Artificial Intelligence (AI)

AI Technology & Applications

“Artificial intelligence was founded as an academic discipline in 1956” according to Wikipedia. In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Computer science defines AI research as the study of “intelligent agents“: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term “artificial intelligence” is used to describe machines that mimic “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

In recent times, Artificial Intelligence (AI) is the breakthrough technology. AI is being used in many sectors and has opened the doors of implementation of AI in many other emerging sectors. A few examples of the implementation of AI are self-driving cars, chatbots, robotics and image processing. AI is creating a significant impact. In the future, the AI implementation in various sectors will become seamless to our everyday lives.

AI will play a major role in shaping the growth of the core sectors in the countries where it is implemented more fully. We are adopting new technologies and experimenting with many new things for a positive outcome. AI technology will be the finest technology to give a boost to core sectors and help us for a faster digitization. Many sectors have been growing more significantly and contributing to the economy. By adding the technology in these core sectors the result would be more positive. The sectors like infrastructure, financial services, technology, automotive, education, entertainment and healthcare have been growing rapidly.

Our AI R&D Experience

KSE has a group of outstanding experts in AI who have PhD and Master degrees, as well as significant work experience in Industry. We provide R&D services and have experience in the following areas:

Domain & Problems

  • Domain Expert Systems:

    This consists of tasks that are based on learning multiple bodies of knowledge such as: financial, legal, etc. A process is then formulated where the machine will be able to simulate an expert in the given field.

  • Machine Learning & Recommendation Systems:

    In this case, the machine learns a complex body of knowledge i.e. information regarding existing medication, and then suggests new ideas to the domain itself, for instance new drugs for curing diseases.

  • Complex Planning:

    There are many logistics and scheduling projects which can be done by current (non AI) algorithms. But as optimization keeps developing and gets more complex , we apply AI to find heuristic ways to solve these complex planning problems.

  • Natural Language Processing:

    AI and deep learning can offer benefits to many communication modes such as intelligent agents and much more to solve the problems of text mining, text processing, and recommendation systems.

  • Pattern Recognition:

    This is the problem of image and form recognition, with text processing. Deep learning and AI can be capable of producing newer forms of perception which enable new services like autonomous automobiles and more.

Case Studies

  • Form OCR:

Hard-copy forms are used in a large variety of documents in daily life such as bills, cards, invoices, survey, etc. Digital form is a special representation of information in these documents. The form recognition task becomes essential in document analysis, which provides the recognition information for title, objectives, sender, destination, and handwriting parts in the documents.

KSE Solution: The KSE-developed framework is able to detect different components in a form and learn the content-based forms, then classify the pieces into predefined categories. The framework is integrated with a supervised learning system for recognition and an OCR system for detecting text.

  • Recommendation Systems:

Currently, due to the development of e-commerce along with the rapid increase in collection of transaction data and customer procurement data, the issue of customer segmentation is very promising in businesses, especially for large-scale retailers. Customer segmentation or grouping helps to achieve success in marketing, product promotion and understanding the needs of customers.

KSE Solution: The system developed by KSE aims to learn the customer behaviour and groups customers according to certain patterns. The system includes an unsupervised learning approach that helps in detecting customer communities and finding a suitable community for new customers.

  • Traffic Trajectory Analysis:

With the development of society , the number of transport vehicles grows and causes both economic and environmental damage. Due to this, a good management for transport is in high demand. Research is ongoing to find a good method to study the behavior of movement based on the trajectory data. The data is generally provided by GPS. Data Mining and Machine Learning can be taken into account to study moving patterns.

KSE Solution: We provide a solution for trajectory analysis which is combined between an interactive visualization and a dynamic structure for learning trajectories. The system can rank and determine moving behaviour of studied objects.

  • Trend and Sentiment Analysis:

Millions of users and reporters around the world write news, articles, and comments to express their opinions on various subjects, such as reviews on consumer products and movies, news, politics, etc. on social media website such as Twitter and Facebook. Textual content analysis may enable early detection of emerging issues, topics, and trends in areas of interest. An emerging trend is a topic area that is growing in interest and utility over time on social media sites.

KSE Solution: A framework proposed by KSE is used to identify discussed topics and users’ sentiments which are growing in importance within a specific span of time, then to study the evolution of topics over time. Trend and sentiment analysis in any domain is essential for companies, governments, and society so that they can identify which issues the public is having problems with and to develop strategies to remedy them. Extracting useful information on a particular time series and making it possible to forecast future events is in high demand.

Technologies & Tools

  • Machine Learning, Deep Learning, Neural Networks (NN)
  • Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)
  • Pattern Recognition, Optical Character Recognition (OCR)
  • Text Mining, NLP, FastText, Word2Vec, Semantic Models
  • Transformer Models, Attention Mechanism, ELMo, BERT
  • TensorFlow, Numpy, Torch, Caffe, Theano
  • Scikit-learn, Django, Flask, Apache Mahout
  • Tesseract OCR, CNN, Keras, Amazon Machine Learning
  • Python, Matlab/Simulink, R, C/C++, Java, Hadoop