Asian ICT Human Resources Development: the Missing Bridge

Kanchana Kanchanasut 정보통신정책연구원 2003 세미나및공청회 Vol.2003 No.

Plasmodium vivax Drug Resistance Genes; Pvmdr1 and Pvcrt-o Polymorphisms in Relation to Chloroquine Sensitivity from a Malaria Endemic Area of Thailand

Kanchana Rungsihirunrat,Poonuch Muhamad,Wanna Chaijaroenkul,Jiraporn Kuesap,Kesara Na-Bangchang 대한기생충학열대의학회 2015 The Korean Journal of Parasitology Vol.53 No.1

Internet of Cars through Commodity V2V and V2X Mobile Routers: Applications for Developing Countries

Kanchana Kanchanasut,Saroch Boonsiripant,Apinun Tunpan,김회경,Mongkol Ekpanyapong 대한토목학회 2015 KSCE Journal of Civil Engineering Vol.19 No.6

In developing countries, government agencies focus its transportation system developments on building more roads and bridge structures to alleviate the traffic, undermining the idea of using Intelligent Transportation Systems to maximize the capacity of existing infrastructure. When the public sectors perform poorly in managing traffic, a decentralized traffic management approach seems to be more appropriate. The vehicle-to-vehicle and vehicle-to-any communication systems are among the top choices in the decentralized system. Despite the potential benefits of V2V and V2X communication applications, there are many challenges in implementing them in developing countries. We identified the obstacles in various aspects and then designed the system within the framework of such constraints. Also, we envisioned the deployment of small, commercially available off-the-shelf routers which rely on open Internet standards, and used one or more unlicensed frequencies followed by setting a simple experiment up. We have successfully demonstrated that V2V mobile routers can be built from commodity components which are available in developing countries without depending on regulated radio frequencies hence achieving the freedom to use these V2V mobile routers almost everywhere. Since smart devices such as Smartphones and tablets can connect to the proposed mobile routers, there are endless possibilities to develop useful applications.

A Deep Learning Model for Predicting User Personality Using Social Media Profile Images

Kanchana, T.S.,Zoraida, B.S.E. International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.11

Social media is a form of communication based on the internet to share information through content and images. Their choice of profile images and type of image they post can be closely connected to their personality. The user posted images are designated as personality traits. The objective of this study is to predict five factor model personality dimensions from profile images by using deep learning and neural networks. Developed a deep learning framework-based neural network for personality prediction. The personality types of the Big Five Factor model can be quantified from user profile images. To measure the effectiveness, proposed two models using convolution Neural Networks to classify each personality of the user. Done performance analysis among two different models for efficiently predict personality traits from profile image. It was found that VGG-69 CNN models are best performing models for producing the classification accuracy of 91% to predict user personality traits.

Product Innovation Accounting, Customer Response Capability and Market Success: An Empirical Investigation in Thailand

Kanchana SUKANTHASIRIKUL,Kornchai PHORNLAPHATRACHAKORN 한국유통과학회 2021 The Journal of Asian Finance, Economics and Busine Vol.8 No.10

This study aims at investigating the effect of product innovation accounting on the market success of instant food and convenience food businesses in Thailand with customer response capability as the mediator. In addition, it examines the effects of management accounting systems, marketing intelligence, and technology orientation on product innovation accounting. The sample for this study is 258 instant food and convenience food businesses in Thailand. To test the research relationships, a structural equation model is used. The results of this study show that product innovation accounting has a significant effect on both customer response capability and market success. Similarly, customer response capability significantly leads to market success while it mediates the product innovation accounting-market success relationship. Testing the antecedents of the research relationships, management accounting system, marketing intelligence, and technology orientation potentially affect product innovation accounting. Accordingly, product innovation accounting is a key source of competitive advantage. Product innovation accounting must be recognized by company management as a strategic tool for competing in markets and environments. They must invest their resources and capabilities to create and develop product innovation accounting principles, as well as encouraging their staff to implement and use these principles in the workplace.

Differentiation of Homogenous and Speckled Objects in HEp-2 Specimen Images using Keypoint Features and an Adaptive Cuckoo Search Algorithm

D. Kanchana,G. Nagarajan,S. Ramakrishnan 대한전자공학회 2019 IEIE Transactions on Smart Processing & Computing Vol.8 No.1

In this work, an attempt has been made to differentiate human epithelial type-2 (HEp-2) specimen images using Bag of Features (BoF) and Adaptive Cuckoo Search (ACS) feature selection. For this, 420 images consisting of homogenous and speckled patterns were obtained from a publicly available International Conference on Pattern Recognition (ICPR) 2016 database. These images are preprocessed using edge-aware local contrast enhancement and subjected to a speededup robust feature (SURF) descriptor for feature extraction. The optimal features are identified using the ACS method and are then fed into a support vector machine (SVM) for classification. The results show that the proposed approach is able to distinguish homogenous and speckled patterns. It is found that the features identified using ACS-based feature selection are significant. The proposed approach yields an average accuracy of 97.90% using the SVM classifier. Because automated analysis and classification of HEp-2 specimen images is important for the diagnosis of autoimmune diseases, this study seems to be clinically relevant.

The Power of Living in the Present Moment among Patients with Diabetes

Thearmtanachok, Kanchana Center for Asian Public Opinion ResearchCollaborat 2015 Asian journal for public opinion research Vol.2 No.2

"Living in the present moment," a Buddhist concept, was applied in this research. This concept urges the patients to cling neither to the past nor the future as well as being mindful of their body, feelings, mind, and mental qualities. The purpose of the study was to develop a "living in the present moment" model and to evaluate the power of "living in the present moment" in terms of physical and mental results. The study used non-participatory action research with quasi-experimental research design that included 3 camps composed of 6 main activities. The percentages, SD, and paired t-test statistics were used to analyze and compare 17 purposively selected diabetic patients from Pak Thong Chai Hospital before and after they attended the 3 camps. The patients improved significantly in terms of waistline, body weight, body mass index (BMI) and blood pressure (SBP and DBP). The mean of fasting plasma glucose (FPG) level was also changed considerably. The results revealed that the treatment helped the patients to gain self-awareness and self-realization (Yonisomanasikara), as well as knowledge and increased support from friends (Kalyanamitta). They also let go of their attachment to their physical and mental oppressions. This helped the patients to relieve their daily pain, fatigue, insomnia, and diabetes-related complications. About 75% of all patients were able to achieve lifestyle modifications. Therefore, implementation of the model should be expanded and utilized in other diabetic centers. The model might also be expanded to pre-diabetes.

Ischemic stroke lesion detection, characterization and classification in CT images with optimal features selection

R. Kanchana,R. Menaka 대한의용생체공학회 2020 Biomedical Engineering Letters (BMEL) Vol.10 No.3

Ischemic stroke is the dominant disorder for mortality and morbidity. For immediate diagnosis and treatment plan of ischemicstroke, computed tomography (CT) images are used. This paper proposes a histogram bin based novel algorithm to segmentthe ischemic stroke lesion using CT and optimal feature group selection to classify normal and abnormal regions. Stepsfollowed are pre-processing, segmentation, extracting texture features, feature ranking, feature grouping, classifi cation andoptimal feature group (FG) selection. The fi rst order features, gray level run length matrix features, gray level co-occurrencematrix features and Hu’s moment features are extracted. Classifi cation is done using logistic regression (LR), support vectormachine classifi er (SVMC), random forest classifi er (RFC) and neural network classifi er (NNC). This proposed approacheff ectively detects ischemic stroke lesion with a classifi cation accuracy of 88.77%, 97.86%, 99.79% and 99.79% obtained bythe LR, SVMC, RFC and NNC when FG12 is opted, which is validated by fourfold cross validation.

Hybrid Genetic Algorithms for Solving Reentrant Flow-Shop Scheduling with Time Windows

Chettha Chamnanlor,Kanchana Sethanan,Chen-Fu Chien,Mitsuo Gen 대한산업공학회 2013 Industrial Engineeering & Management Systems Vol.12 No.4

The semiconductor industry has grown rapidly, and subsequently production planning problems have raised many important research issues. The reentrant flow-shop (RFS) scheduling problem with time windows constraint for harddisk devices (HDD) manufacturing is one such problem of the expanded semiconductor industry. The RFS scheduling problem with the objective of minimizing the makespan of jobs is considered. Meeting this objective is directly related to maximizing the system throughput which is the most important of HDD industry requirements. Moreover, most manufacturing systems have to handle the quality of semiconductor material. The time windows constraint in the manufacturing system must then be considered. In this paper, we propose a hybrid genetic algorithm (HGA) for improving chromosomes/offspring by checking and repairing time window constraint and improving offspring by leftshift routines as a local search algorithm to solve effectively the RFS scheduling problem with time windows constraint. Numerical experiments on several problems show that the proposed HGA approach has higher search capability to improve quality of solutions.

Baclofen-induced Changes in the Resting Brain Modulate Smoking Cue Reactivity: A Double-blind Placebo-controlled Functional Magnetic Resonance Imaging Study in Cigarette Smokers

Ariel Ketcherside,Kanchana Jagannathan,Sudipto Dolui,Nathan Hager,Nathaniel Spilka,Chaela Nutor,Hengyi Rao,Teresa Franklin,Reagan Wetherill 대한정신약물학회 2020 CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE Vol.18 No.2

Objective: Smoking cue-(SC) elicited craving can lead to relapse in SC-vulnerable individuals. Thus, identifying treatments that target SC-elicited craving is a top research priority. Reduced drug cue neural activity is associated with recovery and is marked by a profile of greater tonic (resting) activation in executive control regions, and increased connectivity between executive and salience regions. Evidence suggests the GABA-B agonist baclofen can reduce drug cue-elicited neural activity, potentially through its actions on the resting brain. Based on the literature, we hypothesize that baclofen’s effects in the resting brain can predict its effects during SC exposure. Methods: In this longitudinal, double blind, placebo-controlled neuropharmacological study 43 non-abstinent, sated treatment-seeking cigarette smokers (63% male) participated in an fMRI resting-state scan and a SC-reactivity task prior to (T1) and 3 weeks following randomization (T2; baclofen: 80 mg/day; n = 21). Subjective craving reports were acquired before and after SC exposure to explicitly examine SC-induced craving. Results: Whole-brain full-factorial analysis revealed a group-by-time interaction with greater resting brain activation of the right dorsolateral prefrontal cortex (dlPFC) at T2 in the baclofen group (BAC) (pFWEcorr = 0.02), which was associated with reduced neural responses to SCs in key cue-reactive brain regions; the anterior ventral insula and ventromedial prefrontal cortex (pFWEcorr < 0.01). BAC, but not the placebo group reported decreased SC-elicited craving (p = 0.02). Conclusion: Results suggest that baclofen mitigates the reward response to SCs through an increase in tonic activation of the dlPFC, an executive control region. Through these mechanisms, baclofen may offer SC-vulnerable smokers protection from SC-induced relapse.