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HYBRID FUZZY PI CONTROLLED MULTI-INPUT DC/DC CONVERTER FOR ELECTRIC VEHICLE APPLICATION

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                                                         Power electronic interface with its effective control scheme plays a major role in the utilization of energy sources for electric vehicle application. For this purpose, a hybrid fuzzy PI based control scheme for a multiple input converter (MIC) topology is proposed. The proposed hybrid fuzzy PI controller includes a conventional PI controller at steady state and fuzzy PI at transient state. Also, the proposed control design helps in tracking a predefined speed profile to have complete realization of electric vehicle. Detailed simulation study and performance comparisons with conventional controller are performed. The results show that the developed control scheme is robust providing bidirectional power management, fast tracking capability with less steady state error, better dynamic response by enhancing the flexibility and proper utilization of energy sources. Simulation in MATLAB/SIMULINK environment is carried out to verify th
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FAKE NEWS DETECTION

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                                                   In modern days, a tremendous amount of data is being generated online every day. However, an unprecedented amount of the data flooded on the Internet is fake news, which is generated to attract the audience, to influence beliefs and decisions of people, to increase the revenue generated by clicking, and to affect major events such as political elections. Readers are misguided by deliberately spreading false information. Obtaining and spreading information through social media platforms has become extremely trouble-free, which makes it difficult and nontrivial to detect based merely on the content of news.                   
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Bi-directional Buck-Boost Converter Based on Electric Vehicle Hybrid Energy Storage for V2G System

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COVID -19 X-RAY DETECTION

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                                  The study proposes an automated deep learning-based classification model, based on a Convolutional Neural Network, that demonstrates a rapid detection rate for COVID-19. The training dataset consists of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images which were then augmented. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models. MobileNetV2 showed enough promise to make it a candidate for further modification. The resulting model produced the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods.
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BEST FINAL YEAR MATLAB PROJECT CENTER IN CHENNAI

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                                                Chest X-ray images play a crucial role in pneumonia diagnosis, with deep transfer learning being a widely adopted method for pneumonia detection. However, effectively handling feature data extracted from deep models without succumbing to the challenges of feature dimensionality remains a formidable task. In response to this complex issue, we propose a novel two-stage deep feature selection (FS) method utilizing the voting differential evolution (VDE) algorithm. In this approach, a dimension adaptive search strategy is meticulously devised to ensure robust feature selection while concurrently reducing the dimension. To expedite the optimization process, we devise a CR adaptive adjustment method to enhance the efficiency of the algorithm
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AUTOMATIC SPEED CONTROL

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                                                                                             Considering the road transport data inclusive of traffic and accidents, the idea of automatic speed control is very crucial since it aims to provide maximum road safety as well as driving ease at traffic with the use of technology. In a world where everyone rushes till the nth hour, a system like this is mandatory, to automatically control the speed of any vehicle at smart zones like schools, hospitals, etc. This indeed envisions a future that is accident-free and stresses the importance of road safety and rules beyond human errors and false testimony approval. This project is designed in such a way that speed is regulated and confined at the marked smart zones with the help of the RF module. When the vehicle enters the smart zone, the speed would be automatically controlled with the help of IOT app. This happens automatically beyond manual control when the region is committed to that particula
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A_Single-Phase_35-levels_Cascaded_PUC_Multilevel_Inverter_Fed_by_a_Single_DC-Source

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                         This work proposes a new multilevel inverter topology composed of a PUC5 and an MPUC7 convert cascaded connected fed by only one dc power source by using a highfrequency link. This topology can provide 35-level voltage by using four asymmetrical dc-links, and only twelve power switches. The second dc-link operates as a floating capacitor, while the two smaller dc-links are supplied by the dc-source through a high-frequency transformer (HFT) rated at 7.6% of the total load power when operating at nominal voltage. A self-balance sensor-less strategy is integrated into the switching control and is used to control the voltage of the floating capacitor while the HFT balances two dc-links providing the required power flow. A Nearest Level Control (NLC) and an analysis of power distribution among each dc-link are presented in detail. Besides, comparisons with other multilevel topologies using HFT are carried out. Experimental results are presented to validate the pr
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