Arificial Intelligence

00 INTRODUCTION

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.

01 Approach

Deep Learning - Image Recognition with OvesEnterprise

  • Setting the goal: what do you intend to do with the image-data?
  • Setting the project specific parameters
  • Establishing the type of data that the project will work with. Are there existing data bases? Should new databases be created? If yes, who, how and when?
  • Problem assessment: image classification, image detection or image segmentation?
  • Evaluating the methods of classification.
  • Developing the deep learning algorithms.
  • Analysis of alternatives.
  • The software development itself includes data processing, learning algorithms and quality assessment.
  • The best model goes into iterations and the process can go for further improvements and optimizations.
  • In the end, the AI software may be integrated in a larger software ecosystem where it should play its specific role.
AI applications

Artificial intelligence has made its way into a number of areas. Here are some examples.

  • In agriculture, for example there are complex data to be harvested and interpreted in order to analyze and predict environmental impact related to livestock issues, land and water issues, and the use of pesticides, climate change and sustainability.
  • Forestry related issues contour the dilemmas of forest conservation and globalization, forest mechanization and genetic diversity. AI software development from Romania in the field of forestry may benefit of the existing specialists in the field and the data repositories.
  • Mapping seen form the deep learning / image recognition perspective involves automated map building by robots or other systems, route or path planning and navigation scenarios, predictive routing issues, flood mappings and other natural phenomena mapping.
  • Healthcare is ever more complex as it involves so many disciplines. The problems defined in healthcare cover medical imagery to epidemic monitoring across geographical areas.
  • Insurance sector is based on the predictive modelling for various fields of current life: health issues, real estate developments, crops and livestock, industrial output, extraordinary events.
  • Energy and infrastructure mainly deal to users’ behavior and consumption, to clean energy sources availability and real-time adjustments. Observation of the state of infrastructure with imagery is also a resourceful tool for learning and predicting changes.
  • Business intelligence makes use of machine learning systems for data-driven decision making, for business dashboard, sales enablement and business insights.
  • Defense: we speak here about pattern recognition systems, unmanned vehicles and cyber-security. Image recognition technologies plays a great role in the overall defense artificial intelligence deployment as it implies that men stay safe while explorer devices go into field operations.
  • Social Impact is an ever-growing arena for all type of technologies, including the rising AI. With artificial intelligence systems and tools, we will be able to monitor and predict the impact on society of various demographic, economic and political phenomena and particular events.
Areas of Progress:

Progress with AI software development has been achieved in particular areas like: features extraction and measurements from intensity images, pattern recognition, Image texture detection, automatic extraction of semantic information, camera calibration, binocular and trinocular stereovision, stereovision for mobile robots and automotive applications.

In the field of Driving Assistance Systems and Autonomous Driving progress has been noted for 3D lane detection, 3D objects detection and tracking, 3D objects classification, pedestrian detection, 3D structured/unstructured environment modeling.

In the area of medical image processing the works highlight advances in texture based detection and classification of diffuse and focal illness from Ultrasound Images, Structured reporting for medical images, DICOM infrastructure implementation.

05THE RESULTS

We measured the following results

  • 80 %
    Less misfuels

    Which means much lower fuel costs and, of course, much less frustration on all sides.

  • 80 %
    Less delays

    And that means a big cost reduction, since we all know delays cost a lot of money.

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