surveillance

telecome-consumer-complaints-data-analytics-python

Data Dictionary Ticket #: Ticket number assigned to each complaint Customer Complaint: Description of complaint Date: Date of complaint Time: Time of complaint Received Via: Mode of communication of the complaint City: Customer city State: Customer state Zipcode: Customer zip Status: Status of complaint Filing on behalf of someone Analysis Task To perform these tasks, you can use any of the different Python libraries such as NumPy, SciPy, Pandas, scikit-learn, matplotlib, and BeautifulSoup. - Import data into Python environment. - Provide the trend chart for the number of complaints at monthly and daily granularity levels. - Provide a table with the frequency of complaint types. Which complaint types are maximum i.e., around internet, network issues, or across any other domains. - Create a new categorical variable with value as Open and Closed. Open & Pending is to be categorized as Open and Closed & Solved is to be categorized as Closed. - Provide state wise status of complaints in a stacked bar chart. Use the categorized variable from Q3. Provide insights on: Which state has the maximum complaints Which state has the highest percentage of unresolved complaints - Provide the percentage of complaints resolved till date, which were received through the Internet and customer care calls.

turbulence-mitigation

DeTurbulence using non-deep methods.

video-analytics-

Basic python libraries for video analytics like tensorflow, scikit learn

video-quality-metrics

Video quality metrics are algorithms designed to predict how actual viewers would gauge video quality. These metrics are used for a range of activities, from comparing codecs and different encoding configurations, to assisting in production and live quality of experience (QoE) monitoring. Image quality can degrade due to distortions during image acquisition and processing. Examples of distortion include noise, blurring, ringing, and compression artifacts. Efforts have been made to create objective measures of quality. For many applications, a valuable quality metric correlates well with the subjective perception of quality by a human observer. Quality metrics can also track unperceived errors as they propagate through an image processing pipeline, and can be used to compare image processing algorithms. If an image without distortion is available, you can use it as a reference to measure the quality of other images. For example, when evaluating the quality of compressed images, an uncompressed version of the image provides a useful reference. In these cases, you can use full-reference quality metrics to directly compare the target image and the reference image. If a reference image without distortion is not available. you can use a no-reference image quality metric instead. These metrics compute quality scores based on expected image statistics.

video-quality-metrics-with-graph-visualiation

video-to-frames

Basic requirements for video analytics -- frame separation, frame rate, frame resize, Average of the frames

yolov2

A single convolutional network simultaneously predicts multiple bounding boxes and class probabilities for those boxes. YOLO trains on full images and directly optimizes detection performance. This unified model has several benefits over traditional methods of object detection. First, YOLO is extremely fast. Since we frame detection as a regression problem we don’t need a complex pipeline. We simply run our neural network on a new image at test time to predict detections.

zoho-analytics-for-environmental-science

A dashboard consisting of simple visualizations and analytics of environmental dataset using Zoho Analytics tool.