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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Chandar, Vijay Nair, Vishnu G Rajeswari, D. SRM Institute of Science and Technology Kattankulathur School of Computing College of Engineering and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798350343632

The motive of underwater image enhancement is to improve and enhance the quality of photographs taken underwater. Various traditonal image processing techniques and deep learning models have been developed to work on this task. But most often the deep learning models and conventional methods of image processing work in silos, leading to subpar results. This research proposes a strategy that utilizes a combination of both approaches to solve this issue. Initially, traditional image processing techniques like colour correction and image stretching are used to enhance the colour of the images. The enhanced images are then fed into a custom Real-ESRGAN model that has been fine-tuned on an underwater image dataset to optimize performance in this context. This hybrid approach has the potential to outperform the models utilizing either traditional or deep learning methods in isolation by leveraging the strengths of both. In addition, batch processing is used in the pipeline to efficiently handle multiple images at the same time. This effectively addresses the scalability problem that arises frequently in image enhancement tasks. With eye-catching visuals, the proposed methodology achieves a Peak Signal-to-Noise Ratio (PSNR) of 23.66 and a Mean Squared Error (MSE) of 0.28, suggesting excellent image restoration and noise reduction. The qualitative assessments show that the improved image graphs have better colour, contrast, and clarity than the originals. © 2023 IEEE.

关键词： Batch Processing Image Enhancement Image Stretching MSI PSNR Real-ESRGAN

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Music Recommendation based on Facial Expressions using data Augmentation 3

Music Recommendation based on Facial Expressions using Data ...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Kapoor, Tanisha Ganguly, Arnaja Rajeswari, D. SRM Institute of Science and Technology Kattankulathur School of Computing College of Engineering and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798350343632

Emotion recognition from facial expressions plays a pivotal role in human-computer interaction, psychology, marketing, and various other domains. This research paper presents a comprehensive study and implementation of an automated emotion recognition system based on deep learning techniques. The project addresses the challenge of accurately classifying a wide range of emotions from facial images in real-time. Leveraging Convolutional Neural Networks (CNNs) and modern deep learning approaches, the system demonstrates high accuracy in identifying emotions, including happiness, sadness, anger, and more. This project used the image dataset FER-2013 for the mood prediction portion of the project, and Spotify music data to predict emotions for music recommendation. Both the datasets utilized are freely available on Kaggle. The proposed system can achieve an accuracy of 91.23% in predicting the users' emotion based on their facial expressions. © 2023 IEEE.

关键词： CNN Emotion recognition Facial expression analysis FER-2013 Music Recommendation

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Predicting the Impact of Road Conditions on Battery Health Via Transfer Learning 3

Predicting the Impact of Road Conditions on Battery Health V...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Nair, Rahul Anil Suresh, Dev Karan Rajeswari, D. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9781665462167

Battery life of EV (electric vehicles) depends on various factors. These factors include temperature of the battery, duration of use or the type and condition of the road traversed by the vehicle. This may cause the battery to discharge faster than it used to. Understanding what may cause the battery life to shorten can help us alleviate the given problem and can preserve the battery for longer time. This research focuses on the effects road conditions have on battery life to be able to recommend the favourable conditions to extend it. It proposes to find the routes taken by the vehicles and classifying them based on their battery health upon reaching the destination. In this paper, an attempt has been made to predict the category of the routes by employing Transfer learning via a pre-trained model VGG-16. Observations indicate that paths which have more turns and curves are generally worse for battery life as compared to straight and narrow paths. Another model which is an artificial neural network has been developed to predict the based on the average vehicle speed and the distance covered by the vehicle. The VGG-16 model predicts the data with which it is validated with an accuracy of 84.29% and the testing data with an accuracy of 83.56%. The artificial neural network model predicts the category of the data with an accuracy of 88.02%. © 2023 IEEE.

关键词： Deep learning

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Battery Health Prediction Using Deep Hybrid Learning 3

Battery Health Prediction Using Deep Hybrid Learning

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3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Nair, Rahul Anil Suresh, Dev Karan Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350322842

The type and condition of the road the vehicle travels;how long the battery is used for are just a few of the variables that affect the battery life of electric cars (EVs). These elements might affect the battery's lifespan by causing it to drain more quickly than usual. By understanding the various factors that impact battery life, work can be done to mitigate these issues and extend the useful life of the battery. To find the best circumstances for increasing the battery's longevity, this research paper is concentrated on examining the impacts of road conditions on battery life. The previous article classified routes with an accuracy of 83.56% based on battery health while arriving at the destination [1]. This article examines EV routed and categorises them on the basis of the number of brakes applied upon arrival at the destination. By doing so, it aims to develop recommendations for drivers on how to optimize their driving habits to extend the battery's lifespan. Observations indicate that bad routes generally involve a lot more brakes as compared to good routes. Vehicle speed has a negative correlation with heater power in a bad route and a positive correlation in a good route. Battery voltage and Battery state- of-charge have opposing correlations in both categories. In this research, a prediction of the route category was attempted by using Deep Hybrid Learning to integrate three pre-trained models., VGGI6, Resnet50 and VGG19 with two boosting algorithms:- XGBoost and AdaBoost. The accuracy of the six deep hybrid learning models in predicting the category of these routes are as follows:-0.87625, 0.84875, 0.80125, 0.7925, 0.8425 and 0.855. © 2023 IEEE.

关键词： Deep learning

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Provably secure multilayers certificateless proxy signature for industrial internet of things

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Cluster computing 2025年第5期28卷 1-10页

作者： Ullah, Rafiq Mehmood, Amjad Khan, Muhammad Altaf Ullah, Insaf Institute of Computing Kohat University of Science & Technology Kohat26000 Pakistan WMG University of Warwick CoventryCV4 7AL United Kingdom Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom Rose Lab Kust KP Kohat Pakistan

The emergent nature of Industrial Internet of Things (IIoTs) raises significant authentication concerns in Certificateless Proxy Signature (CLPS) to ensure protection from modern forgery attacks. Due to some modern forgery attacks, single layer authentication is unable to provide secure CLPS with optimal computational cost and communication overhead in IIoTs. Therefore, multilayer authentication is required to secure the identities of the participants on open channel using Zero-Knowledge Proof (ZKP) mechanism. It offers a promising opportunity for addressing authentication vulnerabilities in IIoTs. This article proposes an optimal and secure multilayer authentication technique with minimum computational cost and communication overhead. The proposed mechanism evolved CLPS with zero knowledge proof using Schnorr signature in a transparent way, whereas, the security hardness of the proposed work is demonstrated informally with Hyperelliptic Curve (HEC). Moreover, for formal verification, AVISPA tool is used. Performance analysis depicts that the proposed approach exhibits not only lower computational cost and communication overhead but also provides optimal security as compared to existing approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Authentication

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Detection of Brain Tumor from MRI Images Using VGG-16 CNN Model with Transfer Learning 3

Detection of Brain Tumor from MRI Images Using VGG-16 CNN Mo...

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3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Singh Rathore, Bhanu Pratap Dave, Rutul Dhanasekaran, K. Priyadarsini, K. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur603203 India

ISBN: (纸本)9798350322842

Brain tumors are a serious health concern caused by the abnormal growth of cells in or around the brain. These tumors can either be benign or malignant. Few of the causes for Brain tumors are genetic mutations, exposure to radiation, or environmental factors. Early detection of brain tumors is essential for effective treatment and improved patient outcomes. To address this, a novel approach has been proposed for brain tumor detection using transfer learning with the VGG-16 convolutional neural network (CNN) architecture. By leveraging the pre-trained VGG-16 model on the ImageNet dataset, which provides a broad range of data for learning and representing complex features of various objects, meaningful features can be extracted from MRI scans and differentiate between tumor and normal tissue accurately. This refined VGG-16 model can provide a more precise and reliable diagnosis, which is crucial for successful treatment. This research study demonstrates that the utilization of VGG-16 model for brain tumor diagnosis can provide an efficient and accurate method for identifying brain tumors. Moreover, this approach can be applied to develop automated systems to assist medical professionals in diagnosing and treating brain tumors more effectively. © 2023 IEEE.

关键词： Convolutional neural networks

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Smart Home Based on IoT by Using Machine Learning 1

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6th International Conference on Microelectronics and Telecommunication Engineering, ICMETE 2022

作者： Lavanya, K. Archana, K.S. CSE Vels Institute of Science Technology & Advanced Studies Chennai India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9789811995125

ISBN: (纸本)9789811995118

Smart homes have surfaced as the coming wave of technological advancement. It is a collection of smaller systems connected to home network and comprising a variety of communication technologies which can assist inhabitants’ convenience, security, safety, and comfort. This paper examines several IoT based smart house approaches, which were divided as 2 categories depending on application: smart home security system and smart home automation. We explored several sensors, doors, and communication protocols utilized in IoT-based smart homes in this paper. We also discussed about research problems and also open research objectives. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise 38

Resfusion: Denoising Diffusion Probabilistic Models for Imag...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shi, Zhenning Zheng, Haoshuai Xu, Chen Dong, Changsheng Pan, Bin Xie, Xueshuo He, Along Li, Tao Fu, Huazhu College of Computer Science Nankai University China School of Statistics and Data Science Nankai University China Haihe Lab of ITAI China Institute of High Performance Computing A*STAR Singapore

Recently, research on denoising diffusion models has expanded its application to the field of image restoration. Traditional diffusion-based image restoration methods utilize degraded images as conditional input to effectively guide the reverse generation process, without modifying the original denoising diffusion process. However, since the degraded images already include low-frequency information, starting from Gaussian white noise will result in increased sampling steps. We propose Resfusion, a general framework that incorporates the residual term into the diffusion forward process, starting the reverse process directly from the noisy degraded images. The form of our inference process is consistent with the DDPM. We introduced a weighted residual noise, named resnoise, as the prediction target and explicitly provide the quantitative relationship between the residual term and the noise term in resnoise. By leveraging a smooth equivalence transformation, Resfusion determine the optimal acceleration step and maintains the integrity of existing noise schedules, unifying the training and inference processes. The experimental results demonstrate that Resfusion exhibits competitive performance on ISTD dataset, LOL dataset and Raindrop dataset with only five sampling steps. Furthermore, Resfusion can be easily applied to image generation and emerges with strong versatility. Our code and model are available at https://***/nkicsl/Resfusion. © 2024 Neural information processing systems foundation. All rights reserved.

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Forest-based Evolutionary Algorithm for Reconstructing Boolean Gene Regulatory Networks 21

Forest-based Evolutionary Algorithm for Reconstructing Boole...

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21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Stranieri, Nicolò Buffa, Francesca M. Tangherloni, Andrea Bocconi University Department of Computing Sciences Milan Italy University of Oxford Department of Oncology Oxford United Kingdom Bocconi University Bocconi Institute for Data Science and Analytics Department of Computing Sciences Milan Italy

ISBN: (纸本)9798350356632

Gene Regulatory Networks (GRNs) play a fundamental role in orchestrating the expression of our genes through complex interactions between DNA, RNA, proteins, and other molecules. Accurately reconstructing such networks from gene expression data is a critical yet challenging task in Systems Biology due to their intricate nature and limited data availability. In this work, we introduce a novel Forest-based Evolutionary Algorithm (FP) designed for reconstructing Boolean GRNs from time series data of gene expressions. Unlike traditional methods that struggle with scalability and accurate representation of regulatory interactions, FP utilizes a forest structure where each tree represents the logical relationships between genes, enhancing the model's capacity to depict complex networks efficiently. Our comprehensive testing indicates that FP rapidly converges towards potential solutions within a limited number of generations, although a higher fitness score does not always equate to a more accurate GRN representation. Implementing mini-batching techniques, inspired by their effectiveness in gradient descent optimization, shows promise in improving computational efficiency without sacrificing performance. A comparative analysis against the main state-of-the-art approaches reveals FP's tendency towards conservative predictions, emphasizing precision over recall, making it particularly suitable for contexts where the cost of false positives is high. These initial results suggest that FP is a robust and efficient tool for GRN inference. © 2024 IEEE.

关键词： Genetic programming

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RoCEL: Advancing Table Entity Linking through Distinctive Row and Column Contexts

RoCEL: Advancing Table Entity Linking through Distinctive Ro...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Wang, Yuanzheng Fan, Yixing Guo, Jiafeng Zhang, Ruqing Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798891761643

Table entity linking (TEL) aims to map entity mentions in the table to their corresponding entities in a knowledge base (KB). The core of this task is to leverage structured contexts, specifically row and column contexts, to enhance the semantics of mentions in entity disambiguation. Most entity linking (EL) methods primarily focus on understanding sequential text contexts, making it difficult to adapt to the row and column structure of tables. Additionally, existing methods for TEL indiscriminately mix row and column contexts together, overlooking their semantic differences. In this paper, we explicitly distinguish the modeling of row and column contexts, and propose a method called RoCEL to capture their distinct semantics. Specifically, for row contexts in tables, we take the attention mechanism to learn the implicit relational dependencies between each cell and the mention. For column contexts in tables, we employ a set-wise encoder to learn the categorical information about the group of mentions. At last, we merge both contexts to obtain the final mention embedding for link prediction. Experiments on four benchmarks show that our approach outperforms the state-of-the-art (SOTA) baseline by about 1.5% on the in-domain dataset, and by 3.7% on average across three out-of-domain datasets. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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