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- Telco & utilities
- Healthcare & Pharma
- Machinery
- Manufacturing
- Media & Publishing
- Other

z = −1 + i
nα = θ + 2πk
f(x) = 3x − 2
z = r (cosθ + isinθ)
z = r (cosθ + isinθ)
ax + by + cz + d = 0
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
######
Telco & utilities

####
Dynamic Rating

Personalized and dynamic estimate of the creditworthiness and expected life of potential customers.

z = −1 + i
nα = θ + 2πk
f(x) = 3x − 2
z = r (cosθ + isinθ)
z = r (cosθ + isinθ)
ax + by + cz + d = 0
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
######
Telco & utilities

####
Making forecasts to optimize the electricity grid management

Accurate forecasts that support operators in making effective decisions for managing electricity grid.

z = −1 + i
nα = θ + 2πk
u = ρ (cosα + isinα)
ax + by + cz + d = 0
a (f(x + T) = f(x), ∀ x ∈ IR)
(f(−x) = f(x), ∀ x ∈ D)
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
######
Telco & utilities

####
Customer credit behavior prediction

A tool that allows to have a complete view of customers.

a (f(x + T) = f(x), ∀ x ∈ IR)
(f(−x) = f(x), ∀ x ∈ D)
−x + 2y + z + t = −1
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
(f(−x) = f(x), ∀ x ∈ D)
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
######
Telco & utilities

####
Mathematical models to reduce customer churn rate

Allowing customer service to take targeted actions to prevent and refine the offer.

z = −1 + i
nα = θ + 2πk
u = ρ (cosα + isinα)
f(x) = 3x − 2
−x + 2y + z + t = −1
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
[x0 −r, x0 + r] (cosnα + isinnα) = (cosα + isinα)n
######
Telco & utilities

####
Forecasting in natural gas logistics

Enhancement of forecasting tools for different market clusters.

z = −1 + i
nα = θ + 2πk
f(x) = 3x − 2
z = r (cosθ + isinθ)
(f(−x) = f(x), ∀ x ∈ D)
ax + by + cz + d = 0
−x + 2y + z + t = −1
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
######
Telco & utilities

####
Mathematics to optimize advertising investments

An accurate measure of the return of online and offline advertising investments

z = −1 + i
nα = θ + 2πk
f(x) = 3x − 2
z = r (cosθ + isinθ)
(f(−x) = f(x), ∀ x ∈ D)
ax + by + cz + d = 0
−x + 2y + z + t = −1
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
######
Telco & utilities

####
Model innovation for Lead/Credit Scoring

Evolve and improve the performance of a FinTech algorithm.

z = −1 + i
f(x) = 3x − 2
z = r (cosθ + isinθ)
ax + by + cz + d = 0
nα = θ + 2πk
−x + 2y + z + t = −1
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
######
Healthcare & Pharma

####
Market analysis for anti-influenza products

Better plan your marketing campaigns in correspondence with the peak periods of influenza.

u = ρ (cosα + isinα)
f(x) = 3x − 2
z = r (cosθ + isinθ)
a (f(x + T) = f(x), ∀ x ∈ IR)
(f(−x) = f(x), ∀ x ∈ D)
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
######
Healthcare & Pharma

####
Data science at the service of hospitals

SmartAid Medical Solution, a digital platform based on artificial intelligence algorithms.

z = −1 + i
nα = θ + 2πk
u = ρ (cosα + isinα)
u = ρ (cosα + isinα)
f(x) = 3x − 2'
−x + 2y + z + t = −1
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
######
Healthcare & Pharma

####
Machine learning for public and private tenders

Improve the activities of building a pre-competitive scenario for tenders.

ax + by + cz + d = 0
(f(−x) = f(x), ∀ x ∈ D)
[x0 −r, x0 + r] (cosnα + isinnα) = (cosα + isinα)n
z = −1 + i
nα = θ + 2πk
z = r (cosθ + isinθ)
(f(−x) = f(x), ∀ x ∈ D)
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
######
Healthcare & Pharma

####
Mathematics to choose the most suitable protesis

Algorithm to support surgeons in choosing the best prosthesis for each patient.

−x + 2y + z + t = −1
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
z = −1 + i
u = ρ (cosα + isinα)
ax + by + cz + d = 0
−x + 2y + z + t = −1
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
######
Healthcare & Pharma

####
Algorithms at the service of diagnostic imaging

Automatically detect the presence of possible anomalies in medical imaging.

ax + by + cz + d = 0
a (f(x + T) = f(x), ∀ x ∈ IR)
(f(−x) = f(x), ∀ x ∈ D)
−x + 2y + z + t = −1
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
[x0 −r, x0 + r] (cosnα + isinnα) = (cosα + isinα)n
######
Machinery

####
Virtual prototyping in the design process

Standardize the design process and to be able to create the layout of a new plant in less time.

z = −1 + i
nα = θ + 2πk
u = ρ (cosα + isinα)
f(x) = 3x − 2
ax + by + cz + d = 0
C = {z = x + iy x, y ∈ IR}
[x0 −r, x0 + r] (cosnα + isinnα) = (cosα + isinα)n
######
Machinery

####
Production scheduling optimization

Create scheduling software for rapid and effective planning of processes.

z = −1 + i
nα = θ + 2πk
z = r (cosθ + isinθ)
2x + 2y + z + 4t = 0
[x0 −r, x0 + r] (cosnα + isinnα) = (cosα + isinα)n
2x + 2y + z + 4t = 0
−x + 2y + z + t = −1
C = {z = x + iy x, y ∈ IR}
######
Machinery

####
Mathematical modeling applied to liquid packaging machinery

Simulation algorithms that allow to test the various parameters and different configurations of the production machine.

z = r (cosθ + isinθ)
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
z = −1 + i
f(x) = 3x − 2
ax + by + cz + d = 0
C = {z = x + iy x, y ∈ IR}
[x0 −r, x0 + r] (cosnα + isinnα) = (cosα + isinα)n
######
Machinery

####
A mathematical estimator for the design of large plants

Estimate the types and quantities of materials needed for a new project in a shorter time.

z = r (cosθ + isinθ)
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
z = −1 + i
f(x) = 3x − 2
ax + by + cz + d = 0
C = {z = x + iy x, y ∈ IR}
[x0 −r, x0 + r] (cosnα + isinnα) = (cosα + isinα)n
######
Machinery

####
Multidisciplinary optimization to improve the design process

A unified approach to managing complexity.

nα = θ + 2πk
u = ρ (cosα + isinα)
f(x) = 3x − 2'
z = r (cosθ + isinθ)
x + by + cz + d = 0'
f(x) = 3x − 2
z = r (cosθ + isinθ)
ax + by + cz + d = 0
######
Manufacturing

####
Early warning to identify and manage product defects

Promptly identify and manage any problems in production batches.

x + by + cz + d = 0'
a (f(x + T) = f(x), ∀ x ∈ IR)
z = −1 + i
(f(−x) = f(x), ∀ x ∈ D)
−x + 2y + z + t = −1
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
[x0 −r, x0 + r] (cosnα + isinnα) = (cosα + isinα)n
######
Manufacturing

####
Remote monitoring of household appliances’ operation

Predict when a failure will occur and which component of the washing machine will fail.

nα = θ + 2πk
u = ρ (cosα + isinα)
a (f(x + T) = f(x), ∀ x ∈ IR)
(f(−x) = f(x), ∀ x ∈ D)
−x + 2y + z + t = −1
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
[x0 −r, x0 + r] (cosnα + isinnα) = (cosα + isinα)n
######
Manufacturing

####
Anomaly detection during flight tests

Identification and removal of anomalous data recorded by on-board sensors during prototype flight tests.

z = −1 + i
nα = θ + 2πk
u = ρ (cosα + isinα)
f(x) = 3x − 2
z = r (cosθ + isinθ)
ax + by + cz + d = 0
f(x) = 3x − 2'
2x + 2y + z + 4t = 0
######
Manufacturing

####
Real time monitoring of energy cables’ health status

Controlled, automatic and reliable detection of electrical cable anomalies.

z = r (cosθ + isinθ)
ax + by + cz + d = 0
a (f(x + T) = f(x), ∀ x ∈ IR)
(f(−x) = f(x), ∀ x ∈ D)
−x + 2y + z + t = −1
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
######
Manufacturing

####
A mathematical estimator for on commission valves

To a new request it is essential to be able to respond with an accurate estimate as quickly as possible.

z = r (cosθ + isinθ)
ax + by + cz + d = 0
a (f(x + T) = f(x), ∀ x ∈ IR)
(f(−x) = f(x), ∀ x ∈ D)
−x + 2y + z + t = −1
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
######
Manufacturing

####
Mathematical models to optimize process and product quality

Support engineers in monitoring, predicting and improving all aspects of the manufacturing process that affect the quality of a product.

f(x) = 3x − 2
z = r (cosθ + isinθ)
a (f(x + T) = f(x), ∀ x ∈ IR)
(f(−x) = f(x), ∀ x ∈ D)
−x + 2y + z + t = −1
2x + 2y + z + 4t = 0
[x0 −r, x0 + r] (cosnα + isinnα) = (cosα + isinα)n
######
Media & Publishing

####
Mobile advertising strategies

Deep profiling algorithm capable of classifying users with respect to their offline behaviors.

z = r (cosθ + isinθ)
ax + by + cz + d = 0
a (f(x + T) = f(x), ∀ x ∈ IR)
−x + 2y + z + t = −1
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
[x0 −r, x0 + r] (cosnα + isinnα) = (cosα + isinα)n
######
Media & Publishing

####
Mathematics to predict audience ratings

Predict the audience in the short term, in order to better plan schedules and purchases of audiovisual content.

z = −1 + i
nα = θ + 2πk
f(x) = 3x − 2
z = −1 + i
−x + 2y + z + t = −1
2x + 2y + z + 4t = 0
C = {z = x + iy x, y ∈ IR}
[x0 −r, x0 + r] (cosnα + isinnα) = (cosα + isinα)n
######
Media & Publishing

####
Support to the planning of a television schedule

Decision support tool for channel manager scheduling.

z = −1 + i
(f(−x) = f(x), ∀ x ∈ D)
u = ρ (cosα + isinα)
z = r (cosθ + isinθ)
ax + by + cz + d = 0
a (f(x + T) = f(x), ∀ x ∈ IR)
(f(−x) = f(x), ∀ x ∈ D)
C = {z = x + iy x, y ∈ IR}
######
Media & Publishing

####
Algorithms for school publishing

Support teachers in selecting and adopting the best textbooks for their students.

C = {z = x + iy x, y ∈ IR}
z = −1 + i
nα = θ + 2πk
u = ρ (cosα + isinα)
f(x) = 3x − 2
ax + by + cz + d = 0
[x0 −r, x0 + r] (cosnα + isinnα) = (cosα + isinα)n
######
Other

####
Allocation of products in the fashion & luxury goods sector

Automate and improve the process of allocating products in stock to retailers.

z = −1 + i
nα = θ + 2πk
u = ρ (cosα + isinα)
f(x) = 3x − 2
z = r (cosθ + isinθ)
ax + by + cz + d = 0
C = {z = x + iy x, y ∈ IR}
######
Other

####
Virtual coach: artificial intelligence on the bench

Suggest to the coach the best tactic to adopt, monitoring the opponents' play and their vulnerabilities.

z = −1 + i
nα = θ + 2πk
u = ρ (cosα + isinα)
z = r (cosθ + isinθ)
ax + by + cz + d = 0
−x + 2y + z + t = −1
C = {z = x + iy x, y ∈ IR}
######
Other

####
Mathematics applied to fundraising for a non-profit organization

A dynamic, fast and concise view of donations and fundraising campaigns through a web platform.

z = −1 + i
nα = θ + 2πk
u = ρ (cosα + isinα)
z = r (cosθ + isinθ)
ax + by + cz + d = 0
−x + 2y + z + t = −1
C = {z = x + iy x, y ∈ IR}
######
Other

####
Detection of abnormalities on signals from olfactory sensors

Machine learning systems to support farmers in the optimal management of hygiene in farms.

Personalized and dynamic estimate of the creditworthiness and expected life of potential customers.

Accurate forecasts that support operators in making effective decisions for managing electricity grid.

A tool that allows to have a complete view of customers.

Allowing customer service to take targeted actions to prevent and refine the offer.

Enhancement of forecasting tools for different market clusters.

An accurate measure of the return of online and offline advertising investments

Evolve and improve the performance of a FinTech algorithm.

Better plan your marketing campaigns in correspondence with the peak periods of influenza.

SmartAid Medical Solution, a digital platform based on artificial intelligence algorithms.

Improve the activities of building a pre-competitive scenario for tenders.

Algorithm to support surgeons in choosing the best prosthesis for each patient.

Automatically detect the presence of possible anomalies in medical imaging.

Standardize the design process and to be able to create the layout of a new plant in less time.

Create scheduling software for rapid and effective planning of processes.

Simulation algorithms that allow to test the various parameters and different configurations of the production machine.

Estimate the types and quantities of materials needed for a new project in a shorter time.

A unified approach to managing complexity.

Promptly identify and manage any problems in production batches.

Predict when a failure will occur and which component of the washing machine will fail.

Identification and removal of anomalous data recorded by on-board sensors during prototype flight tests.

Controlled, automatic and reliable detection of electrical cable anomalies.

To a new request it is essential to be able to respond with an accurate estimate as quickly as possible.

Support engineers in monitoring, predicting and improving all aspects of the manufacturing process that affect the quality of a product.

Deep profiling algorithm capable of classifying users with respect to their offline behaviors.

Predict the audience in the short term, in order to better plan schedules and purchases of audiovisual content.

Decision support tool for channel manager scheduling.

Support teachers in selecting and adopting the best textbooks for their students.

Automate and improve the process of allocating products in stock to retailers.

Suggest to the coach the best tactic to adopt, monitoring the opponents' play and their vulnerabilities.

A dynamic, fast and concise view of donations and fundraising campaigns through a web platform.

Machine learning systems to support farmers in the optimal management of hygiene in farms.