Scientific Program

Conference Series Ltd invites all the participants across the globe to attend World Congress on Computer Science, Machine Learning and Big Data Dubai, UAE.

Day 1 :

Keynote Forum

Shabir Momin

ZengaTV, Singapore

Keynote: Data mining, context creation which lead to sale

Time : 08:45 - 09:25

Conference Series Computer Science Meet 2018 International Conference Keynote Speaker Shabir Momin photo
Biography:

Shabir Momin became the youngest CTO, he was also awarded as the Entrepreneur of the Year in 2013 by TIE. He has had a successful track record in various technology and business positions at a CXO level for few years before he became an entrepreneur. He has sold few ventures in the past and his current ventures that he has founded and co-founded are ZengaTV, one of the leading OTT services in India, OneDigital Entertainment Largest, a Digital content company in India, OneAxcess.com. He is a successful Entrepreneur and a professional with an excellent track record in technology innovation, especially in the digital media and convergence technology. In the past, he has been the Head of Asia for a technology innovation company called Picsel Technologies (UK) and Sentac Inc. (USA). He has also been CEO Asia for I-Connect Inc., a US based MNC, an operation comprising more than 9000 employees working out of the Asia Pacific region.

Abstract:

The world is evolving faster than we know. Brands communication and sales approach are changing and now becoming more driven by data mined, predictive and AI-based decision making. Gut calls will still be there but would be taking a back seat. While the data-driven market is growing exponentially, brands these days are not yet using the data-driven information to do the predictive sale. The Deep analysis can also be used for decision making strategy to do right positioning among its competition. The data-driven ecosystem will grow manifold in the near future resulting in the data explosion. With this, brands use the data for better outcomes if used strategically through predictive analytics which helps to understand the business insights. The challenge which the brands face is that they do not know which data to collect and how to analyze the collected data. A lot of brands are facing the same issue. It is a matter of utmost importance for any business survive first and then drive its growth, therefore it is necessary to build the right data science strategy through collecting the right data along with the right analysis to build the expected business ecosystem to get the best results.

Keynote Forum

Anu Kukar

KPMG, Australia

Keynote: Risk management as you go for implementing emerging technologies AI, RPA & ML

Time : 09:25 - 10:05

Conference Series Computer Science Meet 2018 International Conference Keynote Speaker Anu Kukar photo
Biography:

Anu helps organizations reduce the cost of effective risk management through:

  • Applying data, digital and emerging technologies for better risk outcomes
  • Integrating a RiskLens on emerging technology and transformation initiatives such as: AI, Machine Learning, RPA, IoT, Cyber, XTechs, Vx, Chatbots etc.
  • Equipping Risk Teams with new skills, behaviour and mindsets for tomorrow and the future

Anu brings 18 years of experience across Risk & Compliance management, Internal Audit, Governance, Regulatory, Management Consulting and Tax. She has also worked across many industries: Insurance, Bank, Government, Corporates, Manufacturing, Energy and Telecommunications. She is also a global speaker and has spoken at conferences in Dubai, Denmark, Singapore, Thailand, India and Australia. Anu is a graduate from Australian Institute of Company Directors (AICD), a Chartered Accountant (ICAANZ) and holds a Bachelor of Commerce in Accounting and Information Systems (UNSW). Certifications from HarvardX in Cyber & Data risk management, MIT in Artificial Intelligence (ML, RPA, NLP)

Abstract:

Help! My Bot is not listening to me.  Can a Bot be risky? Can a Bot be non-compliant? Can a Bot be governed?

Whether you are choosing the business process, undertaking a proof of concept or piloting for RPA- are you making the most common GRRC mistake? Having set up a project team ensuring project risks are managed is usually done by everyone. Then there is identifying the new risks arising from RPA such as reputational risks, impact on employees, increased cyber risk, privacy and security etc. This is usually considered as part of the business case and project implementation. Managing risk during the change, such as undertaking RPA implementation, can often lead to elements of the risk and compliance management framework being overlooked or forgotten. Imagine your implementing your RPA project and forget to ensure your Business Continuity Plan (BCP) reflects the change in staff and the requirements to support your bots which have not been implemented. Your workforce composition and location has no doubt changed and so will your business requirements. Or take the vendor or strategic alliances agreements you have entered into to deliver this project and support the business in meeting their strategy, objectives and servicing their customer needs. Have you notified the regulator if it is a material provider? What about the contract arrangements, SLA’s, cybersecurity how they will be monitored to ensure reputational, operational, strategic, compliance risks are appropriately managed. Assessing the impact and implementing changes to all the impacted components of the risk and compliance management framework can save lots of unwanted headache financially and non-financially! The element of good governance and commercial risk management is often overlooked, left too late or the team is fatigued out by the 3 mentioned components-

(1) Project risk management
(2) Identifying new risks and
(3) Managing risk during change

What is equally if not the critical component of GRRC management in implementing RPA is considered it at each of the stages choosing the process, proof of concept, pilot, implementation, and post-implementation. Hear, see and learn practical ways to integrate and consider GRRC into the relevant stage of your RPA journey and ensure your bot is listening to you!

Break: Networking and Refreshments Break along with Group Photo @10:05 - 10:30

Keynote Forum

Mahmoud Moussa

Microsoft Corporation, UAE

Keynote: Search the world like you search the web (computer vision and object detection)

Time : 10:30 - 11:10

Conference Series Computer Science Meet 2018 International Conference Keynote Speaker Mahmoud Moussa photo
Biography:

Mahmoud Moussa is a Cloud Solution Architect for Big Data and AI in Microsoft, with the Middle East and Africa Coverage. 16 Years of Experience in the field of Data and a variety of Business Markets Bridging the Technology and Business is the key focus.

Abstract:

The world is Getting Smarter, Imagine you can search your physical World same way you search the web, With the Advances of Artifical Intelligence and Cameras we Currently have, People can look for objects, People the same way we search the internet. From a factories to hospitals the technology can provide a great help into tune the life we have and make us do things more efficient, Object Detection is tightly coupled with Object Tracker and not just identifying Objects but also tracking them, during this talk we will discuss the different algorithms and market trends in both object detection and object tracker and some real-life examples to apply the technology to business solutions. The 30 minutes talk  will discuss the different algorisms and techniques that can help both developers and data scientist understand the best fit for each in building business solutions.

Algorithms discussed,

  • YOLO
  • Tensorflow Object Detection
  • MobileNet
  • Mask R CNN
  • BOOSTING Tracker
  • MIL Tracker
  • KCF Tracker (Kernelized Correlation Filters)
  • CRSY Tracker

With the help of Microsoft Azure Cognitive Services can help developers with a limited Machine learning and Artifical Intelligence Knowledge build cutting-edge solutions that cover lots of the use cases in the business with a minimal amount of coding and reach an outstanding solution.

Keynote Forum

Gerald C Hsu

EclaireMD Foundation, USA

Keynote: Health-maintaining tips for diabetes travelers

Time : 10:00-11:00

Conference Series Computer Science Meet 2018 International Conference Keynote Speaker Gerald C Hsu photo
Biography:

Gerald C Hsu has completed his PhD in Mathematics and has been majored in Engineering at MIT. He has attended different universities over 17 years and studied seven academic disciplines. He has spent 20,000 hours in T2D research. First, he studied six metabolic diseases and food nutrition during 2010-2013, then conducted research during 2014-2018. His approach is math-physics and quantitative medicine based on mathematics, physics, engineering modeling; signal processing, computer science, big data analytics, statistics, machine learning and AI. His main focus is on preventive medicine using prediction tools. He believes that the better the prediction, the more control you have.

Abstract:

For the past 6.5 years (2012-2018), the author has made 179 trips by air which included 69 long-haul travels and 110 short-distance travels. The average trip was 14 days. This paper provides his experience on maintaining his health during traveling days. Prior to 2015, both of his daily average glucose and Metabolism Index (MI), which has a 73.5% break-even level, were high. After 2015, his glucose and MI levels improved to a healthy state; however, he did not meet his own targets- glucose 117 mg/dL and MI 59%. Nevertheless, by following the guidelines listed below from the period after 2015, the author had better results. Therefore, other busy T2D travelers can also maintain their healthy level of both glucose and metabolism during their traveling days by using the same method. The traveling tips summary- (1) Try to avoid having meals at the airport, airline lounge and in-flight food. (2) Don’t indulge yourself, avoid soft drinks, high carbs/sugar food (<15 grams/meal); eat mostly vegetables (size: ~2 fists) and eat berries and tomatoes, not overly sweet fruits. (3) Maintain exercise regimen. After eating, find places to walk 4,000 steps. If inside the airport, walk along the hallway between gates, wherever is safe. (4) Drink 2,000 to 3,000 cc of water each day, dress comfortably, control your weight, maintain sufficient sleep hours, keep a positive mindset and avoid getting sick or injured.

  • PRODUCT LAUNCH BY SHABIR MOMIN & SANTOSH GODBOLE (11:10 - 11:25)

Session Introduction

SHABIR MOMIN & SANTOSH GODBOLE

SSN Solutions, London

Title: Product Launch

Time : 11:10 - 11:25

Biography:

Abstract:

  • WORKSHOP
Speaker
Biography:

Tanya Dixit has completed her BTech from BITS Pilani in Electrical and Electronics Engineering. She is working as an Engineer at Qualcomm, a pioneer in wireless and IOT chips. She is a machine learning and artificial intelligence enthusiast and is a deep thinker. She has a firm grasp of psychology, cognitive science, embedded systems, machine learning and hopes to build products that make the life of the common man better.

Abstract:

Deep learning is an emerging area in machine learning. It is based entirely on the understanding of neural networks and their different architectures to achieve specific goals. Convolutional neural networks are used in computer vision to help machines identify faces, animals, cars, etc. Different convolutional neural network architectures have been developed in recent times to make computer vision tasks easier. Transfer learning can be applied to use the weights and architecture from an already trained network like VGG16 or VGG19 and apply it to relevant tasks. Recurrent neural networks are another class of neural networks that allows context to be preserved and hence is used for language processing. There are various types such as LSTMs, GRUs. Each has their advantages. This study discusses the basics of neural networks, mathematics of back-propagations, intricacies of CNNs, RNNs and then advance architectures like residual networks and ladder networks.

Break: Lunch Break 12:10-13:10
  • Computer Science and Technology | Machine Learning | Big Data, Data Science and Data Mining | Big Data Analytics | Business Intelligence | The role of AI & Machine Learning in Medical Science | Deep Learning | Object Detection with Digits | Computer Vision and Image Processing | Pattern Recognition | Robotic Process Automation (RPA) | Natural Language Processing (NLP) and Speech Recognition | Deep Learning Frameworks
Speaker

Chair

Niladri Shekhar Dutta

Ericsson, UAE

Speaker

Co-Chair

Abbas M Al-Bakry

University of Information Technology and Communications, Iraq

Session Introduction

Vinay Bansal

Faststream Technologies, India

Title: Session Chair
Speaker
Biography:

Abstract:

Jayatu Sen Chaudhury

American Express, India

Title: Machine learning applications in credit card domain

Time : 13:10 - 13:40

Speaker
Biography:

Jayatu Sen Chaudhury is the Vice President, Global Commercial and Merchant Data Science and Head of Enterprise Digital & Analytics India for American Express, India. Prior to this role, he was the Head of Global Information Management, Big Data Labs & Advanced Risk Capabilities. He has been a part of American Express since 2001, working in the various decision science functions for both US and international markets. He has earned his PhD in Financial Economics from IGIDR, Economic Research Institute funded by the Central Bank of the Country (Reserve Bank of India). Prior to joining American Express, he has worked in decision science for two years each in GE Capital and ICICI Bank.

Abstract:

Given the huge volumes of data available (both structured and un-structured) for American Express card members, American Express has adopted machine learning in all its core business processes of credit and fraud risk management, marketing analytics and operations. Work entailed building in-house data warehouses with right level of privacy controls and then using state of art machine learning algorithms from open sources to solve unique business problems across various business verticals. Adoption of machine learning has ensured building of robust economic models leveraging the best possible information, delivering the highest predictive power with utmost accuracy. The models are updated at the highest possible frequency ensuring the models incorporate the most recent information. This has led to significant improvement in the controls for fraud risk and also improved the targeting of appropriate segments with far higher accuracy in marketing. As a part of the presentation, 3-4 actual use cases of core American Express processes and how machine learning has completely changed the game will be discussed. Discussion will also include the new areas where company is thinking of doing research and bringing the best value for its card members.

Speaker
Biography:

Niladri Shekhar Dutta is a seasoned professional with more than 13 years of global consulting experience. He is a Consulting Practitioner with focus on operations and transformation consulting for top-tier telecommunication operators globally. He has worked in more than 30+ consulting engagements in varied culture and markets across Western Europe, Middle East, North and Central Africa, India and New Zealand. His expertise is primarily around C-level advisory, digital transformation, digital enterprise architecture, business and operational process management, IT operational strategy, digital risk and revenue management consulting. He is responsible for driving consulting business across the MEA region for Ericsson and is involved in both sales and delivery. He has his MBA in Marketing and Finance from Symbiosis, Pune, India and is an Engineering graduate in Electronics from University of Nagpur, India. He is presently also undergoing a specialized PG program from MIT Sloan and Columbia facilitated by Emeritus on Digital Business.

Abstract:

With the advent of technology transformation in the fast changing and ever evolving world of Information, Communications and Technology (ICT), the importance of data is supreme. This is often being referred to as big data and is perhaps the single most entity which forms the backbone of any major transformation within any large global corporation across industries. Data is no longer being looked and used as a tactical medium for storage or operations; on the contrary it becomes extremely strategic in nature. In fact the 3 main pillars of today’s disruptive world of digital are driven by big data, IoT and cloud. Out of which big data is the nucleus of transformation. In the world of digital, this is very well centered on three main life cycle entities. They are the customer, the product and the revenue. Each of these, i.e. customer life cycle, product life cycle and revenue life cycles behave very differently from one another. The practical emphases of data in each of these entities are also very different and unique. The concepts of big data within these 3 life cycles are core to the change we witness in the world of digital. Each data entity centered on these life cycles is instrumental in C-level decision making and major change management that happens within the organization. The data element acts as a central aspect to strategic decisions whether it comes to newproduct/service development or behavior of customer or user data, appreciation or acknowledgement of revenue. All use cases around big data will be largely centered on these and any specific case would be a secondary derivation of the above. With big data being so strategic in nature a large part of the focus has now shifted to data extraction and normalization to ensure meaningful information is extracted and utilized for business benefits by customers. Like the traditional mindset used to be, focus was largely around data operations and reporting. We will soon see a world where we cannot live without any form of data and in truest sense the phrase big data would essentially be big and super imposed in all aspects of our lives, right from our behavior, buying and consumption of products and services to distribution of our resources. The extraction and transformation of data for key benefits will be very much a business as usual thing, without which survival will become questionable within ICT industry, especially whilst looking at the concept of digital disruption. This article largely focuses on the key aspects of the same within the world of ICT and how a corporation is heavily dependent on such aspects for generation of its sales and management of its operations.

Speaker
Biography:

Manoj Mishra has completed his Bachelor of Engineering in Computer Science and a Certification in Data Science from Johns Hopkins University. He has more than two decade of experience spreading across multiple geographies (US, Europe, India and Middle East) working with organizations like Adobe Systems, Dell, Perot Systems, CEB-Gartner, Rolta and Tata Group. He is currently a Chief Manager of Business Intelligence and Data with Union Insurance and currently leading their data strategy and technology transformations through data analytics, research and various AI initiatives.

Abstract:

In order to have the competitive advantage, organizations worldwide are driving the need for better analytics (historical, realtime, predictive and cognitive) of data across various domains including customers, products, services and operations. Due to this, the data available for such analytics is exploding in size, technology and complexity. For many years companies have invested in technologies like data warehouses, data marts, OLAP tools, Big Data/Hadoop systems and streaming real-time analytics platforms to take advantage of these opportunities. Total value preposition to the business is maximized only when these are combined into an integrated analytics platform. However, traditional tools cannot integrate streaming data and dataat-rest especially when the data is spread on-premises, cloud, websites and documents everywhere. Data virtualization can be used to provide cross platform logical views of data and analytic insights across the enterprise to provide an integrated analytics platform. By utilizing native integration with in-memory data grids for data processing, data virtualization can deliver a unified and centralized data services fabric with security and real-time integration across multiple traditional and big data sources, including Hadoop, NoSQL, cloud and software-as-a-service (SaaS). Hence data virtualization is becoming a need to address the unique challenges of data explosion in today’s changing business climate.

Speaker
Biography:

Eman Abu Khousa is a Researcher-Instructor (Big Data Applications) at the College of Information Technology, UAE.

Najati Ali Hasan is an experienced health information technology (IT) professional with 25-year experience in the field. Najati is an expert in advising GCC clients on strategies for selections & implementations of health IT with focus on achieving demonstrable clinical, operational and financial benefits. Najati is well versed in the revenue-cycle-management (RCM) field with knowledge of the various nuances and requirements of GCC countries. Najati’s other areas of expertise include smart use of health IT for enhanced patient experience, EDI, data analytics and applications of Artificial Intelligence/Machine Learning (AI/ML) in healthcare. Najati has co-authored three articles for conferences and journals – one having received a best-paper award. Najati’s work experience spans top USA medical centers to world class suppliers of health IT.

Abstract:

The losses from healthcare fraud, over-prescribing and improperly coded insurance claims leading to claim-denials are estimated in the billions of dollars annually. The costs associated with fraud and acts of abuse are increasing insurance premiums for patients and cuts into the profitability of healthcare service providers and payers. The continuing adoption of Electronic Health Records (EHRs) and the advances of machine learning and big data analytics enable more efficient and automated methods for detecting and effectively mitigating the risk of fraudulent activities and illegitimate claims. This paper provides an overview of the new systems and methods to reduce medial claims fraud and a review of open issues and challenges. This paper also proposes a predictive analytics approach to detect potential fraudulent patterns using a set of supervised and unsupervised learning techniques. The proposed approach incorporates both historical and real-time data to identify illegal claims and prevent payouts to fraudsters early in the claims management process lifecycle.

Speaker
Biography:

Tilila is currently a Data Scientists and a Technical Evangelist for data and AI working at Microsoft. She accompanies partners in architecting and building their
cloud based, AI powered solutions. She was previously a Technology Strategist for enterprise accounts including Education and healthcare industry. She’s also a Fulbright scholar who earned, in 2012, a master in Computer Science and Business from San Francisco State University where her focus was on Bioinformatics including Genomics and Biomedical Image Analysis.

Abstract:

Next Generation Sequencing (NGS) allows performing massively parallelled DNA sequencing and is currently revolutionizing biological studies. Instead of sequencing a specific set of genes solely, NGS allows to sequence a wider portion of the genome (even a whole genome), which opens the door for a wider analysis of biological pathways within an individual. Researchers have never before accessed such a wealth of genomic data which holds the promise of unvealing the secrets of the most daunting deseases of the century such as cancer. It also comes with its own set of challenges for the management and analysis of Big Data to extract meaningful and actionable insights. Cloud computing and machine learning do have the capacity to solve this challenge. In this talk we will show how Cloud-based Genomics platform allows to manage petabytes of genomic data as well as foster fast and agile Secondary Analysis. We’ll also also use genomics specific machine learning packages to perform Tertiary Analysis on gene variants data and visualization tools to expose and share the results with the scientific community. We will begin the talk with the introduction to the genomics field and the commonly used genomic analysis process and will present practical applications of the above services and analysis.

Break: Networking and Refreshments Break 15:40-16:00

Abbas M Al-Bakry

University of Information Technology and Communications, Iraq

Title: Computer aided diagnosis In cloud environment based on multi agents system

Time : 16:00 - 16:30

Speaker
Biography:

Abbas M Al Bakry has completed his PhD in Computer Science (Artificial Intelligence) from the University of Technology, Baghdad, Iraq. Currently, he is the President of the University of IT and Communications in Baghdad, General Chair of the NTICT annual conference-Baghdad, Editor in Chief of IJCI Iraqi Journal for Computers and Informatics, Editor-in-Chief, Intelligent Computing in the IJNC International Journal of Network Computing and Advanced Information Management and Editor in JCIT, AISS, JINT, IJACT, IJIIP international journals.

Abstract:

In this speech we address solutions for the problems of the low accurate decision; low availability especially in maintains procedures and the scalability in online Computer Aided Diagnosis (CADs). Most CADs became available online and provide a high importance medical services which develop the health of the human beings. CADs are to increase the detection of disease by reducing the false negative rate due to observational oversights. The online CADs face three major problems: (1) The CADs cannot diagnose some diseases because the symptoms of these diseases are not available in the knowledge bases of this systems, (2) problem is the availability of CADs is depends on the web server which hosted them. Web server may possible to stop for maintenance that will implies to stop the CADs systems. (3) The problem is scalability related with the cost if their admins want to expand them to cover more medical problems. In this lecture we proposed a new framework to solve the above problems. The framework is composed of multi agents system to work on the environment of the cloud computing. The framework consists from three Sections: SaaS components, PaaS components and IaaS components. Each section has its own algorithms and procedures. To evaluate the resulted framework we make a survey in for 150 persons from medical health sector, students, specialists, physicians and other. The results pointed to good ratio of acceptance from the users.

Speaker
Biography:

Rohit Agarwal is working as a Data Scientist in Mobisy Technologies Pvt. Ltd., Bangalore where he leads a team of data scientists and software engineers, focusing on sales force automation by applying state of the art ML and deep learning techniques. He has 12 years of industry experience with 11 years in GE where he worked on conceptualizing, designing, prototyping a number of software and data solutions using cutting edge technologies for solving large industrial problems. He has completed his Masters in IT from IIIT, Bangalore and Bachelors in Computer Science from IET, Lucknow.
 

Gaurav Pawar has completed his BE degree in Electronics and Telecommunication with more than 3 years of practical hands-on experience in computer vision and machine learning. He is specialized in building quick prototypes using python environment by leveraging GPU platform. He has expertise in using popular deep learning libraries such as TensorFlow, PyTorch and Keras. Currently, he is interested in solving data science problems in Indian retail industry using images and other source of data.

Abstract:

We have used image recognition and machine learning technology to automate some of the time consuming and error prone auditing use cases pertinent for SME retail stores (aka mom-n-pop stores) like which all SKU type are present in particular store. Check if the store has put out advertising of the brand as was agreed upon. Current process of store auditing is conducted through feet on street sales force/external auditing agencies which is manual, biased, time consuming, costly affair and is not scalable. Leveraging the state of art deep learning image recognition technology, our platform helps in automating store auditing process by acting as eyes for tracking all types of in-store visibility executions like window displays, POS material and outdoor advertising/banners with high degree of precision (>90%) which is much better than classical approached like SVM (~80%). The platform can analyze millions of retail store images to generate actionable insights for brands/company. Role of the sales force is limited to take pictures of the stores and upload them to our platform. Most of the current image analytics platform works with high quality images of organized environment like supermarkets, this makes our platform different as it has been specifically designed for mom-n-pop store setup which symbolizes unorganized shelves and cluttered environment. The images obtained are also low quality as they are typically shot by sales force using low quality mobile camera. Some of the common issues with these images are low light, partial visibility, occlusion, glare, incorrect angle, etc. In this, we intend to give a technical overview of the platform, highlight its capability to analyze images of varying nature, showcase few use cases in SME domain that can be implemented using this platform.

Speaker
Biography:

S Sharan Kumar is pursuing final year engineering at St Joseph’s College of Engineering, Chennai, India. He has participated in world’s biggest hackathon and was one among top 10 teams in the respective node.
 
R Rakhsanth is pursuing final year engineering at St Joseph’s College of Engineering, Chennai, India. He has participated in world’s biggest hackathon and was one among top 10 teams in the respective node.

Abstract:

Multiple object detection and tracking is still facing some difficulties like efficiency, reliability and creation of datasets. In this paper we are going to present about the efficient way of detecting and tracking of multiple objects regardless of the speed of its motion. We use tensor-flow in combination with OpenCV in which both are open source. We use tensor-flow for training the data sets for object identification and Kalman filtering algorithm for motion tracking and embed them into OpenCV for real-time image/video manipulations. We use SSD (Single Shot multi-box Detector) architecture of convolutional neural network for training the dataset for minimizing the GPU requirement than RCNN (Region Convolution Neural Network). We have also implemented a special algorithm that will boost the SSD model’s accuracy to some extent. The datasets for any training model needs to be well suited and labeled which is somewhat difficult to create that dataset. To reduce the difficulty of that we have proposed another technic by which we can just send the dataset with noise into the OpenCV only with labels. Then system will automatically crop those image from the datasets according to the requirement of the architecture (SSD) used. This will help us in creating datasets in a simple and cost efficient way.

Aisha Al Owais

Sharjah Center for Astronomy and Space Sciences, UAE

Title: Deep learning - An application of machine learning to classify images

Time : 17:30 - 18:00

Speaker
Biography:

Aisha Al Owais has completed her BSc in Computer Science from the College of Engineering at the American University of Sharjah. She is working as a Research
Assistant in the Meteorites Center at the Sharjah Center for Astronomy and Space Sciences.

Abstract:

Living in the 21st century, mankind’s most powerful weapon is technology. The field of technology we are interested in is computer science, specifically Artificial Intelligence (AI). As the name suggests, AI is about turning devices into intelligent agents that take actions based on the environment they perceive. They are also flexible in terms of changing their goal- what they are meant to do as well as adjusting their actions depending on its changing environment. What makes AI agents peculiar is their ability to learn and remember from their mistakes. Furthermore, Machine Learning (ML) is one of AI’s applications that enable systems to learn automatically, improve through experience and adjust its actions without human intervention. This takes us to Deep Learning (DL), a new subfield of ML concerned with algorithms inspired by the structure and function of the human’s brain called artificial neural networks. It has networks which are capable of learning data obtained from instructed or unlabeled data; therefore it is also known Deep Neural Network (DNN). All those terms lead us to what we are mostly interested in, Convolutional Neural Networks (CNNs), which is a deep neural network that is particularly wall-adapted to classify images, in our case to classify images of meteorites.

Break: Panel Discussion