Search results for: suport vector machine

Authors: Mikheil Kalmakhelidze

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Record linkage (RL) problem has become more and more important in recent years due to the growing interest towards big data analysis. The problem can be formulated in a very simple way: Given two entries a and b of a database, decide whether they represent the same object or not. There are two classical deterministic and probabilistic ways of solving the RL problem. Using simple Bayes classifier in many cases produces useful results but sometimes they show to be poor. In recent years several successful approaches have been made towards solving specific RL problems by neural network algorithms including single layer perception, multilayer back propagation network etc. In our work, we model the RL problem for specific dataset of student applications in fuzzy description logic (FDL) where linkage of specific pair (a,b) depends on the truth value of corresponding formula A(a,b) in a canonical FDL model. As a main result, we build neural network for deciding truth value of FDL formulas in a canonical model and thus link RL problem to machine learning. We apply the approach to dataset with 10000 entries and also compare to classical RL solving approaches. The results show to be more accurate than standard probabilistic approach.

Keywords: description logic, fuzzy logic, neural networks, record linkage

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Authors: Endri Rama, Genci Capi, Shigenori Kawahara

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The mobile robot ability to navigate autonomously in its environment is very important. Even though the advances in technology, robot self-localization and goal directed navigation in complex environments are still challenging tasks. In this article, we propose a novel method for robot navigation based on rat’s brain signals (Local Field Potentials). It has been well known that rats accurately and rapidly navigate in a complex space by localizing themselves in reference to the surrounding environmental cues. As the first step to incorporate the rat’s navigation strategy into the robot control, we analyzed the rats’ strategies while it navigates in a multiple Y-maze, and recorded Local Field Potentials (LFPs) simultaneously from three brain regions. Next, we processed the LFPs, and the extracted features were used as an input in the artificial neural network to predict the rat’s next location, especially in the decision-making moment, in Y-junctions. We developed an algorithm by which the robot learned to imitate the rat’s decision-making by mapping the rat’s brain signals into its own actions. Finally, the robot learned to integrate the internal states as well as external sensors in order to localize and navigate in the complex environment.

Keywords: brain-machine interface, decision-making, mobile robot, neural network

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Authors: Rhadinia Tayag-Relanes, Felina C. Young

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This study was conducted to analyze the noodle production process at Yan Hu Food Manufacturing for the basis of production improvement. The study utilized the PDCA approach and record review in the gathering of data for the calendar year 2019 from August to October data of the noodle products miki, canton, and misua. Causal-comparative research was used in this study; it attempts to establish cause-effect relationships among the variables such as descriptive statistics and correlation, both were used to compute the data gathered. The study found that miki, canton, and misua production has different cycle time sets for each production and has different production outputs in every set of its production process and a different number of wastages. The company has not yet established its allowable rejection rate/ wastage; instead, this paper used a 1% wastage limit. The researcher recommended the following: machines used for each process of the noodle product must be consistently maintained and monitored; an assessment of all the production operators by checking their performance statistically based on the output and the machine performance; a root cause analysis for finding the solution must be conducted; and an improvement on the recording system of the input and output of the production process of noodle product should be established to eliminate the poor recording of data.

Keywords: production, continuous improvement, process, operations, PDCA

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Authors: Temidayo O. Omotehinwa, David O. Oyewola, Friday J. Agbo

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This study aims to gather a proper perspective of the development landscape of Computer Science research in Nigeria. Therefore, a bibliometric analysis of 4,333 bibliographic records of Computer Science research in Nigeria in the last 31 years (1991-2021) was carried out. The bibliographic data were extracted from the Scopus database and analyzed using VOSviewer and the bibliometrix R package through the biblioshiny web interface. The findings of this study revealed that Computer Science research in Nigeria has a growth rate of 24.19%. The most developed and well-studied research areas in the Computer Science field in Nigeria are machine learning, data mining, and deep learning. The social structure analysis result revealed that there is a need for improved international collaborations. Sparsely established collaborations are largely influenced by geographic proximity. The funding analysis result showed that Computer Science research in Nigeria is under-funded. The findings of this study will be useful for researchers conducting Computer Science related research. Experts can gain insights into how to develop a strategic framework that will advance the field in a more impactful manner. Government agencies and policymakers can also utilize the outcome of this research to develop strategies for improved funding for Computer Science research.

Keywords: bibliometric analysis, biblioshiny, computer science, Nigeria, science mapping

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Authors: Damien Kunjal, Faeezah Peerbhai

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Despite the increasing popularity of exchange-traded funds (ETFs), ETF investment choices may not always be rational. Excess trading volume, misevaluations of securities, and excess return volatility present in financial markets can be attributed to the influence of the overconfidence bias. Whilst previous research has explored the overconfidence bias in stock markets; this study focuses on trading in ETF markets. Therefore, the objective of this study is to investigate the presence of investor overconfidence in the South African ETF market. Using vector autoregressive models, the lead-lag relationship between market turnover and the market return is examined for the market of South African ETFs tracking domestic benchmarks and for the market of South African ETFs tracking international benchmarks over the period November 2000 till August 2019. Consistent with the overconfidence hypothesis, a positive relationship between current market turnover and lagged market return is found for both markets, even after controlling for market volatility and cross-sectional dispersion. This relationship holds for both market and individual ETF turnover suggesting that investors are overconfident when trading in South African ETFs tracking domestic benchmarks and South African ETFs tracking international benchmarks since trading activity depends on past market returns. Additionally, using the global recession as a structural break, this study finds that investor overconfidence is more pronounced after the global recession suggesting that investors perceive ETFs as risk-reducing assets due to their diversification benefits. Overall, the results of this study indicate that the overconfidence bias has a significant influence on ETF investment choices, therefore, suggesting that the South African ETF market is inefficient since investors’ decisions are based on their biases. As a result, the effect of investor overconfidence can account for the difference between the fair value of ETFs and its current market price. This finding has implications for policymakers whose responsibility is to promote the efficiency of the South African ETF market as well as ETF investors and traders who trade in the South African ETF market.

Keywords: exchange-traded fund, market return, market turnover, overconfidence, trading activity

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Authors: C. O. Igberi, M. U. Amadi

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This research paper on the impact of import restriction on rice production in Nigeria is aimed at finding/proffering valid solutions to the age long problem of rice self-sufficiency, through a better understanding of policy measures used in the past, in this case, the effectiveness of rice import restriction of the early 90’s. It tries to answer the questions of; import restriction boosting domestic rice production and the macroeconomic determining factors of Gross Domestic Rice Product (GDRP). The research probe is investigated through literature and analytical frameworks, such that time series data on the GDRP, Gross Fixed Capital Formation (GFCF), average foreign rice producers’ prices(PPF), domestic producers’ prices (PPN) and the labour force (LABF) are collated for analysis (with an import restriction dummy variable, POL1). The research objectives/hypothesis are analysed using; Cointegration, Vector Error Correction Model (VECM), Impulse Response Function (IRF) and Granger Causality Test(GCT) methodologies. Results show that in the short-run error correction specification for GDRP, a percentage (1%) deviation away from the long-run equilibrium in a current quarter is only corrected by 0.14% in the subsequent quarter. Also, the rice import restriction policy had no significant effect on the GDRP at this time. Other findings show that the policy period has, in fact, had effects on the PPN and LABF. The choice variables used are valid macroeconomic factors that explain the GDRP of Nigeria, as adduced from the IRF and GCT, and in the long-run. Policy recommendations suggest that the import restriction is not disqualified as a veritable tool for improving domestic rice production, rather better enforcement procedures and strict adherence to the policy dictates is needed. Furthermore, accompanying policies which drive public and private capital investment and accumulation must be introduced. Also, employment rate and labour substitution in the agricultural sector should not be drastically changed, rather its welfare and efficiency be improved.

Keywords: import restriction, gross domestic rice production, cointegration, VECM, Granger causality, impulse response function

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Authors: Mohsin Ali Shaikh, Song Weiguo, Muhammad Kashan Surahio, Usman Shahid, Rehmat Karim

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When the area of a structure that is threatened by a disaster affects personal safety, the effectiveness of disaster prevention, evacuation, and rescue operations can be summarized by three assessment indicators: personal safety, property preservation, and attribution of responsibility. These indicators are applicable regardless of the disaster that affects the building. People need to get out of the hazardous area and to a safe place as soon as possible because there's no other way to respond. The results of the tragedy are thus closely related to how quickly people are advised to evacuate and how quickly they are rescued. This study considers present fire prevention systems to address catastrophes and improve building safety. It proposes the methods of Prevention Level for Deployment in Advance and Spatial Transformation by Human-Machine Collaboration. We present and prototype a real-time fire protection system architecture for building disaster prevention, evacuation, and rescue operations. The design encourages the use of simulations to check the efficacy of evacuation, rescue, and disaster prevention procedures throughout the planning and design phase of the structure.

Keywords: prevention level, building information modeling, quality management system, simulated reality

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Authors: Jinhoa Lee

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The relationships between environmental quality, energy use and economic output have created growing attention over the past decades among researchers and policy makers. Focusing on the empirical aspects of the role of CO2 emissions and energy use in affecting the economic output, this paper is an effort to fulfill the gap in a comprehensive case study at a country level using modern econometric techniques. To achieve the goal, this country-specific study examines the short-run and long-run relationships among energy consumption (using disaggregated energy sources: crude oil, coal, natural gas, electricity), carbon dioxide (CO2) emissions and gross domestic product (GDP) for Turkey using time series analysis from the year 1980-2010. To investigate the relationships between the variables, this paper employs the Phillips–Perron (PP) test for stationarity, Johansen maximum likelihood method for cointegration and a Vector Error Correction Model (VECM) for both short- and long-run causality among the research variables for the sample. All the variables in this study show very strong significant effects on GDP in the country for the long term. The long-run equilibrium in the VECM suggests negative long-run causalities from consumption of petroleum products and the direct combustion of crude oil, coal and natural gas to GDP. Conversely, positive impacts of CO2 emissions and electricity consumption on GDP are found to be significant in Turkey during the period. There exists a short-run bidirectional relationship between electricity consumption and natural gas consumption. There exists a positive unidirectional causality running from electricity consumption to natural gas consumption, while there exists a negative unidirectional causality running from natural gas consumption to electricity consumption. Moreover, GDP has a negative effect on electricity consumption in Turkey in the short run. Overall, the results support arguments that there are relationships among environmental quality, energy use and economic output but the associations can to be differed by the sources of energy in the case of Turkey over of period 1980-2010.

Keywords: CO2 emissions, energy consumption, GDP, Turkey, time series analysis

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Authors: Sharif Muddsair, Eisels Martin, Giesbrecht Eugenie

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With development of society increase the demand for the movement of people also increases gradually. The various modes of the transport in different extent which expat impacts, which depends on mainly technical-operating conditions. The up-to-date telematics systems provide the transport industry a revolutionary. Appropriate use of these systems can help to substantially improve the efficiency. Vehicle monitoring and fleet tracking are among services used for improving efficiency and effectiveness of utility vehicle. There are many telematics systems which may contribute to eco-driving. Generally, they can be grouped according to their role in driving cycle. • Before driving - eco-route selection, • While driving – Advanced driver assistance, • After driving – remote analysis. Our point of interest is regulated in third point [after driving – remote analysis]. TS [Telematics-system] make it possible to record driving patterns in real time and analysis the data later on, So that driver- classification-specific hints [fast driver, slow driver, aggressive driver…)] are given to imitate eco-friendly driving style. Together with growing number of vehicle and development of information technology, telematics become an ‘active’ research subject in IT and the car industry. Telematics has gone a long way from providing navigation solution/assisting the driver to become an integral part of the vehicle. Today’s telematics ensure safety, comfort and become convenience of the driver.

Keywords: internet of things, iot, connected vehicle, cv, ts, telematics services, ml, machine learning

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Authors: Jiawen Wang, Qijun Chen

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The performance of most machine-learning methods for image inpainting depends on the quantity and quality of the training samples. However, it is very expensive or even impossible to obtain a great number of training samples in many scenarios. In this paper, an image inpainting model based on a generative adversary network (GAN) is constructed for the cases when the number of training samples is small. Firstly, a feature extraction network (F-net) is incorporated into the GAN network to utilize the available information of the inpainting image. The weighted sum of the extracted feature and the random noise acts as the input to the generative network (G-net). The proposed network can be trained well even when the sample size is very small. Secondly, in the phase of the completion for each damaged image, a genetic algorithm is designed to search an optimized noise input for G-net; based on this optimized input, the parameters of the G-net and F-net are further learned (Once the completion for a certain damaged image ends, the parameters restore to its original values obtained in the training phase) to generate an image patch that not only can fill the missing part of the damaged image smoothly but also has visual semantics.

Keywords: image inpainting, generative adversary nets, genetic algorithm, small-sample size

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Authors: S. Chockalingam, U. Natarajan, D. M. Santhoshsarang

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Particle damping is a promising vibration attenuating technique in boring tool than other type of damping with minimal effect on the strength, rigidity and stiffness ratio of the machine tool structure. Due to the cantilever nature of boring tool holder in operations, it suffers chatter when the slenderness ratio of the tool gets increased. In this study, Copper-Stainless steel (SS) particles were packed inside the boring tool which acts as a damper. Damper suppresses chatter generated during machining and also improves the machining efficiency of the tool with better slenderness ratio. In the first approach of particle damping, combined Cu-SS particles were packed inside the vibrating tool, whereas Copper and Stainless steel particles were selected separately and packed inside another tool and their effectiveness was analysed in this simulation. This study reveals that the efficiency of finite element simulation of the boring tools when equipped with particles such as copper, stainless steel and a combination of both. In this study, the newly modified boring tool holder with particle damping was simulated using ANSYS12.0 with and without particles. The aim of this study is to enhance the structural rigidity through particle damping thus avoiding the occurrence of resonance in the boring tool during machining.

Keywords: boring bar, copper-stainless steel, chatter, particle damping

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Authors: Zuliani Mahmood, Thirumulu Ponnuraj Kannan, Yean Yean Chan, Salahddin A. Al-Hudhairy

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Caries is the most common childhood disease which develops due to disturbances in the physiological equilibrium in the dental plaque resulting in demineralization of tooth structures. Plaque and dentine samples were collected from three different tooth surfaces representing caries progression (intact, over carious lesion and dentine) in children with early childhood caries (ECC, n=36). In caries free (CF) children, plaque samples were collected from sound tooth surfaces at baseline and after one year (n=12). The genomic DNA was extracted from all samples and subjected to 16S rRNA PCR amplification. The end products were cloned into pCR®2.1-TOPO® Vector. Five randomly selected positive clones collected from each surface were sent for sequencing. Identification of the bacterial clones was performed using BLAST against GenBank database. In the ECC group, the frequency of Lactobacillus sp. detected was significantly higher in the dentine surface (p = 0.031) than over the cavitated lesion. The highest frequency of bacteria detected in the intact surfaces was Fusobacterium nucleatum subsp. polymorphum (33.3%) while Streptococcus mutans was detected over the carious lesions and dentine surfaces at a frequency of 33.3% and 52.7% respectively. Fusobacterium nucleatum subsp. polymorphum was also found to be highest in the CF group (41.6%). Follow up at the end of one year showed that the frequency of Corynebacterium matruchotii detected was highest in those who remained caries free (16.6%), while Porphyromonas catoniae was highest in those who developed caries (25%). In conclusion, Streptococcus mutans and Porphyromonas catoniae are strongly associated with caries progression, while Lactobacillus sp. is restricted to deep carious lesions. Fusobacterium nucleatum subsp. polymorphum and Corynebacterium matruchotii may play a role in sustaining the healthy equilibrium in the dental plaque. These identified bacteria show promise as potential biomarkers in diagnosis which could help in the management of dental caries in children.

Keywords: early childhood caries, genotypic identification, oral bacteria, 16S rRNA

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Authors: C. Flynn, Q. Yuan, C. Reinhardt

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Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder affecting broad areas of the cerebral cortex and hippocampus. More than 95% of AD cases are representative of sporadic AD, where both genetic and environmental risk factors play a role. The main pathological features of AD include the widespread deposition of amyloid-beta and neurofibrillary tau tangles in the brain. The earliest brain pathology related to AD has been defined as hyperphosphorylated soluble tau in the noradrenergic locus coeruleus (LC) neurons, characterized by Braak. However, the cause of this pathology and the ultimate progression of AD is not understood. Increasing research points to a connection between the gut microbiota and the brain, and mounting evidence has shown that there is a bidirectional interaction between the two, known as the gut-brain axis. This axis can allow for bidirectional movement of neuroinflammatory cytokines and pathogenic misfolded proteins, as seen in AD. Prebiotics and probiotics have been shown to have a beneficial effect on gut health and can strengthen the gut-barrier as well as the blood-brain barrier, preventing the spread of these pathogens across the gut-brain axis. Our laboratory has recently established a pretangle tau rat model, in which we selectively express pseudo-phosphorylated human tau (htauE14) in the LC neurons of TH-Cre rats. LC htauE14 produced pathological changes in rats resembling those of the preclinical AD pathology (reduced olfactory discrimination and LC degeneration). In this work, we will investigate the effects of pre/probiotic ingestion on AD behavioral deficits, blood inflammation/cytokines, and various brain markers in our experimental rat model of AD. Rats will be infused with an adeno-associated viral vector containing a human tau gene pseudophosphorylated at 14 sites (common in LC pretangles) into 2-3 month TH-Cre rats. Fecal and blood samples will be taken at pre-surgery, and various post-surgery time points. A collection of behavioral tests will be performed, and immunohistochemistry/western blotting techniques will be used to observe various biomarkers. This work aims to elucidate the relationship between gut health and AD progression by strengthening gut-brain relationship and aims to observe the overall effect on tau formation and tau pathology in AD brains.

Keywords: alzheimer’s disease, aging, gut microbiome, neurodegeneration

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Authors: Husam Kaid, Abdulrahman Al-Ahmari, Zhiwu Li

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This paper develops a robust deadlock control technique for shared and unreliable resources in automated manufacturing systems (AMSs) based on structural analysis and colored Petri nets, which consists of three steps. The first step involves using strict minimal siphon control to create a live (deadlock-free) system that does not consider resource failure. The second step uses an approach based on colored Petri net, in which all monitors designed in the first step are merged into a single monitor. The third step addresses the deadlock control problems caused by resource failures. For all resource failures in the Petri net model a common recovery subnet based on colored petri net is proposed. The common recovery subnet is added to the obtained system at the second step to make the system reliable. The proposed approach is evaluated using an AMS from the literature. The results show that the proposed approach can be applied to an unreliable complex Petri net model, has a simpler structure and less computational complexity, and can obtain one common recovery subnet to model all resource failures.

Keywords: automated manufacturing system, colored Petri net, deadlocks, siphon

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Authors: Asma Ameur, Dhafer Malouche

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Hotel competitiveness research is an essential phase of the marketing strategy for any hotel. Certainly, knowing the hotels' competitors helps the hotelier to grasp its position in the market and the citizen to make the right choice in picking a hotel. Thus, competitiveness is an important indicator that can be influenced by various factors. In fact, the issue of competitiveness, this ability to cope with competition, remains a difficult and complex concept to define and to exploit. Therefore, the purpose of this article is to make an exploratory study to calculate a competitiveness indicator for hotels. Further on, this paper makes it possible to determine the criteria of direct or indirect effect on the image and the perception of a hotel. The actual research is used to look into the right model for hotel ‘competitiveness. For this reason, we exploit different theoretical contributions in the field of machine learning. Thus, we use some statistical techniques such as the Principal Component Analysis (PCA) to reduce the dimensions, as well as other techniques of statistical modeling. This paper presents a survey covering of the techniques and methods in hotel competitiveness research. Furthermore, this study allows us to deduct the significant variables that influence the determination of hotel’s competitors. Lastly, the discussed experiences in this article found that the hotel competitors are influenced by several factors with different rates.

Keywords: competitiveness, e-reputation, hotels' competitors, online hotel’ review, principal component analysis, statistical modeling

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Authors: Luna Degife, Desalegn Belay, Yoseph Worku, Tigist Tesfaye, Assefa Tufa, Abyot Bekele, Zegeye Hailemariam, Abay Hagos

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Half of the world’s population is at risk of Dengue Fever (DF), a highly under-recognized and underreported mosquito-borne viral disease with high prevalence in the tropical and subtropical regions. Globally, an estimated 50 to 200 million cases and 20, 000 DF deaths occur annually as per the world health organization report. In Ethiopia, the first outbreak occurred in 2013 in Diredawa administration city. Afterward, three outbreaks have been reported from the eastern part of the country. We received a report of the fifth DF outbreak for Ethiopia and the second for Diredawa city on October 4, 2015. We conducted the investigation to confirm the outbreak, identify the risk factors for the repeatedly occurrence of the disease and implement control measures. We conducted un- matched case-control study and defined a suspected DF case as any person with fever of 2-7 days and 2 or more of the following: a headache, arthralgia, myalgia, rash, or bleeding from any part of the body. Controls were residents of Diredawa city without DF symptoms. We interviewed 70 Cases and 140 controls from all health facilities in Diredawa city from October 7 to 15; 2015. Epi Info version 7.1.5.0 was used to analyze the data and multivariable logistic regression was conducted to assess risk factors for DF. Sixty-nine blood samples were collected for Laboratory confirmation.The mean age for cases was 23.7±9.5 standard deviation (SD) and for controls 31.2±13 SD. Close contact with DF patient (Adjusted odds ratio (AOR)=5.36, 95% confidence interval(CI): 2.75-10.44), nonuse of long-lasting insecticidal nets (AOR=2.74, 95% CI: 1.06-7.08) and availability of stagnant water in the village (AOR=3.61, 95% CI:1.31-9.93) were independent risk factors associated with higher rates of the disease. Forty-two samples were tested positive. Endemicity of DF is becoming a concern for Diredawa city after the first outbreak. Therefore, effective vector control activities need to be part of long-term preventive measures.

Keywords: dengue fever, Diredawa, outbreak, risk factors, second

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Authors: Abe Degale D., Cheng Jian

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When creating automated video surveillance systems, violent action recognition is crucial. In recent years, hand-crafted feature detectors have been the primary method for achieving violence detection, such as the recognition of fighting activity. Researchers have also looked into learning-based representational models. On benchmark datasets created especially for the detection of violent sequences in sports and movies, these methods produced good accuracy results. The Hockey dataset's videos with surveillance camera motion present challenges for these algorithms for learning discriminating features. Image recognition and human activity detection challenges have shown success with deep representation-based methods. For the purpose of detecting violent images and identifying aggressive human behaviours, this research suggested a deep representation-based model using the transfer learning idea. The results show that the suggested approach outperforms state-of-the-art accuracy levels by learning the most discriminating features, attaining 99.34% and 99.98% accuracy levels on the Hockey and Movies datasets, respectively.

Keywords: violence detection, faster RCNN, transfer learning and, surveillance video

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Authors: Caspar S. Carson, Gabriel W. Prather, Nicholas E. Wong, Jeffery R. Anton, William H. McCoy

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Cutibacterium acnes is a commensal bacterium found on human skin that has been linked to acne. C. acnes can also be an opportunistic pathogen when it infiltrates the body during surgery. This pathogen can cause dangerous infections of medical implants, such as shoulder replacements, leading to life-threatening blood infections. Compounding this issue, C. acnes resistance to many antibiotics has become an increasing problem worldwide, creating a need for special forms of treatment. C. acnes expresses the protein RoxP, and it requires this protein to colonize human skin. Though this protein is required for C. acnes skin colonization, its function is not yet understood. Inhibition of RoxP function might be an effective treatment for C. acnes infections. To develop such reagents, the McCoy Laboratory generated four unique anti-RoxP antibodies. Preliminary studies in the McCoy Lab have established that each antibody binds a distinct site on RoxP. To assess the potential of these antibodies as therapeutics, it is necessary to specifically characterize these antibody epitopes and evaluate them in assays that assess their ability to inhibit RoxP-dependent C. acnes growth. To provide material for these studies, an antibody expression construct, Fv-clasp(v2), was adapted to encode anti-RoxP antibody sequences. The author hypothesizes that this expression strategy can produce sufficient amounts of >95% pure antibody fragments for further characterization of these antibodies. Four anti-RoxP Fv-clasp(v2) expression constructs (pET vector-based) were transformed into E. coli BL21-Gold(DE3) cells and a small-scale expression and purification trial was performed for each construct to evaluate anti-RoxP Fv-clasp(v2) yield and purity. Successful expression and purification of these antibody constructs will allow for their use in structural studies, such as protein crystallography and cryogenic electron microscopy. Such studies would help to define the antibody binding sites on RoxP, which could then be leveraged in the development of certain methods to treat C. acnes infection through RoxP inhibition.

Keywords: structural biology, protein expression, infectious disease, antibody, therapeutics, E. coli

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Authors: Ayliana Dharmawan, Heng Yong Sheng, Zhang Xiaojin, Tan Thai Lian

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To facilitate discharge planning, doctors are presently required to assign an Estimated Discharge Date (EDD) for each patient admitted to the hospital. This assignment of the EDD is largely based on the doctor’s judgment. This can be difficult for cases which are complex or relatively new to the doctor. It is hypothesized that a data-driven approach would be able to facilitate the doctors to make accurate estimations of the discharge date. Making use of routinely collected data on inpatient discharges between January 2013 and May 2016, a predictive model was developed using machine learning techniques to predict the Length of Stay (and hence the EDD) of inpatients, at the point of admission. The predictive performance of the model was compared to that of the clinicians using accuracy measures. Overall, the best performing model was found to be able to predict EDD with an accuracy improvement in Average Squared Error (ASE) by -38% as compared to the first EDD determined by the present method. It was found that important predictors of the EDD include the provisional diagnosis code, patient’s age, attending doctor at admission, medical specialty at admission, accommodation type, and the mean length of stay of the patient in the past year. The predictive model can be used as a tool to accurately predict the EDD.

Keywords: inpatient, estimated discharge date, EDD, prediction, data-driven

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Authors: Imine Zakaria, Meftah Sidi Mohamed El Amine

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One of the most vital aerodynamic organs of a flying machine is the wing, which allows it to fly in the air efficiently. The flow around the wing is very sensitive to changes in the angle of attack. Beyond a value, there is a phenomenon of the boundary layer separation on the upper surface, which causes instability and total degradation of aerodynamic performance called a stall. However, controlling flow around an airfoil has become a researcher concern in the aeronautics field. There are two techniques for controlling flow around a wing to improve its aerodynamic performance: passive and active controls. Blowing and suction are among the active techniques that control the boundary layer separation around an airfoil. Their objective is to give energy to the air particles in the boundary layer separation zones and to create vortex structures that will homogenize the velocity near the wall and allow control. Blowing and suction have long been used as flow control actuators around obstacles. In 1904 Prandtl applied a permanent blowing to a cylinder to delay the boundary layer separation. In the present study, several numerical investigations have been developed to predict a turbulent flow around an aerodynamic profile. CFD code was used for several angles of attack in order to validate the present work with that of the literature in the case of a clean profile. The variation of the lift coefficient CL with the momentum coefficient

Keywords: CFD, control flow, lift, slot

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Authors: Fei Gao, Rodolfo C. Raga Jr.

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This research proposal will ascertain the major risk factors for diabetes and to design a predictive model for risk assessment. The project aims to improve diabetes early detection and management by utilizing data science techniques, which may improve patient outcomes and healthcare efficiency. The phase relation values of each attribute were used to analyze and choose the attributes that might influence the examiner's survival probability using Diabetes Health Indicators Dataset from Kaggle’s data as the research data. We compare and evaluate eight machine learning algorithms. Our investigation begins with comprehensive data preprocessing, including feature engineering and dimensionality reduction, aimed at enhancing data quality. The dataset, comprising health indicators and medical data, serves as a foundation for training and testing these algorithms. A rigorous cross-validation process is applied, and we assess their performance using five key metrics like accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). After analyzing the data characteristics, investigate their impact on the likelihood of diabetes and develop corresponding risk indicators.

Keywords: diabetes, risk factors, predictive model, risk assessment, data science techniques, early detection, data analysis, Kaggle

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Authors: Khaled M. Alhawiti

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This research study aims to present a retrospective study about speech recognition systems and artificial intelligence. Speech recognition has become one of the widely used technologies, as it offers great opportunity to interact and communicate with automated machines. Precisely, it can be affirmed that speech recognition facilitates its users and helps them to perform their daily routine tasks, in a more convenient and effective manner. This research intends to present the illustration of recent technological advancements, which are associated with artificial intelligence. Recent researches have revealed the fact that speech recognition is found to be the utmost issue, which affects the decoding of speech. In order to overcome these issues, different statistical models were developed by the researchers. Some of the most prominent statistical models include acoustic model (AM), language model (LM), lexicon model, and hidden Markov models (HMM). The research will help in understanding all of these statistical models of speech recognition. Researchers have also formulated different decoding methods, which are being utilized for realistic decoding tasks and constrained artificial languages. These decoding methods include pattern recognition, acoustic phonetic, and artificial intelligence. It has been recognized that artificial intelligence is the most efficient and reliable methods, which are being used in speech recognition.

Keywords: speech recognition, acoustic phonetic, artificial intelligence, hidden markov models (HMM), statistical models of speech recognition, human machine performance

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Authors: Duyen Lam, Thuong Hoang, Atul Sajjanhar, Feifei Chen

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With the emerging interactive technology applications helping users connect progressively with cultural artefacts in new approaches, the cultural heritage sector gains significantly. The interactive apps’ issues can be tested via several techniques, including usability surveys and usability evaluations. The severe usability problems for museums’ interactive technologies commonly involve interactions, control, and navigation processes. This study confirms the low quality of being immersive for audio guides in navigating the exhibition and involving experience in the virtual environment, which are the most vital features of new interactive technologies such as AR and VR. In addition, our usability surveys and heuristic evaluations disclosed many usability issues of these interactive technologies relating to interaction functions. Additionally, we use the Wayback Machine to examine what interactive apps/technologies were deployed on these websites during the physical visits limited due to the COVID-19 pandemic lockdown. Based on those inputs, we propose the reflection framework to enhance the UX in the cultural heritage domain with detailed guidelines.

Keywords: framework, user experience, cultural heritage, interactive technology, museum, COVID-19 pandemic, usability survey, heuristic evaluation, guidelines

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Authors: Jacqueline Rose T. Alipo-on, Francesca Isabelle F. Escobar, Myles Joshua T. Tan, Hezerul Abdul Karim, Nouar Al Dahoul

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Electrocardiogram (ECG) signal analysis and processing are crucial in the diagnosis of cardiovascular diseases, which are considered one of the leading causes of mortality worldwide. However, the traditional rule-based analysis of large volumes of ECG data is time-consuming, labor-intensive, and prone to human errors. With the advancement of the programming paradigm, algorithms such as machine learning have been increasingly used to perform an analysis of ECG signals. In this paper, various deep learning algorithms were adapted to classify five classes of heartbeat types. The dataset used in this work is the synthetic MIT-BIH Arrhythmia dataset produced from generative adversarial networks (GANs). Various deep learning models such as ResNet-50 convolutional neural network (CNN), 1-D CNN, and long short-term memory (LSTM) were evaluated and compared. ResNet-50 was found to outperform other models in terms of recall and F1 score using a five-fold average score of 98.88% and 98.87%, respectively. 1-D CNN, on the other hand, was found to have the highest average precision of 98.93%.

Keywords: heartbeat classification, convolutional neural network, electrocardiogram signals, generative adversarial networks, long short-term memory, ResNet-50

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Authors: Sonia Perez-Gamboa, Qingquan Sun, Yan Zhang

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Non-intrusive sensor-based human activity recognition (HAR) is utilized in a spectrum of applications, including fitness tracking devices, gaming, health care monitoring, and smartphone applications. Deep learning models such as convolutional neural networks (CNNs) and long short term memory (LSTM) recurrent neural networks (RNNs) provide a way to achieve HAR accurately and effectively. In this paper, we design a multi-layer hybrid architecture with CNN and LSTM and explore a variety of multi-layer combinations. Based on the exploration, we present a lightweight, hybrid, and multi-layer model, which can improve the recognition performance by integrating local features and scale-invariant with dependencies of activities. The experimental results demonstrate the efficacy of the proposed model, which can achieve a 94.7% activity recognition rate on a benchmark human activity dataset. This model outperforms traditional machine learning and other deep learning methods. Additionally, our implementation achieves a balance between recognition rate and training time consumption.

Keywords: deep learning, LSTM, CNN, human activity recognition, inertial sensor

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Authors: Diogo José Morgado Rebelo, Francisco António Carneiro Pacheco de Andrade, Paulo Jorge Freitas de Oliveira Novais

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Not everything in AI-power consumer credit scoring turns out to be a wonder. When using AI in Creditworthiness Assessment (CWA), opacity and unfairness ‘sins’ must be considered to the task be deemed Responsible. AI software is not always 100% accurate, which can lead to misclassification. Discrimination of some groups can be exponentiated. A hetero personalized identity can be imposed on the individual(s) affected. Also, autonomous CWA sometimes lacks transparency when using black box models. However, for this intended purpose, human analysts ‘on-the-loop’ might not be the best remedy consumers are looking for in credit. This study seeks to explore the legality of implementing a Multi-Agent System (MAS) framework in consumer CWA to ensure compliance with the regulation outlined in Article 14(4) of the Proposal for an Artificial Intelligence Act (AIA), dated 21 April 2021 (as per the last corrigendum by the European Parliament on 19 April 2024), Especially with the adoption of Art. 18(8)(9) of the EU Directive 2023/2225, of 18 October, which will go into effect on 20 November 2026, there should be more emphasis on the need for hybrid oversight in AI-driven scoring to ensure fairness and transparency. In fact, the range of EU regulations on AI-based consumer credit will soon impact the AI lending industry locally and globally, as shown by the broad territorial scope of AIA’s Art. 2. Consequently, engineering the law of consumer’s CWA is imperative. Generally, the proposed MAS framework consists of several layers arranged in a specific sequence, as follows: firstly, the Data Layer gathers legitimate predictor sets from traditional sources; then, the Decision Support System Layer, whose Neural Network model is trained using k-fold Cross Validation, provides recommendations based on the feeder data; the eXplainability (XAI) multi-structure comprises Three-Step-Agents; and, lastly, the Oversight Layer has a 'Bottom Stop' for analysts to intervene in a timely manner. From the analysis, one can assure a vital component of this software is the XAY layer. It appears as a transparent curtain covering the AI’s decision-making process, enabling comprehension, reflection, and further feasible oversight. Local Interpretable Model-agnostic Explanations (LIME) might act as a pillar by offering counterfactual insights. SHapley Additive exPlanation (SHAP), another agent in the XAI layer, could address potential discrimination issues, identifying the contribution of each feature to the prediction. Alternatively, for thin or no file consumers, the Suggestion Agent can promote financial inclusion. It uses lawful alternative sources such as the share of wallet, among others, to search for more advantageous solutions to incomplete evaluation appraisals based on genetic programming. Overall, this research aspires to bring the concept of Machine-Centered Anthropocentrism to the table of EU policymaking. It acknowledges that, when put into service, credit analysts no longer exert full control over the data-driven entities programmers have given ‘birth’ to. With similar explanatory agents under supervision, AI itself can become self-accountable, prioritizing human concerns and values. AI decisions should not be vilified inherently. The issue lies in how they are integrated into decision-making and whether they align with non-discrimination principles and transparency rules.

Keywords: creditworthiness assessment, hybrid oversight, machine-centered anthropocentrism, EU policymaking

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Authors: Neslihan Taskale Karatug, Mustafa Akcelik

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Some literature data show that misL protein play a role on host immune response formed against Salmonella Typhimurium. The aim of the present study is to learn the role of the protein in S. Typhimurium pathogenicity. To describe certain functions of the protein, primarily recombinant misL protein was produced and purified. PCR was performed using a primer set targeted to passenger domain of the misL gene on S. Typhimurium LT2 genome. Amplicon and pet28a vector were enzymatically cleaved with EcoRI and NheI. The digested DNA materials were purified with High Pure PCR Product Purification Kit. The ligation reaction was achieved with the pure products. After preparation of competent Escherichia coli Dh5α, ligation mix was transformed into the cell by electroporation. To confirm the existence of insert gene, recombinant plasmid DNA of Dh5α was isolated with high pure plasmid DNA kit. Proved the correctness of recombinant plasmid was electroporated to BL21. The cell was induced by IPTG. After induction, the presence of recombinant protein was checked by SDS-PAGE. The recombinant misL protein was purified using HisPur Ni-NTA spin colon. The pure protein was shown by SDS-PAGE and western blot immünoassay. The concentration of the protein was measured BCA Protein Assay kit. In the wake of ligation with digested products (2 kb misL and 5.4 kb pet28a) visualised on gel size of the band was about 7.4 kb and was named as pNT01. The pNT01 recombinant plasmid was transformed into Dh5α and colonies were chosen in selective medium. Plasmid DNA isolation from them was carried out. PCR was achieved on the pNT01 to check misL and 2 kb band was observed on the agarose gel. After electroporation of the plasmid and induction of the cell, 68 kDa misL protein was seen. Subsequent to the purification of the protein, only a band was observed on SDS-PAGE. Association of the pure protein with anti-his antibody was verified by the western blot assay. The concentration of the pure misL protein was determined as 345 μg/mL. Production of polyclonal antibody will be achieved by using the obtained pure recombinant misL protein as next step. The role of the protein will come out on the immune system together some assays.

Keywords: cloning, Escherichia coli, recombinant protein purification, Salmonella Typhimurium

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Authors: Md Serajul Islam

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High Dose Rate (HDR) Brachytherapy is used for cancer patients. In our country’s prospect, we are using only cervices and breast cancer treatment by using HDR. The air kerma rate in air at a reference distance of less than a meter from the source is the recommended quantity for the specification of gamma ray source Ir-192 in brachytherapy. The absorbed dose for the patients is directly proportional to the air kerma rate. Therefore the air kerma rate should be determined before the first use of the source on patients by qualified medical physicist who is independent from the source manufacturer. The air kerma rate will then be applied in the calculation of the dose delivered to patients in their planning systems. In practice, high dose rate (HDR) Ir-192 afterloader machines are mostly used in brachytherapy treatment. Currently, HDR-Co-60 increasingly comes into operation too. The essential advantage of the use of Co-60 sources is its longer half-life compared to Ir-192. The use of HDRCo-60 afterloading machines is also quite interesting for developing countries. This work describes the dosimetry at HDR afterloading machines according to the protocols IAEA-TECDOC-1274 (2002) with the nuclides Ir-192 and Co-60. We have used 3 different measurement methods (with a ring chamber, with a solid phantom and in free air and with a well chamber) in dependence of each of the protocols. We have shown that the standard deviations of the measured air kerma rate for the Co-60 source are generally larger than those of the Ir-192 source. The measurements with the well chamber had the lowest deviation from the certificate value. In all protocols and methods, the deviations stood for both nuclides by a maximum of about 1% for Ir-192 and 2.5% for Co-60-Sources respectively.

Keywords: Ir-192 source, cancer, patients, cheap treatment cost

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Authors: Vivek Krishna, Nikhil Jain, A. Mary Posonia A., Albert Mayan J

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Terrorism and insurgency are significant global issues that require constant attention and effort from governments and scientists worldwide. To combat these threats, nations invest billions of dollars in developing new defensive technologies to protect civilians. Breakthroughs in vehicle automation have led to the use of sophisticated machines for many dangerous and critical anti-terrorist activities. Our concept of an "Unmanned Ground Vehicle" can carry out tasks such as border security, surveillance, mine detection, and active combat independently or in tandem with human control. The robot's movement can be wirelessly controlled by a person in a distant location or can travel to a pre-programmed destination autonomously in situations where personal control is not feasible. Our defence system comprises two units: the control unit that regulates mobility and the motion tracking unit. The remote operator robot uses the camera's live visual feed to manually operate both units, and the rover can automatically detect movement. The rover is operated by manpower who controls it using a joystick or mouse, and a wireless modem enables a soldier in a combat zone to control the rover via an additional controller feature.

Keywords: robotics, computer vision, Machine learning, Artificial intelligence, future of AI

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Authors: Owen Harrington-Fernandez, Pilar Alderete-Diez

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Up until recently, translation studies treated children’s literature as something of a marginal preoccupation, but the recent attention that this text type has attracted suggests that it may be fertile ground for research. This paper contributes to this new research avenue by applying a sociolinguistic theoretical framework to explore issues around the intersubjective co-construction of identity in the American English translation of the Spanish children’s story, Manolito Gafotas. The application of Bucholtz and Hall’s framework achieves two objectives: (1) it identifies shifts in the translation of the main character’s behaviour as culturally and morally motivated manipulations, and (2) it demonstrates how the context of translation becomes the very censorship machine that delegitimises the identity of the main character, and, concomitantly, the identity of the implied reader(s). If we take identity to be an intersubjective phenomenon, then it logicall follows that expurgating the identity of the main character necessarily shifts the identity of the implied reader(s) also. It is a double censorship of identity carried out under the auspices of an intellectual colonisation of a Spanish text. After reporting on the results of the analysis, the paper ends by raising the question of censorship in translation, and, more specifically, in children’s literature, in order to promote debate around this topic.

Keywords: censorship, identity, sociolinguistics, translation

Procedia PDF Downloads 248