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检索条件"机构=Computer Science and Engineering Nitte Meenakshi Intitute of Technology"

共 1229 条记录，以下是11-20 订阅

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Financial Zen: Achieving Market Harmony through Reinforcement Learning and Algorithmic Trading 2

Financial Zen: Achieving Market Harmony through Reinforcemen...

引用

2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Sowmya, M.R. Chhetri, Diwash Khatri Chai, Kumar Rohit Ashraf, Suhail Nitte Meenakshi Institute Of Technology Department Of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350381665

Algorithmic trading, utilizing reinforcement learning, has attracted significant attention for its adaptability and potential in present financial markets. This research combines Deep Deterministic Policy Gradients (DDPG), Advantage Actor-Critic (AAC), and Proximal Policy Optimization (PPO) algorithms for a comprehensive trading system. To address memory constraints, a load-on-demand technique manages large datasets. Performance is evaluated across 30 Dow Jones stocks, compared to the Dow Jones Industrial Average (DJIA) and a minimum-variance portfolio. Results show the integrated approach outperforms traditional strategies in annualized return, Sharpe ratio, and maximum drawdown. This study contributes to algo-trading literature by leveraging multiple reinforcement learning algorithms, offering benefits for investors in dynamic markets. © 2024 IEEE.

关键词： Reinforcement learning

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Energy-Efficient Autism Spectrum Disorder Data Collection Mechanism using Wireless Sensor Networks and Internet-of-Things 5

Energy-Efficient Autism Spectrum Disorder Data Collection Me...

引用

5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Vijaya Shetty, S. Srujana, S.N. Naidu, P. Ramesh Neeli, Jyoti Sarojadevi, H. Prakash, Ramya Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350356236

A patient's cognitive health may be better assessed using smart monitoring and assisted living technologies. Social skills, repetitive habits, verbal and nonverbal communication, and adjusting to new environments are among the challenges children on the autism spectrum face. Because of their reduced cognitive capacities, children with autism spectrum disorder (ASD) present a significant public health challenge because it is difficult to comprehend their feelings. The main purpose of this research is to assist autistic newborns in adapting to their environment via Internet-of-things (IoT) technologies and wireless sensor networks. The study indicates an Internet of Things (IoT)-WSNs system for data collection, using an energy-efficient cluster-based routing optimization (EECRO) method that sends data to an IoT gateway server as quickly as possible, allowing the guardian to access the data in real-time. The simulation outcome indicates the EECRO can significantly reduce energy consumption to deliver messages from autistic children to their guardians. © 2025 IEEE.

关键词： Assisted living

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Blockchain Voting: A Solution to the Challenges of Traditional Electoral Systems 1

Blockchain Voting: A Solution to the Challenges of Tradition...

引用

1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Sowmya, M.R. Hamal, Manash Chaudhary, Avishek Patel, Johnson Kumar Shrestha, Soyuz Man Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350352931

Voting is a prominent part of the political process, especially in a democratic country like India, where it plays a key role in electing government officials and shaping public policy. Conventional paper-based methods are easy to implement but have considerable limitations, such as security vulnerabilities, paper usage, and the requirement for a large workforce. Common issues such as booth capturing, vote tampering, and manipulation compromise the integrity of elections. To deal with these challenges, blockchain technology offers a solution by providing an immutable and decentralized voting system. This method guarantees that, once added, data cannot be modified, thus preventing manipulation. The decentralized nature of the system allows for continuous availability, as any node failure can be compensated by another node. Moreover, it reduces the need for manual oversight, speeds up the voting process, enhances security and provides transparency, which traditional systems lack. Incorporating blockchain technology into the voting process could significantly enhance the integrity and efficiency of elections, paving the way for a more trustworthy process. © 2024 IEEE.

关键词： Distributed ledger

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Citizen Cane - An Object Detection and Image Description System for the Visually Impaired 3

Citizen Cane - An Object Detection and Image Description Sys...

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Navhule, Akarsh Akif Byndoor, Keerthan Mohammed Nayaz, A.N. Shetty, Deepthi Sarojadevi, H. Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331518592

Visually impaired persons can use informed visual perception to generate understanding of the environment around them to bridge the gap between them and world. This can be done by helping those individuals to identify visual features with the help of linguistic expression through aural/vocal or haptic means. Images are one example of visual perception;humans can identify images, describe it with their words (linguistic expressions) through speech (vocal). The proposed system provides the visually impaired persons with a Smart Cane, that provides the same haptic feedback for object detection, and Smart Glasses that capture the surroundings in the form of images that are captioned based on Encoder-Decoder using deep learning algorithms- Resnet101 which recognizes objects in the images and the LSTM (Long Short-Term Memory) which generates the image caption. The proposed model helps describe the image's features in a meaningful sentence with good accuracy and efficiency. © 2024 IEEE.

关键词： Deep learning

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Credit Risk Assessment using Ensemble Models and Explainable AI 3

Credit Risk Assessment using Ensemble Models and Explainable...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srikanteswara, Ramya Naghera, Krinal Kukkaje, Shashank Balnad Kumar, Adarsh Shrey, P. Dept of Computer Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9798331527549

New challenges are still there in the credit risk management in this context of the financial system, primarily because of the lack of good models for prognosis as well as low interpretability. To address this issue, this research brings together CatBoost, XGBoost, and LightGBM to improve the prospect of identifying high-risk borrowers. A minimal level of Explainable AI (XAI) is utilized to make confirmations that the model follows them and to build confidence in the outcome. Moreover, based on a difference to basic models, the obtained ROC-AUC score equals 0.9693, proving the feasibility of the proposed ensemble approach in questions of impact developing default risks. The end product is therefore a robust method with ensemble modeling and compliant with regulatory requirements in operational financial environments. © 2025 IEEE.

关键词： Credit Risk Credit Risk and Machine Learning Algorithms Ensemble Learning and Risk Interpretation in Financial Institutions Explainable AI and Credit Risk Management Risk Models

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Breast Cancer Image Classification using Convolutional Neural Networks 2

Breast Cancer Image Classification using Convolutional Neura...

引用

2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Ravi Teja, S. Joshi, Sujata Department of Computer Science & Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9798331504960

This study aimed to develop and evaluate a deep learning-based classification model for breast ultrasound images. The primary objectives were to classify ultrasound images into three categories: benign, malignant, and normal. Breast cancer detection using ultrasound imaging is challenging due to class imbalance and insufficient annotations in datasets. This research focuses on enhancing classification accuracy by leveraging a pre-trained ResNet-50 model. Methods included data augmentation to address class imbalance and the integration of masks overlayed on ultrasound images to emphasize regions of interest. These enhancements significantly improved the model’s ability to distinguish between benign, malignant, and normal breast tissue. The model achieved an accuracy of 98%, up from an initial 86% on unseen data, demonstrating the impact of detailed annotations and fine-tuning. The results underscore the potential of AI-driven models in improving diagnostic accuracy. Future work involves incorporating additional data sources and validating the model across diverse clinical settings, with the goal of advancing early detection and personalized treatment strategies in breast cancer care. © 2024 IEEE.

关键词： Ultrasonic imaging

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Comparative Analysis of Deep Learning Architectures for Multilingual Digit Recognition 2

Comparative Analysis of Deep Learning Architectures for Mult...

引用

2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Mamatha Bai, B.G. Sarangi, Akruti Kashyap, Akanksha Yadav, Himanshi Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350373110

Digit recognition is foundational in pattern recog-nition and machine learning, with applications in document processing and optical character recognition. Current research often targets English digits, overlooking language-independent systems. This paper bridges this gap by analyzing deep-learning architectures and activation functions for Hindi, English, and Kannada digits. We evaluate CNN, LeNet, ResNet, MobileNet, and Inception (GoogleNet) models, and activation functions like sigmoid, tanh, linear, ELU, Leaky ReLU, and ReLU. The results indicate ResNet achieves 99.498% accuracy in English, 99.43% in Hindi, and 97.67% in Kannada datasets. This study enhances the design and optimization of robust digit recognition systems across diverse languages and scripts, promoting efficient models for practical applications. Despite advancements with CNNs, research predominantly focuses on English digits, highlighting the need for lanauage-independent recognition systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning for Skin Lesion Classification using Pre-Trained Neural Networks

Deep Learning for Skin Lesion Classification using Pre-Train...

引用

2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Anjali, M. Sowmya, P. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360660

The increasing prevalence of skin cancer underscores the importance of accurate and efficient diagnostic tools. Deep Convolutional Neural Networks (DCNN) have demonstrated remarkable success in computer vision tasks, particularly in skin cancer classification. However, adapting these networks to specific domains, such as dermatoscopic images, poses challenges. This project addresses the need for robust skin cancer classification by fine-tuning DCNNs pre-trained on ImageNet for the specific task of identifying 7 types of skin lesions using the HAM10000 dataset, which contains 10,000 dermatoscopic images. To comprehensively assess the efficiency of different fine-tuning strategies, a comparative analysis is conducted across multiple architectures, including the baseline model, VGG16, Inception V3, InceptionResNetV2, and DenseNet201. The initial examination involves fine-tuning the top layers of each architecture. Subsequently, a more intricate analysis is performed by fine-tuning all layers for Inception V3, InceptionResNetV2, and DenseNet201. This study aimed to improve dermatological image classification by combining cross-learning and ensemble learning. The ensemble included the best versions of Inception V3 and DenseNet 201, achieving an accuracy of 88.52% on the test set and 88.8% on the validation set. The fitting of the whole model improved the results and the speed of convergence. © 2024 IEEE.

关键词： Convolutional neural networks

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Velocity Powered Sprint: Gesture Controlled Car Racing Game 2

Velocity Powered Sprint: Gesture Controlled Car Racing Game

引用

2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Mamatha Bai, B.G. Singh, Priyanshu Pramod Tuttagunta, Sowmya Goutam, Vachaspati Nitte Meenakshi Institute of Technology Computer Science and Engineering Karnataka Benguluru India

ISBN: (纸本)9798350372892

An innovative real-time car racing game controlled by hand gestures is introduced, employing OpenCV and advanced AI techniques. The primary goal is to offer players an immersive gaming experience by allowing them to steer a virtual car along a racing track using intuitive hand movements. The system utilizes OpenCV's computer vision algorithms, analyzing live video feeds from a webcam to instantly detect and interpret hand gestures. These gestures, including commands like 'move forward,' 'move backward,' 'turn left,' and 'turn right,' act as input signals for directing the car within the game. To enhance gesture recognition accuracy, AI methods are integrated, training a model with machine learning algorithms such as Convolutional Neural Networks (CNNs) to precisely categorize various hand gestures. The identified gestures are then translated into control signals for the virtual car, resulting in a seamless and captivating gaming experience. Featuring visually stunning graphics, challenging racecourses, and competitive adversaries, this project exemplifies the synergy of computer vision, AI, and gaming technologies, offering a user-friendly and immersive hand gesture-controlled car racing game. It not only provides entertainment but also opens up avenues for potential applications in gaming, virtual reality, and human-computer interaction. © 2024 IEEE.

关键词： Gesture recognition

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Cotton Leaf Disease Classification and Pesticide Recommendation 2

Cotton Leaf Disease Classification and Pesticide Recommendat...

引用

2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Jamadar, Bhagesh Harikant, Bhavana Manjunath Chaithra, B.J. Mohit, J.S. Vasudevan, Vani Nitte Meenakshi Institute of Technology Computer Science Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350372892

The decline in cotton production due to various diseases poses significant challenges to agriculture. In response, this paper presents the development of a software application titled 'Cotton Leaf Disease Classification and Pesticide Recommendation.' The application aims to classify diseases affecting cotton leaves, recommend suitable pesticides, and suggest nearby shops for pesticide procurement. Focusing primarily on prevalent cotton leaf diseases such as cercospora, bacterial blight, curl virus, fusarium, and Ascochyta, the software employs image preprocessing techniques to process cotton leaf images. Convolutional neural network technologies are utilized for training the datasets to accurately classify diseases. Upon classification, the application suggests appropriate pesticides and nearby vendors, and sends the results to the user's email address. This software endeavors to mitigate the impact of cotton leaf diseases on agricultural productivity, particularly in regions like Karnataka, India. © 2024 IEEE.

关键词： Application programs

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