This document discusses risk assessment techniques for projects. It describes qualitative and quantitative risk analysis. Qualitative risk analysis involves defining the probability and impact of risks using classes or scores, and organizing risks into a matrix. Quantitative risk analysis uses numerical techniques like expected monetary value analysis with decision trees to assign probabilities and impacts to risks. The document outlines strategies for responding to risks like avoiding, transferring, mitigating, exploiting, and accepting risks. The goal of risk assessment is to prioritize and manage project risks.
This document discusses risk management and analysis. It defines risk management as identifying, analyzing, and responding to risks. Risk analysis helps identify potential problems that could undermine projects or initiatives. The key steps of risk analysis include identifying threats, estimating the likelihood and impact of each threat, and developing risk mitigation strategies. Quantitative techniques like decision trees and expected monetary value analysis can also be used. Ongoing risk monitoring and control is important to evaluate risks and ensure responses remain effective.
This document discusses risk analysis techniques for information technology projects. It outlines the goals of risk analysis as improving decision making for project direction, scheduling and budgeting. Key points made include:
- Risk analysis identifies risks, assesses impact, and develops contingencies, while risk management works to mitigate risks.
- Qualitative and quantitative risk analysis methods are described for identifying and prioritizing risks.
- Bias must be addressed in risk analysis to provide accurate information for decision making.
- Proactive risk analysis can improve project schedule, costs, and quality outcomes.
Risk analysis is a systematic process to estimate the probability and impact of identified project risks. There are qualitative and quantitative approaches to risk analysis. Qualitative approaches use scales to assess probability and impact and assign risk levels like low, medium, high. Quantitative approaches use techniques like expected value analysis to generate probabilistic estimates of project outcomes. Monte Carlo simulation is commonly used to model project risks and determine the likelihood of meeting objectives within given cost and schedule constraints. Effective risk management involves identifying, analyzing, prioritizing and developing response plans for risks throughout the project lifecycle.
This document discusses project risk management for an IT project management course. It defines risk management and identifies key risk management processes: planning, identification, analysis, response planning, and monitoring/control. Various risk analysis techniques are described like probability/impact matrices and decision trees. The goal of risk management is to minimize negative risks while maximizing positive opportunities through risk avoidance, acceptance, transference, or mitigation strategies.
This document discusses project risk management. It defines risk as an uncertain event that can positively or negatively impact project objectives. Risk management is the systematic process of identifying, analyzing, and responding to project risks. The six processes of risk management are: 1) plan risk management, 2) identify risks, 3) perform qualitative risk analysis, 4) perform quantitative risk analysis, 5) plan risk responses, and 6) monitor and control risks. Tools used include risk breakdown structures, probability and impact matrices to assess risks, and decision trees to evaluate responses. The goal is to prioritize and respond to risks to help ensure project success.
Heba is following a risk mitigation strategy to respond to the identified risk of resource attrition on a software migration project at a bank. Mitigation strategies aim to reduce the probability and/or impact of adverse risks. Specifically, Heba is providing good increments to team members, which helps retain resources and mitigate the risk of attrition. Quantitative risk analysis uses modeling techniques like decision trees and Monte Carlo simulation to numerically analyze the effects of risks on project objectives. If the team cannot identify a suitable risk response strategy, the default is typically risk acceptance, where the project management plan is not changed to account for that risk.
Risk management involves identifying potential risks, assessing their probability and impact, prioritizing risks, developing strategies to mitigate high-priority risks, and continuously monitoring risks throughout the project. There are different categories of risk including project risks, technical risks, business risks, known risks, and unpredictable risks. Effective risk management requires proactively identifying risks, tracking them over time, taking steps to reduce impact or likelihood, and open communication across teams.
This document discusses risk management and analysis. It defines risk management as identifying, analyzing, and responding to risks. Risk analysis helps identify potential problems that could undermine projects or initiatives. The key steps of risk analysis include identifying threats, estimating the likelihood and impact of each threat, and developing risk mitigation strategies. Quantitative techniques like decision trees and expected monetary value analysis can also be used. Ongoing risk monitoring and control is important to evaluate risks and ensure responses remain effective.
This document discusses risk analysis techniques for information technology projects. It outlines the goals of risk analysis as improving decision making for project direction, scheduling and budgeting. Key points made include:
- Risk analysis identifies risks, assesses impact, and develops contingencies, while risk management works to mitigate risks.
- Qualitative and quantitative risk analysis methods are described for identifying and prioritizing risks.
- Bias must be addressed in risk analysis to provide accurate information for decision making.
- Proactive risk analysis can improve project schedule, costs, and quality outcomes.
Risk analysis is a systematic process to estimate the probability and impact of identified project risks. There are qualitative and quantitative approaches to risk analysis. Qualitative approaches use scales to assess probability and impact and assign risk levels like low, medium, high. Quantitative approaches use techniques like expected value analysis to generate probabilistic estimates of project outcomes. Monte Carlo simulation is commonly used to model project risks and determine the likelihood of meeting objectives within given cost and schedule constraints. Effective risk management involves identifying, analyzing, prioritizing and developing response plans for risks throughout the project lifecycle.
This document discusses project risk management for an IT project management course. It defines risk management and identifies key risk management processes: planning, identification, analysis, response planning, and monitoring/control. Various risk analysis techniques are described like probability/impact matrices and decision trees. The goal of risk management is to minimize negative risks while maximizing positive opportunities through risk avoidance, acceptance, transference, or mitigation strategies.
This document discusses project risk management. It defines risk as an uncertain event that can positively or negatively impact project objectives. Risk management is the systematic process of identifying, analyzing, and responding to project risks. The six processes of risk management are: 1) plan risk management, 2) identify risks, 3) perform qualitative risk analysis, 4) perform quantitative risk analysis, 5) plan risk responses, and 6) monitor and control risks. Tools used include risk breakdown structures, probability and impact matrices to assess risks, and decision trees to evaluate responses. The goal is to prioritize and respond to risks to help ensure project success.
Heba is following a risk mitigation strategy to respond to the identified risk of resource attrition on a software migration project at a bank. Mitigation strategies aim to reduce the probability and/or impact of adverse risks. Specifically, Heba is providing good increments to team members, which helps retain resources and mitigate the risk of attrition. Quantitative risk analysis uses modeling techniques like decision trees and Monte Carlo simulation to numerically analyze the effects of risks on project objectives. If the team cannot identify a suitable risk response strategy, the default is typically risk acceptance, where the project management plan is not changed to account for that risk.
Risk management involves identifying potential risks, assessing their probability and impact, prioritizing risks, developing strategies to mitigate high-priority risks, and continuously monitoring risks throughout the project. There are different categories of risk including project risks, technical risks, business risks, known risks, and unpredictable risks. Effective risk management requires proactively identifying risks, tracking them over time, taking steps to reduce impact or likelihood, and open communication across teams.
This document provides an overview of project risk management. It defines project risk as an event that could have a positive or negative impact on a project. Risk management involves identifying risks and developing plans to minimize their effects. The key steps in risk management are risk identification, analysis, response planning, monitoring and control. Managing risks helps improve project success rates, schedule and cost performance by moving from reactive to proactive decision making.
This document provides an overview of project risk management. It discusses the goals of risk management, including identifying and planning for risks to help projects succeed. The key aspects covered are identifying risks, analyzing their probability and impact, planning responses, and continuously monitoring risks. Qualitative and quantitative approaches to analysis are outlined. The overall process aims to move projects from reactive "firefighting" to proactive risk-based decision making.
This document provides an overview of project risk management. It discusses what project risk is, the risk management process, and tools for risk identification, analysis, response planning, monitoring and control. The risk management process involves planning risk management, identifying risks, analyzing their probability and impact, developing response plans, monitoring risks throughout the project, and using tools like risk logs and templates. Managing risks proactively helps improve project success rates.
This document provides an overview of project risk management. It discusses the goals of risk management, including identifying and planning for risks to help projects succeed. The key aspects covered are identifying risks, analyzing their probability and impact, planning responses, and continuously monitoring risks. Qualitative and quantitative approaches to analysis are outlined. The overall process aims to move projects from reactive "firefighting" to proactive risk-based decision making.
This document discusses decision analysis and risk management. It covers decision making under certainty, ignorance, and risk. Key concepts include expected monetary value, maximax, maximin, and expected return decision rules. Under certainty, the decision maker knows the state of nature with certainty. Under ignorance, all states are possible but probabilities are unknown. Under risk, probabilities of states are known. Expected monetary value quantifies risks by multiplying probability and impact. Maximax selects the strategy with highest possible return, while maximin selects the strategy with the lowest possible loss. Expected return selects the alternative with the highest expected long-term return based on probabilities of outcomes. The document emphasizes applying decision analysis concepts to project risk management.
This document provides an overview of project risk management. It defines risk as the possibility of suffering loss and discusses how risk changes throughout a project's life. Key aspects of risk management are identified, including risk identification, assessment, response planning, monitoring and control. Various risk management techniques are described, such as risk maps, hazard control matrices, and defining risk ownership. The document emphasizes that effective risk management can help improve project success.
The document discusses project risk management. It provides an overview of the risk management process, including the key inputs, tools and techniques, and outputs of each process. Specifically, it describes the processes of risk planning, identification, analysis, and monitoring. It defines risk and outlines the objectives of risk management. It also provides details about developing a risk management plan, identifying risks, performing qualitative analysis using tools like probability/impact matrices, and updating the risk register.
Project risk management involves identifying potential risks, analyzing their likelihood and impact, and developing responses to address threats and opportunities. The key processes include planning risk management, identifying risks, performing qualitative and quantitative risk analyses to prioritize risks, and planning risk responses. Qualitative analysis involves assessing probability and impact, while quantitative analysis uses numerical methods to evaluate risk exposure and determine contingency reserves. Risks are continually monitored and the risk register updated throughout the project life cycle.
This document discusses various tools and techniques for project risk management. It covers the key steps in risk management including risk planning, identification, analysis, response, and monitoring. Some common tools for risk identification include brainstorming, checklists, and SWOT analysis. Qualitative analysis involves assessing probability and impact, while quantitative analysis uses expected monetary value and decision trees to assign numerical values to risk. Risk responses can involve avoiding, transferring, mitigating, or accepting risks. Commercial risk management software tools are also listed.
This document outlines the risk management process for a case study project. It includes:
1. The risk management process with 6 steps: plan, identify, analyze qualitatively, analyze quantitatively, plan responses, and control risks.
2. A risk register containing 16 identified risks for the project with details like probability, impact, risk response strategies.
3. An update to the risk register after 29 months with one risk removed.
The document provides an overview of the risk management process applied to a case study project and the resulting risk register and monitoring.
This document provides an overview of project risk management processes and techniques. It discusses the six key processes: (1) plan risk management, (2) identify risks, (3) perform qualitative risk analysis, (4) perform quantitative risk analysis, (5) plan risk responses, and (6) monitor and control risks. For each process, it describes important inputs, tools and techniques, and outputs to consider when managing project risks. The goal of risk management is to proactively identify and mitigate risks that could negatively impact a project.
This document provides an overview of project risk management processes and techniques. It discusses the six key processes: (1) plan risk management, (2) identify risks, (3) perform qualitative risk analysis, (4) perform quantitative risk analysis, (5) plan risk responses, and (6) monitor and control risks. For each process, it describes important inputs, tools and techniques, and outputs to consider when managing project risks. The goal of risk management is to proactively identify and mitigate risks that could negatively impact a project.
Risk management involves identifying potential problems, assessing their likelihood and impacts, and developing strategies to address them. There are two main risk strategies - reactive, which addresses risks after issues arise, and proactive, which plans ahead. Key steps in proactive risk management include identifying risks through checklists, estimating their probability and impacts, developing mitigation plans, monitoring risks and mitigation effectiveness, and adjusting plans as needed. Common risk categories include project risks, technical risks, and business risks.
The document provides an overview of key aspects of developing a business plan, including the purpose of a business plan, an executive summary, product/service plans, market and industry analysis, and risk management. It discusses the importance of identifying risks and analyzing them both qualitatively and quantitatively. The risk management process involves risk planning, identification, analysis, response planning, and monitoring. Quantitative analysis includes using probability distributions and simulation to numerically analyze risks and their potential impacts on project objectives.
Final Class Presentation on Determining Project Stakeholders & Risks.pptxGeorgeKabongah2
“A person or group of people who have a vested interest in the success of an organization or project and the environment in which the organization/ project operates”
This document discusses risk management for engineering projects. It defines risk as potential problems that could impact a project's budget, timeline or deliverables. The risk management process involves identifying risks, analyzing their likelihood and impact, planning strategies to avoid or minimize risks, and monitoring risks throughout the project. Common risk types are technology, people, organizational, tools and requirements risks. Risk analysis assesses the probability and consequences of each risk. Risk planning develops avoidance, minimization and contingency strategies. Risk monitoring tracks risks and determines if their likelihood or impact changes over time.
This document discusses risk management in major projects. It defines risk and outlines the risk management process. This includes identifying risks, analyzing their potential impacts, and developing responses to mitigate negative impacts and maximize positive ones. Key steps involve identifying risks, assessing their impacts, running simulations to evaluate scenarios, and interpreting the results, which can indicate the probability of costs and the most influential risks. Risk management tools and establishing risk owners are also covered.
Review of Enterprise Security Risk ManagementRand W. Hirt
The document discusses enterprise security risk management and provides details on the risk assessment process. It defines risk as the likelihood of an adverse event occurring multiplied by the impact. Risk management aims to identify and mitigate risks to acceptable levels. The risk assessment process involves determining scope, gathering information, assessing risks, recommending controls, and determining residual risk. Controls can reduce risk through preventative, detective or corrective measures. Ongoing monitoring ensures the organization's risk posture remains consistent over time.
The document defines risk and issue, outlines the risk lifecycle and management cycle, and provides details on risk identification, analysis, assessment, and management. Key points include:
- A risk is a potential future event that could negatively impact objectives, while an issue is a current problem.
- The risk management cycle includes identifying risks, assessing them, selecting strategies, implementing controls, and monitoring/evaluating.
- Risk identification involves knowing the organization's assets and sources of risk. Risk analysis assesses the likelihood and impact of risks.
This document provides an overview of project risk management. It defines project risk as an event that could have a positive or negative impact on a project. Risk management involves identifying risks and developing plans to minimize their effects. The key steps in risk management are risk identification, analysis, response planning, monitoring and control. Managing risks helps improve project success rates, schedule and cost performance by moving from reactive to proactive decision making.
This document provides an overview of project risk management. It discusses the goals of risk management, including identifying and planning for risks to help projects succeed. The key aspects covered are identifying risks, analyzing their probability and impact, planning responses, and continuously monitoring risks. Qualitative and quantitative approaches to analysis are outlined. The overall process aims to move projects from reactive "firefighting" to proactive risk-based decision making.
This document provides an overview of project risk management. It discusses what project risk is, the risk management process, and tools for risk identification, analysis, response planning, monitoring and control. The risk management process involves planning risk management, identifying risks, analyzing their probability and impact, developing response plans, monitoring risks throughout the project, and using tools like risk logs and templates. Managing risks proactively helps improve project success rates.
This document provides an overview of project risk management. It discusses the goals of risk management, including identifying and planning for risks to help projects succeed. The key aspects covered are identifying risks, analyzing their probability and impact, planning responses, and continuously monitoring risks. Qualitative and quantitative approaches to analysis are outlined. The overall process aims to move projects from reactive "firefighting" to proactive risk-based decision making.
This document discusses decision analysis and risk management. It covers decision making under certainty, ignorance, and risk. Key concepts include expected monetary value, maximax, maximin, and expected return decision rules. Under certainty, the decision maker knows the state of nature with certainty. Under ignorance, all states are possible but probabilities are unknown. Under risk, probabilities of states are known. Expected monetary value quantifies risks by multiplying probability and impact. Maximax selects the strategy with highest possible return, while maximin selects the strategy with the lowest possible loss. Expected return selects the alternative with the highest expected long-term return based on probabilities of outcomes. The document emphasizes applying decision analysis concepts to project risk management.
This document provides an overview of project risk management. It defines risk as the possibility of suffering loss and discusses how risk changes throughout a project's life. Key aspects of risk management are identified, including risk identification, assessment, response planning, monitoring and control. Various risk management techniques are described, such as risk maps, hazard control matrices, and defining risk ownership. The document emphasizes that effective risk management can help improve project success.
The document discusses project risk management. It provides an overview of the risk management process, including the key inputs, tools and techniques, and outputs of each process. Specifically, it describes the processes of risk planning, identification, analysis, and monitoring. It defines risk and outlines the objectives of risk management. It also provides details about developing a risk management plan, identifying risks, performing qualitative analysis using tools like probability/impact matrices, and updating the risk register.
Project risk management involves identifying potential risks, analyzing their likelihood and impact, and developing responses to address threats and opportunities. The key processes include planning risk management, identifying risks, performing qualitative and quantitative risk analyses to prioritize risks, and planning risk responses. Qualitative analysis involves assessing probability and impact, while quantitative analysis uses numerical methods to evaluate risk exposure and determine contingency reserves. Risks are continually monitored and the risk register updated throughout the project life cycle.
This document discusses various tools and techniques for project risk management. It covers the key steps in risk management including risk planning, identification, analysis, response, and monitoring. Some common tools for risk identification include brainstorming, checklists, and SWOT analysis. Qualitative analysis involves assessing probability and impact, while quantitative analysis uses expected monetary value and decision trees to assign numerical values to risk. Risk responses can involve avoiding, transferring, mitigating, or accepting risks. Commercial risk management software tools are also listed.
This document outlines the risk management process for a case study project. It includes:
1. The risk management process with 6 steps: plan, identify, analyze qualitatively, analyze quantitatively, plan responses, and control risks.
2. A risk register containing 16 identified risks for the project with details like probability, impact, risk response strategies.
3. An update to the risk register after 29 months with one risk removed.
The document provides an overview of the risk management process applied to a case study project and the resulting risk register and monitoring.
This document provides an overview of project risk management processes and techniques. It discusses the six key processes: (1) plan risk management, (2) identify risks, (3) perform qualitative risk analysis, (4) perform quantitative risk analysis, (5) plan risk responses, and (6) monitor and control risks. For each process, it describes important inputs, tools and techniques, and outputs to consider when managing project risks. The goal of risk management is to proactively identify and mitigate risks that could negatively impact a project.
This document provides an overview of project risk management processes and techniques. It discusses the six key processes: (1) plan risk management, (2) identify risks, (3) perform qualitative risk analysis, (4) perform quantitative risk analysis, (5) plan risk responses, and (6) monitor and control risks. For each process, it describes important inputs, tools and techniques, and outputs to consider when managing project risks. The goal of risk management is to proactively identify and mitigate risks that could negatively impact a project.
Risk management involves identifying potential problems, assessing their likelihood and impacts, and developing strategies to address them. There are two main risk strategies - reactive, which addresses risks after issues arise, and proactive, which plans ahead. Key steps in proactive risk management include identifying risks through checklists, estimating their probability and impacts, developing mitigation plans, monitoring risks and mitigation effectiveness, and adjusting plans as needed. Common risk categories include project risks, technical risks, and business risks.
The document provides an overview of key aspects of developing a business plan, including the purpose of a business plan, an executive summary, product/service plans, market and industry analysis, and risk management. It discusses the importance of identifying risks and analyzing them both qualitatively and quantitatively. The risk management process involves risk planning, identification, analysis, response planning, and monitoring. Quantitative analysis includes using probability distributions and simulation to numerically analyze risks and their potential impacts on project objectives.
Final Class Presentation on Determining Project Stakeholders & Risks.pptxGeorgeKabongah2
“A person or group of people who have a vested interest in the success of an organization or project and the environment in which the organization/ project operates”
This document discusses risk management for engineering projects. It defines risk as potential problems that could impact a project's budget, timeline or deliverables. The risk management process involves identifying risks, analyzing their likelihood and impact, planning strategies to avoid or minimize risks, and monitoring risks throughout the project. Common risk types are technology, people, organizational, tools and requirements risks. Risk analysis assesses the probability and consequences of each risk. Risk planning develops avoidance, minimization and contingency strategies. Risk monitoring tracks risks and determines if their likelihood or impact changes over time.
This document discusses risk management in major projects. It defines risk and outlines the risk management process. This includes identifying risks, analyzing their potential impacts, and developing responses to mitigate negative impacts and maximize positive ones. Key steps involve identifying risks, assessing their impacts, running simulations to evaluate scenarios, and interpreting the results, which can indicate the probability of costs and the most influential risks. Risk management tools and establishing risk owners are also covered.
Review of Enterprise Security Risk ManagementRand W. Hirt
The document discusses enterprise security risk management and provides details on the risk assessment process. It defines risk as the likelihood of an adverse event occurring multiplied by the impact. Risk management aims to identify and mitigate risks to acceptable levels. The risk assessment process involves determining scope, gathering information, assessing risks, recommending controls, and determining residual risk. Controls can reduce risk through preventative, detective or corrective measures. Ongoing monitoring ensures the organization's risk posture remains consistent over time.
The document defines risk and issue, outlines the risk lifecycle and management cycle, and provides details on risk identification, analysis, assessment, and management. Key points include:
- A risk is a potential future event that could negatively impact objectives, while an issue is a current problem.
- The risk management cycle includes identifying risks, assessing them, selecting strategies, implementing controls, and monitoring/evaluating.
- Risk identification involves knowing the organization's assets and sources of risk. Risk analysis assesses the likelihood and impact of risks.
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.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
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
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.
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.
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.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
2. Risk Assessment
Goal:
• being able to prioritize risks according to
their impact and likeness on the project
• Making explicit the information
necessary to define the risk
management strategies (risk
management planning)
3. Techniques
• Two techniques:
– Qualitative risk analysis
• Simpler
• Can be used when no precise information about
probabilities of risk is available
– Quantitative risk analysis
• More systematic
• Suitable for mathematical analysis
• Provide figures on the (economial) impact of risks
4. Qualitative Risk Analysis
A three-step process
• Define probability, impact, and score
• Organize risk
• Highlight significant risks
5. Qualitative Risk Assessment
• Define classes of probabilities and classes of
impact
• Example
– Probability: Very low, low, moderate, high, very
high
– Impact: negligible, low, moderate, severe,
catastrophic
– Risk Score: low, medium, high
6. Qualitative Risk Assessment
… or numeric:
Risk Score = P x I
Very Low 0.1 1
Low 0.3 2
Moderata 0.5 3
High 0.7 4
Very High 0.9 5
Negligible 0.1 1
Low 0.3 2
Moderate 0.5 3
Severe 0.7 4
Catastrophic 0.9 5
7. Risk analysis (i)
Risk Probability Effects
Organisational financial problems force reductions in
the project budge t.
Low Catastrophic
It is impossible to recruit staff with the skill s required
for the project.
High Catastrophic
Key staff are ill at critical times in the project. Moderate Serious
Software components that should be reused contain
defects which limit their func tionality.
Moderate Serious
Changes to requirements that require major design
rework are proposed.
Moderate Serious
The organisation is restructured so that diff erent
manage ment are responsible for the project.
High Serious
8. Risk analysis (ii)
Risk Probability Effects
The database used in the system canno t process as
many transactions per second as expec ted.
Moderate Serious
The time required to deve lop the software is
unde restimated.
High Serious
CASE tools canno t be integrated. High Tolerable
Customers fail to unde rstand the impact of
requirements change s.
Moderate Tolerable
Required training for staff is not available. Moderate Tolerable
The rate of defect repair is underestimated. Moderate Tolerable
The size of the software is unde restimated. High Tolerable
The cod e generated by CASE tools is ineffi cient. Moderate Insignificant
9. Risk Matrix
Negligible Low Moderate Severe Catastrophic
Very High R1 R5
High R2 R6, R7, R8
Moderate R3
Low R4
Very Low R9, R10
10. Risk Matrix
Negligible Low Moderate Severe Catastrophic
Very High R1 R5
High R2 R6, R7, R8
Moderate R3
Low R4
Very Low R9, R10
11. Risk Matrix
Negligible Low Moderate Severe Catastrophic
Very High R1 R5
High R2 R6, R7, R8
Moderate R3
Low R4
Very Low R9, R10
12. Socially constructed risk
Two problems with qualitative risk
• People will believe some things are
risk, even when the statistics
indicate they aren't (and vice
versa). We are "risk illiterate”
• Who says what the probabilities
are? How do we calculate the risk
exposures objectively?
13. Socially Constructed Risk
• When seeking to put people's minds at rest,
qualitative risk assessment may not be
enough
• When assessing risk "objectively", we are in
fact using subjective judgements
… People are emotional!
(and fortunately so)
14. Some examples of real risks
Did you know:
• you should be more frightened of taking a bath than of
walking down a dark alleyway
• you should be more wary of yourself than of flying in a plane
Chances are your death will be by:
• being shot by a stranger...1 in 22,500
• drowning in the bath...1 in 17,500
• plane crash...1 in 800,000
• car accident...1 in 300
• suicide...1 in 160
• accidental fall...1 in 150
• cancer...1 in 4
15. Quantitative Risk Analysis
Similar as qualitative:
• Define probability and impact (in a sense:
which depends on the techniques; how
depends on the domain)
• Use techniques to numerically assess risks
and to visualize data
• Highlight significant risks
16. Quantitative Risk Assessment
• Approach: Expected monetary value analysis. It
computes the expected monetary outcome (according
to different statistical criteria) of a decision/risk
– Technique: Decision tree analysis. Technique that
helps solving the EMV analysis.
• Approach: Modeling. Provide a model of the project.
– Technique: Sensitivity analysis. Helps determining
which risks have the most impact by examining one
variable at a time. (Tornado diagrams)
– Technique: simulation, monte-carlo technique.
18. Decision Theory
• Si: states of the system
• Dj: decisions (risks)
• Cij: cost associated to Dj in Si
19. Decision Theory
Choose cost of decision according to different
strategies:
• Minimax, take the decision which has the
maximum minimum gain associated do D
• Average, take the decision which has the
maximum average gain associated
• Max, take the decision which has the
maximum gain associated
… who’s optimistic, who’s pessimistic?
20. EMV
• Decision D has probability pj of
generating gain gj (j = 1..N, SUM(pj) =
1)
• Expected Monetary Value associated to
D is
– EMV(D) = SUMj(pj * gj)
• Take decision with maximum EMV
21. Decision Trees
• A way of computing EMV
• It graphically represents all the possible
outcomes in a tree
• Costs are associated to leaves (and
propagate to nodes)
• Probability are associated to labels
23. Modeling
• Define a model for the decision/project
(some formula describing how inputs
are transformed into outputs)
• “Play” with the formula to understand
the main factors (sensitivity analysis) or
to get a global value
24. Developing a tornado diagram
(100) (50) - 50 100 150 200 250
NPV (Primary Criterion)
15%
6%
100,000
Engineering Budget
Investment
Material Cost
Labor Cost
Market Size
Market Share 10%
120,000
100
150,000
120 60
Uncertainties are
sorted in descending
order of impact
on NPV
Third
Base Value $100 First
Length of bar
indicated impact
on NPV
one variable
at a time
Second
25. Montecarlo Simulation
• Automatically varies input variables
(according to their statistical distribution)
to get a probability distribution of the
outputs
26. Quantitative Risk Assessment:
Outputs
• Probabilistic Analysis of the project:
estimates of the possible schedule and
cost overruns with their probabilities
• Prioritized list of quantified risks:
risks that pose the greatest threat or the
greatest opportunity to the project
• Trends (by repeating the process,
trends may emerge)
27. Risk Response Planning
• Risk Response Planning
– Goal: define the strategies for taking
care/exploit risks
28. Strategies: Menaces
• Avoid.
– Change the plan to eliminate the threat (increase
time, relax objectives, take corrective actions -
increase time to do requirements)
• Transfer.
– Shift the negative outcome to a third party. It
transfers responsibility, it does not eliminate the
risk (insurance, contracts to transfer liability… they
require to pay you a price)
• Mitigate
– Reduce probability or impact (often better than
trying and repare the damage; prototyping)
29. Strategies: Opportunities
• Exploit
– Eliminate uncertainty relate to the occurrence of
the opportunity (e.g. assign more talented people,
provide better quality)
• Share
– Allocate responsibility of exploitation to a third
party (joint-ventures, partnerships, …)
• Enhance
– Modify the size of an opportunity by increasing
probability and/or positive impact
30. Strategy for both Threats and
Opportunities
• Acceptance
– Difficult to deal with all the risks
– May be:
• Passive: just let the team deal with them
• Active: provide some buffer (time, money, …)
• Contingent Response Planning
– Prepare a plan to implement if the risk
occur
31. Risk management strategies
(i)
Risk Strategy
Organisational
financ ial problems
Prepare a briefing document for senior manage ment
showing how th e project is making a very important
contribution to the goals of the business.
Recruitment
problems
Alert customer of potential difficulties and the
possibility of delays, inves tigate buying- in
components.
Staff illness Reorganise team so that there is more overlap of work
and people therefore und erstand each other’s jobs.
Defective
components
Replace potentially defective components withbough t-
in components of known reliabilit y.
32. Risk management strategies
(ii)
Risk Strategy
Requirements
chang es
Derive traceabili ty info rmation to assess requirements
chang e impact, maximi se information hid ing in the
design.
Organisational
restructuring
Prepare a briefing document for senior manage ment
showing how th e project is making a very important
contribution to the goals of the business.
Database
performance
Inves tigate the possibilit yof buying a high er-
performance database.
Unde restimated
development time
Inves tigate buying in components, inve stigate use ofa
program gene rator
33. Risk Response Planning:
Outputs
• Strategies for dealing with the risks
• Triggers (elements used to monitor and
understand whether a risk has
occurred)
• People responsible of monitoring the
risk
• People responsible of applying
contingency plans
34. Risk Monitoring and Control
• Process
– Analyse deviations
– Identify causes
– Evaluate corrective actions
– Modify current plan
• Mind:
– Planned risks dealt with as above
– Unplanned risks require the full process!
37. Risk homeostasis
People accept a certain degree of risk, regardless
of what you do to reduce it
• Today, life is "safer" than ever before, but mortality rates
remain static (Gerald Wilde, cited in Bryson, 1997)
• Cars with ABS (anti-lock braking systems) no longer attract
insurance discounts because their drivers drive more
recklessly/carelessly
• As we take measures to make our projects more predictable
and safer, we can expect people to ask us to undertake more
risky work
39. Risk management Principles
• Global perspective Viewing software development within the context of the larger systems-
level definition, design, and development. Recognizing both the potential value of
opportunity and the potential impact of adverse effects.
• Forward-looking view Thinking toward tomorrow, identifying uncertainties, anticipating
potential outcomes.Managing project resources and activities while anticipating
uncertainties.
• Open communication Encouraging free-flowing information at and between all project
levels.Enabling formal, informal, and impromptu communication. Using processes that value
the individual voice (bringing unique knowledge and insight to identifying and managing
risk).
• Integrated management Making risk management an integral and vital part of project
management. Adapting risk management methods and tools to a project's infrastructure and
culture.
• Continuous process Sustaining constant vigilance. Identifying and managing risks
routinely through all phases of the project's life cycle.
• Shared product vision Mutual product vision based on common purpose, shared
ownership, and collective communication. Focusing on results.
• Teamwork Working cooperatively to achieve common goal. Pooling talents, skills, and
knowledge.
40. Most Common Errors
• Do not identify a maximum risk value.
– Give up a project if too risky
• Do not write a balanced risk management
plan
– Not to big, not to simplicistic
• Misinterpret effects as causes
– Being late with the project
– We may be charged 100.000 euros as a penalty
• Do not apply contingency plans
– Dealing with risk when they occur is more error-
prone than think about the strategies before they
occur
41. Most Common Errors
• Do not involve actors
– Make sure stakeholders understand
consequences of the risk (share the risk);
involved stakeholders in dealing with them
• Do not update the plan
– Helps keeping the contingency plans really
applicable
42. Exercise
• Write the risk management plan for the
digital-divide project
• Write the risk management plan for the
e-procurement project