Collaborating Authors


Reputation acquires Nuvi to add social listening, visualization to its CX platform


Reputation said it will acquire social customer experience management provider Nuvi to add social listening to its platform. Terms of the deal weren't disclosed. Nuvi, based in Lehi, Utah, will add another pillar to Reputation's platform, which includes survey-based experience management, business listings and experience management. Reputation, known for its Reputation Score X, said its goal is to provide a 360-degree view of sentiment and Nuvi adds social listening. Via Nuvi, Reputation will add social listening and data visualization tools to its RXM platform and cover Twitter, Facebook and Reddit among others.

Music Circles: An interactive data visualization tool that helps users discover new music


Today, users can listen to music and discover new artists, songs or albums on a variety of music streaming platforms, including Spotify, Apple Music, Amazon Music Unlimited and more. Many developers have been trying to create tools that could improve these services, such as music recommendation systems that suggest new songs or playlists to users based on their preferences and on music they listened to in the past. Researchers at Seoul National University recently created an interactive data visualization tool that could enhance both existing and emerging music streaming services. This tool, called Music Circles, can represent songs as unique vectors and then calculate similarities between different vectors to group similar songs into clusters. "As music lovers with different tastes, we came together for a project that would find novel ways of visually representing and grouping abstract music data," Seokgi Kim, Jihye Park, Kihong Seong, Namwoo Cho, Junho Min and Hwajung Hong, the researchers who carried out the study, told TechXplore via email.

Python for Machine Learning with Numpy, Pandas & Matplotlib


Python for Machine Learning with Numpy, Pandas & Matplotlib Learn to Code in Python and How to use NumPy, Pandas, Matplotlib and Seaborn by real time Machine Learning project. Description Are you ready to start your path to becoming a Data Scientist or ML Engineer? This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!

Can you explain this?


It's the year 2030, we are living in an age of increasing automation and artificially intelligent bots are powering this. All the trivial decisions are driven by machines and they are redesigning our ways of life. Lorem Ipsum is frustrated with the work lately and has a splitting headache, the automated recommendations from his Google health don't help and he gets an appointment with his doctor. It takes him just 30 minutes to go get through all the arduous stages of his brain test until he sees the doctor finally. "The scan results look negative for any abnormality, the brain health score is on the positive side", the doctor concludes it is just a headache and prescribes the medicines.

Interpretability in Machine Learning: An Overview


This essay provides a broad overview of the sub-field of machine learning interpretability. While not exhaustive, my goal is to review conceptual frameworks, existing research, and future directions. I follow the categorizations used in Lipton et al.'s Mythos of Model Interpretability, which I think is the best paper for understanding the different definitions of interpretability. We'll go over many ways to formalize what "interpretability" means. Broadly, interpretability focuses on the how. It's focused on getting some notion of an explanation for the decisions made by our models. Below, each section is operationalized by a concrete question we can ask of our machine learning model using a specific definition of interpretability. If you're new to all this, we'll first briefly explain why we might care about interpretability at all.

Machine learning, data is helping medical fraternity: Rahul Sharma


How covid-19 pandemic brought digitization of healthcare to the fore? There are broadly two patterns that have emerged in healthcare digitization since the start of the pandemic. The first is the adoption of digital technologies by the central and state governments to monitor and track the spread of the pandemic, connect with various authorities for decision-making, and provide healthcare services that can help citizens. The pandemic created the need for collaboration among various stakeholders at an unprecedented scale – multiple departments and decision-makers in governance at the ward, city, district, state and national level; administration and medical staff at hospitals; drug manufacturers and distributors; care-givers and health counselors; volunteers; and police and security personnel. This meant a large volume of data flow, data storage, analytics, and visualization, necessitated the need for a robust, scalable, and secure cloud platform to deliver it.

SuSketch: Surrogate Models of Gameplay as a Design Assistant Artificial Intelligence

This paper introduces SuSketch, a design tool for first person shooter levels. SuSketch provides the designer with gameplay predictions for two competing players of specific character classes. The interface allows the designer to work side-by-side with an artificially intelligent creator and to receive varied types of feedback such as path information, predicted balance between players in a complete playthrough, or a predicted heatmap of the locations of player deaths. The system also proactively designs alternatives to the level and class pairing, and presents them to the designer as suggestions that improve the predicted balance of the game. SuSketch offers a new way of integrating machine learning into mixed-initiative co-creation tools, as a surrogate of human play trained on a large corpus of artificial playtraces. A user study with 16 game developers indicated that the tool was easy to use, but also highlighted a need to make SuSketch more accessible and more explainable.

Prediction of lung and colon cancer through analysis of histopathological images by utilizing Pre-trained CNN models with visualization of class activation and saliency maps Artificial Intelligence

Colon and Lung cancer is one of the most perilous and dangerous ailments that individuals are enduring worldwide and has become a general medical problem. To lessen the risk of death, a legitimate and early finding is particularly required. In any case, it is a truly troublesome task that depends on the experience of histopathologists. If a histologist is under-prepared it may even hazard the life of a patient. As of late, deep learning has picked up energy, and it is being valued in the analysis of Medical Imaging. This paper intends to utilize and alter the current pre-trained CNN-based model to identify lung and colon cancer utilizing histopathological images with better augmentation techniques. In this paper, eight distinctive Pre-trained CNN models, VGG16, NASNetMobile, InceptionV3, InceptionResNetV2, ResNet50, Xception, MobileNet, and DenseNet169 are trained on LC25000 dataset. The model performances are assessed on precision, recall, f1score, accuracy, and auroc score. The results exhibit that all eight models accomplished noteworthy results ranging from 96% to 100% accuracy. Subsequently, GradCAM and SmoothGrad are also used to picture the attention images of Pre-trained CNN models classifying malignant and benign images.

4 Data Visualization Tools To Transform Your Data Storytelling


At first glance, data science always appears to be an intricate field -- or maybe I should say a collection of fields. It very broad vague, and one can argue complex. But, the truth is, data science can be defined very simply using one sentence. Data science is the field of interpreting data collected from different resources into useful information. Or in other words, it is all about listening and translating the story some data is trying to deliver.

Time Series Prediction using Spark


These days in high-tech or smart cities the pedestrian counts can be monitored by deploying sensors at certain locations which can count the number of pedestrians every hour(as per the data used for this blog) or as required. From the title of the post itself one can understand that here we will try to predict the count of pedestrians or pedestrian traffic at certain locations for the next hour from the data of previous hour(s). This technique is also called a one-step time-series prediction, where we are predicting the next value with the previous values. Therefore this is a time-series regression type of problem as the data to predict is of continuous nature. Using these predictions we can select the locations with most traffic which can then be used by certain companies to market their products, performers in the music and entertainment industry to make sure that they are heard by the most amount of people, etc.