RenAIssance Team to Present in Sweden Disruptive Innovations in IoT Devices and 5G Technology with Cloud-Based, Medical IP-Rich AI Platform to Deliver High Quality Healthcare Services ��

Khalifa University of Science and Technology has announced RenAIssance, a team of researchers and collaborators, has won the grand prize at Ericsson’s ‘UAE Together Apart Hackathon’ for its solution RenAIssance. The grand prize�� includes a fully-paid visit to showcase the solution at Ericsson’s Headquarters and engage with the entrepreneurial community in Stockholm, Sweden.��

The Hackathon, organized under the patronage of the UAE Ministry of Economy, is inspired by the UAE Vision 2021 objectives, and aims to accelerate the journey towards a more connected future and solving global challenges.��

The RenAIssance team includes Dr. Mecit Can Emre Simsekler, Assistant Professor, Industrial and Systems Engineering, Khalifa University; Dr. Siddiq Anwar, Physician, Sheikh Shakhbout Medical City and Adjunct Associate Professor at Khalifa University College of Medicine and Health Sciences, Khalifa University alumni and Engineering Systems and Management Master’s graduate Himanshu Upadhyay, and Dr. Mohammad Yaqub, Assistant Professor, Mohamed bin Zayed University of Artificial Intelligence.��

RenAIssance re-imagines a world where high-quality medical care can be consistently provided to improve healthcare outcomes across the globe. RenAIssance endeavors to provide risk-based decision-making tools to healthcare providers looking after patients suffering from kidney disease by leveraging its cutting-edge AI platform. It integrates disruptive technologies and innovations in medical IoT devices and 5G technology with its cloud-based, medical intellectual property-rich AI platform to deliver its services.

Clarence Michael
English Editor Specialist
8 April 2022

The post Team of Khalifa University Researchers and Collaborators Wins ‘UAE Together Apart Hackathon’ Grand Prize to Visit Ericsson’s Headquarters in Sweden appeared first on Khalifa University.

Partnership to Leverage Each Other’s Outstanding Assets and Technical Skills ��

Khalifa University of Science and Technology and AIQ, Abu Dhabi National Oil Company’s (ADNOC) artificial intelligence (AI) joint venture with Group 42 (G42), have signed a research and development framework agreement to leverage each other’s outstanding assets and technical skills to jointly develop digital innovations for the energy sector.

The agreement, which was signed by Dr. Arif Sultan Al Hammadi, Executive Vice-President, Khalifa University, and Omar Al Marzooqi, Chief Executive Officer, AIQ will see the two partners pursuing cutting-edge digital research that yields value generation for the energy sector. Artificial intelligence, particularly machine learning, will play a key role in the collaboration.��

Dr. Arif Sultan Al Hammadi said: “As a top-ranked research-oriented university, specializing in most of the advanced technology areas, Khalifa University is delighted to partner with AIQ and establish the right platform that will facilitate innovation in the energy sector. This collaboration will bring digital solutions, particularly applied artificial intelligence, to one of the UAE’s vital economic sectors.”

Omar Al Marzooqi said: “We are delighted to partner with Khalifa University, a world-class, research-intensive institution, to drive the creation of future transformative advanced technologies for the energy sector and further strengthen the position of Abu Dhabi and the UAE as an international hub for AI and advanced technology.”

Khalifa University has established research capabilities in all areas of digital technology as well as renewable energy, oil and gas. The university leads several research projects that apply artificial intelligence techniques for industrial applications. Khalifa University’s Robotics and Intelligent Systems Institute brings together the university’s research in robotics, artificial intelligence, data science, next-gen networks, semiconductor technologies and cybersecurity under a single umbrella for application in key sectors, such as energy.

At the same time, Khalifa University’s Petroleum Institute is a research institute dedicated to obtaining new technologies and solutions in hydrocarbon exploration and production. The Institute houses two research centers – the Center of Catalysis and Separation (CeCaS) and the Research and Innovation Center on carbon dioxide and hydrogen (RICH Center) – driving energy innovation to maintain the UAE’s position at the forefront of the energy industry.

AIQ focuses on developing and commercializing artificial intelligence products and applications for the energy industry, and accelerating industry adoption of advanced technologies in the UAE. AIQ is working on a number of key AI projects across the oil and gas value chain such as drilling performance, reservoir modelling, corrosion detection, and product quality.

Clarence Michael
English Editor Specialist
31 January 2022

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Project Framework to Integrate Computer Vision, Machine Learning, and Expert Systems, Capable of Accurately Locating Markings and Symbols on Aircraft ��

Khalifa University and Lockheed Martin today announced the signing of an agreement to create a framework to utilize Artificial Intelligence (AI) and computer vision to inspect and verify measurements, in order to improve the speed and accuracy of logo inspections during aircraft production cycle.��

The agreement was executed by Dr. Arif Sultan Al Hammadi, Executive Vice-President, Khalifa University, and Dr. Steve Walker, Vice President and Chief Technology Officer, Lockheed Martin, at Dubai Airshow 2021, held from 14-18 November 2021 at DWC.��

Lockheed Martin and Khalifa University will collaborate on a Computer Vision & Pattern Recognition (CVPR) project using AI reinforcement learning and training sets. CVPR techniques have become more ubiquitous as computer hardware and software techniques have improved over the past 20 years. CVPR has been applied to biometrics, target identification, aimpoint refinement and automated control. It is of interest to Lockheed Martin for use across all domains.��

“Khalifa University’s collaboration agreement with Lockheed Martin signifies our status not only as a top-ranked research-intensive university but also as a center for academic and innovation excellence in advanced technologies such as machine learning and augmented reality systems through our research centers,” said Dr. Arif Sultan Al Hammadi. “We believe this partnership will lead to an outcome that will benefit industry stakeholders while ushering in quality systems to assist professionals in the global aviation sector.”��

“We are excited by the opportunity to collaborate with Khalifa University on this AI project based on the Memorandum of Understanding we signed last year,” said Dr. Steven Walker. “Khalifa University is a leading research institute in the region and the results will be applicable to the production line and a variety of technology developments efforts across space, air, land, sea, underwater and cyber.”��

This effort will research techniques to improve the ability of CVPR to accurately register standard symbology in imagery. The techniques developed can then be applied to any product that is attempting to accurately locate markings and symbols.��

Dr. Naoufel Werghi, Professor of Electrical Engineering and Computer Sciences, Khalifa University, will be the Principal Investigator, while Dr. Ernesto Damiani, Director, Research Center on Cyber-Physical Systems (C2PS), will be the Co-PI.

Clarence Michael
English Editor Specialist
12 December 2021

The post Khalifa University and Lockheed Martin to Collaborate on Image Analysis System for Aircraft appeared first on Khalifa University.

Teaching robots to follow a moving object is more difficult than you think, requiring complex algorithms and a different way of thinking.

Take a look around. What do you see? Most of us have two eyes and we use those eyes to collect light that reflects off the objects around us. The eyes convert that light into electrical signals that are processed by our brain. This builds a representation of the world and we use that to navigate during our everyday lives. Even robots that are the most like humans in appearance, however, don’t see the world the way we do.

Instead, algorithms recognize features in images collected by a robot’s sensors and cameras. The software may create a very basic map of the environment and learn to recognize patterns to help the robot understand its surroundings. This means that robots are being programmed by humans to see things the human thinks the robot will need to see. While this has many very successful examples, no robot is capable of navigating the world using just vision for static recognition.

If you spot a bird outside, you can watch that bird fly through the sky until it lands or disappears from view. This is visual object tracking, and it’s a simple task for humans: spot the object and follow it. For robots, it’s much more difficult.

To improve visual object tracking in robotic applications, Dr. Sajid Javed, Assistant Professor, Dr. Jorge Dias, Professor, Dr. Lakmal Seneviratne, Professor, and Dr. Naoufel Werghi, Professor, all from the Department of Electrical Engineering and Computer Science at Khalifa University, collaborated with Dr. Arif Mahmood from Information Technology University, Pakistan, to develop an AI algorithm that is both highly accurate and quick when detecting and tracking a generic object. Their proposed solution was published in.

“Visual object tracking is a fundamental and challenging task in many high-level vision and robotics applications,” Dr. Javed explains. “Typically, the difficulties lie in developing detection algorithms that can handle blurred images from fast motion, ignore background clutter and deal with significant scale and light variations.”

Object tracking is an application of deep learning where a program takes an initial set of object detections and follows them as they move around frames in a video. The algorithms allow the robot to automatically identify an object in a video and interpret it as a set of trajectories to predict where it will end up.

The first step in tracking an object is to detect it. The research team’s solution narrows a search area down and instructs the robot to find all object instances of one or more pre-determined object classes. The algorithm is trained on a series of examples of these object classes to learn what it is looking for, regardless of the object’s scale, location or pose and despite any partial occlusions or poor lighting conditions.

Once the object has been identified, it needs to be followed. Robots can do this by continuously re-identifying the object in subsequent images, but for visual object tracking to be useful in robotic applications, interpreting the object as a set of trajectories with high accuracy is required.

Algorithms for tracking objects need to accurately perform detections and localize objects of interest in the least amount of time possible. This is especially imperative for real-time object tracking models.

“Discriminative correlation filters (DCF) are well suited to object tracking because of their impressive performance in terms of speed and accuracy,” Dr. Javed says. “In most DCF methods, an online correlation filter is trained from the region of interest in the current frame and then employed to track the target object in subsequent frames.”

High detection accuracy and fast processing speed are difficult to combine: More accurate tracking tasks often require longer processing times, while quicker responses are more prone to errors. In the research team’s solution, accuracy and speed are achieved by constructing a spatiotemporal graph that models and predicts where an object is likely to appear based on its previous identified location. Out of a series of possible trajectories, the most probable is selected by the DCF, which filters the background noise and any other distractions.

To evaluate their algorithm, the team tested it on six challenging benchmark datasets and compared it with 33 existing state-of-the-art trackers. Their results were excellent, achieving higher accuracy than existing trackers on many tests and ranking among the top three for the remaining tests.

As mobile robots and autonomous machines are increasingly deployed, object detection systems are becoming more important. Although great progress is being made, we are still far from achieving human-level performance, but solutions like this are a vital step towards that level of performance.��

Jade Sterling
Science Writer
28 November 2021

The post A New Solution for Visual Object Tracking in Robotics appeared first on Khalifa University.

Khalifa University of Science and Technology has announced it has won the right to host the Middle East’s first-ever IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), one of the world’s largest and most impactful robotics research conferences, in Abu Dhabi in 2024.

The Khalifa University bid, submitted in collaboration with the Abu Dhabi Tourism and Culture Authority and the UAE Ministry of Education, won against global competition from other leading international universities. Abu Dhabi’s status as a major conference destination, the emirate’s focus on supporting new technologies such as robotics and artificial intelligence systems, Khalifa University’s Robotics and Intelligent Systems Institute, and the Center for Autonomous Robotic Systems (KU-CARS), played a significant role in clinching the deal in favor of the university.

Dr Arif Sultan Al Hammadi, Executive Vice-President, Khalifa University of Science and Technology, said: “Following the successful hosting of the MBZIRC 2020 that brought international acclaim to Abu Dhabi, we feel privileged to have won the international bidding now in collaboration with other UAE Government institutions to bring IROS, one of the world’s largest and most impactful robotics research conferences, not only to Abu Dhabi but to the Middle East, for the first time. The IROS 2024 conference will help portray the UAE’s readiness to accept modern technologies such as artificial intelligence and Khalifa University’s involvement in furthering scientific innovation in this area. We believe the conference will substantially intensify adoption of robotic systems, machine learning and smart devices, while highlighting the advances made in this sector.”

Khalifa University continues to play an undeniably critical role in the advancement of robotics and machine intelligence technologies. In addition to launching the Robotics and Intelligent Systems Institute, the university also plans to offer new academic programs in Robotics and Artificial Intelligence.

Moreover, the university’s flagship KU-CARS has over 50 faculty and staff working on several diverse projects, while researchers are conducting groundbreaking research to discover new ways to advance robotics for extreme environments, industrial applications, and infrastructure inspection. KU-CARS also has state-of-the-art labs including a new Marine Robotics Pool and an Autonomous Car Lab. In addition to KU-CARS, other Khalifa University research centers that deploy intelligent systems include the Aerospace and Research Innovation Center (ARIC), and EBTIC.

Khalifa University’s initiatives in intelligence systems and robotics are in line with the UAE’s drive towards advancing the tide of progress in AI, deep learning and automation. The UAE is already exploring how public sector entities can leverage AI solutions in areas including cyber security, to detect and monitor malicious activity; behavioral analysis to aid police by predicting crimes; monitoring economic growth through time lapse satellite imagery; predicting climate change; and improving doctors’ abilities to provide accurate diagnoses.

Clarence Michael
English Editor Specialist
3 November 2021

The post Khalifa University Wins International Bid to Bring Prestigious IEEE Intelligent Robots and Systems (IROS) Conference to Abu Dhabi in 2024 appeared first on Khalifa University.

When data is sparse and medical knowledge of a disease is limited, combining modelling approaches can lead to more accurate predictions of clinical outcomes

Big data in healthcare is nothing new. Hospital records, medical records, results of medical examinations and biomedical research generate vast quantities of information that need to be handled carefully and accurately.

Sometimes, clinicians and researchers don’t understand how a disease progresses or the biochemical mechanisms behind a disease. Other times the data available is sparse because it depends on when the patient physically attends a clinic or appointment.

In the first case, the predictive accuracy of clinical outcomes for any mathematical model is limited as the underlying biological mechanisms are only partly understood. Machine learning techniques do not require knowledge of the underlying interactions in biomedical problems but infrequent data does impact their use, restraining any algorithm from accurately inferring the corresponding disease dynamics.����

Combining the two approaches could be the solution. Dr. Haralampos Hatzikirou, Associate Professor of Mathematics at Khalifa University, proposed a method to improve individualized predictions in cancer patients based on the Bayesian coupling of mathematical modelling and machine learning. This approach was tested on a simulated dataset for brain tumor patients and on two real cohorts of patients with leukemia and ovarian cancer. The results were closely aligned with the actual clinical data for individual patients, suggesting its potential use in enabling accurate personalized clinical predictions in healthcare.

Dr. Hatzikirou worked with Pietro Mascheroni, Michael Meyer-Hermann and Juan Carlos Lopez Alfonso from Braunschweig Integrated Center of Systems Biology and Helmholtz Centre for Infectious Research, Germany, and Dr. Symeon Savvopoulos, Mathematics Post-doctoral Fellow at Khalifa University.

The results were published in

In oncology, this clinical data is the cornerstone of providing personalized healthcare to the patient, but using the data is more challenging.

“Although mathematical models can be extremely powerful in proposing biological hypotheses, they require adequate knowledge of the underlying biological mechanisms of the analyzed system,” Dr. Hatzikirou explained. “Typically, this knowledge is not complete and we only know a limited amount of mechanistic interactions, such as molecular pathways, seen in cancer. Therefore, even though mathematical models provide a good description of an idealized version of what’s going on in cancer dynamics, they can’t always allow for accurate and quantitative predictions.

“On the other hand, machine learning techniques can handle the inherent complexity of biomedical problems. While mathematical models rely on causality, statistical learning methods identify correlations among data so they can systematically process large amounts of data and infer hidden data patterns. However, the data for each patient is limited to being collected whenever a patient is in the hospital or clinic. This doesn’t provide the algorithm with enough data to make meaningful individualized inferences.”

Dr. Hatzikirou and the research team proposed the first Bayesian method that combines the two techniques.

Bayes’ Theorem deals with probabilities based on prior experiences. These priors provide some information but once there is more data, the priors can be updated. It’s the law of probability governing the strength of evidence, saying how much to revise our probabilities when we observe new evidence. For example, if you know your patient has a positive cancer test result, and that’s all you know, you can look at how many people with a positive test result actually have cancer, and that is input to determining the probability that your patient has cancer.

The team first tested their approach on a simulated dataset of 500 virtual patients with brain cancer. Their model accounted for oxygen consumption by tumor cells, formation of new blood vessels due to the cancer spreading, and detecting the compression of other blood vessels by tumors growing and squashing them. For this demonstration, the team considered the tumor cell density to be the ‘modelable’ variable and the other variables as ‘unmodelable’ to represent the unknown mechanisms of disease progression. In the simulation, each patient attended appointments over a three-year period to serve as the sparse data collection opportunities.

First, a mathematical model for brain tumor growth was simulated, then a machine learning model, before finally the combined approach for comparison against the two individual models. The team found that their combined approach performs particularly well for prediction times larger than six months.

“Our method was able to correct the mathematical model predictions for most of the patients, particularly at later times,” Dr. Savvopoulos said. “We then tested our method on two cohorts of real life patients, using their data to more carefully test the effects of ‘unmodelable’ variables, or those unknowns. We used clinical datasets from patients with leukemia and patients with ovarian cancer.”

“Our proposed method aspires to solve a dire problem in personalized medicine that is related to the limited time-points of patient data collection and limited knowledge in cancer biology,” Dr. Hatzikirou said. “In all our tests, we found our model had excellent predictive capacity, but we did recognize some limitations that should be addressed when applying the methodology to real cases.”

Most importantly, this new combined approach is not restricted to Oncology. Most applications of clinical predictions concern data that is heterogeneous and sparse and there are always unknowns in our knowledge of disease mechanisms.

Jade Sterling
Science Writer
24 October 2021

The post Combining Mathematical Modeling and Machine Learning to Better Predict Tumor Growth appeared first on Khalifa University.

Reward Workshop ‘CliftonStrengths Assessment’ Helps Winning YFEL Members to Dive Deep into Their Own Talents and Strengths

Khalifa University of Science and Technology today announced 10 members of Young Future Energy Leaders (YFEL) outreach program were honored at a special reward workshop for winning the 2021 YFEL Case Study Competition on ‘AI for Sustainable Farming’, conducted by the Emirates ICT Innovation Center (EBTIC).��

The 10 winning YFEL members included three UAE national students, one international student from Khalifa University, one young professional from Dubai Municipality, and five international members from Mexico, Colombia, the US, China, and Hong Kong.��

Dr. Arif Sultan Al Hammadi, Executive Vice-President, Khalifa University, said: “Our outreach programs and sustainability initiatives are designed to encourage wider participation by youth and professionals. We are grateful to BASF, partner of YFEL, for organizing CliftonStrengths, the reward workshop, and helping the YFEL winners to explore and augment their personal skills and talents. Also, EBTIC, one of our research centers, has played a commendable role in conducting this case study competition on machine intelligence in farming. We thank the partners and hope the 2021 YFEL members will continue with their progress in obtaining sustainable innovations as laid down by the UAE leadership.”��

The winners of YFEL Case Study Competition were evaluated by an elite panel of judges including Dr. Nawaf I. Almoosa, Director of EBTIC and Head of Smart Infrastructure Research; and Assistant Professor, Electrical Engineering and Computer Science, Khalifa University; Dr. Dymitr Ruta and Dr. Kin Danny Poon, Chief Researchers, EBTIC-Research.��

Dr. Ruta and Dr. Poon introduced the case study topic to the YFEL members, and engaged them in group discussions. The YFEL members were given six days after the initial introduction to find solutions and create innovative ideas. The winners were assessed by the judges for novelty, strength and robustness of the design, feasibility of the implementation plan and the analysis-backed efficiencies delivered to the farming industry. All indoor and outdoor solutions were accepted for evaluation, especially those energy-efficient sustainable solutions suitable for the arid climate in the Middle East.��

Industry studies indicate AI can provide farmers with real-time insights from their fields, allowing them to identify areas that need irrigation, fertilization, or pesticide treatment. Also, innovative farming practices like vertical agriculture may help increase food production while minimizing the use of resources.��

The 2021 YFEL Case Study Competition aimed to design a novel solution, system or process within a broad-minded smart farming domain that leverages technologies in artificial intelligence/machine learning/internet of things to provide significant measurable benefits compared to the equivalent currently used farming practices.��

BASF, world’s largest chemical company and YFEL program partner for over four years, organized the reward workshop ‘CliftonStrengths assessment’. CliftonStrengths, a web-based assessment of normal personality from the perspective of Positive Psychology, helps individuals to dive deep into their own talents and strengths. Developed expressly for the Internet, this tool helps explore what makes a person unique and the value this person can bring to a team or the workplace.��

EBTIC is a research and innovation center founded by Khalifa University, Etisalat and BT (British Telecom), and supported by the Telecommunication and UAE Digital Government Authority’s (TDRA) ICT Fund.��

Clarence Michael
English Editor Specialist
30 August 2021

The post BASF Honors 10 Winners of Khalifa University’s YFEL Case Study Competition on ‘AI for Sustainable Farming’ Organized by EBTIC appeared first on Khalifa University.

EBTIC and BT Team Win International Awards Based on Innovation, Relevance and Evidence of Success, for their SPL Solution and Trial ��

The SPL team led by Salwa Alzahmi, a senior researcher at KU’s Emirates ICT Innovation Center (EBTIC), has won two awards in two categories at the prestigious 2021 Artificial Intelligence (AI) and Machine Learning Awards in the UK.��

EBTIC is a research and innovation center focused on driving research and innovation in intelligent systems and applications founded by Khalifa University, Etisalat and BT (British Telecom), and supported by the Telecommunication and Digital Government Authority’s (TDRA) ICT Fund. It is based at the Khalifa University campus in Abu Dhabi.��

The Computing magazine’s AI and Machine Learning Awards recognise the best companies, individuals, and projects in artificial intelligence. The judging process covers several industry segments such as security, ethics, data analysis, and innovation, while showcasing the movers and shakers including the technology heroes and projects that deserve industry-wide praise.��

The awards won by EBTIC’s SPL were in the categories of Best Cloud Automation Tool and Best Use of Automation. An elite panel of 12 judges selected the SPL entries as winners based on the evidence of three factors – innovation, relevance, and achieved success. More specifically, the Best Cloud Automation Tool award was given to EBTIC and BT for the tool’s innovative and fully featured automation solution that is designed to ease the burden of application migration to the Cloud. In their citation, the judges mentioned that the SPL Tool ‘has the opportunity to hugely reduce code complexity, thus improving security and reducing the cost of cloud migration’.��

In the Best Use of Automation award category, the SPL tool was highly recommended for the automation of migrating legacy software systems to cloud-native architecture using artificial intelligence, hence, addressing the challenges of complexity and high associated costs. The judges’ citation mentioned that the SPL team is highly recommended for this category for its ‘SPL project that has a strong topical story and told it well’.��

Dr. Nawaf I. Almoosa, Acting Director, Head of Smart Infrastructure Research, EBTIC, and Associate Professor, Electrical Engineering and Computer Science, Khalifa University, said: “Achieving this success in the 2021 Artificial Intelligence and Machine Learning Awards across two categories strongly indicates the extent of cutting-edge technological innovation being produced by the EBTIC’s SPL team productive partnership with BT to accurately target the future requirements of the industry in these areas. Our team members have demonstrated their talent in another prestigious international competition, and we hope to continue exploring innovation through working on new projects in AI and ML-related areas.”

SPL is a platform developed at EBTIC and uses intelligent methods to simplify and streamline software governance, development, and migration to the Cloud. The technology is currently undergoing commercialisation as a start-up in collaboration with Khalifa Innovation Center (KIC). Salwa Alzahmi, an Emirati researcher and tech entrepreneur, is leading the SPL platform development and commercialization, and has been instrumental in driving the technology, its commercialisation and the collaboration with BT Applied Research to evaluate the tool’s business value. As part of the drive for commercialisation, Alzahmi has set up a UAE-based tech start-up in the Khalifa Innovation Center – KIC khalifainnovation.ac.ae that is registered at Abu Dhabi Global Market. The EBTIC-SPL research team also includes Ahmed Suliman, Corrado Mio and Sid Shakya.

At the Computing AI and Machine Learning Awards 2021 in London, one of the UK’s leading business technology information resource, EBTICs SPL won awards for “Best Cloud or Networking Automation Tool” and was highly commended in the category of “Best Use of Automation”

The judges commented that SPL, in winning the “Best Cloud or Networking Automation Tool” category “has the opportunity to hugely reduce code complexity, thus improving security and reducing the cost of cloud migration.”

Alzahmi, Senior Research at EBTIC who leads the SPL team, said “I’m so proud of my team to be recognised in such a fantastic way. It is testament to all the hard work that has been put in to make the SPL tool such a compelling solution.”

Dr. Nawaf Almoosa, Acting Director of EBTIC, said: “EBTIC has a history of innovating practical AI solutions and have been a leader in intellectual property generation in UAE for the past 10 years. These awards are a further testament to EBTIC’s expertise of applying AI techniques to the operations of its partner organizations, and to their effort to promote Intelligent Systems research in the UAE and the region. We are immensely proud for the SPL team to be winning such prestigious awards, and we have high hopes for the future of SPL. I’d like to thank the SPL Team: Salwa Alzahmi, Corrado Mio, Ahmad Suliman, and Sid Shakya for this great achievement.”

Computing’s AI & Machine Learning Awards recognise the best companies, individuals, and projects in the AI space today. The judging process covers every corner of the industry: security, ethics, data analysis, innovation and more, as well as showcasing the movers and shakers: the technology heroes and projects that deserve industry-wide praise. With judges from prestigious organisations, such as Expedia Group, Network Rail, MET Office, London Stock Exchange and Computing itself, the winning of these awards proves that SPL demonstrates an outstanding, innovative approach in the field of AI.

EBTIC spun-out SPL.Co Ltd as a start-up in its own right, incorporated in 2019 and registered at Abu Dhabi Global Market. It came about following the success of SPL as a research project in 2015. SPL.Co provides corporations with an intelligent software modernization solution to accelerate and reduce the high costs associated with upgrading software systems into a cloud infrastructure.

EBTIC has a very successful history with Computing awards. In 2019, EBTIC’s spares optimization project Inuitu, won two awards for ‘Outstanding AI/Machine Learning Project’ and ‘Most Innovative use of AI/Machine Learning’.

EBTIC has produced more than 500 scientific publications, developed more than 75 inventions, resulting in 60 granted patents, with more pending, and has trained more than 350 students,�� and 250 professionals. It has also organized 10 international technical workshops and developed numerous technologies and worked on many projects in collaboration with its partners and stakeholders.

Clarence Michael
English Editor Specialist
10 August 2021

The post EBTIC’s SPL Team Wins Two Artificial Intelligence and Machine Learning Awards in UK appeared first on Khalifa University.

The features of a cell nucleus can reveal much about the health of a cell, but finding the nucleus among the background noise of a tissue sample can be laborious and time-consuming when done manually. Khalifa University researchers are exploring how AI can be leveraged to speed up the process of detecting a cell’s nucleus.

Known as the cell’s ‘command center’, the nucleus is a large organelle that stores the cell’s DNA. The nucleus controls all of the cell’s activities, such as growth and metabolism, using the DNA’s genetic information.

Pathologists use features of the cell nucleus to distinguish benign from malignant cells. They examine tissue samples for cells with increased nuclear size and irregularities in the nuclear membrane or abnormal distribution of chromatin, a substance within the chromosomes in the nucleus.

Artificial intelligence techniques are being investigated to streamline the process of nuclei detection. However, these techniques struggle when the tissue samples are noisy or when nuclei appear crowded.

Dr. Naoufel Werghi, Associate Professor, has collaborated with Dr. Sajid Javed, Assistant Professor, and Dr. Jorge Dias, Professor, to develop a machine learning solution that can sift through the background noise and more easily identify individual nuclei. Their results were published in.

In an ideal world, a sample of tissue would contain only that tissue, but most samples contain a rich mix of several other types. Most normal epithelial cells have nuclei which are round to oval-shaped, most lymphoid cells have round nuclei, while stromal cells have ovoid to spindle-shaped nuclei. Any machine learning algorithm would need to be trained on the specific nucleus shape for the nucleus it is tasked to detect, adding a further layer of training required if the algorithm were to be used in other applications.

“Nucleus detection is a challenging task because of the nuclear clutter and diverse shapes and sizes,” said Dr. Werghi. “Additionally, computation challenges arise because the images analyzed are multi-gigapixel images, and could contain billions of pixels and tens of thousands of cell nuclei.”

A number of potential methods have been reported for automatic detection of cell nuclei, including deep learning methods to train a convolutional neural network (CNN) to generate probability maps of where the cell nuclei are present.

Existing approaches are promising, but they require a significant amount of training data and expensive platforms to cater to the high computational requirements. The model proposed by the research team can be trained using much smaller training datasets that can be executed on a typical desktop computer.

“Our solution uses correlation filters to sort through the data and identify nuclei,” explained Dr. Werghi. “Compared to end-to-end deep learning in previous methods, correlation filters are computationally effective and require significantly less training data. The correlation filters are also flexible and can detect complex and irregular-shaped nuclei without requiring handcrafted features.”

These correlation filters help the algorithm to better discriminate different nuclear components from the non-nuclear regions and also to discern each nucleus from the remaining nuclei where they are clustered.

Constraints are placed on the spatial structure of the nucleus and its local contextual information in the correlation filter framework to handle varying nuclei shapes, texture, and clutter. The first considers the spatial structure of the nucleus, while the second discriminates between the nucleus and the non-nucleus region. Both of these help to reduce the noise in any given sample.

The team plans to explore the strength of correlation filters in analysis classifying cell nuclei and tissue phenotyping problems, such as cancer detection.

This research was funded by Khalifa University Center for Autonomous Robotic Systems (KUCARS).

Jade Sterling
Science Writer
5 July 2021

The post Sifting Through the Noise to Find the Nucleus with Artificial Intelligence appeared first on Khalifa University.

Khalifa University researchers find a way to use convolutional neural networks to identify cancer in tissue samples, which could speed up diagnosis and improve outcomes in patients with colorectal cancer.

Colorectal cancer is the third most common cancer among men and women worldwide, and the second most common cause of cancer-related mortality. Most colorectal cancers are due to old age and lifestyle factors, with only a small number of cases due to underlying genetic disorders. It typically starts as a benign tumor, such as a polyp, which over time becomes cancerous. Like all forms of cancer, early diagnosis and differentiation of the tumor are crucial for a patient’s survival and wellbeing.

Colorectal cancer may be diagnosed by obtaining a sample of the colon and using histopathology – the study of changes in tissues caused by diseases – to determine the characteristics of the tumour tissue at the microscopic level.

Histology is the study of the microanatomy of cells, tissues and organs as seen through a microscope. The structure of each tissue in the body is directly related to its function and diseases affect tissues in distinctive ways. Studying the histology of a tissue can be very useful in making a diagnosis and determining the severity and progress of a condition.

Because of the great variety of tests that are available, and the high level of skill needed to carry out and interpret them, researchers are beginning to turn to computational pathology and artificial intelligence techniques to identify in tissue samples diseases like cancer.

Dr. Sajid Javed, Assistant Professor, and Dr. Naoufel Werghi, Associate Professor of Electrical Engineering and Computer Science, have collaborated with researchers from around the world to develop algorithms to identify samples of colorectal cancer tissue. A paper based on this research has been published in.

“Computational pathology is a fast-growing research area in cancer diagnosis and can play an instrumental role in helping medical professionals detect and classify tumors,” said Dr. Javed.

Cancer histology reveals underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes.

The phenotype is the set of observable characteristics or traits of an organism or a tissue. Image-based phenotyping aims to develop the computer vision techniques and tools needed to recover quantitative data from a wide range of images. But phenotyping presents challenging problems, particularly in images from colorectal cancer tissues.

Aided by advances in slide scanning microscopes and computing, convolutional neural networks (CNNs) have emerged as an important image analysis tool. CNNs use a network of interconnected layers of filters that highlight important patterns in the images and can continue to learn from previous results.

“Manual examination of tissue samples is time-consuming, highly subjective, and often affected by the observer,” explained Dr. Javed. “Meanwhile, algorithms analyzing digitized Whole Slide Images (WSIs) can examine hundreds of thousands of cells and billions of pixels to differentiate seven distinct tissue phenotypes.”

Deep learning methods require large amounts of annotated histology data for training, which may be tedious to obtain. Additionally, while these methods may be effective in determining tumor tissue, the tissues in colorectal cancer also contain a rich mix of several other types of tissue, including smooth muscle, inflammatory, necrotic, and benign tissue. Any algorithm must be taught to distinguish between these tissue types to be effective.

Texture analysis is a commonly used approach for tissue phenotyping, where texture features are computed to train classifiers, which are then used to predict distinct tissue types.

“Texture analysis may be attractive due to its simplicity but it does not fully capture the biological diversity of tissue components,” explained Dr. Javed.

“Recent methods have proposed integrating cellular connectivity features, which are used as a proxy to cellular interaction features. The notion of cellular connectivity features is based on the fact that spatially adjacent cells have a higher probability of receiving inter-cellular signals from each other than from cells that are farther away. It has also been shown that inter-cellular signals between various types of cells can influence the progression of cancer. However, a dynamic network of tumor growth cannot be adequately modelled by a single type of interaction. Our technique uses a multiplex network model to represent the intricate relationships between cell populations. We propose four different types of cellular networks integrating a variety of features representing tissue characteristics at different levels.”

In the researchers’ model, cells from a WSI are detected and classified into five distinct categories using a deep neural network. Then, four different types of cellular interaction features are computed and used to construct a four-layer multiplex graph. Since each slide contains thousands of cells, the slides are segmented into tiles or patches, which helps the algorithm determine the distribution of different types of tissues across the cells.

“There are many directions in which this work can be further extended,” added Dr. Javed. “Further cellular types such as blood cells could improve performance and also reveal more micro-level tissue communities. Additionally, our framework could be adapted to WSIs of different types of cancer. Of course, in clinical practice, our work can help medical practitioners understand the contents of the WSI and make more accurate and timely diagnoses.”

This work was funded by the Khalifa University of Science and Technology and the UK Medical Research Council. The collaborators were also supported by the PathLAKE digital pathology consortium, which is funded from the Data to Early Diagnosis and Precision Medicine strand of the UK government’s Industrial Strategy Challenge Fund, managed and delivered by UK Research and Innovation.��

Jade Sterling
Science Writer
5 July 2021

The post Leveraging AI to Detect Colorectal Cancer appeared first on Khalifa University.

Etisalat Leadership Offers Insights into Future Careers for Students of AI and Engineering ��

Leaders from Emirates Telecommunication Group (Etisalat), and graduate recruits from the Etisalat AI Centre of Excellence shared their experience and offered insights into the role of artificial intelligence in industry with an emphasis on Etisalat’s AI strategy, and how students at Khalifa University can shape their academic journey to pursue a career in AI.

The seminar, developed jointly by Khalifa University Career Services Office, Etisalat, and the Emirates ICT Innovation Center (EBTIC), was titled ‘Embarking on a Career in AI – How Etisalat supports career ambitions of graduates’. Student participants learnt about changes at the workplace due to both the COVID-19 pandemic and the beneficial impact that artificial intelligence has already had on Etisalat’s business operations. Members from the Etisalat AI Centre of Excellence include more than 20 recently recruited Emiratis.

Dena Ali Al Mansoori, Group Chief Human Resources Officer, Etisalat, offered an insightful presentation on the future of work, the impact of COVID-19 pandemic on recruitment processes, and how AI has helped the company continue with important recruitment initiatives. Speaking on AI and learning, Joseph Hayes, Director of Learning and Development Planning, discussed the ‘Power Skills’ that companies look for in the individuals that they employ, and how Etisalat uses a new AI-powered training platform to help develop their employees. Three Etisalat AI Team representatives, Hend Al Jasmi, Haifa AlHosani and Alyazia Ahli, also shared their experiences.

Al Mansoori said: “This was a great platform for the next generation of innovators to gain insights into the future of work. We were able to deep-dive into how AI has changed the competitive landscape and customer expectations, ultimately forcing organisations to change and adapt.

“With Etisalat’s vision to ‘Drive the digital future to empower societies’, we have undertaken our own digital transformation within our services, operations and internal capabilities. At a group level, Etisalat is now focused on acquiring, as well as developing and reskilling its existing workforce to meet the evolving and ever-changing requirements of its digital and traditional domains of business.”

Dr. Ahmed Al Shoaibi, Senior Vice-President, Academic and Student Services, and Professor of Practice, Khalifa University, said: “Since Etisalat is looking into boosting its AI workforce, the seminar offered an excellent opportunity for Khalifa University students to learn how to hone their skills and forge a future career in the AI discipline. We believe the event also helped undergraduates and postgraduates gain an understanding of the promising career opportunities available with Etisalat, while hearing first-hand from the AI Team about their experience in business, the projects they have delivered, and the AI training program at Etisalat.”

Dr. Ernesto Damiani, Director, Khalifa University Center for Cyber Physical Systems (C2PS), Director, Information Security Center, Professor, Electrical and Computer Engineering, who works with EBTIC, offered an overview of the current and future artificial intelligence and machine learning program offerings, including a Minor in Artificial Intelligence, at Khalifa University.

The Etisalat AI Centre of Excellence graduates go through the bespoke development pathway that Etisalat has built, in alliance with its strategic partners, EBTIC, Microsoft and Accenture. EBTIC, a research and innovation center established by Etisalat, BT, and Khalifa University, and supported by ICT Fund, has helped the AI team with developing and upskilling in AI/Machine Learning (ML). Senior EBTIC researchers and innovation specialists supervise and collaborate with the AI team, to develop and deploy projects that deliver real-world business value to Etisalat.

EBTIC, along with Dr. Damiani, have also conducted a series of tailored IEEE-certified AI/Big Data and Data Scientist courses for the AI Team throughout 2020, upskilling them in practitioner skills, helping them to become advanced data scientists.

The three Etisalat AI Team members specifically discussed Etisalat’s AI strategy and objectives. They offered valuable insights and advice, gained from their own experiences, in order to inspire like-minded individuals in the current Khalifa University student body.

Clarence Michael
English Editor Specialist
9 May 2021

The post Etisalat AI Centre of Excellence Staff Members Share Experience with Khalifa University Students appeared first on Khalifa University.

The original computers were designed around a human brain model. Since then, developments in artificial intelligence and computer science continue to take inspiration from the brain.

Read Arabic story .

The human brain has always been under study for inspiration of computing systems. Although there’s a very long way to go until we can achieve a computing system that matches the efficiency of the human brain for cognitive tasks, several brain-inspired computing paradigms are being researched. Convolutional neural networks are a widely used machine learning approach for AI-related applications due to their significant performance relative to rules-based or symbolic approaches. Nonetheless, for many tasks machine learning requires vast amounts of data and training to converge to an acceptable level of performance.

A PhD student from Khalifa University, Eman Hasan, is investigating another AI computation methodology called ‘hyperdimensional computing’, which can possibly take AI systems a step closer toward human-like cognition. The work is supervised by Dr. Baker Mohammad, Associate Professor and Director of the System-on-Chip Lab (SOCL), and Dr. Yasmin Halawani, Postdoctoral Fellow.

Hasan’s work, which was published recently in the journal, analyses different models of hyperdimensional computing and ��ݮ��Ƶ the advantages of this computing paradigm. Hyperdimensional computing, or HDC, is a relatively new paradigm for computing using large vectors (like 10000 bits each) and is inspired by patterns of neural activity in the human brain. The means by which can allow AI-based computing systems to retain memory can reduce their computing and power demands.

HDC vectors, by nature, are also extremely robust against noise, much like the human’s central nervous system. Intelligence requires detecting, storing, binding and unbinding noisy patterns, and HDC is well-suited to handling noisy patterns. Inspired by an abstract representation of neuronal circuits in the human brain, developing an HDC architecture involves encoding, training, and comparison stages.

The human brain is excellent at recognizing patterns and using those patterns to infer information about other things. For example, humans generally understand that just because a chair is missing a leg, that doesn’t mean it’s no longer a chair. An AI system may look at this three-legged chair and decide it is a completely new object that needs a new classification. HDC vectors, however, offer some margin for error. With HDC, recognizing certain features will generate a vector that is similar enough to a chair that the computer can infer the object is a chair from its memory of what a chair looks like. Hence, the three-legged chair will remain a chair in hyperdimensional computing while in traditional object recognition this is a difficult task.

“In a HD vector, we can represent data holistically, meaning that the value of an object is distributed among many data points,” explained Hasan. “Therefore, we can reconstruct the vector’s meaning as long as we have 60% of its content.”��

The structure of the vectors leads to one of the strongest advantages of the HDC approach, which is that it can tolerate errors and therefore is a great option for approximate computing applications. This arises from the representation of the hyper vectors, where a bit value is independent of its location in the bit sequence.

HDC is also powerful in that it is memory-centric, which makes it capable of performing complex calculations while requiring less computing power. This type of computing is particularly useful for ‘edge’ computing, which refers to computing that’s done at or near the source of data. In a growing number of devices, including in autonomous vehicles, computations must be carried out immediately and at the point of the data collection, instead of relying on computing done in the cloud at a data center.

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“Hyperdimensional computing is a promising model for edge devices as it does not include the computationally demanding training step found in the widely used convolutional neural network,” explained Hasan. “However, hyperdimensional computing comes with its own challenges as encoding alone takes about 80 percent of the execution time of its training and some encoding algorithms result in the encoded data growing to twenty times its original size.”

Hasan studied the HDC paradigm and its main algorithms in one-dimensional and two-dimensional applications. Research has shown that HDC outperforms digital neural networks in one dimensional data set applications, such as speech recognition, but the complexity increases once it is expanded to 2D applications.

“HDC has shown promising results for one dimensional applications, using less power, and with lower latency than state-of-the-art simple deep neural networks,” explained Hasan. “But in 2D applications, convolutional neural networks still achieve higher classification accuracy, but at the expense of more computations.”

Hasan concluded that HDC is still considered a new paradigm and faces challenges requiring further analysis.��

Jade Sterling
Science Writer
29 April 2021

The post A Step Closer to Brain Like AI with Hyperdimensional Computing appeared first on Khalifa University.