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检索条件"机构=Dept. of Computing and Data Science Institute"

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Machine Learning in Action: Supervised Learning Models for Classifying Credit Card Fraud

Machine Learning in Action: Supervised Learning Models for C...

引用

2024 International Conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Pendalwar, Rishab Verma, Aditya Patil, Ratna Vishwakarma Institute of Information Technology Dept. of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350378092

Financial institutions face a relentless battle against fraudulent credit card transactions. Machine learning (ML) offers a promising approach for tackling this challenge. This paper explores six supervised machine learning models (Support Vector Machine (SVM), Random Forest, Naive Bayes, K-Nearest Neighbours (KNN), XGBoost, and LIGHT GBM) for detecting credit card fraud using a benchmark dataset. We explore the impact of dimensionality reduction with Principal Component Analysis (PCA) and data balancing with Generative Adversarial Networks (GANs) on imbalanced class distributions. The assessment relies on metrics such as accuracy, precision, recall, and F1-score. The results indicate that Random Forest demonstrates the best overall performance, especially when paired with GAN-based data balancing techniques. This study highlights the importance of addressing class imbalance and exploring dimensionality reduction techniques for enhancing credit card fraud detection capabilities. © 2024 IEEE.

关键词： Generative adversarial networks

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Grey Wolf Optimizer and Deep Neural Network based Feature Selection and Classification in Medical Image Analysis 18

Grey Wolf Optimizer and Deep Neural Network based Feature Se...

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Chandrashekhar, Katha Leela, P. Madhavi, K. Reddy Avanija, J. Tangudu, Naresh Indur Institute of Engineering and Technology Computer Science and Engineering Siddipet India Mohan Babu University School of Computing Dept. of Data Science Tirupati India Mohan Babu University School of Computing Tirupati India Aditya Institute of Technology and Management Tekkali India

ISBN: (纸本)9789380544519

Accurate and efficient medical image analysis is required for modern healthcare applications, facilitating timely disease diagnosis and treatment. Considering the importance of optimization algorithms and deep learning methodologies, this work presents a novel approach, Grey Wolf Optimizer-Assisted Deep Neural Networks (GWO-DNN), for feature selection and classification in medical image analysis. The proposed framework utilizes the power of the Grey Wolf Optimizer to intelligently select the most relevant features from complex medical images. The selected features are then integrated into Deep Neural Networks, to create accurate models for medical image classification tasks. This integrated approach is designed to improve diagnostic accuracy and enhance patient care. The experimentation and evaluation, reveal that the proposed GWO-DNN based model for image analysis is more efficient than the existing methods. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep Neural Networks (DNN) Disease Classification Feature Selection Grey Wolf Optimization (GWO) Medical Image Analysis

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FitTip: Merging Technology and Fitness for Posture Improvement 3

FitTip: Merging Technology and Fitness for Posture Improveme...

引用

3rd International Conference on Trends in Electrical, Electronics and Computer Engineering, TEECCON 2024

作者： Tembhurnikar, Atharv Sarvankar, Girish Yadav, Gitanjali Vaidya, Rohan Shelar, Aditya Bhage, Kartik Vishwakarma Institute of Information Technology Dept. of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798331505714

Poor form is a primary contributor to exercise-related accidents, which can result in permanent physical handicap and deter people from maintaining a regular fitness regimen. This study suggests FitTip, an intelligent posture identification and correction device that uses computer vision and machine learning, as a means of preventing these kinds of injuries. Using a proprietary dataset of yoga and gym routines, we developed a convolutional neural network (CNN) integrated with the Mediapipe framework to accurately categorize and correct workout positions in real time. Position analysis is used in FitTip' s design to provide real-time feedback and change recommendations. The algorithm was evaluated on a dataset of five workout poses, and the results indicated that it could get 98.73% training and validation accuracy. These findings show how well algorithm works to improve overall fitness results and workout safety. The suggested algorithm offers a framework that is scalable for enhancing posture during activity, and it may eventually be integrated with wearables and fitness applications. It includes a real-time camera feed for pose detection, a key-point detection module, and a feedback algorithm that suggests corrections based on the angles between body joints. This paper also addresses challenges such as limited dataset size and variability in user movements, proposing solutions through enhanced preprocessing and pose analysis techniques. FitTip algorithm makes training safer and more accessible for people of all fitness levels, which adds to the expanding field of fitness technology. © 2024 IEEE.

关键词： Convolutional neural networks

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Clear Text Visibility: A CNN-LSTM Approach for Frame of Interest Identification in Book Flipping Videos

Clear Text Visibility: A CNN-LSTM Approach for Frame of Inte...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent computing, MoSICom 2024

作者： Buddhawar, Gaurav Dave, Dharin Jariwala, Krupa Chattopadhyay, Chiranjoy Sardar Vallabhbhai National Institute of Technology Dept. of Computer Science and Engineering Surat India Pune India Flame University School of Computing and Data Sciences Pune India

ISBN: (纸本)9798331533311

Book flipping videos present a distinctive challenge for information extraction, requiring the identification of frames with clear text visibility during dynamic page turns. This paper introduces a novel approach to frame classification in book flipping videos, leveraging the combined effect of Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs). In particular, a CNN-LSTM model is employed, where a pre-trained CNN captures spatial features, and LSTMs extend the model's capability to distinguish temporal dependencies critical for page turn detection. Unlike conventional methods, this approach processes entire video sequences, enabling the model to learn intricate Frame of Interest (FoI) detection features indicative of page turns and text visibility. The proposed model offers improved interpretability, effectively classifying frames without compromising transparency. The research opens avenues for automated book digitization, digital library creation, and educational technology applications, enhancing information extraction and reliability from book flipping videos. © 2024 IEEE.

关键词： Convolutional neural networks

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Oral Vision: Advanced Image Recognition for Oral Cancer 5

Oral Vision: Advanced Image Recognition for Oral Cancer

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Ghosh, Joita Mishra, Khushi Sindhuja, M. Nagadevi, S. Anand, M. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Dept. of Computing Technologies Tamil Nadu Kattankulathur India

ISBN: (纸本)9798331530013

Cancer that affects the lining of the mouth, lips, or upper throat is called oral cancer. Other names for it include tongue cancer, mouth cancer, and oral cavity cancer. There is an urgent need for early cancer detection due to the rise in cancer cases in all age groups. Alcohol consumption and tobacco usage are risk factors. The risk of oral cancer is fifteen times higher for people who use both alcohol and tobacco than for people who do not. This paper presents a deep learning-based technique that uses the ResNet50 convolution neural network model to classify the images of malignant and non-malignant oral cell images. To enhance the pretrained ResNet50 model's capacity to differentiate malignant and benign samples, it was trained using a dataset of histopathological oral tissue pictures. A special virtual voice assistant has also been integrated along with the model to make it more assistive for all kinds of users. In order to track the advancement of these methods, this paper also analyses how combining NLP with point-of-care tools makes use of a variety of algorithms and sets. © 2024 IEEE.

关键词： Lung cancer

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Sparse Channel Estimation in Massive MIMO Systems Using Compressed Sensing and Neural Networks

Sparse Channel Estimation in Massive MIMO Systems Using Comp...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

In this paper, based on compressed sensing and neural networks, we present an intelligent sparse channel estimation technique for Massive MIMO systems. We show that our approach improves considerably the estimation accuracy in terms of Mean Squared Error (MSE) and Normalized Mean Squared Error (NMSE) with respect to classical methods. We demonstrate [Figure: ] that our approach is computationally more efficient, requires less time and resources (memory/disk) compared to the existing literature but for a robustness of performance across distal or low Signal-to-Noise Ratios (SNRs). We also confirm our method exhibits fast convergence and efficiently accommodates a variety of sparsity levels. By visualizing it using heatmaps and ROC curves we show that this method is able to effectively differentiate between signal and noise, asserting our approach as a powerful tool for the contemporary set of problems present in wireless communications. © 2024 IEEE.

关键词： Signal to noise ratio

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E-Waste Alchemy: A Unified Solution for Recycling and Sustainability 5

E-Waste Alchemy: A Unified Solution for Recycling and Sustai...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Sharma, Vansh Sameer, Md Sindhuja, M. Nagadevi, S. Anand, M. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Dept. of Computing Technologies Tamil Nadu Kattankulathur India

ISBN: (纸本)9798331530013

Through user-centric solutions and advanced features, E-Waste Alchemy is an innovative platform that aims to transform the administration of electronic waste (e-waste). The Recycle Centre on the platform is comprehensive, allowing users to specify the brand and model of laptops, smartphones, and a variety of accessories in order to schedule and select the retrieval. E-Waste Alchemy further improves user convenience by enabling real-time tracking of nearby recyclers based on the user's current location. Furthermore, the platform employs predictive analytics to determine the market values of the components contained within each device, including plastic, aluminum, and nickel. The responsible disposal of e-waste is promoted by this predictive feature, which also contributes to environmental sustainability by optimizing recycling processes. E-Waste Alchemy is a forward-thinking and adaptable solution that facilitates the administration of e-waste and promotes a circular economy. © 2024 IEEE.

关键词： Circular economy

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A Detailed Analysis of Secrecy and Performance Metrics in Hybrid RF/ FSO Communication Systems: Implications of Composite Weibull and Log-normal Fading with M Turbulent Channels

A Detailed Analysis of Secrecy and Performance Metrics in Hy...

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

ISBN: (纸本)9798350379525

In this paper, we have analyzed the secrecy performance and system operation performance for hybrid RF/FSO systems against composite Weibull-log-normal fading channel with M turbulent channels. The performance is evaluated by carrying out extensive simulations to calculate Signal-to-Noise Ratio (SNR), Bit Error Rate (BER) and outage probability, we also re-evaluate channel capacity and secrecy capacity with respect of environmental conditions. Compared with RF only system, FSO only standalone scheme and some meaningful orthogonal-modulation multicarrier IM/DD-based schemes proposed by the literatures. Similarly, the utilization of adaptive modulation schemes improved resistance to fading and roughness in systems. The above results confirm the hybridization of both RF and FSO a comprehensive solution for dependable communication in different and even hostile surrounding conditions which set this work as reliable foundation point towards forthcoming research activity or application relevance within all range telecommunication sector. © 2024 IEEE.

关键词： Bit error rate

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Transfer Learning Approaches for Channel State Prediction in Heterogeneous Fading Networks

Transfer Learning Approaches for Channel State Prediction in...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

ISBN: (纸本)9798350379525

In this work, we leverage state-of-the-art machine learning algorithms to predict Channel State Information (CSI), for enhancing performance of wireless communication systems. The simulation and analysis stop with transfer learning to finetune, adaptive domain adaptations, multitasking (MTL) as well a few shot meta-learning models framework under different network packet loss rate scenarios. By generalizing better from one domain to another, transfer learning pushes the performance beyond what traditional methods can obtain. Further, fine-tuning also helps to align the source and target domain distributions in feature space by a partial (or full) reduction of re-stylization error. This work proposes multi-Task learning as a tool to mitigate loses in light of predicting across fading scenarios. Considering multi-task learning also provides benefits from shared learnings, so experience is pooled between the tasks which indirectly improve prediction as well and it helps in training both of these algorithms together. Moreover, the few-shot learning models prove to be highly desirable as they deliver state-of-the-art performance with minimal training data for difficult problems. These results show the potential of AI-based methods to improve CSI prediction, and have implications for improving reliability and throughput when deploying real wireless communication systems. © 2024 IEEE.

关键词： Contrastive Learning

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StreamBucket: In-Network Adaptation for Late-Binding Stream Processing Systems 13

StreamBucket: In-Network Adaptation for Late-Binding Stream ...

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13th IEEE International Conference on Cloud Networking, CloudNet 2024

作者： Ramprasad, Brian Mishra, Pritish Peixoto, Maycon L. M. De Lara, Eyal University of Toronto Dept. of Computer Science Canada Institute of Computing Federal University of Bahia Brazil

ISBN: (纸本)9798350376562

Stream processing applications are increasingly de-ployed on hierarchical geo-distributed edge-cloud environments. Applications in such environments require frequent deployment reconfigurations to balance performance and cost by optimally utilizing the diverse resources. Recently, late-binding stream processing frameworks using a hierarchical network of routers have been introduced to seamlessly execute such reconfigurations with minimal impact on application performance. However, late-binding frameworks face scalability challenges as the routers can become a bottleneck. This restricts the maximum throughput that can be achieved by the network. We propose StreamBucket, a novel protocol that increases the capacity of late-binding routers by intelligently batching the tuples at various levels of the routing network and employs header compression techniques to reduce bandwidth. We design a model to predict the optimal batch size for unseen workloads, which reduces the amount of costly profiling. Our evaluations show that StreamBucket achieves up to 5x improvement in throughput using multi-tier batching and between 15% to 85% in bandwidth savings when using our protocol's compressed header. © 2024 IEEE.

关键词： Bandwidth compression

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