• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.79-86
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• 2021

Alzheimer's is a chronic progressive disease which exhibits varied symptoms and behavioural traits from person to person. The deterioration in cognitive abilities is more noticeable through their Activities and Instrumental Activities of Daily Living rather than biological markers. This information discussed in social media communities was collected and features were extracted by using the proposed fuzzy logic based algorithm to address the uncertainties and imprecision in the data reported. The data thus obtained is used to train machine learning models in order to predict the longevity of the patients. Models built on features extracted using the proposed algorithm performs better than models trained on full set of features. Important findings are discussed and Support Vector Regressor with RBF kernel is identified as the best performing model in predicting the longevity of Alzheimer's patients. The results would prove to be of high value for healthcare practitioners and palliative care providers to design interventions that can alleviate the trauma faced by patients and caregivers due to chronic diseases.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.411-422
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• 2021

Technological adaption and innovative activities foster small and medium enterprises (SMEs) growth, especially women-owned SMEs in Pakistan, However, the impact of technological adaption and innovative activities on SMEs growth in the context of Pakistan has been examined by very researchers. This study aims to identify the effect of technology and open innovation policies on the growth of women-owned SMEs and the present trends and management challenges for successful full implementation of open innovation. The study considered a sample of 693 women enterprises located in different cities in Pakistan. Open innovation is measured through eight innovative practices, reflecting the exploration and exploitation of technology in SMEs. Study findings revealed that women enterprises were involved in several open innovation policies during the last five years. Moreover, the study indicated no significant differences between manufacturing and service SMEs regarding open innovation practices; however, women enterprises are more impressively engaged in open innovation practices. Findings also reveal that women-owned SMEs follow open innovation, mainly for market-related intentions, to compete with competitors and meet customers' demands. Thus, it is suggested that government policy relating to thriving SMEs owned by women should be innovation-oriented. The study contributes to the theoretical and practical implications. Further, the study is helpful for SMEs, researchers, practitioners, and decision-makers.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.319-344
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• 2020

Inverse analysis of non-linear reinforced concrete bridge pier using recursive Gaussian filtering for in-situ condition assessment is the main theme of this work. For this purpose, minimum variance unbiased estimation using unscented sigma points is adopted here. The uniqueness of this inverse analysis lies in its approach for strain based updating of engineering demand parameters, where appropriate bound and constrained conditions are introduced to ensure numerical stability and convergence. In this analysis, seismic input is also identified, which is an added advantage for the structures having no dedicated sensors for earthquake measurement. First, the proposed strategy is tested with a simulated example whose hysteretic properties are obtained from the slow-cyclic test of a frame to investigate its efficiency and accuracy. Finally, the experimental test data of a full-scale bridge pier is used to study its in-situ condition in terms of Park & Ang damage index. Overall the study shows the ability of the augmented minimum variance unbiased estimation based recursive time-marching algorithm for non-linear system identification with the aim to estimate the engineering damage parameters that are the fundamental information necessary for any future decision making for retrofitting/rehabilitation.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.89-100
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• 2021

Smart Grid Network (SGN) is a next generation electrical power network which digitizes the power distribution grid and achieves smart, efficient, safe and secure operations of the electricity. The backbone of the SGN is information communication technology that enables the SGN to get full control of network station monitoring and analysis. In any network where communication is involved security is essential. It has been observed from several recent incidents that an adversary causes an interruption to the operation of the networks which lead to the electricity theft. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we have introduced a machine learning based SVM method that detects malicious nodes in a smart grid network. The algorithm collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) classifies nodes into Normal or malicious nodes giving the statues of 1 for normal nodes and status of -1 for malicious -abnormal-nodes. Once the malicious nodes have been detected, we have done a trust evaluation based on the nodes history and recorded data. In the simulation, we have observed that our detection rate is almost 98% where the false alarm rate is only 2%. Moreover, a Trust value of 50 was achieved. As a future work, countermeasures based on the trust value will be developed to solve the problem remotely.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.2
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• pp.262-268
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• 2019

Previous dynamic models of hysteresis motor use an extended induction machine equivalent circuit or somewhat different equivalent circuit with conventional one, which makes unsatisfiable results. In this paper, the hysteresis dynamic characteristics of the motor rotor are analyzed using the inverse Preisach model and the hysteresis motor equivalent circuit considering eddy current effect. The hysteresis loop for the rotor ring is analyzed under full-load voltage source static state. The calculated hysteresis loop is then approximated to an ellipse for simplicity of dynamic computation. The permeability and delay angle of the elliptic loop apply to the dynamic analysis model. As a result, it is possible to dynamically analyze the hysteresis motor according to the applied voltage and the rotor material. With this method, the motor speed, generated torque, load angle, rotor current as well as synchronous entry time, hunting effect can be calculated.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.717-729
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• 2021

This paper examines the efficacy of the default risk factor in an emerging market context using the Fama-French five-factor model. Our aim is to test whether the Fama-French five-factor model augmented with a default risk factor improves the predictability of returns of portfolios sorted on the firm's characteristics as well as on industry. The default risk factor is constructed by estimating the probability of default using a hybrid version of dynamic panel probit and artificial neural network (ANN) to proxy default risk. This study also provides evidence on the temporal stability of risk premiums obtained using the Fama-MacBeth approach. Using a sample of 3,806 firm-year observations on non-financial listed companies of Pakistan over 2006-2015 we found that the augmented model performed better when tested across size-investment-default sorted portfolios. The investment factor contains some default-related information, but default risk is independently priced and bears a significantly positive risk premium. The risk premiums are also found temporally stable over the full sample and more recent sample period 2010-2015 as evidence by the Fama-MacBeth regressions. The finding suggests that the default risk factor is not a useless factor and due to mispricing, default risk anomaly prevails in the Pakistani equity market.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.222-232
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• 2021

The power output of solar panels highly depends on environmental situations like weather and air pollution. Due to bad weather or air pollution, it is difficult for solar panels to operate at their full potential. Knowing the power output of solar panels in advance helps set up the solar panels correctly and work their possible potential. This paper presents an approach to predict the power output of solar panels based on weather and air pollution features using machine learning methods. We create machine learning models with three kinds set of features, such as weather, air pollution, and weather and air pollution. Our datasets are collected from the area of Seoul, South Korea, between 2017 and 2019. The experimental results show that the weather and air pollution features can be efficient factors to predict the power output of solar panels.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.124-129
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• 2021

As the 4th industrial era is in full swing, the public's interest in related technologies such as artificial intelligence, big data, and block chain is increasing. As artificial intelligence technology is used in various industrial fields, the need for research methods incorporating artificial intelligence technology in related fields is also increasing. Evidence collection among digital forensic investigation techniques is a very important procedure in the investigation process that needs to prove a specific person's suspicions. However, there may be cases in which evidence is damaged due to intentional damage to evidence or other physical reasons, and there is a limit to the collection of evidence in this situation. Therefore, this paper we intends to propose an artificial intelligence-based evidence collection system that analyzes numerous image files reported by citizens in real time to visually check the location, user information, and shooting time of the image files. When this system is applied, it is expected that the evidence expected data collected in real time can be actually used as evidence, and it is also expected that the risk area analysis will be possible through big data analysis.

• Journal of Information Technology Services
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• v.20 no.6
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• pp.125-139
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• 2021

To explore college students' course satisfaction over the course of the semester during which a full-scale digital transformation was in progress due to COVID-19 pandemic, this study analyzed student survey data from a university located in the metropolitan area. To minimize the respondents' burden to answer long list of detailed questions in repetition, the study utilized a pulse survey method and students were asked to answer a brief and regular set of online questions 5 times throughout the semester. The number of survey respondents ranged from 1,640 to 4,116, with an average of more than 3,700. The main results and implications of this study are summarized as follows. First, the survey data indicated that the overall student satisfaction with online courses was above average (3.46/5). Vast majority of students have chosen pre-recorded, contents-based course over real-time, video-based course as their preferred course delivery method and this tendency remained the same throughout the semester. Second, the results of keyword network analysis of open-ended questions indicated that technical issues, increased workload (e.g., course assignments and course attendance) were main causes of online course dissatisfaction. And students suggested an unified online course platform and more interactive course design to further improve online courses in the future.

• Journal of Interdisciplinary Genomics
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• v.4 no.1
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• pp.15-18
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• 2022

Hemophilia A is a rare X-linked congenital deficiency of clotting factor VIII (FVIII) that is traditionally diagnosed by measuring FVIII activity. Various mutations of the FVIII gene have been reported and they influence on the FVIII protein structure. A deficiency of or reduction in FVIII protein manifests as spontaneous or induced bleeding depending on the disease severity. Mutations of the FVIII gene provide important information on the severity of disease and inhibitor development. FVIII mutations also affect the discrepant activities found using different FVIII assays. FVIII activity is affected differently depending on the mutation site. Long-range PCR is commonly used to detect intron 22 inversion, the most common mutation in severe hemophilia. However, point mutations are also common in patients with hemophilia, and direct Sanger sequencing and copy number variant analysis are being used to screen for full mutations in the FVIII gene. Advances in molecular genetic methods, such as next-generation sequencing, may enable accurate analysis of mutations in the factor VIII gene, which may be useful in the diagnosis of mild to moderate hemophilia. Genetic analysis is also useful in diagnosing carriers and managing bleeding control. This review discusses the current knowledge about mutations in hemophilia and focuses on the clinical aspects associated with these mutations and the importance of genetic analysis.