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Se Jin Kwon
Dept. of Computer Engineering, Kangwon National University, 346 Joongang-ro, 25913, Samcheok, Gangwon-do, South Korea

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Journal article
Published: 19 September 2020 in Human-centric Computing and Information Sciences
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Smart devices are effective in helping people with impairments, overcome their disabilities, and improve their living standards. Braille is a popular method used for communication by visually impaired people. Touch screen smart devices can be used to take Braille input and instantaneously convert it into a natural language. Most of these schemes require location-specific input that is difficult for visually impaired users. In this study, a position-free accessible touchscreen-based Braille input algorithm is designed and implemented for visually impaired people. It aims to place the least burden on the user, who is only required to tap those dots that are needed for a specific character. The user has input English Braille Grade 1 data (a–z) using a newly designed application. A total dataset comprised of 1258 images was collected. The classification was performed using deep learning techniques, out of which 70%–30% was used for training and validation purposes. The proposed method was thoroughly evaluated on a dataset collected from visually impaired people using Deep Learning (DL) techniques. The results obtained from deep learning techniques are compared with classical machine learning techniques like Naïve Bayes (NB), Decision Trees (DT), SVM, and KNN. We divided the multi-class into two categories, i.e., Category-A (a–m) and Category-B (n–z). The performance was evaluated using Sensitivity, Specificity, Positive Predicted Value (PPV), Negative Predicted Value (NPV), False Positive Rate (FPV), Total Accuracy (TA), and Area under the Curve (AUC). GoogLeNet Model, followed by the Sequential model, SVM, DT, KNN, and NB achieved the highest performance. The results prove that the proposed Braille input method for touch screen devices is more effective and that the deep learning method can predict the user's input with high accuracy.

ACS Style

Sana Shokat; Rabia Riaz; Sanam Shahla Rizvi; Abdul Majid Abbasi; Adeel Ahmed Abbasi; Se Jin Kwon. Deep learning scheme for character prediction with position-free touch screen-based Braille input method. Human-centric Computing and Information Sciences 2020, 10, 1 -24.

AMA Style

Sana Shokat, Rabia Riaz, Sanam Shahla Rizvi, Abdul Majid Abbasi, Adeel Ahmed Abbasi, Se Jin Kwon. Deep learning scheme for character prediction with position-free touch screen-based Braille input method. Human-centric Computing and Information Sciences. 2020; 10 (1):1-24.

Chicago/Turabian Style

Sana Shokat; Rabia Riaz; Sanam Shahla Rizvi; Abdul Majid Abbasi; Adeel Ahmed Abbasi; Se Jin Kwon. 2020. "Deep learning scheme for character prediction with position-free touch screen-based Braille input method." Human-centric Computing and Information Sciences 10, no. 1: 1-24.

Research article
Published: 26 August 2020 in Mobile Information Systems
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Diagnosis of heart disease is a difficult job, and researchers have designed various intelligent diagnostic systems for improved heart disease diagnosis. However, low heart disease prediction accuracy is still a problem in these systems. For better heart risk prediction accuracy, we propose a feature selection method that uses a floating window with adaptive size for feature elimination (FWAFE). After the feature elimination, two kinds of classification frameworks are utilized, i.e., artificial neural network (ANN) and deep neural network (DNN). Thus, two types of hybrid diagnostic systems are proposed in this paper, i.e., FWAFE-ANN and FWAFE-DNN. Experiments are performed to assess the effectiveness of the proposed methods on a dataset collected from Cleveland online heart disease database. The strength of the proposed methods is appraised against accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and receiver operating characteristics (ROC) curve. Experimental outcomes confirm that the proposed models outperformed eighteen other proposed methods in the past, which attained accuracies in the range of 50.00–91.83%. Moreover, the performance of the proposed models is impressive as compared with that of the other state-of-the-art machine learning techniques for heart disease diagnosis. Furthermore, the proposed systems can help the physicians to make accurate decisions while diagnosing heart disease.

ACS Style

Ashir Javeed; Sanam Shahla Rizvi; Shijie Zhou; Rabia Riaz; Shafqat Ullah Khan; Se Jin Kwon. Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification. Mobile Information Systems 2020, 2020, 1 -11.

AMA Style

Ashir Javeed, Sanam Shahla Rizvi, Shijie Zhou, Rabia Riaz, Shafqat Ullah Khan, Se Jin Kwon. Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification. Mobile Information Systems. 2020; 2020 ():1-11.

Chicago/Turabian Style

Ashir Javeed; Sanam Shahla Rizvi; Shijie Zhou; Rabia Riaz; Shafqat Ullah Khan; Se Jin Kwon. 2020. "Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification." Mobile Information Systems 2020, no. : 1-11.

Journal article
Published: 28 July 2020 in Sustainability
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Ubiquitous online learning is continuing to expand, and the factors affecting success and educational sustainability need to be quantified. Procrastination is one of the compelling characteristics that students observe as a failure to achieve the weaker outcomes. Past studies have mainly assessed the behaviors of procrastination by describing explanatory work. Throughout this research, we concentrate on predictive measures to identify and forecast procrastinator students by using ensemble machine learning models (i.e., Logistic Regression, Decision Tree, Gradient Boosting, and Forest). Our results indicate that the Gradient Boosting autotuned is a predictive champion model of high precision compared to the other default and hyper-parameterized tuned models in the pipeline. The accuracy we enumerated for the VALIDATION partition dataset is 91.77 percent, based on the Kolmogorov–Smirnov statistics. Additionally, our model allows teachers to monitor each procrastinator student who interacts with the web-based e-learning platform and take corrective action on the next day of the class. The earlier prediction of such procrastination behaviors would assist teachers in classifying students before completing the task, homework, or mastery of a skill, which is useful and a path to developing a sustainable atmosphere for education or education for sustainable development.

ACS Style

Syed Muhammad Raza Abidi; Wu Zhang; Saqib Ali Haidery; Sanam Shahla Rizvi; Rabia Riaz; Hu Ding; Se Jin Kwon. Educational Sustainability through Big Data Assimilation to Quantify Academic Procrastination Using Ensemble Classifiers. Sustainability 2020, 12, 6074 .

AMA Style

Syed Muhammad Raza Abidi, Wu Zhang, Saqib Ali Haidery, Sanam Shahla Rizvi, Rabia Riaz, Hu Ding, Se Jin Kwon. Educational Sustainability through Big Data Assimilation to Quantify Academic Procrastination Using Ensemble Classifiers. Sustainability. 2020; 12 (15):6074.

Chicago/Turabian Style

Syed Muhammad Raza Abidi; Wu Zhang; Saqib Ali Haidery; Sanam Shahla Rizvi; Rabia Riaz; Hu Ding; Se Jin Kwon. 2020. "Educational Sustainability through Big Data Assimilation to Quantify Academic Procrastination Using Ensemble Classifiers." Sustainability 12, no. 15: 6074.

Research article
Published: 26 July 2020 in Mobile Information Systems
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Technology is advancing rapidly in present times. To serve as a useful and connected part of the community, everyone is required to learn and update themselves on innovations. Visually impaired people fall behind in this regard because of their inherent limitations. To involve these people as active participants within communities, technology must be modified for their facilitation. This paper provides a comprehensive survey of various user input schemes designed for the visually impaired for Braille to natural language conversion. These techniques are analyzed in detail with a focus on their accessibility and usability. Currently, considerable effort has been made to design a touch-screen input mechanism for visually impaired people, such as Braille Touch, Braille Enter, and Edge Braille. All of these schemes use location-specific input and challenge visually impaired persons to locate specified places on the touch screen. Most of the schemes require special actions to switch between upper and lowercase and between numbers and special characters, which affects system usability. The key features used for accessing the performance of these techniques are efficiency, accuracy, and usability issues found in the applications. In the end, a comparison of all these techniques is performed. Outcomes of this analysis show that there is a strong need for application that put the least burden on the visually impaired users. Based on this survey, a guideline has been designed for future research in this area.

ACS Style

Sana Shokat; Rabia Riaz; Sanam Shahla Rizvi; Khalil Khan; Farina Riaz; Se Jin Kwon. Analysis and Evaluation of Braille to Text Conversion Methods. Mobile Information Systems 2020, 2020, 1 -14.

AMA Style

Sana Shokat, Rabia Riaz, Sanam Shahla Rizvi, Khalil Khan, Farina Riaz, Se Jin Kwon. Analysis and Evaluation of Braille to Text Conversion Methods. Mobile Information Systems. 2020; 2020 ():1-14.

Chicago/Turabian Style

Sana Shokat; Rabia Riaz; Sanam Shahla Rizvi; Khalil Khan; Farina Riaz; Se Jin Kwon. 2020. "Analysis and Evaluation of Braille to Text Conversion Methods." Mobile Information Systems 2020, no. : 1-14.

Journal article
Published: 19 May 2020 in Electronics
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In recent decades, a large amount of research has been carried out to analyze location-based social network data to highlight their application. These location-based social network datasets can be used to propose models and techniques that can analyze and reproduce the spatiotemporal structures and symmetries in user activities as well as density estimations. In the current study, different density estimation techniques are utilized to analyze the check-in frequency of users in more detail from location-based social network dataset acquired from Sina-Weibo, also referred as Weibo, over a specific period in 10 different districts of Shanghai, China. The aim of this study is to analyze the density of users in Shanghai city from geolocation data of Weibo as well as to compare their density through univariate and bivariate density estimation techniques; i.e., point density and kernel density estimation (KDE) respectively. The main findings of the study include the following: (i) characteristics of users’ spatial behavior, the center of activity based on their check-ins, (ii) the feasibility of check-in data to explain the relationship between users and social media, and (iii) the presentation of evident results for regulatory or managing authorities for urban planning. The current study shows that the point density and kernel density estimation. KDE methods provide useful insights for modeling spatial patterns using geo-spatial dataset. Finally, we can conclude that, by utilizing the KDE technique, we can examine the check-in behavior in more detail for an individual as well as broader patterns in the population as a whole for the development of smart city. The purpose of this article is to figure out the denser places so that the authorities can divide the mobility of people from the same routes or at least they can control the situation from any further inconvenience.

ACS Style

Saqib Ali Haidery; Hidayat Ullah; Naimat Ullah Khan; Kanwal Fatima; Sanam Shahla Rizvi; Se Jin Kwon. Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai. Electronics 2020, 9, 837 .

AMA Style

Saqib Ali Haidery, Hidayat Ullah, Naimat Ullah Khan, Kanwal Fatima, Sanam Shahla Rizvi, Se Jin Kwon. Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai. Electronics. 2020; 9 (5):837.

Chicago/Turabian Style

Saqib Ali Haidery; Hidayat Ullah; Naimat Ullah Khan; Kanwal Fatima; Sanam Shahla Rizvi; Se Jin Kwon. 2020. "Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai." Electronics 9, no. 5: 837.

Journal article
Published: 25 September 2019 in IEEE Access
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Many prior works have investigated on how to increase the job processing performance and energy efficient computing in large scale clusters. However, they employ serialized scheduling approaches encompassed with task straggler “hunting” techniques which launches speculative tasks after detecting slow tasks. These slow tasks are detected through node instrumentation which collects system level information whilst tracking the task execution progress. Such approaches are however detrimental towards achieving maximum processing performance and preserving cluster energy as they increase communication overheads. In this paper, we observe that node instrumentation and serialized scheduling in existing works does not only degrade the job processing performance, but also increase cluster energy consumption. To alleviate this, we propose EPPADS, a light-weight scheduler which eradicates the need for instrumentation modules for job scheduling purposes. EPPADS schedules tasks in two stages, the slow-start phase (SSP) and accelerate phase (AccP). The SSP schedules initial tasks in the queue using baseline FIFO scheduling and records the initial execution times of the processing nodes, whilst tagging the effective and straggling nodes. The AccP uses the initial execution times to compute the processing nodes task distribution ratio of remaining tasks and schedules them in parallel using a single scheduling I/O, boosting up the processing performance. To amortize the computing energy costs, EPPADS implements a power management module that coordinates with the scheduling module and leverage on node tagging information, to place nodes in two different power transition pools, i.e., high and low state power pools. A single power transition signal per pool is then broadcasted to lower or raise the energy state in the low-power state pool and high-power state pool. Our evaluation using a Hadoop cluster shows that EPPADS achieves 30% and 22% 15% to 20% energy savings as compared to the FIFO and DynMon schedulers, respectively.

ACS Style

Prince Hamandawana; Ronnie Mativenga; Se Jin Kwon; Tae-Sun Chung. Towards an Energy Efficient Computing With Coordinated Performance-Aware Scheduling in Large Scale Data Clusters. IEEE Access 2019, 7, 140261 -140277.

AMA Style

Prince Hamandawana, Ronnie Mativenga, Se Jin Kwon, Tae-Sun Chung. Towards an Energy Efficient Computing With Coordinated Performance-Aware Scheduling in Large Scale Data Clusters. IEEE Access. 2019; 7 (99):140261-140277.

Chicago/Turabian Style

Prince Hamandawana; Ronnie Mativenga; Se Jin Kwon; Tae-Sun Chung. 2019. "Towards an Energy Efficient Computing With Coordinated Performance-Aware Scheduling in Large Scale Data Clusters." IEEE Access 7, no. 99: 140261-140277.

Journal article
Published: 12 September 2019 in IEEE Access
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Although flash memory solid state drives (FSSDs) outperform traditional hard disk drives (HDDs), their performance still fails to cope up with the perennial doubling speeds of microprocessors, regardless of the available high bandwidth. To alleviate this bottleneck, many semiconductor companies, such as Intel, Micron, Samsung, and Hynix have already recently manufactured faster and more scalable non-volatile memory (NVM) technology as main memory but none so far have publicly announced their implementation or production of a full NVM Phase Change Memory SSD (PCM-SSD). Considering implementing NVM-PCM as secondary memory, we can build a future PCM-SSD (PSSD) to replace the slow traditional FSSD. However, a careful design, especially for the controller is essential to hide and manage PCM endurance constraints, in-place-updates ability, bit-addressability and enabling it to appear as a block device to the host as their predecessors (HDD and FSSD) do. In this paper, we propose implementing ExTENDS, a hardware assumption of NVM-PCM instead of the NVM-flash memory as our future secondary/persistent memory in storage systems. We further present a PCM file translation layer (PhaseFTL) that can efficiently manage address translations from a host file system to PCM while hiding PCM constrains and allowing the PCM blocks to wear down evenly. Moreover, PhaseFTL can efficiently manipulate the bit-addressability and in-place-update feature of PCM. Our experimental results shows that our proposed PSSD can improve overall SSD performance throughput by an average of 69% compared to traditional FSSDs.

ACS Style

Ronnie Mativenga; Prince Hamandawana; Se Jin Kwon; Tae-Sun Chung. ExTENDS: Efficient Data Placement and Management for Next Generation PCM-Based Storage Systems. IEEE Access 2019, 7, 148718 -148730.

AMA Style

Ronnie Mativenga, Prince Hamandawana, Se Jin Kwon, Tae-Sun Chung. ExTENDS: Efficient Data Placement and Management for Next Generation PCM-Based Storage Systems. IEEE Access. 2019; 7 (99):148718-148730.

Chicago/Turabian Style

Ronnie Mativenga; Prince Hamandawana; Se Jin Kwon; Tae-Sun Chung. 2019. "ExTENDS: Efficient Data Placement and Management for Next Generation PCM-Based Storage Systems." IEEE Access 7, no. 99: 148718-148730.

Journal article
Published: 31 May 2019 in IEEE Access
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Because of the need for processing and managing the massive amounts of big data in smart/wearable devices and driverless vehicles, semiconductor companies are focusing on developing byte-addressable non-volatile memory (NVM)-based storage systems. Byte-addressable NVMs, such as phase-change memory, resistive memory, and magnetoresistive memory, are regarded as an alternative to NAND flash memories. There have been many proposals and studies on the use of NVM as a main memory in the memory hierarchy. However, there has not been much academic research on using NVM as a substitute for NAND flash memories. This paper provides a system architecture for an NVM-based solid state drive based on some speculations/assumptions on the hardware characteristics of NVMs. It applies the previously proposed address-mapping algorithms of conventional solid state drives to the NVM-based solid state drives, and examines their suitability. The optimization of I/O parallelism of static and dynamic address mapping algorithms are compared and analyzed. This paper also observes the effect of log block policies on the hardware characteristics of the NVMs.

ACS Style

Se Jin Kwon. Address Translation Layer for Byte-Addressable Non-Volatile Memory-Based Solid State Drives. IEEE Access 2019, 7, 73207 -73214.

AMA Style

Se Jin Kwon. Address Translation Layer for Byte-Addressable Non-Volatile Memory-Based Solid State Drives. IEEE Access. 2019; 7 (99):73207-73214.

Chicago/Turabian Style

Se Jin Kwon. 2019. "Address Translation Layer for Byte-Addressable Non-Volatile Memory-Based Solid State Drives." IEEE Access 7, no. 99: 73207-73214.

Conference paper
Published: 24 April 2019 in Computer Vision
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The way in which jobs are scheduled is critical to achieve high job processing performance in large scale data clusters. Most existing scheduling mechanism employs a First-In First-Out, serialized approach encompassed with task straggler hunting techniques which launches speculative tasks after detecting slow tasks. This is often achieved through the instrumentation of processing nodes. Such node instrumentation incurs frequent communication overheads as the number of processing nodes increase. Moreover the sequential scheduling of job tasks and the straggler hunting approach fails to meet optimal performance as they increase job waiting time in queue and incurs delayed speculative execution of straggling tasks respectively. In this paper we propose an Enhanced Phase based Performance Aware Dynamic Scheduler (EPPADS), which schedules job tasks without additional instrumentation modules. EPPADS uses a two staged scheduling approach, that is, the slow start phase (SSP) and accelerate phase (AccP). The SSP schedules the initial task in the queue in the normal FIFO way and records the initial execution times of the processing nodes. The AccP uses the initial execution times to compute the processing nodes task distribution ratio of the remaining tasks and schedules them using a single scheduling I/O. We implement EPPADS scheduler in Hadoop’s MapReduce framework. Our evaluation shows that EPPADS can achieve a performance improvement on FIFO scheduler of 30%. Compared with existing Dynamic scheduling approach which uses node instrumentation, EPPADS achieves a better performance of 22%.

ACS Style

Prince Hamandawana; Ronnie Mativenga; Se Jin Kwon; Tae-Sun Chung. EPPADS: An Enhanced Phase-Based Performance-Aware Dynamic Scheduler for High Job Execution Performance in Large Scale Clusters. Computer Vision 2019, 140 -156.

AMA Style

Prince Hamandawana, Ronnie Mativenga, Se Jin Kwon, Tae-Sun Chung. EPPADS: An Enhanced Phase-Based Performance-Aware Dynamic Scheduler for High Job Execution Performance in Large Scale Clusters. Computer Vision. 2019; ():140-156.

Chicago/Turabian Style

Prince Hamandawana; Ronnie Mativenga; Se Jin Kwon; Tae-Sun Chung. 2019. "EPPADS: An Enhanced Phase-Based Performance-Aware Dynamic Scheduler for High Job Execution Performance in Large Scale Clusters." Computer Vision , no. : 140-156.

Research article
Published: 04 April 2019 in Wireless Communications and Mobile Computing
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To keep a network secure, a user authentication scheme that allows only authenticated users to access network services is required. However, the limited resources of sensor nodes make providing authentication a challenging task. We therefore propose a new method of security for a wireless sensor network (WSN). Our technique, Secure User Biometric Based Authentication Scheme (SUBBASe), is based on the user biometrics for WSNs. It achieves a higher security level as well as improved network performance. This solution consists of easy operations and light computations. Herein, the proposed technique is evaluated and compared with previous existing techniques. This scheme increases the performance of the network by reducing network traffic, defending against DOS attacks, and increasing the battery life of a node. Consequently, the functionality and performance of the entire network is improved.

ACS Style

Rabia Riaz; Noor-Ul-Ain Gillani; SanamShahla Rizvi; Sana Shokat; Se Jin Kwon. SUBBASE: An Authentication Scheme for Wireless Sensor Networks Based on User Biometrics. Wireless Communications and Mobile Computing 2019, 2019, 1 -11.

AMA Style

Rabia Riaz, Noor-Ul-Ain Gillani, SanamShahla Rizvi, Sana Shokat, Se Jin Kwon. SUBBASE: An Authentication Scheme for Wireless Sensor Networks Based on User Biometrics. Wireless Communications and Mobile Computing. 2019; 2019 ():1-11.

Chicago/Turabian Style

Rabia Riaz; Noor-Ul-Ain Gillani; SanamShahla Rizvi; Sana Shokat; Se Jin Kwon. 2019. "SUBBASE: An Authentication Scheme for Wireless Sensor Networks Based on User Biometrics." Wireless Communications and Mobile Computing 2019, no. : 1-11.

Journal article
Published: 11 March 2019 in IEEE Transactions on Consumer Electronics
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Artificial intelligence (AI) based Consumer Electronic (CE) devices generate massive amounts of accumulated hot data. Persisting this data into such devices’ storage media like the Universal flash Storage (USF) becomes an issue for flash storage system to manage hot data optimally. Consequently, efforts have been made to support many CE applications and achieve high performance and scalability storage in CE devices. However, it is challenging to find a compatible flash translation layer (FTL) for such flash devices, which can efficiently handle frequent updates imposed by the incoming hot data. Moreover, FTL should be able to handle the hot multimedia data that induces cache-miss penalties which in-turn degrades our flash storage performance. This paper proposes a hybrid flash device for fast address translations called HyFAT which is workload-compatible. HyFAT improves device performance and efficiently manages the combined QuadLevel cell (QLC) for data storage and scalability with SingleLevel cell (SLC) for fast mapping entry storage. The experimental results with realistic workloads show that our approach can improve device performance throughput by 18% on average, compared to traditional DFTL and RFTL.

ACS Style

Ronnie Mativenga; Se Jin Kwon; Tae-Sun Chung. HyFAT: Affordable Hybrid Fast Address Translating Device Driver for Multichannel-Based Flash Devices. IEEE Transactions on Consumer Electronics 2019, 65, 142 -149.

AMA Style

Ronnie Mativenga, Se Jin Kwon, Tae-Sun Chung. HyFAT: Affordable Hybrid Fast Address Translating Device Driver for Multichannel-Based Flash Devices. IEEE Transactions on Consumer Electronics. 2019; 65 (2):142-149.

Chicago/Turabian Style

Ronnie Mativenga; Se Jin Kwon; Tae-Sun Chung. 2019. "HyFAT: Affordable Hybrid Fast Address Translating Device Driver for Multichannel-Based Flash Devices." IEEE Transactions on Consumer Electronics 65, no. 2: 142-149.

Journal article
Published: 01 November 2018 in IEEE Access
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There are several physical and social factors that are associated to the maternal health and may be considered influential towards the C-Section across the world. Several researches have been conducted in different regions of the world, mostly targeting the pregnant women in specific region. The dynamicity of pregnancy and differences among women with respect to region and social life enforces the researchers to widen the sphere of research at regional level to comprehensively explore the significant risk factors associated to mother and expected child. In current study, the region of interest is the native city of authors lacking medical facilities and proper pregnant women healthcare infrastructure. As compared to advanced countries, no such study is ever conducted in this region that involves cause analysis of factors resulting in enhanced cases of C-sections and assisting physicians via providing decision support systems based on knowledge induced from machine learning approaches. The aim of the current study is twofold. The first objective is to collect data regionally, in order to conduct local study, first of its kind in this region, and acquire results that are helpful for public health offices in decision making. Secondly, it is desired to produce different birth classification models and study their applicability on birth data collected previously. The best approach on the basis of correct classification may later be used to produce decision support systems to assist physicians to gain knowledge from the hidden patterns in data. The success of such study is crucial as it will open the doors of interdisciplinary research in two distinct fields of the region.

ACS Style

Syed Ali Abbas; Rabia Riaz; Syed Zaki Hassan Kazmi; Sanam Shahla Rizvi; Se Jin Kwon. Cause Analysis of Caesarian Sections and Application of Machine Learning Methods for Classification of Birth Data. IEEE Access 2018, 6, 67555 -67561.

AMA Style

Syed Ali Abbas, Rabia Riaz, Syed Zaki Hassan Kazmi, Sanam Shahla Rizvi, Se Jin Kwon. Cause Analysis of Caesarian Sections and Application of Machine Learning Methods for Classification of Birth Data. IEEE Access. 2018; 6 (99):67555-67561.

Chicago/Turabian Style

Syed Ali Abbas; Rabia Riaz; Syed Zaki Hassan Kazmi; Sanam Shahla Rizvi; Se Jin Kwon. 2018. "Cause Analysis of Caesarian Sections and Application of Machine Learning Methods for Classification of Birth Data." IEEE Access 6, no. 99: 67555-67561.

Conference paper
Published: 01 September 2018 in 2018 IEEE International Conference on Cluster Computing (CLUSTER)
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Even though flash memory Solid State Drives (FSSDs) outperformed traditional Hard Disk Drives (HDDs), they are still failing to reduce performance gap between microprocessors and storage in computer systems regardless of available high bandwidth. To alleviate this, we propose implementing PCM as main memory in SSDs to replace flash memory. In particular, we present a PCM File Translation Layer (PhaseFTL) that can efficiently manage address translations from host file system to PCM while allowing PCM memory blocks to wear down evenly. PhaseFTL hides PCM's constrains and does not suffer from cache miss because it's address translations are directly linked to the entire mapping table stored on fast PCM main memory.

ACS Style

Ronnie Mativenga; Prince Hamandawana; Se Jin Kwon; Tae-Sun Chung. EDDAPS: An Efficient Data Distribution Approach for PCM-Based SSD. 2018 IEEE International Conference on Cluster Computing (CLUSTER) 2018, 158 -159.

AMA Style

Ronnie Mativenga, Prince Hamandawana, Se Jin Kwon, Tae-Sun Chung. EDDAPS: An Efficient Data Distribution Approach for PCM-Based SSD. 2018 IEEE International Conference on Cluster Computing (CLUSTER). 2018; ():158-159.

Chicago/Turabian Style

Ronnie Mativenga; Prince Hamandawana; Se Jin Kwon; Tae-Sun Chung. 2018. "EDDAPS: An Efficient Data Distribution Approach for PCM-Based SSD." 2018 IEEE International Conference on Cluster Computing (CLUSTER) , no. : 158-159.

Conference paper
Published: 01 September 2018 in 2018 IEEE International Conference on Cluster Computing (CLUSTER)
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A lot of previous works on Map-Reduce improved job completion performance through implementing additional instrumentation modules which collects system level information for making scheduling decisions. However the extra instrumentation may not scale well with increasing number of task-trackers. To this end, we design PADS, a lightweight scheduler which uses time prediction to schedule tasks without additional instrumentation modules. Results shows PADS improves performance by 6%, 12%, and 9% as compared to ESAMR, LA, and DDAS respectively.

ACS Style

Prince Hamandawana; Ronnie Mativenga; Se Jin Kwon; Tae-Sun Chung. PADS: Performance-Aware Dynamic Scheduling for Effective MapReduce Computation in Heterogeneous Clusters. 2018 IEEE International Conference on Cluster Computing (CLUSTER) 2018, 160 -161.

AMA Style

Prince Hamandawana, Ronnie Mativenga, Se Jin Kwon, Tae-Sun Chung. PADS: Performance-Aware Dynamic Scheduling for Effective MapReduce Computation in Heterogeneous Clusters. 2018 IEEE International Conference on Cluster Computing (CLUSTER). 2018; ():160-161.

Chicago/Turabian Style

Prince Hamandawana; Ronnie Mativenga; Se Jin Kwon; Tae-Sun Chung. 2018. "PADS: Performance-Aware Dynamic Scheduling for Effective MapReduce Computation in Heterogeneous Clusters." 2018 IEEE International Conference on Cluster Computing (CLUSTER) , no. : 160-161.

Conference paper
Published: 24 July 2018 in Lecture Notes in Electrical Engineering
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Most large-scale data server systems are having difficulties applying modern data usage patterns to such systems because recent data request patterns of users are sequential, and users tend to request up-to-date data. In this regard, customized systems are necessary for handling such requests efficiently. This paper deals with issues related to how conventional large-scale data server systems utilize memory, and how data are stored in storage devices. In addition, the paper analyzes data usage patterns of users, utilizing a cold storage system, and proposes a main memory system based on the analysis. This paper proposes a hybrid main memory system that utilizes DRAM and phase change memory (PCM). PCM is regarded as the next generation of non-volatile memory. Using a main memory that utilizes PCM, which operates similar to DRAM, and non-volatile storage, the proposed system improves the data processing efficiency. The paper also proposes an algorithm for processing data with the use of DRAM as a buffer. In addition, the paper proposes a system architecture with a tree-type block data and hash-type data block link. Moreover, this study compares the performance of an existing system with that of the proposed system using sequential and random data workloads. The results of the comparison show that performance improves by 10% when using a sequential data load, and remains almost at the same level when using a random data workload.

ACS Style

Tae Hoon Noh; Se Jin Kwon. Memory Management Strategy for PCM-Based IoT Cloud Server. Lecture Notes in Electrical Engineering 2018, 69 -77.

AMA Style

Tae Hoon Noh, Se Jin Kwon. Memory Management Strategy for PCM-Based IoT Cloud Server. Lecture Notes in Electrical Engineering. 2018; ():69-77.

Chicago/Turabian Style

Tae Hoon Noh; Se Jin Kwon. 2018. "Memory Management Strategy for PCM-Based IoT Cloud Server." Lecture Notes in Electrical Engineering , no. : 69-77.

Journal article
Published: 01 November 2017 in IEEE Transactions on Consumer Electronics
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Recently, there have been approaches of using phase change memory (PCM) for the wearable devices. PCM can prolong the lifetime of the wearable devices, because it can endure approximately 10 8 writes per cell. Unfortunately, because previous well-known software translation algorithms were designed to use DRAM as the main memory, they execute frequent write operations on the PCM. As a solution, this paper proposes a software layer called “load-balancing flash translation layer (Load-FTL)” that enhances the performance of the PCM-based wearable devices by efficiently identifying the hot data and managing them in the PCM. Furthermore, Load-FTL prolongs the durability of the PCM using a window-based wear-leveling algorithm.

ACS Style

Se Jin Kwon. Non-volatile translation layer for PCM+NAND in wearable devices. IEEE Transactions on Consumer Electronics 2017, 63, 483 -489.

AMA Style

Se Jin Kwon. Non-volatile translation layer for PCM+NAND in wearable devices. IEEE Transactions on Consumer Electronics. 2017; 63 (4):483-489.

Chicago/Turabian Style

Se Jin Kwon. 2017. "Non-volatile translation layer for PCM+NAND in wearable devices." IEEE Transactions on Consumer Electronics 63, no. 4: 483-489.

Conference paper
Published: 17 June 2017 in Lecture Notes in Electrical Engineering
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Most vendors of e-commerce applications deploy the cache memory to deliver the web objects to clients faster. However, they face many problems in dealing with the cache memory due to limited resources and dynamic access patterns. As a result, we need to efficiently manage the cache memory by evicting the unused data. The performance of cache manager depends upon the efficiency of delete determination. In this paper, we propose ERF, a cache eviction policy using natural exponential function on time with frequency in order to cope with dynamic nature of e-commerce business with limited memory. It sorts the caches in the order of result value which come from coordination between frequency and recency and evicts the caches according to it. We evaluate the performance of ERF by using the workload which reflects the real-world applications and compare it with conventional algorithms. By increasing the cache hit ratio with ERF, we can expect the decrease of copy and delete operations of cache with improving the overall system performance.

ACS Style

Jung Hwa Lee; Se Jin Kwon; Tae-Sun Chung. ERF: Efficient Cache Eviction Strategy for E-commerce Applications. Lecture Notes in Electrical Engineering 2017, 425, 295 -304.

AMA Style

Jung Hwa Lee, Se Jin Kwon, Tae-Sun Chung. ERF: Efficient Cache Eviction Strategy for E-commerce Applications. Lecture Notes in Electrical Engineering. 2017; 425 ():295-304.

Chicago/Turabian Style

Jung Hwa Lee; Se Jin Kwon; Tae-Sun Chung. 2017. "ERF: Efficient Cache Eviction Strategy for E-commerce Applications." Lecture Notes in Electrical Engineering 425, no. : 295-304.

Journal article
Published: 20 February 2016 in ACM Transactions on Embedded Computing Systems
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Current triple-level cell (TLC)-based solids-tate drives used in multimedia storage devices support multichannel access to increase capacity and throughput. Unfortunately, current state-of-the-art FTL algorithms must employ selective caching for inquiring about the address mapping information, which causes low space utilization, a large flash memory requirement, and performance degradation. In this article, the Ca che- b ased Flash Translation Layer (Cab-FTL) is proposed for TLC-based multimedia storage devices. Cab-FTL enhances the read and write performances by achieving high space utilization while reducing the size of the mapping tables to 1.68% compared to DFTL. Despite a caching of the mapping tables in DRAM, Cab-FTL achieves a fast system boot using its fast wake-up mechanism.

ACS Style

Se Jin Kwon. A Cache-Based Flash Translation Layer for TLC-Based Multimedia Storage Devices. ACM Transactions on Embedded Computing Systems 2016, 15, 1 -28.

AMA Style

Se Jin Kwon. A Cache-Based Flash Translation Layer for TLC-Based Multimedia Storage Devices. ACM Transactions on Embedded Computing Systems. 2016; 15 (1):1-28.

Chicago/Turabian Style

Se Jin Kwon. 2016. "A Cache-Based Flash Translation Layer for TLC-Based Multimedia Storage Devices." ACM Transactions on Embedded Computing Systems 15, no. 1: 1-28.

Book chapter
Published: 17 October 2015 in Econometrics for Financial Applications
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Nowadays, NAND flash memory is begin widely used for data storage purposes in all types of digital devices such as in handheld devices like; MP3 players, mobile phones, and digital cameras or in large scale servers. Reasons of so much popularity of flash memory are its characteristics; low power consumption, non-volatility, high performance, shock resistance and portability. However due to some of its hardware characteristics, a software layer called flash translation layer (FTL) is used between file system and flash memory. We propose a new FTL algorithm called SAF. Compared to the previous FTL algorithm, SAF shows higher performance. We also provide performance comparison results of our implementation of SAF and previous algorithm FAST.

ACS Style

Usman Anwar; Se Jin Kwon; Tae-Sun Chung. SAF: States Aware Fully Associative FTL for Multitasking Environment. Econometrics for Financial Applications 2015, 1 -11.

AMA Style

Usman Anwar, Se Jin Kwon, Tae-Sun Chung. SAF: States Aware Fully Associative FTL for Multitasking Environment. Econometrics for Financial Applications. 2015; ():1-11.

Chicago/Turabian Style

Usman Anwar; Se Jin Kwon; Tae-Sun Chung. 2015. "SAF: States Aware Fully Associative FTL for Multitasking Environment." Econometrics for Financial Applications , no. : 1-11.

Journal article
Published: 12 August 2015 in Journal of Circuits, Systems and Computers
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Hot data identification in flash memory is of great interest because it significantly affects the garbage collection and wear-leveling performance. Presently, certain hot and cold data classification schemes based on Bloom filters (BFs) have been proposed. Although BFs are efficient in most cases, there is a significant trade-off between false positive rates, which are the result of hash value collisions and memory utilization. In this paper, we suggest a better data categorization mechanism that is based on a hashing technique called Fingerprinting by Random Polynomials with the aim of reducing false positive rates and achieving lower memory consumption compared to the BF-based schemes. We also introduce a new methodology for classifying write requests by linking the definition of hot and cold write requests to the flash memory software layer, the flash translation layer (FTL) characteristics. Our approach improves space utilization by representing each logical block number (lbn) by one counter in the hash table, and achieves an extremely low error rate by choosing the degree of the hash function based on the address space of the flash memory. In addition, we achieved lower false identification rates. We demonstrate the performance using mathematical analysis and trace-driven simulation.

ACS Style

Sololia Gudeta Ayele; Rize Jin; Se Jin Kwon; Muhammad Attique; Tae-Sung Chung. Efficient FTL-Aware Data Categorization and Identification Scheme for Flash Memory. Journal of Circuits, Systems and Computers 2015, 24, 1550113 .

AMA Style

Sololia Gudeta Ayele, Rize Jin, Se Jin Kwon, Muhammad Attique, Tae-Sung Chung. Efficient FTL-Aware Data Categorization and Identification Scheme for Flash Memory. Journal of Circuits, Systems and Computers. 2015; 24 (8):1550113.

Chicago/Turabian Style

Sololia Gudeta Ayele; Rize Jin; Se Jin Kwon; Muhammad Attique; Tae-Sung Chung. 2015. "Efficient FTL-Aware Data Categorization and Identification Scheme for Flash Memory." Journal of Circuits, Systems and Computers 24, no. 8: 1550113.