^{World Congress on} Computer Science, Machine Learning and Big Data August 30-31, 2018 Dubai, UAE

A distinguished Technology leader driving transformation through innovation in Data Engineering, Data Management and Data Science. Holds a “Bachelor of Engineering” in Computer Science and a certification in “Data Science” from “Johns Hopkins University” Transformational leader, with a track record of developing solutions for future, creating benchmark of performance and success. 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. Moved to Dubai in June 2017 to take a leadership role as “Chief Manager–Business Intelligence & Data” with Union Insurance and currently leading their Data Strategy and Technology Transformations through Data, Analytics, research & various AI initiatives.

In this time of immense machine learning skills shortage, Data and analytics leaders face a dilemma. Without data scientists, venturing into machine learning and data science is difficult. Without any successful pilots, convincing the business to hire data scientists is equally challenging. Auto ML is a concept started by H2o which has been recently gone to the next level by Google's Automated Machine Learning which lets you train custom machine learning models without having to code. My workshop will be focusing on these trends in a little more detail and also a demo on how these trends can be leveraged.

Santosh is the Co-Founder and Chief Product Officer at SSN Solutions Limited. At SSN, his role is to define product and technology roadmap. Prior to SSN, he was a Senior Director of Engineering at ARRIS (India). At ARRIS, he has managed a large team of engineers that was spread across multiple countries. He also held various senior level positions such as Director Product Management at Cisco Video Technologies, Vice President Product Management at NDS Services Pay-TV Technology Pvt. Ltd., Co-Founder and VP Engineering of Sensact Applications and Co-Founder and Architect at Metabyte Networks. He holds Executive General Management Program (EGMP) certificate from IIM Bangalore, MS in Computer Science from IIT, Madras and BE in Computer Science, MS from University of Baroda.

The idea of creating and using consumer personas is not new. Marketers have been going through painstakingly long way to understand and define consumer persona for their products. Further, they go through an intricate process of defining and executing elaborate campaigns to acquire consumer information and map the same to required personas. Even after spending big portion of their budget, marketers face various problems in reaching out to the right consumer: Data acquisition is an expensive task, many times data is not authentic or recent; all this starts to affect the conversion rate of the business making the ROI a far-fetched dream. Typical approach used in data acquisition and persona creation suffers from multiple problems: Most personas built today are static. Yes, the practice of updating consumer profile periodically is helpful but not ideal. Second, there are just too many factors (attributes) involved in the consumer’s decision making process. Marketer’s approach of confining consumer to few personas is quite limiting and inaccurate. The answer to these complex problems is to build a multidimensional consumer profile that is always up-to-date. This is possible by engaging the consumers at various stages during their day, be it online venues such as social network, reviews, blogs, opinions, surveys or offline venues such as surveys, transactions, logs and so on. Developing a multidimensional profile that is up-to-date is not a simple task. It is the kind of problem where tools such as big data, data analytics and machine learning can be used most effectively.

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.

Sociological imagination is the awareness of the relationship between personal experience and the society. This talk is about methods to apply artificial intelligence in ways that can help a human being more connected to the society that he/she is living in. There are three focal points of this talk: Education, health and social interactions. Artificial intelligence can be applied to education and help more students get educated. Wearable’s and virtual reality is the technology to be used in making education accessible and make it application based. Similarly, for medicine, existing IOT products to be linked and generate an online assessment report on each person, and let a text analyzer process that for red flags. Emile Durkheim, in his classic book called suicide, mentions the main cause of suicide as the dissociation of a person from the society. AI products can help solve this dissociation by eliminating boundaries between human body and computers. Humans would be able to access information at the speed of thought and that will help a person get connected to more people like himself, thus eliminating suicide. Also, video camera analyzers to analyze footage and assess the body language of humans in real time thus predicting rapes, assaults and general nuances, thus leading to their timely resolution.

Ahmed has completed his CPA exams at the age of 22 years from California Board of Accountancy. He is the Founder and CEO of Almaqabi, a startup leading blockchain in audit & banking industry in Middle East. He has more than 10 years Big 4 experience in USA and Middle East.

Almaqabi is exploring the use of blockchain, machine learning, artificial intelligence, automation and Internet of Things (IOT) in audit. Department of eAuditchain focus on 4th industrial revolution technologies solutions to banks and financial instituions. Blockchain Audit Council was annouced recently to help top management of companies and bank understand the impact of 4th industrial revolution technologies on how it will change the way we live, work and interact with each other. Almaqabi won and recognized in regional competitions including AIM startup roadshow competition organized by Dubai Ministry of Economy.

Kai Khalid Miethig has completed his architectural study in 2004 at the University of Siegen, previous known as University of Applied Sciences of Siegen and gained further experience in waste management at Lobbe Environmental Consultancy. He has more than a decade experience in project management and lecturing. He is the Managing Director of Tariq Faqeeh Engineering in Bahrain, dedicated to enhance lifestyle and quality of living, offering various unique services for developers, government entities and individuals. He initiated the environmental awareness campaign “A Wave of Change” in cooperation with the Supreme Council of Environment Bahrain, German Embassy Bahrain and CleanUp Bahrain in 2017 and is providing/lectures on environmental awareness as well on the context and connection of architecture and waste management automation.

W-AI-STE - The implementation of artificial intelligence and machine learning in the processes of waste management, segregation and collection in architectural developments. It is a new and unique form of combining the existing technologies with the latest technologies of artificial intelligence and machine learning for an ancient issue the humanity is facing ever since mankind exists: Waste management. Due to the fact that the population is increasing continuously and therewith the production of waste as well, why not using the available technologies and features for supporting that basic task, which no one wants to deal with and is a topic which is now becoming a worldwide problem. Anywhere in the world where humans are present and were present as well even in areas humans never have been waste can be found as a remains of human existence. As we all are aware of smart home systems, e.g. fingerprints for entrance control and access or speech control featuring various functions in the house, this AI and ML technologies can be as well implemented for our daily legacies: WASTE- W-AI-STE. All humans have a certain pattern of whatever they do or their habits, these pattern and habits are as well detectable in their waste. Most waste comes from food packing and packing in general, so people have their favorite food, ingredients and other recurrent favorites which can be recorded, tracked and analyzed with the help of artificial intelligence and machine learning. So in an early stage of developments for residential and commercial purpose the pattern can be used to develop the concept of waste management systems.

Abed Benaichouche has completed his PhD from Ecole Nationale Superieur des Mines de Paris and Postdoctoral studies from French Geological Survey. He is a Senior Research Engineer and Team Leader at Inception Institute of Artificial Intelligence, (IIAI) the UAE’s national research organization, which aims for breakthroughs in fundamental and applied AI research. He has published more than 10 papers in reputed journals and international conferences. Before joining IIAI, he has worked for 2 years in BRGM (France) as Research Engineer. He designed and developed the Suricate-Nat platform; interactive web platform to collect information and analyzing it using NLP techniques on natural disasters emitted by citizens using social networks Twitter. He was also the Project Manager of the development of international interoperable platform for multi-risk analysis.

In recent years, deep learning (DL) has won numerous contests in computer vision and machine learning. In this presentation, we will present real world applications of Conventional Neuronal Network (CNN), Recurrent Network (RNN) and Generative Adversarial Network (GAN) in computer vision area. In the presentation, we will show a selection of recent research that Inception Institute of Artificial Intelligence (IIAI) is leading in the field of computer vision and artificial intelligence. For the CNN, we will present its application for face detection and annotation, demo for object detection and pose camera estimation. For the GANs, we will show its use for image colorization and art style transfer. And finally we present a new approach for face detection and super-resolution using both CNN and GAN models. For each demo we present the designed network, its limitations and the give perspectives for possible improvement.

Balasubramanyam Pisupati is currently working with Robert Bosch Engineering Solution and Business Solution as a Senior Manager in Data Analytics team. He has accomplished senior statistical professional with rich experience of more than 10 years in software industry related to product development, testing and data mining.

Forecasting is necessary for better business understanding and decision making. When it is done manually, it requires a lot of effort and time from multiple departments like logistics, sales, finance, etc. It also involves lot of gut feeling from experienced people and sometimes it might lead to error prone prediction if person is inexperienced or is not aware of past behavior. It is even very challenging for any data scientist to find a forecast model that performs best for all scenarios and in all forecast horizons. In this paper an approach for forecasting using ensemble model is discussed. Ensembling is done using symmetric mean absolute percentage error and mean absolute percentage error calculated from rolling forecast approach. For validation of forecast model, M3 competition data is used. This approach has resulted in better performance on out of sample prediction.

Beesung Kam has his degree in Computer Science and another in Medical Anatomy. He has completed his second PhD from Pusan National University School of Medicine.He is the Director of Maritime Mobile Health Research, a premier bio-soft service organization. He is researching in the area of medical education since 2005 and has digitalized student assessments of different grades.

Although there is a rich literature predicting student performance based on courses and lectures, it is much less studied in predicting degree completion upon affection of student’s personality. Student’s low score of graduation is a critical issue in medical school because students who result in lower scores mostly fail from Korean Medical Licensing Examination (KMLE). This paper introduces a method of supervised learning to predict future rank and score of students by using information provided prior of entrance to the school. Data sets of 256 number of graduated students from Pusan National University School of Medicine school year of 2016 and 2017 were distributed to machine learning by 2500 times. By using method of ordinary least squares regression three groups of students were established depending on their achieved final graduation to low, mid and high scores. These groups were used as a guideline  to pivot personality data of freshmen for further comparison. Prediction analyzed by student’s status, based on their student’s temporal behavior such as age, sex, blood type, school of graduation, district, major, hobby, religious, drinking habit, parent’s status, tuition method of payment and registration to an application can influence on their future earned rank and score. Predicating future student rank and score helps to monitor, review and re-establish student’s road map to enhance learning progress. This prediction not only helps student to know how they do but also encourages them as a feedback to reinforce their current method of learning for further improvement. Although increasing pilot data for this study can enhance the study achievement and improving student personality can be challenging, method of determining low score of graduation by machine learning can sparkle new era to progress learning.

Semakula Abdumajidhu is currently pursuing his Master of Science degree in Computer Science at the College of Computing and Information Sciences (CoCIS), Makerere University. His major is Computer Vision and Image Processing with his current research on crop diseases.

Whiteflies have been observed to cause two forms of damage to the host cassava plants that is feeding damage and sooty damage. The secondary damage, referred to as sooty mold damage, is a result of honeydew dropped by the feeding whiteflies on lower leaves. It is characterized with a sooty darkening of the lower leaves that affect chlorophyll content levels, because chlorophyll is an indicator of plants nutritional stress, photosynthetic capacity and the healthy status of plants. Plants need much possible levels of chlorophyll content to absorb enough light that is to be used during photosynthesis for them to make their own food. Sooty mold damage with its black-darkening-effect abstract the leaves from getting direct light rays during the photosynthesis process which leads to low cassava yields. The use of machine learning for surveilling the health of crops has been looked at in a number of related settings, including the segmentation of diseased leaves and disease-related dis-coloration in citrus fruit. The study will be aimed at understanding the effect of sooty mold on chlorophyll content in cassava plants and see how we can improve and raise on the cassava yields. Five cassava varieties that are narocass 1, nase 14, mkumba, njule and bamunanika will be considered during the research. We shall determine the level of infestation by sooty mold on these cassava varieties and tell which variety is most affected. We shall analyze and relate spectral reading data with chlorophyll content to measure how much of chlorophyll is affected by sooty mold damage in cassava images. We shall develop a Convolutional Neural Network (CNN) model to estimate pigment (chlorophyll) content based on spectrometer readings. Using our model, we shall determine the percentage of the plant being affected and the extent covered by sooty mold. We shall use field spectrometer to take spectral field measurements and also carry out remote sensing analysis of the data. This data will be related to the chlorophyll content to determine the levels of sooty mold effect. The research will lead to increase in cassava yields as a result to a more accurate detection of sooty molds. This will improve the response time for whiteflies and sooty mold infected cassava care. This will no longer be need for handcrafting/manually extracting features.

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 asters 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 sources of data.

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 other sourcesparticular 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.

Noor Alasadi is a Senior Data Scientist at Creditinfo Group, a leading service provider of credit information and risk management solutions worldwide. He is a Graduate Instructor and a Researcher at Damascus University, Department of Artificial Intelligence and Natural Language Processing. He is also a Member of the Scientific Committee of the ACM-ICPC in which he was a Judge, Problem Setter, Coach and Organizer in the Arab Collegiate Programming Contest (2012-2017). He was also involved in multiple private-public partnership projects with companies and authorities in the Middle East to build intelligent security systems.

Social networking websites have enjoyed a great success in recent years, apart from the numerous new opportunities that they are providing, extremist groups and terrorist organizations are using them to promote their ideology to facilitate internal communications and to evoke a planned psychological reaction in their enemies. Many web resources contain information about extremism, but a relatively small proportion comes from terrorist groups themselves and since manually monitoring and analyzing all their content separately during warfare is unattainable, solutions using automated methods are sought. This study applies machine learning techniques to perform automated extremist language detection. In this project, we proposed an approach for detecting extremist content and identifying potential extremist users in social media. The study’s methodology explores features in users’ histories to predict extremism via statistical topic model on an Arabic corpus which detects extremist posts with automatically generated features and a graded structure in which, whether extremism applies to a given person is a matter of degree related to multiple factors. To demonstrate our work, we created a dataset containing over 360,000 web forum posts. Experiments on a sampled data set show precision of 96.20% and recall of 94.90%.

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.

Nabil Belgasmi holds a PhD and an Engineering degree in Computer Science from Manouba University. He is a full stack Data Scientist at Banque de Tunisie, Tunisia. He is involved in three main activities: (1) Applied R&D, (2) Data Analytics Technology Watch and (3) Data Science consulting. He achieved many successful Data Science POCs and Quick-Wins: Credit Scoring, Forecasting, Cash Planning, Anomaly/Fraud Detection, Customers Profiling, Intelligent Transactions Scoring & Monitoring, etc. He is a Member of the Industrial Editorial Board of the Engineering Applications of Artificial Intelligence journal (EAAI).

The current framework of reinforcement learning is based on a single objective performance optimization that is maximizing the expected returns based on scalar rewards that come from either univariate environment response to the agent actions or from a weighted aggregation of a multivariate response. But in many real world situations, tradeoffs must be made among multiple conflicting objectives that have the different order of magnitude, measurement units and business specific contexts related to the problem being solved (i.e. costs, lead time, quality of service, profits, etc.). The aggregation of such sub-rewards to get a scalar reward assumes a perfect knowledge about the decision maker preferences and the way she perceives the importance of each objective. In this study, we consider the problem of learning the best ATM cash replenishment policies in an uncertain multi-objective context given an arbitrary history of cash withdrawals that may be non-stationary and may contain outliers. We propose a model-free Multi-objective Deep Reinforcement Learning approach that allows us to compete against the human decision maker and to find the best policy per ATM that outperforms the current human policy. The idea is to disaggregate the performance of a replenishment policy to form a vector of objective functions. The performance of the human policy is then a multi-dimensional reference point (Rh). The task of the deep reinforcement learning algorithm is to find a policy that generates a set of performance points which Pareto-dominate the current human reference point (Rh).