Yashank Akula

Dynamic and results-oriented professional with experience as a Web Developer Intern at Oasis Infobyte and an AI-ML Intern. Excelled in fast-paced environments, demonstrating exceptional teamwork, critical thinking, and proficiency in Python and web development. Proven track record of quick learning and applying innovative solutions to complex problems. Experienced leader with strong background in guiding teams, managing complex projects, and achieving strategic objectives. Excels in developing efficient processes, ensuring high standards, and aligning efforts with organizational goals. Known for collaborative approach and commitment to excellence.

As a BTECH student with a strong passion for technology and development, I am seeking for a role where
I can utilize my technical skills and contribute t0 innovative project.

• [Oasis Infobyte certificate] [2023]
• [Eduskills] [2023]
• [Intenpe] [2024]
• [coursera] [2023]

DRIVER DROWSINESS DETECTION USING VISUAL FEATURES

• The proposed research focuses on implementing real-time machine learning algorithms for driver drowsiness detection using visual features.
• By utilizing advanced technology, such as computer vision and artificial intelligence, the system aims to accurately and efficiently identify signs of drowsiness in drivers.
• This approach involves extracting relevant features from network traffic data generated by IoT devices.
• Machine learning algorithms then learn to distinguish between normal and attack traffic based on these features. Features could include packet frequency, source-destination relationships, and traffic volume.

DETECTION OF DDOS ATTACK USING IOT NETWORK

• This study explores the application of machine learning techniques for detecting Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) environments.
• By analyzing IoT device communication patterns and employing diverse machine learning algorithms, we enhance the ability to differentiate normal traffic from malicious attacks.
• The main aim of using machine learning to detect DDoS attacks in an IoT network is to develop an automated and proactive approach that can identify abnormal patterns of network traffic associated with DDoS attacks.
• By analyzing the data generated by IoT devices in real-time, machine learning algorithms can learn the normal behavior of the network and identify deviations that might indicate a DDoS attack in progress.
• This helps in early detection and mitigation of such attacks, enhancing the security and reliability of the IoT network