Cloud Hosting

AI and ML in D365

Microsoft has embedded AI and ML capabilities across multiple modules of D365 to augment its functionality and provide users with intelligent insights and automation.

Lets explore some key areas where AI and ML are integrated:

Sales and Marketing

In the sales and marketing modules of D365, AI-powered features enable businesses to enhance customer engagement, personalize marketing campaigns, and identify sales opportunities. ML algorithms analyze customer data, social media interactions, and historical buying patterns to generate predictive lead scoring, recommend personalized product offerings, and optimize marketing strategies. This results in improved targeting, increased conversion rates, and more effective customer segmentation.

Customer Service

D365 leverages AI and ML to improve customer service experiences. Chatbots with natural language processing (NLP) capabilities can understand and respond to customer queries, providing quick and accurate resolutions. ML algorithms analyze customer feedback and sentiment to gauge customer satisfaction levels, allowing organizations to proactively address any issues. This leads to enhanced customer support, reduced response times, and increased customer satisfaction.

Field Service

ML algorithms in D365s Field Service module enable predictive maintenance by analyzing historical data and identifying patterns that indicate equipment failures. This helps organizations optimize maintenance schedules, reduce downtime, and improve operational efficiency. By leveraging AI-powered insights, field service teams can proactively address potential issues, minimize service disruptions, and deliver better service quality.

Finance and Operations

AI and ML capabilities in D365s Finance and Operations modules assist in automating and streamlining financial processes. These include fraud detection, predictive cash flow analysis, intelligent forecasting, and supply chain optimization based on demand patterns and market trends. By leveraging AI and ML, organizations can optimize financial decision-making, reduce risks, and improve overall operational efficiency.

Also Read: Streamlining business operations with D365 Business Central workflows

The integration of AI and ML in D365 offers several significant benefits to businesses:

Enhanced Efficiency

Automation and intelligent algorithms enable businesses to improve efficiency by reducing manual efforts and streamlining processes. Tasks such as data entry, report generation, and customer inquiries can be automated, allowing employees to focus on higher-value tasks and strategic decision-making.

Personalized Experiences

AI and ML capabilities enable D365 to deliver personalized experiences to customers. By analyzing vast amounts of customer data, businesses can tailor their interactions, offers, and recommendations based on individual preferences. This personalized approach increases customer satisfaction, strengthens customer relationships, and drives customer loyalty.

Smarter Decision-Making

With AI and ML insights, organizations can make data-driven decisions based on accurate predictions and trends. By analyzing large datasets, identifying patterns, and generating actionable insights, D365 empowers businesses to identify emerging opportunities, optimize operations, and proactively address potential risks.

Improved Customer Service

The integration of AI-driven chatbots and sentiment analysis tools in D365s customer service modules enhances response times, provides 24/7 support, and improves the overall customer experience. Chatbots can handle routine inquiries and provide instant support, while sentiment analysis tools help organizations gauge customer satisfaction levels and address any issues promptly.

See original here:
Exploring the integration of artificial intelligence (AI) and machine learning (ML) capabilities in D365 - Security Boulevard

Machine learning has become an integral part of our lives, revolutionizing industries and transforming the way we interact with technology. From personalized recommendations on streaming platforms to advanced medical diagnostics, the applications of machine learning are vast and ever-growing. However, there is a hidden cost to this technological marvel that is often overlooked: the energy consumption of machine learning.

The energy consumption of machine learning is surprisingly high, and it is essential to understand the implications of this fact. With the increasing demand for more complex and powerful machine learning models, the energy required to train and run these models is also on the rise. This energy consumption not only contributes to the global energy crisis but also has a significant impact on the environment.

Machine learning models are developed through a process called training, where the model learns from a large dataset to make predictions or decisions. This training process is computationally intensive and requires a significant amount of energy. In fact, the energy consumption of training a single machine learning model can be equivalent to the energy consumed by multiple households in a year.

A study conducted by researchers at the University of Massachusetts, Amherst, found that training a single natural language processing (NLP) model, which is used for tasks such as translation and sentiment analysis, can generate carbon emissions equivalent to nearly five times the lifetime emissions of an average car, including its manufacturing process. This startling revelation highlights the environmental impact of machine learning and the need for more sustainable practices in the field.

The energy consumption of machine learning is primarily driven by the hardware used for training and running the models. Graphics processing units (GPUs) and tensor processing units (TPUs) are commonly used for these tasks due to their high computational capabilities. However, these specialized processors consume a significant amount of energy, contributing to the overall energy consumption of machine learning.

Another factor contributing to the energy consumption of machine learning is the increasing complexity of models. As researchers and developers strive to create more accurate and sophisticated models, the number of parameters and computations required for training increases. This, in turn, leads to higher energy consumption.

Data centers, which house the servers and hardware required for machine learning, also play a significant role in the energy consumption of machine learning. These facilities consume vast amounts of energy to power the servers and maintain optimal operating conditions, such as cooling systems to prevent overheating. As the demand for machine learning services grows, so does the need for more data centers, further exacerbating the energy consumption issue.

To address the energy consumption of machine learning, researchers and developers are exploring various solutions. One approach is to develop more energy-efficient hardware, such as specialized processors designed specifically for machine learning tasks. Another strategy is to optimize machine learning algorithms to reduce the number of computations required for training, thereby reducing energy consumption.

Additionally, there is a growing interest in exploring alternative, more sustainable energy sources for powering data centers. For example, some companies are investing in renewable energy sources, such as solar and wind power, to reduce the environmental impact of their data centers.

In conclusion, the energy consumption of machine learning is a critical issue that must be addressed as the field continues to grow and evolve. By developing more energy-efficient hardware, optimizing algorithms, and exploring sustainable energy sources, the machine learning community can help mitigate the environmental impact of this groundbreaking technology. As we continue to reap the benefits of machine learning in various aspects of our lives, it is crucial to be aware of the hidden costs and strive towards a more sustainable future.

Follow this link:
Hidden Costs: The Energy Consumption of Machine Learning - EnergyPortal.eu

Data

This paper selects parts of China and surrounding areas as the research area. The research data selects the NDVI data of MODIS (NDVIm) and AVHRR (NDVIa) sensors on Terra and Aqua, and the NDVI data of VIRR (NDVIv) sensors on Fengyun satellite31. (I) Compare the NDVIv with the NDVIa, and the NDVIa and NDVIm. (II) Find out the functional relationship between NDVIa and NDVIm, and the functional relationship between NDVIv and NDVIa through comparison. (III) use NDVIa to correct NDVIv data to a level equivalent to NDVIm.

The data used in this study include (see Table 1): NDVIa from 1982 to 2015, NDVIm from 2000 to 2019, and NDVIv from 2015 to 2020, all of which have a resolution of 0.05. Because in 2005, there are both NDVIa data and NDVIm data. Therefore, we use the data of this year to compare NDVIa and NDVIm, and explore the correlation between the two. Because in 2015, there are both NDVIv data and NDVIa data. Therefore, we used the data of this year to compare NDVIv and NDVIa and explore the correlation between the two. Finally, we compared the corrected NDVIv of 2019 with the NDVIm of 2019 to verify the success of the model we constructed.

Figure1 shows the spectral response function curves of different satellite sensors in the visible and near-infrared spectrum32. By comparison, it can be found that in the visible light band, the spectral response function of MODIS is narrower than AVHRR, and the spectral response function of AVHRR is narrower than VIRR. In the near-infrared band, MODIS still has the narrowest spectral response function, followed by VIRR, and AVHRR has the widest spectral response function. The channel, wavelength range, corresponding spectrum and sub-satellite resolution information of MODIS, AVHRR, and VIRR sensors are shown in Table 2.

Spectral response function curves of different satellite sensors in the visible and near-infrared spectrum29.

Linear model is a form of machine learning model. The form of linear model is relatively simple and easy to model. The linear model contains some important basic ideas in machine learning. Many more powerful nonlinear models can be obtained by introducing hierarchical structure or high-dimensional mapping on the basis of linear models. There are many forms of linear models, and linear regression is a common one. Linear regression tries to learn a linear model to predict the real-valued output markers as accurately as possible. By establishing a linear model on the data set, a loss function is established, and finally the model parameters are determined with the goal of optimizing the cost function, so as to obtain the model for subsequent prediction. The general linear regression algorithm process is as presented in Fig.2.

Schematic diagram of the linear regression algorithm flow.

The detailed procedure is as follows33:

The data is standardized and preprocessed. The preprocessing includes data cleaning, screening, organization, etc., so that the data can be input into the machine learning model as feature variables.

Different machine learning algorithms are selected to train a separate data set, and find the best machine learning model, establish a machine learning model based on the normalized vegetation index product retrieved by Fengyun satellite.

Verify and output the long-term series normalized vegetation index of the Fengyun satellite.

For 20012005, there are both AVHRR NDVI data and MODIS NDVI data. Therefore, we used the data of these 5years to compare NDVIa and NDVIm and explore the correlation between the two. Because 2015 has both VIRR's NDVI data and AVHRR's NDVI data. Therefore, we used the data of this year to compare NDVIv and NDVIa and explore the correlation between the two. Finally, we compared the corrected NDVIv of 2019 with the NDVIm of 2019 to verify the success of the model we constructed.

The linear machine learning model is used to construct the optimal functional relationship between the NDVIa and the NDVIm. The formula is as presented in formula (1):

$${text{Y}}_{{{text{NDVIm}}}} = left{ {{text{k2}}00{1},{text{k2}}00{2},{text{k2}}00{3},{text{k2}}00{4},{text{k2}}00{5},{text{kmin}},{text{kmax}},{text{kave}}} right} times {text{X}}_{{{text{NDVIa}}}} + left{ {{text{m2}}00{1},{text{m2}}00{2},{text{m2}}00{3},{text{m2}}00{4},{text{m2}}00{5},{text{mmin}},{text{mmax}},{text{mmean}}} right}$$

(1)

In the formula, XNDVIa is the NDVI value of AVHRR, YNDVIm is the NDVI value of MODIS, k is the coefficient value of the linear function relationship between NDVIa and NDVIm, k2001, k2002, k2003, k2004, k2005, kmin, kmax, kave are the coefficients of 2001, 2002, 2003, 2004, 2005, the 5-year minimum, 5-year maximum, and the 5-year coefficient average respectively. m is the intercept of the linear function relationship between the NDVIa and the NDVIm, m2001, m2002, m2003, m2004, m2005, mmin, mmax, mmean are the intercept of 2001, 2002, 2003, 2004, 2005 Year, 5-year minimum, 55-year maximum, and 5-year average respectively.

Through multiple cross-comparison analysis, the optimal coefficient k and the optimal coefficient m are selected, and then the optimal functional relationship between NDVIa and NDVIm is determined.

Based on the above analysis, we continue to construct the functional relationship between NDVIa and NDVIv, according to formula (2).

$${text{X}}_{{{text{NDVIa}}}} = {text{aZ}}_{{{text{NDVIv}}}} + {text{b}}{.}$$

(2)

In the formula (2), ZNDVIv is the NDVI value of VIRR, XNDVIa is the NDVI value of AVHRR, a is the coefficient value of the linear function relationship between the NDVIv and the NDVIa fitting, and b is the intercept of the linear function relationship between NDVIv and NDVIa fitting.

Replacing the functional relationship between NDVIa and NDVIv into the optimal NDVIa and NDVIm functional relationships filtered out to obtain the refitted NDVIv, which is Yvir_ndvi in the formula (3). The functional relationship formula of the simulated NDVIv is as follows (3):

$${text{C}}_{{{text{NDVIcv}}}} = {text{k}}_{{{text{NDVIa}}}} + {text{m}} = {text{k}}left( {{text{aZ}}_{{{text{NDVIv}}}} + {text{b}}} right) + {text{m}} = {text{kaZ}}_{{{text{NDVIv}}}} + {text{kb}} + {text{m}}{.}$$

(3)

In the formula, CNDVIcv is the corrected NDVIv(NDVIcv), k is the optimal coefficient of the correlation between NDVIa and NDVIm, and m is the optimal intercept of the correlation between NDVIa and NDVIm.

The data of 2005 were selected to compare NDVIm and NDVIa in some parts of China and surrounding areas. The data of 2015 were selected to compare NDVIv and NDVIa in some parts of China and surrounding areas. Through analysis, the correlation among NDVIv, NDVIa and NDVIm is found.

See the article here:
Research on the establishment of NDVI long-term data set based on ... - Nature.com