) achieves 0.1448mg/cm2(spine) and 0.0002 mg/cm2(hip) mean absolute error(MAE) and linear correlation coefficient R2=0.9970(spine) and R2=0.9991(hip)(compared to QCT-Pro(Mindways)) on 155 patients in three-center clinical trial in Guangzhou, China. Conclusion: This study builds a Chinese osteoporosis corpus and develops a fine-tuned and domain-adapted LLM as well as a PL-QCT BMD measurement system. Our fine-tuned GPT model shows better capability than LLaMA model on most testing questions on osteoporosis. Combined with our PL-QCT BMD system, we are looking forward to providing science popularization and early morning screening for potential osteoporotic patients.

Keywords: GPT, phantom-less QCT, large language model, osteoporosis

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Authors: Umar Albalawi, Sang C. Suh, Jinoh Kim

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As internet continues to expand its usage with an enormous number of applications, cyber-threats have significantly increased accordingly. Thus, accurate detection of malicious traffic in a timely manner is a critical concern in today’s Internet for security. One approach for intrusion detection is to use Machine Learning (ML) techniques. Several methods based on ML algorithms have been introduced over the past years, but they are largely limited in terms of detection accuracy and/or time and space complexity to run. In this work, we present a novel method for intrusion detection that incorporates a set of supervised learning algorithms. The proposed technique provides high accuracy and outperforms existing techniques that simply utilizes a single learning method. In addition, our technique relies on partial flow information (rather than full information) for detection, and thus, it is light-weight and desirable for online operations with the property of early identification. With the mid-Atlantic CCDC intrusion dataset publicly available, we show that our proposed technique yields a high degree of detection rate over 99% with a very low false alarm rate (0.4%).

Keywords: intrusion detection, supervised learning, traffic classification, computer networks

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Authors: João Madureira, Ricardo Lagido, Inês Sousa, Fraunhofer Portugal

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Surf is an increasingly popular sport and its performance evaluation is often qualitative. This work aims at using a smartphone to collect and analyze the GPS and inertial sensors data in order to obtain quantitative metrics of the surfing performance. Two approaches are compared for detection of wave rides, computing the number of waves rode in a surfing session, the starting time of each wave and its duration. The first approach is based on computing the velocity from the Global Positioning System (GPS) signal and finding the velocity thresholds that allow identifying the start and end of each wave ride. The second approach adds information from the Inertial Measurement Unit (IMU) of the smartphone, to the velocity thresholds obtained from the GPS unit, to determine the start and end of each wave ride. The two methods were evaluated using GPS and IMU data from two surfing sessions and validated with similar metrics extracted from video data collected from the beach. The second method, combining GPS and IMU data, was found to be more accurate in determining the number of waves, start time and duration. This paper shows that it is feasible to use smartphones for quantification of performance metrics during surfing. In particular, detection of the waves rode and their duration can be accurately determined using the smartphone GPS and IMU.

Keywords: inertial measurement unit (IMU), global positioning system (GPS), smartphone, surfing performance

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Authors: Pitimanus Bunlue

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The objectives of this study were to identify the unique characteristics of Salaya Old market, Phutthamonthon, Nakhon Pathom and develop the effective video media to promote the homeland awareness among local people and the characteristic features of this community were collectively summarized based on historical data, community observation, and people’s interview. The acquired data were used to develop a media describing prominent features of the community. The quality of the media was later assessed by interviewing local people in the old market in terms of content accuracy, video, and narration qualities, and sense of homeland awareness after watching the video. The result shows a 6-minute video media containing historical data and outstanding features of this community was developed. Based on the interview, the content accuracy was good. The picture quality and the narration were very good. Most people developed a sense of homeland awareness after watching the video also as well.

Keywords: audio-visual, creating homeland awareness, Phutthamonthon Nakhon Pathom, research and development

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Authors: Aishik Deb, Rituparna Sinha

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We have developed an algorithm to detect the abnormal segment/"structural variation in the genome across a number of samples. We have worked on simulated as well as real data from the BAM Files and have designed a segmentation algorithm where abnormal segments are detected. This algorithm aims to improve the accuracy and performance of the existing CNV-CH algorithm. The next-generation sequencing (NGS) approach is very fast and can generate large sequences in a reasonable time. So the huge volume of sequence information gives rise to the need for Big Data and parallel approaches of segmentation. Therefore, we have designed a map-reduce approach for the existing CNV-CH algorithm where a large amount of sequence data can be segmented and structural variations in the human genome can be detected. We have compared the efficiency of the traditional and map-reduce algorithms with respect to precision, sensitivity, and F-Score. The advantages of using our algorithm are that it is fast and has better accuracy. This algorithm can be applied to detect structural variations within a genome, which in turn can be used to detect various genetic disorders such as cancer, etc. The defects may be caused by new mutations or changes to the DNA and generally result in abnormally high or low base coverage and quantification values.

Keywords: cancer detection, convex hull segmentation, map reduce, next generation sequencing

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Authors: Asra Yousuf, Ajaykumar Tannirkulum

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Inferring rural-urban migration trends can help design effective policies that promote better urban planning and rural development. In this paper, we describe how machine learning algorithms can be applied to predict internal migration decisions of people. We consider data collected from household surveys in Tamil Nadu to train our model. To measure the performance of the model, we use data on past migration from National Sample Survey Organisation of India. The factors for training the model include socioeconomic characteristic of each individual like age, gender, place of residence, outstanding loans, strength of the household, etc. and his past migration history. We perform a comparative analysis of the performance of a number of machine learning algorithm to determine their prediction accuracy. Our results show that machine learning algorithms provide a stronger prediction accuracy as compared to statistical models. Our goal through this research is to propose the use of data science techniques in understanding human decisions and behaviour in developing countries.

Keywords: development, migration, internal migration, machine learning, prediction

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Authors: Qiang Zhang, Xiaojian Hu1

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Intersection is the place where vehicles and pedestrians must pass through, turn and evacuate. Obtaining the motion data of vehicles near the intersection is of great significance for transportation research. Since there are usually many targets and there are more conflicts between targets, this makes it difficult to obtain vehicle motion parameters in traffic videos of intersections. According to the characteristics of traffic videos, this paper applies video technology to realize the automated track, count and trajectory extraction of vehicles to collect traffic data by roadside surveillance cameras installed near the intersections. Based on the video recognition method, the vehicles in each lane near the intersection are tracked with extracting trajectory and counted respectively in various degrees of occlusion and visibility. The performances are compared with current recognized CPU-based algorithms of real-time tracking-by-detection. The speed of the presented system is higher than the others and the system has a better real-time performance. The accuracy of direction has reached about 94.99% on average, and the accuracy of classification and statistics has reached about 75.12% on average.

Keywords: tracking and statistics, vehicle, signalized intersection, motion parameter, trajectory

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Authors: Ana B. Vivas, Antonia Ypsilanti, Aristea I. Ladas, Angeles F. Estevez

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Mild Cognitive Impairment (MCI) is considered an intermediate stage between normal and pathological aging, as a substantial percentage of people diagnosed with MCI converts later to dementia of the Alzheimer’s type. Memory is of the first cognitive processes to deteriorate in this condition. In the present study we employed the differential outcomes procedure (DOP) to improve visuospatial memory in a group of participants with MCI. The DOP requires the structure of a conditional discriminative learning task in which a correct choice response to a specific stimulus-stimulus association is reinforced with a particular reinforcer or outcome. A group of 10 participants with MCI, and a matched control group had to learn and keep in working memory four target locations out of eight possible locations where a shape could be presented. Results showed that participants with MCI had a statistically significant better terminal accuracy when a unique outcome was paired with a location (76% accuracy) as compared to a non differential outcome condition (64%). This finding suggests that the DOP is useful in improving working memory in MCI patients, which may delay their conversion to dementia.

Keywords: mild cognitive impairment, working memory, differential outcomes, cognitive process

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Authors: Bin Ji, Kai Xu, Shunyu Yao, Jingjing Liu, Ye Pan

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Human pose estimation is a popular research area in computer vision for its important application in human-machine interface. In recent years, 2D human pose estimation based on convolution neural network has got great progress and development. However, in more and more practical applications, people often need to deal with tasks based on video. It’s not far-fetched for us to consider how to combine the spatial and temporal information together to achieve a balance between computing cost and accuracy. To address this issue, this study proposes a new spatiotemporal model, namely Spatiotemporal Net (STNet) to combine both temporal and spatial information more rationally. As a result, the predicted keypoints heatmap is potentially more accurate and spatially more precise. Under the condition of ensuring the recognition accuracy, the algorithm deal with spatiotemporal series in a decoupled way, which greatly reduces the computation of the model, thus reducing the resource consumption. This study demonstrate the effectiveness of our network over the Penn Action Dataset, and the results indicate superior performance of our network over the existing methods.

Keywords: convolutional long short-term memory, deep learning, human pose estimation, spatiotemporal series

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Authors: Xingyu Peng, Qingyuan Hu, Xuebin Zhu, Xi Yuan

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Neutron spectrometry has contributed much to the development of nuclear physics since 1932 and has also become an importance tool in several other fields, notably nuclear technology, fusion plasma diagnostics and radiation protection. Compared with neutron fluxes, neutron spectra can provide more detailed information on the internal physical process of neutron sources, such as fast neutron reactors, fusion plasma, fission-fusion hybrid reactors, and so on. However, high performance neutron spectrometer is not so commonly available as it requires the use of large and complex instrumentation. This work describes the development and characterization of a compact magnetic proton recoil (MPR) spectrometer for high-resolution measurements of fast neutron spectra. The compact MPR spectrometer is featured by its large recoil angle, small size permanent analysis magnet, short beam transport line and dual-purpose detector array for both steady state and pulsed neutron spectra measurement. A 3-dimensional electromagnetic particle transport code is developed to simulate the response function of the spectrometer. Simulation results illustrate that the performance of the spectrometer is mainly determined by n-p recoil foil and proton apertures, and an overall energy resolution of 3% is achieved for 14 MeV neutrons. Dedicated experiments using alpha source and mono-energetic neutron beam are employed to verify the simulated response function of the compact MPR spectrometer. These experimental results show a good agreement with the simulated ones, which indicates that the simulation code possesses good accuracy and reliability. The compact MPR spectrometer described in this work is a valuable tool for fast neutron spectra measurements for the fission or fusion devices.

Keywords: neutron spectrometry, magnetic proton recoil spectrometer, neutron spectra, fast neutron

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Authors: Sylwia Sumińska, Łukasz Kapica, Grzegorz Szczepański

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Introduction: Cognitive skills are a crucial part of everyday functioning. Cognitive skills include perception, attention, language, memory, executive functions, and higher cognitive skills. With the aging of societies, there is an increasing percentage of people whose cognitive skills decline. Cognitive skills affect work performance. The appropriate diagnosis of a worker’s cognitive skills reduces the risk of errors and accidents at work which is also important for senior workers. The study aimed to prepare new cognitive tests for adults aged 20-60 and assess the psychometric properties of the tests. The project responds to the need for reliable and accurate methods of assessing cognitive performance. Computer tests were developed to assess psychomotor performance, attention, and working memory. Method: Two hundred eighty people aged 20-60 will participate in the study in 4 age groups. Inclusion criteria for the study were: no subjective cognitive impairment, no history of severe head injuries, chronic diseases, psychiatric and neurological diseases. The research will be conducted from February - to June 2022. Cognitive tests: 1) Measurement of psychomotor performance: Reaction time, Reaction time with selective attention component; 2) Measurement of sustained attention: Visual search (dots), Visual search (numbers); 3) Measurement of working memory: Remembering words, Remembering letters. To assess the validity and the reliability subjects will perform the Vienna Test System, i.e., “Reaction Test” (reaction time), “Signal Detection” (sustained attention), “Corsi Block-Tapping Test” (working memory), and Perception and Attention Test (TUS), Colour Trails Test (CTT), Digit Span – subtest from The Wechsler Adult Intelligence Scale. Eighty people will be invited to a session after three months aimed to assess the consistency over time. Results: Due to ongoing research, the detailed results from 280 people will be shown at the conference separately in each age group. The results of correlation analysis with the Vienna Test System will be demonstrated as well.

Keywords: aging, attention, cognitive skills, cognitive tests, psychomotor performance, working memory

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Authors: Neşe Alkan

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Purpose: The point that applied/clinical psychology, which is the practice and research discipline of the mental health field, has reached today can be summarized as the necessity of handling the psychological well-being of people from multiple perspectives and the goal of moving it to a higher level. Clients' subjective assessment of their own condition and wellbeing is an integral part of evidence-based interventions. There is a need for tools through which clients can evaluate the effectiveness of the psychotherapy/intervention performed with them and their contribution to the wellbeing and wellbeing of this process in a valid and reliable manner. The aim of this research is to meet this need, to test the reliability and validity of the index in Turkish, and explore its usability in the practices of both researchers and psychotherapists. Method: A total of 213 adults aged between 18-54, 69.5% working and 29.5% university students, were included in the study. Along with their demographic information, the participants were administered a set of scales: wellbeing, life satisfaction, spiritual satisfaction, shopping addiction, and loneliness, namely via an online platform. The construct validity of the wellbeing scale was tested with exploratory and confirmatory factor analyses, convergent and discriminant validity were tested with two-way full and partial correlation analyses and, measurement invariance was tested with one-way analysis of variance. Results: Factor analyzes showed that the scale consisted of six dimensions as it is in its original structure. The internal consistency of the scale was found to be Cronbach α = .82. Two-way correlation analyzes revealed that the wellbeing scale total score was positively correlated with general life satisfaction (r = .62) and spiritual satisfaction (r = .29), as expected. It was negatively correlated with loneliness (r = -.51) and shopping addiction (r = -.15). While the scale score did not vary by gender, previous illness, or nicotine addiction, it was found that the total wellbeing scale scores of the participants who had used antidepressant medication during the past year were lower than those who did not use antidepressant medication (F(1,204) = 7.713, p = .005). Conclusion: It has been concluded that the 12-item wellbeing scale consisting of six dimensions can be used in research and health sciences practices as a valid and reliable measurement tool. Further research which examines the reliability and validity of the scale in different widely used languages such as Spanish and Chinese is recommended.

Keywords: wellbeing, intervention effectiveness, reliability and validity, effectiveness

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Authors: I. Gusti A. A. Rai Asmiwyati, Robert J. Corner, Ashraf M. Dewan

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Change on land use/cover (LULC) dominantly affects spatial structure and function. It can have such impacts by disrupting social culture practice and disturbing physical elements. Thus, it has become essential to understand of the dynamics in time and space of LULC as it can be used as a critical input for developing sustainable LULC. This study was an attempt to map and monitor the LULC change in Bali Indonesia from 2003 to 2013. Using object based classification to improve the accuracy, and change detection, multi temporal land use/cover data were extracted from a set of ASTER satellite image. The overall accuracies of the classification maps of 2003 and 2013 were 86.99% and 80.36%, respectively. Built up area and paddy field were the dominant type of land use/cover in both years. Patch increase dominantly in 2003 illustrated the rapid paddy field fragmentation and the huge occurring transformation. This approach is new for the case of diverse urban features of Bali that has been growing fast and increased the classification accuracy than the manual pixel based classification.

Keywords: land use/cover, urban, Bali, ASTER

Procedia PDF Downloads 542

Authors: Sakshi Dhumale, Madhushree C., Amba Shetty

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The effect of station density on the geostatistical prediction of groundwater levels is of critical importance to ensure accurate and reliable predictions. Monitoring station density directly impacts the accuracy and reliability of geostatistical predictions by influencing the model's ability to capture localized variations and small-scale features in groundwater levels. This is particularly crucial in regions with complex hydrogeological conditions and significant spatial heterogeneity. Insufficient station density can result in larger prediction uncertainties, as the model may struggle to adequately represent the spatial variability and correlation patterns of the data. On the other hand, an optimal distribution of monitoring stations enables effective coverage of the study area and captures the spatial variability of groundwater levels more comprehensively. In this study, we investigate the effect of station density on the predictive performance of groundwater levels using the geostatistical technique of Ordinary Kriging. The research utilizes groundwater level data collected from 121 observation wells within the semi-arid Berambadi watershed, gathered over a six-year period (2010-2015) from the Indian Institute of Science (IISc), Bengaluru. The dataset is partitioned into seven subsets representing varying sampling densities, ranging from 15% (12 wells) to 100% (121 wells) of the total well network. The results obtained from different monitoring networks are compared against the existing groundwater monitoring network established by the Central Ground Water Board (CGWB). The findings of this study demonstrate that higher station densities significantly enhance the accuracy of geostatistical predictions for groundwater levels. The increased number of monitoring stations enables improved interpolation accuracy and captures finer-scale variations in groundwater levels. These results shed light on the relationship between station density and the geostatistical prediction of groundwater levels, emphasizing the importance of appropriate station densities to ensure accurate and reliable predictions. The insights gained from this study have practical implications for designing and optimizing monitoring networks, facilitating effective groundwater level assessments, and enabling sustainable management of groundwater resources.

Keywords: station density, geostatistical prediction, groundwater levels, monitoring networks, interpolation accuracy, spatial variability

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Authors: Kenneth Fon Mbah, Arash Habibi Lashkari, Ali A. Ghorbani

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Phishing e-mails are a security issue that not only annoys online users, but has also resulted in significant financial losses for businesses. Phishing advertisements and pornographic e-mails are difficult to detect as attackers have been becoming increasingly intelligent and professional. Attackers track users and adjust their attacks based on users’ attractions and hot topics that can be extracted from community news and journals. This research focuses on deceptive Phishing attacks and their variants such as attacks through advertisements and pornographic e-mails. We propose a framework called Phishing Alerting System (PHAS) to accurately classify e-mails as Phishing, advertisements or as pornographic. PHAS has the ability to detect and alert users for all types of deceptive e-mails to help users in decision making. A well-known email dataset has been used for these experiments and based on previously extracted features, 93.11% detection accuracy is obtainable by using J48 and KNN machine learning techniques. Our proposed framework achieved approximately the same accuracy as the benchmark while using this dataset.

Keywords: phishing e-mail, phishing detection, anti phishing, alarm system, machine learning

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Authors: Yu. I. Pigolkin, M. A. Garcia Corro

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Age determination of unknown dead bodies in forensic personal identification is a complicated process which involves the application of numerous methods and techniques. Skeletal remains are less exposed to influences of environmental factors. In order to enhance the accuracy of forensic age estimation additional properties of bones correlating with age are required to be revealed. Material and Methods: Dimensional examination of the sella turcica was carried out on cadavers with the cranium opened by a circular vibrating saw. The sample consisted of a total of 90 Russian subjects, ranging in age from two months and 87 years. Results: The tendency of dimensional variations throughout life was detected. There were no observed gender differences in the morphometry of the sella turcica. The shared use of the sella turcica depth and length values revealed the possibility to categorize an examined sample in a certain age period. Conclusions: Based on the results of existing methods of age determination, the morphometry of the sella turcica can be an additional characteristic, amplifying the received values, and accordingly, increasing the accuracy of forensic biological age diagnosis.

Keywords: age–related changes in bone structures, forensic personal identification, sella turcica morphometry, body identification

Procedia PDF Downloads 275

Authors: Desmond Agbolade Ademola

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This work aimed to introduce a theory, called Guided Energy Theory of a particle that answered questions that arise from quantum foundation, quantum mechanics theory, and interpretation such as: what is nature of wavefunction? Is mathematical formalism of wavefunction correct? Does wavefunction collapse during measurement? Do quantum physical entanglement and many world interpretations really exist? In addition, is there uncertainty in the physical reality of our nature as being concluded in the Quantum theory? We have been able to show by the fundamental analysis presented in this work that the way quantum mechanics theory, and interpretation describes nature is not correlated with physical reality. Because, we discovered amongst others that, (1) Guided energy theory of a particle fundamentally provides complete physical observable series of quantized measurement of a particle momentum, force, energy e.t.c. in a given distance and time.In contrast, quantum mechanics wavefunction describes that nature has inherited probabilistic and indeterministic physical quantities, resulting in unobservable physical quantities that lead to many worldinterpretation.(2) Guided energy theory of a particle fundamentally predicts that it is mathematically possible to determine precise quantized measurementof position and momentum of a particle simultaneously. Because, there is no uncertainty in nature; nature however naturally guides itself against uncertainty. Contrary to the conclusion in quantum mechanics theory that, it is mathematically impossible to determine the position and the momentum of a particle simultaneously. Furthermore, we have been able to show by this theory that, it is mathematically possible to determine quantized measurement of force acting on a particle simultaneously, which is not possible on the premise of quantum mechanics theory. (3) It is evidently shown by our theory that, guided energy does not collapse, only describes the lopsided nature of a particle behavior in motion. This pretty offers us insight on gradual process of engagement - convergence and disengagement – divergence of guided energy holders which further highlight the picture how wave – like behavior return to particle-like behavior and how particle – like behavior return to wave – like behavior respectively. This further proves that the particles’ behavior in motion is oscillatory in nature. The mathematical formalism of Guided energy theory shows that nature is certainty whereas the mathematical formalism of Quantum mechanics theory shows that nature is absolutely probabilistics. In addition, the nature of wavefunction is the guided energy of the wave. In conclusion, the fundamental mathematical formalism of Quantum mechanics theory is wrong.

Keywords: momentum, physical entanglement, wavefunction, uncertainty

Procedia PDF Downloads 298