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International Conference on science Technology Engineering and Management (ICONSTEM)

作者： Venkatesh S D. Jeevitha J. Anitha Gnanaselvi Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Department of Computer Science and Engineering Jeppiaar Engineering College Chennai

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The human brain has a simple time analyzing and processing images. The brain is able to rapidly deconstruct and distinguish an image's various components when the eye perceives it. With the Convolutional Neural Network (CNN) as its foundation, this research suggests deep learning conceptual models. When the algorithms are compared, it becomes clear that CNN-based classification of handwritten alphabets performs better than other algorithms in terms of accuracy. The Manual Net, Alex Net, and LeNet Architectures are among the CNN algorithms employed in this research. The convulational layer, max pooling, flattening, feature assortment, rectifier lined unit, and completely linked softmaxx layers are each components of the aforementioned designs. The proposed network is tested using an image dataset comprising 530 training photos and 2756 testing images. The top precision and cost-efficient model will be used in the Django context to build a handler line for supplying the appeal to be recognized and obtaining the productivity outcome of recognized appeal.

关键词： Training Productivity Accuracy Rectifiers User interfaces Mathematical models Classification algorithms

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Dual-path Residual UNet with Convolutional Attention based Swin-Spectral Transformer Network for Segmentation and Detection of Ovarian Cancer

Dual-path Residual UNet with Convolutional Attention based S...

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Advanced computing Technologies and Applications (ICACTA), International Conference on

作者： A Shanthini Arathi Boyanapalli Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai

In this paper, a new transformer based deep learning network (TDLN) model has designed for detecting ovarian cancer (OC) from the input samples. Primarily, the input images are pre-processed to remove the unwanted noises and thereby enhances the quality through HSV color channel conversion, block matching with triple dimensional filtering (BM_TriD-Fil) and improved gabor wavelet transform (IGWT). From the pre-processed images, the affected regions are gets segmented through a new dual-path residual UNet (DPResU-Net) structure. Using the segmented regions, the feature extraction and classification is performed by utilizing a novel convolutional adaptive-attention based swin-spectral transformer network (CA 2 _S 2 TNet) model. In order to tune the parameters of CA 2 _S 2 TNet model, chaotic fire hawks optimization (CFHO) has employed. The proposedTDLN model is implemented in the python platform through TCGA-OV dataset and assessed the performance in terms of different evaluation measures. The maximum classification accuracy obtained by TDLN is 99.91%, superior to the existing approaches for OC classification.

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Computer vision and Generative AI for yield prediction in Digital Agriculture

Computer vision and Generative AI for yield prediction in Di...

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Networking and Communications (ICNWC), International Conference on

作者： Sayan Majumder Yash Khandelwal Sornalakshmi K Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

For the cause of evolution of agriculture to its next generation, the introduction of A.I. and data-driven approach is going to be an important part of the agricultural industry that as per our vision would offer numerous economic, environmental and social benefits. Controlled Environment Agriculture (CEA) is providing more benefits since the weather and other environmental conditions is controlled and predictable. In CEA, the benefits include enhanced productivity in yield, reduced environmental footprints and better resource management. Our solution uses the adoption of Generative AI to visually forecast the potential yield of a crop using stable diffusion and conditional prompts additionally implementing several Generative Adversarial Networks (GAN) architectures for data generation, augmentation and super-resolution. Our proposed system also adopts generative AI for real time monitoring of plants. studying their respective conditions and their autonomous cultivation and harvesting patterns. The dataset used contained over 8479 examples for different fruits alongside annotations. This data was used to train the detection models as well categorically feed the GANs for data generation. data generated by DCGAN has a structural similarity of 0.5-0.7 whereas data generated by pix2pix network returns us a structural similarity of 0.7-0.9.

关键词： Smart agriculture Productivity Generative AI Superresolution Weather forecasting Generative adversarial networks Real-time systems

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Skin Cancer Classification using CNN and Transfer Learning (TL)

Skin Cancer Classification using CNN and Transfer Learning (...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Shashank Patel Mudita Pandey Rajeswari D Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Skin cancer is a predominant and possibly lethal condition that distresses people across the world. Primary detection and precise finding are crucial for leveraging efficacious treatment and enhanced patient consequences. This research study explores the application off Artificial Intelligence (AI) resolutions for the primary finding and analysis of skin cancer. The study leverages a dataset of dermatological images and employs various ML methods, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and decision trees, to analyse the existence of skin cancer with a higher degree of accuracy. By utilizing a combination of image processing and feature extraction, the AI model demonstrates higher performance in classifying skin lesions into malignant or benign categories. The proposed model has achieved an accuracy rate of 98.52%, making the AI-based system a promising tool for dermatologists and healthcare professionals.

关键词： Support vector machines Technological innovation Dermatology Transfer learning Collaboration Medical services Skin

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Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering

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IEEE Transactions on Computational Social Systems 2023年第3期10卷 910-921页

作者： Deng, Ping Li, Tianrui Wang, Hongjun Wang, Dexian Horng, Shi-Jinn Liu, Rui Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Computing and Artificial Intelligence Chengdu611756 China National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan North Sichuan Medical College School of Preclinical Medicine Department of Computer and Mathematics Nanchong637100 China

The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data. However, noise is introduced when data reconstruction is performed;and the square loss method is sensitive to noise, which leads to degradation in the performance of data analysis tasks. To solve this problem, a novel graph regularized sparse NMF (GSNMF) is proposed in this article. To obtain a cleaner data matrix to approximate the high-dimensional matrix, the l1-norm to the low-dimensional matrix is added to achieve the adjustment of data eigenvalues in the matrix and sparsity constraint. In addition, the corresponding inference and alternating iterative update algorithm to solve the optimization problem are given. Then, an extension of GSNMF, namely, graph regularized sparse nonnegative matrix trifactorization (GSNMTF), is proposed, and the detailed inference procedure is also shown. Finally, the experimental results on eight different datasets demonstrate that the proposed model has a good performance. © 2014 IEEE.

关键词： Matrix algebra

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From Text to Visuals: Advancements in Depression Prediction Using AI and Machine Learning Techniques

From Text to Visuals: Advancements in Depression Prediction ...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International Conference on

作者： S. Annapoorani P. Saravanan Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulatur India

Social lifestyle and COVID pandemic situation to produce a direct impact on mental health. Depression remains a primary cause of disability worldwide and contributes significantly to the global health burden. It can typically lead to suicidal ideation and have a negative impact on anyone's professional and personal life. Artificial intelligence, machine learning, and deep learning techniques can forecast depression in a timely manner utilizing a variety of resources. Different kinds of sources, such as surveys, social networking sites, video images, and speech, can be used to accurately identify depression without the assistance of a psychiatrist. People on social networking sites express their feelings through appropriate text, which may be examined using machine learning algorithms. Through visual and speech data, deep learning approaches properly provide remarkable accuracy in predicting depression. This survey study is useful in identifying depression accuracy in advanced ways utilizing diverse resources.

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Deepfake Detection System

Deepfake Detection System

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Networking and Communications (ICNWC), International Conference on

作者： Shaswat Shrivas Aditya Rai M. Lakshmi Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

Detecting deepfake content presents a formidable challenge, necessitating advanced methodologies. This paper proposes a holistic strategy employing Facenet_pytorch, MTCNN, and InceptionResnetV1 for robust deepfake detection. Facenet_pytorch serves as the cornerstone for facial feature recognition, while MTCNN efficiently identifies faces in images during preprocessing. InceptionResnetV1 scrutinizes visual details to detect subtle anomalies indicative of manipulation, such as unnatural facial expressions and incongruent lip synchronization. Our approach underscores the importance of maintaining a delicate balance between accurate detection and individual privacy rights. By leveraging these advanced models, we achieve significant strides in differentiating manipulated content from authentic media, contributing to the ethical deployment of deepfake detection technologies.

关键词： Deepfakes Visualization Social networking (online) Law enforcement Forensics Entertainment industry Biodiversity

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Disease Detection in Arecanut using Convolutional Neural Network

Disease Detection in Arecanut using Convolutional Neural Net...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： V Arun Karthik Jatin Shivaprakash D Rajeswari Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Arecanut, also known as betel nut, is a tropical crop predominantly grown in India. The country holds the second position globally in terms of the production and consumption of arecanut. Throughout its life cycle, the areca nut plant is susceptible to a variety of diseases affecting its roots, trunk, leaves, and fruits. Some of these diseases are visible to the naked eye, while others are not. Farmers traditionally analyze every crop to detect any signs of disease, a process that is extremely *** this study, we propose an automatic system that aids in detecting the diseases of arecanut leaves using Convolutional Neural Networks (CNN) and deep learning algorithms. A Convolutional Neural Network is a Deep Learning algorithm that takes an image as input, assigns learnable weights and biases to various features in the image, and then learns from the results to distinguish one feature from another. This approach significantly reduces the manual effort required in disease detection and increases the efficiency of the process. A datset is developed with many photos of healthy and sick arecanut leaves in order to train and evaluate the CNN model. An 80:20 split of the dataset was made into training and testing data. The goal of training the model over multiple epochs was to maximize validation and test accuracy while minimizing loss. It was discovered that the suggested method for diagnosing arecanut illnesses was precise and successful. This study contributes to the ongoing efforts to leverage technology in agriculture, with the aim of improving crop health and productivity. This study indicate that the proposed system can accurately identify diseases in arecanut leaves with a good and best accuracy. The system was able to distinguish between healthy and diseased leaves, and suggest appropriate remedies for the detected diseases. These findings demonstrate the potential of using Convolutional Neural Networks and deep learning algorithms in the field of agriculture, par

关键词： Deep learning Training Productivity Plant diseases Accuracy Crops Agriculture

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Deep Dynamic Probabilistic Canonical Correlation Analysis

arXiv

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arXiv 2025年

作者： Tang, Shiqin Yu, Shujian Dong, Yining Qin, S. Joe Department of Data Science City University of Hong Kong Hong Kong Department of Artificial Intelligence Vrije Universiteit Amsterdam Amsterdam Netherlands Department of Data Science City University of Hong Kong Hong Kong Department of Computing and Decision Science Lingnan University Hong Kong

This paper presents Deep Dynamic Probabilistic Canonical Correlation Analysis (D2PCCA), a model that integrates deep learning with probabilistic modeling to analyze nonlinear dynamical systems. Building on the probabilistic extensions of Canonical Correlation Analysis (CCA), D2PCCA captures nonlinear latent dynamics and supports enhancements such as KL annealing for improved convergence and normalizing flows for a more flexible posterior approximation. D2PCCA naturally extends to multiple observed variables, making it a versatile tool for encoding prior knowledge about sequential datasets and providing a probabilistic understanding of the system’s dynamics. Experimental validation on real financial datasets demonstrates the effectiveness of D2PCCA and its extensions in capturing latent dynamics. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

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Towards Context-aware Support for Color Vision Deficiency: An Approach Integrating LLM and AR 13

Towards Context-aware Support for Color Vision Deficiency: A...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Morita, Shogo Zhang, Yan Yamauchi, Takuto Chen, Sinan Li, Jialong Tei, Kenji Tokyo Institute of Technology School of Computing Tokyo Japan University of Tokyo International Research Center for Neurointelligence Tokyo Japan Waseda University Department of Computer Science and Engineering Tokyo Japan Kobe University Center of Mathematical and Data Sciences Kobe Japan

ISBN: (纸本)9798350355079

People with color vision deficiency often face challenges in distinguishing colors such as red and green, which can complicate daily tasks and require the use of assistive tools or environmental adjustments. Current support tools mainly focus on presentation-based aids, like the color vision modes found in iPhone accessibility settings. However, offering context-aware support, like indicating the doneness of meat, remains a challenge since task-specific solutions are not cost-effective for all possible scenarios. To address this, our paper proposes an application that provides contextual and autonomous assistance. This application is mainly composed of: (i) an augmented reality interface that efficiently captures context;and (ii) a multi-modal large language model-based reasoner that serves to cognitize the context and then reason about the appropriate support contents. Preliminary user experiments with two color vision deficient users across five different scenarios have demonstrated the effectiveness and universality of our application. © 2024 IEEE.

关键词： Color vision

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