Search Word(s) in Title, Keywords, Authors, and Abstract: ATM

Enhanced Security Framework for E-Health Systems using Blockchain
Mohan Kubendiran, Satyapal Singh and Arun Kumar Sangaiah
Page: 239~250, Vol. 15, No.2, 2019
10.3745/JIPS.04.0106
Keywords: Blockchain, Cloud Computing, Data Integrity, Data Provenance, E-Health System
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An individual’s health data is very sensitive and private. Such data are usually stored on a private or community owned cloud, where access is not restricted to the owners of that cloud. Anyone within the cloud can access this data. This data may not be read only and multiple parties can make to it. Thus, any unauthorized modification of health-related data will lead to incorrect diagnosis and mistreatment. However, we cannot restrict semipublic access to this data. Existing security mechanisms in e-health systems are competent in dealing with the issues associated with these systems but only up to a certain extent. The indigenous technologies need to be complemented with current and future technologies. We have put forward a method to complement such technologies by incorporating the concept of blockchain to ensure the integrity of data as well as its provenance.


Research on the Variable Rate Spraying System Based on Canopy Volume Measurement
Kaiqun Hu and Xin Feng
Page: 1131~1140, Vol. 15, No.5, 2019
10.3745/JIPS.04.0134
Keywords: Canopy Volume Measurement, Tracer Deposition Device, Ultrasonic Sensor, Variable Rate Spraying System
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Characteristics of fruit tree canopies are important target information for adjusting the pesticide application rate in variable rate spraying in orchards. Therefore, the target detection of the canopy characteristics is very important. In this study, a canopy volume measurement method for peach trees was presented and a variable rate spraying system based on canopy volume measurement was developed using the ultrasonic sensing, one of the most effective target detection method. Ten ultrasonic sensors and two flow control units were mounted on the orchard air-assisted sprayer. The ultrasonic sensors were used to detect the canopy diameters and the flow controls were used to modify the flow rate of the nozzles in real time. Two treatments were established: a constant application rate of 300 Lha-1 was set as the control treatment for the comparison with the variable rate application at a 0.095 Lm-3 canopy. The tracer deposition at different parts of peach trees and the tracer losses to the ground (between rows and within rows) were analyzed in detail under constant rate and variable rate application. The results showed that there were no significant differences between two treatments in the liquid distribution and the capability to reach the inner parts of the crop canopies.


Combining Multi-Criteria Analysis with CBR for Medical Decision Support
Mansoul Abdelhak and Atmani Baghdad
Page: 1496~1515, Vol. 13, No.6, 2017
10.3745/JIPS.04.0050
Keywords: Case-Based Reasoning (CBR), Decision Support, Medical Diagnosis, Multi-Criteria Analysis (MCA), Multimodal Reasoning
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One of the most visible developments in Decision Support Systems (DSS) was the emergence of rule-based expert systems. Hence, despite their success in many sectors, developers of Medical Rule-Based Systems have met several critical problems. Firstly, the rules are related to a clearly stated subject. Secondly, a rule-based system can only learn by updating of its rule-base, since it requires explicit knowledge of the used domain. Solutions to these problems have been sought through improved techniques and tools, improved development paradigms, knowledge modeling languages and ontology, as well as advanced reasoning techniques such as case-based reasoning (CBR) which is well suited to provide decision support in the healthcare setting. However, using CBR reveals some drawbacks, mainly in its interrelated tasks: the retrieval and the adaptation. For the retrieval task, a major drawback raises when several similar cases are found and consequently several solutions. Hence, a choice for the best solution must be done. To overcome these limitations, numerous useful works related to the retrieval task were conducted with simple and convenient procedures or by combining CBR with other techniques. Through this paper, we provide a combining approach using the multi-criteria analysis (MCA) to help, the traditional retrieval task of CBR, in choosing the best solution. Afterwards, we integrate this approach in a decision model to support medical decision. We present, also, some preliminary results and suggestions to extend our approach.


Extraction of ObjectProperty-UsageMethod Relation from Web Documents
Chaveevan Pechsiri, Sumran Phainoun and Rapeepun Piriyakul
Page: 1103~1125, Vol. 13, No.5, 2017
10.3745/JIPS.04.0046
Keywords: Medicinal Property, N-Word-Co, Semantic Relation, Usage-Method
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This paper aims to extract an ObjectProperty-UsageMethod relation, in particular the HerbalMedicinalProperty- UsageMethod relation of the herb-plant object, as a semantic relation between two related sets, a herbal- medicinal-property concept set and a usage-method concept set from several web documents. This HerbalMedicinalProperty-UsageMethod relation benefits people by providing an alternative treatment/solution knowledge to health problems. The research includes three main problems: how to determine EDU (where EDU is an elementary discourse unit or a simple sentence/clause) with a medicinal-property/usage-method concept; how to determine the usage-method boundary; and how to determine the HerbalMedicinalProperty- UsageMethod relation between the two related sets. We propose using N-Word-Co on the verb phrase with the medicinal-property/usage-method concept to solve the first and second problems where the N-Word-Co size is determined by the learning of maximum entropy, support vector machine, and nai?ve Bayes. We also apply nai?ve Bayes to solve the third problem of determining the HerbalMedicinalProperty-UsageMethod relation with N-Word-Co elements as features. The research results can provide high precision in the HerbalMedicinalProperty-UsageMethod relation extraction.


Treatment Planning in Smart Medical: A Sustainable Strategy
Fei Hao, Doo-Soon Park, Sang Yeon Woo, Se Dong Min and Sewon Park
Page: 711~723, Vol. 12, No.4, 2016
10.3745/JIPS.04.0026
Keywords: Degree of Membership, Fuzzy Evaluation, Smart Medical, Sustainable, Treatment Plan
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With the rapid development of both ubiquitous computing and the mobile internet, big data technology is gradually penetrating into various applications, such as smart traffic, smart city, and smart medical. In particular, smart medical, which is one core part of a smart city, is changing the medical structure. Specifically, it is improving treatment planning for various diseases. Since multiple treatment plans generated from smart medical have their own unique treatment costs, pollution effects, side-effects for patients, and so on, determining a sustainable strategy for treatment planning is becoming very critical in smart medical. From the sustainable point of view, this paper first presents a three-dimensional evaluation model for representing the raw medical data and then proposes a sustainable strategy for treatment planning based on the representation model. Finally, a case study on treatment planning for the group of “computer autism” patients is then presented for demonstrating the feasibility and usability of the proposed strategy


Image-Centric Integrated Data Model of Medical Information by Diseases: Two Case Studies for AMI and Ischemic Stroke
Meeyeon Lee, Ye-Seul Park and Jung-Won Lee
Page: 741~753, Vol. 12, No.4, 2016
10.3745/JIPS.04.0027
Keywords: Acute Myocardial Infarction, Data Model, Hospital Information System, Ischemic Stroke, Medical Image, Medical Information, Ontology
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In the medical fields, many efforts have been made to develop and improve Hospital Information System (HIS) including Electronic Medical Record (EMR), Order Communication System (OCS), and Picture Archiving and Communication System (PACS). However, materials generated and used in medical fields have various types and forms. The current HISs separately store and manage them by different systems, even though they relate to each other and contain redundant data. These systems are not helpful particularly in emergency where medical experts cannot check all of clinical materials in the golden time. Therefore, in this paper, we propose a process to build an integrated data model for medical information currently stored in various HISs. The proposed data model integrates vast information by focusing on medical images since they are most important materials for the diagnosis and treatment. Moreover, the model is disease-specific to consider that medical information and clinical materials including images are different by diseases. Two case studies show the feasibility and the usefulness of our proposed data model by building models about two diseases, acute myocardial infarction (AMI) and ischemic stroke


Analysis of Semantic Relations Between Multimodal Medical Images Based on Coronary Anatomy for Acute Myocardial Infarction
Yeseul Park, Meeyeon Lee, Myung-Hee Kim and Jung-Won Lee
Page: 129~148, Vol. 12, No.1, 2016
10.3745/JIPS.04.0021
Keywords: Acute Myocardial Infarction, Coronary Anatomy, Coronary Angiography, Data Model, Echocardiography, Medical Images, Multimodality, Semantic Features
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Acute myocardial infarction (AMI) is one of the three emergency diseases that require urgent diagnosis and treatment in the golden hour. It is important to identify the status of the coronary artery in AMI due to the nature of disease. Therefore, multi-modal medical images, which can effectively show the status of the coronary artery, have been widely used to diagnose AMI. However, the legacy system has provided multi- modal medical images with flat and unstructured data. It has a lack of semantic information between multi- modal images, which are distributed and stored individually. If we can see the status of the coronary artery all at once by integrating the core information extracted from multi-modal medical images, the time for diagnosis and treatment will be reduced. In this paper, we analyze semantic relations between multi-modal medical images based on coronary anatomy for AMI. First, we selected a coronary arteriogram, coronary angiography, and echocardiography as the representative medical images for AMI and extracted semantic features from them, respectively. We then analyzed the semantic relations between them and defined the convergence data model for AMI. As a result, we show that the data model can present core information from multi-modal medical images and enable to diagnose through the united view of AMI intuitively.


Spatial Interpolation and Assimilation Methods for Satellite and Ground Meteorological Data in Vietnam
Khac Phong Do, Ba Tung Nguyen, Xuan Thanh Nguyen, Quang Hung Bui, Nguyen Le Tran, Thi Nhat Thanh Nguyen, Van Quynh Vuong, Huy Lai Nguyen and Thanh Ha Le
Page: 556~572, Vol. 11, No.4, 2015
10.3745/JIPS.02.0030
Keywords: Assimilation, Interpolation, Meteorological Variables, Kriging, Vietnam
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This paper presents the applications of spatial interpolation and assimilation methods for satellite and ground meteorological data, including temperature, relative humidity, and precipitation in regions of Vietnam. In this work, Universal Kriging is used for spatially interpolating ground data and its interpolated results are assimilated with corresponding satellite data to anticipate better gridded data. The input meteorological data was collected from 98 ground weather stations located all over Vietnam; whereas, the satellite data consists of the MODIS Atmospheric Profiles product (MOD07), the ASTER Global Digital Elevation Map (ASTER DEM), and the Tropical Rainfall Measuring Mission (TRMM) in six years. The outputs are gridded fields of temperature, relative humidity, and precipitation. The empirical results were evaluated by using the Root mean square error (RMSE) and the mean percent error (MPE), which illustrate that Universal Kriging interpolation obtains higher accuracy than other forms of Kriging; whereas, the assimilation for precipitation gradually reduces RMSE and significantly MPE. It also reveals that the accuracy of temperature and humidity when employing assimilation that is not significantly improved because of low MODIS retrieval due to cloud contamination.


Imputation of Medical Data Using Subspace Condition Order Degree Polynomials
Klaokanlaya Silachan and Panjai Tantatsanawong
Page: 395~411, Vol. 10, No.3, 2014
10.3745/JIPS.04.0007
Keywords: Imputation, Personal Temporal Data, Polynomial Interpolation
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Temporal medical data is often collected during patient treatments that require personal analysis. Each observation recorded in the temporal medical data is associated with measurements and time treatments. A major problem in the analysis of temporal medical data are the missing values that are caused, for example, by patients dropping out of a study before completion. Therefore, the imputation of missing data is an important step during pre-processing and can provide useful information before the data is mined. For each patient and each variable, this imputation replaces the missing data with a value drawn from an estimated distribution of that variable. In this paper, we propose a new method, called Newton’s finite divided difference polynomial interpolation with condition order degree, for dealing with missing values in temporal medical data related to obesity. We compared the new imputation method with three existing subspace estimation techniques, including the k-nearest neighbor, local least squares, and natural cubic spline approaches. The performance of each approach was then evaluated by using the normalized root mean square error and the statistically significant test results. The experimental results have demonstrated that the proposed method provides the best fit with the smallest error and is more accurate than the other methods.


A TRUS Prostate Segmentation using Gabor Texture Features and Snake-like Contour
Sung Gyun Kim and Yeong Geon Seo
Page: 103~116, Vol. 9, No.1, 2013
10.3745/JIPS.2013.9.1.103
Keywords: Gabor Filter Bank, Support Vector Machines, Prostate Segmentation
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Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in the most of countries. In many diagnostic and treatment procedures for prostate disease accurate detection of prostate boundaries in transrectal ultrasound(TRUS) images is required. This is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a method for automatic prostate segmentation in TRUS images using Gabor feature extraction and snake-like contour is presented. This method involves preprocessing, extracting Gabor feature, training, and prostate segmentation. The speckle reduction for preprocessing step has been achieved by using stick filter and top-hat transform has been implemented for smoothing the contour. A Gabor filter bank for extraction of rotation- invariant texture features has been implemented. A support vector machine(SVM) for training step has been used to get each feature of prostate and nonprostate. Finally, the boundary of prostate is extracted by the snake-like contour algorithm. A number of experiments are conducted to validate this method and results showed that this new algorithm extracted the prostate boundary with less than 10.2% of the accuracy which is relative to boundary provided manually by experts


Dynamic knowledge mapping guided by data mining: Application on Healthcare
Menaouer Brahami, Baghdad Atmani and Nada Matta
Page: 1~30, Vol. 9, No.1, 2013
10.3745/JIPS.2013.9.1.001
Keywords: Knowledge Management, Knowledge Mapping (Knowledge Cartography), Knowledge Representation, Boolean Modeling, Cellular Machine, Data Mining, Boolean Inference Engine
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The capitalization of know-how, knowledge management, and the control of the constantly growing information mass has become the new strategic challenge for organizations that aim to capture the entire wealth of knowledge (tacit and explicit). Thus, knowledge mapping is a means of (cognitive) navigation to access the resources of the strategic heritage knowledge of an organization. In this paper, we present a new mapping approach based on the Boolean modeling of critical domain knowledge and on the use of different data sources via the data mining technique in order to improve the process of acquiring knowledge explicitly. To evaluate our approach, we have initiated a process of mapping that is guided by machine learning that is artificially operated in the following two stages: data mining and automatic mapping. Data mining is be initially run from an induction of Boolean case studies (explicit). The mapping rules are then used to automatically improve the Boolean model of the mapping of critical knowledge


Using a Cellular Automaton to Extract Medical Information from Clinical Reports
Fatiha Barigou, Baghdad Atmani and Bouziane Beldjilali
Page: 67~84, Vol. 8, No.1, 2012
10.3745/JIPS.2012.8.1.067
Keywords: Clinical Reports, Information Extraction, Cellular Automaton, Boolean Inference Engine
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An important amount of clinical data concerning the medical history of a patient is in the form of clinical reports that are written by doctors. They describe patients, their pathologies, their personal and medical histories, findings made during interviews or during procedures, and so forth. They represent a source of precious information that can be used in several applications such as research information to diagnose new patients, epidemiological studies, decision support, statistical analysis, and data mining. But this information is difficult to access, as it is often in unstructured text form. To make access to patient data easy, our research aims to develop a system for extracting information from unstructured text. In a previous work, a rule-based approach is applied to a clinical reports corpus of infectious diseases to extract structured data in the form of named entities and properties. In this paper, we propose the use of a Boolean inference engine, which is based on a cellular automaton, to do extraction. Our motivation to adopt this Boolean modeling approach is twofold: first optimize storage, and second reduce the response time of the entities extraction.


En-Route Trajectory calculation using Flight Plan Information for Effective Air Traffic Management
Yong-Kyun Kim, Yun-Hyun Jo, Jin-Won Yun, Taeck-Keun Oh, Hee-Chang Roh, Sang-Bang Choi and Hyo-Dal Park
Page: 375~384, Vol. 6, No.3, 2010
10.3745/JIPS.2010.6.3.375
Keywords: ATC, ATM, Trajectory Prediction, ATFM
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Trajectory modeling is foundational for 4D-Route modeling, conflict detection and air traffic flow management. This paper proposes a novel algorithm based Vincenty’s fomulas for trajectory calculation, combined with the Dijkstra algorithm and Vincenty’s formulas. Using flight plan simulations our experimental results show that our method of En-route trajectory calculation exhibits much improved performance in accuracy.