C* Summit EU 2013: Analytics On Top of Cassandra and HadoopDataStax Academy
The document discusses performing analytics on Cassandra data using Hadoop to calculate over 100 different statistics like top N values, time series, minimum/maximum/average values. It describes implementing various statistics like top N, time series, and extremum values using MapReduce jobs where the map phase generates key-value pairs and the reduce phase aggregates values before writing results to Cassandra. The approach scales elastically by reading and writing only to Cassandra and Hadoop and Cassandra both support elastic scalability with no bottlenecks.
The document discusses the 8085 microprocessor instruction set's arithmetic group. It contains instructions for addition, subtraction, incrementing, decrementing, and decimal adjustment. Some key instructions are ADD, ADC, SUB, INR, DCR, and DAA. ADD adds a register or memory to the accumulator. DAA is used after addition/increment instructions to adjust the result in the accumulator to decimal format.
World War 2 was caused by four main factors: (1) the harsh terms of the Treaty of Versailles imposed on Germany after WWI, (2) Hitler's secret rearmament of Germany in the 1930s, (3) the failure of the appeasement policy towards German aggression, and (4) the inability of the League of Nations to prevent war. Major events of WWII included the German invasion of Poland starting the war in Europe, the German invasion of the Soviet Union, the Holocaust genocide, Japanese imperialism leading to the attack on Pearl Harbor, the Normandy landings in Europe, and the atomic bombings of Hiroshima and Nagasaki ending the war.
Code review is a systematic examination of computer source code to find mistakes. In SAOS, an online Polish court judgment analysis system, code review is conducted using GitHub pull requests in a lightweight manner according to Scrum methodology. Each task takes at most two days to complete and is reviewed by a partner to catch errors before being merged. Observations found that code review improves code quality and catches bugs, though it takes about 20% of time. It has also strengthened collaboration and skills within the SAOS team.
Programming in Spark - Lessons Learned in OpenAire projectŁukasz Dumiszewski
This document discusses lessons learned from rewriting parts of the OpenAire project to use Apache Spark. It covers choosing Java and Kryo serialization for efficiency, understanding that spark.closure.serializer controls code serialization, using accumulators carefully, and testing Spark jobs including unit tests and integration with Oozie workflows. The rewrite resulted in faster execution times for some modules like CitationMatching.
The document summarizes Martin Odersky's talk at Scala Days 2016 about the road ahead for Scala. The key points are:
1. Scala is maturing with improvements to tools like IDEs and build tools in 2015, while 2016 sees increased activity with the Scala Center, Scala 2.12 release, and rethinking Scala libraries.
2. The Scala Center was formed to undertake projects benefiting the Scala community with support from various companies.
3. Scala 2.12 focuses on optimizing for Java 8 and includes many new features. Future releases will focus on improving Scala libraries and modularization.
4. The DOT calculus provides a formal
C* Summit EU 2013: Analytics On Top of Cassandra and HadoopDataStax Academy
The document discusses performing analytics on Cassandra data using Hadoop to calculate over 100 different statistics like top N values, time series, minimum/maximum/average values. It describes implementing various statistics like top N, time series, and extremum values using MapReduce jobs where the map phase generates key-value pairs and the reduce phase aggregates values before writing results to Cassandra. The approach scales elastically by reading and writing only to Cassandra and Hadoop and Cassandra both support elastic scalability with no bottlenecks.
The document discusses the 8085 microprocessor instruction set's arithmetic group. It contains instructions for addition, subtraction, incrementing, decrementing, and decimal adjustment. Some key instructions are ADD, ADC, SUB, INR, DCR, and DAA. ADD adds a register or memory to the accumulator. DAA is used after addition/increment instructions to adjust the result in the accumulator to decimal format.
World War 2 was caused by four main factors: (1) the harsh terms of the Treaty of Versailles imposed on Germany after WWI, (2) Hitler's secret rearmament of Germany in the 1930s, (3) the failure of the appeasement policy towards German aggression, and (4) the inability of the League of Nations to prevent war. Major events of WWII included the German invasion of Poland starting the war in Europe, the German invasion of the Soviet Union, the Holocaust genocide, Japanese imperialism leading to the attack on Pearl Harbor, the Normandy landings in Europe, and the atomic bombings of Hiroshima and Nagasaki ending the war.
Code review is a systematic examination of computer source code to find mistakes. In SAOS, an online Polish court judgment analysis system, code review is conducted using GitHub pull requests in a lightweight manner according to Scrum methodology. Each task takes at most two days to complete and is reviewed by a partner to catch errors before being merged. Observations found that code review improves code quality and catches bugs, though it takes about 20% of time. It has also strengthened collaboration and skills within the SAOS team.
Programming in Spark - Lessons Learned in OpenAire projectŁukasz Dumiszewski
This document discusses lessons learned from rewriting parts of the OpenAire project to use Apache Spark. It covers choosing Java and Kryo serialization for efficiency, understanding that spark.closure.serializer controls code serialization, using accumulators carefully, and testing Spark jobs including unit tests and integration with Oozie workflows. The rewrite resulted in faster execution times for some modules like CitationMatching.
The document summarizes Martin Odersky's talk at Scala Days 2016 about the road ahead for Scala. The key points are:
1. Scala is maturing with improvements to tools like IDEs and build tools in 2015, while 2016 sees increased activity with the Scala Center, Scala 2.12 release, and rethinking Scala libraries.
2. The Scala Center was formed to undertake projects benefiting the Scala community with support from various companies.
3. Scala 2.12 focuses on optimizing for Java 8 and includes many new features. Future releases will focus on improving Scala libraries and modularization.
4. The DOT calculus provides a formal
Putting the Spark into Functional Fashion Tech AnalysticsGareth Rogers
A talk highlighting the power of functional paradigms for big data pipeline building and Clojure and Apache Spark are a good fit. We cover the lambda architecture and some of the pros and cons that we discovered while using Spark at Metail.
Metail allows users to discover clothes on their body shape online with minimum measurements from the user. With your avatar you can create outfits and coupled with our size advice this gives you a confidence in the size and fit.
I'm part of the team within Metail that has built a pipeline to collection, enriched and serve data to the company and our clients, and which has been used to validate Metail's product. This talk was given at the AWS Loft in London 21st April 2016 where I gave an overview of the end-to-end pipeline and then went into detail how we're using AWS' EMR to perform a batch processing of the collected data which is then served internally with Redshift.
Understanding Parallelization of Machine Learning Algorithms in Apache Spark™Databricks
Machine Learning (ML) is a subset of Artificial Intelligence (AI). In this talk, Richard Garris, Principal Architect at Databricks will explain how various ML algorithms are parallelized in Apache Spark. Andrew Ng calls the algorithms the "rocket ship" and the data "the fuel that you feed machine learning" to build deep learning applications. We will start with an understanding of machine learning pipelines built using single machine algorithms including Pandas, scikit-learn, and R. Then we will discuss how Apache Spark MLlib can be used to parallelize your machine learning pipeline with Linear Regression and Random Forest. Lastly, we will discuss ways to parallelize single machine algorithms in Spark by broadcasting the data and then performing distributed feature selection, model creation or hyperparameter tuning.
Combining Machine Learning Frameworks with Apache SparkDatabricks
This document discusses combining machine learning frameworks with Apache Spark. It provides an overview of Apache Spark and MLlib, describes how to distribute TensorFlow computations using Spark, and discusses managing machine learning workflows with Spark through features like cross validation, persistence, and distributed data sources. The goal is to make machine learning easy, scalable, and integrate with existing workflows.
1. The document discusses the process of productionalizing a financial analytics application built on Spark over multiple iterations. It started with data scientists using Python and data engineers porting code to Scala RDDs. They then moved to using DataFrames and deployed on EMR.
2. Issues with code quality and testing led to adding ScalaTest, PR reviews, and daily Jenkins builds. Architectural challenges were addressed by moving to Databricks Cloud which provided notebooks, jobs, and throwaway clusters.
3. Future work includes using Spark SQL windows and Dataset API for stronger typing and schema support. The iterations improved the code, testing, deployment, and use of latest Spark features.
Production ready big ml workflows from zero to hero daniel marcous @ wazeIdo Shilon
This document provides an overview of production-ready machine learning workflows. It discusses challenges of big ML including skill gaps, dimensionality, and model complexity. The solution is presented as a workflow that includes preprocessing, naive implementation, monitoring with dashboards, optimization, A/B testing, and iteration. Key steps are to measure first before optimizing, start small and grow, test infrastructure, and establish a baseline before optimizing models. The document provides examples of applying these workflows at Waze for tasks like irregular traffic event detection, dangerous place identification, and speed limit inference.
Tuning ML Models: Scaling, Workflows, and ArchitectureDatabricks
This document discusses best practices for tuning machine learning models. It covers architectural patterns like single-machine versus distributed training and training one model per group. It also discusses workflows for hyperparameter tuning including setting up full pipelines before tuning, evaluating metrics on validation data, and tracking results for reproducibility. Finally it provides tips for handling code, data, and cluster configurations for distributed hyperparameter tuning and recommends tools to use.
My slides from the Big Data Applications meetup on 27th of July, talking about FlinkML. Also some things about open-source ML development and an illustation of interactive Flink machine learning with Apache Zeppelin.
Scaling Machine Learning with Apache SparkDatabricks
Spark has become synonymous with big data processing, however the majority of data scientists still build models using single machine libraries. This talk will explore the multitude of ways Spark can be used to scale machine learning applications. In particular, we will guide you through distributed solutions for training and inference, distributed hyperparameter search, deployment issues, and new features for Machine Learning in Apache Spark 3.0. Niall Turbitt and Holly Smith combine their years of experience working with Spark to summarize best practices for scaling ML solutions.
Neo4j GraphTalk Basel - Building intelligent Software with GraphsNeo4j
The document discusses using graphs and Neo4j to build intelligent solutions. It outlines Neo4j's professional services which include training, solution delivery, and packaged services. Typical technical requirements and a methodology for delivering solutions from use case to implementation are presented. Examples of graph-based solutions and how machine learning can be integrated are provided. Finally, a case study of Adobe migrating from Cassandra to Neo4j is summarized, reducing infrastructure costs significantly.
Databricks: What We Have Learned by Eating Our Dog FoodDatabricks
"Databricks Unified Analytics Platform (UAP) is a cloud-based service for running all analytics in one place - from highly reliable and performant data pipelines to state-of-the-art Machine Learning. From the original creators of Apache Spark and MLflow, it provides data science and engineering teams ready to use pre-packaged clusters with optimized Apache Spark and various ML frameworks coupled with powerful collaboration capabilities to improve productivity across the ML lifecycle. Yada yada yada... But in addition to being a vendor Databricks is also a user of UAP.
So, what have we learned by eating our own dogfood? Attend a “from the trenches report” from Suraj Acharya, Director Engineering responsible for Databricks’ in-house data engineering team how his team put Databricks technology to use, the lessons they have learned along the way and best practices for using Databricks for data engineering.
"
Putting the Spark into Functional Fashion Tech AnalysticsGareth Rogers
Metail uses Apache Spark and a functional programming approach to process and analyze data from its fashion recommendation application. It collects data through various pipelines to understand user journeys and optimize business processes like photography. Metail's data pipeline is influenced by functional paradigms like immutability and uses Spark on AWS to operate on datasets in a distributed, scalable manner. The presentation demonstrated Metail's use of Clojure, Spark, and AWS services to build a functional data pipeline for analytics purposes.
Take advantage of ScyllaDB’s wide column NoSQL features such as workload prioritization to balance the needs of OLTP and OLAP in the same cluster. Plus learn about the different compaction strategies and which one would be right for your workload. With additional insights on properly sizing your database and using open source tools for observability.
WBDB 2015 Performance Evaluation of Spark SQL using BigBencht_ivanov
In this paper we present the initial results of our work to run BigBench on Spark. First, we evaluated the data scalability behavior of the existing MapReduce implementation of BigBench. Next, we executed the group of 14 pure HiveQL queries on Spark SQL and compared the results with the respective Hive results. Our experiments show that: (1) for both MapReduce and Spark SQL, BigBench queries perform with the increase of the data size on average better than the linear scaling behavior and (2) pure HiveQL queries perform faster on Spark SQL than on Hive.
http://clds.sdsc.edu/wbdb2015.ca/program
My talk from SICS Data Science Day, describing FlinkML, the Machine Learning library for Apache Flink.
I talk about our approach to large-scale machine learning and how we utilize state-of-the-art algorithms to ensure FlinkML is a truly scalable library.
You can watch a video of the talk here: https://youtu.be/k29qoCm4c_k
Qubole is a big data as a service platform that allows users to run analytics jobs on AWS infrastructure. It integrates tightly with various AWS services like EC2, S3, Redshift, and Kinesis. Qubole handles cluster provisioning and management, provides tools for interactive querying using Presto, and allows customers to access data across different AWS data platforms through a single interface. Some key benefits of Qubole include simplified management of AWS resources, optimized performance through techniques like auto-scaling and caching, and unified analytics platform for tools like Hive, Spark and Presto.
3 query tuning techniques every sql server programmer should knowRodrigo Crespi
This document discusses various query tuning techniques that SQL Server programmers should know. It covers understanding how queries work and set theory, testing queries, formatting T-SQL for readability, using appropriate predicates and set theory operations, avoiding implicit conversions, and using locks appropriately. The document also includes diagrams of the SQL Server architecture and physical query tree to illustrate how queries are processed.
Winning performance challenges in oracle standard editionsPini Dibask
This document provides a summary of a presentation about winning performance challenges in Oracle Standard Editions. The presentation discusses:
1. The performance tuning challenges that exist in Oracle Standard Editions due to the lack of diagnostics packs.
2. Approaches for performance monitoring and diagnostics using Statspack and Oracle dictionary views in Standard Editions.
3. How Foglight for Databases can provide visibility into database performance and workloads across all Oracle editions and configurations.
Putting the Spark into Functional Fashion Tech AnalysticsGareth Rogers
A talk highlighting the power of functional paradigms for big data pipeline building and Clojure and Apache Spark are a good fit. We cover the lambda architecture and some of the pros and cons that we discovered while using Spark at Metail.
Metail allows users to discover clothes on their body shape online with minimum measurements from the user. With your avatar you can create outfits and coupled with our size advice this gives you a confidence in the size and fit.
I'm part of the team within Metail that has built a pipeline to collection, enriched and serve data to the company and our clients, and which has been used to validate Metail's product. This talk was given at the AWS Loft in London 21st April 2016 where I gave an overview of the end-to-end pipeline and then went into detail how we're using AWS' EMR to perform a batch processing of the collected data which is then served internally with Redshift.
Understanding Parallelization of Machine Learning Algorithms in Apache Spark™Databricks
Machine Learning (ML) is a subset of Artificial Intelligence (AI). In this talk, Richard Garris, Principal Architect at Databricks will explain how various ML algorithms are parallelized in Apache Spark. Andrew Ng calls the algorithms the "rocket ship" and the data "the fuel that you feed machine learning" to build deep learning applications. We will start with an understanding of machine learning pipelines built using single machine algorithms including Pandas, scikit-learn, and R. Then we will discuss how Apache Spark MLlib can be used to parallelize your machine learning pipeline with Linear Regression and Random Forest. Lastly, we will discuss ways to parallelize single machine algorithms in Spark by broadcasting the data and then performing distributed feature selection, model creation or hyperparameter tuning.
Combining Machine Learning Frameworks with Apache SparkDatabricks
This document discusses combining machine learning frameworks with Apache Spark. It provides an overview of Apache Spark and MLlib, describes how to distribute TensorFlow computations using Spark, and discusses managing machine learning workflows with Spark through features like cross validation, persistence, and distributed data sources. The goal is to make machine learning easy, scalable, and integrate with existing workflows.
1. The document discusses the process of productionalizing a financial analytics application built on Spark over multiple iterations. It started with data scientists using Python and data engineers porting code to Scala RDDs. They then moved to using DataFrames and deployed on EMR.
2. Issues with code quality and testing led to adding ScalaTest, PR reviews, and daily Jenkins builds. Architectural challenges were addressed by moving to Databricks Cloud which provided notebooks, jobs, and throwaway clusters.
3. Future work includes using Spark SQL windows and Dataset API for stronger typing and schema support. The iterations improved the code, testing, deployment, and use of latest Spark features.
Production ready big ml workflows from zero to hero daniel marcous @ wazeIdo Shilon
This document provides an overview of production-ready machine learning workflows. It discusses challenges of big ML including skill gaps, dimensionality, and model complexity. The solution is presented as a workflow that includes preprocessing, naive implementation, monitoring with dashboards, optimization, A/B testing, and iteration. Key steps are to measure first before optimizing, start small and grow, test infrastructure, and establish a baseline before optimizing models. The document provides examples of applying these workflows at Waze for tasks like irregular traffic event detection, dangerous place identification, and speed limit inference.
Tuning ML Models: Scaling, Workflows, and ArchitectureDatabricks
This document discusses best practices for tuning machine learning models. It covers architectural patterns like single-machine versus distributed training and training one model per group. It also discusses workflows for hyperparameter tuning including setting up full pipelines before tuning, evaluating metrics on validation data, and tracking results for reproducibility. Finally it provides tips for handling code, data, and cluster configurations for distributed hyperparameter tuning and recommends tools to use.
My slides from the Big Data Applications meetup on 27th of July, talking about FlinkML. Also some things about open-source ML development and an illustation of interactive Flink machine learning with Apache Zeppelin.
Scaling Machine Learning with Apache SparkDatabricks
Spark has become synonymous with big data processing, however the majority of data scientists still build models using single machine libraries. This talk will explore the multitude of ways Spark can be used to scale machine learning applications. In particular, we will guide you through distributed solutions for training and inference, distributed hyperparameter search, deployment issues, and new features for Machine Learning in Apache Spark 3.0. Niall Turbitt and Holly Smith combine their years of experience working with Spark to summarize best practices for scaling ML solutions.
Neo4j GraphTalk Basel - Building intelligent Software with GraphsNeo4j
The document discusses using graphs and Neo4j to build intelligent solutions. It outlines Neo4j's professional services which include training, solution delivery, and packaged services. Typical technical requirements and a methodology for delivering solutions from use case to implementation are presented. Examples of graph-based solutions and how machine learning can be integrated are provided. Finally, a case study of Adobe migrating from Cassandra to Neo4j is summarized, reducing infrastructure costs significantly.
Databricks: What We Have Learned by Eating Our Dog FoodDatabricks
"Databricks Unified Analytics Platform (UAP) is a cloud-based service for running all analytics in one place - from highly reliable and performant data pipelines to state-of-the-art Machine Learning. From the original creators of Apache Spark and MLflow, it provides data science and engineering teams ready to use pre-packaged clusters with optimized Apache Spark and various ML frameworks coupled with powerful collaboration capabilities to improve productivity across the ML lifecycle. Yada yada yada... But in addition to being a vendor Databricks is also a user of UAP.
So, what have we learned by eating our own dogfood? Attend a “from the trenches report” from Suraj Acharya, Director Engineering responsible for Databricks’ in-house data engineering team how his team put Databricks technology to use, the lessons they have learned along the way and best practices for using Databricks for data engineering.
"
Putting the Spark into Functional Fashion Tech AnalysticsGareth Rogers
Metail uses Apache Spark and a functional programming approach to process and analyze data from its fashion recommendation application. It collects data through various pipelines to understand user journeys and optimize business processes like photography. Metail's data pipeline is influenced by functional paradigms like immutability and uses Spark on AWS to operate on datasets in a distributed, scalable manner. The presentation demonstrated Metail's use of Clojure, Spark, and AWS services to build a functional data pipeline for analytics purposes.
Take advantage of ScyllaDB’s wide column NoSQL features such as workload prioritization to balance the needs of OLTP and OLAP in the same cluster. Plus learn about the different compaction strategies and which one would be right for your workload. With additional insights on properly sizing your database and using open source tools for observability.
WBDB 2015 Performance Evaluation of Spark SQL using BigBencht_ivanov
In this paper we present the initial results of our work to run BigBench on Spark. First, we evaluated the data scalability behavior of the existing MapReduce implementation of BigBench. Next, we executed the group of 14 pure HiveQL queries on Spark SQL and compared the results with the respective Hive results. Our experiments show that: (1) for both MapReduce and Spark SQL, BigBench queries perform with the increase of the data size on average better than the linear scaling behavior and (2) pure HiveQL queries perform faster on Spark SQL than on Hive.
http://clds.sdsc.edu/wbdb2015.ca/program
My talk from SICS Data Science Day, describing FlinkML, the Machine Learning library for Apache Flink.
I talk about our approach to large-scale machine learning and how we utilize state-of-the-art algorithms to ensure FlinkML is a truly scalable library.
You can watch a video of the talk here: https://youtu.be/k29qoCm4c_k
Qubole is a big data as a service platform that allows users to run analytics jobs on AWS infrastructure. It integrates tightly with various AWS services like EC2, S3, Redshift, and Kinesis. Qubole handles cluster provisioning and management, provides tools for interactive querying using Presto, and allows customers to access data across different AWS data platforms through a single interface. Some key benefits of Qubole include simplified management of AWS resources, optimized performance through techniques like auto-scaling and caching, and unified analytics platform for tools like Hive, Spark and Presto.
3 query tuning techniques every sql server programmer should knowRodrigo Crespi
This document discusses various query tuning techniques that SQL Server programmers should know. It covers understanding how queries work and set theory, testing queries, formatting T-SQL for readability, using appropriate predicates and set theory operations, avoiding implicit conversions, and using locks appropriately. The document also includes diagrams of the SQL Server architecture and physical query tree to illustrate how queries are processed.
Winning performance challenges in oracle standard editionsPini Dibask
This document provides a summary of a presentation about winning performance challenges in Oracle Standard Editions. The presentation discusses:
1. The performance tuning challenges that exist in Oracle Standard Editions due to the lack of diagnostics packs.
2. Approaches for performance monitoring and diagnostics using Statspack and Oracle dictionary views in Standard Editions.
3. How Foglight for Databases can provide visibility into database performance and workloads across all Oracle editions and configurations.
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.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
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.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
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.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
10. Table of Contents
● Overview and Approach
● Performance comparison
● Scope For Improvement
10
11. Scope For Improvement
11
● Emphasis on Data Mining Techniques
○ Attribute Ranking
○ Removal of bias, etc
● MLLib is black-box! Generalization is harmful!