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Hello, I'm
V V V N Udayaditya
Software Developer
Hello, I'm
Software Developer
Get To Know More
Hey there! I'm a computer science student who loves creating software solutions. Throughout my journey, in software engineering I've gained a foundation and a hunger for taking on challenges. Collaborating with professionals to develop cutting edge projects and scalable systems is where I thrive.
3 years
Projects
Bachelors Degree in
Computer Science and Engineering with Artificial Intelligence
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Urban Harmony: Vital Role of Parking Slot Management in City Life highlights the importance of efficient parking systems in reducing congestion, enhancing traffic flow, optimizing urban space. Smart parking solutions improve accessibility, reduce pollution, and support sustainable city planning, ensuring a balanced and organized urban environment for residents and commuters.
This study focuses on proactive assessment and predictive modeling for chronic kidney disease (CKD). By integrating machine learning and clinical data, it enables early detection, risk prediction, and preventive measures. The approach enhances healthcare efficiency, reducing progression risks and improving patient outcomes through timely intervention and personalized care.
Integrating robotics, voice AI, and chatbots revolutionizes healthcare by enhancing patient experience, automating tasks, and improving accessibility. AI-driven systems provide personalized assistance, reduce wait times, and support medical staff, leading to efficient, responsive, and patient-centric healthcare services, ultimately improving operational efficiency and overall patient satisfaction in modern healthcare facilities.
In Python, machine learning models analyze data from temperature, sound, and vibration sensors to detect vehicle anomalies. This multi-sensor approach enhances accuracy, enabling early identification of potential issues. It supports predictive maintenance, reducing failures and improving vehicle performance, ensuring safety and efficiency in automotive systems.
This approach leverages deep learning models like YOLO, Faster R-CNN, and SSD to detect and track objects in real time. By utilizing GPUs and optimized architectures, it ensures high-speed and accurate detections, making it ideal for applications in surveillance, autonomous vehicles, and smart security systems.
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