This document discusses clinical information systems and their role in healthcare. It begins with background on healthcare and how information technology has helped address issues with declining resources and rapid knowledge growth. It then defines and discusses hospital information systems, clinical information systems, clinical decision support systems, and electronic medical records. It explains how these systems help with tasks like data management, decision making, and improving quality of care. The document also covers healthcare strategy making and how clinical information systems are developed and integrated.
The document discusses an existing pharmacy management system called Pharmaserv. It provides an integrated software solution for pharmacy management that combines point-of-sale, inventory management, accounts receivable, and reporting functions. The system aims to help pharmacists manage their businesses more efficiently by streamlining operations and providing comprehensive tools and services in one integrated platform. It notes that pharmacists face challenges like staffing shortages and shrinking profit margins, making an integrated pharmacy management system essential for running pharmacy operations.
This document describes an advanced machine learning approach for predicting skin cancer. It discusses using machine learning algorithms like Naive Bayes, Decision Tree, Random Forest on a dataset to estimate disease risk and determine algorithm accuracy. The paper focuses on developing a system that integrates symptom and medical data using machine learning algorithms like K-means to provide accurate disease predictions.
IRJET - E-Health Chain and Anticipation of Future DiseaseIRJET Journal
The document proposes an E-Health Chain system that uses machine learning algorithms to predict future diseases and maintain electronic health records more efficiently than traditional paper-based systems. Key features include digital prescriptions that allow patients to purchase medicines using a unique ID, electronic health records that give doctors access to patient data from remote locations, emergency response features like ambulance dispatch, and medication reminders for patients. The system aims to help patients take preventive measures by predicting future diseases using algorithms trained on historical health data. A literature review covers previous research on disease prediction using decision trees, neural networks, and other machine learning methods applied to medical datasets.
An AI-based Decision Platform built using unified data model, incorporating systems biology topics for unit analysis using semi-supervised learning models
Expert Systems vs Clinical Decision Support SystemsAdil Alpkoçak
The document discusses expert systems and clinical decision support systems. It provides an overview of key concepts including:
- Medical artificial intelligence aims to perform diagnosis and make therapy recommendations using symbolic models of disease.
- Clinical decision support systems directly assist health professionals with decision making, though there is scope for ambiguity in inputs like patient history.
- Expert systems are interactive programs that enhance decision making through a knowledge base and rules. They are used to solve problems typically addressed by human experts.
- Key components of expert systems include the knowledge base containing rules, a working memory, and an inference engine to derive conclusions from rules and data.
This document summarizes a research paper on using Markov Decision Processes (MDPs) to improve inpatient hospital care. It discusses challenges in the current healthcare system and how machine learning and artificial intelligence could help address issues like overtreatment, inconsistent care quality, and high costs. The paper proposes using MDPs and other algorithms to analyze patient electronic health record data, detect abnormal care patterns, and make real-time predictions to optimize treatment and resource allocation. A web application with modules for patients, doctors and administrators is designed to facilitate this approach. Simulation results suggest it could increase care efficiency by better connecting patients and doctors. Future work may expand this to personalized treatment planning, diagnostic testing optimization and knowledge discovery from medical literature.
This document describes a patient management system project for a university. The system aims to automate a hospital's manual patient record keeping system. It will computerize patient, doctor, and hospital details to make record keeping more efficient. The system will allow scheduling appointments, tracking medical bills and patient rooms. It will generate reports on patient information and utilize databases to store records. Diagrams including data flow diagrams and entity-relationship diagrams are provided to illustrate the system's design and data structure.
This document discusses clinical information systems and their role in healthcare. It begins with background on healthcare and how information technology has helped address issues with declining resources and rapid knowledge growth. It then defines and discusses hospital information systems, clinical information systems, clinical decision support systems, and electronic medical records. It explains how these systems help with tasks like data management, decision making, and improving quality of care. The document also covers healthcare strategy making and how clinical information systems are developed and integrated.
The document discusses an existing pharmacy management system called Pharmaserv. It provides an integrated software solution for pharmacy management that combines point-of-sale, inventory management, accounts receivable, and reporting functions. The system aims to help pharmacists manage their businesses more efficiently by streamlining operations and providing comprehensive tools and services in one integrated platform. It notes that pharmacists face challenges like staffing shortages and shrinking profit margins, making an integrated pharmacy management system essential for running pharmacy operations.
This document describes an advanced machine learning approach for predicting skin cancer. It discusses using machine learning algorithms like Naive Bayes, Decision Tree, Random Forest on a dataset to estimate disease risk and determine algorithm accuracy. The paper focuses on developing a system that integrates symptom and medical data using machine learning algorithms like K-means to provide accurate disease predictions.
IRJET - E-Health Chain and Anticipation of Future DiseaseIRJET Journal
The document proposes an E-Health Chain system that uses machine learning algorithms to predict future diseases and maintain electronic health records more efficiently than traditional paper-based systems. Key features include digital prescriptions that allow patients to purchase medicines using a unique ID, electronic health records that give doctors access to patient data from remote locations, emergency response features like ambulance dispatch, and medication reminders for patients. The system aims to help patients take preventive measures by predicting future diseases using algorithms trained on historical health data. A literature review covers previous research on disease prediction using decision trees, neural networks, and other machine learning methods applied to medical datasets.
An AI-based Decision Platform built using unified data model, incorporating systems biology topics for unit analysis using semi-supervised learning models
Expert Systems vs Clinical Decision Support SystemsAdil Alpkoçak
The document discusses expert systems and clinical decision support systems. It provides an overview of key concepts including:
- Medical artificial intelligence aims to perform diagnosis and make therapy recommendations using symbolic models of disease.
- Clinical decision support systems directly assist health professionals with decision making, though there is scope for ambiguity in inputs like patient history.
- Expert systems are interactive programs that enhance decision making through a knowledge base and rules. They are used to solve problems typically addressed by human experts.
- Key components of expert systems include the knowledge base containing rules, a working memory, and an inference engine to derive conclusions from rules and data.
This document summarizes a research paper on using Markov Decision Processes (MDPs) to improve inpatient hospital care. It discusses challenges in the current healthcare system and how machine learning and artificial intelligence could help address issues like overtreatment, inconsistent care quality, and high costs. The paper proposes using MDPs and other algorithms to analyze patient electronic health record data, detect abnormal care patterns, and make real-time predictions to optimize treatment and resource allocation. A web application with modules for patients, doctors and administrators is designed to facilitate this approach. Simulation results suggest it could increase care efficiency by better connecting patients and doctors. Future work may expand this to personalized treatment planning, diagnostic testing optimization and knowledge discovery from medical literature.
This document describes a patient management system project for a university. The system aims to automate a hospital's manual patient record keeping system. It will computerize patient, doctor, and hospital details to make record keeping more efficient. The system will allow scheduling appointments, tracking medical bills and patient rooms. It will generate reports on patient information and utilize databases to store records. Diagrams including data flow diagrams and entity-relationship diagrams are provided to illustrate the system's design and data structure.
Parna Das' paper discusses hospital information systems. It begins with an introduction explaining that hospital information systems are used to manage patient data and hospital operations. It then covers objectives like centralized patient management; functions like online appointments and payments; hardware and software requirements; and classifications by functional area and management level. The paper references sources on hospital information systems and acknowledges help from teachers.
Smart Healthcare Prediction System Using Machine LearningIRJET Journal
1. The document discusses a smart healthcare prediction system using machine learning algorithms like Naive Bayes to predict diseases based on patient symptoms and medical data.
2. It proposes a system with modules for patients, doctors, and administrators where patients can input symptoms, the system predicts diseases, and doctors can view patient histories.
3. The system uses Naive Bayes and decision tree algorithms to classify medical data and symptoms to predict diseases accurately and reduce the workload for healthcare professionals.
This document defines clinical decision support systems (CDSS) and outlines their key components and challenges. It begins by defining CDSS as computer programs that help health professionals make clinical decisions. It then describes the main categories of CDSS, including diagnostic assistance, therapy planning, and image recognition. The document outlines the typical system architecture of CDSS including tools for information management, focusing attention, and patient-specific consultation. It also discusses the need for CDSS, potential applications, disadvantages, and challenges to implementation. Throughout, it provides examples to illustrate different types of CDSS.
This document summarizes a research paper on developing a cloud-based health prediction system. The system allows users to enter their health issues and details like weight and height online. It then provides an accurate health prediction by matching the user's data to an analysis database. The cloud-based system is designed to be user-friendly and accessible from anywhere at any time. It aims to help users identify potential health problems early without visiting a doctor. The system architecture uses HTML, CSS, JavaScript, PHP and a MySQL database. It flows user data through registration, selecting health details, and logout for security.
Group project health_care_informatics[2guest1e610e
1) The document discusses components of a Clinical Information System (CIS) including an overview presented by four speakers and components of the Electronic Health Record such as health information, results management, and decision support.
2) It also discusses education and training needed for a CIS, safety and costs associated with electronic health records, and clinical decision making systems within a CIS.
3) References are provided at the end to support information discussed throughout the document.
This document discusses various healthcare information systems and their functions. It identifies six main types of systems: electronic medical records, practice management software, master patient indexes, patient portals, remote patient monitoring, and clinical decision support. It also defines hospital information systems, administrative information systems, and clinical information systems. The document differentiates the nursing process from critical pathways in nursing system design. Finally, it outlines the five main components of a basic database system: hardware, software, data, procedures, and database access language.
A Greybox Hospital Information System in the Medical Center Tobruk Libya base...IOSR Journals
This document presents a study on developing a greybox hospital information system for the Medical Center in Tobruk, Libya based on the Three-layer Graph-based Model (3LGM). The study aims to model the current information system and propose improvements using 3LGM. It describes modeling the main functions, logical and physical layers, use cases, and databases for patient, doctor, and clinical documentation data. Tables compare 3LGM to other models. Figures illustrate the domain layers, tools layers, use cases, and database tables. The conclusion is that all tasks were successfully completed to develop and implement an information system model to support management of patient, doctor, and clinical data using 3LGM.
This document provides definitions for various terms related to health informatics. It defines terms such as algorithm, bioinformatics, clinical coding system, clinical data system, clinical decision tool, communication, database, electronic health record, and medical knowledge. The definitions cover topics such as the use of informatics methods and technologies in research, clinical practice, public health, and consumer health contexts.
This document provides definitions for various terms related to health informatics. It defines terms such as algorithm, bioinformatics, clinical coding system, clinical data system, clinical decision tool, communication, database, electronic health record, and medical knowledge. The definitions cover topics such as the use of informatics methods and technologies in clinical care, research, public health, and consumer health contexts.
A clinical decision support system (CDSS) is an interactive computer program that uses patient data to generate advice to help clinicians make decisions. A CDSS uses a dynamic knowledge base and rules derived from experts to make suggestions, which clinicians can then use along with their own expertise to determine diagnoses and treatments. CDSS systems are used at the point of care to assist clinicians before, during, and after making diagnoses. They work by taking in patient data, applying medical knowledge, and providing recommendations to aid clinical decision making.
Patterns discovered from based on collected molecular profiles of patient tumour samples, and also clinical metadata, could be used to provide personalized cancer treatment to patients with
similar molecular subtypes. Computational algorithms for cancer diagnosis, prognosis, and therapeutics that can recognize specific functions and aid in classifiers based on a plethora of
publicly accessible cancer research outcomes are needed. Machine learning, a branch of artificial intelligence, has a great deal of potential for problem solving in cryptic cancer
datasets, as per a literature study. We focus on the new state of machine learning applications in cancer research in this study, illustrating trends and analysing major accomplishments,
roadblocks, and challenges along the way to clinic implementation. In the context of noninvasive treating cancer using diet-based and natural biomarkers, we propose a novel machine learning algorithm.
Cis evaluation final_presentation, nur 3563 sol1SBU
An overview of a Computer Information System (CIS) and considerations that need to be taken with implementing an Electronic Health Record (EHR) in a healthcare setting.
Computer applications are now widely used in pharmacy for tasks like storing patient data, analyzing drug interactions, monitoring medications, and providing drug information. Some key uses of computers discussed include using software programs to analyze patient pharmacokinetic data and predict drug concentrations, developing mathematical models for drug design, and maintaining patient records and inventory in hospitals. Mobile technologies and automated dispensing systems are also discussed as emerging areas where computers are being applied in pharmacy.
Team Sol2 01 Health Care Informatics Power PointMessner Angie
The document discusses clinical information systems and their components. It provides an overview of electronic health records and describes key parts of a clinical information system including health information, order entry, decision support, and clinical documentation. It also discusses clinical decision making systems and their importance in reducing variation, costs, and improving diagnosis. Safety, education and costs related to clinical information systems are also evaluated.
The document discusses clinical decision support systems (CDSS), which are software designed to aid clinical decision making by matching patient characteristics to a computerized knowledge base. It describes several types of CDSS including knowledge-based systems, alerts and reminders, diagnostic assistance, therapy critiquing and prescribing decision support. It also discusses different knowledge representations, functionally classified systems, benefits and limitations of CDSS, and their future directions.
Evaluation of a clinical information system (cis)nikita024
This power point presentation provides an overview of a clinical information system (CIS). It discusses what a CIS is, how CIS have evolved, and the key players involved in designing CIS. It also examines the electronic health record component of a CIS and discusses the eight basic components that make up an EHR. Additional topics covered include clinical decision making systems, safety, costs, and education regarding CIS. The presentation was created by four students with each student covering specific slides and aspects of the topic.
A clinical decision support system (CDSS) is a tool that analyzes patient data to help healthcare providers make decisions and improve patient care. It does this by providing clinicians with targeted clinical knowledge, recommendations, and alerts to help diagnose patients and avoid errors. CDSS tools can flag patients who were improperly diagnosed, missed medications, or faced other issues. This helps improve quality of care while increasing efficiency and reducing costs. However, CDSS tools also face limitations like interoperability issues, inability to incorporate all new medical research quickly, and alert fatigue for clinicians.
The document outlines many uses of computers in healthcare, including maintaining electronic patient records, clinical imaging, telemedicine, inventory management, and communication. Computers are used for tasks like documenting patient treatment and monitoring, assisting with surgical procedures, making diagnoses, and conducting research. They allow important patient information to be securely stored and easily accessible to healthcare professionals, improving efficiency, accuracy, and care coordination.
Multi Disease Detection using Deep LearningIRJET Journal
1) The document proposes a system for multi-disease detection using deep learning that could provide early detection of chronic diseases like heart disease, cancer, and diabetes from medical data and save lives.
2) It reviews literature on disease prediction using machine learning algorithms like CNN, KNN, decision trees, and support vector machines. CNN showed slightly better accuracy than KNN for general disease detection.
3) The proposed system would use deep learning models to detect and classify diseases from medical images and data with high accuracy, helping doctors verify test results and enhancing their experience with diseases. It aims to reduce the costs of diagnostic testing for chronic conditions.
cognitive computing for electronic medical record selamu shirtawi
This document discusses applying cognitive computing to electronic medical records (EMRs) using IBM Watson. It describes a cognitive computing system called Watson EMRA that can generate a problem-oriented summary of a patient's EMR. The summary aggregates key data like problems, medications, labs, notes, and procedures. It also identifies relationships between these data aggregates to present them in a clinically meaningful way. This type of cognitive system has the potential to reduce physicians' cognitive load when reviewing patient records and fulfilling their various information needs in clinical workflows.
Parna Das' paper discusses hospital information systems. It begins with an introduction explaining that hospital information systems are used to manage patient data and hospital operations. It then covers objectives like centralized patient management; functions like online appointments and payments; hardware and software requirements; and classifications by functional area and management level. The paper references sources on hospital information systems and acknowledges help from teachers.
Smart Healthcare Prediction System Using Machine LearningIRJET Journal
1. The document discusses a smart healthcare prediction system using machine learning algorithms like Naive Bayes to predict diseases based on patient symptoms and medical data.
2. It proposes a system with modules for patients, doctors, and administrators where patients can input symptoms, the system predicts diseases, and doctors can view patient histories.
3. The system uses Naive Bayes and decision tree algorithms to classify medical data and symptoms to predict diseases accurately and reduce the workload for healthcare professionals.
This document defines clinical decision support systems (CDSS) and outlines their key components and challenges. It begins by defining CDSS as computer programs that help health professionals make clinical decisions. It then describes the main categories of CDSS, including diagnostic assistance, therapy planning, and image recognition. The document outlines the typical system architecture of CDSS including tools for information management, focusing attention, and patient-specific consultation. It also discusses the need for CDSS, potential applications, disadvantages, and challenges to implementation. Throughout, it provides examples to illustrate different types of CDSS.
This document summarizes a research paper on developing a cloud-based health prediction system. The system allows users to enter their health issues and details like weight and height online. It then provides an accurate health prediction by matching the user's data to an analysis database. The cloud-based system is designed to be user-friendly and accessible from anywhere at any time. It aims to help users identify potential health problems early without visiting a doctor. The system architecture uses HTML, CSS, JavaScript, PHP and a MySQL database. It flows user data through registration, selecting health details, and logout for security.
Group project health_care_informatics[2guest1e610e
1) The document discusses components of a Clinical Information System (CIS) including an overview presented by four speakers and components of the Electronic Health Record such as health information, results management, and decision support.
2) It also discusses education and training needed for a CIS, safety and costs associated with electronic health records, and clinical decision making systems within a CIS.
3) References are provided at the end to support information discussed throughout the document.
This document discusses various healthcare information systems and their functions. It identifies six main types of systems: electronic medical records, practice management software, master patient indexes, patient portals, remote patient monitoring, and clinical decision support. It also defines hospital information systems, administrative information systems, and clinical information systems. The document differentiates the nursing process from critical pathways in nursing system design. Finally, it outlines the five main components of a basic database system: hardware, software, data, procedures, and database access language.
A Greybox Hospital Information System in the Medical Center Tobruk Libya base...IOSR Journals
This document presents a study on developing a greybox hospital information system for the Medical Center in Tobruk, Libya based on the Three-layer Graph-based Model (3LGM). The study aims to model the current information system and propose improvements using 3LGM. It describes modeling the main functions, logical and physical layers, use cases, and databases for patient, doctor, and clinical documentation data. Tables compare 3LGM to other models. Figures illustrate the domain layers, tools layers, use cases, and database tables. The conclusion is that all tasks were successfully completed to develop and implement an information system model to support management of patient, doctor, and clinical data using 3LGM.
This document provides definitions for various terms related to health informatics. It defines terms such as algorithm, bioinformatics, clinical coding system, clinical data system, clinical decision tool, communication, database, electronic health record, and medical knowledge. The definitions cover topics such as the use of informatics methods and technologies in research, clinical practice, public health, and consumer health contexts.
This document provides definitions for various terms related to health informatics. It defines terms such as algorithm, bioinformatics, clinical coding system, clinical data system, clinical decision tool, communication, database, electronic health record, and medical knowledge. The definitions cover topics such as the use of informatics methods and technologies in clinical care, research, public health, and consumer health contexts.
A clinical decision support system (CDSS) is an interactive computer program that uses patient data to generate advice to help clinicians make decisions. A CDSS uses a dynamic knowledge base and rules derived from experts to make suggestions, which clinicians can then use along with their own expertise to determine diagnoses and treatments. CDSS systems are used at the point of care to assist clinicians before, during, and after making diagnoses. They work by taking in patient data, applying medical knowledge, and providing recommendations to aid clinical decision making.
Patterns discovered from based on collected molecular profiles of patient tumour samples, and also clinical metadata, could be used to provide personalized cancer treatment to patients with
similar molecular subtypes. Computational algorithms for cancer diagnosis, prognosis, and therapeutics that can recognize specific functions and aid in classifiers based on a plethora of
publicly accessible cancer research outcomes are needed. Machine learning, a branch of artificial intelligence, has a great deal of potential for problem solving in cryptic cancer
datasets, as per a literature study. We focus on the new state of machine learning applications in cancer research in this study, illustrating trends and analysing major accomplishments,
roadblocks, and challenges along the way to clinic implementation. In the context of noninvasive treating cancer using diet-based and natural biomarkers, we propose a novel machine learning algorithm.
Cis evaluation final_presentation, nur 3563 sol1SBU
An overview of a Computer Information System (CIS) and considerations that need to be taken with implementing an Electronic Health Record (EHR) in a healthcare setting.
Computer applications are now widely used in pharmacy for tasks like storing patient data, analyzing drug interactions, monitoring medications, and providing drug information. Some key uses of computers discussed include using software programs to analyze patient pharmacokinetic data and predict drug concentrations, developing mathematical models for drug design, and maintaining patient records and inventory in hospitals. Mobile technologies and automated dispensing systems are also discussed as emerging areas where computers are being applied in pharmacy.
Team Sol2 01 Health Care Informatics Power PointMessner Angie
The document discusses clinical information systems and their components. It provides an overview of electronic health records and describes key parts of a clinical information system including health information, order entry, decision support, and clinical documentation. It also discusses clinical decision making systems and their importance in reducing variation, costs, and improving diagnosis. Safety, education and costs related to clinical information systems are also evaluated.
The document discusses clinical decision support systems (CDSS), which are software designed to aid clinical decision making by matching patient characteristics to a computerized knowledge base. It describes several types of CDSS including knowledge-based systems, alerts and reminders, diagnostic assistance, therapy critiquing and prescribing decision support. It also discusses different knowledge representations, functionally classified systems, benefits and limitations of CDSS, and their future directions.
Evaluation of a clinical information system (cis)nikita024
This power point presentation provides an overview of a clinical information system (CIS). It discusses what a CIS is, how CIS have evolved, and the key players involved in designing CIS. It also examines the electronic health record component of a CIS and discusses the eight basic components that make up an EHR. Additional topics covered include clinical decision making systems, safety, costs, and education regarding CIS. The presentation was created by four students with each student covering specific slides and aspects of the topic.
A clinical decision support system (CDSS) is a tool that analyzes patient data to help healthcare providers make decisions and improve patient care. It does this by providing clinicians with targeted clinical knowledge, recommendations, and alerts to help diagnose patients and avoid errors. CDSS tools can flag patients who were improperly diagnosed, missed medications, or faced other issues. This helps improve quality of care while increasing efficiency and reducing costs. However, CDSS tools also face limitations like interoperability issues, inability to incorporate all new medical research quickly, and alert fatigue for clinicians.
The document outlines many uses of computers in healthcare, including maintaining electronic patient records, clinical imaging, telemedicine, inventory management, and communication. Computers are used for tasks like documenting patient treatment and monitoring, assisting with surgical procedures, making diagnoses, and conducting research. They allow important patient information to be securely stored and easily accessible to healthcare professionals, improving efficiency, accuracy, and care coordination.
Multi Disease Detection using Deep LearningIRJET Journal
1) The document proposes a system for multi-disease detection using deep learning that could provide early detection of chronic diseases like heart disease, cancer, and diabetes from medical data and save lives.
2) It reviews literature on disease prediction using machine learning algorithms like CNN, KNN, decision trees, and support vector machines. CNN showed slightly better accuracy than KNN for general disease detection.
3) The proposed system would use deep learning models to detect and classify diseases from medical images and data with high accuracy, helping doctors verify test results and enhancing their experience with diseases. It aims to reduce the costs of diagnostic testing for chronic conditions.
cognitive computing for electronic medical record selamu shirtawi
This document discusses applying cognitive computing to electronic medical records (EMRs) using IBM Watson. It describes a cognitive computing system called Watson EMRA that can generate a problem-oriented summary of a patient's EMR. The summary aggregates key data like problems, medications, labs, notes, and procedures. It also identifies relationships between these data aggregates to present them in a clinically meaningful way. This type of cognitive system has the potential to reduce physicians' cognitive load when reviewing patient records and fulfilling their various information needs in clinical workflows.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
3. z
Introduction
1.Computer assisted medical decision making (CAMD) refers to the use of
computer systems and algorithms to help health care professionals to make
informed decision about patient care.
2. It involves the integration of software, data and medical knowledge to
support clinical decision making processes.
3. CAMD systems can analyze patient data such as medical history, symptoms,
laboratory results and imaging studies to provide recommendations to
healthcare professionals.
4. These systems can assist with diagnosis treatment selection, medication
dosing, and monitoring patient progress.
5.CAMD can improve the accuracy and efficiency of medical decision making
processes and reduce medical errors and enhance patients outcomes.
5. z
Structure of computer assisted
medical decision making system
cont…
1. The basic structure of the CMD system is given in figure.
2. In this system the input to a CMD system is a description of a special
case(e.g, the size and location of a lung tumor) and the output is the
useful information about that case(e.g, tumor stage, patients
prognosis).
3.CMD system composed of two components:- a knowledge base and
an inference mechanism.
4. The knowledge base is a collection of encoded knowledge that is
needed to solve problems in some specific medical area.
5. The inference mechanism is a program, that gives a case
description, uses the information in the knowledge base to reach
conclusions about that case.
7. z
Development of computer program
Cont…
The computer programs need to provide diagnostic and
therapeutic strategies as depicted in Figure 6.15
1. Information Structure: it contains the information processed
and obtained from different sources of the program. It can convert
the program for use in a new problem area by reforming the
information structure. It is application based Structure changing
from one program to another. It consists of information about
patient history along with probabilities and utilities.The probabilities
relate with diseases and their symptoms.
8. z
Development of computer program
Cont…
2. Inference function:The inference part of the
programencompassesthe reasoning ppprocess
undermining the aptitude skills of the physician.It employs
the probabilistic approach based on Bayes theorem for
obtaining the probability distribution providing the
likelihood of each disease under the available evidence to
date and medical experience.
3. Test or treatment selection funcrion:- the purpose of treatment
selection is to select appropriate test or treatment procedure to
be followedat each stagein the problem solving process on the
patient.
9. z
Modelling the diagnostic and
treatment problem
1. The CMD system could be employed as a deductive reasoningtool
which is built into the hospital healthcare system that can augment or
replace the activities or requirements of the physician in patient care.
2.It can assist the physician in meaningful dialogue taking note of
patient history, clinical findings, laboratory tests reports and alerting the
physician during diagnoses , and suggesting remedial procefures to be
followed in patient care.
3. It reduces routine jobs to be carried out by the physician on daily
basisand allow him to concentrate on the application of bedside skills ,
the management of disease, and the application of clinical knowledge
and acumen for making good judgement in providing clinical care.
10. z
Modelling the diagnostic and treatment
problem cont...
4. The CMD system also allows non physician personell toassist
the physician in patient care and management.
5. These healthcare specialistssuppported by computerised
know how and aided by a variety of automated devices like
collectingpatient history, blood and laboratory test reports could
be trained to perform physical examination usually undertaken
by the physician, to assist him by taking over the chunk of
responsibility for the delivery of primary medical care to patients
visiting these centres.