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  • 20 Mins
    Talk
    Beginner

    Healthcare industry across the world has progressed very rapidly over the last two decades. However, the healthcare industry is behind other sectors in adopting the newer IT technologies. This talk primarily focuses on imbibing Artificial Intelligence (AI) in medical domain innovations. Like other revolutionary advances in medicine, AI is to be integrated into healthcare practices.

    Healthcare using Artificial Intelligence is amongst the fastest growing research area across the globe. A massive amount of heterogeneous data generated in healthcare sector offers opportunities for big data analytics. Such analysis transforms big data into real and actionable insights to healthcare practices, thus provide new understanding and ways for better and quicker treatment and improve overall individual and population health.

    Recent advancements in AI are proving beneficial in development of applications in various spheres of healthcare such as microbiological analysis, discovery of drug, disease diagnosis, Genomics, medical imaging and bioinformatics. Due to increasing availability of electronic healthcare data (structured as well as unstructured data) and rapid progress of analytics techniques, a lot of research is being carried out in this area. Popular AI techniques include machine learning/deep learning for structured data and natural language processing for unstructured data. Guided by relevant clinical questions, automation using AI can unlock clinically relevant information hidden in the massive amount of structured/unstructured data, which in turn can assist clinical decision making.

    The talk connects three contemporary areas of research: AI, Healthcare and Bigdata Analytics. It will provide attendees a collective update on developments in healthcare using AI, major challenges, opportunities and future research directions.

  • 20 Mins
    Talk
    Beginner

    Machine Learning has been rapidly adopted in various spheres of healthcare for translating medical data into improved human healthcare. Deep learning based healthcare applications assist physicians to make faster, cheaper and more accurate diagnosis. This talk will focus on “Early Detection of Hypothyroidism in Infants using Machine Learning”.

    Thyroid is a hormone secreting gland which influences all metabolic activities in our body. Hypothyroidism is a common disorder of thyroid that occurs when thyroid gland produces an insufficient amount of thyroid hormone. Deficiency of thyroid hormone at birth leads to hypothyroidism in babies. The common hypothyroidism symptoms in infants are prolong jaundice, protruding tongue, hoarse cry, puffy face, pain and swelling in joints, goiter and umbilical hernia. During early stage of hypothyroidism, babies may not have noticeable symptoms and hence, doctors (Physicians, Paediatricians and Paediatric Endocrinologists) face difficulty in recognizing hypothyroidism in infants. If hypothyroidism in infants isn’t treated during early stage, severe complications such as mental retardation, slower linear growth, loss of IQ, poor muscle tone, sensorinueral deafness, speech disorder and vision problem may arise. As a consequence, infant’s growth cannot be proceeded as healthy infants. To prevent such complications, we have developed a novel approach to diagnose hypothyroidism in infants during its early stage. To the best of our knowledge, this is the first attempt to detect hypothyroidism based on only facial symptoms viz. puffy face, jaundice, swelling around eyes, protruding tongue and flat bridged nose with broad fleshy tip.

    This talk will include motivation for this work, precise problem statement and its solution, data set generated consulting Pediatric Endocrinologists and experimental results. Finally, possible extensions of this work and the future scope of research in healthcare sector will be discussed.

  • Liked Vrishank Gupta
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    Vrishank Gupta / Saakshi Bhargava - Assembling a Perfect Personal Computer using Genetic Algorithms

    20 Mins
    Demonstration
    Beginner

    Assembling a perfect personal computer, that meets various varying requirements of a family such as gaming, regular usage, programming, etc., in such a huge market of features is quite a challenge nowadays.

    Despite the efforts put by consumers to customize their computers to meet the different requirements, the percentage of satisfied consumers is very less. This session aims to propose and demonstrate a genetic algorithm approach to find the optimum set of features, given that each feature adds to the cost of the computer but provides some benefit to the consumer, the selected features must be fulfilled within a given budget. The experimental result yields the average fitness convergence at value 5524 which is a marked improvement over 23% over a recently published paper that used the Group Selection Technique along with single-point crossover for hardware selection.

  • 20 Mins
    Talk
    Beginner

    Deep Learning has been rapidly adopted in various spheres of healthcare for translating medical data into improved human healthcare. Deep learning based healthcare applications assist physicians to make faster, cheaper and more accurate diagnosis. This talk will focus on how commonly occurring Dry Eye Disease (DED) can be diagnosed accurately and speedily using deep learning based automated approach.

    DED is one of the commonly occurring chronic disease in the world today. It causes severe discomfort in eye, visual disturbance and blurred vision impacting the quality of life of patient. Certain factors such as prolonged use of electronic gadgets, old age, environmental conditions, medication, smoking habits and use of contact lens can disturb the tear film balance and can lead to evaporation of moisture from tear film which causes dry eye disease. If DED is left untreated, it can cause infection, corneal ulcer or blindness. However, diagnosis of dry eye is a difficult task because it occurs due to different factors. An ophthalmologist sometimes requires multiple tests or repetitive tests for proper diagnosis. Moreover, the major drawbacks of clinical diagnosis are: 1) Higher time in clinical diagnosis as it is done manually. This has severe impact during mass screening in Civil Hospitals and Multispecialilty Hospitals 2) Diagnosis is subjective in nature 3) Accurate severity level of DED is not identified and 4) Medication may be prescribed for incorrect period on the basis of inaccurate severity level. To overcome these drawbacks, we have developed a deep learning based automated approach to diagnose DED considering Tear Film Breakup Time (TBUT) which is a standard diagnostic procedure. This automated approach is to assist ophthalmologist and optometrist to bring objectivity in diagnosis, to increase diagnosis accuracy and to make diagnosis faster so that ophthalmologist can devote more time in counselling of patients.

    The talk will include motivation, precise problem statement, proposed solution, data set generated consulting ophthalmologists and experimental results. Finally, the possible extensions of our work and the future scope of research in healthcare sector will be discussed.

  • Liked Saakshi Bhargava
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    Saakshi Bhargava / Vrishank Gupta - Generative Adversarial Networks (GANs)

    20 Mins
    Demonstration
    Beginner

    Deep learning has accomplished pronounced triumph in the field of artificial intelligence, there are many deep learning models that have been developed in the recent time. Generative Models (GAN) are one of the deep learning models, that was given based on the game theory called zero-sum and now has been treated as the hot area for research. Generative Models are modern techniques used in computer vision. Unlike other neural networks that are used for predictions from images, generative models can generate new images as well for specific objectives. They can be used for generating huge datasets. This session will review several applications of generative modeling such as image generation and image translation, video frame prediction using CNNs and GANs.

  • Liked Rakshit Prabhakar
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    Rakshit Prabhakar - Smart AI Drones : As Emergency Responders During Accidents

    Rakshit Prabhakar
    Rakshit Prabhakar
    Innovator
    AIM
    schedule 3 months ago
    Sold Out!
    20 Mins
    Talk
    Beginner

    Growing population, growing vehicles and lack of awareness has led to the problem of accidents happening at low to high density vehicle and human population. A witness in an accident has no medical training and often becomes a bystander. Drones as emergency responders exploits the use of ICT with AI to as an add-on to emergency services. With a push of a button by a witness, the drones use the GPS of the phone to reach the spot, the AI model is trained to classify the accident and shares its results with the emergency responders including hospitals. The ambulance reaching the spot is prepared with the basic of what has to be done and the nearest hospital well prepared to act.

  • Liked Amarjeet Dua
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    Amarjeet Dua - Case Study: Fusing Machine Learning into Operations Research Techniques to solve Complex Optimization Problems

    20 Mins
    Experience Report
    Intermediate

    Machine Learning (ML) and Operations Research (OR) have co-existed for long. There have been amazing applications driven by ML and OR that we come across in our day-to-day lives. These applications range from matching algorithms on dating websites to solving large scale vehicle routing problems for complex supply chains. But, have you ever wondered what happens when these two areas of mathematical science come together to solve complex real-world optimization problems?

    Are you curious to know how OR-applications can benefit from the power of ML?

    In this talk, we’ll go through a real-world case study where we used the power of (ML+OR) to create significant dollar savings in the area of airline flight schedules. I will also take you through cases where ML can help OR solutions shine further to solve more generic problems.

  • Liked Abdul Azeez
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    Abdul Azeez - Photo-Realistic Single Image Super-Resolution using SRGAN

    Abdul Azeez
    Abdul Azeez
    Data Scientist
    Nexquare
    schedule 3 months ago
    Sold Out!
    20 Mins
    Demonstration
    Advanced

    As we know, a basic GAN has two neural Networks – the Discriminator (D) and the Generator (G). The Generator attempts to generate images that look like real images. The Discriminator tries to distinguish the generated images from real images. By combined loss minimization of these two neural networks, the entire model trains and eventually reach a state of equilibrium, where the Discriminator no longer can distinguish the fake images, generated by the Generator, from real images.


    In this talk, I'll be discussing how GANs can be used to achieve Super Resolution. Super Resolution is the process of upscaling and or improving the details within an image. Often a Low Resolution image is taken as an input and the same image is upscaled to a higher resolution, which is the output. The details in the High Resolution output are filled in where the details are essentially unknown.

    The traditional way of upscaling an image was to perform interpolation over the pixels such as Bicubic Interpolation. Drawbacks of this method are images that get smoothened or details and definitions are lost.

    The need for high resolution is extensive right from a portrait we capture from our phone to image recognition, forensic science, etc.

  • Liked Gouthaman Asokan
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    Gouthaman Asokan - Real Time Multi Person Pose Estimation

    Gouthaman Asokan
    Gouthaman Asokan
    AI Researcher
    Cellstrat
    schedule 3 months ago
    Sold Out!
    20 Mins
    Demonstration
    Intermediate

    Openpose is a library written in C++ with python wrapper available for real time multi person key point detection and multithreading. This model predicts the location of various human keypoints such as chest, hips, shoulder, neck, elbows, knees. This model uses part affinity fields and greedy inference to connect these localized keypoints.

    In this talk, I'll be discussing how Openpose helps in the real time multi person detection system to jointly detect human body,hand,facial and foot keypoints detection and the part affinity field.

    Also,discuss the model architecture,comparing with other models like Mask RCNN and AlphaPose. Finally show how pose estimation can be done on single as well as multiple person images using pretrained models

  • Liked Vivek Singhal
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    Vivek Singhal - How to start and grow an AI Lab

    20 Mins
    Experience Report
    Beginner

    I will discuss how I started CellStrat AI Lab and what goes to make it successful as one of the leading AI research groups in India.

    I will cover facets such as origination, growth, researcher motivation, content portfolio, project activity, research areas, marketing techniques and talent development pipeline.

    This presentation will give an idea to corporate and academic institutions as to how they can create and nurture a world-class AI Lab within their organizations.

  • Liked Vishal Singhal
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    Vishal Singhal - How to Fulfill Management’s Enterprise AI Initiative

    20 Mins
    Talk
    Executive

    Artificial Intelligence fulfillment is a hot topic these days across continents and boardrooms of all small to mid-range to large companies alike. A common question for everybody is to understand as to how they can utilise the technology, deploy it in their business and what will it take to achive this business objective. “How to Fulfill Management’s Enterprise AI Initiative” topic addresses most of such queries and attempts to answer them taking a well-rounded perspective in the Indian Enterprise context.

  • Liked Vivek Singhal
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    Vivek Singhal / Niraj Kale - Scalable and Efficient Object Detection with EfficientDet

    20 Mins
    Talk
    Intermediate

    There has been much research in efficient and scalable model approaches required for network design and object detection. Feature Pyramid Networks (FPN) enabled feature fusion at different scales. Path Aggregation Networks (PANet) conduct bottom-up path augmentation in an FPN, which shortens information transfer from bottom layers to topmost features. A more recent scheme, NAS-FPN (Neural Architecture Search-Feature Pyramid Network), combines RL-based NAS with an FPN to enable combination of top-down and bottom-up connections to fuse features across scales.

    A recent state-of-the art model EfficientDet uses a weighted bi-directional BiFPN with multi-scale feature fusion across layers to account for learnable feature importance while applying bottom-up and top-down feature fusion. EfficientDet combines BiFPN with efficient model scaling techniques proposed in EfficientNet model, such that the baseline network, the feature network and the bounding box/class prediction networks are all scaled uniformly and efficiently using compound scaling technique across resolution/width/depth dimensions. The EfficientNet-D6 achieves top of the line accuracy (mAP) on COCO dataset with 4x lesser parameters and 13x fewer FLOPS then the recent comparable NAS-FPN model.

  • Liked Anupam Ranjan
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    Anupam Ranjan / Yash Raj - SQUAD application through Knowledge Graph for COVID-19 Literature

    20 Mins
    Demonstration
    Advanced

    There are numerous documents and research papers being published for COVID-19 and doctors are not able to absorb the content of all the literature. It has become a real challenge to extract relevant information in a short span of time.

    Knowledge Graph along with SQUAD application can help process multiple documents and extract precise information from a set of documents quickly. This will be a very handy application for healthcare professional to extract relevant information without going in detail with each application.

    The session will demonstrate the following:

    a) Text Processing of COVID-19 literature

    b) Named Entity Extraction from the documents using BERT/Spacy

    c) Building a Knowledge Graph of the documents

    d) Building question-answer application

  • Liked Saumya Suvarna
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    Saumya Suvarna / Mashrin Srivastava - Redefining Ethics and Privacy in the Age of AI

    20 Mins
    Talk
    Intermediate

    The session ‘Redefining Ethics and Privacy in the age of AI’ provides a holistic view of the need to change the current outlook on privacy and ethics with the wide use and deployment of various machine learning and deep learning technologies. This session covers the ways in which privacy can be lost in machine learning and deep learning deployments and how differential privacy can be used as a privacy preserving mechanism. It also explores the possibility of using synthetic databases with privacy preservation. Lastly, it includes the ethical challenges that are currently being faced and will be faced in the future.

  • Liked Aditya Bhattacharya
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    Aditya Bhattacharya - Application of Masked RCNN for segmentation of brain haemorrhage from Computed Tomography Images

    20 Mins
    Demonstration
    Intermediate

    Automated analysis of CT scan images using AI solutions to diagnose abnormalities will help in overcoming the costly, time consuming and prone to error from manual analysis. Deep Learning has proved to be quite efficient to mimic human cognitive abilities (and even exceed that in many cases), especially with unstructured data.

    DL algorithms can detect, localize and quantify a growing list of brain pathologies including intra-cerebral bleeds and their subtypes, infarcts, mass effect, midline shift, and cranial fractures. So, with advanced DL algorithms, analysis of radiographic data can be easily achieved and this can accelerate early detection of certain critical medical conditions, powered by AI.

    As mentioned, Deep Learning algorithms for computer vision use cases has been extremely successful for classification and localization related problems. With the availability of annotated dataset, object of interest or region of interest segmentation using Deep Learning has been plausible.

    Algorithms like Regional Convolutional Neural Network (RCNN) and it’s evolved forms, Faster RCNN and Masked RCNN is being widely used in the field of advanced radiology to auto detect medical conditions through radio-graphic images.

    For this session, I am particularly going to talk about application of Masked RCNN for detection of regions of brain haemorrhage from CT scan images of the brain.

  • Liked Aditya Bhattacharya
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    Aditya Bhattacharya - Using Deep Learning to identify medical conditions related to Thorax Region from Radiographic X-Ray Images

    20 Mins
    Talk
    Intermediate

    Automated analysis of Chest X-ray images to diagnose various pathologies will help in overcoming the costly, time consuming and prone to error from manual analysis of them, especially using deep learning based approaches. One of such recent efforts in this direction is Classification of Common Thorax which combines the advantages of CNN based feature extraction and problem transformation methods in multi-label classification task.

    So this is one of the key areas where deep learning based solution has already made an impact and has the potential to come up with even a better and well improved performance.

    For this session, I am going to discuss about the problem at hand, the data-set, several approaches that has been explored and that worked quite well so far in this research. Also I am going to mention about the potential use case and the real world impact of such a real world healthcare application that can save millions of lives by early and effective detection.

    Also I am going to mention about some of the key challenges faced during this research and how it can be scaled to build an end to end software solution!

  • Liked Bharati Patidar
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    Bharati Patidar - AI/ML under the covers of modern Master Data Management

    20 Mins
    Talk
    Intermediate

    Data quality is utmost important in Master Data Management solutions. Data curation and standardisation involves multiple iterations of exchange between customers and its’ vendors. Rules written for validations and corrections, pile up and their maintenance gets costlier with time. Data quality rules can run from 500+ to 20K, many of which get outdated, but cannot be taken out risking any regressions. To address these challenges, we turned to machine learning to enable autocorrection of the human errors and standardisation of the content across products on-boarded.

    This talk is about our journey to fix the problem at hand where we started with implementing a simple spell check algorithm using edit distance/Levenshtein distance to more complex language models. We used state of the art approaches such as a char-to-char sequence model with encode decoder, auto encoders, attention based transformers and even BERT. The result from these models were getting better, but not good enough to the quality expected. These experiments with latest techniques helped us build a strong intuition and understanding of language models.

    I will also be touching upon the data collection, it’s challenges and our work arounds. The key takeaway will be performance comparisons of the various techniques and approaches from the experiments, (in the context of our use case) something similar that I had once longed to see before starting on this journey. I will also share my experience on intuitions learned and common mistakes to be aware of.

    If there is anything that blocks you today from trying new techniques, or keeps you wondering how and where to start from, or anything that I could help you with, please leave a comment and I will work to get answers to, in this talk (if the talk gets accepted, if not pls reach out to me on linkedIn and I will be happy to help.).

  • Liked Kriti Doneria
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    Kriti Doneria - Trust Building in AI systems: A critical thinking perspective

    Kriti Doneria
    Kriti Doneria
    Data Science
    Practitioner
    schedule 9 months ago
    Sold Out!
    20 Mins
    Talk
    Beginner

    How do I know when to trust AI,and when not to?

    Who goes to jail if a self driving car kills someone tomorrow?

    Do you know scientists say people will believe anything,repeated enough

    Designing AI systems is also an exercise in critical thinking because an AI is only as good as its creator.This talk is for discussions like these,and more.

    With the exponential increase in computing power available, several AI algorithms that were mere papers written decades ago have become implementable. For a data scientist, it is very tempting to use the most sophisticated algorithm available. But given that its applicability has moved beyond academia and out into the business world, are numbers alone sufficient? Putting context to AI, or XAI (explainable AI) takes the black box out of AI to enhance human-computer interaction. This talk shall revolve around the interpret-ability-complexity trade-off, challenges, drivers and caveats of the XAI paradigm, and an intuitive demo of translating inner workings of an ML algorithm into human understandable formats to achieve more business buy-ins.

    Prepare to be amused and enthralled at the same time.

  • Liked Ujwala Musku
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    Ujwala Musku - Supply Path Optimization in Video Advertising Landscape

    Ujwala Musku
    Ujwala Musku
    Data Scientist II
    MiQ Digital
    schedule 3 months ago
    Sold Out!
    20 Mins
    Talk
    Beginner

    In the programmatic era, with a lot of players in the market, it is quite complex for a buyer to reach the destination, namely advertising slot from the source, namely publisher. Auction Duplication, internal deals between DSP & SSP, and fraudulent activities are making the existing complex route even more complex day by day. Due to the aforementioned reasons, it is fairly evident that a single impression is being sold through multiple routes by multiple sellers at multiple prices. The new dilemma that has emerged recently is: Which route/path should the buyer choose and what should be the fair price to pay?

    In this talk, we will discuss a framework that solves the problem of choosing the best path at the right price in programmatic Video Advertising. Initially, we will give an overview of all the different approaches tried i.e., Clustering, Classification Modelling, DEA, and Scoring based on Classification modeling. Out of these, DEA and Scoring Methodology had better results, and hence a detailed comparison of results and why a particular approach worked better will be illustrated. The final framework explains the two best-worked techniques: 1. Data Envelopment Analysis and 2.Scoring based on Classification Modeling. DEA is a non-parametric method used to rank the Unsupervised dataset of various supply paths by estimating the relative efficiencies. These efficiencies are calculated by comparing all the possible production frontiers of decision-making units (here supply paths). As a statistical and machine learning hybrid, the Scoring method calculates the score against each supply path, helping us decide whether a path is worth bidding.

    The results of these models are compared with each other to choose the best one based on campaign KPI i.e., CPM (Cost per 1000 impressions) and CPCV (Cost per completed view of the video ad). A 4 - 8% improvement in CPM is observed in multiple test video ad campaigns, however, there is a dip in the number of impressions delivered. This is tackled by including impressions as an input in both the techniques. These clear improvements in CPM indicate that the technique results in better ROI compared to the heuristic approach. This approach can be used in various sectors like Banks (determining Credit Score) and Retail Industries(supply path optimization in Operations).

  • Liked Gunjan Dewan
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    Gunjan Dewan - Developing a match-making algorithm between customers and Go-Jek products!

    Gunjan Dewan
    Gunjan Dewan
    Data Scientist
    Go-Jek
    schedule 4 months ago
    Sold Out!
    20 Mins
    Talk
    Beginner

    20+ products. Millions of active customers. Insane amount of data and complex domain. Come join me in this talk to know the journey we at Gojek took to predict which of our products a user is most likely to use next.

    A major problem we faced, as a company, was targeting our customers with promos and vouchers that were relevant to them. We developed a generalized model that takes into account the transaction history of users and gives a ranked list of our services that they are most likely to use next. From here on, we are able to determine the vouchers that we can target these customers with.

    In this talk, I will be talking about how we used recommendation engines to solve this problem, the challenges we faced during the time and the impact it had on our conversion rates. I will also be talking about the different iterations we went through and how our problem statement evolved as we were solving the problem.