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Unit 3 part i Data mining
To cover the basics of data mining concepts..Can get introduction of types of data ,KDD process,applications of data mining..
Subhaschamdrabhosesubhqschndrachose.pptx
Congress,[x] and a new mood in Britain for rapid decolonisation in India.[y][41][44]
Bose's legacy is mixed. Among many in India, he is seen as a hero, his saga serving as a would-be counterpoise to the many actions of regeneration, negotiation, and reconciliation over a quarter-century through which the independence of India was achieved.[z][aa][ab] His collaborations with Japanese Fascism and Nazism pose serious ethical dilemmas,[ac] especially his reluctance to publicly criticize the worst excesses of German anti-Semitism from 1938 onwards or to offer refuge in India to its victims.
Chittaranjan Das, a voice for aggressive nationalism in Bengal. In 1923, Bose was elected the President of Indian Youth Congress and also the Secretary of the Bengal State Congress. He became the editor of the newspaper "Forward", which had been founded by Chittaranjan Das.[81] Bose worked as the CEO of the Calcutta Municipal Corporation for Das when the latter was elected mayor of Calcutta in 1924.[82] During the same year, when Bose was leading a protest march in Calcutta, he, Maghfoor Ahmad Ajazi and other leaders were arrested and imprisoned.[83][failed verification] After a roundup of nationalists in 1925, Bose was sent to prison in Mandalay, British Burma, where he contracted tuberculosis.[84]
Subhas Bose (in military uniform) with Congress president, Motilal Nehru taking the salute. Annual meeting, Indian National Congress, 29 December 1928
In 1927, after being released from prison, Bose became general secretary of the Congress party and worked with Jawaharlal Nehru for independence. In late December 1928, Bose organised the Annual Meeting of the Indian National Congress in Calcutta.[85] His most memorable role was as General officer commanding (GOC) Congress Volunteer Corps.[85] Author Nirad Chaudhuri wrote about the meeting:
Bose organized a volunteer corps in uniform, its officers were even provided with steel-cut epaulettes ... his uniform was made by a firm of British tailors in Calcutta, Harman's. A telegram addressed to him as GOC was delivered to the British General in Fort William and was the subject of a good deal of malicious gossip in the (British Indian) press. Mahatma Gandhi as a sincere pacifist vowed to non-violence, did not like the strutting, clicking of boots, and saluting, and he afterward described the Calcutta session of the Congress as a Bertram Mills circus, which caused a great deal of indignation among the Bengalis.[85]
A little later, Bose was again arrested and jailed for civil disobedience; this time he emerged to become Mayor of Calcutta in 1930.[84]
Chittaranjan Das, a voice for aggressive nationalism in Bengal. In 1923, Bose was elected the President of Indian Youth Congress and also the Secretary of the Bengal State Congress. He became the editor of the newspaper "Forward", which had been founded by Chittaranjan Das.[81] Bose worked as the CEO of the Calcutta Municipal Corporation for Das when the latter was elected ma
Lecture2 (1).ppt
Data mining is the process of extracting patterns from large data sets to identify useful information. It involves applying machine learning algorithms to detect patterns in sample data and then using the learned patterns to predict future behaviors or outcomes. Data mining utilizes techniques from machine learning, statistics, databases, and visualization to analyze large datasets and discover hidden patterns. The goal of data mining is to extract useful information from large datasets and transform it into an understandable structure for further use.
NCCT.pptx
This document provides an overview of data mining. It discusses the introduction to data mining, its importance and applications. The key techniques of data mining discussed include classification, prediction, clustering, association and summarization. Examples of data mining applications mentioned are in healthcare, banking/finance, retail and web mining. The document concludes with discussing future trends in data mining involving new algorithms and data types, as well as computing resources like cloud computing.
Unit-V-Introduction to Data Mining.pptx
Data mining involves extracting useful patterns from large data sets to help businesses make informed decisions. It allows organizations to obtain knowledge from data, make improvements, and aid decision making in a cost-effective manner. However, data mining tools can be difficult to use and may not always provide precise results. Knowledge discovery is the overall process of discovering useful information from data, which includes steps like data cleaning, integration, selection, transformation, and mining followed by pattern evaluation and presentation of knowledge.
UNIT - 5: Data Warehousing and Data Mining
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Mining Object, Spatial, Multimedia, Text, and Web Data: Multidimensional Analysis and Descriptive Mining of Complex Data Objects – Spatial Data Mining – Multimedia Data Mining – Text Mining – Mining the World Wide Web.
Recommended
Unit 3 part i Data mining
To cover the basics of data mining concepts..Can get introduction of types of data ,KDD process,applications of data mining..
Subhaschamdrabhosesubhqschndrachose.pptx
Congress,[x] and a new mood in Britain for rapid decolonisation in India.[y][41][44]
Bose's legacy is mixed. Among many in India, he is seen as a hero, his saga serving as a would-be counterpoise to the many actions of regeneration, negotiation, and reconciliation over a quarter-century through which the independence of India was achieved.[z][aa][ab] His collaborations with Japanese Fascism and Nazism pose serious ethical dilemmas,[ac] especially his reluctance to publicly criticize the worst excesses of German anti-Semitism from 1938 onwards or to offer refuge in India to its victims.
Chittaranjan Das, a voice for aggressive nationalism in Bengal. In 1923, Bose was elected the President of Indian Youth Congress and also the Secretary of the Bengal State Congress. He became the editor of the newspaper "Forward", which had been founded by Chittaranjan Das.[81] Bose worked as the CEO of the Calcutta Municipal Corporation for Das when the latter was elected mayor of Calcutta in 1924.[82] During the same year, when Bose was leading a protest march in Calcutta, he, Maghfoor Ahmad Ajazi and other leaders were arrested and imprisoned.[83][failed verification] After a roundup of nationalists in 1925, Bose was sent to prison in Mandalay, British Burma, where he contracted tuberculosis.[84]
Subhas Bose (in military uniform) with Congress president, Motilal Nehru taking the salute. Annual meeting, Indian National Congress, 29 December 1928
In 1927, after being released from prison, Bose became general secretary of the Congress party and worked with Jawaharlal Nehru for independence. In late December 1928, Bose organised the Annual Meeting of the Indian National Congress in Calcutta.[85] His most memorable role was as General officer commanding (GOC) Congress Volunteer Corps.[85] Author Nirad Chaudhuri wrote about the meeting:
Bose organized a volunteer corps in uniform, its officers were even provided with steel-cut epaulettes ... his uniform was made by a firm of British tailors in Calcutta, Harman's. A telegram addressed to him as GOC was delivered to the British General in Fort William and was the subject of a good deal of malicious gossip in the (British Indian) press. Mahatma Gandhi as a sincere pacifist vowed to non-violence, did not like the strutting, clicking of boots, and saluting, and he afterward described the Calcutta session of the Congress as a Bertram Mills circus, which caused a great deal of indignation among the Bengalis.[85]
A little later, Bose was again arrested and jailed for civil disobedience; this time he emerged to become Mayor of Calcutta in 1930.[84]
Chittaranjan Das, a voice for aggressive nationalism in Bengal. In 1923, Bose was elected the President of Indian Youth Congress and also the Secretary of the Bengal State Congress. He became the editor of the newspaper "Forward", which had been founded by Chittaranjan Das.[81] Bose worked as the CEO of the Calcutta Municipal Corporation for Das when the latter was elected ma
Lecture2 (1).ppt
Data mining is the process of extracting patterns from large data sets to identify useful information. It involves applying machine learning algorithms to detect patterns in sample data and then using the learned patterns to predict future behaviors or outcomes. Data mining utilizes techniques from machine learning, statistics, databases, and visualization to analyze large datasets and discover hidden patterns. The goal of data mining is to extract useful information from large datasets and transform it into an understandable structure for further use.
NCCT.pptx
This document provides an overview of data mining. It discusses the introduction to data mining, its importance and applications. The key techniques of data mining discussed include classification, prediction, clustering, association and summarization. Examples of data mining applications mentioned are in healthcare, banking/finance, retail and web mining. The document concludes with discussing future trends in data mining involving new algorithms and data types, as well as computing resources like cloud computing.
Unit-V-Introduction to Data Mining.pptx
Data mining involves extracting useful patterns from large data sets to help businesses make informed decisions. It allows organizations to obtain knowledge from data, make improvements, and aid decision making in a cost-effective manner. However, data mining tools can be difficult to use and may not always provide precise results. Knowledge discovery is the overall process of discovering useful information from data, which includes steps like data cleaning, integration, selection, transformation, and mining followed by pattern evaluation and presentation of knowledge.
UNIT - 5: Data Warehousing and Data Mining
UNIT-V
Mining Object, Spatial, Multimedia, Text, and Web Data: Multidimensional Analysis and Descriptive Mining of Complex Data Objects – Spatial Data Mining – Multimedia Data Mining – Text Mining – Mining the World Wide Web.
2 introductory slides
This document provides an introduction to data mining techniques. It discusses how data mining emerged due to the problem of data explosion and the need to extract knowledge from large datasets. It describes data mining as an interdisciplinary field that involves methods from artificial intelligence, machine learning, statistics, and databases. It also summarizes some common data mining frameworks and processes like KDD, CRISP-DM and SEMMA.
Data mining
Data mining refers to extracting knowledge from large amounts of data and involves techniques from machine learning, statistics, and databases. A typical data mining system includes a database, data mining engine, pattern evaluation module, and graphical user interface. The knowledge discovery in data (KDD) process involves data cleaning, integration, selection, transformation, mining, evaluation, and presentation to extract useful patterns from data. KDD is the overall process while data mining is one step, applying algorithms to extract patterns for analysis.
Dma unit 1
This document outlines the learning objectives and resources for a course on data mining and analytics. The course aims to:
1) Familiarize students with key concepts in data mining like association rule mining and classification algorithms.
2) Teach students to apply techniques like association rule mining, classification, cluster analysis, and outlier analysis.
3) Help students understand the importance of applying data mining concepts across different domains.
The primary textbook listed is "Data Mining: Concepts and Techniques" by Jiawei Han and Micheline Kamber. Topics that will be covered include introduction to data mining, preprocessing, association rules, classification algorithms, cluster analysis, and applications.
Data, Text and Web Mining
Brief description of the 3 mining techniques and we give a brief description of the differences between them and the similarities. Finally we talked about the shared techniques.
Ch~2.pdf
Data science involves extracting knowledge and insights from structured, semi-structured, and unstructured data using scientific processes. It encompasses more than just data analysis. The data value chain describes the process of acquiring data and transforming it into useful information and insights. It involves data acquisition, analysis, curation, storage, and usage. There are three main types of data: structured data that follows a predefined model like databases, semi-structured data with some organization like JSON, and unstructured data like text without a clear model. Metadata provides additional context about data to help with analysis. Big data is characterized by its large volume, velocity, and variety that makes it difficult to process with traditional tools.
Additional themes of data mining for Msc CS
Data mining involves using computational techniques from machine learning, statistics, and database systems to discover patterns in large data sets. There are several theoretical foundations of data mining including data reduction, data compression, pattern discovery, probability theory, and inductive databases. Statistical techniques like regression, generalized linear models, analysis of variance, and time series analysis are also used for statistical data mining. Visual data mining integrates data visualization techniques with data mining to discover implicit knowledge. Audio data mining uses audio signals to represent data mining patterns and results. Collaborative filtering is commonly used for product recommendations based on opinions of other customers. Privacy and security of personal data are important social concerns of data mining.
Data mining introduction
This document introduces data mining. It defines data mining as the process of extracting useful information from large databases. It discusses technologies used in data mining like statistics and machine learning. It also covers data mining models and tasks such as classification, regression, clustering, and forecasting. Finally, it provides an overview of the data mining process and examples of data mining tools.
unit 1 big data.pptx
This document discusses web data extraction and analysis using Hadoop. It begins by explaining that web data extraction involves collecting data from websites using tools like web scrapers or crawlers. Next, it describes that the data extracted is often large in volume and requires processing tools like Hadoop for analysis. The document then provides details about using MapReduce on Hadoop to analyze web data in a parallel and distributed manner by breaking the analysis into mapping and reducing phases.
DOWLD SLIDES.pptx
This document provides an overview of data mining including:
- Data mining techniques like classification, prediction, clustering which are used to analyze patterns in data.
- The importance of data mining for applications in fields like banking, retail, and healthcare to discover useful knowledge from large datasets.
- Issues with data mining like security, performance, and methodology challenges as well as future trends like using more advanced algorithms and computing resources to handle diverse and large datasets.
dwdm unit 1.ppt
This document provides information about Dr. Sunil Bhutada, including his educational background and professional experience. It then outlines the syllabus for a course on data warehousing and data mining, including an introduction to key concepts and textbooks. Finally, it shares slides on additional topics related to data warehousing, data mining, and business intelligence.
BAS 250 Lecture 1
The document provides an introduction to data mining, including:
1. Defining data mining as the process of discovering patterns in large data sets using methods from artificial intelligence, machine learning, statistics, and database systems.
2. Explaining the CRISP-DM process as the standard method for data mining projects, which includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
3. Noting some challenges of data mining like data quality, privacy, and ensuring findings are meaningful and not just random patterns.
ch2 DS.pptx
The document provides an overview of key concepts in data science and big data including:
1) It defines data science, data scientists, and their roles in extracting insights from structured, semi-structured, and unstructured data.
2) It explains different data types like structured, semi-structured, unstructured and their characteristics from a data analytics perspective.
3) It describes the data value chain involving data acquisition, analysis, curation, storage, and usage to generate value from data.
4) It introduces concepts in big data like the 3V's of volume, velocity and variety, and technologies like Hadoop and its ecosystem that are used for distributed processing of large datasets.
data mining and data warehousing
Data mining involves analyzing large amounts of data to discover patterns that can be used for purposes such as increasing sales, reducing costs, or detecting fraud. It allows companies to better understand customer behavior and develop more effective marketing strategies. Common data mining techniques used by retailers include loyalty programs to track purchasing patterns and target customers with personalized coupons. Data mining software uses techniques like classification, clustering, and prediction to analyze data from different perspectives and extract useful information and patterns.
Data Mining-2023 (2).ppt
- Data mining is the process of discovering interesting patterns and knowledge from large amounts of data. It involves steps like data cleaning, integration, selection, transformation, mining, pattern evaluation and knowledge presentation.
- There are various types of data that can be mined, including database data, data warehouses, transactional data, text data, web data, time-series data, images, audio, video and others. Common data mining techniques include characterization, discrimination, clustering, classification, regression, and outlier detection. The goal is to extract useful patterns from data for tasks like prediction and description.
Ch_2.pdf
Data is unprocessed facts and figures that can be represented using characters. Information is processed data used to make decisions. Data science uses scientific methods to extract knowledge from structured, semi-structured, and unstructured data. The data processing cycle involves inputting data, processing it, and outputting the results. There are different types of data from both computer programming and data analytics perspectives including structured, semi-structured, and unstructured data. Metadata provides additional context about data.
Data Mining & Data Warehousing Lecture Notes
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# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
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All types of mining and trends indata mining
This document discusses trends in data mining, including the need for scalable and interactive methods to handle vast amounts of data, tighter integration with database and data warehouse systems, and expanding applications to new domains like biology, software engineering, and web mining. It also outlines ongoing challenges in mining complex data types like text, multimedia, spatial, and streaming data, as well as emerging areas like real-time and distributed data mining.
9. Data Warehousing & Mining.pptx
The document discusses data warehousing, data mining, and business intelligence. It defines data warehousing as a solution for fast analysis of information that operational systems cannot provide, due to limitations like unavailable historical data and poor query performance. It describes the architecture of data warehousing and lists databases, data warehouses, and transactional data as sources for data mining. The data mining process involves data collection, feature extraction, cleaning, and analytical algorithms. Common techniques are discussed as well. Business intelligence is defined as converting corporate data through processing and analysis into useful information and knowledge to trigger profitable business decisions.
Unit 4 Advanced Data Analytics
Unit 4 Advanced Data AnalyticsRani Channamma University, Sangolli Rayanna First Grade Constituent College, Belagavi
1. The document discusses various advanced data analytics techniques including data mining, online analytical processing (OLAP), pivot tables, power pivot, power view in Excel, and different types of data mining techniques like classification, clustering, regression, association rules, outlier detection, sequential patterns, and prediction.
2. It provides details on each technique including definitions, applications, and examples.
3. The key data analytics techniques covered are data mining, OLAP, pivot tables, power pivot and power view in Excel, and various classification methods for advanced data analysis.Unit i
This document discusses data mining and related topics. It begins by defining data mining as the process of discovering patterns in large datasets using methods from machine learning, statistics, and database systems. The document then discusses data warehouses, how they work, and their role in data mining. It describes different data mining functionalities and tasks such as classification, prediction, and clustering. The document outlines some common data mining applications and issues related to methodology, performance, and diverse data types. Finally, it discusses some social implications of data mining involving privacy, profiling, and unauthorized use of data.
学校原版美国波士顿大学毕业证学历学位证书原版一模一样
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This document provides an introduction to data mining techniques. It discusses how data mining emerged due to the problem of data explosion and the need to extract knowledge from large datasets. It describes data mining as an interdisciplinary field that involves methods from artificial intelligence, machine learning, statistics, and databases. It also summarizes some common data mining frameworks and processes like KDD, CRISP-DM and SEMMA.
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This document outlines the learning objectives and resources for a course on data mining and analytics. The course aims to:
1) Familiarize students with key concepts in data mining like association rule mining and classification algorithms.
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3) Help students understand the importance of applying data mining concepts across different domains.
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This document discusses web data extraction and analysis using Hadoop. It begins by explaining that web data extraction involves collecting data from websites using tools like web scrapers or crawlers. Next, it describes that the data extracted is often large in volume and requires processing tools like Hadoop for analysis. The document then provides details about using MapReduce on Hadoop to analyze web data in a parallel and distributed manner by breaking the analysis into mapping and reducing phases.
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2. Explaining the CRISP-DM process as the standard method for data mining projects, which includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
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2) It explains different data types like structured, semi-structured, unstructured and their characteristics from a data analytics perspective.
3) It describes the data value chain involving data acquisition, analysis, curation, storage, and usage to generate value from data.
4) It introduces concepts in big data like the 3V's of volume, velocity and variety, and technologies like Hadoop and its ecosystem that are used for distributed processing of large datasets.
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FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
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# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
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学校原版美国波士顿大学毕业证学历学位证书原版一模一样
原版一模一样【微信：741003700 】【美国波士顿大学毕业证学历学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
22CYT12-Unit-V-E Waste and its Management.ppt
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.
Recycled Concrete Aggregate in Construction Part III
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.
The Python for beginners. This is an advance computer language.
Python language is very important language at this time. we can easily understand this language by these notes.
Advanced control scheme of doubly fed induction generator for wind turbine us...
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.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Comparative analysis between traditional aquaponics and reconstructed aquapon...
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.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
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%.
Computational Engineering IITH Presentation
This Presentation will give you a brief idea about what Computational Engineering at IIT Hyderabad has to offer.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM
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
New techniques for characterising damage in rock slopes.pdf
rock mass characterization and New techniques for characterising damage in rock slopes
Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
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basic-wireline-operations-course-mahmoud-f-radwan.pdf
basic-wireline-operations-course-mahmoud-f-radwan.pdf
Recycled Concrete Aggregate in Construction Part III
Recycled Concrete Aggregate in Construction Part III
The Python for beginners. This is an advance computer language.
The Python for beginners. This is an advance computer language.
Advanced control scheme of doubly fed induction generator for wind turbine us...
Advanced control scheme of doubly fed induction generator for wind turbine us...
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM
New techniques for characterising damage in rock slopes.pdf
New techniques for characterising damage in rock slopes.pdf
Manufacturing Process of molasses based distillery ppt.pptx
Manufacturing Process of molasses based distillery ppt.pptx
Embedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoring
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
Data mining slide for data mining process
- 1. Data Mining • Data mining refers to extracting or mining knowledge from large amounts of data. • Data mining should have been more appropriately named as knowledge mining which emphasis on mining from large amounts of data. • It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. • The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. • The key properties of data mining are a) Automatic discovery of patterns b) Prediction of likely outcomes c) Creation of actionable information d) Focus on large datasets and databases
- 2. Data Mining Functionalities • Data mining functionalities are used to specify the kind of patterns to be found in data mining tasks. • In general, data mining tasks can be classified into two categories: descriptive and predictive. a) Descriptive mining tasks characterize the general properties of the data in the database. b) Predictive mining tasks perform inference on the current data in order to make predictions. • Data mining system can able to mine multiple kinds of patterns to accommodate different user expectations or applications. • Data mining systems should be able to discover patterns at various granularity (i.e., different levels of abstraction). • Data mining systems should also allow users to specify hints to guide or focus the search for interesting patterns.
- 3. Major Issues In Data Mining
- 4. Performance Issues • Efficiency and scalability of data mining algorithms − In order to effectively extract the information from huge amount of data in databases, data mining algorithm must be efficient and scalable. • Parallel, distributed, and incremental mining algorithms − The factors such as huge size of databases, wide distribution of data, and complexity of data mining methods motivate the development of parallel and distributed data mining algorithms. These algorithms divide the data into partitions which is further processed in a parallel fashion. Then the results from the partitions is merged. The incremental algorithms, update databases without mining the data again from scratch.
- 5. Diverse Data Types Issues • Handling of relational and complex types of data − The database may contain complex data objects, multimedia data objects, spatial data, temporal data etc. It is not possible for one system to mine all these kind of data. • Mining information from heterogeneous databases and global information systems − The data is available at different data sources on LAN or WAN. These data source may be structured, semi structured or unstructured. Therefore mining the knowledge from them adds challenges to data mining.