This document provides an introduction to machine learning basics. It defines machine learning as a field of artificial intelligence that uses algorithms to learn from data and improve performance on tasks. The document outlines common machine learning problems like text classification, disease diagnosis, and chess playing. It also discusses key machine learning concepts like training experiences, target functions, feature engineering, and different types of intelligent programs and learning algorithms. Resources for learning more about machine learning are provided.
This document provides an introduction to machine learning basics. It defines machine learning as a field of artificial intelligence that uses algorithms to learn from data and improve performance on tasks. The document outlines common machine learning problems like text classification, disease diagnosis, and chess playing. It also discusses machine learning approaches, including supervised learning algorithms, feature engineering, and different types of models. Resources for learning more about machine learning are provided at the end.
This document provides an overview of machine learning concepts including:
- Defining machine learning as the study of how to build systems that improve with experience.
- Designing a learning system for the task of playing checkers by choosing the training experience, representation, and learning algorithm.
- Common machine learning applications like speech recognition, computer vision, and robot control.
This document provides an introduction to an artificial intelligence course on machine learning. It discusses different machine learning tasks like classification, regression, transcription, and machine translation. It also covers the concepts of experience (datasets), performance evaluation, supervised vs unsupervised learning, and examples of tasks like face recognition, search queries prediction, and medical imaging analysis that are well-suited for machine learning. Key algorithms discussed include neural networks, decision trees, naive Bayes, and support vector machines.
The document provides an overview of artificial intelligence and expert systems. It discusses the major branches of AI including perceptive systems, learning systems, natural language processing, neural networks, robotics, and expert systems. It also describes the components, development process, applications, and evolution of expert systems.
The document provides an overview of artificial intelligence and expert systems. It discusses the major branches of AI including perceptive systems, learning systems, natural language processing, neural networks, robotics, and expert systems. It also describes the components, development process, applications, and evolution of expert systems.
This presentation briefs about International Collegiate Programming Contest(ICPC) which is organized by ACM and sponsored by IBM.
This is delivered at VB Siddardha Colleges, Vijayawada on 10th Mar 2015. Somehow Indian participation is not attractive. I am encouraging Indian students to participate in this competition by delivering lectures like this.
This document summarizes a machine learning lecture given by William H. Hsu. The 3-sentence summary is:
The lecture outlined the course information, including format, exams, homework and grading. It provided an overview of machine learning topics to be covered, such as learning algorithms, models and methodologies. Examples of applications of machine learning were also discussed, such as database mining, reasoning, and acting.
Ayush Kumar, Sarah Kohail, Amit Kumar, Asif Ekbal, Chris Biemann
IIT Patna, India
TU Darmstadt, Germany
Presented by: Alexander Panchenko, TU Darmstadt, Germany
This document provides an introduction to machine learning basics. It defines machine learning as a field of artificial intelligence that uses algorithms to learn from data and improve performance on tasks. The document outlines common machine learning problems like text classification, disease diagnosis, and chess playing. It also discusses machine learning approaches, including supervised learning algorithms, feature engineering, and different types of models. Resources for learning more about machine learning are provided at the end.
This document provides an overview of machine learning concepts including:
- Defining machine learning as the study of how to build systems that improve with experience.
- Designing a learning system for the task of playing checkers by choosing the training experience, representation, and learning algorithm.
- Common machine learning applications like speech recognition, computer vision, and robot control.
This document provides an introduction to an artificial intelligence course on machine learning. It discusses different machine learning tasks like classification, regression, transcription, and machine translation. It also covers the concepts of experience (datasets), performance evaluation, supervised vs unsupervised learning, and examples of tasks like face recognition, search queries prediction, and medical imaging analysis that are well-suited for machine learning. Key algorithms discussed include neural networks, decision trees, naive Bayes, and support vector machines.
The document provides an overview of artificial intelligence and expert systems. It discusses the major branches of AI including perceptive systems, learning systems, natural language processing, neural networks, robotics, and expert systems. It also describes the components, development process, applications, and evolution of expert systems.
The document provides an overview of artificial intelligence and expert systems. It discusses the major branches of AI including perceptive systems, learning systems, natural language processing, neural networks, robotics, and expert systems. It also describes the components, development process, applications, and evolution of expert systems.
This presentation briefs about International Collegiate Programming Contest(ICPC) which is organized by ACM and sponsored by IBM.
This is delivered at VB Siddardha Colleges, Vijayawada on 10th Mar 2015. Somehow Indian participation is not attractive. I am encouraging Indian students to participate in this competition by delivering lectures like this.
This document summarizes a machine learning lecture given by William H. Hsu. The 3-sentence summary is:
The lecture outlined the course information, including format, exams, homework and grading. It provided an overview of machine learning topics to be covered, such as learning algorithms, models and methodologies. Examples of applications of machine learning were also discussed, such as database mining, reasoning, and acting.
Ayush Kumar, Sarah Kohail, Amit Kumar, Asif Ekbal, Chris Biemann
IIT Patna, India
TU Darmstadt, Germany
Presented by: Alexander Panchenko, TU Darmstadt, Germany
- A high-level overview of artificial intelligence
- The importance of predictions across different domains of life
- Big (text) data
- Competition as a discovery process
- Domain-general learning
- Computer vision and natural language processing
- Elements of a machine learning system
- A hierarchy of problem classes
- Data collection
- The purpose of a model
- Logistic loss function
- Likelihood, log likelihood and maximum likelihood
- Ockham's Razor
- Intelligence as sequence prediction
- Building blocks of neural networks: neurons, weights and layers
- Logistic regression as a neural network
- Sigmoid function
- A look at backpropagation
- Gradient descent
- Convolutional neural networks
- Max-pooling
- Deep neural networks
Fundamentals of computer system and Programming EC-105NUST Stuff
This document provides an overview of the EC-102 Computer System and Programming course. It includes information about the course title, code, semester, required textbooks and references. The document also outlines the course contents which cover topics like computer organization, programming, data types, operators, selection statements, repetition structures, functions, arrays, pointers and more. Finally, it lists the course learning outcomes and some policies like no assignment deadline extensions and penalties for plagiarism.
This document provides an overview of machine learning, including:
1) It defines machine learning as teaching computational machines to solve problems by giving examples to automatically infer rules for associating inputs and outputs.
2) It discusses different machine learning algorithms like linear classifiers, support vector machines, ensemble methods, and deep learning.
3) It emphasizes the need for scalable deployment of machine learning models to handle large and streaming data, covering approaches like distributed and parallel processing using MapReduce and cloud services.
The document discusses aspects of being a professional including being highly educated, working autonomously on intellectually challenging tasks, defining technical terms, reading books, referring to references, thinking before working and complaining, and not being overly pedantic. It provides examples of some technical terms and concepts along with explanations to illustrate how to think like a professional.
This document outlines the assessment scheme, learning outcomes, and content for a module on the Introduction to Artificial Intelligence. It includes:
- The assessment scheme which is 80% theory and 20% practical, with a 40% continuous assessment and 60% end term examination. The continuous assessment includes components like class tests, assignments, and presentations.
- The learning outcomes which are for students to understand AI, its applications, and analyze problems to identify computing solutions.
- An introduction to AI, its definitions, applications in games, vision, robotics, and other fields. It also discusses different philosophies of AI like thinking humanly versus rationally.
- Examples of AI in puzzles, games and how
Lecture 01: Machine Learning for Language Technology - IntroductionMarina Santini
This document provides an introduction to a machine learning course being taught at Uppsala University. It outlines the schedule, reading list, assignments, and examination. The course covers topics like decision trees, linear models, ensemble methods, text mining, and unsupervised learning. It discusses the differences between supervised and unsupervised learning, as well as classification, regression, and other machine learning techniques. The goal is to introduce students to commonly used methods in natural language processing.
1. CS154 teaches the theory of computation through models like finite automata, context-free grammars, and Turing machines.
2. These concepts have many practical applications in areas like regular expressions, programming languages, and determining what problems can and cannot be solved by computers.
3. The course covers regular and context-free languages, their descriptors, decidability of problems, and intractable problems through lectures, homework, exams, and the textbook.
This document provides an introduction to machine learning, including definitions, applications, and types of learning. It defines machine learning as the study of algorithms that improve performance on tasks with experience. The main types of learning covered are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Supervised learning uses labeled training data, unsupervised learning uses unlabeled data, and reinforcement learning involves sequences of actions with rewards. Machine learning has many applications and the field is growing rapidly.
Introduction AI ML& Mathematicals of ML.pdfGandhiMathy6
Machine learning uses probability theory to deal with uncertainty that arises from noisy data, limited data sets, and ambiguity. Probability theory provides a framework to quantify and manipulate uncertainty. It allows optimal predictions given available information, even if that information is incomplete. Key concepts in probability theory for machine learning include defining sample spaces and events, calculating probabilities, working with joint, conditional, and independent probabilities, and using Bayes' rule. These concepts help machine learning algorithms make inferences from data.
Machine Learning: Past, Present and Future - by Tom DietterichBigML, Inc
There are many uses to Machine Learning. This technology began as a form of Data-Driven Software Engineering; but a more recent development is Machine Learning for Data Science: its tools can help us understand the many forms of data that are collected by companies, scientists and governments. Another important trend is Machine Learning for Optimizing Operations: for example, logistics, scheduling, advertisement placement, etc. Also, recent advances in anomaly detection are helping us understand when the results of previous Machine Learning cannot be trusted or when changes in the inputs are surprising.
Find more details here: http://www.madridml.com/en/.
This document provides an overview of machine learning with three key points:
1) Machine learning is a method of data analysis that allows computers to learn from data without being explicitly programmed. It builds models from sample data known as training data.
2) Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.
3) The goals of machine learning are to automatically create programs that can learn from data and perform predictive tasks without needing to be programmed with rules, and to devise learning algorithms that learn automatically from data without human intervention.
Expert systems are computer programs that contain knowledge from human experts and use logical rules to solve problems in a specific domain. They have four main components: a knowledge base that stores rules and data, an inference engine that applies rules to solve problems, an explanation facility to explain solutions, and a user interface. While expert systems were widely developed in the 1980s and 1990s, they have limitations such as a narrow domain of knowledge and inability to learn.
An introduction to machine learning. I gave a talk on this, the video can be found here:
http://www.techgig.com/expert-speak/Introduction-to-Machine-Learning-616
A 3 sentence summary of the document:
The document discusses deep learning for medical image analysis, focusing on applications in neonatal medical imaging. It provides an overview of deep learning and convolutional neural networks, including examples of their use for tasks like brain tissue segmentation in MRI scans of newborns. The presenter describes their research using deep learning for segmentation and diagnosis of hypoxic ischemic encephalopathy in MRI scans of newborns.
This document provides an overview of deep learning and its applications in medical image analysis. It begins with an introduction to the speaker and their background in biomedical image analysis. It then discusses machine learning and how deep learning uses neural networks with many layers to automatically determine useful features from data. Convolutional neural networks are described as being well-suited for image analysis. Several examples of deep learning applications in medical images are given, including brain MRI segmentation, detection of prostate cancer in ultrasound images, and the speaker's own work on neonatal brain injury assessment from MRI scans. Resources for getting started with deep learning are also listed.
Machine learning involves using data to answer questions and make predictions. There are three main types of machine learning problems: supervised learning which involves predicting outputs given labeled examples; unsupervised learning which finds hidden patterns in unlabeled data; and reinforcement learning where an agent learns through trial-and-error interactions with an environment. To solve a machine learning problem typically involves five steps: gathering and preprocessing data, engineering features, selecting and training an algorithm, and using the trained model to make predictions.
Deep Learning And Business Models (VNITC 2015-09-13)Ha Phuong
Deep Learning and Business Models
Tran Quoc Hoan discusses deep learning and its applications, as well as potential business models. Deep learning has led to significant improvements in areas like image and speech recognition compared to traditional machine learning. Some business models highlighted include developing deep learning frameworks, building hardware optimized for deep learning, using deep learning for IoT applications, and providing deep learning APIs and services. Deep learning shows promise across many sectors but also faces challenges in fully realizing its potential.
SSTRM - StrategicReviewGroup.ca - HSI Technical Workshop Process - Phil Carr ...Phil Carr
The document summarizes the process and goals of a workshop on integrating human and system capabilities for future soldier systems. The workshop aimed to identify key integration challenges, potential solutions, and areas for research and development collaboration across industry, government, military, and academia. Participants discussed challenges related to physical, cognitive and external integration, then proposed solutions and enabling technologies. They identified priority technologies requiring further R&D and potential collaborators.
This document provides an overview of artificial intelligence and expert systems. It discusses key concepts in artificial intelligence including machine learning, problem solving, and visual processing. The major branches of AI are described as perceptive systems, robotics, expert systems, learning systems, natural language processing, and neural networks. Expert systems are defined as storing knowledge and making inferences. The components, advantages, and applications of expert systems are also summarized.
Effort to develop computer-based systems that behave like humans:
learn languages
accomplish physical tasks
use a perceptual apparatus
emulate human thinking
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
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 high-level overview of artificial intelligence
- The importance of predictions across different domains of life
- Big (text) data
- Competition as a discovery process
- Domain-general learning
- Computer vision and natural language processing
- Elements of a machine learning system
- A hierarchy of problem classes
- Data collection
- The purpose of a model
- Logistic loss function
- Likelihood, log likelihood and maximum likelihood
- Ockham's Razor
- Intelligence as sequence prediction
- Building blocks of neural networks: neurons, weights and layers
- Logistic regression as a neural network
- Sigmoid function
- A look at backpropagation
- Gradient descent
- Convolutional neural networks
- Max-pooling
- Deep neural networks
Fundamentals of computer system and Programming EC-105NUST Stuff
This document provides an overview of the EC-102 Computer System and Programming course. It includes information about the course title, code, semester, required textbooks and references. The document also outlines the course contents which cover topics like computer organization, programming, data types, operators, selection statements, repetition structures, functions, arrays, pointers and more. Finally, it lists the course learning outcomes and some policies like no assignment deadline extensions and penalties for plagiarism.
This document provides an overview of machine learning, including:
1) It defines machine learning as teaching computational machines to solve problems by giving examples to automatically infer rules for associating inputs and outputs.
2) It discusses different machine learning algorithms like linear classifiers, support vector machines, ensemble methods, and deep learning.
3) It emphasizes the need for scalable deployment of machine learning models to handle large and streaming data, covering approaches like distributed and parallel processing using MapReduce and cloud services.
The document discusses aspects of being a professional including being highly educated, working autonomously on intellectually challenging tasks, defining technical terms, reading books, referring to references, thinking before working and complaining, and not being overly pedantic. It provides examples of some technical terms and concepts along with explanations to illustrate how to think like a professional.
This document outlines the assessment scheme, learning outcomes, and content for a module on the Introduction to Artificial Intelligence. It includes:
- The assessment scheme which is 80% theory and 20% practical, with a 40% continuous assessment and 60% end term examination. The continuous assessment includes components like class tests, assignments, and presentations.
- The learning outcomes which are for students to understand AI, its applications, and analyze problems to identify computing solutions.
- An introduction to AI, its definitions, applications in games, vision, robotics, and other fields. It also discusses different philosophies of AI like thinking humanly versus rationally.
- Examples of AI in puzzles, games and how
Lecture 01: Machine Learning for Language Technology - IntroductionMarina Santini
This document provides an introduction to a machine learning course being taught at Uppsala University. It outlines the schedule, reading list, assignments, and examination. The course covers topics like decision trees, linear models, ensemble methods, text mining, and unsupervised learning. It discusses the differences between supervised and unsupervised learning, as well as classification, regression, and other machine learning techniques. The goal is to introduce students to commonly used methods in natural language processing.
1. CS154 teaches the theory of computation through models like finite automata, context-free grammars, and Turing machines.
2. These concepts have many practical applications in areas like regular expressions, programming languages, and determining what problems can and cannot be solved by computers.
3. The course covers regular and context-free languages, their descriptors, decidability of problems, and intractable problems through lectures, homework, exams, and the textbook.
This document provides an introduction to machine learning, including definitions, applications, and types of learning. It defines machine learning as the study of algorithms that improve performance on tasks with experience. The main types of learning covered are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Supervised learning uses labeled training data, unsupervised learning uses unlabeled data, and reinforcement learning involves sequences of actions with rewards. Machine learning has many applications and the field is growing rapidly.
Introduction AI ML& Mathematicals of ML.pdfGandhiMathy6
Machine learning uses probability theory to deal with uncertainty that arises from noisy data, limited data sets, and ambiguity. Probability theory provides a framework to quantify and manipulate uncertainty. It allows optimal predictions given available information, even if that information is incomplete. Key concepts in probability theory for machine learning include defining sample spaces and events, calculating probabilities, working with joint, conditional, and independent probabilities, and using Bayes' rule. These concepts help machine learning algorithms make inferences from data.
Machine Learning: Past, Present and Future - by Tom DietterichBigML, Inc
There are many uses to Machine Learning. This technology began as a form of Data-Driven Software Engineering; but a more recent development is Machine Learning for Data Science: its tools can help us understand the many forms of data that are collected by companies, scientists and governments. Another important trend is Machine Learning for Optimizing Operations: for example, logistics, scheduling, advertisement placement, etc. Also, recent advances in anomaly detection are helping us understand when the results of previous Machine Learning cannot be trusted or when changes in the inputs are surprising.
Find more details here: http://www.madridml.com/en/.
This document provides an overview of machine learning with three key points:
1) Machine learning is a method of data analysis that allows computers to learn from data without being explicitly programmed. It builds models from sample data known as training data.
2) Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.
3) The goals of machine learning are to automatically create programs that can learn from data and perform predictive tasks without needing to be programmed with rules, and to devise learning algorithms that learn automatically from data without human intervention.
Expert systems are computer programs that contain knowledge from human experts and use logical rules to solve problems in a specific domain. They have four main components: a knowledge base that stores rules and data, an inference engine that applies rules to solve problems, an explanation facility to explain solutions, and a user interface. While expert systems were widely developed in the 1980s and 1990s, they have limitations such as a narrow domain of knowledge and inability to learn.
An introduction to machine learning. I gave a talk on this, the video can be found here:
http://www.techgig.com/expert-speak/Introduction-to-Machine-Learning-616
A 3 sentence summary of the document:
The document discusses deep learning for medical image analysis, focusing on applications in neonatal medical imaging. It provides an overview of deep learning and convolutional neural networks, including examples of their use for tasks like brain tissue segmentation in MRI scans of newborns. The presenter describes their research using deep learning for segmentation and diagnosis of hypoxic ischemic encephalopathy in MRI scans of newborns.
This document provides an overview of deep learning and its applications in medical image analysis. It begins with an introduction to the speaker and their background in biomedical image analysis. It then discusses machine learning and how deep learning uses neural networks with many layers to automatically determine useful features from data. Convolutional neural networks are described as being well-suited for image analysis. Several examples of deep learning applications in medical images are given, including brain MRI segmentation, detection of prostate cancer in ultrasound images, and the speaker's own work on neonatal brain injury assessment from MRI scans. Resources for getting started with deep learning are also listed.
Machine learning involves using data to answer questions and make predictions. There are three main types of machine learning problems: supervised learning which involves predicting outputs given labeled examples; unsupervised learning which finds hidden patterns in unlabeled data; and reinforcement learning where an agent learns through trial-and-error interactions with an environment. To solve a machine learning problem typically involves five steps: gathering and preprocessing data, engineering features, selecting and training an algorithm, and using the trained model to make predictions.
Deep Learning And Business Models (VNITC 2015-09-13)Ha Phuong
Deep Learning and Business Models
Tran Quoc Hoan discusses deep learning and its applications, as well as potential business models. Deep learning has led to significant improvements in areas like image and speech recognition compared to traditional machine learning. Some business models highlighted include developing deep learning frameworks, building hardware optimized for deep learning, using deep learning for IoT applications, and providing deep learning APIs and services. Deep learning shows promise across many sectors but also faces challenges in fully realizing its potential.
SSTRM - StrategicReviewGroup.ca - HSI Technical Workshop Process - Phil Carr ...Phil Carr
The document summarizes the process and goals of a workshop on integrating human and system capabilities for future soldier systems. The workshop aimed to identify key integration challenges, potential solutions, and areas for research and development collaboration across industry, government, military, and academia. Participants discussed challenges related to physical, cognitive and external integration, then proposed solutions and enabling technologies. They identified priority technologies requiring further R&D and potential collaborators.
This document provides an overview of artificial intelligence and expert systems. It discusses key concepts in artificial intelligence including machine learning, problem solving, and visual processing. The major branches of AI are described as perceptive systems, robotics, expert systems, learning systems, natural language processing, and neural networks. Expert systems are defined as storing knowledge and making inferences. The components, advantages, and applications of expert systems are also summarized.
Effort to develop computer-based systems that behave like humans:
learn languages
accomplish physical tasks
use a perceptual apparatus
emulate human thinking
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
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.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
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%.
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.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
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
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
3. Machine Learning Basics: 1. General Introduction
Intelligence
Intelligence
Ability to solve problems
Examples of Intelligent Behaviors or
Tasks
Classification of texts based on content
Heart disease diagnosis
Chess playing
4. Machine Learning Basics: 1. General Introduction
Example 1: Text Classification (1)
Huge oil platforms dot the Gulf like
beacons -- usually lit up like Christmas
trees at night.
One of them, sitting astride the
Rostam offshore oilfield, was all but
blown out of the water by U.S.
Warships on Monday.
The Iranian platform, an unsightly
mass of steel and concrete, was a
three-tier structure rising 200 feet
(60 metres) above the warm waters of
the Gulf until four U.S. Destroyers
pumped some …
Human
Judgment
Crude
Ship
5. Machine Learning Basics: 1. General Introduction
Example 1: Text Classification (2)
The Federal Reserve is expected to
enter the government securities
market to supply reserves to the
banking system via system repurchase
agreements, economists said.
Most economists said the Fed would
execute three-day system
repurchases to meet a substantial
need to add reserves in the current
maintenance period, although some
said a more …
Human
Judgment
Money-fx
6. Machine Learning Basics: 1. General Introduction
Example 2: Disease Diagnosis (1)
Patient 1’s data
Age: 67
Sex: male
Chest pain type: asymptomatic
Resting blood pressure: 160mm Hg
Serum cholestoral: 286mg/dl
Fasting blood sugar: < 120mg/dl
…
Doctor
Diagnosis
Presence
7. Machine Learning Basics: 1. General Introduction
Example 2: Disease Diagnosis (2)
Patient 2‘s data
Age: 63
Sex: male
Chest pain type: typical angina
Resting blood pressure: 145mm Hg
Serum cholestoral: 233mg/dl
Fasting blood sugar: > 120mg/dl
…
Doctor
Diagnosis
Absence
8. Machine Learning Basics: 1. General Introduction
Example 3: Chess Playing
Chess Game
Two players playing one-by-one under
the restriction of a certain rule
Characteristics
To achieve a goal: win the game
Interactive
9. Machine Learning Basics: 1. General Introduction
Artificial Intelligence
Artificial Intelligence
Ability of machines in conducting
intelligent tasks
Intelligent Programs
Programs conducting specific intelligent
tasks
Input
Intelligent
Processing
Output
10. Machine Learning Basics: 1. General Introduction
Example 1: Text Classifier (1)
…
fiber = 0
…
huge = 1
…
oil = 1
platforms = 1
…
Classification
…
Crude = 1
…
Money-fx = 0
…
Ship = 1
…
Text File:
Huge oil
platforms dot
the Gulf like
beacons --
usually lit up …
Preprocessing
11. Machine Learning Basics: 1. General Introduction
Example 1: Text Classifier (2)
…
enter = 1
expected = 1
…
federal = 1
…
oil = 0
…
Classification
…
Crude = 0
…
Money-fx = 1
…
Ship = 0
…
Text File:
The Federal
Reserve is
expected to
enter the
government …
Preprocessing
12. Machine Learning Basics: 1. General Introduction
Example 2: Disease Classifier (1)
Preprocessed data of patient 1
Age = 67
Sex = 1
Chest pain type = 4
Resting blood pressure = 160
Serum cholestoral = 286
Fasting blood sugar = 0
…
Classification
Presence = 1
13. Machine Learning Basics: 1. General Introduction
Example 2: Disease Classifier (2)
Preprocessed data of patient 2
Age = 63
Sex = 1
Chest pain type = 1
Resting blood pressure = 145
Serum cholestoral = 233
Fasting blood sugar = 1
…
Classification
Presence = 0
14. Machine Learning Basics: 1. General Introduction
Example 3: Chess Program
Best move -
New matrix
Opponent’s
playing his move
Matrix representing
the current board
Searching and
evaluating
15. Machine Learning Basics: 1. General Introduction
AI Approach
Reasoning with Knowledge
Knowledge base
Reasoning
Traditional Approaches
Handcrafted knowledge base
Complex reasoning process
Disadvantages
Knowledge acquisition bottleneck
16. Machine Learning Basics: 1. General Introduction
Outline
Artificial Intelligence
Machine Learning: Modern
Approaches to Artificial Intelligence
Machine Learning Problems
Research and Resources
Our Course
17. Machine Learning Basics: 1. General Introduction
Machine Learning
Machine Learning (Mitchell 1997)
Learn from past experiences
Improve the performances of intelligent
programs
Definitions (Mitchell 1997)
A computer program is said to learn
from experience E with respect to some
class of tasks T and performance
measure P, if its performance at the
tasks improves with the experiences
18. Machine Learning Basics: 1. General Introduction
Example 1: Text Classification
Text
classifier
New text file class
Classified text files
Text file 1 trade
Text file 2 ship
… …
Training
19. Machine Learning Basics: 1. General Introduction
Example 2: Disease Diagnosis
Disease
classifier
New patient’s
data
Presence or
absence
Database of medical records
Patient 1’s data Absence
Patient 2’s data Presence
… …
Training
20. Machine Learning Basics: 1. General Introduction
Example 3: Chess Playing
Strategy of
Searching and
Evaluating
New matrix
representing
the current
board
Best move
Games played:
Game 1’s move list Win
Game 2’s move list Lose
… …
Training
21. Machine Learning Basics: 1. General Introduction
Examples
Text Classification
Task T
Assigning texts to a set of predefined
categories
Performance measure P
Precision and recall of each category
Training experiences E
A database of texts with their
corresponding categories
How about Disease Diagnosis?
How about Chess Playing?
22. Machine Learning Basics: 1. General Introduction
Why Machine Learning Is Possible?
Mass Storage
More data available
Higher Performance of Computer
Larger memory in handling the data
Greater computational power for
calculating and even online learning
23. Machine Learning Basics: 1. General Introduction
Advantages
Alleviate Knowledge Acquisition
Bottleneck
Does not require knowledge engineers
Scalable in constructing knowledge base
Adaptive
Adaptive to the changing conditions
Easy in migrating to new domains
24. Machine Learning Basics: 1. General Introduction
Success of Machine Learning
Almost All the Learning Algorithms
Text classification (Dumais et al. 1998)
Gene or protein classification optionally
with feature engineering (Bhaskar et al.
2006)
Reinforcement Learning
Backgammon (Tesauro 1995)
Learning of Sequence Labeling
Speech recognition (Lee 1989)
Part-of-speech tagging (Church 1988)
25. Machine Learning Basics: 1. General Introduction
Outline
Artificial Intelligence
Machine Learning: Modern
Approaches to Artificial Intelligence
Machine Learning Problems
Machine Learning Resources
Our Course
26. Machine Learning Basics: 1. General Introduction
Choosing the Training Experience
Choosing the Training Experience
Sometimes straightforward
Text classification, disease diagnosis
Sometimes not so straightforward
Chess playing
Other Attributes
How the training experience is controlled
by the learner?
How the training experience represents
the situations in which the performance
of the program is measured?
27. Machine Learning Basics: 1. General Introduction
Choosing the Target Function
Choosing the Target Function
What type of knowledge will be learned?
How it will be used by the program?
Reducing the Learning Problem
From the problem of improving
performance P at task T with experience
E
To the problem of learning some
particular target functions
28. Machine Learning Basics: 1. General Introduction
Solving Real World Problems
What Is the Input?
Features representing the real world
data
What Is the Output?
Predictions or decisions to be made
What Is the Intelligent Program?
Types of classifiers, value functions, etc.
How to Learn from experience?
Learning algorithms
29. Machine Learning Basics: 1. General Introduction
Feature Engineering
Representation of the Real World Data
Features: data’s attributes which may be useful
in prediction
Feature Transformation and Selection
Select a subset of the features
Construct new features, e.g.
Discretization of real value features
Combinations of existing features
Post Processing to Fit the Classifier
Does not change the nature
30. Machine Learning Basics: 1. General Introduction
Intelligent Programs
Value Functions
Input: features
Output: value
Classifiers (Most Commonly Used)
Input: features
Output: a single decision
Sequence Labeling
Input: sequence of features
Output: sequence of decisions
31. Machine Learning Basics: 1. General Introduction
Examples of Value Functions
Linear Regression
Input: feature vectors
Output:
)
,
,
,
( 2
1 n
x
x
x
x
n
i
i
i b
x
w
b
f
1
)
( x
w
x
)
,
,
,
( 2
1 n
x
x
x
x
b
e
f
x
w
x
1
1
)
(
Logistic Regression
Input: feature vectors
Output:
32. Machine Learning Basics: 1. General Introduction
Examples of Classifiers
Linear Classifier
Input: feature vectors
Output:
)
,
,
,
( 2
1 n
x
x
x
x
)
sgn(
)
sgn(
1
n
i
i
i b
x
w
b
y x
w
Rule Classifier
Decision tree
A tree with nodes representing condition
testing and leaves representing classes
Decision list
If condition 1 then class 1 elseif condition 2
then class 2 elseif ….
33. Machine Learning Basics: 1. General Introduction
Examples of Learning Algorithms
Parametric Functions or Classifiers
Given parameters of the functions or
classifier, e.g.
Linear functions or classifiers: w, b
Estimating the parameters, e.g.
Loss function optimization
Rule Learning
Condition construction
Rules induction using divide-and-conquer
34. Machine Learning Basics: 1. General Introduction
Machine Learning Problems
Methodology of Machine Learning
General methods for machine learning
Investigate which method is better under
some certain conditions
Application of Machine Learning
Specific application of machine learning
methods
Investigate which feature, classifier,
method should be used to solve a certain
problem
35. Machine Learning Basics: 1. General Introduction
Methodology
Theoretical
Mathematical analysis of performances of
learning algorithms (usually with
assumptions)
Empirical
Demonstrate the empirical results of
learning algorithms on datasets
(benchmarks or real world applications)
36. Machine Learning Basics: 1. General Introduction
Application
Adaptation of Learning Algorithms
Directly apply, or tailor learning
algorithms to specific application
Generalization
Generalize the problems and methods in
the specific application to more general
cases
37. Machine Learning Basics: 1. General Introduction
Outline
Artificial Intelligence
Machine Learning: Modern
Approaches to Artificial Intelligence
Machine Learning Problems
Machine Learning Resources
Our Course
38. Machine Learning Basics: 1. General Introduction
Introduction Materials
Text Books
T. Mitchell (1997). Machine Learning,
McGraw-Hill Publishers.
N. Nilsson (1996). Introduction to
Machine Learning (drafts).
Lecture Notes
T. Mitchell’s Slides
Introduction to Machine Learning
39. Machine Learning Basics: 1. General Introduction
Technical Papers
Journals, e.g.
Machine Learning, Kluwer Academic
Publishers.
Journal of Machine Learning Research,
MIT Press.
Conferences, e.g.
International Conference on Machine
Learning (ICML)
Neural Information Processing Systems
(NIPS)
40. Machine Learning Basics: 1. General Introduction
Others
Data Sets
UCI Machine Learning Repository
Reuters data set for text classification
Related Areas
Artificial intelligence
Knowledge discovery and data mining
Statistics
Operation research
…
41. Machine Learning Basics: 1. General Introduction
Outline
Artificial Intelligence
Machine Learning: Modern
Approaches to Artificial Intelligence
Machine Learning Problems
Machine Learning Resources
Our Course
42. Machine Learning Basics: 1. General Introduction
What I will Talk about
Machine Learning Methods
Simple methods
Effective methods (state of the art)
Method Details
Ideas
Assumptions
Intuitive interpretations
43. Machine Learning Basics: 1. General Introduction
What I won’t Talk about
Machine Learning Methods
Classical, but complex and not effective
methods (e.g., complex neural networks)
Methods not widely used
Method Details
Theoretical justification
44. Machine Learning Basics: 1. General Introduction
What You will Learn
Machine Learning Basics
Methods
Data
Assumptions
Ideas
Others
Problem solving techniques
Extensive knowledge of modern
techniques
45. Machine Learning Basics: 1. General Introduction
References
H. Bhaskar, D. Hoyle, and S. Singh (2006). Machine
Learning: a Brief Survey and Recommendations for
Practitioners. Computers in Biology and Medicine, 36(10),
1104-1125.
K. Church (1988). A Stochastic Parts Program and Noun
Phrase Parser for Unrestricted Texts. In Proc. ANLP-
1988, 136-143.
S. Dumais, J. Platt, D. Heckerman and M. Sahami
(1998). Inductive Learning Algorithms and
Representations for Text Categorization. In Proc. CIKM-
1998, 148-155.
K. Lee (1989). Automatic Speech Recognition: The
Development of the Sphinx System, Kluwer Academic
Publishers.
T. Mitchell (1997). Machine Learning, McGraw-Hill
Publishers.
G. Tesauro (1995). Temporal Difference Learning and
TD-gammon. Communications of the ACM, 38(3), 58-68.