Kolli Himantha Rao’s research while affiliated with SAVEETHA ENGINEERING COLLEGE and other places

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Publications (4)


Figure 1. Proposed methodology.
Figure 2. Accuracy.
Figure 3. Precision.
Figure 4. Recall.
Figure 5. F1 score.
Development of greenhouse automation using machine learning with remote monitoring control
  • Chapter
  • Full-text available

January 2025

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22 Reads

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S. Sophia

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C. Aravindan

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[...]

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K. Shiva Bhavani
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Fig 2. Python Program used in this Research.
Fig 3. Developed Robot in this Research.
Fig 4. Result of Mapping a) Case 1 b) Case 2 For the case 1 shown in Fig 4a the time metric showed the largest variations, with a distance variation of 30 seconds among the finest and nastiest time. Inconsistencies were experiential during direction finding, especially when turning around corners into the next corridor. It is likely that the robot circumnavigated too near to the ramparts as it curved the corner, causing the direction-finding system to reduce its speed significantly and temporarily stop when the robot is too near to the object. The possible solution at this situation would be to increase the sensitivity to obstacles on the map. To test the robot's obstacle avoidance abilities in case 2, two static obstacles were placed 1.92 meters apart in a hallway (as depicted in Fig 4b). A person was then added as a dynamic obstacle in the path after the second static obstacle. The robot effectively steered everywhere the lively obstacle in 6 out of 5 trials, demonstrating its adaptability. However, in the fifth trial, the robot failed to locate its location at the twitch and did not navigate to the destination. During the trials, there was a noticeable variance in navigation time due to differences in obstacle perception and distance. The robot reduced its speed when it approached an obstacle, which impacted its navigation time.
Fig 5. Experimental Result of The Case 3
Machine learning and Sensor-Based Multi-Robot System with Voice Recognition for Assisting the Visually Impaired

July 2023

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282 Reads

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22 Citations

Journal of Machine and Computing

Navigating through an environment can be challenging for visually impaired individuals, especially when they are outdoors or in unfamiliar surroundings. In this research, we propose a multi-robot system equipped with sensors and machine learning algorithms to assist the visually impaired in navigating their surroundings with greater ease and independence. The robot is equipped with sensors, including Lidar, proximity sensors, and a Bluetooth transmitter and receiver, which enable it to sense the environment and deliver information to the user. The presence of obstacles can be detected by the robot, and the user is notified through a Bluetooth interface to their headset. The robot's machine learning algorithm is generated using Python code and is capable of processing the data collected by the sensors to make decisions about how to inform the user about their surroundings. A microcontroller is used to collect data from the sensors, and a Raspberry Pi is used to communicate the information to the system. The visually impaired user can receive instructions about their environment through a speaker, which enables them to navigate their surroundings with greater confidence and independence. Our research shows that a multi-robot system equipped with sensors and machine learning algorithms can assist visually impaired individuals in navigating their environment. The system delivers the user with real-time information about their surroundings, enabling them to make informed decisions about their movements. Additionally, the system can replace the need for a human assistant, providing greater independence and privacy for the visually impaired individual. The system can be improved further by incorporating additional sensors and refining the machine learning algorithms to enhance its functionality and usability. This technology has the possible to greatly advance the value of life for visually impaired individuals by increasing their independence and mobility. It has important implications for the design of future assistive technologies and robotics.

Citations (1)


... those techniques search for uncommon styles of conduct that a fraudster is likely to use and is able to stumble on in a much quicker time than manual detection methods. An instance of this is using cluster evaluation to discover clusters of similar transactions that are possible to be fraudulent [3].while mixed, each supervised and unsupervised mastering strategy can form a complete fraud detection gadget. ...

Reference:

Secured Banking Systems for Critical Fraud Detection using Machine Learning Model
Machine learning and Sensor-Based Multi-Robot System with Voice Recognition for Assisting the Visually Impaired

Journal of Machine and Computing