The document describes and compares supervised and reinforcement learning models for self-driving cars. It discusses:
1) A supervised learning model that predicts steering angle based on camera images using a convolutional neural network trained on human-driven data. Accuracy improved from 20% to 85% with more training data.
2) A reinforcement learning model that trains an agent car to navigate an environment and avoid obstacles using rewards and Q-learning. The car learns to optimize its path in real-time as new barriers are added.
3) The key differences are that the supervised model only considers the car's motion, while the reinforcement model also accounts for external obstacles by giving rewards/penalties. The reinforcement model requires no explicit data
A machine learning model for average fuel consumption in heavy vehiclesVenkat Projects
The document describes a machine learning model that uses artificial neural networks to predict average fuel consumption in heavy vehicles. Seven features are extracted from vehicle dataset to train the ANN model, including number of stops, time stopped, average speed, etc. The trained model is then used to predict fuel consumption for new test vehicles based on their feature values. Screenshots of a software implementation of this ANN model for fuel consumption prediction are also included.
The document discusses an optimal fault tolerance model for real-time cloud computing. It proposes running variant algorithms on multiple virtual machines and using an adjudicator to select the output. The adjudicator contains modules to verify results, check timing, assess reliability, make decisions, and enable recovery. It provides both forward and backward recovery. Experimental results show the system can tolerate various failure scenarios and dynamically adjust reliability weights to improve fault tolerance for real-time tasks running on cloud infrastructure.
IRJET- Self Driving RC Car using Behavioral CloningIRJET Journal
This document describes a project to convert a remote-controlled car into a self-driving car using behavioral cloning and deep learning. The researchers recorded video of themselves driving the RC car manually and used this data to train a convolutional neural network to map images from the car's camera to steering commands. Their model achieved 84.5% accuracy in testing and was able to autonomously drive the car forward, left, and right based on the training. While successful, the approach has limitations such as requiring an expert driver for training and not handling dynamically changing scenarios well. The researchers suggest ways to improve the system such as using more powerful hardware and adding navigation and object detection capabilities.
PUMA 560 TRAJECTORY CONTROL USING NSGA-II TECHNIQUE WITH REAL VALUED OPERATORSijscmc
In the industry, Multi-objectives problems are a big defy and they are also hard to be conquered by conventional methods. For this reason, heuristic algorithms become an executable choice when facing this kind of problems. The main objective of this work is to investigate the use of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) technique using the real valued recombination and the real valued mutation in the tuning of the computed torque controller gains of a PUMA560 arm manipulator. The NSGA-II algorithm with real valued operators searches for the controller gains so that the six Integral of the Absolute Errors (IAE) in joint space are minimized. The implemented model under MATLAB allows an optimization of the Proportional-Derivative computed torque controller parameters while the cost functions and time are simultaneously minimized.. Moreover, experimental results also show that the real valued recombination and the real valued mutation operators can improve the performance of NSGA-II effectively.
PUMA 560 Trajectory Control Using NSGA-II Technique with Real Valued Operatorsijscmcj
In the industry, Multi-objectives problems are a big defy and they are also hard to be conquered by conventional methods. For this reason, heuristic algorithms become an executable choice when facing this kind of problems. The main objective of this work is to investigate the use of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) technique using the real valued recombination and the real valued mutation in the tuning of the computed torque controller gains of a PUMA560 arm manipulator. The NSGA-II algorithm with real valued operators searches for the controller gains so that the six Integral of the Absolute Errors (IAE) in joint space are minimized. The implemented model under MATLAB allows an optimization of the Proportional-Derivative computed torque controller parameters while the cost functions and time are simultaneously minimized.. Moreover, experimental results also show that the real valued recombination and the real valued mutation operators can improve the performance of NSGA-II effectively.
Puma 560 trajectory control using nsga ii technique with real valued operatorsijscmcj
In the industry, Multi-objectives problems are a big defy and they are also hard to be conquered by conventional methods. For this reason, heuristic algorithms become an executable choice when facing this kind of problems. The main objective of this work is to investigate the use of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) technique using the real valued recombination and the real valued mutation in the tuning of the computed torque controller gains of a PUMA560 arm manipulator. The NSGA-II algorithm with real valued operators searches for the controller gains so that the six Integral of the Absolute Errors (IAE) in joint space are minimized. The implemented model under MATLAB allows an optimization of the Proportional-Derivative computed torque controller parameters while the cost functions and time are simultaneously minimized.. Moreover, experimental results also show that the real valued recombination and the real valued mutation operators can improve the performance of NSGA-II effectively.
IRJET-Automatic Self-Parking Chair using Nissan TechnologyIRJET Journal
The document describes an automatic self-parking chair system using Nissan technology. Webcams are used to capture images of a room and detect the positions of chairs using MATLAB image processing. When a meeting ends, the system can automatically rearrange misplaced chairs to their proper positions. It works by sending commands from the MATLAB program to an FPGA controller and motor drivers on each chair via an RF link. This allows chairs to be autonomously repositioned without manual labor, saving time after meetings.
This document summarizes a project that aims to classify heartbeats (ECG signals) into four classes (normal, congestive heart failure, ventricular tachyarrhythmia, atrial fibrillation) using support vector machines with error correcting output codes. The project implements feature extraction via discrete wavelet transform on ECG signals, then uses SVMs with an error correcting code for classification, achieving an average accuracy of classifying 2-3 out of 4 classes correctly. The document outlines the methodology, implementations, experiments and results of the project.
A machine learning model for average fuel consumption in heavy vehiclesVenkat Projects
The document describes a machine learning model that uses artificial neural networks to predict average fuel consumption in heavy vehicles. Seven features are extracted from vehicle dataset to train the ANN model, including number of stops, time stopped, average speed, etc. The trained model is then used to predict fuel consumption for new test vehicles based on their feature values. Screenshots of a software implementation of this ANN model for fuel consumption prediction are also included.
The document discusses an optimal fault tolerance model for real-time cloud computing. It proposes running variant algorithms on multiple virtual machines and using an adjudicator to select the output. The adjudicator contains modules to verify results, check timing, assess reliability, make decisions, and enable recovery. It provides both forward and backward recovery. Experimental results show the system can tolerate various failure scenarios and dynamically adjust reliability weights to improve fault tolerance for real-time tasks running on cloud infrastructure.
IRJET- Self Driving RC Car using Behavioral CloningIRJET Journal
This document describes a project to convert a remote-controlled car into a self-driving car using behavioral cloning and deep learning. The researchers recorded video of themselves driving the RC car manually and used this data to train a convolutional neural network to map images from the car's camera to steering commands. Their model achieved 84.5% accuracy in testing and was able to autonomously drive the car forward, left, and right based on the training. While successful, the approach has limitations such as requiring an expert driver for training and not handling dynamically changing scenarios well. The researchers suggest ways to improve the system such as using more powerful hardware and adding navigation and object detection capabilities.
PUMA 560 TRAJECTORY CONTROL USING NSGA-II TECHNIQUE WITH REAL VALUED OPERATORSijscmc
In the industry, Multi-objectives problems are a big defy and they are also hard to be conquered by conventional methods. For this reason, heuristic algorithms become an executable choice when facing this kind of problems. The main objective of this work is to investigate the use of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) technique using the real valued recombination and the real valued mutation in the tuning of the computed torque controller gains of a PUMA560 arm manipulator. The NSGA-II algorithm with real valued operators searches for the controller gains so that the six Integral of the Absolute Errors (IAE) in joint space are minimized. The implemented model under MATLAB allows an optimization of the Proportional-Derivative computed torque controller parameters while the cost functions and time are simultaneously minimized.. Moreover, experimental results also show that the real valued recombination and the real valued mutation operators can improve the performance of NSGA-II effectively.
PUMA 560 Trajectory Control Using NSGA-II Technique with Real Valued Operatorsijscmcj
In the industry, Multi-objectives problems are a big defy and they are also hard to be conquered by conventional methods. For this reason, heuristic algorithms become an executable choice when facing this kind of problems. The main objective of this work is to investigate the use of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) technique using the real valued recombination and the real valued mutation in the tuning of the computed torque controller gains of a PUMA560 arm manipulator. The NSGA-II algorithm with real valued operators searches for the controller gains so that the six Integral of the Absolute Errors (IAE) in joint space are minimized. The implemented model under MATLAB allows an optimization of the Proportional-Derivative computed torque controller parameters while the cost functions and time are simultaneously minimized.. Moreover, experimental results also show that the real valued recombination and the real valued mutation operators can improve the performance of NSGA-II effectively.
Puma 560 trajectory control using nsga ii technique with real valued operatorsijscmcj
In the industry, Multi-objectives problems are a big defy and they are also hard to be conquered by conventional methods. For this reason, heuristic algorithms become an executable choice when facing this kind of problems. The main objective of this work is to investigate the use of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) technique using the real valued recombination and the real valued mutation in the tuning of the computed torque controller gains of a PUMA560 arm manipulator. The NSGA-II algorithm with real valued operators searches for the controller gains so that the six Integral of the Absolute Errors (IAE) in joint space are minimized. The implemented model under MATLAB allows an optimization of the Proportional-Derivative computed torque controller parameters while the cost functions and time are simultaneously minimized.. Moreover, experimental results also show that the real valued recombination and the real valued mutation operators can improve the performance of NSGA-II effectively.
IRJET-Automatic Self-Parking Chair using Nissan TechnologyIRJET Journal
The document describes an automatic self-parking chair system using Nissan technology. Webcams are used to capture images of a room and detect the positions of chairs using MATLAB image processing. When a meeting ends, the system can automatically rearrange misplaced chairs to their proper positions. It works by sending commands from the MATLAB program to an FPGA controller and motor drivers on each chair via an RF link. This allows chairs to be autonomously repositioned without manual labor, saving time after meetings.
This document summarizes a project that aims to classify heartbeats (ECG signals) into four classes (normal, congestive heart failure, ventricular tachyarrhythmia, atrial fibrillation) using support vector machines with error correcting output codes. The project implements feature extraction via discrete wavelet transform on ECG signals, then uses SVMs with an error correcting code for classification, achieving an average accuracy of classifying 2-3 out of 4 classes correctly. The document outlines the methodology, implementations, experiments and results of the project.
IRJET- Self-Driving Cars: Automation Testing using Udacity SimulatorIRJET Journal
This document describes a methodology for performing automated testing of self-driving vehicles using the Udacity simulator. The methodology involves collecting training data by manually driving a vehicle in the simulator and recording camera images and steering angles. This data is then augmented and used to train a convolutional neural network model. The trained model is tested by running it autonomously in the simulator on tracks it was and was not trained on. The vehicle is able to navigate both tracks successfully with minimal deviations, demonstrating the methodology can be used to test self-driving vehicles in a safe, automated manner using a simulator.
IRJET - Steering Wheel Angle Prediction for Self-Driving CarsIRJET Journal
This document discusses using a convolutional neural network to predict steering wheel angle for self-driving cars. The network is trained using human driving data from a simulator. The network architecture includes convolutional and fully connected layers to map input images to steering angles. The network is evaluated in the simulator and is able to drive autonomously for periods of time without human intervention, demonstrating its ability to predict steering wheel angles needed for navigation.
This document describes a proposed self-driving radio controlled car model that uses computer vision and deep learning techniques. The model was trained in a virtual environment using a convolutional neural network to detect lanes, obstacles, and traffic signs. The physical model uses a Raspberry Pi, camera, ultrasonic sensor, and other hardware to capture images and detect its environment, sending the outputs to an Arduino microcontroller to control the car. The document outlines the proposed system, reviews related work, discusses the implementation including algorithms and testing, and presents the results, concluding the model provides a cost-effective way to demonstrate basic autonomous driving functions.
Self-Driving Car to Drive Autonomously using Image Processing and Deep LearningIRJET Journal
This document discusses a proposed system for creating a self-driving car using image processing and deep learning techniques. The system would use sensors like radar, lidar and cameras to understand the vehicle's environment. An artificial intelligence model would be trained to detect obstacles, interpret sensory data to plan a navigation path, and spot traffic signs. The proposed system would allow the car to change lanes autonomously, park itself, and make U-turns without human input. It would use techniques like object and curb detection, vehicle tracking, and traffic condition monitoring. The goal is to develop a fully autonomous self-driving vehicle capable of safely navigating roads on its own.
Comparative Analysis of Tuning Hyperparameters in Policy-Based DRL Algorithm ...IRJET Journal
The document compares the performance of the PPO deep reinforcement learning algorithm at different hyperparameters in a simulated self-driving environment. It finds that agents trained with PPO performed best with a discount factor of 0.99, clip range of 0.2, and learning rate of 0.0003, which are the default baseline values. The agents' performance on evaluation metrics like mean episode reward was analyzed while varying the hyperparameters over 200,000 timesteps of training. Tuning the hyperparameters away from the baseline values generally resulted in suboptimal performance compared to using the default values.
This document summarizes a research project that aims to develop an application to predict airline ticket prices using machine learning techniques. The researchers collected over 10,000 records of flight data including features like source, destination, date, time, number of stops, and price. They preprocessed the data, selected important features, and applied machine learning algorithms like linear regression, decision trees, and random forests to build predictive models. The random forest model provided the most accurate predictions according to performance metrics like MAE, MSE, and RMSE. The researchers propose deploying the best model in a web application using Flask for the backend and Bootstrap for the frontend so users can input flight details and receive predicted price outputs.
19004 Practice school finalReportformat Model (1).pdfAkshayKumar983983
This document is a project report submitted by Karumuri Dhanesh in partial fulfillment of the requirements for a Bachelor of Technology degree. The report details a simulation project conducted using IPG CarMaker to evaluate test cases for a virtualized Category 3 vehicle. The report includes an introduction to IPG CarMaker simulations, the methodology used, components and tools involved, workflow diagrams, code snippets, results and discussions, and conclusions. The project aims to simulate a Tesla Model S electric vehicle under different battery configurations and state of charge levels to analyze energy consumption and current draw.
The document describes a traffic sign recognition model that uses a convolutional neural network (CNN) algorithm to recognize traffic signs from images with 94.98% accuracy. The model was trained on the German Traffic Sign Recognition Benchmark dataset containing over 50,000 images split into 80% for training and 20% for testing. The CNN model extracts features from the images and classifies the traffic signs. The results show the network can accurately classify traffic signs and could be integrated into advanced driver assistance systems to help vehicles recognize road signs.
Traffic Flow Prediction Using Machine Learning AlgorithmsIRJET Journal
This document discusses using machine learning algorithms to predict traffic flow and control traffic lights. Specifically, it explores using deep reinforcement learning (DRL) with Q-learning. The DRL agent is trained in the SUMO traffic simulation environment to optimize traffic light timing and reduce overall vehicle wait times at intersections. The agent represents traffic light states as actions and vehicle positions/speeds as states. It is rewarded based on decreasing total wait times. Through experience replay and training on historical state-action-reward data, the agent learns which traffic light patterns minimize congestion. The experiments showed the DRL approach improved traffic flow compared to traditional reinforcement learning methods for high-dimensional problems like city-wide traffic control.
IRJET- A Review on Deep Reinforcement Learning Induced Autonomous Driving Fra...IRJET Journal
This document discusses using deep reinforcement learning to develop an autonomous driving framework. It proposes using a simulator called UDACITY to train an autonomous driving agent. Sensors would fuse data on other vehicles' speeds and positions. A recurrent neural network would integrate information to enable the car to handle partially observable scenarios. The goal is to develop a deep learning framework for autonomous driving using this simulator and deep learning for training the autonomous driving agent.
A Comparative Study on Identical Face Classification using Machine LearningIRJET Journal
This document presents research on classifying identical faces using machine learning techniques like support vector machines (SVM). The researchers aim to develop an accurate technique for identifying the same faces from facial photographs. They discuss using SVM classifiers and combining multiple SVM classifiers using plurality voting. They compare the SVM classification approach to standard identical face classification methods. The document also provides background on machine learning and supervised learning techniques like logistic regression, SVM, and random forest classifiers. It discusses related work applying SVM, neural networks, and other methods to tasks like facial expression classification, emotion classification, age and gender recognition.
IRJET - Obstacle Detection using a Stereo Vision of a CarIRJET Journal
This document describes a study that uses deep reinforcement learning to build a self-driving car agent. The agent takes raw sensory inputs from a simulation environment and learns to navigate and control the car through a deep Q-network trained with Q-learning. The authors implement the self-driving car using PyTorch and evaluate its performance against other standard agents. The results show that their agent is able to successfully control the car to navigate within the simulation environment.
IRJET- Parking Space Detection using Image Processing in MATLABIRJET Journal
The document presents a system for detecting available parking spaces using image processing in MATLAB. A camera is installed above a parking lot to capture images. The images are processed to identify and count parked vehicles, and determine the number of available spots. Specifically, the system first acquires images of the empty parking lot and lot with cars. It then subtracts the images to identify parking slots with and without cars. By processing captured images instead of using embedded sensors, the system can efficiently detect available spaces and help drivers find parking spots more quickly at a low cost. The system was tested and able to successfully count parked cars and available spaces in real-time using a camera feed and GUI display.
Demonstrate the implementation PI controller to regulate speed of DC Servo Mo...MIbrar4
Servo system plays an important role in the electromechanical system. Accompanying the
progress of technology and industry, servo driving technology can be completely implemented
in digital form, which gives much more convenience.
Motion Studio is an intelligent servo controller development environment with high
performance window visual software, which can be used to control servo systems containing
Techno soft intelligent servo drive. Motion system (includes motion system element definition
and controller parameter measurement) can be configured, and superior integrated tools can be
used to design motion program, which gives TML codes automatically. Open code development
tools allow further edit, direct compile, link, generate execute codes and send them to IPM
driver. Finally, advanced graphic view tools such as data record, control button and TML
variables observer can be used for analyzing the motion of system. Its interface is shown as
follows:
IRJET- Mango Classification using Convolutional Neural NetworksIRJET Journal
This document presents research on classifying different types of mangoes using convolutional neural networks (CNNs). The researchers collected a dataset of over 5000 mango images across 5 classes. They used transfer learning with the Inception v3 CNN model pre-trained on ImageNet, removing the final classification layer and retraining a new one for the mango classes. The CNN achieved over 99% accuracy on the test set at classifying mango types, demonstrating that CNNs can effectively perform fine-grained image classification of mangoes and distinguish between similar types.
Obstacle Detection and Collision Avoidance SystemIRJET Journal
This document summarizes an obstacle detection and collision avoidance system. It begins by introducing the topic of obstacle detection and how accidents can often be avoided. It then describes the design of an obstacle detector that can detect discontinuities in terrain and alert users of potential hazards. Key components discussed include using MATLAB, a camera, a PIC controller, and image processing techniques. The document reviews related literature on vision-based vehicle detection and classification methods. It also discusses adaptive cruise control systems using ultrasonic sensors and nonlinear coordinated control strategies for autonomous vehicles. Motion planning and trajectory planning frameworks are described for generating collision-free paths.
This document outlines a mini project for students to design software to control a stepper motor. The objectives are to design software that smoothly rotates the motor to specified positions while considering its maximum operating speed. Over two days, students will create programs to move the motor to positions using delays and then interrupts, and investigate how load affects the motor's maximum rotation speed. They will implement a speed profile to move the motor as quickly as possible.
deep-reinforcement-learning-framework.pdfYugank Aman
This document discusses using deep reinforcement learning for navigation in autonomous vehicles. It proposes using a convolutional neural network to process image inputs from a simulator and output steering commands. The neural network is trained using behavioral cloning by recording images and steering angles during manual driving. The trained model is then tested in the simulator to autonomously navigate the track by adjusting speed and following curves and turns. In summary, it aims to implement autonomous vehicle navigation through reinforcement learning using a CNN for image processing and behavioral cloning for training in a simulator environment.
IRJET- Prediction of Crime Rate Analysis using Supervised Classification Mach...IRJET Journal
This document presents a study that uses machine learning techniques to predict crime rates. Specifically, it aims to analyze crime data using supervised machine learning classification algorithms like decision trees, support vector machines, logistic regression, k-nearest neighbors, and random forests. The document outlines collecting and preprocessing crime data, selecting relevant features, training models on a portion of the data and testing them on the remaining data. It finds that random forest achieved the best prediction accuracy compared to other algorithms tested. The goal is to help law enforcement agencies better predict and reduce crime rates by analyzing historical crime data patterns.
IRJET- American Sign Language ClassificationIRJET Journal
This document describes a system for classifying American Sign Language (ASL) gestures into their corresponding English alphabet letters and then converting those letters to audio using text-to-speech. The system uses convolutional neural networks (CNNs) with transfer learning using the VGG16 model to classify ASL images. It achieved 0.99 precision on classification. The CNN model was trained on a dataset of ASL alphabet gestures. Once a gesture is classified, the letter is converted to audio using the gTTS library to play the sound of the classified letter. The system provides a pipeline from image classification to audio output to help facilitate communication between deaf and hearing communities.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
More Related Content
Similar to IRJET-Survey on Simulation of Self-Driving Cars using Supervised and Reinforcement Learning Models
IRJET- Self-Driving Cars: Automation Testing using Udacity SimulatorIRJET Journal
This document describes a methodology for performing automated testing of self-driving vehicles using the Udacity simulator. The methodology involves collecting training data by manually driving a vehicle in the simulator and recording camera images and steering angles. This data is then augmented and used to train a convolutional neural network model. The trained model is tested by running it autonomously in the simulator on tracks it was and was not trained on. The vehicle is able to navigate both tracks successfully with minimal deviations, demonstrating the methodology can be used to test self-driving vehicles in a safe, automated manner using a simulator.
IRJET - Steering Wheel Angle Prediction for Self-Driving CarsIRJET Journal
This document discusses using a convolutional neural network to predict steering wheel angle for self-driving cars. The network is trained using human driving data from a simulator. The network architecture includes convolutional and fully connected layers to map input images to steering angles. The network is evaluated in the simulator and is able to drive autonomously for periods of time without human intervention, demonstrating its ability to predict steering wheel angles needed for navigation.
This document describes a proposed self-driving radio controlled car model that uses computer vision and deep learning techniques. The model was trained in a virtual environment using a convolutional neural network to detect lanes, obstacles, and traffic signs. The physical model uses a Raspberry Pi, camera, ultrasonic sensor, and other hardware to capture images and detect its environment, sending the outputs to an Arduino microcontroller to control the car. The document outlines the proposed system, reviews related work, discusses the implementation including algorithms and testing, and presents the results, concluding the model provides a cost-effective way to demonstrate basic autonomous driving functions.
Self-Driving Car to Drive Autonomously using Image Processing and Deep LearningIRJET Journal
This document discusses a proposed system for creating a self-driving car using image processing and deep learning techniques. The system would use sensors like radar, lidar and cameras to understand the vehicle's environment. An artificial intelligence model would be trained to detect obstacles, interpret sensory data to plan a navigation path, and spot traffic signs. The proposed system would allow the car to change lanes autonomously, park itself, and make U-turns without human input. It would use techniques like object and curb detection, vehicle tracking, and traffic condition monitoring. The goal is to develop a fully autonomous self-driving vehicle capable of safely navigating roads on its own.
Comparative Analysis of Tuning Hyperparameters in Policy-Based DRL Algorithm ...IRJET Journal
The document compares the performance of the PPO deep reinforcement learning algorithm at different hyperparameters in a simulated self-driving environment. It finds that agents trained with PPO performed best with a discount factor of 0.99, clip range of 0.2, and learning rate of 0.0003, which are the default baseline values. The agents' performance on evaluation metrics like mean episode reward was analyzed while varying the hyperparameters over 200,000 timesteps of training. Tuning the hyperparameters away from the baseline values generally resulted in suboptimal performance compared to using the default values.
This document summarizes a research project that aims to develop an application to predict airline ticket prices using machine learning techniques. The researchers collected over 10,000 records of flight data including features like source, destination, date, time, number of stops, and price. They preprocessed the data, selected important features, and applied machine learning algorithms like linear regression, decision trees, and random forests to build predictive models. The random forest model provided the most accurate predictions according to performance metrics like MAE, MSE, and RMSE. The researchers propose deploying the best model in a web application using Flask for the backend and Bootstrap for the frontend so users can input flight details and receive predicted price outputs.
19004 Practice school finalReportformat Model (1).pdfAkshayKumar983983
This document is a project report submitted by Karumuri Dhanesh in partial fulfillment of the requirements for a Bachelor of Technology degree. The report details a simulation project conducted using IPG CarMaker to evaluate test cases for a virtualized Category 3 vehicle. The report includes an introduction to IPG CarMaker simulations, the methodology used, components and tools involved, workflow diagrams, code snippets, results and discussions, and conclusions. The project aims to simulate a Tesla Model S electric vehicle under different battery configurations and state of charge levels to analyze energy consumption and current draw.
The document describes a traffic sign recognition model that uses a convolutional neural network (CNN) algorithm to recognize traffic signs from images with 94.98% accuracy. The model was trained on the German Traffic Sign Recognition Benchmark dataset containing over 50,000 images split into 80% for training and 20% for testing. The CNN model extracts features from the images and classifies the traffic signs. The results show the network can accurately classify traffic signs and could be integrated into advanced driver assistance systems to help vehicles recognize road signs.
Traffic Flow Prediction Using Machine Learning AlgorithmsIRJET Journal
This document discusses using machine learning algorithms to predict traffic flow and control traffic lights. Specifically, it explores using deep reinforcement learning (DRL) with Q-learning. The DRL agent is trained in the SUMO traffic simulation environment to optimize traffic light timing and reduce overall vehicle wait times at intersections. The agent represents traffic light states as actions and vehicle positions/speeds as states. It is rewarded based on decreasing total wait times. Through experience replay and training on historical state-action-reward data, the agent learns which traffic light patterns minimize congestion. The experiments showed the DRL approach improved traffic flow compared to traditional reinforcement learning methods for high-dimensional problems like city-wide traffic control.
IRJET- A Review on Deep Reinforcement Learning Induced Autonomous Driving Fra...IRJET Journal
This document discusses using deep reinforcement learning to develop an autonomous driving framework. It proposes using a simulator called UDACITY to train an autonomous driving agent. Sensors would fuse data on other vehicles' speeds and positions. A recurrent neural network would integrate information to enable the car to handle partially observable scenarios. The goal is to develop a deep learning framework for autonomous driving using this simulator and deep learning for training the autonomous driving agent.
A Comparative Study on Identical Face Classification using Machine LearningIRJET Journal
This document presents research on classifying identical faces using machine learning techniques like support vector machines (SVM). The researchers aim to develop an accurate technique for identifying the same faces from facial photographs. They discuss using SVM classifiers and combining multiple SVM classifiers using plurality voting. They compare the SVM classification approach to standard identical face classification methods. The document also provides background on machine learning and supervised learning techniques like logistic regression, SVM, and random forest classifiers. It discusses related work applying SVM, neural networks, and other methods to tasks like facial expression classification, emotion classification, age and gender recognition.
IRJET - Obstacle Detection using a Stereo Vision of a CarIRJET Journal
This document describes a study that uses deep reinforcement learning to build a self-driving car agent. The agent takes raw sensory inputs from a simulation environment and learns to navigate and control the car through a deep Q-network trained with Q-learning. The authors implement the self-driving car using PyTorch and evaluate its performance against other standard agents. The results show that their agent is able to successfully control the car to navigate within the simulation environment.
IRJET- Parking Space Detection using Image Processing in MATLABIRJET Journal
The document presents a system for detecting available parking spaces using image processing in MATLAB. A camera is installed above a parking lot to capture images. The images are processed to identify and count parked vehicles, and determine the number of available spots. Specifically, the system first acquires images of the empty parking lot and lot with cars. It then subtracts the images to identify parking slots with and without cars. By processing captured images instead of using embedded sensors, the system can efficiently detect available spaces and help drivers find parking spots more quickly at a low cost. The system was tested and able to successfully count parked cars and available spaces in real-time using a camera feed and GUI display.
Demonstrate the implementation PI controller to regulate speed of DC Servo Mo...MIbrar4
Servo system plays an important role in the electromechanical system. Accompanying the
progress of technology and industry, servo driving technology can be completely implemented
in digital form, which gives much more convenience.
Motion Studio is an intelligent servo controller development environment with high
performance window visual software, which can be used to control servo systems containing
Techno soft intelligent servo drive. Motion system (includes motion system element definition
and controller parameter measurement) can be configured, and superior integrated tools can be
used to design motion program, which gives TML codes automatically. Open code development
tools allow further edit, direct compile, link, generate execute codes and send them to IPM
driver. Finally, advanced graphic view tools such as data record, control button and TML
variables observer can be used for analyzing the motion of system. Its interface is shown as
follows:
IRJET- Mango Classification using Convolutional Neural NetworksIRJET Journal
This document presents research on classifying different types of mangoes using convolutional neural networks (CNNs). The researchers collected a dataset of over 5000 mango images across 5 classes. They used transfer learning with the Inception v3 CNN model pre-trained on ImageNet, removing the final classification layer and retraining a new one for the mango classes. The CNN achieved over 99% accuracy on the test set at classifying mango types, demonstrating that CNNs can effectively perform fine-grained image classification of mangoes and distinguish between similar types.
Obstacle Detection and Collision Avoidance SystemIRJET Journal
This document summarizes an obstacle detection and collision avoidance system. It begins by introducing the topic of obstacle detection and how accidents can often be avoided. It then describes the design of an obstacle detector that can detect discontinuities in terrain and alert users of potential hazards. Key components discussed include using MATLAB, a camera, a PIC controller, and image processing techniques. The document reviews related literature on vision-based vehicle detection and classification methods. It also discusses adaptive cruise control systems using ultrasonic sensors and nonlinear coordinated control strategies for autonomous vehicles. Motion planning and trajectory planning frameworks are described for generating collision-free paths.
This document outlines a mini project for students to design software to control a stepper motor. The objectives are to design software that smoothly rotates the motor to specified positions while considering its maximum operating speed. Over two days, students will create programs to move the motor to positions using delays and then interrupts, and investigate how load affects the motor's maximum rotation speed. They will implement a speed profile to move the motor as quickly as possible.
deep-reinforcement-learning-framework.pdfYugank Aman
This document discusses using deep reinforcement learning for navigation in autonomous vehicles. It proposes using a convolutional neural network to process image inputs from a simulator and output steering commands. The neural network is trained using behavioral cloning by recording images and steering angles during manual driving. The trained model is then tested in the simulator to autonomously navigate the track by adjusting speed and following curves and turns. In summary, it aims to implement autonomous vehicle navigation through reinforcement learning using a CNN for image processing and behavioral cloning for training in a simulator environment.
IRJET- Prediction of Crime Rate Analysis using Supervised Classification Mach...IRJET Journal
This document presents a study that uses machine learning techniques to predict crime rates. Specifically, it aims to analyze crime data using supervised machine learning classification algorithms like decision trees, support vector machines, logistic regression, k-nearest neighbors, and random forests. The document outlines collecting and preprocessing crime data, selecting relevant features, training models on a portion of the data and testing them on the remaining data. It finds that random forest achieved the best prediction accuracy compared to other algorithms tested. The goal is to help law enforcement agencies better predict and reduce crime rates by analyzing historical crime data patterns.
IRJET- American Sign Language ClassificationIRJET Journal
This document describes a system for classifying American Sign Language (ASL) gestures into their corresponding English alphabet letters and then converting those letters to audio using text-to-speech. The system uses convolutional neural networks (CNNs) with transfer learning using the VGG16 model to classify ASL images. It achieved 0.99 precision on classification. The CNN model was trained on a dataset of ASL alphabet gestures. Once a gesture is classified, the letter is converted to audio using the gTTS library to play the sound of the classified letter. The system provides a pipeline from image classification to audio output to help facilitate communication between deaf and hearing communities.
Similar to IRJET-Survey on Simulation of Self-Driving Cars using Supervised and Reinforcement Learning Models (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.