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tonymattny · 1 month ago

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Transform Your Future with AI ML Development Services by ideyaLabs

Artificial Intelligence and Machine Learning transform how organizations operate, deliver value, and engage customers. Every business strives to outpace competition with innovation and agility. ideyaLabs stands as a pioneer, delivering premium AI ML Development Services that drive progress. Business leaders trust ideyaLabs to unlock the potential of technology for tomorrow’s success.

Modernized Operations with Advanced AI ML Solutions

Most organizations handle large amounts of data every day. Manual processing drains time and leads to errors. AI ML technologies automate data handling and business workflows. ideyaLabs engineer’s intelligent solutions that parse data, recognize patterns, and automate processes. Decision-makers access accurate information in real-time, leading to improved efficiency and faster growth.

Organizations deploy ideyaLabs' custom AI ML models to manage supply chains, plan resources, and process financial data. Engineers design solutions that adapt to new data and learn from interactions. Each implementation fits the client's needs and goals.

Deliver Personalized Experiences at Scale

Customers expect brands to understand and anticipate their needs. AI ML makes hyper-personalization possible. ideyaLabs crafts recommendation engines and personalization frameworks that tailor online experiences for every user. Businesses segment audiences, deliver unique content, and boost engagement.

ideyaLabs’ personalization engines drive loyalty and increase revenue for retail, healthcare, and financial services. Customized product recommendations, dynamic pricing, and personalized communication help organizations delight customers and build loyalty.

Empower Decisions with Predictive Analytics

Successful businesses anticipate market movements. Predictive analytics guides leaders to make strategic moves. ideyaLabs uses AI and ML to analyze historical data, spot trends, and predict customer behavior.

Clients gain powerful forecasting tools that minimize risks and maximize opportunities. Organizations in retail, finance, and logistics benefit from demand forecasting, fraud detection, and churn prediction. ideyaLabs equips leaders with actionable insights to outperform competitors.

Boost Productivity through Intelligent Automation

Automation remains central to modernizing business operations. AI ML Development Services from ideyaLabs deploy smart bots for tasks like invoice management, HR assistance, and customer support. Workflows run faster, operational costs drop, and human error decreases.

ideyaLabs' intelligent automation gives employees freedom to innovate and focus on strategic goals. Businesses scale seamlessly without hiring in proportion to growth. Automated systems maintain full accuracy and compliance at every step.

Enable Communication with Natural Language Processing

Businesses must communicate without friction, in every language and at any volume. ideyaLabs integrates state-of-the-art Natural Language Processing (NLP) for customer service, content moderation, and feedback analysis. Chatbots, voice assistants, and automated translation tools deliver fast and accurate responses.

NLP models process unstructured text, interpret sentiment, and provide insights from customer reviews, social media, and survey data. Organizations turn every conversation into a source of actionable intelligence.

Accelerate Innovation with AI-Powered Visual Recognition

Manual inspection, classification, and analysis of images or videos take time. AI vision solutions can streamline quality assurance, monitoring, and security. ideyaLabs designs AI image recognition systems that identify defects, monitor compliance, and classify digital content.

Manufacturers use these tools to spot faults early. Media companies automate content moderation and tagging. Healthcare providers use visual recognition to support diagnostics. ideyaLabs’ innovative visual solutions lower costs and raise quality standards.

Enhance Security and Risk Management

Security threats and fraud attempts grow more sophisticated every day. AI ML technologies safeguard businesses with real-time monitoring and anomaly detection. ideyaLabs implements AI-powered systems that detect unusual patterns, flag suspicious activity, and automate responses.

Financial institutions use these models to identify fraud instantly. Enterprises monitor networks and systems for cyber threats. ideyaLabs ensures confidential data remains protected, assets remain secure, and compliance remains uncompromised.

Drive Business Growth Across Every Sector

Every industry seeks the advantages of machine learning and artificial intelligence. ideyaLabs delivers domain-specific AI ML solutions for retail, banking, manufacturing, healthcare, and logistics. Retailers predict stock demand, personalize shopping journeys, and optimize pricing. Healthcare providers accelerate diagnostics, improve patient care, and reduce administrative workload.

Banks deploy ideyaLabs’ predictive models for risk assessment and customer retention. Manufacturers spot defects on production lines automatically. Logistics companies optimize routes and cut delivery times. ideyaLabs empowers organizations everywhere to innovate, cut costs, and reach new markets at speed.

Collaboration and Expertise for Every Project

The ideyaLabs team consists of experts in data science, engineering, and project management. Project managers work alongside clients to identify business needs and set achievable goals. Data scientists craft robust solutions using the latest machine learning frameworks. Engineers deploy, test, and refine solutions for optimal results.

Clients receive regular updates, actionable insights, and clear documentation. ideyaLabs supports businesses through every stage, from ideation to MVP, to full production rollout.

Seamless Integration and Scalable Deployment

Businesses need solutions that scale with demand and integrate without disruption. ideyaLabs guarantees seamless integration with legacy systems, CRMs, ERPs, and cloud environments. Scalable architecture supports business growth and data expansion.

Technical support and monitoring keep systems operating at peak performance. ideyaLabs stays ahead of technology trends, ensuring every client benefits from the latest advancements.

Commitment to Ethical and Transparent AI

Responsible AI matters to every stakeholder. ideyaLabs follows strict ethical guidelines, implements bias controls, and ensures transparency in every model. Clients gain full visibility into data, algorithms, and decision-making processes.

Privacy regulations and compliance requirements receive full attention during every project. ideyaLabs values the trust of its clients and their customers by building AI people can rely on.

Choose ideyaLabs for Industry-Leading AI ML Development Services

Your organization deserves a partner that understands your goals and delivers measurable results. ideyaLabs combines industry expertise, technical excellence, and client-centric service. Transform data into insights, automate routine work, personalize customer journeys, and lead your market.

AI ML Development Services from ideyaLabs unlock growth and innovation for the connected economy. Contact the team and explore customized solutions that set your business apart. Embrace the future with confidence and speed.

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autoaceresearcher · 11 months ago

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https://www.einpresswire.com/article/721530258/fault-detection-and-classification-market-to-cross-usd-10-15-billion-at-9-2-cagr-by-2031-report-by-sns-insider

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govindhtech · 11 months ago

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Vector Databases Tutorial: Data Access for Advanced AI Apps

Vector Database Tutorial

Vector databases are revolutionising artificial intelligence and machine learning. These databases are revolutionising data storage and access and advancing AI and machine learning applications. This essay will analyse vector databases’ unique abilities and how they are changing numerous sectors.

What is Vector Database?

Knowing the Fundamentals

Specialised database systems called vector databases are made with the purpose of effectively storing, managing, and retrieving high-dimensional vectors. Vectors are numerical representations of data items, such as words, photos, or human behaviours, that encapsulate their key characteristics in a multidimensional space in the context of artificial intelligence (AI) and machine learning. Since vector databases can manage complicated data types, they are perfect for tasks involving similarity search, clustering, and classification, in contrast to standard databases that store scalar values (such as integers and texts).

AI Vector database

Important Vector Database Features

These are particularly good at handling high-dimensional data, which frequently has hundreds or thousands of dimensions.

Effective Similarity Search

These databases carry out quick and precise similarity searches a necessary function for applications such as picture recognition and recommendation systems by utilising sophisticated indexing algorithms.

Scalability

These can manage massive data volumes without sacrificing performance because of its horizontal scalability design.

Integration with AI Workflows

They easily interface with pipelines and models for machine learning, making it easier to train, implement, and infer AI models.

Reasons Vector Databases Are Unbelievably Excellent

Improved Features for Search

The capacity of vector databases to conduct quick and precise similarity searches is one of their best qualities. High-dimensional data presents challenges for traditional databases, which frequently result in sluggish and inaccurate search results. On the other hand, this use methods like locality-sensitive hashing (LSH) and approximate nearest neighbour (ANN) search to swiftly identify the most similar vectors. This feature is especially useful for the following applications:

Recommendation Systems

Vector databases are remarkably accurate in providing personalised suggestions based on analysis of user behaviour and preferences.

Image and Video Search

They change the way media libraries are managed by providing quick access to comparable images or movies based on visual content.

Natural Language Processing, or NLP

Natural Language Processing NLP uses these to make semantic search more effective and precise by retrieving information based on word meanings rather than exact matches.

Processing Data in Real Time

Real-time data processing and analysis is essential in the big data era. Because vector databases can manage real-time data streams, they are perfect for applications that need quick decisions and responses. This comprises:

Fraud Detection

By using vector databases to track transactions in real-time, financial institutions can accurately detect and stop fraudulent activity.

Predictive Maintenance

Real-time sensor data analysis by vector databases in manufacturing enables the prediction of equipment faults and the proactive scheduling of maintenance.

Personalised Marketing

Based on user behaviour, marketers can send personalised adverts and promotions by using these to analyse user interactions in real-time.

Better Processes for Machine Learning

Machine learning procedures and vector databases work together seamlessly to improve the efficacy and efficiency of AI models. These databases’ ability to store the vectors generated by models allows for:

Model Training

Large volumes of training data can be quickly accessed and stored in vector databases, facilitating both model training and retraining.

Inference

Vector databases are able to quickly obtain pertinent vectors during inference, guaranteeing AI applications minimal latency replies.

Continuous Learning

They help maintain the accuracy and currentness of AI systems by enabling models to be updated in real-time with fresh data points.

Vector Database Applications in Industry

Retail and E-Commerce

Vector databases are revolutionising customer interactions in the retail and e-commerce industries. Using vector databases, businesses can:

Improved Product Suggestions

Provide extremely tailored product suggestions according to user preferences and behaviour.

Optimise Search Results

By offering more precise and pertinent search results based on vector similarity, you can enhance search functionality.

Analyse Customer Sentiment

To better understand consumer sentiment and develop goods and services, examine customer reviews and feedback.

Medical Care

The potential of vector databases is also advantageous to the healthcare sector. Examples of applications are:

Medical Image Analysis

By quickly storing and retrieving medical images, vector databases can help in illness diagnosis and treatment.

Genomics

Research and personalised therapy are made easier by their ability to analyse high-dimensional genomic data.

Patient Monitoring

The ability to process data in real-time facilitates ongoing patient health monitoring and prompt intervention.

Money

Vector databases are improving the speed and accuracy of a number of applications in the finance sector, including:

Risk management

Evaluate and effectively manage risks by analysing high-dimensional financial data.

Algorithmic Trading

Use real-time data processing to quickly and intelligently decide which trades to make.

Customer Insights

Acquire a deeper understanding of consumer behaviour and preferences in order to customise financial services and goods.

Vector Databases’ Future

Vector databases will become even more crucial as AI and machine learning become more widely used. Potential future advancements in this field could be:

Improved Integration with AI Platforms

More thorough integration with AI frameworks and platforms, which facilitates the deployment and management of AI applications even more.

Improvements in Indexing Methods

Indexing methods have been refined to increase similarity search speed and accuracy.

Use Case Expansion

As new and creative use cases are found in a variety of industries, vector databases are being adopted more widely.

In summary

Without a doubt, vector databases are changing the field of artificial intelligence and machine learning. They are absurdly strong at handling high-dimensional data, processing real-time data, conducting effective similarity searches, and improving machine learning operations. Vector databases will be essential in opening doors and spurring innovation as industries investigate and use this technology.

Read more on Govindhtech.com

#artificialintelligence #datastorage #MachineLearning #databasesystems #naturallanguageprocessing #datastreams #dataanalysis #AIModels #vectordatabases #AIApplications #healthmonitoring #News #technews #technology #technologynews #technologytrends #govindhtech

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vishalsaini11 · 1 year ago

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Top 7 Data Mining Techniques You Should Know

Data mining is the process of discovering patterns and knowledge from large amounts of data. The data sources can include databases, data warehouses, the internet, and other repositories. With the exponential growth of data in today’s world, data mining techniques have become essential for extracting useful information and gaining insights that drive decision-making. Here are the top seven data mining techniques you should know:

1. Classification

Classification is a supervised learning technique used to predict the categorical labels of new observations. It involves building a model that can classify data into predefined classes or categories. Common algorithms used for classification include decision trees, random forests, k-nearest neighbors (KNN), support vector machines (SVM), and neural networks.

Decision Trees: These are tree-like structures where each node represents a feature (attribute), each branch represents a decision rule, and each leaf represents the outcome. They are easy to understand and interpret.

Random Forests: This technique uses an ensemble of decision trees to improve accuracy and control overfitting.

Support Vector Machines (SVM): SVMs find the hyperplane that best separates the classes in the feature space.

Neural Networks: These are used for complex pattern recognition tasks and involve layers of interconnected nodes (neurons) that can learn from data.

2. Clustering

Clustering is an unsupervised learning technique used to group similar data points into clusters based on their features. Unlike classification, clustering does not rely on predefined categories and is used to explore data to find natural groupings.

K-Means Clustering: This algorithm partitions the data into K clusters, where each data point belongs to the cluster with the nearest mean.

Hierarchical Clustering: This technique builds a hierarchy of clusters either agglomeratively (bottom-up) or divisively (top-down).

DBSCAN (Density-Based Spatial Clustering of Applications with Noise): This algorithm groups together points that are closely packed and marks points that are far away as outliers.

3. Association Rule Learning

Association Rule Learning is used to discover interesting relationships or associations between variables in large datasets. It is often used in market basket analysis to find associations between products purchased together.

Apriori Algorithm: This is a classic algorithm used to find frequent itemsets and generate association rules. It operates on the principle that if an itemset is frequent, then all its subsets must also be frequent.

FP-Growth (Frequent Pattern Growth): This algorithm compresses the dataset using a structure called an FP-tree and extracts frequent itemsets without candidate generation.

4. Regression

Regression is a technique used to predict a continuous target variable based on one or more predictor variables. It helps in understanding the relationship between variables and forecasting future trends.

Linear Regression: This is the simplest form of regression that models the relationship between two variables by fitting a linear equation to the observed data.

Multiple Regression: This extends linear regression by using multiple predictors to model the relationship.

Logistic Regression: Though used for classification, it models the probability of a binary outcome using a logistic function.

5. Anomaly Detection

Anomaly Detection identifies rare items, events, or observations that differ significantly from the majority of the data. This technique is crucial for fraud detection, network security, and fault detection.

Statistical Methods: These include z-scores, modified z-scores, and the Grubbs' test to identify outliers.

Machine Learning Methods: Algorithms like Isolation Forests, One-Class SVM, and Autoencoders can learn the normal behavior and identify deviations.

6. Text Mining

Text Mining involves extracting useful information and knowledge from unstructured text data. Given the large volume of text data available, this technique is valuable for applications like sentiment analysis, topic modeling, and document classification.

Natural Language Processing (NLP): This field encompasses techniques for processing and analyzing text, including tokenization, stemming, lemmatization, and part-of-speech tagging.

Topic Modeling: Techniques like Latent Dirichlet Allocation (LDA) are used to identify topics in large text corpora.

Sentiment Analysis: This involves determining the sentiment expressed in a text, which can be positive, negative, or neutral.

7. Dimensionality Reduction

Dimensionality Reduction is used to reduce the number of random variables under consideration by obtaining a set of principal variables. This technique is crucial for simplifying models, reducing computation time, and visualizing data.

Principal Component Analysis (PCA): This technique transforms the data into a new coordinate system where the greatest variances are represented by the first few coordinates (principal components).

t-Distributed Stochastic Neighbor Embedding (t-SNE): This is a technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets.

Linear Discriminant Analysis (LDA): This technique is used for both classification and dimensionality reduction by finding the linear combinations of features that best separate classes.

Conclusion

These top seven data mining techniques offer a robust toolkit for extracting valuable insights from vast amounts of data. Whether you are dealing with structured or unstructured data, supervised or unsupervised learning problems, these techniques can help you uncover patterns, relationships, and trends that are crucial for making informed decisions. As data continues to grow in volume and complexity, mastering these techniques will be increasingly important for data scientists, analysts, and professionals across various fields.

#data

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superaakash24 · 6 years ago

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Top 9 Applications of Machine Learning in Real World

uture of Machine Learning “ A Learning Machine is any device Whose actions are influenced by past experience.” - N.Jhon.Nilsson

Machine Learning is a science to make the machine capable of taking the decision itself. These systems also have the ability to learn from past experience or analyze historical data. It provides results according to its experience.

Here, we will explore Machine Learning Applications. These Applications of Machine Learning shows the area or scope of Machine Learning.

So, let’s start Machine learning Applications.

Machine Learning Applications

As we move forward into the digital age, One of the modern innovations we’ve seen is the creation of Machine Learning. This incredible form of artificial intelligence is already being used in various industries and professions. For Example, Image and Speech Recognition, Medical Diagnosis, Prediction, Classification, Learning Associations, Statistical Arbitrage, Extraction, Regression. Today we’re looking at all these Machine Learning Applications in today’s modern world.

These are the real world Machine Learning Applications, let’s see them one by one-

1. Image Recognition

It is one of the most common machine learning applications. There are many situations where you can classify the object as a digital image. For digital images, the measurements describe the outputs of each pixel in the image.

In the case of a black and white image, the intensity of each pixel serves as one measurement. So if a black and white image has N*N pixels, the total number of pixels and hence measurement is N2.

Let’s discuss ANN in Machine Learning

In the coloured image, each pixel considered as providing 3 measurements of the intensities of 3 main colour components ie RGB. So N*N coloured image there are 3 N2 measurements.

For face detection – The categories might be face versus no face present. There might be a separate category for each person in a database of several individuals.

For character recognition – We can segment a piece of writing into smaller images, each containing a single character. The categories might consist of the 26 letters of the English alphabet, the 10 digits, and some special characters.

2. Speech Recognition

Speech recognition (SR) is the translation of spoken words into text. It is also known as “automatic speech recognition” (ASR), “computer speech recognition”, or “speech to text” (STT).

In speech recognition, a software application recognizes spoken words. The measurements in this Machine Learning application might be a set of numbers that represent the speech signal. We can segment the signal into portions that contain distinct words or phonemes. In each segment, we can represent the speech signal by the intensities or energy in different time-frequency bands.

Although the details of signal representation are outside the scope of this program, we can represent the signal by a set of real values.

Do you know about Artificial Neural Network Model

Speech recognition, Machine Learning applications include voice user interfaces. Voice user interfaces are such as voice dialing, call routing, domotic appliance control. It can also use as simple data entry, preparation of structured documents, speech-to-text processing, and plane.

3. Medical Diagnosis

ML provides methods, techniques, and tools that can help in solving diagnostic and prognostic problems in a variety of medical domains. It is being used for the analysis of the importance of clinical parameters and of their combinations for prognosis, e.g. prediction of disease progression, for the extraction of medical knowledge for outcomes research, for therapy planning and support, and for overall patient management. ML is also being used for data analysis, such as detection of regularities in the data by appropriately dealing with imperfect data, interpretation of continuous data used in the Intensive Care Unit, and for intelligent alarming resulting in effective and efficient monitoring.

It is argued that the successful implementation of ML methods can help the integration of computer-based systems in the healthcare environment providing opportunities to facilitate and enhance the work of medical experts and ultimately to improve the efficiency and quality of medical care.

Let’s take a tour of Neural Network Algorithms

In medical diagnosis, the main interest is in establishing the existence of a disease followed by its accurate identification. There is a separate category for each disease under consideration and one category for cases where no disease is present. Here, machine learning improves the accuracy of medical diagnosis by analyzing data of patients.

The measurements in this Machine Learning applications are typically the results of certain medical tests (example blood pressure, temperature and various blood tests) or medical diagnostics (such as medical images), presence/absence/intensity of various symptoms and basic physical information about the patient(age, sex, weight etc). On the basis of the results of these measurements, the doctors narrow down on the disease inflicting the patient.

4. Statistical Arbitrage

In finance, statistical arbitrage refers to automated trading strategies that are typical of a short-term and involve a large number of securities. In such strategies, the user tries to implement a trading algorithm for a set of securities on the basis of quantities such as historical correlations and general economic variables. These measurements can be cast as a classification or estimation problem. The basic assumption is that prices will move towards a historical average.

Do you know about Kernel Functions

We apply machine learning methods to obtain an index arbitrage strategy. In particular, we employ linear regression and support vector regression (SVR)onto the prices of an exchange-traded fund and a stream of stocks. By using principal component analysis (PCA) in reducing the dimension of feature space, we observe the benefit and note the issues in the application of SVR. To generate trading signals, we model the residuals from the previous regression as a mean reverting process.

In the case of classification, the categories might be sold, buy or do nothing for each security. I the case of estimation one might try to predict the expected return of each security over a future time horizon. In this case, one typically needs to use the estimates of the expected return to make a trading decision(buy, sell, etc.)

5. Learning Associations

Learning association is the process of developing insights into various associations between products. A good example is how seemingly unrelated products may reveal an association to one another. When analyzed in relation to buying behaviors of customers.

Let’s discuss Deep learning and Neural Networks in Machine Learning

One application of machine learning- Often studying the association between the products people buy, which is also known as basket analysis. If a buyer buys ‘X’, would he or she force to buy ‘Y’ because of a relationship that can identify between them? This leads to the relationship that exists between fish and chips etc. when new products launch in the market a Knowing these relationships it develops a new relationship. Knowing these relationships could help in suggesting the associated product to the customer. For a higher likelihood of the customer buying it, It can also help in bundling products for a better package.

This learning of associations between products by a machine is learning associations. Once we found an association by examining a large amount of sales data, Big Data analysts. It can develop a rule to derive a probability test in learning a conditional probability.

6. Classification

Classification is a process of placing each individual from the population under study in many classes. This is identified as independent variables.

Have a look at Convolutional Neural Networks Architecture

Classification helps analysts to use measurements of an object to identify the category to which that object belongs. To establish an efficient rule, analysts use data. Data consists of many examples of objects with their correct classification.

For example, before a bank decides to disburse a loan, it assesses customers on their ability to repay the loan. By considering factors such as customer’s earning, age, savings and financial history we can do it. This information is taken from the past data of the loan. Hence, Seeker uses to create a relationship between customer attributes and related risks.

7. Prediction

Consider the example of a bank computing the probability of any of loan applicants faulting the loan repayment. To compute the probability of the fault, the system will first need to classify the available data in certain groups. It is described by a set of rules prescribed by the analysts.

Let’s revise Recurrent Neural Networks

Once we do the classification, as per need we can compute the probability. These probability computations can compute across all sectors for varied purposes

The current prediction is one of the hottest machine learning algorithms. Let’s take an example of retail, earlier we were able to get insights like sales report last month / year / 5-years / Diwali / Christmas. These type of reporting is called as historical reporting. But currently business is more interested in finding out what will be my sales next month / year / Diwali, etc. So that business can take a required decision (related to procurement, stocks, etc.) on time.

8. Extraction

Information Extraction (IE) is another application of machine learning. It is the process of extracting structured information from unstructured data. For example web pages, articles, blogs, business reports, and e-mails. The relational database maintains the output produced by the information extraction.

The process of extraction takes input as a set of documents and produces a structured data. This output is in a summarized form such as an excel sheet and table in a relational database.

Nowadays extraction is becoming a key in the big data industry.

As we know that the huge volume of data is getting generated out of which most of the data is unstructured. The first key challenge is handling unstructured data. Now conversion of unstructured data to structured form based on some pattern so that the same can stored in RDBMS.

Apart from this in current days data collection mechanism is also getting change. Earlier we collected data in batches like End-of-Day (EOD), but now business wants the data as soon as it is getting generated, i.e. in real time.

9. Regression

We can apply Machine learning to regression as well.

Assume that x= x1, x2, x3, … xn are the input variables and y is the outcome variable. In this case, we can use machine learning technology to produce the output (y) on the basis of the input variables (x). You can use a model to express the relationship between various parameters as below:

Have a look at Advantages and Disadvantages of Machine Learning

Y=g(x) where g is a function that depends on specific characteristics of the model. In regression, we can use the principle of machine learning to optimize the parameters. To cut the approximation error and calculate the closest possible outcome.

We can also use Machine learning for function optimization. We can choose to alter the inputs to get a better model. This gives a new and improved model to work with. This is known as response surface design.

So, this was all about Machine Learning Applications. Hope you like our explanation.

3. Conclusion

In conclusion, Machine learning is an incredible breakthrough in the field of artificial intelligence. While it does have some frightening implications when you think about it, these Machine Learning Applications are several of the many ways this technology can improve our lives. In the next few years Future of Machine Learning will be very bright.

If you found any other Machine Learning applications, So, please let us know in the comments!

#machine learning #machine learning application #deeplearning #artificial intelligence

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sueheaven · 3 years ago

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Low-voltage Circuit Breakers Market is set for a Potential Growth Worldwide: Excellent Technology Trends with Business Analysis

Latest edition released by AMA on Global Low-voltage Circuit Breakers Market to regulate the balance of demand and supply. This intelligence report on Low-voltage Circuit Breakers includes Investigation of past progress, ongoing market scenarios, and future prospects. Data True to market on the products, strategies and market share of leading companies of this particular market are mentioned. It’s a 360-degree overview of the global market’s competitive landscape. The report further predicts the size and valuation of the global market during the forecast period. Some of the key players profiled in the study are :

ABB (Switzerland)

Schneider Electric (France)

Mitsubishi Electric Corporation (Japan)

Siemens AG (Germany)

Eaton (Ireland)

Shanghai Liangxin Electrical Co. Ltd. (China)

Delixi Electric Co., Ltd. (China)

HangShen Electric (China)

CHINT Group (China)

Hyundai Electric & Energy Systems Co., Ltd. (South Korea) The low voltage circuit breaker is defined as the circuit breakers which operate on voltages less than 1000 volts. The important function of the low voltage circuit breaker is interrupted current flow after a fault is detected. It is widely used in houses, certain industrial uses, and small businesses. The main classifications of low-voltage circuit breakers are two-step stored energy mechanism circuit breakers and toggle mechanism. Continued growth of construction & developmental activities and increase access to electricity in developing countries are some of the major drivers which are propelling the growth of the market.

Opportunities: Upcoming Smart Cities Projects in Developing Countries Such as India, China and others

Market Drivers: The Growing Demand for New Electrical Distribution Infrastructure

Growth in Renewable Power Generation

Challenges: Problem related to cyber security threats

Presented By

AMA Research & Media LLP

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delightfullyatomiccollector-blog · 1 month ago

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Global Fault Detection and Classification (FDC) Market Poised for Robust Growth

The global fault detection and classification market was valued at USD 4.4 billion in 2022 and is projected to reach USD 7.4 billion by 2028; it is expected to register a CAGR of 8.9% between 2023 and 2028 according to a new report by MarketsandMarkets™. The rise in demand for FDC systems is attributed to the increased complexity of systems, strong focus of manufacturers on automating quality control and quality assurance processes, and stringent health and safety measures imposed by governments and standards organizations on global manufacturing firms.

#Fault Detection and Classification (FDC) Market

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messunggroup · 4 years ago

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PREVENT ELECTRICAL FIRES & HAZARDS WITH RESIDUAL CURRENT MONITORING

Residual Current is a critical condition impacting hospitals, data centres, industrial plants, etc. It can lead to dangerous situations for both personnel and equipment.

Moreover, it can cause unexpected interruptions, malfunctions and EMC problems that cost time and money. Hence, Residual Current Monitoring is critical for safety, high availability and productivity in industrial and purpose-built environments.

Inside environments like data centres where shielded cables are present, residual current may spread through cable shielding across different parts of the data centre if the racks are insufficiently earthed. This can lead to fires, unexplained IT systems failures, tripping and disconnecting of critical equipment.

Let’s understand RCM, its dangers and the solutions a little better.

WHAT IS RESIDUAL CURRENT?

Residual Current is electrical current that occurs in an unwanted conductive path. It flows from either AC or DC circuit in equipment to the chassis, or to the ground.

Residual currents are typically the result of insulation faults, defective equipment and components (power supplies, electrical loads) causing outflow-related residual currents and stray currents in TN-S systems. RCM classification is based on frequency and waveform of the current that they candetect - Type A, Type B, Type B+.

WHY USE RCM (EARTH LEAKAGE MONITORING)?

RCM is essential to ensure Continuous System Availability. In market segments with sensitive applications (e.g., Critical power locations, Hospitals, Industrial Manufacturing), an uninterrupted power system is of crucial importance.

Uninterrupted operation (Increased Uptime & Availability) of an installation is only ensured by continuous monitoring.Continuous Residual Current Monitoring is the only method to detect dangerous fault currents at an early stage.

Early detection of insulation faults, as well as preventive maintenance and servicing outside of operating hours, can prevent unexpected shutdowns of machines, servers and systems, thus avoiding unwanted interruptions in operation, property damage and high costs.

RCM measurement in accordance with DIN EN 62020 offers the most reliable and safe alternative wherever the realization of Insulation Resistance Measurement or RCDs are not practical in an installation.

PROBLEMS CAUSED BY FAULT CURRENTS

While electricity has become an indispensable component of our lives, the fact is, it comes with its own hazards to human life and property. The major risks include:

· Electric Shock

· Breaker Tripping

· Fire Hazard

· Server and Computer Shutdown

· Interruption of Communication Networks

· Temperature Rise in Conductors resulting in Insulation Breakdown

· Fast Corrosion of Metal Pipes

· Production Shutdown

· VSD / Inverter Faults

· Sensor Malfunction

· Overvoltage Problems

· Overloaded Neutral Conductor

REASONS FOR INSULATION FAILURE

The purpose of insulation is to prevent the flow of electric current between points of different potential in an electrical system. Failure of insulation is one of the most common failures in an electrical equipment.

Insulation of an electrical circuit becomes weak due to various causes:

· Natural deterioration due to aging

· Accelerated by excessive heat and moisture

· Heat, moisture and dirt are main causes of insulation failure

· Chemical deterioration

· Mechanical damage

· Sunlight

· Excessive voltage stresses

HOW RCM WORKS

Residual currents are typically the result of insulation faults, defective equipment and components (power supplies, electrical loads).

Conductors (L1, L2, L3, N) of the specific measurement point are monitored and passed through the RCM CT. In a fault-free system the sum of all currents is ZERO, so that in the current transformer no current is induced.

If a fault current is flowing, the current difference induces in the differential CT an mA current that is detected by Janitza’s UMG device.

TYPES OF RCM

There are 3 types of RCM:

Type A: Sinusoidal alternating current pulsed direct current. This type of RCM is applied to single-phase electronic devices with electronic regulation & control such as power supplies, computers, lighting systems, single-phase drives, heat pumps, single-phase dimmers, single-phase electronic devices in the three-phase network, etc.

Type B: Smooth & pulsating direct current as well as alternating currents up to 2 kHz. Applications include devices with three-phase bridge circuits and purely direct current devices like photovoltaic systems, controlled three-phase motors, UPS systems, dimmers, medical devices, etc.

Type B+: Smooth & pulsating direct current as well as alternating currents upto 20 kHz.

RCM STANDARDS

RCMs monitor residual currents in electrical installations in real time. They report the level of residual current value and signal when it exceeds a threshold. They comply with DIN EN 62020.

IEC 62020 is a standard used to test RCM accuracy. It defines residual operating current IΔN as the leakage current threshold where the RCM reports an alarm.

Where a circuit is permanently monitored by an RCM in accordance with IEC 62020 or an IMD in accordance with IEC 61557-8, it is not necessary to measure the insulation resistance if the functioning of the IMD or RCM is correct.

When RCM (Earth Leakage Monitoring) is installed and operational, there is “NO” requirement for Mains Power disconnection.

WHERE IS RCM USED?

There are many applications of residual current monitors; they include:

· TN-S Systems

· Drive Systems (Frequency Converters)

· DC Applications

· Hospital & Medical Facilities

· Industrial Manufacturing

· Data Centre & Critical Power Applications

· Power Distribution Busbar

· Rail Networks

ALTERNATIVES FOR RESIDUAL CURRENT MONITORING

Insulation Testing as part of Preventive Maintenance Both the ends of the cable need to be disconnected and then meggered for finding the insulation. Long testing times results in long shutdown times and personnel have to deal with the cumbersome procedure of disconnecting the terminated cables. Periodicity of the tests needs long term planning as well.

Insulation Monitors - Online This is a popular but cost prohibitive measuring method wherein a measuring DC voltage is superimposed between the phase and PE conductor. This measurement procedure is suitable for monitoring conventional AC, 3(N)AC systems. If it is used in AC, 3(N)AC systems containing galvanically connected DC components, these DC currents will distort the measurement result.

Residual Current Breakers / Earth Leakage Relays These devices trip the circuit causing incidents. Faults and fires are not predicted, only prevented, and surprise shutdowns can be extremely expensive for operations that require uninterrupted power supply.

RCM WITH JANITZA

Janitza offers a wide range of digital energy meters that provide residual current monitoring in conjunction with energy measurement, which constitute a measure for fire protection and maintenance prevention. Downtimes and the associated costs are thereby reduced.

Timely and preventative maintenance – facilitated through the information additionally gained from an RCM measuring device – also significantly enhances the efficiency and availability of a system.

Scope: Janitza's range of smart RCM devices covers the complete electrical system from utility incomer to the final circuit:

1. MV & LV supply (PCC)

2. LV Main Distribution Switchboard

3. Sub-distribution Switchboards, UPS Outputs

4. PDU, Critical Power Supply Points & Busway Feed Box

5. Outgoing Circuits, Final Sub-measurements & Busway Tap-offs

Devices: Type A RCM measuring devices such as the UMG 96RM-E, UMG 509-PRO, UMG 512-PRO, UMG 20CM from Janitza are suitable for monitoring alternating currents, pulsing DC currents per IEC/TR 60755 (2008-01) and can be used for continuously checking for residual currents in TN-S systems.

Other Type A RCM devices include UMG 96 RM-PN, UMG 96 PA, UMG 806, RCM 201-ROGO, RCM 202 AB.

Type B RCM devices include UMG 96 RM-E, UMG 96 PA, UMG PQ-L, RCM 202 AB. The last three devices are also capable of Type B+ RCM.

Software & Apps: Janitza's power grid monitoring software, GridVis generates the RCM report which allows clear and uncomplicated display of measurement data from residual current measurements. The GridVis RCM Report enables users to:

· Analyse and evaluate residual current violations

· Receive statistics regarding threshold violations with graphically highlighted overviews

· Set up to 4 threshold levels

· Support of dynamic thresholds (RCM measuring device configuration!)

· Custom text, customer logo and export in PDF and XLS format

· Report creation can be carried out at custom time schedule

· Automatically dispatch per E-Mail

Janitza's RCM analysis App is a homepage add-on application with extensive options for setting limit values and for detailed analysis of residual currents. Up to 20 RCM channels can be managed and evaluated via a gateway*. The evaluation includes all types of residual currents and an associated frequency analysis.

For example, 50 Hz, pure DC or high-frequency residual currents in the 20 kHz range can be displayed individually. In addition, the application enables the proven dynamic limit value formation with Janitza energy measuring devices.

Energy measuring devices can be assigned to each of the 20 RCM channels and limit values can be calculated as a function of power.

SUMMARY

Highly automated production systems, computer centres and systems with constant processes (e.g. food sector, cable fabrication, paper production) require a reliable power supply - often even high availability, i.e. an availability of at least 99.9%, frequently even 99.9999 %.

An insulation measurement is required for the repeat testing of fixed electrical systems for which the system must be switched off. Production processes and administration processes are interrupted. This means an increase in work and often also significant costs.

In order to avoid this, the standards offer an alternative: Continuous residual current monitoring, with which it is also possible to locate faults faster. With continuous RCM, it is possible to avoid shut-downs and minimise test work. Constant checking of the system takes place, which enables the immediate detection of faults.

With Janitza’s range of solutions for energy monitoring and management system, comprehensive RCM of the power supply takes place at all levels: from CGP and outputs requiring monitoring in the LVDS and sub-distribution systems, right through to individual critical loads. Together with the GridVis® energy data acquisition software and the integrated alarm management, users can avoid expensive downtimes and prevent fire hazards that are latent due to creeping insulation faults.

In India, Janitza products are brought to you by Messung Electrical Engineering division. Power quality analysers, digital energy meters, active filters, and apps – Messung offers the complete Janitza range of power quality and energy management solutions.visualisation software

#Messung Electrical Engineering #Power quality analysers #digital energy meters #active filters #power quality #energy management solutions.#energy monitoring and management system #energy monitoring

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messungelectricalengineering · 4 years ago

Text

PREVENT ELECTRICAL FIRES & HAZARDS WITH RESIDUAL CURRENT MONITORING

Residual Current is a critical condition impacting hospitals, data centres, industrial plants, etc. It can lead to dangerous situations for both personnel and equipment.

Let’s understand RCM, its dangers and the solutions a little better.

WHAT IS RESIDUAL CURRENT?

Residual Current is electrical current that occurs in an unwanted conductive path. It flows from either AC or DC circuit in equipment to the chassis, or to the ground.

WHY USE RCM (EARTH LEAKAGE MONITORING)?

PROBLEMS CAUSED BY FAULT CURRENTS

While electricity has become an indispensable component of our lives, the fact is, it comes with its own hazards to human life and property. The major risks include:

· Electric Shock

· Breaker Tripping

· Fire Hazard

· Server and Computer Shutdown

· Interruption of Communication Networks

· Temperature Rise in Conductors resulting in Insulation Breakdown

· Fast Corrosion of Metal Pipes

· Production Shutdown

· VSD / Inverter Faults

· Sensor Malfunction

· Overvoltage Problems

· Overloaded Neutral Conductor

REASONS FOR INSULATION FAILURE

Insulation of an electrical circuit becomes weak due to various causes:

· Natural deterioration due to aging

· Accelerated by excessive heat and moisture

· Heat, moisture and dirt are main causes of insulation failure

· Chemical deterioration

· Mechanical damage

· Sunlight

· Excessive voltage stresses

HOW RCM WORKS

Residual currents are typically the result of insulation faults, defective equipment and components (power supplies, electrical loads).

If a fault current is flowing, the current difference induces in the differential CT an mA current that is detected by Janitza’s UMG device.

TYPES OF RCM

There are 3 types of RCM:

Type B+: Smooth & pulsating direct current as well as alternating currents upto 20 kHz.

RCM STANDARDS

RCMs monitor residual currents in electrical installations in real time. They report the level of residual current value and signal when it exceeds a threshold. They comply with DIN EN 62020.

IEC 62020 is a standard used to test RCM accuracy. It defines residual operating current IΔN as the leakage current threshold where the RCM reports an alarm.

When RCM (Earth Leakage Monitoring) is installed and operational, there is “NO” requirement for Mains Power disconnection.

WHERE IS RCM USED?

There are many applications of residual current monitors; they include:

· TN-S Systems

· Drive Systems (Frequency Converters)

· DC Applications

· Hospital & Medical Facilities

· Industrial Manufacturing

· Data Centre & Critical Power Applications

· Power Distribution Busbar

· Rail Networks

ALTERNATIVES FOR RESIDUAL CURRENT MONITORING

RCM WITH JANITZA

Scope: Janitza's range of smart RCM devices covers the complete electrical system from utility incomer to the final circuit:

1. MV & LV supply (PCC)

2. LV Main Distribution Switchboard

3. Sub-distribution Switchboards, UPS Outputs

4. PDU, Critical Power Supply Points & Busway Feed Box

5. Outgoing Circuits, Final Sub-measurements & Busway Tap-offs

Other Type A RCM devices include UMG 96 RM-PN, UMG 96 PA, UMG 806, RCM 201-ROGO, RCM 202 AB.

Type B RCM devices include UMG 96 RM-E, UMG 96 PA, UMG PQ-L, RCM 202 AB. The last three devices are also capable of Type B+ RCM.

· Analyse and evaluate residual current violations

· Receive statistics regarding threshold violations with graphically highlighted overviews

· Set up to 4 threshold levels

· Support of dynamic thresholds (RCM measuring device configuration!)

· Custom text, customer logo and export in PDF and XLS format

· Report creation can be carried out at custom time schedule

· Automatically dispatch per E-Mail

Energy measuring devices can be assigned to each of the 20 RCM channels and limit values can be calculated as a function of power.

SUMMARY

In India, Janitza products are brought to you by Messung Electrical Engineering division. Power quality analysers, digital energy meters, active filters, visualisation software and apps – Messung offers the complete Janitza range of power quality and energy management solutions.

0 notes

smartgen · 5 years ago

Photo

Marine Diesel Control Solutions of SmartGen

MGCP100B Series Diesel Control Box

After ten years of consistent R&D and product promotion in marine diesel control and power management control of automatic power station, MGCP100B series diesel control boxes and HMC6 power management modules provided by SmartGen with advanced technology, full functions and complete certificates have increased in the high-end marine market year by year, gaining rich experience in field of diesel control of marine propulsion engines, marine auxiliary engines and marine gensets with different uses.

Features of SmartGen marine diesel control solutions

1. MGCP100B series control boxes are appliable for marine diesel of different speed regulation (machanical , electronic and ECM control). 2. Master control module with backlight LCD display, Chinese/English optional, the main parameters can be set through panel to realize “one button operation”. 3. Up to 16 remote monitoring modules can be extended by CANBUS. 4. 2 pieces of 16 digital inputs (DIN16A), 2 pieces of 16 relay outputs (DOUT16B), 2 pieces of 16 PT100 inputs (AIN16 - PT), 2 pieces of 16 4 ~ 20 mA inputs (AIN16 - C), 2 pieces of 16 analog integrated extension modules (AIN16 - M02) can be extended by CANBUS, which can realize multi-channel digital inputs, temperature, pressure, speed data sampling, relay and 4 ~ 20 mA output can be configured at the same time. 5. CANBUS can be used to extend the security module (RPU560A) to achieve multiple protection of the engine. 6. It can be controlled and communicated by CANBUS and dozens of EFI diesels with J1939 interface. 7. With RTC, alarm information display and event log, it is convenient to daily maintenance and fault inquiry.

Standardized control modules and extension modules

Classification societies have strict regulations for marine diesel gensets and marine engines control and protection, SmartGen adopts the main control module and extension module topological structure to build control system, strictly in accordance with the classification rules for design, with 22 years experience in the field of diesel control to ensure the highly standardized modules development process and the product functions.

Marine Diesel Control Series

MGCP100 series diesel engine control box with various functions

MGCP100L and MGCP100B series control boxes are based on the design concept of master control module and extension module. The master control module and extension module are connected through the standard CANBUS interface, following a unified network topology. At the same time, the master control module is pre-built with a set of programmable configuration parameters, so the user can change the working mechanism of the control system without changing the circuit.

HMC4000/HMC6000A/HMC9000A control modules have the models to support J1939 interface, suitable for makers of marine EFI diesel controlled by ECM, the speed control unit and some sensors could be saved from the topology.

Control Topology of HMC6000A Engine Control Module in Complex Applications

Control Topology of HMC9000A Engine Control Module in Complex Applications

Users can use MGCP100B-2 diesel control box and its supporting remote transmission module on the diesel master genset, emergency genset and main propulsion diesel to make a one-stop marine power and automatic power station solution with the HMC6 power management control module developed by SmartGen.

Power Management Application

One-stop Service

SmartGen has a professional marine products service team, which can provide customers with one-stop services such as solution design, production, product certification (CCS, BV) and debugging guidance from the diesel control and automatic power station power management.

Solutions

MGCPB-2 Series Diesel Control Box

A 20 meters patrol boat from a marine bureau equipped with two Scania DI13 EFI diesel; MGCP100B-2 diesel control box and HRM3300 remote monitoring module in the control console of SmartGen adopted to realize side and remote control operation. The bimotored driven and twin-screw boat is mainly used for law enforcement patrols along the Yangtze River waterway to ensure safe and orderly shipping.

Power Station Solution: MGCP100B-2 + HLS300 Power Sharing Module

Three official ships of a shipyard equipped with Cummins main genset. MGCP100B-2 diesel control boxes with HMC9000A as the master control modules are used, the switch panel is an automatic power station built by HLS300 load distributor and HSM300 sync control module.

MGCP100B-2 Series Diesel Control Box

4 main gensets (Cummins EFI diesel) on a surveying ship are equipped with MGCP100B-2 diesel control box with HMC9000A master control module.

The MGCP100B-2 diesel control box is installed on the main propulsion and the main control module is HMC9000A.

HRM3300 and HMC6000RM Remote Transmission Module Application

HRM3300 and HMC6000RM remote transmission modules installed on the driving console and AMS alarm monitoring platform.

MGCP100B-2 Series Diesel Control Box

Cummins master engine: HMC6000 diesel control box with security module, switch expansion control module, remote transmission module, which can detect engine speed, water temperature, oil temperature, oil pressure, water pressure, battery voltage and other parameters. With water level alarm input, fuel leakage alarm input, low pressure alarm input, anti-condensation heating functions, as well as multiple alarm outputs that the alarm point can be extended.

MGCP100B-2 Series Diesel Control Box

VOLOV D7 master engine: HMC6000 diesel control box with power conversion module, digital output expansion module, battery charger and two sets of remote monitoring modules; it can monitor the engine speed, oil pressure, oil temperature, water temperature, water pressure, battery voltage and other parameters, with fuel leakage, low water level alarm input functions.

www.smartgen.cn

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i-globalone · 6 years ago

Quote

In recent past years, there has been a great evolution in the field of software testing. Heading into 2019, where we can expect to reach even more critical milestones in the IT industry, as several new software testing trends are emerging.Keeping this in mind, here are the top trends of software testing in 2019 that can shape you in the future.Are you Ready to Check the Impressive Software Testing Trends for your future?Currently, we witness enormous changes in the technological advancements as the world is becoming more digitized. So everyone must know the evolving Top Software Testing Trends to anticipate in the coming years. Let’s get started!1. Agile and DevOpsMany organizations today have embraced Agile as a response to constantly changing requirements and DevOps as a response to the demand for speed.As DevOps involves rules, practices, processes, and tools that help in integrating development and operation activities. This in order to decrease the time starting from the development to operations. Also nowadays, DevOps has become a widely accepted solution for every organization, which is looking at different ways to reduce their software lifecycles starting from development to delivery and operation.So, the adoption of both Agile and DevOps helps the software development teams to quickly develop and deliver quality software, which in turn is also known as “Quality of Speed”. It is also noticeable that this adoption has gained much interest over the past five years and continues to intensify in the future.2. Test Automation:In order to execute DevOps practices efficiently as well as effectively, many software teams can’t ignore test automation because as it became an essential element of the DevOps process.Also, automation testing gives opportunities to replace manual testing. Thus, considering test automation an important element of DevOps, at a minimum, most regression testing must be automated.Given the popularity of DevOps, but the fact that test automation is underutilized, with less than 20% of testing is automated. So to better utilize the automation testing, advanced methods and tools should emerge coming projects.There are already some popular automation tools available in the market such as Selenium, Katalon, and TestComplete. This continues to evolve with new features and aspects that make automation much more easier and effective.3. API and Services Test Automation:Disassociating the client and server is today’s evolving trend in web design i.e. both Web and mobile applications.API and services both are reused in more than one application or component. These changes require the teams to test both API and services independently from the applications using them.While using API and services across client applications and components, then testing them becomes more effective and efficient than testing the client. The trend here is that the need for API and services test automation continues to increase constantly, possibly outpacing that of the functionality used by the end-users on user interfaces.Therefore, having the right process, tool and solution for API automation test became more critical than ever. Hence, it is worth your effort in learning the best API Testing Tools for your testing your upcoming projects.4. Artificial Intelligence for TestingAs we know, the approach of artificial intelligence and machine learning (AI/ML) in the software industry is not new. But the recent advancements in AI/ML with a large amount of data available pave a gateway for new opportunities to apply AI/ML in testing.Related PostsHowever, the application of Artificial Intelligence and Machine learning in testing is still in the early stages. There are organizations that still find better ways to optimize their testing practices in AI/ML.As AI/ML algorithms are developed in a way to produce better test cases, test data, test scripts, and reports. Thus, Predictive models will help you make decisions about where what, and when to test. Additionally, Smart analytics and visualization, in turn, support the teams to notice faults, to understand test coverage, areas of high risk, etc.As many Software testing class and software testing institute convey that they have hopes to see more applications of AI/ML in addressing problems such as quality prediction, fault classification, test case prioritization, and assignment in the coming years.5. Mobile Test Automation:Currently, the trend of mobile app development continues to grow rapidly as mobile devices are now increasingly becoming more capable.Also to completely support DevOps, mobile test automation should be a part of DevOps toolchains. However, the utilization of mobile test automation is now currently very low. This is due to the lack of methods and tools.But the trend of automated testing for mobile apps continues to grow rapidly. This trend is coming into force for the need to shorten the time-to-market and more advanced methods and tools for mobile test automation.Thus, many software testing courses say that the integration between cloud-based mobile device labs like Kobiton and test automation tools like Katalon will help in bringing mobile automation to a new level in the coming years.6. Test Environments and Data:There is a continuous growth of the Internet of Things (IoT). That is more software systems that are operating in numerous different environments. This probably places a challenge for many testing teams to ensure the right level of test coverage. Also, when applying to test in agile projects, the lack of test environments and data is being a top challenge.Thus, we will notice a growth in offering and using cloud-based and containerized test environments. Therefore, the reputed software testing training institutes implies that the application of AI/ML to produce test data and the growth of data projects are some key solutions for the lack of test data.7. Integration of Tools and ActivitiesFor application lifecycle management, it is tough to use any testing tool that is not integrated with the other tools. Therefore, every Software team has to integrate the tools that are used for all development phases and activities. Because of this, many multi-source data can be collected together to apply AI/ML approaches effectively.For Instance, if you are using AI/ML to detect where to focus testing on, that not only requires data from the testing phase but also from the requirements, design, and implementation phases.Along with the trends of growing transformation towards DevOps, test automation, and AI/ML, we will see many testing tools that allow integration with the other tools and activities in the upcoming ALM.Final Thoughts:As we live in a world of unprecedented exponential changes that are driven by digital transformation and technologies. The above are the Emerging Software Testing Trends that one must know in 2019Therefore to dominate the software testing industry, every organization and individual must keep themselves aware of the developments in the industry.To keep you with these trends, you can also make use of Software testing online courses. This would give the opportunity for every organization and team to stay ahead of the curve.About Author:-Janani is working as a Digital Marketing Content Manager at Zuan Technologies. She is keen to explore new and better avenues for doing digital marketing. Her research-based content writings validate contemporary techniques used in marketing. This, in turn, encourages digital marketers. Also, users are tempted to explore new products.

http://www.globalone.com.np/2019/11/the-top-emerging-trends-in-software.html

#SEO #Local SEO training

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parvinasseaynk72insurance-blog · 6 years ago

Text

BMW 340i Cheap Insurance

BEST ANSWER: Try this site where you can compare free quotes :COVERAGE-FINDER.NET

SOURCES:

BMW 340i Cheap Insurance

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BMW 340i Cheap Insurance

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