Search results for: medi-cal data

Authors: Melissa Angus Baboun

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Utilizing a Critical Discourse Analysis (CDA) methodology and a theoretical framework based on disability studies, how Jamaican and Trinidadian newspapers discussed issues relating to the Deaf community were examined. The term deaf was inputted into the search engine tool of the online website for the Jamaica Observer and the Trinidad & Tobago Guardian. All 27 articles that contained the term deaf in its content and were written between August 1, 2017 and November 15, 2017 were chosen for the study. The data analysis was divided into three steps: (1) listing and analysis instances of metaphorical deafness (e.g. fall on deaf ears), (2) categorization of the content of the articles into the models of disability discourse (the medical, socio-cultural, and superscrip models of disability narratives), and (3) the analysis of any additional data found. A total of 42% of the articles pulled for this study did not deal with the Deaf community in any capacity, but rather instances of the use of idiomatic expressions that use deafness as a metaphor for a non-physical, undesirable trait. The most common idiomatic expression found was fall on deaf ears. Regarding the models of disability discourse, eight articles were found to follow the socio-cultural model, two were found to follow the medical model, and two were found to follow the superscrip model. The additional data found in these articles include two instances of the term deaf and mute, an overwhelming use of lower case d for the term deaf, and the misuse of the term translator (to mean interpreter).

Keywords: deafness, disability, news coverage, Caribbean newspapers

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Authors: Grace Temiloluwa Agbede, Rodwell Makombe, Gift Mheta

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The study adopts a sociolinguistic framework to analyse trado-medical and church advertisements in Nigeria. The study employs a qualitative case-study approach to examine the language of trado-medical and church adverts in Nigeria. Obviously, language serves as an instrument of thought. Thus, it is safe to say that language is at the centre of every human activity and experience because it differentiates human beings from all other animals. The study analyses the appropriateness of language and visual elements in trado-medical and church advertisements in relation to their meaning. It focuses on billboard advertisements as well as selected Newspapers in Nigeria. It then became clearer that society influences language and vice versa. Thus, the justification for this study is predicated on the fact that more work still needs to be done to unpack the intertwined relationship among sociolinguistics, visual culture and advertisement. Given that this research focuses on visual advertisements by traditional medical practitioners and churches in Nigeria, it is therefore necessary to investigate the interplay between language and visuality in advertisements by traditional medical practitioners and churches.

Keywords: commercials, culture, language, visuality

Procedia PDF Downloads 178

Authors: González A. Julio, Ramírez L. Leonardo, Puerta A. Gabriel

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Telemedicine services use a large amount of data, most of which are diagnostic images in Digital Imaging and Communications in Medicine (DICOM) and Health Level Seven (HL7) formats. Metadata is generated from each related image to support their identification. This study presents the use of decision trees for the optimization of information search processes for diagnostic images, hosted on the cloud server. To analyze the performance in the server, the following quality of service (QoS) metrics are evaluated: delay, bandwidth, jitter, latency and throughput in five test scenarios for a total of 26 experiments during the loading and downloading of DICOM images, hosted by the telemedicine group server of the Universidad Militar Nueva Granada, Bogotá, Colombia. By applying decision trees as a data mining technique and comparing it with the sequential search, it was possible to evaluate the search times of diagnostic images in the server. The results show that by using the metadata in decision trees, the search times are substantially improved, the computational resources are optimized and the request management of the telemedicine image service is improved. Based on the experiments carried out, search efficiency increased by 45% in relation to the sequential search, given that, when downloading a diagnostic image, false positives are avoided in management and acquisition processes of said information. It is concluded that, for the diagnostic images services in telemedicine, the technique of decision trees guarantees the accessibility and robustness in the acquisition and manipulation of medical images, in improvement of the diagnoses and medical procedures in patients.

Keywords: cloud storage, decision trees, diagnostic image, search, telemedicine

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Authors: Tosin Ige

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Ethics must be a condition of the world, like logic. (Ludwig Wittgenstein, 1889-1951). As important as data mining is, it possess a significant threat to ethics, privacy, and legality, since data mining makes it difficult for an individual or consumer (in the case of a company) to control the accessibility and usage of his data. This research focuses on Current issues and the latest research and development on Privacy preserving data mining methods as at year 2022. It also discusses some advances in those techniques while at the same time highlighting and providing a new technique as a solution to an existing technique of privacy preserving data mining methods. This paper also bridges the wide gap between Data mining and the Web Application Programing Interface (web API), where research is urgently needed for an added layer of security in data mining while at the same time introducing a seamless and more efficient way of data mining.

Keywords: data, privacy, data mining, association rule, privacy preserving, mining technique

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Authors: A. Alexandru, C. A. Alexandru, D. Coardos, E. Tudora

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Big Data (BD) is associated with a new generation of technologies and architectures which can harness the value of extremely large volumes of very varied data through real time processing and analysis. It involves changes in (1) data types, (2) accumulation speed, and (3) data volume. This paper presents the main concepts related to the BD paradigm, and introduces architectures and technologies for BD and BD sets. The integration of BD with the Hadoop Framework is also underlined. BD has attracted a lot of attention in the public sector due to the newly emerging technologies that allow the availability of network access. The volume of different types of data has exponentially increased. Some applications of BD in the public sector in Romania are briefly presented.

Keywords: big data, big data analytics, Hadoop, cloud

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Authors: Aïcha Ben Salem, Faouzi Boufares, Sebastiao Correia

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The subject covered in this paper aims at assisting the user in its quality approach. The goal is to better extract, mix, interpret and reuse data. It deals with the semantic schema recognition of a data source. This enables the extraction of data semantics from all the available information, inculding the data and the metadata. Firstly, it consists of categorizing the data by assigning it to a category and possibly a sub-category, and secondly, of establishing relations between columns and possibly discovering the semantics of the manipulated data source. These links detected between columns offer a better understanding of the source and the alternatives for correcting data. This approach allows automatic detection of a large number of syntactic and semantic anomalies.

Keywords: schema recognition, semantic data profiling, meta-categorisation, semantic dependencies inter columns

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Authors: Winfred Okoe Addy, Jean Jacques Dominique Beraud

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Access control systems (ACs) are some of the most important components in safety areas. Inaccuracies of regulatory frameworks make personal policies and remedies more appropriate than standard models or protocols. This problem is exacerbated by the increasing complexity of software, such as integrated Big Data (BD) software for controlling large volumes of encrypted data and resources embedded in a dedicated BD production system. This paper proposes a general access control strategy system for the diffusion of Big Data domains since it is crucial to secure the data provided to data consumers (DC). We presented a general access control circulation strategy for the Big Data domain by describing the benefit of using designated access control for BD units and performance and taking into consideration the need for BD and AC system. We then presented a generic of Big Data access control system to improve the dissemination of Big Data.

Keywords: access control, security, Big Data, domain

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Authors: Oluwaponmile D. Alao

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In this paper, two models have been developed to ascertain the best network needed for diagnosis of breast cancer. Breast cancer has been a disease that required the attention of the medical practitioner. Experience has shown that misdiagnose of the disease has been a major challenge in the medical field. Therefore, designing a system with adequate performance for will help in making diagnosis of the disease faster and accurate. In this paper, two models: backpropagation neural network and support vector machine has been developed. The performance obtained is also compared with other previously obtained algorithms to ascertain the best algorithms.

Keywords: breast cancer, data mining, neural network, support vector machine

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Authors: Nassim Salmi

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Postoperative mobilization is an important part of fundamental care. Increased mobilization has a positive effect on recovery, but immobilization is still a challenge in postoperative care. Aims: To report how the establishment of a national nursing database was used to measure postoperative mobilization in patients undergoing surgery for ovarian cancer. Mobilization was defined as at least 3 hours out of bed on postoperative day 1, with the goal set at achieving this in 60% of patients. Clinical nurses on 4400 patients with ovarian cancer performed data entry. Findings: 46.7% of patients met the goal for mobilization on the first postoperative day, but variations in duration and type of mobilization were observed. Of those mobilized, 51.8% had been walking in the hallway. A national nursing database creates opportunities to optimize fundamental care. By comparing nursing data with oncological, surgical, and pathology data, it became possible to study mobilization in relation to cancer stage, comorbidity, treatment, and extent of surgery.

Keywords: postoperative care, gynecology, nursing documentation, database

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Authors: Ahmedou Moulaye Idriss, Yahya Tfeil

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Medical imaging technology has become an indispensable tool in many branches of the biomedical, health area, and research and is vitally important for the training of professionals in these fields. It is not only about the tools, technologies, and knowledge provided but also about the community that this training project proposes. In order to be able to raise the level of anatomy teaching in the medical school of Nouakchott in Mauritania, it is necessary and even urgent to facilitate access to modern technology for African countries. The role of technology as a key driver of justifiable development has long been recognized. Anatomy is an essential discipline for the training of medical students; it is a key element for the training of medical specialists. The quality and results of the work of a young surgeon depend on his better knowledge of anatomical structures. The teaching of anatomy is difficult as the discipline is being neglected by medical students in many academic institutions. However, anatomy remains a vital part of any medical education program. When anatomy is presented in various planes medical students approve of difficulties in understanding. They do not increase their ability to visualize and mentally manipulate 3D structures. They are sometimes not able to correctly identify neighbouring or associated structures. This is the case when they have to make the identification of structures related to the caudate lobe when the liver is moved to different positions. In recent decades, some modern educational tools using digital sources tend to replace old methods. One of the main reasons for this change is the lack of cadavers in laboratories with poorly qualified staff. The emergence of increasingly sophisticated mathematical models, image processing, and visualization tools in biomedical imaging research have enabled sophisticated three-dimensional (3D) representations of anatomical structures. In this paper, we report our current experience in the Faculty of Medicine in Nouakchott Mauritania. One of our main aims is to create a local learning community in the fields of anatomy. The main technological platform used in this project is called 3D Slicer. 3D Slicer platform is an open-source application available for free for viewing, analysis, and interaction with biomedical imaging data. Using the 3D Slicer platform, we created from real medical images anatomical atlases of parts of the human body, including head, thorax, abdomen, liver, and pelvis, upper and lower limbs. Data were collected from several local hospitals and also from the website. We used MRI and CT-Scan imaging data from children and adults. Many different anatomy atlases exist, both in print and digital forms. Anatomy Atlas displays three-dimensional anatomical models, image cross-sections of labelled structures and source radiological imaging, and a text-based hierarchy of structures. Open and free online anatomical atlases developed by our anatomy laboratory team will be available to our students. This will allow pedagogical autonomy and remedy the shortcomings by responding more fully to the objectives of sustainable local development of quality education and good health at the national level. To make this work a reality, our team produced several atlases available in our faculty in the form of research projects.

Keywords: anatomy, education, medical imaging, three dimensional

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Authors: Michael Gidey Gebru

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Most of Data Envelopment Analysis models operate in a static environment with input and output parameters that are chosen by deterministic data. However, due to ambiguity brought on shifting market conditions, input and output data are not always precisely gathered in real-world scenarios. Fuzzy numbers can be used to address this kind of ambiguity in input and output data. Therefore, this work aims to expand crisp Data Envelopment Analysis into Data Envelopment Analysis with fuzzy environment. In this study, the input and output data are regarded as fuzzy triangular numbers. Then, the Data Envelopment Analysis model with fuzzy environment is solved using a multi-objective method to gauge the Decision Making Units' efficiency. Finally, the developed Data Envelopment Analysis model is illustrated with an application on real data 50 educational institutions.

Keywords: efficiency, Data Envelopment Analysis, fuzzy, higher education, input, output

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Authors: Betül sönmez, Fatma Azizoğlu, S. Bilge Hapçıoğlu, Aytolan Yıldırım

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Background: Social innovativeness contains the procurement of a sustainable benefit for a number of problems from working conditions to education, social development, health, and from environmental control to climate change, as well as the development of new social productions and services. Objectives: This study was conducted to determine the correlation between the social innovation tendency of nursing and medical faculty students and value types. Methods and participants: The population of this correlational study consisted of third-year students studying at a medical faculty and a nursing faculty in a public university in Istanbul. Ethics committee approval and permission from the school administrations were obtained in order to conduct the study and voluntary participation of the students in the study was ensured. 524 questionnaires were obtained with a total return rate of 57.1% (65.0% in nurse student and 52.1% in physic students). The data of the study were collected by using the Portrait Values Questionnaire and a questionnaire containing the Social Innovativeness Scale. Results: The effect of the subscale scores of Portrait Values Questionnaire on the total score of Social Innovativeness Scale was 26.6%. In the model where a significance was determined (F=37.566; p<0.01), the highest effect was observed in the subscale of universalism. The effect of subscale scores obtained from the Portrait Values Questionnaire, as well as age, gender and number of siblings was 25% on the Social Innovativeness in nursing students and 30.8% in medical faculty students. In both models where a significance was determined (p<0.01), the nursing students had the values of power, universalism and kindness, whereas the medical faculty students had the values of self-direction, stimulation, hedonism and universalism showed the highest effect in both models. Conclusions: Universalism is the value with the highest effect upon the social innovativeness in both groups, which is an expected result by the nature of professions. The effect of the values of independent thinking and self-direction, as well as openness to change involving quest for innovation (stimulation), which are observed in medical faculty students, also supports the literature of innovative behavior. These results are thought to guide educators and administrators in terms of developing socially innovative behaviors.

Keywords: social innovativeness, portrait values questionnaire, nursing students, medical faculty students

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Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: Wen-Yu Chen, Chia-Yuan Hsu, Sasinee Vongsrikul

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The present study is to explore the perception of Taiwanese females towards medical tourism in Thailand for the development of applicable marketing strategy, integrating travel motivation and cosmetic surgery trend to attract potential medical tourists from Taiwan. Since previous studies relevant to this research issue are limited, qualitative study is firstly employed by using one focus group interview and in-depth interviews with Taiwanese females. Moreover, the present research collected questionnaires from 290 Taiwanese females to provide greater understanding of research results. The top three factors that affect Taiwanese females’ decision for not going to Thailand for medical tourism are “physicians and nurses cannot speak Chinese”, “low quality of the cosmetic surgery product that I want to do”, and “the county does not have laws to protect medical tourists’ right”. The finding of the empirical part would suggest the area in medical tourism industry which Thailand should promote and emphasizes in order to increase its presence as a hub for cosmetic surgery and attract Taiwanese female market. Therefore, the study contributes to the potential development of marketing strategy for medical tourism, specifically in the area of cosmetic surgery in Thailand while targeting Taiwan market.

Keywords: Thailand, Taiwanese female tourists, medical tourism, cosmetic surgery

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Authors: Y. Atilgan Şengül

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Health information technologies promise higher quality, safer care and much more for both patients and professionals. Despite their promise, they are costly to develop and difficult to implement. On the other hand, user acceptance and usage determine the success of implemented information technology in healthcare. This study provides a model to understand health professionals’ perception and expectation of health information technology. Extensive literature review has been conducted to determine the main factors to be measured. A questionnaire has been designed as a measurement model and submitted to the personnel of an in vitro fertilization clinic. The respondents’ degree of agreement according to five-point Likert scale was 72% for convenient access to data and 69.4% for the importance of data security. There was a significant difference in acceptance of electronic data storage for female respondents. Also, other significant differences between professions were obtained.

Keywords: healthcare, health informatics, medical record system, questionnaire

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Authors: Umamaheswari Shanmugam, Silvia Ronchi

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Software, artificial intelligence, and machine learning can improve healthcare through innovative and advanced technologies that can use the large amount and variety of data generated during healthcare services every day; one of the significant advantages of these new technologies is the ability to get experience and knowledge from real-world use and to improve their performance continuously. Healthcare systems and institutions can significantly benefit because the use of advanced technologies improves the efficiency and efficacy of healthcare. Software-defined as a medical device, is stand-alone software that is intended to be used for patients for one or more of these specific medical intended uses: - diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of a disease, any other health conditions, replacing or modifying any part of a physiological or pathological process–manage the received information from in vitro specimens derived from the human samples (body) and without principal main action of its principal intended use by pharmacological, immunological or metabolic definition. Software qualified as medical devices must comply with the general safety and performance requirements applicable to medical devices. These requirements are necessary to ensure high performance and quality and protect patients' safety. The evolution and the continuous improvement of software used in healthcare must consider the increase in regulatory requirements, which are becoming more complex in each market. The gap between these advanced technologies and the new regulations is the biggest challenge for medical device manufacturers. Regulatory requirements can be considered a market barrier, as they can delay or obstacle the device's approval. Still, they are necessary to ensure performance, quality, and safety. At the same time, they can be a business opportunity if the manufacturer can define the appropriate regulatory strategy in advance. The abstract will provide an overview of the current regulatory framework, the evolution of the international requirements, and the standards applicable to medical device software in the potential market all over the world.

Keywords: artificial intelligence, machine learning, SaMD, regulatory, clinical evaluation, classification, international requirements, MDR, 510k, PMA, IMDRF, cyber security, health care systems

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Authors: Kacper Tomasz Kröber-Mulawa

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This paper will address the topic of data ownership from an economic perspective, and examples of economic limitations of data property rights will be provided, which have been identified using methods and approaches of economic analysis of law. To properly build a background for the economic focus, in the beginning a short perspective of data and data ownership in the EU’s legal system will be provided. It will include a short introduction to its political and social importance and highlight relevant viewpoints. This will stress the importance of a Single Market for data but also far-reaching regulations of data governance and privacy (including the distinction of personal and non-personal data, data held by public bodies and private businesses). The main discussion of this paper will build upon the briefly referred to legal basis as well as methods and approaches of economic analysis of law.

Keywords: antitrust, data, data ownership, digital economy, property rights

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Authors: Trevor Kroeger, Hayden Williams, Edward Holzinger, David Coleman, Brian Haberman

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The network connectivity of medical devices is increasing at a rapid rate. Many medical devices, such as vital sign monitors, share information via wireless or wired connections. However, these connectivity options suffer from a variety of well-known limitations. Wireless connectivity, especially in the unlicensed radio frequency bands, can be disrupted. Such disruption could be due to benign reasons, such as a crowded spectrum, or to malicious intent. While wired connections are less susceptible to interference, they inhibit the mobility of the medical devices, which could be critical in a variety of scenarios. This work explores the application of Light Fidelity (Li-Fi) communication to enhance the security, performance, and mobility of medical devices in connected healthcare scenarios. A simple bridge for connected devices serves as an avenue to connect traditional medical devices to the Li-Fi network. This bridge was utilized to conduct bandwidth tests on a small Li-Fi network installed into a Mock-ICU setting with a backend enterprise network similar to that of a hospital. Mobile and stationary tests were conducted to replicate various different situations that might occur within a hospital setting. Results show that in room Li-Fi connectivity provides reasonable bandwidth and latency within a hospital like setting.

Keywords: hospital, light fidelity, Li-Fi, medical devices, security

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Authors: Rajae Tahri, Omar Chokairi, Asmae Saadi, Souad Chaouir

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Background: The factors motivating students to choose a medical career is a long-standing topic of publication and discussion. To our knowledge, no national study on the motivation for choosing medical studies has been published to date. Population and methods: This is an observational, descriptive, and cross-sectional study of first-year medical students at the Faculty of Medicine and Pharmacy of Rabat. An anonymous questionnaire comprising 16 questions was developed and distributed to students during Embryology tutorials. The students were free to fill it in or not. The number of students who consented to participate in the survey was 266. The variables studied are the socio-demographic variables of the students and the reasons for choosing medical studies. Results: The most strongly and frequently chosen reasons for choice by our students were saving lives (64.9%), helping others (62.1%), love of medicine (57%), and reducing suffering (56.5%). The comparison of the results according to gender showed a significant difference between the degree of self-motivation of girls compared to that of boys (p <0.001). The reason that stood out the most for them was teamwork. The presence of a health professional in the family was associated with strong extrinsic motivation (p = 0.005). Conclusion: Understanding medical student career choices would improve our knowledge of the factors that influence medical student learning and performance. This knowledge will make it possible to adapt the educational strategies to maintain the motivation of the students throughout their course as well as during their exercise.

Keywords: motivation, motivational profiles, medical studies, Morocco

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Authors: Abdullah Alsaeed

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Virtualized computing and cloud computing infrastructures are no longer fuzz or marketing term. They are a core reality in today’s corporate Information Technology (IT) organizations. Hence, developing an effective and efficient methodologies for data backup and data recovery is required more than any time. The purpose of data backup and recovery techniques are to assist the organizations to strategize the business continuity and disaster recovery approaches. In order to accomplish this strategic objective, a variety of mechanism were proposed in the recent years. This research paper will explore and examine the latest techniques and solutions to provide data backup and restoration for the cloud computing platforms.

Keywords: data backup, data recovery, cloud computing, business continuity, disaster recovery, cost-effective, data encryption.

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Authors: Salehah Tahkin, Norlaila Mustafa, Dayang Anita Abdul Aziz

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Knowledge of medical ethics is important to all practitioners so the best care can be delivered to all patients through safe practice. Surgeons are not exceptions to this. Knowledge, attitude, and practice (KAP) of medical ethics among paediatric surgeons and trainees in Malaysia has not been evaluated before. This study aims to determine the level of KAP regarding medical ethics among these groups. This was a cross-sectional study involving three groups of samples, i.e., paediatric surgeons (PS), paediatric surgical trainees (PST), and medical officers with a special interest in paediatric surgery (MO). A validated KAP questionnaire was used. Standard formulas were used to calculate objective indexes for measuring KAP, which were then compared for statistical significance across different sample groups; p less than 0.05 is taken as significant. The index is rated into 5 classes using a score of 0 to 10, i.e., poor (1-2.99), fair (3-4.99), good (5-6.99), very good (7-8.99), and excellent (9-10). There were 117 samples, i.e., PS n=45 (38.5%), PST n=25 (21.3%), and MO n=47 (40.2%). For knowledge, all three groups display a good index score (mean score of 5.44). For attitude, PS and MO also display an index score of good (mean score of 5.81), while the PST index score was fair (4.82). For practice, our study shows a highest score of 7.14 (very good) among PST. However, these differences were not statistically significant (p> 0.05). Conclusion: Training in paediatric surgery must continue to emphasize professionalism and medical ethics education to deliver the best health care services.

Keywords: KAP, medical ethics, paediatric, surgeons, trainees

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Authors: JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim

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In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.

Keywords: data estimation, link data, machine learning, road network

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Authors: Shima Hashemi, Firoozeh Ghazanfari

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Introduction: Eating disorder is a psychiatric disorder that is increasingly growing. This study aimed to determine the relationship between eating disorders (anorexia and bulimia nervosa) with some of the demographic factors among Lorestan University of Medical Sciences students. Materials and Methods: This study is a cross-sectional and descriptive study that was done at Lorestan University of Medical Sciences in 2019. Four hundred fifty students were studied by stratified and cluster sampling methods. For gathering data, we use the standard questionnaire Eating Attitudes Test EAT (26). Data were analyzed using statistical software SPSS. Results: According to the results, 144 (32%) males and 305 (67.8%) females were studied. 88.7% were single, and 8.9% were married. In the anorexia nervosa group, the results showed that there was a significant meaning between demographic information, and the number of family members, marital status, BMI, level of education, family income, father and mother education, as well as in the bulimia nervosa group, there was no significant meaning with any demographic information (p>0.05). Conclusion: Anorexia and bulimia nervosa are two known types of eating disorders, and some demographic factors can be effective in causing or aggravating these disorders.

Keywords: eating disorder, anorexia nervosa, bulimia nervosa, students

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Authors: Jinyan Li, Simon Fong, Raymond Wong, Mohammed Sabah, Fiaidhi Jinan

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Clinical data analysis and forecasting have make great contributions to disease control, prevention and detection. However, such data usually suffer from highly unbalanced samples in class distributions. In this paper, we target at the binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat-inspired algorithm, and combine both of them with the synthetic minority over-sampling technique (SMOTE) for processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reveal that while the performance improvements obtained by the former methods are not scalable to larger data scales, the later one, which we call Adaptive Swarm Balancing Algorithms, leads to significant efficiency and effectiveness improvements on large datasets. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. Leading to more credible performances of the classifier, and shortening the running time compared with the brute-force method.

Keywords: Imbalanced dataset, meta-heuristic algorithm, SMOTE, big data

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Authors: Nihar Bheda

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Protecting digital assets such as networks, systems, and data from advanced cyber threats is the aim of Digital Immunity Systems (DIS), which are a subset of cybersecurity. With features like continuous monitoring, coordinated reactions, and long-term adaptation, DIS seeks to mimic biological immunity. This minimizes downtime by automatically identifying and eliminating threats. Traditional security measures, such as firewalls and antivirus software, are insufficient for enterprises, such as healthcare providers, given the rapid evolution of cyber threats. The number of medical record breaches that have occurred in recent years is proof that attackers are finding healthcare data to be an increasingly valuable target. However, obstacles to enhancing security include outdated systems, financial limitations, and a lack of knowledge. DIS is an advancement in cyber defenses designed specifically for healthcare settings. Protection akin to an "immune system" is produced by core capabilities such as anomaly detection, access controls, and policy enforcement. Coordination of responses across IT infrastructure to contain attacks is made possible by automation and orchestration. Massive amounts of data are analyzed by AI and machine learning to find new threats. After an incident, self-healing enables services to resume quickly. The implementation of DIS is consistent with the healthcare industry's urgent requirement for resilient data security in light of evolving risks and strict guidelines. With resilient systems, it can help organizations lower business risk, minimize the effects of breaches, and preserve patient care continuity. DIS will be essential for protecting a variety of environments, including cloud computing and the Internet of medical devices, as healthcare providers quickly adopt new technologies. DIS lowers traditional security overhead for IT departments and offers automated protection, even though it requires an initial investment. In the near future, DIS may prove to be essential for small clinics, blood banks, imaging centers, large hospitals, and other healthcare organizations. Cyber resilience can become attainable for the whole healthcare ecosystem with customized DIS implementations.

Keywords: digital immunity system, cybersecurity, healthcare data, emerging technology

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Authors: Monica Lia

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This article presents a customer data analysis model using business intelligence tools for data modelling, transforming, data visualization and dynamic reports building. Economic organizational customer’s analysis is made based on the information from the transactional systems of the organization. The paper presents how to develop the data model starting for the data that companies have inside their own operational systems. The owned data can be transformed into useful information about customers using business intelligence tool. For a mature market, knowing the information inside the data and making forecast for strategic decision become more important. Business Intelligence tools are used in business organization as support for decision-making.

Keywords: customer analysis, business intelligence, data warehouse, data mining, decisions, self-service reports, interactive visual analysis, and dynamic dashboards, use cases diagram, process modelling, logical data model, data mart, ETL, star schema, OLAP, data universes

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Authors: Boukhatem Mohamed Belkaid, Lahdi Mourad

Abstract:

In this paper, we propose a new encryption system for security issues medical images. The hybrid encryption scheme is based on AES and RSA algorithms to validate the three security services are authentication, integrity, and confidentiality. Privacy is ensured by AES, authenticity is ensured by the RSA algorithm. Integrity is assured by the basic function of the correlation between adjacent pixels. Our system generates a unique password every new session of encryption, that will be used to encrypt each frame of the medical image basis to strengthen and ensure his safety. Several metrics have been used for various tests of our analysis. For the integrity test, we noticed the efficiencies of our system and how the imprint cryptographic changes at reception if a change affects the image in the transmission channel.

Keywords: AES, RSA, integrity, confidentiality, authentication, medical images, encryption, decryption, key, correlation

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Authors: Rao Muhammad Afzal Khan

Abstract:

Nano technology is emerging as a useful technology in nearly all areas of Science and Technology. Its role in medical imaging is attracting the researchers towards existing and new imaging modalities and techniques. This presentation gives an overview of the development of the work done throughout the world. Furthermore, it lays an idea into the scope of the future use of this technology for diagnosing different diseases. A comparative analysis has also been discussed with an emphasis to detect diseases, in general, and cancer, in particular.

Keywords: medical imaging, cancer detection, diagnosis, nano-imaging, nanotechnology

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Authors: K. Hardy, A. Maurushat

Abstract:

Policy makers are increasingly looking to make evidence-based decisions. Evidence-based decisions have historically used rigorous methodologies of empirical studies by research institutes, as well as less reliable immediate survey/polls often with limited sample sizes. As we move into the era of Big Data analytics, policy makers are looking to different methodologies to deliver reliable empirics in real-time. The question is not why did these people do this for the last 10 years, but why are these people doing this now, and if the this is undesirable, and how can we have an impact to promote change immediately. Big data analytics rely heavily on government data that has been released in to the public domain. The open data movement promises greater productivity and more efficient delivery of services; however, Australian government agencies remain reluctant to release their data to the general public. This paper considers the barriers to releasing government data as open data, and how these barriers might be overcome.

Keywords: big data, open data, productivity, data governance

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Authors: Tri Murti Tugiman, Awalyya Fasha

Abstract:

This research aims to analyze the magnitude of the economic losses incurred as a result of a person suffering from a particular disease of the ten highest in the productive age diseases in Hospitals Inche Abdul Moeis Samarinda. This research was a descriptive survey research and a secondary data analysis. For the analysis of economic losses populations used are all in patients who suffer from the 10 highest diseases in the productive age in hospitals IA Moeis Samarinda in 2011. Sampling was performed by using a stratified random sampling with samples of 77 people. Research results indicate that the direct cost community incurred to obtain medical services in hospitals IA Moeis is IDR 74437520. The amount of indirect costs incurred during service in a community hospital is IDR 10562000. The amount lost due to sickness fee is IDR 5377800. The amount of economic lost people to obtain medical services in hospitals IA Moeis is IDR 90377320. The number of days of hospitalization was as much as 171 respondents throughout the day. This study suggests the economic loss could be prevented by changes in the lifestyle of the people who clean and healthy along with the following insurance.

Keywords: hospitalized, economic lost, productive age diseases, secondary data analysis

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