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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qian Huang ZhongQi Chen Chang Li WeiWen Qian College of Computer Science and Software Engineering Hohai University NanJing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Human Action Recognition (HAR) has widespread applications in areas such as human-computer interaction, elderly care, and home healthcare. However, current sensor-based HAR faces challenges of low fine-grained recognition performance and difficulty in distinguishing similar actions. To solve this problem, this paper proposes a model based on Multilevel Convolutional Time Series Attention Network (MCTSANet). By Multi-ResCNN to pay attention to different levels of features and using Time Series Attention (TSA) to pay attention to the more important data in the channel, so as to improve the ability of confusable action recognition. Experiments on three public datasets show that the proposed method outperforms state-of-the-art sensor-based HAR approaches.

关键词： Human computer interaction Convolution Time series analysis Noise Speech recognition Medical services Human activity recognition Speech processing Older adults Faces

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Optimizing Image Recognition Model through Transfer Learning and Robust Feature Extraction in Neural Networks 2

Optimizing Image Recognition Model through Transfer Learning...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Ramesh, G. Agoramoorthy, Moorthy Ahamed Ali, S. Sujatha, D. Michael Raj, T.F. K.L.N.College of Engineering Department of Information Technology Sivagangai Tamilnadu Pottapalayam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Easwari Engineering College Computer Science and Engineering Chennai Ramapuram India St. Peters College of Engineering and Technology Department of Computer Science and Engineering Chennai Avadi India Sclas Simats Department of Computer Applications Tamilnadu Chennai India

ISBN: (纸本)9798350369960

These advances in image identification's technology have created massive changes, mainly in the health care facilities plus self-directed systems. Transfer learning strategies coupled with robust feature extraction techniques are used in the model to promote efficiency in the imaging system. A technique of fine-tuning the weights used for recognition tasks on pre-trained convolutional neural network architectures is proposed. State of art feature extraction pipeline composed with edge detection, texture analysis and spatial frequency analysis to enrich network capacity of distinguishing intricate visual textures and patterns. This combination of techniques helps in faster model training with top class prediction results on mixed image datasets. After rigorous testing on various benchmark datasets, including CIFAR-10 and ImageNet, considerable enhancement in recognition performance beyond baselines was observed. Additionally, the proposed approach is resilient to changes in image quality, illumination, and occlusions. The proposed framework enhances the existing model efficiency, flexibility, and robustness for application in real-time image identification. This approach opens new capabilities for the medical imagery, self-navigation cars, and object recognize mechanisms whose image classification must be exact. © 2023 IEEE.

关键词： Feature Extraction Image Recognition Neural Network Transfer Learning

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A Multi-channel Convolutional Neural Network for Multilabel Sentiment Classification Using Abilify Oral User Reviews

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第1期47卷 109-114页

作者： Trueman, Tina Esther Jayaraman, Ashok Kumar Jasmine, S. Ananthakrishnan, Gayathri Narayanasamy, P. Department of Computer Science University of the People Pasadena United States Department of Information Science and Technology Anna University Chennai India Department of Information Technology Vellore Institute of Technology Vellore India Department of Electrical and Electronics Engineering PSG College of Technology Coimbatore India

Nowadays, patients and caregivers have become very active in social media. They are sharing a lot of information about their medication and drugs in terms of posts or comments. Therefore, sentiment analysis plays an active role to compute those posts or comments. However, each post is associated with multilabel such as ease of use, effectiveness, and satisfaction. To solve this kind of problem, we propose a multichannel convolution neural network for multilabel sentiment classification using Abilify oral user comments. The multichannel represents the multiple versions of the standard model with different strides. Specifically, we use the pre-trained model to generate word vectors. The proposed model is evaluated with multilabel metrics. The results indicate that the proposed multichannel convolutional network model outperforms the traditional machine learning algorithms. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Sentiment analysis

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Deep Learning-Based Diagnosis System for Liver Cancer Through Multimodal Determination for Early Treatment 4

Deep Learning-Based Diagnosis System for Liver Cancer Throug...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Dandotiya, Monika Singh, Anupam Agarwal, Nishi Jaheda, Shaik Gulati, Samridhi Poornima University Department of Computer Science & Engineering Jaipur India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Dev Bhoomi Uttrakhand University Department of Computer Science and Engineering Dehradun India Cvr College of Engineering Department of Information Technology Hyderabad India Raj Kumar Goel Institute of Technology Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350364729

Liver cancer is still one of the top causes of worldwide mortality from cancer, which highlights the demand for earlier diagnostic tools aimed at improved treatment outcomes. Although these traditional diagnostic practices are helpful, they may take time and also be subject to human error. The present study aims to fill the gap in existing diagnostic tools for liver cancer by evaluating prostate deep learning models (VGG 16, VGG 19 and ResNet 50) on CT scan images. This problem statement is related to improving the accuracy and reliability of liver cancer detection using powerful computational techniques. We processed a dataset of 1020 CT scan images by resizing, then normalizing the data, and performing an augmentation and some noise reduction. The performance of each model was evaluated in terms of precision, recall and F1 score using Dice Similarity Coefficient to identify the liver tumours. VGG 19 was the top performer in terms of accuracy and another score, followed by VGG 16 and ResNet 50. The use of such models in clinical settings can significantly improve diagnostic accuracy and, ultimately, diagnosis time, which will lead to better patient outcomes. The study ultimately underscores the promise of deep learning models (specifically VGG 19) as an essential resource in medical imaging by developing new and prospective methods for computerized liver cancer diagnosis that will help improve healthcare. © 2024 IEEE.

关键词： Diseases

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Spatially Compact Dense Block Mining in Spatial Tensors 25

Spatially Compact Dense Block Mining in Spatial Tensors

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Weike Tang Dingming Wu Tsz Nam Chan Kezhong Lu College of Computer Science & Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798400712456

Spatial tensors have been extensively used in a wide range of applications, including remote sensing, geospatial information systems, conservation planning, and urban planning. We study the problem of Spatially Compact Dense (SCD) block mining in a spatial tensor, which targets for discovering dense blocks that cover small spatial regions. However, most of existing dense block mining (DBM) algorithms cannot solve the SCD-block mining problem since they only focus on maximizing the density of candidate blocks, so that the discovered blocks are spatially loose, i.e., covering large spatial regions. Therefore, we first formulate the problem of mining top-k Spatially Compact Dense blocks (SCD-blocks) in spatial tensors, which ranks SCD-blocks based on a new scoring function that takes both the density value and the spatial coverage into account. Then, we adopt a filter-refinement framework that first generates candidate SCD-blocks with good scores in the filtering phase and then uses the traditional DBM algorithm to further maximize the density values of the candidates in the refinement phase. Due to the NP-hardness of the problem, we develop two types of solutions in the filtering phase, namely the top-down solution and the bottom-up solution, which can find good candidate SCD-blocks by approximately solving the new scoring function. The evaluations on four real datasets verify that compared with the dense blocks returned by existing DBM algorithms, the proposed solutions are able to find SCD-blocks with comparable density values and significantly smaller spatial coverage.

关键词： dense block

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Zebra Optimization Algorithm with Convolutional Neural Network for Detecting Subtle Changes in Skin Pigmentation for Melanoma Screening 1

Zebra Optimization Algorithm with Convolutional Neural Netwo...

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

作者： Sriram, T. Snehachaturya, A. Shnain, Ammar Hameed Ravi, G. Kumar, S. Senthil Kristu Jayanti College Department of Life Sciences Bangalore India Maisammaguda School of Computer Science and Engineering Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sona College of Technology Department of Electronics and Communication Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350352931

Melanoma is easily detectable by visual examination since it occurs on the skin surface. Traditionally approach inefficiently to capture to diagnosis, it need to facilitate the diagnosis process while still yielding an accurate of diagnosis. This paper, Zebra optimization Algorithm with Convolutional Neural Network (ZOA-CNN) combined to performed in the classification skin pigmentation for melanoma screening achieve better accuracy. The ZOA is used to optimize the CNN technique enhance the classification accuracy. ZOA balances exploration as well as exploitation that supports the CNN avoid local minima and attain best solutions at the time of training. This flexibility enhances the robustness of the model in detecting melanoma. Initially data obtained from the ISIC 2019 dataset and pre-processing involved the Histogram Equalization and Convert RGB images to grayscale images, which is enhance the image quality. Segmentation utilized U-net architecture includes skip connections that allow high resolution feature from the encoder and decoder path. Feature extraction using MobileNet is designed for efficiency to extracted the high level feature and depthwise separable convolutions reduces the amount of parameters while maintaining the performance. The performance of proposed model evaluated the various metrics and it attains the better accuracy of 0.91 compared to GoogleNet. © 2024 IEEE.

关键词： Dermatology

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Improve Accuracy in Healthcare Data Analysis using Competitive Ensemble Deep Learning Model 18

Improve Accuracy in Healthcare Data Analysis using Competiti...

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

作者： Sravanthi, Jakkula Reddy, Chada Sampath Mahendar, A. Kumar, V. Ravi Buragadda, Swathi Ghantasala, G. S. Pradeep Gupta, Gaurav Vnr Vignana Jyoti Institute of Engineering and Technology Department of Information Technology Telangana India Sree Chaitanya Institute of Technological Sciences Department of Computer Science Engineering Telangana Karimnagar India Telangana Hyderabad India Ace Engineering College Department of Computer Science Engineering Ghatkesar India Lakireddy Balireddy College of Engineering Department of Computer Science Engineering Kakinada India Alliance University Department of Computer Science and Engineering Bengaluru India Shoolini University Yogananda School of Artificial Intelligence Computers and Data Sciences Solan India

ISBN: (纸本)9789380544519

This paper discusses the significance of Machine Learning (ML) and Deep Learning (DL) techniques for structured and unstructured healthcare data. As healthcare data is increasing tremendously, it is difficult to identify hidden patterns in huge amounts of data. DL handles a massive amount of clinical data and provides better outcomes. A novel competitive ensemble deep learning model has been proposed to improve the classification performance of structured data. However, dealing with unstructured data, the proposed work highlights a competitive DL model for Twitter sentiment analysis. In addition, this paper discusses the proposed Competitive Ensemble Deep Learning (CEPL) algorithm for text data. The proposed model is compared with a traditional model to evaluate the model's performance in the range of 0.2%-0.5%. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Accuracy CEPL Deep Learning Healthcare data Machine learning Methods

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Detecting Deepfakes: Using CNN to Identify Manipulated Visual Media 8

Detecting Deepfakes: Using CNN to Identify Manipulated Visua...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sunanda, N. Shailaja, K. Babu, Jampani Satish Vamsi Krishna, K. Lakshmanpratap, N. Motupalli, Ravi Kanth and Ai&ds Hyderabad India Vasavi College of Engineering & Technology Department of Cse Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Vaddeswaram India Prowess Software Services Hyderabad India Mallareddy Engineering College Department of Ece Hyderabad India Vnr Vignana Jyothi Institute of Engineering & Technology Department of Cse Hyderabad India

ISBN: (纸本)9798350376425

Deepfake detection is a rapidly evolving field with significant implications for the integrity of visual media. This review explores techniques, challenges, and future directions in deepfake detection. Traditional image forensics techniques, deep learning models, and multi-modal fusion approaches are employed to differentiate real content from deepfakes. Addressing the challenges of evolving deepfake algorithms and the need for real-time detection, future research should focus on novel architectures, training strategies, temporal and contextual information, and ethical considerations. By advancing deepfake detection methods, researchers can contribute to combating the proliferation of synthetic content and preserving the authenticity of visual media in the digital age. © 2024 IEEE.

关键词： Convolutional neural networks

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Cardiac Signal Monitoring System using Non-Contact Measurement for Physiological Features 5

Cardiac Signal Monitoring System using Non-Contact Measureme...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Balaraman, Kalaiselvi Claret Angelin, S.P. Department of Computer Science College of Science and Humanities SRM Institute of Science and Technology India Department of Computational Intelligence Faculty of Engineering and Technology SRM Institute of Science and Technology India

ISBN: (纸本)9798400709418

Healthcare is a crucial part of human beings;thus, keeping a check by recording and monitoring certain essential functions of the body is important. Using sensors to record multiple physiological processes can be discomforting for the body. This is especially true if the recording is done for a prolonged period, due to this reason that methods for remote physiological monitoring are becoming popular. These methods do not involve the physical interaction of any sensors with the human body or skin. Several studies have proposed the detection of Blood Volume Pulse, or BVP, to calculate Heart Rate and Heart Rate Variability. In this particular study, the main focus is on measuring the Heart Rate (HR). The algorithm utilized in this study is Independent Component Analysis (ICA). The primary function of this algorithm is to separate the main source signal essential for measurement from the noise in the RGB channels of the facial video. In this paper, the generalization of existential methods is addressed. In addition, the process considered in this approach is much more accurate. It is possible due to the implementation of a light equalization scheme. This scheme helps reduce issues of unequal facial light and minimize the effect of shadows - the selection of the best and most accurate signal as outputted by the ICA module. © 2023 ACM.

关键词： Independent component analysis

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Enhancing Knowledge Graph Attention by Temporal Modeling for Entity Alignment with Sparse Seeds 28th

Enhancing Knowledge Graph Attention by Temporal Modeling for...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Sun, Chenchen Jin, Yuyuan Shen, Derong Nie, Tiezheng Wang, Xite Xiao, Yingyuan School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Computer Science and Engineering Northeastern University Shenyang China College of Information Science and Technology Dalian Maritime University Dalian China

ISBN: (纸本)9783031306716

As a fundamental task of knowledge graph integration, entity alignment (EA) matches equivalent entities across knowledge graphs (KGs). Temporal knowledge graphs (TKGs) enhance static KGs with temporal information. Traditional EA approaches tackle alignments of static KGs, but cannot effectively deal with TKGs. Therefore, temporal EA solutions are called for. To this end, we propose a time-aware graph attention network for entity alignment (TGA-EA). Generally, we learn high-quality temporal-relational entity embeddings for alignments by systematically integrating temporal information into KG embeddings. We propose three temporal modeling methods to effectively represent and integrate temporal information into both entities and relations. Then we construct temporal enhanced graph attention to produce target temporal-relational entity embeddings with temporal entities and temporal relations. Thanks to the powerful design, TGA-EA achieves promising performances with sparse alignment seeds. Extensive experiments on five datasets demonstrate our approach’s obvious advantages over previous works. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Alignment

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