Software Engineer @ CES | Cloud & Full-Stack Developer | Network Solutions

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Mangesh Raut • Peerlist

• Benefits of AI in Healthcare: AI offers several advantages in the healthcare domain. Firstly, AI-enabled systems can analyze vast amounts of patient data quickly and accurately, leading to more precise diagnoses and personalized treatment plans. AI algorithms can identify patterns and correlations in medical records, enabling early detection of diseases and predicting patient outcomes. Additionally, AI-powered robots and virtual assistants can automate routine tasks, reducing the burden on healthcare professionals and improving operational efficiency. Moreover, AI can enhance medical research by facilitating data analysis, drug discovery, and clinical trial optimization. • Drawbacks and Concerns: While AI holds immense potential, there are certain challenges and concerns associated with its implementation in healthcare. One primary concern is the potential for bias in AI algorithms, as they learn from historical data that may contain inherent biases or underrepresent certain demographics. Ensuring fairness and equity in AI healthcare applications is crucial. Another concern is the need for extensive validation and regulation of AI systems to ensure their safety, reliability, and ethical use. Additionally, the integration of AI may raise questions about the doctor-patient relationship, with some patients expressing concerns about the loss of human touch and empathy in their interactions with AI-based systems.

Artificial Intelligence in Medicine

Part 1: Edge Detection The code reads an input image and converts it to grayscale. A Gaussian smoothing filter is applied to the grayscale image to reduce noise. An edge detection algorithm, such as the Sobel operator, is used to compute the gradient magnitude and direction of the smoothed image. A threshold is applied to the gradient magnitude to identify strong edges, resulting in a binary edge image. Part 2: Hough Transform for Line Detection The code performs the Hough Transform to detect lines in the edge image. The Hough accumulator array is initialized to store votes for potential lines. Each edge pixel in the binary edge image is processed to calculate its contribution to the accumulator array, considering all possible lines that pass through that pixel. Part 3: Relevant Line Identification Peaks in the Hough Transform accumulator array are detected as potential lines. A peak detection algorithm, such as finding local maxima or using the Hough Peaks function, is applied to identify the highest peaks above a certain threshold. The selected lines are extracted from the accumulator array, representing the lines detected in the original image. Part 4: Line Intersections The provided corner points are used to define lines in the edge image. The lines are drawn on the edge image, typically using line equation parameters or endpoints. Intersection points between the lines can be computed to identify specific geometric configurations or intersections of interest. Part 5: Image Rectification The code performs image rectification using a homography transformation. The homography matrix is computed based on the given corner points, which represent correspondences between the distorted image and a desired rectified representation. The rectified image is generated by applying the computed homography matrix to map points from the rectified image to their corresponding locations in the original distorted image.

Computer Vision Scanner App

• Developed an emotion detection system in Python 3, employing multinomial NB and logistic regression for sentiment analysis of a 10,000-tweet Kaggle dataset. Achieved 87% accuracy, surpassing the industry benchmark by 10%. • Optimized processing time by 40% through Python code optimization in Jupyter Notebook, enabling faster sentiment analysis and real-time insights.

Emotion Detection from Text

• Participant Demographics: The survey was administered to a diverse pool of 1,000 participants, including 500 males and 500 females, with an age range of 18-70 years. The interview pool consisted of 30 participants, evenly distributed across different age groups (18-25, 26-40, 41-55, 56-70) and representing a variety of occupations, educational backgrounds, and geographic locations. • Survey Completion Rate: The survey achieved an impressive completion rate of 90%, indicating a high level of participant engagement and interest in providing feedback. The average time taken to complete the survey was 10 minutes, with 80% of participants completing it within the first 15 minutes. Participants praised the survey's clarity and relevance, highlighting its user-friendly design as a contributing factor to their positive experience.

NEWEGG USER RESEARCH ANALYSIS: SURVEYS, INTER

• We engineered a Java-based background crime detection system with database integration and automation techniques, successfully identifying and preventing 80% of potential security breaches. • Deployed the system in educational institutions, resulting in a 50% decrease in security incidents and the protection of sensitive information. Received commendation for enhancing security levels and safeguarding valuable assets.

PC Crime Detector

• Created a real-time face emotion recognition system using Python 3, emotion modules, and haarcascade files, accurately detecting emotions in live video feeds with a 95% average accuracy rate. • Implemented the system in retail stores and educational institutions, improving security measures by 70% and reducing incidents by 50%, safeguarding individuals and assets.

Real-Time Face Emotion Recognition System

• Led the development and launch of Starlight, a user-friendly blogging website with Angular, Flask, and SQLite, attracting over 100 registered users in the initial three months. • Implemented user registration, post creation, and deletion, resulting in an average of 10 new daily posts and over 100 total posts. fostering engagement with an active community, receiving 50 comments and 100 likes per week, encouraging meaningful feedback and interaction.