Decision tree learning involves creating a decision tree that classifies examples by sorting them from the root node to a leaf node. Each node tests an attribute and branches correspond to attribute values. Instances are classified by traversing the tree in this way. The ID3 algorithm uses information gain to select the attribute that best splits examples at each node, creating a greedy top-down search through possible trees. It calculates information gain, which measures the expected reduction in entropy (impurity), to determine which attribute best classifies examples at each step.
This document provides an overview of classification and decision tree induction. It discusses basic concepts of classification and prediction. Classification involves analyzing labeled datasets to build a model, while prediction involves forecasting future trends. Decision tree induction is then explained as a common classification technique. It involves learning classification rules from training data and using test data to evaluate the rules. The document outlines the decision tree induction process and algorithms. It also discusses attribute selection measures, pruning techniques, and compares decision trees to naive Bayesian classification.
The document discusses decision tree learning and the ID3 algorithm. It begins by introducing decision trees and how they are used to classify instances by sorting them from the root node to a leaf node. It then discusses how ID3 builds decision trees in a top-down greedy manner by selecting the attribute that best splits the data at each node based on information gain. The document also covers issues like overfitting, handling continuous attributes, and pruning decision trees.
This document provides an overview of decision tree algorithms for machine learning. It discusses key concepts such as:
- Decision trees can be used for classification or regression problems.
- They represent rules that can be understood by humans and used in knowledge systems.
- The trees are built by splitting the data into purer subsets based on attribute tests, using measures like information gain.
- Issues like overfitting are addressed through techniques like reduced error pruning and rule post-pruning.
This document discusses decision trees and entropy. It begins by providing examples of binary and numeric decision trees used for classification. It then describes characteristics of decision trees such as nodes, edges, and paths. Decision trees are used for classification by organizing attributes, values, and outcomes. The document explains how to build decision trees using a top-down approach and discusses splitting nodes based on attribute type. It introduces the concept of entropy from information theory and how it can measure the uncertainty in data for classification. Entropy is the minimum number of questions needed to identify an unknown value.
Decision trees are a supervised learning method that represents concepts as decision trees to classify data. The algorithm works by selecting the best attribute to test at each node using information gain, which measures the reduction in entropy from partitioning data by an attribute. It builds the tree in a top-down greedy manner, recursively selecting the attribute that best splits the data until the leaf nodes are pure or no further information gain is possible. The tree can then be converted to classification rules by tracing paths from the root to leaf nodes.
Decision trees are a type of supervised learning algorithm used for classification and regression. ID3 and C4.5 are algorithms that generate decision trees by choosing the attribute with the highest information gain at each step. Random forest is an ensemble method that creates multiple decision trees and aggregates their results, improving accuracy. It introduces randomness when building trees to decrease variance.
This document provides an overview of decision tree classification algorithms. It defines key concepts like decision nodes, leaf nodes, splitting, pruning, and explains how a decision tree is constructed using attributes to recursively split the dataset into purer subsets. It also describes techniques like information gain and Gini index that help select the best attributes to split on, and discusses advantages like interpretability and disadvantages like potential overfitting.
This document provides an overview of classification and decision tree induction. It discusses basic concepts of classification and prediction. Classification involves analyzing labeled datasets to build a model, while prediction involves forecasting future trends. Decision tree induction is then explained as a common classification technique. It involves learning classification rules from training data and using test data to evaluate the rules. The document outlines the decision tree induction process and algorithms. It also discusses attribute selection measures, pruning techniques, and compares decision trees to naive Bayesian classification.
The document discusses decision tree learning and the ID3 algorithm. It begins by introducing decision trees and how they are used to classify instances by sorting them from the root node to a leaf node. It then discusses how ID3 builds decision trees in a top-down greedy manner by selecting the attribute that best splits the data at each node based on information gain. The document also covers issues like overfitting, handling continuous attributes, and pruning decision trees.
This document provides an overview of decision tree algorithms for machine learning. It discusses key concepts such as:
- Decision trees can be used for classification or regression problems.
- They represent rules that can be understood by humans and used in knowledge systems.
- The trees are built by splitting the data into purer subsets based on attribute tests, using measures like information gain.
- Issues like overfitting are addressed through techniques like reduced error pruning and rule post-pruning.
This document discusses decision trees and entropy. It begins by providing examples of binary and numeric decision trees used for classification. It then describes characteristics of decision trees such as nodes, edges, and paths. Decision trees are used for classification by organizing attributes, values, and outcomes. The document explains how to build decision trees using a top-down approach and discusses splitting nodes based on attribute type. It introduces the concept of entropy from information theory and how it can measure the uncertainty in data for classification. Entropy is the minimum number of questions needed to identify an unknown value.
Decision trees are a supervised learning method that represents concepts as decision trees to classify data. The algorithm works by selecting the best attribute to test at each node using information gain, which measures the reduction in entropy from partitioning data by an attribute. It builds the tree in a top-down greedy manner, recursively selecting the attribute that best splits the data until the leaf nodes are pure or no further information gain is possible. The tree can then be converted to classification rules by tracing paths from the root to leaf nodes.
Decision trees are a type of supervised learning algorithm used for classification and regression. ID3 and C4.5 are algorithms that generate decision trees by choosing the attribute with the highest information gain at each step. Random forest is an ensemble method that creates multiple decision trees and aggregates their results, improving accuracy. It introduces randomness when building trees to decrease variance.
This document provides an overview of decision tree classification algorithms. It defines key concepts like decision nodes, leaf nodes, splitting, pruning, and explains how a decision tree is constructed using attributes to recursively split the dataset into purer subsets. It also describes techniques like information gain and Gini index that help select the best attributes to split on, and discusses advantages like interpretability and disadvantages like potential overfitting.
This document provides an overview of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. It then discusses decision tree learning and decision trees in more detail. Decision tree algorithms like ID3 and C4.5 are explained as popular inductive inference algorithms that use an information gain measure to select attributes at each step of growing the decision tree. The document also covers converting decision trees to rules and splitting information. Linear models and artificial neural networks are briefly introduced, with the backpropagation algorithm explained as the gradient descent learning rule used in multilayer feedforward neural networks.
The document discusses decision tree induction algorithms. It begins with an introduction to decision trees, describing their structure and how they are used for classification. It then covers the basic algorithm for constructing decision trees, including the ID3, C4.5, and CART algorithms. Next, it discusses different attribute selection measures that can be used to determine the best attribute to split on at each node, including information gain, gain ratio, and the Gini index. It provides details on how information gain is calculated.
This document discusses decision trees and their use for classification. It provides examples to illustrate key concepts:
- Decision trees classify instances by sorting them down the tree from root to leaf node, where each leaf represents a classification outcome. Nodes test attribute values and branches represent test outcomes.
- An example decision tree classifies whether to play golf based on weather attributes like temperature and humidity. It generates rules like "if sunny and humidity below 75% then play."
- Classification accuracy is measured by how many test instances the tree correctly classifies. Information gain is used to select the most informative attribute to split on at each node, improving classification.
Machine learning session6(decision trees random forrest)Abhimanyu Dwivedi
Concepts include decision tree with its examples. Measures used for splitting in decision tree like gini index, entropy, information gain, pros and cons, validation. Basics of random forests with its example and uses.
The document discusses various decision tree learning methods. It begins by defining decision trees and issues in decision tree learning, such as how to split training records and when to stop splitting. It then covers impurity measures like misclassification error, Gini impurity, information gain, and variance reduction. The document outlines algorithms like ID3, C4.5, C5.0, and CART. It also discusses ensemble methods like bagging, random forests, boosting, AdaBoost, and gradient boosting.
1. The document discusses decision trees, bagging, and random forests. It provides an overview of how classification and regression trees (CART) work using a binary tree data structure and recursive data partitioning. It then explains how bagging generates diverse trees by bootstrap sampling and averages the results. Finally, it describes how random forests improve upon bagging by introducing random feature selection to generate less correlated and more accurate trees.
The document discusses decision tree learning, including:
- Decision trees represent a disjunction of conjunctions of constraints on attribute values to classify instances.
- The ID3 and C4.5 algorithms use information gain to select the attribute that best splits the data at each node, growing the tree in a top-down greedy manner.
- Decision trees can model nonlinearity and are generally easy to interpret, but may overfit more complex datasets.
1. The document describes the C4.5 algorithm for building decision trees from a set of training data. It involves choosing attributes that best differentiate the training instances and creating tree nodes with child links for each attribute value.
2. It then discusses concepts like entropy, information gain, and using information gain to select the optimal attribute to test at each node.
3. The document provides a weather data example to illustrate how a decision tree is constructed recursively using these concepts.
The document discusses decision trees and random forest algorithms. It begins with an outline and defines the problem as determining target attribute values for new examples given a training data set. It then explains key requirements like discrete classes and sufficient data. The document goes on to describe the principles of decision trees, including entropy and information gain as criteria for splitting nodes. Random forests are introduced as consisting of multiple decision trees to help reduce variance. The summary concludes by noting out-of-bag error rate can estimate classification error as trees are added.
This document discusses decision tree algorithms C4.5 and CART. It explains that ID3 has limitations in dealing with continuous data and noisy data, which C4.5 aims to address through techniques like post-pruning trees to avoid overfitting. CART uses binary splits and measures like Gini index or entropy to produce classification trees, and sum of squared errors to produce regression trees. It also performs cost-complexity pruning to find an optimal trade-off between accuracy and model complexity.
A Decision Tree Based Classifier for Classification & Prediction of Diseasesijsrd.com
In this paper, we are proposing a modified algorithm for classification. This algorithm is based on the concept of the decision trees. The proposed algorithm is better then the previous algorithms. It provides more accurate results. We have tested the proposed method on the example of patient data set. Our proposed methodology uses greedy approach to select the best attribute. To do so the information gain is used. The attribute with highest information gain is selected. If information gain is not good then again divide attributes values into groups. These steps are done until we get good classification/misclassification ratio. The proposed algorithms classify the data sets more accurately and efficiently.
Decision trees classify instances by starting at the root node and moving through the tree recursively according to attribute tests at each node, until a leaf node determining the class label is reached. They work by splitting the training data into purer partitions based on the values of predictor attributes, using an attribute selection measure like information gain to choose the splitting attributes. The resulting tree can be pruned to avoid overfitting and reduce error on new data.
The document discusses decision tree algorithms. It begins with an introduction and example, then covers the principles of entropy and information gain used to build decision trees. It provides explanations of key concepts like entropy, information gain, and how decision trees are constructed and evaluated. Examples are given to illustrate these concepts. The document concludes with strengths and weaknesses of decision tree algorithms.
The document discusses decision tree algorithms. It begins with an introduction and example, then covers the principles of entropy and information gain used to build decision trees. It provides explanations of key concepts like evaluating decision trees using training and testing accuracy. The document concludes with strengths and weaknesses of decision tree algorithms.
Research scholars evaluation based on guides view using id3eSAT Journals
Abstract Research Scholars finds many problems in their Research and Development activities for the completion of their research work in universities. This paper gives a proficient way for analyzing the performance of Research Scholar based on guides and experts feedback. A dataset is formed using this information. The outcome class attribute will be in view of guides about the scholars. We apply decision tree algorithm ID3 on this dataset to construct the decision tree. Then the scholars can enter the testing data that has comprised with attribute values to get the view of guides for that testing dataset. Guidelines to the scholar can be provided by considering this constructed tree to improve their outcomes.
The document discusses decision trees and their algorithms. It introduces decision trees, describing their structure as having root, internal, and leaf nodes. It then discusses Hunt's algorithm, the basis for decision tree induction algorithms like ID3 and C4.5. Hunt's algorithm grows a decision tree recursively by partitioning training records into purer subsets based on attribute tests. The document also covers methods for expressing test conditions based on attribute type, measures for selecting the best split like information gain, and advantages and disadvantages of decision trees.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
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.
This document provides an overview of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. It then discusses decision tree learning and decision trees in more detail. Decision tree algorithms like ID3 and C4.5 are explained as popular inductive inference algorithms that use an information gain measure to select attributes at each step of growing the decision tree. The document also covers converting decision trees to rules and splitting information. Linear models and artificial neural networks are briefly introduced, with the backpropagation algorithm explained as the gradient descent learning rule used in multilayer feedforward neural networks.
The document discusses decision tree induction algorithms. It begins with an introduction to decision trees, describing their structure and how they are used for classification. It then covers the basic algorithm for constructing decision trees, including the ID3, C4.5, and CART algorithms. Next, it discusses different attribute selection measures that can be used to determine the best attribute to split on at each node, including information gain, gain ratio, and the Gini index. It provides details on how information gain is calculated.
This document discusses decision trees and their use for classification. It provides examples to illustrate key concepts:
- Decision trees classify instances by sorting them down the tree from root to leaf node, where each leaf represents a classification outcome. Nodes test attribute values and branches represent test outcomes.
- An example decision tree classifies whether to play golf based on weather attributes like temperature and humidity. It generates rules like "if sunny and humidity below 75% then play."
- Classification accuracy is measured by how many test instances the tree correctly classifies. Information gain is used to select the most informative attribute to split on at each node, improving classification.
Machine learning session6(decision trees random forrest)Abhimanyu Dwivedi
Concepts include decision tree with its examples. Measures used for splitting in decision tree like gini index, entropy, information gain, pros and cons, validation. Basics of random forests with its example and uses.
The document discusses various decision tree learning methods. It begins by defining decision trees and issues in decision tree learning, such as how to split training records and when to stop splitting. It then covers impurity measures like misclassification error, Gini impurity, information gain, and variance reduction. The document outlines algorithms like ID3, C4.5, C5.0, and CART. It also discusses ensemble methods like bagging, random forests, boosting, AdaBoost, and gradient boosting.
1. The document discusses decision trees, bagging, and random forests. It provides an overview of how classification and regression trees (CART) work using a binary tree data structure and recursive data partitioning. It then explains how bagging generates diverse trees by bootstrap sampling and averages the results. Finally, it describes how random forests improve upon bagging by introducing random feature selection to generate less correlated and more accurate trees.
The document discusses decision tree learning, including:
- Decision trees represent a disjunction of conjunctions of constraints on attribute values to classify instances.
- The ID3 and C4.5 algorithms use information gain to select the attribute that best splits the data at each node, growing the tree in a top-down greedy manner.
- Decision trees can model nonlinearity and are generally easy to interpret, but may overfit more complex datasets.
1. The document describes the C4.5 algorithm for building decision trees from a set of training data. It involves choosing attributes that best differentiate the training instances and creating tree nodes with child links for each attribute value.
2. It then discusses concepts like entropy, information gain, and using information gain to select the optimal attribute to test at each node.
3. The document provides a weather data example to illustrate how a decision tree is constructed recursively using these concepts.
The document discusses decision trees and random forest algorithms. It begins with an outline and defines the problem as determining target attribute values for new examples given a training data set. It then explains key requirements like discrete classes and sufficient data. The document goes on to describe the principles of decision trees, including entropy and information gain as criteria for splitting nodes. Random forests are introduced as consisting of multiple decision trees to help reduce variance. The summary concludes by noting out-of-bag error rate can estimate classification error as trees are added.
This document discusses decision tree algorithms C4.5 and CART. It explains that ID3 has limitations in dealing with continuous data and noisy data, which C4.5 aims to address through techniques like post-pruning trees to avoid overfitting. CART uses binary splits and measures like Gini index or entropy to produce classification trees, and sum of squared errors to produce regression trees. It also performs cost-complexity pruning to find an optimal trade-off between accuracy and model complexity.
A Decision Tree Based Classifier for Classification & Prediction of Diseasesijsrd.com
In this paper, we are proposing a modified algorithm for classification. This algorithm is based on the concept of the decision trees. The proposed algorithm is better then the previous algorithms. It provides more accurate results. We have tested the proposed method on the example of patient data set. Our proposed methodology uses greedy approach to select the best attribute. To do so the information gain is used. The attribute with highest information gain is selected. If information gain is not good then again divide attributes values into groups. These steps are done until we get good classification/misclassification ratio. The proposed algorithms classify the data sets more accurately and efficiently.
Decision trees classify instances by starting at the root node and moving through the tree recursively according to attribute tests at each node, until a leaf node determining the class label is reached. They work by splitting the training data into purer partitions based on the values of predictor attributes, using an attribute selection measure like information gain to choose the splitting attributes. The resulting tree can be pruned to avoid overfitting and reduce error on new data.
The document discusses decision tree algorithms. It begins with an introduction and example, then covers the principles of entropy and information gain used to build decision trees. It provides explanations of key concepts like entropy, information gain, and how decision trees are constructed and evaluated. Examples are given to illustrate these concepts. The document concludes with strengths and weaknesses of decision tree algorithms.
The document discusses decision tree algorithms. It begins with an introduction and example, then covers the principles of entropy and information gain used to build decision trees. It provides explanations of key concepts like evaluating decision trees using training and testing accuracy. The document concludes with strengths and weaknesses of decision tree algorithms.
Research scholars evaluation based on guides view using id3eSAT Journals
Abstract Research Scholars finds many problems in their Research and Development activities for the completion of their research work in universities. This paper gives a proficient way for analyzing the performance of Research Scholar based on guides and experts feedback. A dataset is formed using this information. The outcome class attribute will be in view of guides about the scholars. We apply decision tree algorithm ID3 on this dataset to construct the decision tree. Then the scholars can enter the testing data that has comprised with attribute values to get the view of guides for that testing dataset. Guidelines to the scholar can be provided by considering this constructed tree to improve their outcomes.
The document discusses decision trees and their algorithms. It introduces decision trees, describing their structure as having root, internal, and leaf nodes. It then discusses Hunt's algorithm, the basis for decision tree induction algorithms like ID3 and C4.5. Hunt's algorithm grows a decision tree recursively by partitioning training records into purer subsets based on attribute tests. The document also covers methods for expressing test conditions based on attribute type, measures for selecting the best split like information gain, and advantages and disadvantages of decision trees.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
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.
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.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
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.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
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%.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
2. Decision tree learning is a method for approximating
discrete-valued target functions, in which the learned
function is represented by a decision tree.
2
3. DECISION TREE REPRESENTATION
FIGURE: A
decision tree for the
concept PlayTennis.
An example is
classified by sorting
it through the tree to
the appropriate leaf
node, then returning
the classification
associated with this
leaf
3
4. 4
• Decision trees classify instances by sorting them down the tree from the root to
some leaf node, which provides the classification of the instance.
• Each node in the tree specifies a test of some attribute of the instance, and each
branch descending from that node corresponds to one of the possible values for
this attribute.
• An instance is classified by starting at the root node of the tree, testing the
attribute specified by this node, then moving down the tree branch corresponding
to the value of the attribute in the given example. This process is then repeated
for the subtree rooted at the new node.
5. 5
• Decision trees represent a disjunction of conjunctions of constraints on the
attribute values of instances.
• Each path from the tree root to a leaf corresponds to a conjunction of attribute
tests, and the tree itself to a disjunction of these conjunctions
For example,
The decision tree shown in above figure corresponds to the expression
(Outlook = Sunny 𝖠 Humidity = Normal)
𝗏
𝗏
(Outlook = Overcast)
(Outlook = Rain 𝖠 Wind = Weak)
6. 6
APPROPRIATE PROBLEMS FOR
DECISION TREE LEARNING
Decision tree learning is generally best suited to problems with the following
characteristics:
1. Instances are represented by attribute-value pairs – Instances are described by
a fixed set of attributes and their values
2. The target function has discrete output values – The decision tree assigns a
Boolean classification (e.g., yes or no) to each example. Decision tree methods
easily extend to learning functions with more than two possible output values.
3. Disjunctive descriptions may be required
7. 7
4. The training data may contain errors – Decision tree learning methods are
robust to errors, both errors in classifications of the training examples and errors
in the attribute values that describe these examples.
5. The training data may contain missing attribute values – Decision tree
methods can be used even when some training examples have unknown values
• Decision tree learning has been applied to problems such as learning to classify
medical patients by their disease, equipment malfunctions by their cause, and
loan applicants by their likelihood of defaulting on payments.
• Such problems, in which the task is to classify examples into one of a discrete set
of possible categories, are often referred to as classification problems.
8. 8
THE BASIC DECISION TREE LEARNING
ALGORITHM
• Most algorithms that have been developed for learning decision trees are
variations on a core algorithm that employs a top-down, greedy search through the
space of possible decision trees. This approach is exemplified by the ID3
algorithm and its successor C4.5
9. 9
What is the ID3 algorithm?
• ID3 stands for Iterative Dichotomiser 3
• ID3 is a precursor to the C4.5Algorithm.
• The ID3 algorithm was invented by Ross Quinlan in 1975
• Used to generate a decision tree from a given data set by employing a top-down,
greedy search, to test each attribute at every node of the tree.
• The resulting tree is used to classify future samples.
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ID3 algorithm
ID3(Examples, Target_attribute,Attributes)
Examples are the training examples. Target_attribute is the attribute whose value is to be predicted
by the tree. Attributes is a list of other attributes that may be tested by the learned decision tree.
Returns a decision tree that correctly classifies the given Examples.
Create a Root node for the tree
If all Examples are positive, Return the single-node tree Root, with label = +
If all Examples are negative, Return the single-node tree Root, with label = -
If Attributes is empty, Return the single-node tree Root, with label = most common value of
Target_attribute in Examples
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Otherwise Begin
A← the attribute fromAttributes that best* classifies Examples
The decision attribute for Root ←A
For each possible value, vi, ofA,
Add a new tree branch below Root, corresponding to the testA= vi
Let Examples vi, be the subset of Examples that have value vi for A
If Examples vi , is empty
Then below this new branch add a leaf node with label = most common value of
Target_attribute in Examples
Else below this new branch add the subtree
ID3(Examples vi, Targe_tattribute,Attributes – {A}))
End
Return Root
* The best attribute is the one with highest information gain
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Which Attribute Is the Best Classifier?
• The central choice in the ID3 algorithm is selecting which attribute to test at each
node in the tree.
• A statistical property called information gain that measures how well a given
attribute separates the training examples according to their target classification.
• ID3 uses information gain measure to select among the candidate attributes at
each step while growing the tree.
13. ENTROPY MEASURES HOMOGENEITY OF EXAMPLES
• To define information gain, we begin by defining a measure called entropy.
Entropy measures the impurity of a collection of examples.
• Given a collection S, containing positive and negative examples of some target
concept, the entropy of S relative to this Boolean classification is
Where,
p+ is the proportion of positive examples in S
p- is the proportion of negative examples in S.
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14. Example: Entropy
• Suppose S is a collection of 14 examples of some boolean concept, including 9
positive and 5 negative examples. Then the entropy of S relative to this boolean
classification is
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• The entropy is 0 if all members of S belong to the same class
• The entropy is 1 when the collection contains an equal number of positive and
negative examples
• If the collection contains unequal numbers of positive and negative examples, the
entropy is between 0 and 1
17. INFORMATION GAIN MEASURES THE EXPECTED
REDUCTION IN ENTROPY
• Information gain, is the expected reduction in entropy caused by partitioning the
examples according to this attribute.
• The information gain, Gain(S, A) of an attribute A, relative to a collection of
examples S, is defined as
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Example: Information gain
Let, Values(Wind) = {Weak, Strong}
S
SWeak
SStrong
= [9+, 5−]
= [6+, 2−]
= [3+, 3−]
Information gain of attribute Wind:
Gain(S, Wind) = Entropy(S) − 8/14 Entropy (SWeak) − 6/14 Entropy (SStrong)
= 0.94 – (8/14)* 0.811 – (6/14) *1.00
= 0.048
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An Illustrative Example
• To illustrate the operation of ID3, consider the learning task represented by the
training examples of below table.
• Here the target attribute PlayTennis, which can have values yes or no for
different days.
• Consider the first step through the algorithm, in which the topmost node of the
decision tree is created.
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Day Outlook Temperature Humidity Wind PlayTennis
D1 Sunny Hot High Weak No
D2 Sunny Hot High Strong No
D3 Overcast Hot High Weak Yes
D4 Rain Mild High Weak Yes
D5 Rain Cool Normal Weak Yes
D6 Rain Cool Normal Strong No
D7 Overcast Cool Normal Strong Yes
D8 Sunny Mild High Weak No
D9 Sunny Cool Normal Weak Yes
D10 Rain Mild Normal Weak Yes
D11 Sunny Mild Normal Strong Yes
D12 Overcast Mild High Strong Yes
D13 Overcast Hot Normal Weak Yes
D14 Rain Mild High Strong No
21. ID3 determines the information gain for each candidate attribute (i.e., Outlook,
Temperature, Humidity, and Wind), then selects the one with highest information
gain
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= 0.246
= 0.151
= 0.048
The information gain values for all four attributes are
• Gain(S, Outlook)
• Gain(S, Humidity)
• Gain(S, Wind)
• Gain(S, Temperature) = 0.029
• According to the information gain measure, the Outlook attribute provides the
best prediction of the target attribute, PlayTennis, over the training examples.
Therefore, Outlook is selected as the decision attribute for the root node, and
branches are created below the root for each of its possible values i.e., Sunny,
Overcast, and Rain.