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检索条件"机构=MTech Computer Science and Engineering School of Computing Science and Engineering"

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Ice hockey game using Deep Reinforcement Learning

Ice hockey game using Deep Reinforcement Learning

引用

International Conference for Convergence of Technology (I2CT)

作者： Y. Mounika Y. Lakshmi Prasanna Y. Tarakaram J. Amudha K. Aiswariya Milan Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Modern deep Reinforcement Learning (RL) techniques are quite good at choosing the best possible rules to maximise rewards. By using rich visual information for policy selection, this method combined with Deep Learning methodologies shows promise for difficult tasks. In this work, we have used Double DQN and also three variants of Duelling DQN to implement the ice hockey environment. The Duelling DQN outperformed the Double DQN and gave better results.

关键词： Visualization Games Deep reinforcement learning Ice Task analysis Sports Convergence

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Enhanced Pomegranate Grading Using InceptionResNetV2 with Transformer Integration

Enhanced Pomegranate Grading Using InceptionResNetV2 with Tr...

引用

IEEE International WIE Conference on Electrical and computer engineering (WIECON-ECE)

作者： V. Gopi Kiran T. Hemanth Babu Vidula N. A Rimjhim Padam Singh Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331535476

ISBN: (纸本)9798331535483

Sorting pomegranates based on quality grades is a crucial stage in their export preparation and packing process. Traditionally reliant on manual sorting methods which are time-consuming, prone to inaccuracies and incur very high costs due to the need for a large workforce prove to be very inefficient. Moreover, manual grading of export-quality of foods poses additional challenges. This paper addresses such issues by proposing an automated system employing modern digital image processing and deep learning techniques for pomegranate quality grading and classification. Initially, pomegranate images are subjected to several augmentation techniques like extracting HSV color features, Local Binary Pattern features, horizontal and vertical transformations, etc. Then a novel classification model having InceptionResNetv2 convolutional network as its backbone and integrated with optimally placed transformer blocks is proposed. The augmented dataset is utilized to train six state-of-art deep learning models namely, Vision transformers, XceptionNet, EfficientNetV2B0, DenseNet121, InceptionResNetV2 and NasNet-Large. It is observed that the proposed InceptionResNetV2 model containing transformer blocks exhibited superior performance, achieving an accuracy of 90.56%. This underscores the ability of the proposed deep learning model in automating quality evaluation and sorting procedures for pomegranates.

关键词： Deep learning Training Measurement Accuracy Image color analysis Manuals Transformers Feature extraction Robustness Sorting

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Hyperspectral Patterns With Deep Learning For Classification For Indian Pines

Hyperspectral Patterns With Deep Learning For Classification...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Vaishnav Vishwanath K Sreekanth Jishnu Prakash Adithya Rajendran G Gopakumar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The Indian Pines test site, located primarily in north-western Indiana, is renowned for its Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) scene, which has been extensively studied over the years. The hyperspectral nature of this image introduces challenges related to pixel mixture complexity, creating difficulties for supervised subpixel detection and mixed-pixel classification. Despite the prevalent use of supervised approaches for this scene due to the complexity of forming an appropriate sample pool for supervised classification, there is limited research on supervised classification for this image. The study leverages cutting-edge deep learning models and transfer learning techniques, including ResNet50 (99.68% accuracy), InceptionV3 (99.55% accuracy), DenseNet121 (99.10% accuracy), and MobileNetV3 (99.94% accuracy). These models are employed for predictive analysis, highlighting their effectiveness in handling the complexities of the Indian Pines scene and showcasing their high accuracy rates in supervised image classification. This research contributes valuable insights into the application of deep learning and transfer learning methodologies, offering a promising approach to addressing the challenges presented by complex hyperspectral imagery.

关键词： Deep learning Solid modeling Accuracy Transfer learning Training data Robustness Complexity theory Hyperspectral imaging Image classification Residual neural networks

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Unified Detection Framework for Robbery Events: Integrating YOLOv8, Fast R-CNN, and RetinaNet with Explainable AI Validation and Real-time Android Application Integration

Unified Detection Framework for Robbery Events: Integrating ...

引用

computer Vision and Machine Intelligence (CVMI), International Conference on

作者： P Sai Mrudhun Mc Keerthana K Guru Nithysh T Akash Manjusha R Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

This research proposes a Unified Detection Framework for Robbery Events by combining YOLOv8, Fast R-CNN, and RetinaNet models. The framework incorporates Explainable AI Validation for transparency and real-time detection capabilities. Additionally, an Android application integration is implemented for practical deployment. Through experimentation and evaluation, the effectiveness of the framework in detecting robbery events is demonstrated. The fusion of multiple object detection models enhances accuracy and efficiency, while the integration of explainable AI validation ensures interpretability of results. The proposed framework shows promising results for addressing security concerns in real-world scenarios.

关键词： Deep learning Technological innovation Explainable AI Law enforcement Surveillance Object detection Real-time systems Security Object recognition Machine intelligence

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On – Device Translation of Dynamically Typed and Interpreted Languages

On – Device Translation of Dynamically Typed and Interprete...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： H Pranav Muppavarapu Sriharshini Rachuri Tarun Meena Belwal Dept of Computer Science Engineering Amrita school of computing Amrita Viswa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

On Device - translation of expression form languages with dynamic nature and using interpreted languages like MATLAB and Python requires a precise and automated form of processing the error-free conversion. With the help of the automation, the code in MATLAB is well-defined to Python so that translation is precise and the originality as well as functionality is ensured. This paper describes a robust and redundant methodology for converting maintaining the functionality and originality of the MATLAB code helping in smooth transition of code from MATLAB to python. The procedure being entirely local also ensures to protect privacy of the code.

关键词： Privacy Codes Systematics Automation Generators Complexity theory Logic MATLAB

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Comparative Image Analysis of Chest X-RAY Image Encryption using Symmetric and Asymmetric Key Encryption Algorithms

Comparative Image Analysis of Chest X-RAY Image Encryption u...

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Bhupathi Vishva Pavani Challagulla Sai Bhumika Dumpa Venkata Sai Jagadeesh C.R. Kavitha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Assuring the medicinal data is very important for protecting the patient’s privacy and their trust in the team. Prohibited access to confidential medicinal data gives rise to privacy breaches, healthcare scams, and improper health treatment. The technique that is suggested offers an opportunity for the encryption of chest scan images. The approach makes use of a multilayer encryption mechanism that contains a hashing layer, a digital signature layer, and the final encryption layer. The utilization of multiple algorithms for the layers mentioned above is analyzed and contrasted. For the encryption of an image, two algorithms are used which are AES and DES. For creating a digital signature, RSA and DSS are utilized. For all the three methods mentioned, RSA Key Encryption and SHA-256 hash algorithm are been frequently used. As for the evaluation metrics for the three approaches, time and space complexities are computed. The methods mentioned above prove the CIA triad which is confidentiality, Integrity, and authentication of the chest X-ray images. The most effective approach proves to be AES image encryption with RSA digital signature as it takes a short amount of time to decrypt.

关键词： Authentication Privacy breach Nonhomogeneous media Real-time systems Encryption Time complexity X-ray imaging Digital signatures Biomedical imaging Testing

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Smart Traffic Flow Optimization with IoT and Machine Learning

Smart Traffic Flow Optimization with IoT and Machine Learnin...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： D Poojitha Tanmay Darak Ushnish Samaddar Manju Khanna Department of Computer Science and Engineering Amrita School Of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Traffic signal optimization is crucial for the effective traffic management within cities with the primary goals being the reduction of bottleneck occurrences, avoidance of the extension of travelling time, and improvement of overall city street network function. Historically used approaches can hardly be adjusted to fluctuating traffic situation. Hence, a new methodology involving the use of machine learning-based traffic signal control with the assistance of the IR (Infrared) /UV (Ultraviolet) sensors for traffic flow sensing in real-time environment is introduced in this study. The UV sensors make congestion distance measurements in real time which are assisted by IR sensors for traffic information acquisition. Historic data with data gathered from the traffic sensors are used to train the machine learning model to predict traffic. The strategy employs the use of heuristic search methodologies in the determination of signal timings with reference to congestion information that has been forecasted as well as congestion information that is being experienced. This paper discusses the fact that machine learning-based approaches, supported by sensor technology, can be used for effective traffic management systems that modify with the help of data. The success of this traffic management system is proved by its ability to minimize the MSE (Mean Squared Error) of congestion predictions to an average of 0.69.

关键词： Machine learning algorithms Urban areas Machine learning Predictive models Prediction algorithms Real-time systems Sensor systems Sensors Timing Optimization

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Application of Generative Adversarial Network for Data Augmentation in Diabetic Retinopathy

Application of Generative Adversarial Network for Data Augme...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Anuj Kumar Rimjhim Padam Singh Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Image generation through generative adversarial network (GAN) has been a well-studied problem. It has been specifically implemented in medical domain for a number of use cases but training GAN has its own challenges. Application of GAN on generation of eye fundus images for the disease diabetic retinopathy (DR) has not been explored in depth. This paper shows a novel approach to generate low resolution eye fundus images for DR using GAN and validate it though a CNN classifier using samplers and schedulers module. The proposed approach beats the current benchmark in terms of generated image quality as well as classification accuracy. The low-quality generated images of 224*224 pixels manages to retain the key aspects of DR which aids in identifying the DR grade during classification predominantly from generated images only.

关键词： Training Image quality Diabetic retinopathy Image resolution Image synthesis Computational modeling Generative adversarial networks Data augmentation Diseases Biomedical imaging

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Modeling Plants vs Zombies Gameplay using Finite automata

Modeling Plants vs Zombies Gameplay using Finite automata

引用

North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Archana Reddy P. Divya Jyothi G. Velumury Varshita Chennupati Akshitha D Radha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364576

Playing video games on personal computers is known as PC gaming. PC games are available in a variety of genres, offer both single-player and multiplayer options, and have spawned an international e-sports industry. Modding communities are active, digital distribution systems are essential, and hardware can be customized. Future developments in gaming, such as virtual reality, cloud gaming, and artificial intelligence, portend more innovation in the industry. The game, Plants vs Zombies is represented by Finite Automata and it is ruled by DFAs and NFAs which describe the actions and behaviours of both the plants and the zombies and the result of their actions. In the proposed work you can notice how the NFA will be converted to DFA and vice versa due to its handy functions. Overall, "Plants vs Zombies" is a fun and interactive way for players to explore computational thinking while learning about the complex ideas of NFA and DFA in Automata Theory. The advantages are Synchronize State Management, Consistent State Transitions, Easier Development, and Unambiguous Animation Staging.

关键词： Video games Technological innovation Solid modeling Analytical models Systematics Learning automata Computational modeling Biological system modeling Games Virtual reality

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Comparative Deep Learning Approach for Intrusion Detection

Comparative Deep Learning Approach for Intrusion Detection

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Nabin Kumar Sah Mahi Kolli Kadam Prajwal Dharmaraj H.N. Vishwas Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In the modern world, the security of computer networks from harmful activities is very important since cyber threats keep changing from time to time. However, Intrusion Detection Systems (IDS) have not been able to match this pace using traditional methods. This research is on checking how well deep learning models which are Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNNs) help in enhancing Intrusion Detection capabilities. The NSL-KDD dataset available on Kaggle provides various attack types and normal activities through diverse network traffic data. Rigorous data preprocessing has been done during the study through feature engineering, encoding categorical variables, and normalizing numerical attributes. After that, custom deep learning architectures are created and evaluated with accuracy being one of the standard metrics used for testing their performance among others like recall, precision, or F1-score so as to improve intrusion detection efficiency.

关键词： Deep learning Training Transfer learning Intrusion detection Telecommunication traffic Predictive models Long short term memory Standards Testing Resilience

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