The task of medical diagnosis with the help different intelligent system techniques is always crucial because it require high level of accuracy and less time consumption in decision making.
Among all other AI techniques Artificial Neural Networks (ANN) as a tool for medical diagnosis has become the most popular in last few decades due to its flexibility and accuracy. ANN was
developed after getting the inspiration from biological neurons. There are various diseases that are still needed to be diagnosed. Among many other critical diseases like cancer, thyroid disorder, diabetes, heart diseases, neuro diseases, asthma disease was also tried to bediagnosed
effectively with various ANN mechanisms by different researchers. Due to various uncertainties about symptoms the study of Neuro-Fuzzy technique in this context became very popular in last few years. Neuro-Fuzzy now-a-days is one of the most advanced technique that is mainly concatenation of two model-neural networks and the fuzzy logic. In this model various
parameters are used that are much crucial if ill-chosen and may led to failure of the whole system. Recent trend in analysis is following this model for advanced expert work. In this study
an enhanced Neuro-fuzzy model has been proposed for the proper diagnosis of adult Asthma disease and to foster the proper aid or medication to the patients and make physicians alert forthe upcoming disease pattern otherwise they may lack in the process of providing improper medication at right time. In the first phase data collected from various hospitals are used to
train by three different types of learning of ANN like ANN with Self Organizing Maps (SOM),ANN with Learning Vector Quantization (LVQ) and ANN with Backpropagation Algorithm
(BPA) through NF tool for much accurate result. In the second phase fuzzy rule base is appliedto the classified data for the diagnosis of the disease.
A study on “impact of artificial intelligence in covid19 diagnosis”Dr. C.V. Suresh Babu
Although the lungs are one of the most vital organs in the body, they are vulnerable to infection and injury. COVID-19 has put the entire world in an unprecedented difficult situation, bringing life to a halt and claiming thousands of lives all across the world. Medical imaging, such as X-rays and computed tomography (CT), is essential in the global fight against COVID-19, and newly emerging artificial intelligence (AI) technologies are boosting the power of imaging tools and assisting medical specialists. AI can improve job efficiency by precisely identifying infections in X-ray and CT images and allowing further measurement. We focus on the integration of AI with X-ray and CT, both of which are routinely used in frontline hospitals, to reflect the most recent progress in medical imaging and radiology combating COVID-19.
A study on “Diagnosis Test of Diabetics and Hypertension by AI”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
A study on the impact of data analytics in COVID-19 health care systemDr. C.V. Suresh Babu
Through the disperse of novel coronavirus illness globally, existence became considerably contrived. Data analytics have experienced powerful development over the past few years. As it happens, it’s exceptionally considerable to take advantage of data analytics to assist mankind in a prompt as well as factually precise method to forestall additionally restrain the advancement of the widespread, sustain gregarious balance and evaluate the influence of the widespread. The unforeseen significant number of coronavirus disease instances has disturbed medical care system in many economies furthermore eventuated in an insufficiency of dormitory in the hospices. For this reason, prognosticating quantity of coronavirus infection instances is indispensable for administrations to adopt the necessary measures. The count of coronavirus disease instances could be correctly anticipated by taking into account historical records of announced instances side by side few extraneous components that impact the disseminate of the COVID-19 . Hence, the principal aim out of this research is to contemporaneously consider historical data and the extraneous components. This paper explores how data analytics can play a role in health care especially in novel coronavirus illness.
A study on “the impact of data analytics in covid 19 health care system”Dr. C.V. Suresh Babu
A Study on “The Impact of Data Analytics in COVID-19 Health Care System”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
Integrated E-Health Approach For Early Detection of Human Body Disorders in R...IOSR Journals
This document presents an integrated e-health approach for early detection of human body disorders in real-time. It monitors heart rate, respiration, and body temperature using sensors and transfers the data wirelessly via ZigBee to a PC. The values are tested against a reference database to detect any abnormalities. Simulations were performed using LabVIEW to acquire, monitor, and synthesize the vital sign data in real-time. This non-invasive method allows for early diagnosis and prevention of cardiovascular diseases and respiratory issues.
Personalized Medicine: A Utilization In Pharmaceutical Field.(A Review) Makrani Shaharukh
Personalized Medicine (PM) is an emerging exercise of medicine that uses a person‟s genetic summary to monitor judgments made in favor to the diagnosis, inhibition and treatment of diseases. Personalized medicine is presence innovative through data from the Human Genome Project. It is initial to complete its aim of “the right therapy to the right patient at the right time”. Currently PM is moving us closer to more exact, predictable and powerful medication tailored for an individual patient. By the way the genomic data is the dynamic force late PM. Combined understanding of genetics is approving us to provide greater diagnoses, safer medication advising, and more effective treatment of the diseases and conditions that have affected us throughout history. This review focus on various aspects of personalized medicine.
This document summarizes a knowledge-driven personalized contextual mobile health service called kHealth for asthma management in children. kHealth collects data from sensors and patients to provide personalized and actionable information to help manage asthma. It was tested with four asthma patients collecting environmental, physiological and activity data. Preliminary analysis found relationships between symptoms, medication use and triggers like pollen levels and exhaled nitric oxide. The goal is to help doctors and patients better understand individual responses to triggers to improve personalized treatment for the heterogeneous and variable condition of asthma. Future work includes a larger clinical trial, formulating a patient vulnerability score, and adding new sensors.
This document discusses epidemiological research and treatment studies. It defines epidemiology as the scientific study of disease patterns in human populations and the application of this study to disease control. The aims of epidemiology are described as describing disease occurrence and distribution, identifying disease causes, and providing data to plan, implement and evaluate prevention and treatment services. Epidemiological studies include descriptive studies like case reports and surveys, and analytical studies like cross-sectional, cohort and case-control studies to test hypotheses. Disease prevention and control involves preventing disease sources, early diagnosis, treatment, quarantine, interrupting transmission, and preventing susceptible hosts through immunization and health education.
A study on “impact of artificial intelligence in covid19 diagnosis”Dr. C.V. Suresh Babu
Although the lungs are one of the most vital organs in the body, they are vulnerable to infection and injury. COVID-19 has put the entire world in an unprecedented difficult situation, bringing life to a halt and claiming thousands of lives all across the world. Medical imaging, such as X-rays and computed tomography (CT), is essential in the global fight against COVID-19, and newly emerging artificial intelligence (AI) technologies are boosting the power of imaging tools and assisting medical specialists. AI can improve job efficiency by precisely identifying infections in X-ray and CT images and allowing further measurement. We focus on the integration of AI with X-ray and CT, both of which are routinely used in frontline hospitals, to reflect the most recent progress in medical imaging and radiology combating COVID-19.
A study on “Diagnosis Test of Diabetics and Hypertension by AI”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
A study on the impact of data analytics in COVID-19 health care systemDr. C.V. Suresh Babu
Through the disperse of novel coronavirus illness globally, existence became considerably contrived. Data analytics have experienced powerful development over the past few years. As it happens, it’s exceptionally considerable to take advantage of data analytics to assist mankind in a prompt as well as factually precise method to forestall additionally restrain the advancement of the widespread, sustain gregarious balance and evaluate the influence of the widespread. The unforeseen significant number of coronavirus disease instances has disturbed medical care system in many economies furthermore eventuated in an insufficiency of dormitory in the hospices. For this reason, prognosticating quantity of coronavirus infection instances is indispensable for administrations to adopt the necessary measures. The count of coronavirus disease instances could be correctly anticipated by taking into account historical records of announced instances side by side few extraneous components that impact the disseminate of the COVID-19 . Hence, the principal aim out of this research is to contemporaneously consider historical data and the extraneous components. This paper explores how data analytics can play a role in health care especially in novel coronavirus illness.
A study on “the impact of data analytics in covid 19 health care system”Dr. C.V. Suresh Babu
A Study on “The Impact of Data Analytics in COVID-19 Health Care System”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
Integrated E-Health Approach For Early Detection of Human Body Disorders in R...IOSR Journals
This document presents an integrated e-health approach for early detection of human body disorders in real-time. It monitors heart rate, respiration, and body temperature using sensors and transfers the data wirelessly via ZigBee to a PC. The values are tested against a reference database to detect any abnormalities. Simulations were performed using LabVIEW to acquire, monitor, and synthesize the vital sign data in real-time. This non-invasive method allows for early diagnosis and prevention of cardiovascular diseases and respiratory issues.
Personalized Medicine: A Utilization In Pharmaceutical Field.(A Review) Makrani Shaharukh
Personalized Medicine (PM) is an emerging exercise of medicine that uses a person‟s genetic summary to monitor judgments made in favor to the diagnosis, inhibition and treatment of diseases. Personalized medicine is presence innovative through data from the Human Genome Project. It is initial to complete its aim of “the right therapy to the right patient at the right time”. Currently PM is moving us closer to more exact, predictable and powerful medication tailored for an individual patient. By the way the genomic data is the dynamic force late PM. Combined understanding of genetics is approving us to provide greater diagnoses, safer medication advising, and more effective treatment of the diseases and conditions that have affected us throughout history. This review focus on various aspects of personalized medicine.
This document summarizes a knowledge-driven personalized contextual mobile health service called kHealth for asthma management in children. kHealth collects data from sensors and patients to provide personalized and actionable information to help manage asthma. It was tested with four asthma patients collecting environmental, physiological and activity data. Preliminary analysis found relationships between symptoms, medication use and triggers like pollen levels and exhaled nitric oxide. The goal is to help doctors and patients better understand individual responses to triggers to improve personalized treatment for the heterogeneous and variable condition of asthma. Future work includes a larger clinical trial, formulating a patient vulnerability score, and adding new sensors.
This document discusses epidemiological research and treatment studies. It defines epidemiology as the scientific study of disease patterns in human populations and the application of this study to disease control. The aims of epidemiology are described as describing disease occurrence and distribution, identifying disease causes, and providing data to plan, implement and evaluate prevention and treatment services. Epidemiological studies include descriptive studies like case reports and surveys, and analytical studies like cross-sectional, cohort and case-control studies to test hypotheses. Disease prevention and control involves preventing disease sources, early diagnosis, treatment, quarantine, interrupting transmission, and preventing susceptible hosts through immunization and health education.
Dr Mike Repacholi gave a presentation on common questions about the health effects of mobile phone use. He discussed the following key points in 3 sentences:
Extensive research has been conducted on whether mobile phone use causes brain cancer, but large studies like Interphone have found no consistent evidence of increased brain tumor risk except possibly among very heavy long-term users. While the IARC classified RF fields as possibly carcinogenic, this is the lowest cancer risk classification and means more research is still needed. Repacholi summarized that the current scientific consensus is that there is no evidence mobile phone use causes health issues, though more research on long-term and child use is still warranted.
Video: https://youtu.be/Yv6L_b8ZrtU
Abstract:
Asthma, chronic pulmonary disease, is one of the major health issues in the United States. Given its chronic nature, the demand for continuous monitoring of patient’s adherence to the medication care plan, assessment of their environment triggers, and management of asthma control level can be challenging in traditional clinical settings and taxing on clinical professionals. A shift from a reactive to a proactive asthma care can improve health outcomes and reduce expenses. On the technology spectrum, smart conversational systems and Internet-of-Things (IoTs) are rapidly gaining popularity in the healthcare industry. By leveraging such technological prevalence, it is feasible to design a system that is capable of monitoring asthmatic patients for a prolonged period and empowering them to manage their health better.
In this thesis, we describe kBot, a knowledge-driven personalized chatbot system designed to continuously track medication adherence of pediatric asthmatic patients (age 8 to 15) and monitor relevant health and environmental data. The outcome is to help asthma patients self manage their asthma progression by generating trigger alerts and educate them with various self-management strategies. kBOT takes the form of an Android application with a frontend chat interface capable of conversing both text and voice-based conversations and a backend cloud-based server application that handles data collection, processing, and dialogue management. The domain knowledge component is pieced together from the Asthma and Allergy Foundation of America, Mayoclinic, and Verywell Health as well as our clinical collaborator. Whereas, the personalization aspect is derived from the patient’s history of asthma collected from the questionnaires and day-to-day conversations. The system has been evaluated by eight asthma clinicians and eight computer science researchers for chatbot quality, technology acceptance, and system usability. kBOT achieved an overall technology acceptance score of greater than 8 on an 11-point Likert scale and a mean System Usability Score (SUS) greater than 80 from both evaluation groups.
https://www.facebook.com/pg/Kno.e.sis/photos/?tab=album&album_id=2560068547361311
E-HEALTH BIOSENSOR PLATFORM FOR NONINVASIVE HEALTH MONITORING FOR THE ELDERLY...ijbesjournal
New technologies in the field of tele-health using biosensor systems for non-invasive vital signs monitoring of patients, especially elderly people who need long-term care, and marginalized areas with hard to reach health care services are emerging. A study involving a self-care approach within the cardiac domain, where late detection increases the likelihood of patient disability or of premature death is proposed. In the
study the application of e-health biosensors platform in medical services is experimented. The study resulted into the synthesis of vital signs from various body positions with biosensors that does not require a full coupled system. A model for the prevention of cardiovascular disease management based on noninvasive personal health monitoring systems with easy access for everybody, at any time or location is designed. A personal vital sign system such as ECG sensor which contain the functionality, allows recording anywhere and at any time a diagnostic quality ECG and analyzing it “on-board” by comparing it to a reference ECG, is modelled. The model called Mobile Health for the Elderly Persons (MOHELP)
which relies on with application in estimation and control of boolean processes based on noisy and incomplete measurements is designed. This enabled a reliable recommendation from a digital artificial intelligence-based diagnosis, which can support an elderly person to take timely and correct decisions upon his (her) health status. In a case of urgency, the assistant puts the elderly person in a contact with
healthcare providers. The signal pattern sensitivity related to sensors placement is one of the issues this study addressed using e-sensor platform. Sensors displacement errors have a direct impact on the medical diagnosis, especially if the diagnostic procedure is automated. The study resulted into the formulation of a methodology for e-Health Sensor Platform, in software architecture terms, that permits use of system
biosensors to adapt to the user-specific context for self-healthcare
This document provides an overview of epidemiology, including its basic concepts, principles, scope, and measurement tools. Some key points:
- Epidemiology is the study of disease distribution and determinants in populations, and is used to prevent and control health problems. It describes disease patterns and identifies risk factors.
- Epidemiological principles are applied in various areas like clinical research, disease prevention, and health services evaluation. Measurement tools include rates, ratios, and proportions to quantify disease frequency and burden.
- The scope of epidemiology includes measuring mortality, morbidity, disability, births, risk factors, and assessing health needs in populations. Different study designs are used to investigate disease etiology and evaluate interventions.
This document provides an introduction to and overview of a textbook on epidemiology. It discusses how epidemiology aims to discover the causes of disease in populations in order to enable disease prevention. The textbook focuses on observational epidemiological studies, which have the advantage over experimental studies of being able to study issues where randomized trials would be unethical or impractical, but have limitations due to lack of randomization. It describes the contents of the textbook, which covers study design, study design issues, conducting epidemiological studies, and analyzing and interpreting study results. The introduction emphasizes that epidemiology focuses on prevention rather than treatment and on populations rather than individuals.
Computer systems and the internet have greatly improved healthcare in several ways:
1. Electronic medical records allow doctors to access complete patient histories instantly and share information between hospitals. Computerized prescriptions reduce errors.
2. Diagnostic tools like CT scans, MRIs, and ultrasounds can identify medical issues much faster and more accurately than before. Monitoring equipment keeps close tabs on patients' vital signs.
3. Treatments are also enhanced through robotics in surgery, pacemakers, ventilators, and prosthetics that can mimic natural limb movement. Online support groups and research databases help patients.
4. However, self-diagnosis online risks missing issues, and purchasing medications without a prescription
The document discusses the field of health informatics and provides definitions and examples. It defines health informatics as the application of information science to healthcare and biomedical research. It describes the relationships between health informatics and other fields like computer science, engineering, and the medical sciences. The document also discusses different areas of health informatics like clinical informatics, public health informatics, and consumer health informatics. It provides examples of common health information technologies used in healthcare settings like electronic health records, computerized physician order entry, and picture archiving systems.
Efficient Fuzzy-Based System for the Diagnosis and Treatment of Tuberculosis ...Editor IJCATR
The aim of this study is to design a FuzzyBased Expert System for Tuberculosis diagnosis
and Treatment. The designed system made use of General Hospital Adikpo, patient database. The system
has 18 input fields and five outputs field. Input fields are Chest pain (CP), cough duration (CD), fever
duration (FV), night sweats (NS), weight loss (WL), loss of appetite (LOA), change in bowel habits
(CBH), variations in mental behaviour (VMB), masses along the neck (MAN), draining sinus (DS),
coma (seizure) (CO), stiff Neck (SN), headache (HD), abdominal Pain (AP), painful or uncomfortable
urination (PU), hemopysis (coughing up blood) (CUB), fatigue (FA) and blood present in urine (BPU).
The output fields refers to the class/group of tuberculosis disease in the patient. This system uses
Mamdani inference method. The results obtained from designed system are compared with the data in
the database and observed results of designed system are correct. The system was designed with Java
(Jfuzzylogic), Microsoft visio (2013), mySql workbench, MySql database, JSP and XHML.
Epidemiological intelligence involves collecting disease data, analyzing it, and disseminating findings to relevant parties. Data collection involves methods like mortality registration, ongoing morbidity reporting from farms and hospitals, and diagnostic laboratory records. Data is collated and analyzed to identify disease determinants and support control strategies. Analysis results and ongoing reports on control efforts are expressed and interpreted, then promptly distributed to data providers, decision makers, and the public using formats like tables, graphs, maps, and verbal/written communications. The goal is providing early warning of health threats and supporting evidence-based decisions.
The document provides lecture notes on epidemiology for environmental and occupational health students, covering topics such as the definition of epidemiology, the history of epidemiology, disease causation, levels of disease prevention, infectious disease epidemiology, descriptive epidemiology, and measurements of morbidity and mortality. The notes were developed by Yigzaw Kebede of the University of Gondar in Ethiopia in collaboration with various Ethiopian government ministries and international partners.
An overview of HEALTH & Financial forecasting in hospitals By Dr.Mahboob Khan...Healthcare consultant
This document provides an overview of health forecasting. It defines health forecasting as predicting future health events or situations to facilitate preventative healthcare and resource planning. The document outlines some key principles of health forecasting, including measuring uncertainty, focusing on aggregated population data for improved accuracy, and defining appropriate forecasting horizons. It also discusses challenges like data limitations and demonstrating forecast performance. Overall, the document aims to stimulate discussion on standardizing approaches to health forecasting to better support healthcare delivery and services.
Monitoring and improving quality of mental health care in EuropeTHL
This document discusses quality of mental healthcare in Europe. It describes factors that influence continuity of care after hospitalization like travel times to services and discharge planning. It also summarizes data on various quality indicators like readmission rates, length of stay, and community follow-up. Quality is measured differently between countries, for example some have mandatory patient satisfaction surveys while others do not formally assess needs or quality of care. Integrating social and mental health services improves quality but ratios of community to hospital beds vary significantly between locations.
This document provides information on recommended international scientific journals in the fields of public health, environmental health, and occupational medicine. It lists over 100 journals that are indexed in major international databases like PubMed, Scopus, and Web of Science, which indicates they meet international standards for scholarly publishing. The journals cover topics like epidemiology, health promotion, disease prevention, community health, and environmental health. The list was compiled by an expert and provides links to obtain more details on the journal titles and publication information.
Giris basics of biomedical informatics generalSerkan Turkeli
At the end of this course, students will be able to
• Define medical informatics
• Define information management, information technology and informatics
• Define concepts of medical informatics
• Selecting best techniques to manage a medical informatics project.
Tele-ophthalmology: the new normal in current timesObaidur Rehman
Covers telehealth and telemedicine in general. Tele-ophthalmology development in India. Practice and patterns as defined by concerned authorities. Guidelines as set up Govt of India. Current tele-ophthalmology projects in India
- Bio-Medical Informatics (BMI) is the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, motivated by efforts to improve human health.
- Emergency Medical Informatics (EMI) applies BMI principles to emergency patient care and operations by facilitating the collection, management, processing, and application of emergency patient care and operational data.
- As an emergency physician, it is important to ensure that any emergency department information system (EDIS) is safe, effective, and patient-centered, and to find ways to measure its impact and make it more valuable for clinical practice and research. National organizations like the Korean
This document summarizes the key findings from several studies on patients' attitudes towards privacy and sharing of their medical information electronically. The studies found that patients have significant concerns about privacy and security of their medical records. They worry about sensitive information being shared without consent and lack confidence in security measures. Preliminary results from an ongoing New Zealand study show wide variation in patients' comfort levels depending on the level of identification, content, reason for access, and whether sensitive issues are involved. The document stresses the importance of considering privacy and involving patients to build trust in electronic health information systems.
The document discusses the emerging field of precision medicine and how it represents a shift from symptom-based to evidence-based to personalized medicine. Precision medicine utilizes large datasets including multi-omics data, imaging, and other clinical data combined with machine learning algorithms and reference databases to generate personalized molecular profiles and enable targeted prevention and treatment. Key to realizing precision medicine's potential is establishing standards, processes, and reference databases to facilitate large-scale data analysis and ensure results can be reproduced.
ONLINE FUZZY-LOGIC KNOWLEDGE WAREHOUSING AND MINING MODEL FOR THE DIAGNOSIS A...ijcsity
This document presents a model for an online fuzzy-logic knowledge warehousing and mining system for diagnosing and treating HIV/AIDS. The system would store patient data and medical knowledge about HIV/AIDS. It uses fuzzy logic and data mining to predict HIV/AIDS status, monitor patient health over time, and determine recommended treatment plans. The system was tested on real patient data from a hospital in Nigeria. It aims to provide an efficient way to diagnose, treat, and monitor people living with HIV/AIDS.
Detection of myocardial infarction on recent dataset using machine learningIJICTJOURNAL
In developing countries such as India, with a large aging population and limited access to medical facilities, remote and timely diagnosis of myocardial infarction (MI) has the potential to save the life of many. An electrocardiogram is the primary clinical tool utilized in the onset or detection of a previous MI incident. Artificial intelligence has made a great impact on every area of research as well as in medical diagnosis. In medical diagnosis, the hypothesis might be doctors' experience which would be used as input to predict a disease that saves the life of mankind. It is been observed that a properly cleaned and pruned dataset provides far better accuracy than an unclean one with missing values. Selection of suitable techniques for data cleaning alongside proper classification algorithms will cause the event of prediction systems that give enhanced accuracy. In this proposal detection of myocardial infarction using new parameters is proposed with increased accuracy and efficiency of the existing model. Additional parameters are used to predict MI with more accuracy. The proposed model is used to predict an early diagnosis of MI with the help of expertise experiences and data gathered from hospitals.
Dr Mike Repacholi gave a presentation on common questions about the health effects of mobile phone use. He discussed the following key points in 3 sentences:
Extensive research has been conducted on whether mobile phone use causes brain cancer, but large studies like Interphone have found no consistent evidence of increased brain tumor risk except possibly among very heavy long-term users. While the IARC classified RF fields as possibly carcinogenic, this is the lowest cancer risk classification and means more research is still needed. Repacholi summarized that the current scientific consensus is that there is no evidence mobile phone use causes health issues, though more research on long-term and child use is still warranted.
Video: https://youtu.be/Yv6L_b8ZrtU
Abstract:
Asthma, chronic pulmonary disease, is one of the major health issues in the United States. Given its chronic nature, the demand for continuous monitoring of patient’s adherence to the medication care plan, assessment of their environment triggers, and management of asthma control level can be challenging in traditional clinical settings and taxing on clinical professionals. A shift from a reactive to a proactive asthma care can improve health outcomes and reduce expenses. On the technology spectrum, smart conversational systems and Internet-of-Things (IoTs) are rapidly gaining popularity in the healthcare industry. By leveraging such technological prevalence, it is feasible to design a system that is capable of monitoring asthmatic patients for a prolonged period and empowering them to manage their health better.
In this thesis, we describe kBot, a knowledge-driven personalized chatbot system designed to continuously track medication adherence of pediatric asthmatic patients (age 8 to 15) and monitor relevant health and environmental data. The outcome is to help asthma patients self manage their asthma progression by generating trigger alerts and educate them with various self-management strategies. kBOT takes the form of an Android application with a frontend chat interface capable of conversing both text and voice-based conversations and a backend cloud-based server application that handles data collection, processing, and dialogue management. The domain knowledge component is pieced together from the Asthma and Allergy Foundation of America, Mayoclinic, and Verywell Health as well as our clinical collaborator. Whereas, the personalization aspect is derived from the patient’s history of asthma collected from the questionnaires and day-to-day conversations. The system has been evaluated by eight asthma clinicians and eight computer science researchers for chatbot quality, technology acceptance, and system usability. kBOT achieved an overall technology acceptance score of greater than 8 on an 11-point Likert scale and a mean System Usability Score (SUS) greater than 80 from both evaluation groups.
https://www.facebook.com/pg/Kno.e.sis/photos/?tab=album&album_id=2560068547361311
E-HEALTH BIOSENSOR PLATFORM FOR NONINVASIVE HEALTH MONITORING FOR THE ELDERLY...ijbesjournal
New technologies in the field of tele-health using biosensor systems for non-invasive vital signs monitoring of patients, especially elderly people who need long-term care, and marginalized areas with hard to reach health care services are emerging. A study involving a self-care approach within the cardiac domain, where late detection increases the likelihood of patient disability or of premature death is proposed. In the
study the application of e-health biosensors platform in medical services is experimented. The study resulted into the synthesis of vital signs from various body positions with biosensors that does not require a full coupled system. A model for the prevention of cardiovascular disease management based on noninvasive personal health monitoring systems with easy access for everybody, at any time or location is designed. A personal vital sign system such as ECG sensor which contain the functionality, allows recording anywhere and at any time a diagnostic quality ECG and analyzing it “on-board” by comparing it to a reference ECG, is modelled. The model called Mobile Health for the Elderly Persons (MOHELP)
which relies on with application in estimation and control of boolean processes based on noisy and incomplete measurements is designed. This enabled a reliable recommendation from a digital artificial intelligence-based diagnosis, which can support an elderly person to take timely and correct decisions upon his (her) health status. In a case of urgency, the assistant puts the elderly person in a contact with
healthcare providers. The signal pattern sensitivity related to sensors placement is one of the issues this study addressed using e-sensor platform. Sensors displacement errors have a direct impact on the medical diagnosis, especially if the diagnostic procedure is automated. The study resulted into the formulation of a methodology for e-Health Sensor Platform, in software architecture terms, that permits use of system
biosensors to adapt to the user-specific context for self-healthcare
This document provides an overview of epidemiology, including its basic concepts, principles, scope, and measurement tools. Some key points:
- Epidemiology is the study of disease distribution and determinants in populations, and is used to prevent and control health problems. It describes disease patterns and identifies risk factors.
- Epidemiological principles are applied in various areas like clinical research, disease prevention, and health services evaluation. Measurement tools include rates, ratios, and proportions to quantify disease frequency and burden.
- The scope of epidemiology includes measuring mortality, morbidity, disability, births, risk factors, and assessing health needs in populations. Different study designs are used to investigate disease etiology and evaluate interventions.
This document provides an introduction to and overview of a textbook on epidemiology. It discusses how epidemiology aims to discover the causes of disease in populations in order to enable disease prevention. The textbook focuses on observational epidemiological studies, which have the advantage over experimental studies of being able to study issues where randomized trials would be unethical or impractical, but have limitations due to lack of randomization. It describes the contents of the textbook, which covers study design, study design issues, conducting epidemiological studies, and analyzing and interpreting study results. The introduction emphasizes that epidemiology focuses on prevention rather than treatment and on populations rather than individuals.
Computer systems and the internet have greatly improved healthcare in several ways:
1. Electronic medical records allow doctors to access complete patient histories instantly and share information between hospitals. Computerized prescriptions reduce errors.
2. Diagnostic tools like CT scans, MRIs, and ultrasounds can identify medical issues much faster and more accurately than before. Monitoring equipment keeps close tabs on patients' vital signs.
3. Treatments are also enhanced through robotics in surgery, pacemakers, ventilators, and prosthetics that can mimic natural limb movement. Online support groups and research databases help patients.
4. However, self-diagnosis online risks missing issues, and purchasing medications without a prescription
The document discusses the field of health informatics and provides definitions and examples. It defines health informatics as the application of information science to healthcare and biomedical research. It describes the relationships between health informatics and other fields like computer science, engineering, and the medical sciences. The document also discusses different areas of health informatics like clinical informatics, public health informatics, and consumer health informatics. It provides examples of common health information technologies used in healthcare settings like electronic health records, computerized physician order entry, and picture archiving systems.
Efficient Fuzzy-Based System for the Diagnosis and Treatment of Tuberculosis ...Editor IJCATR
The aim of this study is to design a FuzzyBased Expert System for Tuberculosis diagnosis
and Treatment. The designed system made use of General Hospital Adikpo, patient database. The system
has 18 input fields and five outputs field. Input fields are Chest pain (CP), cough duration (CD), fever
duration (FV), night sweats (NS), weight loss (WL), loss of appetite (LOA), change in bowel habits
(CBH), variations in mental behaviour (VMB), masses along the neck (MAN), draining sinus (DS),
coma (seizure) (CO), stiff Neck (SN), headache (HD), abdominal Pain (AP), painful or uncomfortable
urination (PU), hemopysis (coughing up blood) (CUB), fatigue (FA) and blood present in urine (BPU).
The output fields refers to the class/group of tuberculosis disease in the patient. This system uses
Mamdani inference method. The results obtained from designed system are compared with the data in
the database and observed results of designed system are correct. The system was designed with Java
(Jfuzzylogic), Microsoft visio (2013), mySql workbench, MySql database, JSP and XHML.
Epidemiological intelligence involves collecting disease data, analyzing it, and disseminating findings to relevant parties. Data collection involves methods like mortality registration, ongoing morbidity reporting from farms and hospitals, and diagnostic laboratory records. Data is collated and analyzed to identify disease determinants and support control strategies. Analysis results and ongoing reports on control efforts are expressed and interpreted, then promptly distributed to data providers, decision makers, and the public using formats like tables, graphs, maps, and verbal/written communications. The goal is providing early warning of health threats and supporting evidence-based decisions.
The document provides lecture notes on epidemiology for environmental and occupational health students, covering topics such as the definition of epidemiology, the history of epidemiology, disease causation, levels of disease prevention, infectious disease epidemiology, descriptive epidemiology, and measurements of morbidity and mortality. The notes were developed by Yigzaw Kebede of the University of Gondar in Ethiopia in collaboration with various Ethiopian government ministries and international partners.
An overview of HEALTH & Financial forecasting in hospitals By Dr.Mahboob Khan...Healthcare consultant
This document provides an overview of health forecasting. It defines health forecasting as predicting future health events or situations to facilitate preventative healthcare and resource planning. The document outlines some key principles of health forecasting, including measuring uncertainty, focusing on aggregated population data for improved accuracy, and defining appropriate forecasting horizons. It also discusses challenges like data limitations and demonstrating forecast performance. Overall, the document aims to stimulate discussion on standardizing approaches to health forecasting to better support healthcare delivery and services.
Monitoring and improving quality of mental health care in EuropeTHL
This document discusses quality of mental healthcare in Europe. It describes factors that influence continuity of care after hospitalization like travel times to services and discharge planning. It also summarizes data on various quality indicators like readmission rates, length of stay, and community follow-up. Quality is measured differently between countries, for example some have mandatory patient satisfaction surveys while others do not formally assess needs or quality of care. Integrating social and mental health services improves quality but ratios of community to hospital beds vary significantly between locations.
This document provides information on recommended international scientific journals in the fields of public health, environmental health, and occupational medicine. It lists over 100 journals that are indexed in major international databases like PubMed, Scopus, and Web of Science, which indicates they meet international standards for scholarly publishing. The journals cover topics like epidemiology, health promotion, disease prevention, community health, and environmental health. The list was compiled by an expert and provides links to obtain more details on the journal titles and publication information.
Giris basics of biomedical informatics generalSerkan Turkeli
At the end of this course, students will be able to
• Define medical informatics
• Define information management, information technology and informatics
• Define concepts of medical informatics
• Selecting best techniques to manage a medical informatics project.
Tele-ophthalmology: the new normal in current timesObaidur Rehman
Covers telehealth and telemedicine in general. Tele-ophthalmology development in India. Practice and patterns as defined by concerned authorities. Guidelines as set up Govt of India. Current tele-ophthalmology projects in India
- Bio-Medical Informatics (BMI) is the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, motivated by efforts to improve human health.
- Emergency Medical Informatics (EMI) applies BMI principles to emergency patient care and operations by facilitating the collection, management, processing, and application of emergency patient care and operational data.
- As an emergency physician, it is important to ensure that any emergency department information system (EDIS) is safe, effective, and patient-centered, and to find ways to measure its impact and make it more valuable for clinical practice and research. National organizations like the Korean
This document summarizes the key findings from several studies on patients' attitudes towards privacy and sharing of their medical information electronically. The studies found that patients have significant concerns about privacy and security of their medical records. They worry about sensitive information being shared without consent and lack confidence in security measures. Preliminary results from an ongoing New Zealand study show wide variation in patients' comfort levels depending on the level of identification, content, reason for access, and whether sensitive issues are involved. The document stresses the importance of considering privacy and involving patients to build trust in electronic health information systems.
The document discusses the emerging field of precision medicine and how it represents a shift from symptom-based to evidence-based to personalized medicine. Precision medicine utilizes large datasets including multi-omics data, imaging, and other clinical data combined with machine learning algorithms and reference databases to generate personalized molecular profiles and enable targeted prevention and treatment. Key to realizing precision medicine's potential is establishing standards, processes, and reference databases to facilitate large-scale data analysis and ensure results can be reproduced.
ONLINE FUZZY-LOGIC KNOWLEDGE WAREHOUSING AND MINING MODEL FOR THE DIAGNOSIS A...ijcsity
This document presents a model for an online fuzzy-logic knowledge warehousing and mining system for diagnosing and treating HIV/AIDS. The system would store patient data and medical knowledge about HIV/AIDS. It uses fuzzy logic and data mining to predict HIV/AIDS status, monitor patient health over time, and determine recommended treatment plans. The system was tested on real patient data from a hospital in Nigeria. It aims to provide an efficient way to diagnose, treat, and monitor people living with HIV/AIDS.
Detection of myocardial infarction on recent dataset using machine learningIJICTJOURNAL
In developing countries such as India, with a large aging population and limited access to medical facilities, remote and timely diagnosis of myocardial infarction (MI) has the potential to save the life of many. An electrocardiogram is the primary clinical tool utilized in the onset or detection of a previous MI incident. Artificial intelligence has made a great impact on every area of research as well as in medical diagnosis. In medical diagnosis, the hypothesis might be doctors' experience which would be used as input to predict a disease that saves the life of mankind. It is been observed that a properly cleaned and pruned dataset provides far better accuracy than an unclean one with missing values. Selection of suitable techniques for data cleaning alongside proper classification algorithms will cause the event of prediction systems that give enhanced accuracy. In this proposal detection of myocardial infarction using new parameters is proposed with increased accuracy and efficiency of the existing model. Additional parameters are used to predict MI with more accuracy. The proposed model is used to predict an early diagnosis of MI with the help of expertise experiences and data gathered from hospitals.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Dissertation on Computer Science: Machine Learning Algorithm to Predict Covid...PhD Assistance
Artificial intelligence and data science play a vital role in the health-care business in this era of automation. Medical practitioners may simply manage their duties and patient care since these technologies is so well-connected. Dependence on automated systems such as AI has increased in healthcare services.ML can identify illness and viral infections more precisely, allowing patients’ ailments to be identified earlier, severe phases of diseases to be avoided, and fewer people to be treated.
Read More: https://bit.ly/3HGu9NY
For Enquiry:
India: +91 91769 66446
UK: +44 7537144372
Email: info@phdassistance.com
This document describes a fuzzy rule-based medical expert system called MExS that can diagnose 25 human diseases based on patient symptoms. MExS uses fuzzy logic to evaluate symptoms and determine the likely disease. It collects patient medical records and symptom data, categorizes diseases and symptoms, formulates weighted fuzzy rules relating symptoms to diseases, and has a graphical user interface for users to input symptoms and receive a diagnosis. The system aims to assist doctors in diagnosis by evaluating initial symptoms and suggesting possible diseases and specialist referrals.
Dissertation on Computer Science: Machine Learning Algorithm to Predict Covid...PhD Assistance
Artificial intelligence and data science play a vital role in the health-care business in this era of automation. Medical practitioners may simply manage their duties and patient care since these technologies is so well-connected. Dependence on automated systems such as AI has increased in healthcare services.ML can identify illness and viral infections more precisely, allowing patients’ ailments to be identified earlier, severe phases of diseases to be avoided, and fewer people to be treated.
Read More: https://bit.ly/3HGu9NY
For Enquiry:
India: +91 91769 66446
UK: +44 7537144372
Email: info@phdassistance.com
Preliminary Diagnostic System for Endocrine related diseasesandreigumabao
This document provides background information on developing a preliminary diagnostic system for endocrine diseases. It discusses diabetes and thyroid disorders, which can cause nail discoloration, brittleness, and other changes. Previous research has found glycated keratin in fingernails can indicate tissue damage in diabetic patients. The objectives of this study are to design a prototype diagnostic system that captures fingernail images and temperature, analyzes for possible diabetes or thyroidism, and prints/emails results. The scope is limited to these two diseases and depends on severity levels for accuracy.
Chronic disease (CD) such as kidney disease and causes severe challenging issues to the people all around the world. Chronic kidney disease (CKD) and diabetes mellitus (DM) are considered in this paper. Predicting the diseases in earlier stage, gives better preventive measures to the people. Healthcare domain leads to tremendous cost savings and improved health status of the society. The main objective of this paper is to develop an algorithm to predict CKD occurrence using machine learning (ML) technique. The commonly used classification algorithms namely logistic regression (LR), random forest (RF), conditional random forest (CRF), and recurrent neural networks (RNN) are considered to predict the disease at an earlier stage. The proposed algorithm in this paper uses medical code data to predict disease at an earlier stage.
This document discusses how information technology can help address pandemics of influenza, AIDS, and heart disease. It describes how influenza and HIV/AIDS spread, and notes the challenges of developing vaccines for rapidly mutating influenza strains and the complex life cycle of HIV. The document also discusses how public health organizations use information technology to monitor disease outbreaks, ensure data security, and educate the public on prevention and risks. Overall, the document advocates that information technology and public health resources can help reduce rates of these potentially fatal diseases.
Neuro-Fuzzy Approach for Diagnosing and Control of Tuberculosirinzindorjej
ABSTRACT
Tuberculosis is the second leading cause of death from an infectious disease worldwide, after the human immunodeficiency virus. The main aim of this research work is to develop a Neuro-Fuzzy system for diagnosing tuberculosis. The system is structured with to accept symptoms with the help of three domain Medical expertise as inputs that are used to automatically generate rules that are injected in to the
knowledge based where the system would use to make decisions and draw a conclusion. MATLAB 7.0 is used to implement this experiment using fuzzy logic and Neural Network toolbox. In this experiment linguistic variables are evaluated using Gaussian membership function. This system will offer potential assistance to medical practitioners and healthcare sector in making prompt decision during the diagnosis
of tuberculosis. In this work basic emblematic approach using Neuro-fuzzy methodology is presented that describes a technique to forecast the existence of mycobacterium and provides support platform to researchers in the related field.
KEYWORDS
Tuberculosis, Neuro-fuzzy, Fuzzy logic, Artificial Neural Network, Diagnosing, Mycobacterium
NEURO-FUZZY APPROACH FOR DIAGNOSING AND CONTROL OF TUBERCULOSISijcsitcejournal
Tuberculosis is the second leading cause of death from an infectious disease worldwide, after the human
immunodeficiency virus. The main aim of this research work is to develop a Neuro-Fuzzy system for diagnosing tuberculosis. The system is structured with to accept symptoms with the help of three domain Medical expertise as inputs that are used to automatically generate rules that are injected in to the knowledge based where the system would use to make decisions and draw a conclusion. MATLAB 7.0 is used to implement this experiment using fuzzy logic and Neural Network toolbox. In this experiment linguistic variables are evaluated using Gaussian membership function. This system will offer potential assistance to medical practitioners and healthcare sector in making prompt decision during the diagnosis of tuberculosis. In this work basic emblematic approach using Neuro-fuzzy methodology is presented that describes a technique to forecast the existence of mycobacterium and provides support platform to researchers in the related field.
Your nurse manager has asked you to form a small team to explore i.docxodiliagilby
Your nurse manager has asked you to form a small team to explore incorporating patient care technology in the care of patients with pulmonary disease. He has asked that your team prepare a presentation for the nurses on the following topics: asthma and COPD.
Discuss at least two patient-care technologies that would assist in the care of patients suffering from asthma and COPD. Please include in-text citations to support your response.
Peer Response 1:
Alyson Ferguson posted
Asthma and chronic obstructive pulmonary disease (COPD) are two of the most common obstructive pulmonary diseases. Asthma is a familial disorder causing chronic inflammation of the bronchial mucosa which leads to bronchial hyperresponsiveness, airway constriction, and airway obstruction which is reversible. COPD is a common, progressive, preventable yet not fully reversible, airflow limitation (McCance 2019).
There are many technologies patients can utilize to help monitor and manage their airway and breathing utilizing certain technologies. There is a mobile application that came out in 2009 called the Personal Wheezometor. This application measures the sound waves of a patients’ breath, it then can alert the patient if an asthma attack is likely to happen or is happening. The app is designed to limit the use of the rescue inhaler if not needed, and to prevent overuse (Meyers 2011).
Another innovation for asthma management for home use is the “Smart inhaler.” The Smart Inhaler uses Bluetooth technology to monitor and detect use of the inhaler linked to the device. The inhaler can alert the patient on overuse of the device also, suggesting possible symptoms worsening, or a decreased in asthma control. That monitoring device can be a great tool for the patient’s primary doctor to see how many asthma attacks the patient is having, and how often, to then determine if any medications need to be altered (Mohammadi, 2017).
McCance, K. L., & Huether, S. E. (2019). Pathophysiology: the biologic basis for disease in adults and children (8th ed.). St. Louis, MO: Elsevier.
Mohammadi, D. (2017). Smart Inhalers: will they help improve asthma care? The Pharmacuetical Journal.
Rosenberg, S. R., & Kalhan, R. (2017). Recent advances in the management of chronic obstructive pulmonary disease. F1000Research, 6, 863.
Peer Response 2:
Carrie Eyman posted
Chronic Obstructive Pulmonary Disease (COPD), is considered a progressive lung disease that entails an obstructed airflow from the lungs. There is no cure for this, the management of symptoms, and reducing risk factors, such as smoking, indoor air pollutions, that exacerbate this disease is of the upmost importance. “The World Health Organization estimates that COPD will be the third leading cause of death in the world by 2030” (Dixon, Ward, Smith, Holmes, & Mhadeva, 2016, p.330). Since there is no cure, managing the symptoms and the disease progression is vital. There are many technologies today that can a ...
ADAPTIVE LEARNING EXPERT SYSTEM FOR DIAGNOSIS AND MANAGEMENT OF VIRAL HEPATITISijaia
Viral hepatitis is the regularly found health problem throughout the world among other easily transmitted
diseases, such as tuberculosis, human immune virus, malaria and so on. Among all hepatitis viruses, the
uppermost numbers of deaths are result from the long-lasting hepatitis C infection or long-lasting hepatitis
B. In order to develop this system, the knowledge is acquired using both structured and semi-structured
interviews from internists of St.Paul Hospital. Once the knowledge is acquired, it is modeled and
represented using rule based reasoning techniques. Both forward and backward chaining is used to infer
the rules and provide appropriate advices in the developed expert system. For the purpose of developing
the prototype expert system SWI-prolog editor also used. The proposed system has the ability to adapt with
dynamic knowledge by generalizing rules and discover new rules through learning the newly arrived
knowledge from domain experts adaptively without any help from the knowledge engineer.
Adaptive Learning Expert System for Diagnosis and Management of Viral Hepatitisgerogepatton
Viral hepatitis is the regularly found health problem throughout the world among other easily transmitted diseases, such as tuberculosis, human immune virus, malaria and so on. Among all hepatitis viruses, the uppermost numbers of deaths are result from the long-lasting hepatitis C infection or long-lasting hepatitis B. In order to develop this system, the knowledge is acquired using both structured and semi-structured interviews from internists of St.Paul Hospital. Once the knowledge is acquired, it is modeled and represented using rule based reasoning techniques. Both forward and backward chaining is used to infer the rules and provide appropriate advices in the developed expert system. For the purpose of developing the prototype expert system SWI-prolog editor also used. The proposed system has the ability to adapt with dynamic knowledge by generalizing rules and discover new rules through learning the newly arrived knowledge from domain experts adaptively without any help from the knowledge engineer
Basics of Information support of the hospitalEneutron
Telemedicine involves using technology to provide medical services from a distance. It includes teleconsultations, teleeducation, mobile medical services, remote patient monitoring, and telesurgery. Screening in various medical fields helps detect diseases early through simple and standardized tests. This allows for preventive measures that can improve health outcomes. Information systems also support doctors by providing medical information and decision support. They help increase the quality of diagnosis and treatment.
The global ecosystem analyst - the date broker of personal medical data based on artificial intelligence and blockchain technologies.The personal ecosystem for diagnosing a human body in real time.Finds sources, patterns of development of different diseases and prevents future illnesses. Insurance Health life.
A Review of the Issues by Which a Blockchain Solution Could Improve the Preva...BRNSSPublicationHubI
This document discusses how blockchain technology could potentially disrupt and improve the current healthcare system. It notes that while new medical technologies promise changes, they often just provide incremental efficiencies and do not advance the underlying biomedical model. The author argues that an alternative neurological approach, using a technology called Strannik, shows promise in more accurately diagnosing and treating diseases in a safer, non-invasive way at lower cost compared to current biomedical tests and treatments. However, more research is still needed to fully validate this approach. Overall, the document explores the limitations of current biomedicine and how a technology like blockchain or Strannik could offer an alternative paradigm for delivering more effective and affordable healthcare.
IRJET- Survey on Risk Estimation of Chronic Disease using Machine LearningIRJET Journal
This document summarizes research on using machine learning to predict chronic disease risk. It discusses how healthcare generates massive amounts of data that can be used for prediction. The paper proposes a new convolutional neural network (CNN) based model that uses both structured and unstructured data from hospitals to predict disease risk. It compares this multimodal approach to existing unimodal prediction models. The document also reviews several other studies applying machine learning to tasks like heart disease prediction using large healthcare datasets. The goal is to develop effective machine learning models for predicting disease outbreaks in communities using real hospital data.
Similar to A PROPOSED NEURO-FUZZY MODEL FOR ADULT ASTHMA DISEASE DIAGNOSIS (20)
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
This document discusses using social media technologies to promote student engagement in a software project management course. It describes the course and objectives of enhancing communication. It discusses using Facebook for 4 years, then switching to WhatsApp based on student feedback, and finally introducing Slack to enable personalized team communication. Surveys found students engaged and satisfied with all three tools, though less familiar with Slack. The conclusion is that social media promotes engagement but familiarity with the tool also impacts satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The document proposes a blockchain-based digital currency and streaming platform called GoMAA to address issues of piracy in the online music streaming industry. Key points:
- GoMAA would use a digital token on the iMediaStreams blockchain to enable secure dissemination and tracking of streamed content. Content owners could control access and track consumption of released content.
- Original media files would be converted to a Secure Portable Streaming (SPS) format, embedding watermarks and smart contract data to indicate ownership and enable validation on the blockchain.
- A browser plugin would provide wallets for fans to collect GoMAA tokens as rewards for consuming content, incentivizing participation and addressing royalty discrepancies by recording
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This document discusses the importance of verb suffix mapping in discourse translation from English to Telugu. It explains that after anaphora resolution, the verbs must be changed to agree with the gender, number, and person features of the subject or anaphoric pronoun. Verbs in Telugu inflect based on these features, while verbs in English only inflect based on number and person. Several examples are provided that demonstrate how the Telugu verb changes based on whether the subject or pronoun is masculine, feminine, neuter, singular or plural. Proper verb suffix mapping is essential for generating natural and coherent translations while preserving the context and meaning of the original discourse.
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The document discusses automated penetration testing and provides an overview. It compares manual and automated penetration testing, noting that automated testing allows for faster, more standardized and repeatable tests but has limitations in developing new exploits. It also reviews some current automated penetration testing methodologies and tools, including those using HTTP/TCP/IP attacks, linking common scanning tools, a Python-based tool targeting databases, and one using POMDPs for multi-step penetration test planning under uncertainty. The document concludes that automated testing is more efficient than manual for known vulnerabilities but cannot replace manual testing for discovering new exploits.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
The document proposes a new validation method for fuzzy association rules based on three steps: (1) applying the EFAR-PN algorithm to extract a generic base of non-redundant fuzzy association rules using fuzzy formal concept analysis, (2) categorizing the extracted rules into groups, and (3) evaluating the relevance of the rules using structural equation modeling, specifically partial least squares. The method aims to address issues with existing fuzzy association rule extraction algorithms such as large numbers of extracted rules, redundancy, and difficulties with manual validation.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHcscpconf
Data collection is an essential, but manpower intensive procedure in ecological research. An
algorithm was developed by the author which incorporated two important computer vision
techniques to automate data cataloging for butterfly measurements. Optical Character
Recognition is used for character recognition and Contour Detection is used for imageprocessing.
Proper pre-processing is first done on the images to improve accuracy. Although
there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify
words of basic fonts. Contour detection is an advanced technique that can be utilized to
measure an image. Shapes and mathematical calculations are crucial in determining the precise
location of the points on which to draw the body and forewing lines of the butterfly. Overall,
92% accuracy were achieved by the program for the set of butterflies measured.
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXTcscpconf
This article presents Part of Speech tagging for Nepali text using General Regression Neural
Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is
trained and validated on both training and testing data. It is observed that 96.13% words are
correctly being tagged on training set whereas 74.38% words are tagged correctly on testing
data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Assessment and Planning in Educational technology.pptxKavitha Krishnan
In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
2. 192 Computer Science & Information Technology (CS & IT)
Asthma is one of the chronic inflammatory diseases now prevailing world wide and affecting
huge mass of the population including the youths and the adults. It has been seen that from very
starting of the information system, the health care system is one of the important field that has
ever found. This medical service is mainly used for the medication of the human being. As it was
also told “Prevention is better than cure”. So pre-diagnosis is very much important for the
prevention. Mass of the population is suffering from many diseases like eye diseases, cardiac
diseases, cancers, tumors, pulmonary diseases, brain cancers and tumors, urinary diseases, etc.
These all diseases are much chronic to diagnose and it is hard to take preventive measures for
those.
‘Medical diagnosis’ word is actually stating its meaning that diagnosis in medical field. It is done
to identify a probable disease and also to know how to cure that one. During diagnosis tests are
needed to be done as per the reference of the doctors or the advisors. There are various diseases
and their categorization can be done as infectious diseases, contagious diseases, communicable
diseases, non-communicable diseases, etc.These diseases are needed to be properly diagnosed.
Only the simple medical methods are not enough to easily diagnose a disease and effectively. So
with the advancement of the modern technology, we need to do something new and eye catchy.
Side by side of various medical and scientific methods there are so much computational methods
that was already been started in the year 1970 with the development of MYCIN at Stanford
University.
MYCIN was actually a type of expert systems that had used the computer applications so as to
embody some of the non-algorithmic expertise that could solve certain types of problems. For
example, expert systems can be used for diagnostic purposes that service both for people and
machinery. They also can perform decisions making, monitoring real time systems, do work as
like human expertise. Earliest expert system MYCIN was developed so as to identify the bacteria
that might cause severe diseases and could be resolved by the implementation of the specific
antibiotics in the body.
Cancer is a malignant disease that causes unregulated and uncontrolled growth of the cells in the
body. Earlier the cancer diagnosis was done by the methods of Laser- induced Fluorescence (LIF)
and Raman Spectra and nanoparticles (breast cancer) that combine laser spectroscopy,
biomedical, nanoparticle removing technology with specific target agents and computer
technology [1,2]. Again Incremental Background Knowledge has been used in medical field for
diagnosing the breast cancer for introducing the unlabeled data into proper training sets [3]. But
with the advancement of expert system an evolutionary type of NF model [4] has been developed
and also by using the fuzzy rules the cost effective models [5] has also been developed for breast
cancer diagnosis and its treatment.
Thyroid disease is one of the hormonal disorders that causes either hyperthyroidism (Grave’s
disease, pituitary gland malfunctioning) or hypothyroidism (intake of excessive iodine). This
disease can be diagnosed by clinical evaluation, blood tests (T3, T4), imaging tests, biopsies, and
other tests like saliva and urine testing. Ultrasound elastography for FNA biopsy for thyroid
diagnosis was developed earlier [6]. But it takes long time to perform the tests medically and
scientifically. This conventional method has now been replaced by the application of expert
system for prognosis of this thyroid disease [7]. Again Progressive Learning Vector Quantization
Neural Network (PLVQNN) is used for thyroid disease diagnosis with thyroid segmentation and
volume estimation [8].
Similarly asthma disease is needed to be diagnosed. It is one of the chronic diseases that can be
either diagnosed by applying the scientific methods or the expertise methods.
3. Computer Science & Information Technology (CS & IT) 193
In the below sections the short description about the Asthma disease is given. Then in the nest
subsection there is brief note on Adult Asthma and its prevalence. After that in the nest
proceeding sections the knowledge of expert system and NF has been provided for medical
diagnosis. After that the methodology of my proposed work has been described thoroughly.
Lastly conclusion and references for the proposed work has been made.
2. ASTHMA
Asthma is one of the common occurring chronic inflammatory diseases where actually effect
found on the airways that are mainly used for respiration .i.e. inhale of oxygen and exhale of
carbon di-oxide.It may occur at any age. The inner walls of an asthmatic's airways actually get
swollen creating difficulty in breathing. Here appears the change that is either gets stimulated by
the allergens or by the environmental triggers. Airway inflammation plays a significant role
among wheezing, asthma, cystic fibrosis (CF) and chronic obstructive pulmonary disease
(COPD). So there is a need to identify the airway inflammatory changes.
Figure 1. Asthma affecting human lungs
Symptoms of asthma are wheezing, dysponea, coughing, fever, nausea, loss of appetite, etc.
Changes that appear in Asthma disease give response like:
• Hyper-reactive Response
• Inflammatory Response
Asthma disease is found globally but specially affecting the peoples of developing countries. The
term ‘poorly controlled asthma’,we meant to say that the increase and instant rate of morbidity,
mortality and socio-economic are found. Asthma management is needed to be the essential plan
for asthma control.
4. 194 Computer Science & Information Technology (CS & IT)
It is one of the prevailing diseases in our country India. It can also be caused by the addition of
environmental as well as genetic factors.
As per the survey it has been noted that estimation of every one in 15 persons is suffering from
this type of lung or pulmonary disease. It is a type of chronic disorder that notaffects only the
adult but also the childhood. The prevalence and occurrence of this childhood asthma is
increasing day-by-day. It is actually causing huge problems as like considerable morbidity,
disability and occasional mortality at all ages. In between 12-15 million people in the United
States near about 5 million children are having Asthma and for those patients about 3 million
doctors are allotted the work of visiting to those patients every hour.
Asthma is one of the incurable illnesses. Although good treatment and management available but
still people cannot live normally healthy.
2.1 Adult Asthma
When asthma is diagnosed at an age older than 20 is called as adult-onset asthma. It is estimated
that about half of adults have asthma with allergies.
Who may have adult-onset asthma are as follows [9]:
• Women having hormonal changes, who are pregnant or who are experiencing
menopause.
• Women take estrogen in a regular manner due to menopause for 10 years or longer.
• People with certain cold or flu.
• People who have the pets in their home.
People do smoking in a regular manner or work in the place with much mold, dust or irritants.
Asthma in adult human body is called as adult-asthma.
2.2 Prevalence of Asthma
There are mainly three metrics that are used to describe asthma prevalence [9]. These are as
follow:
• Lifetime prevalence which is used to measure how many individuals in the population
will be diagnosed with asthma for at least once in his or her lifetime.
• Current prevalence that is used to measure how many individuals will be diagnosed
recently with asthma in a given year.
• Attack prevalence that is used to measure how many individuals in the population who
are asthma attack for a given year?
5. Computer Science & Information Technology (CS & IT) 195
Table 1.Prevalence of Asthma.
AGE /GENDER CATEGORY
ASTHMA PREVALENCE RATE PER 1000
POPULATION (YEAR 2001)
GIRLS OF AGE 0-17 74.4
BOYS OF AGE 0-17 99.0
ALL FEMALES 82.6
ALL MALES 63.6
OVERALL FAMILY 73.4
From the above chart it has been observable that females are having 10 percent higher lifetime
prevalence rate than that of the male population.
In 2001 the current prevalence rate of the U.S. population who actually had asthma was estimated
about 73.4 per 1000 persons. The highest and the current prevalence that was found at the age of
5-17 years were of rate 98.1 per 1000 persons.
The latest prevalence rate in females of 82.6 in per head of 1000 persons was about of 30 percent
more than that for male population of 63.6 per head of 1000 persons. But for the children the
estimated current asthma prevalence rate for boys was approximately of 30 percent more than for
girl.
Earlier by using the scientific and medical methods the asthma can be diagnosed by the
measuring the complexity of airflow pattern with the changes in the entropy by Approximate
Entropy method (ApEnQ) [10]. Again diagnosis can be done by checking the frequency of the
cough sound using wavelet analysis. These methods are long term. So to improve the process
there is the emergence of the expert system [9].
2. EXPERT SYSTEM IN MEDICAL DIAGNOSIS
Health care issues in the worldwide are increasing its prevalence in many countries by the
upcoming trends of urbanization and also the industrialization that is mostly prevailing in many
areas. In our country India alone has burden of the people suffering due to various diseases are
more than 15 million patients. Despite the existence of effective medicines, many peoples are
suffering due to the disease asthma. By observing the earlier studies it has been found that the
important factor that leads to under-treatment of asthma are due to the non-availability and also
the under-usage of the basic tools as like the spirometer for diagnosis and also for managing the
asthma. Further, there are some other reasons like the non-availability of essential inhalation
medicines mostly in the public sector hospitals and also the low affordability by the people of the
developing countries make asthma management difficult.
It has been noted that the poor knowledge of both patients and doctors cause barrier in the
effective treatment and also the management of asthma. So there is a need for some expert
method that can do some better diagnosis in the field of computer science as well as in the
medical field.
Expert system can be efficiently applied in the field of medicine. This system is more flexible as
compared to that of the conventional system as it is not so much improved one and here testing of
each and every patient are done every time and then medicines are prescribed. So it is a long task
to do for every-one. So the emergence of the expert system has come into its way.
6. 196 Computer Science & Information Technology (CS & IT)
Figure 2. Expert system
These are the components of the expert system that are needed to be considered when we are
going for making any system that works with expertize methods.
Various works has been done on various diseases like Thyroid, Cancer, Diabetes, etc. for
Diabetes disease identification and proper medical an automated system was developed that
eradicated the problem of communication gap between the patients and the doctors [18]. An
expert system was developed for Cardiac disease diagnosis on the basis of various computing
techniques as like fuzzy rule base learning, Genetic algorithm and ANN [19]. A fuzzy rule based
system was developed for the diagnosis of liver disorder [20].Again an expert system was
developed on the basis of supervised learning methodology for lung cancer diagnosis [22].
3. NEURO-FUZZY SYSTEM ARCHITECTURE
Neuro fuzzy systems are a kind of enhancement over simple fuzzy expert systems.The term
neuro-fuzzy derives from the terms ANN and fuzzy logic. This term neuro-fuzzy was proposed
firstly by J. S. R. Jang. A NF system is actually based on a fuzzy system with a trained learning
algorithm that has been developed from neural network. The learning procedure which is
heuristic in nature mainly applies on the host data, and doing modification for making developed
training sets with fuzzy system.
Figure 3. Artificial Neural Network (ANN)
7. Computer Science & Information Technology (CS & IT) 197
Figure 4. Neuro-Fuzzy (NF)
Neuro-fuzzy system is the aggregation of two techniques- one is the reasoning style of fuzzy
systems and the other is the learning methodology of neural network. Neuro-fuzzy hybridization
is widely termed as Fuzzy Neural Network (FNN). NF system uses reasoning style of fuzzy
systems through the use of fuzzy sets that uses the concepts of the rule making with IF-THEN
that is termed as the fuzzy rules.
• Neuro-fuzzy systems consist mainly of two requirements in fuzzy modeling that is
contradictory in nature: interpretability versus accuracy. The model that describes the
neuro-fuzzy technique with linguistic method is defined with the Mamdani model [20]
and the model with accuracy is referred as the Takagi-Sugeno-Kang (TSK) model.
• The implementation of Neuro-Fuzzy Logic helps in probing the various approximation
techniques from neural networks to get the exact parameter that are useful in the
functioning of a fuzzy system.
Various works has already been done in diseases like diabetes, Thyroid, Cancer, etc. In Thyroid
disease the work has already done on the basis of NF for diagnosing the disease with the
symptoms and settings parameters for those symptoms so as to proper diagnosing the disease [7].
Similarly in Diabetes the work on fuzzy rule base system has done for diagnosing the disease
with developing the decision support system for knowledge construction [15].Similarly in cancer
disease various works has already been done. In Cancers like Ovarian, Oral and Cervical there
has been a paper that describes the classification rules for chromatogram analysis by comparing
the PCA and fuzzy clustering techniques with development of rules for diagnosis [16]. Similarly
for identifying the sleeping stages in the child a neuro fuzzy classifier model was proposed that
took inputs as the parameters of sleeping [17].
Fuzzy Rule-Base Expert System was developed for the evaluation of Asthma [11]. After
developing the system the performance of the rules based system was checked. Again a decision
support system was developed with five inputs and evaluating one output by designing the fuzzy
inference system with fuzzy logics [12].
Only the expert system is not enough. So we have to move into the idea of more advanced
technology that is neural network.
8. 198 Computer Science & Information Technology (CS & IT)
Classification of Impulse Oscillometry (IOS) patterns are analyzed for lung functioning in asthma
patients by artificial neural network [13].
Previous works are done on the medical diagnosis of asthma. But there is also need to develop a
more improved model.The combination of the fuzzy technique with that of the expert system is
helpful in developing an effective intelligent system that is required for evaluating the asthma
expert's so as to derive a cost-effective intelligence systems for diagnosing the occurring of
asthma disease in the adult human being [14].
In this study various symptoms for adult asthma are considered. Then an optimum NF model has
been proposed where the performance of the models have been tested on 300 patients data
collected by the sincere co-operation of Dr. Kanai LalPatra and Prof.J.L.Ghosh From Amri Super
Speciality Hospital, Salt Lake, Kolkata- 700 028, Health Point Respiratory Care Clinic,
Tarakeswar, Hooghly District, West Bengal-712410 And Tarakeswar Rural General Government
Hospital, District: - Hooghly and R.G.Kar Medical College& Hospital, Kolkata, West Bengal.
Here by using the various methods of learning like SOM, LVQ, BPA the proposed NF model has
been compared. The accuracy of the systems istested by observing the results from the regression
graph available in the NF tool of MATLAB and the best methods among them areproposed in my
proposed work.
4. NEURO FUZZY MODEL FOR ASTHMA DISEASE
The proposed paper is actually based firstly on neural network and various learning algorithm of
neural network. As we know that there are various types of learning methods in neural network.
This may be supervised, unsupervised or reinforcement learning’s. By using the learning methods
this paper further apply those in fuzzy environment so that development of the fuzzy sets done by
creating the membership functions. After studying various papers on medical diagnosis for
ASTHMA the method of learning are compared. By applying this, neuro-fuzzy techniques
applied for diagnosing the disease bronchial ASTHMA on the basis of their symptoms named
breathing problem, cough, and fever for days, nausea, wheezing, loss of appetite and dysponea.
This diagnosis is done mainly for the patients‟ medication and proper treatment. As ASTHMA is
a chronic disorder, recurrent recovery measures to be taken.
In this paper diagnosis on Adult ASTHMA disease can be performed and we have to find best
accuracy by comparing the performance by checking through the learning algorithms of back
propagation. By using this we can get to know the best neuro-fuzzy network that give more
effected result. Problem formulation is done based on the filtering out the real patients data,
whose symptoms are much effective for disease diagnosis. The dataset for adult ASTHMA
disease is obtained from medical reports of the patients that have been collected. In this paper
approximately we are presenting 300 patients data with their symptoms and factors for checking
and diagnosis of the disease.
The main aim or objective of the proposed work is to diagnosis the adult ASTHMA disease and
applies algorithms of ANN with learning on the disease and also apply neuro-fuzzy classifier.
The very first step towards making the diagnosis for the disease is that we will collect the
patients’ data. The patients’ data collection is done on the basis of their symptoms of the disease.
Then we get theentirehealth condition history and other relevant facts. After that detailed medical
examination with the relevant symptoms will be done. Patients present with or complain about
certain symptoms. These are subjective reports. The physician or the doctors under that disease
will carry out the medical tests so as to identify signs related to an illness.
9. Computer Science & Information Technology (CS & IT) 199
Proposed work follows the following steps:
1. Collection of the medical reports of the patients having Asthma or not having Asthma.
2. Making the dataset for the symptoms for Adult Asthma disease of the patients.
3. The Adult patients having asthma disease are specified with 1 and the patients not having
Asthma are specified with 0.
4. Feedback taken from doctors as well patients.
5. Perform classification of the patients using Backpropagation, LVQ, SOM learning methods
and compare them to find the most accurate method.
6. Apply the rules of fuzzy.
7. Network will be developed that is chosen as a best network model for disease.
8. Provide patients with proper medication and treatment so that in future other will get alarm of
the disease occurrence.
Research Methodology means the guideline that is used for solving a problem, with tasks,
techniques and tools.
In this proposed work diagnosis on adult ASTHMA disease is performed on the basis of ANN
learning algorithms. From the medical reports of patients, the data with same symptoms are taken
and then making a diagnosis of those symptoms. After diagnosis the paper will show that how
many patients are suffering from the disease and how many patients are not. The patients
suffering from ASTHMA are coded 1 and others are coded as 0. The learning algorithm will give
more effected result that can formulate the hypothesis.
Figure 5. Research Methodology
5. DESIGN OF THE PROPOSED MODEL
The medical diagnosis process of Asthmahappens with an individual patient consulting with the
doctor. After consultation doctors set up a set of complaints named as symptoms of the disease.
Data collection is done on the basis of patients past health condition, age (18 years and above),
hygiene. A physical test is needed to be conducted, observations are analyzed and a medical
treatment is needed to be started as per the reference of the doctors. Symptoms are arranged in
specific orders and then they are made available for the classification. A treatment plan is needed
10. 200 Computer Science & Information Technology (CS & IT)
to be proposed and physically the tests are performed to determine if the patient is having asthma
disease. Neuro-fuzzy inference model is developed for Adult Asthma disease diagnosis.
The knowledge base has the entire set of the database. The fuzzy logic is set and neural networks
provide the structure for the inference engine. The inference engine has the production rules that
are fuzzy logic driven. The filters are present in the decision support engine for ranking the
patients in terms of the presence or absence of Asthma. This representation is done in the
computer system in the forms of binary where the presence of Asthma is represented by 1(input
vector) and otherwise 0 (symptom vector).
The set of symptoms are poured into the network model so as to make the patients lists as per
their symptoms of having the disease or not.
Figure 6. Neuro Fuzzy Model for Adult Asthma Diagnosis
11. Computer Science & Information Technology (CS & IT) 201
Figure 7. Methodology to diagnose the Adult Asthma disease
6. IMPLEMENTATION AND RESULT ANALYSIS
In this section the implementation for the ASYHMA disease diagnosis has been shown by using
the NF tool.
In the NF tool we have chosen about 300 patients data whom either having ASTHMA or not
having ASTHMA. The patients having ASTHMA is coded by 1 otherwise awarded 0. Below
snapshots are given after doing training of the data.
Figure 8. Analysis after training of the patients’ data by NF tools
12. 202 Computer Science & Information Technology (CS & IT)
In the above snapshot we have trained the data by using NF Fitting tool through Backpropagation
Algorithm. Here Training of the data will get automatically stops when its generalization stops
further improvement, as indicated by an increase in the mean square error of the validation
samples. Out of 300, 210 samples are taken for training, 45 samples for testing and rest 45 are
taken for validation. These all are done by calculating the Mean Squared Error value and
regression value R. The mse value for 210 samples is 4.13641e-14, for 45 testing samples is
3.13760e-14 and finally for validation the mse value is of 5.35984e-14.
Mean Squared Error (mse) is calculated as the average squared by considering the difference
between outputs and targets. If we are having much lower values then our inputs consideration
are much better. If we are getting Zero mean squared error then we are not getting any error
value. This is actually taken for observing the performance of my system. From the above figure
it has been observed that performance is 0 so no error found and also the snapshot proceeding to
that performance is shown in below figure 9.
Figure 9. Performance validation with respect to mse and epochs
Here from the above snapshot it has been found that we are getting the best performance of about
3.1376e-014. This is found by 9 iterations called as epochs which are one of the steps in neural
network process and its representation is iterations for 1000 epochs. It means the numbers of
iterations are found for 1000 epochs and when we get the minimum iteration value for 1000
epochs then goal value for net.trainParam is achieved. So, there is a need to stop the training
process.
Finally the error histogram is provided below for showing the accuracy of my proposed work.
14. 204 Computer Science & Information Technology (CS & IT)
From the above figure 10 it has been found that the error rate is much lower so giving much good
result. If we get less error then we can say that our proposed work is running in the proper
direction and can be done further with much better tool. After retraining is has been found the
error rate is gradually decreasing after certain period of time and this is noted by noticing the
value of mse.
7. CONCLUSION
Considering the prevalence of asthma and its increasing rate of the mortality and the morbidity,
there is a need to develop an intelligence system that can diagnose adult asthma disease and can
be applied to the hospitals for primary care with aims of:
1. Diagnosing.
2. Controlling with manageable measures.
3. Classifying the patients.
This main aim of this proposed work is actually to design asystem that is helpful for the diagnosis
of adult Asthma using Neuro Fuzzy Logic. This will be helpful for the common people who
suspects Asthma and will get the proper expected result. By observing those results proper and
effective cure in diagnosing the Asthma are taken. The observed efficacy of the system developed
is presented to the doctors so that they can give proper suggestion about the system to be
developed in the field of observation. This system is available to all of the doctors through the
internet facility. With consulting the doctors effective curative measures are to be taken as per
their suggestion and also it is filled up in the proposed system to make available for further use.
The efficiency of this system was analyzed using asthmatic patients data and acceptable result is
developed. Future work can be done with more sensitive data for eradicating the system’s good
and effective performance. Tuning the neural network for better performance and applying the
same model for other diseases. This will help in developing more developed system with less
focus on the laboratory data
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