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Detection of communicable and non-communicable diseases using hyperparameter optimization with Bi-LSTM model in pathology images

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International Journal of Intelligent Computing and Cybernetics 2023年第4期16卷 649-664页

作者： Shiva Sumanth Reddy C.Nandini Department of Computer Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangaloreIndia Dayananda Sagar Academy of Technology and Management BangaloreIndia

Purpose:The present research work is carried out for determining haemoprotozoan diseases in cattle and breast cancer diseases in humans at early *** combination of LeNet and bidirectional long short-term memory(Bi-LSTM)model is used for the classification of heamoprotazoan samples into three classes such as theileriosis,babesiosis and ***,BreaKHis dataset image samples are classified into two major classes as malignant and *** hyperparameter optimization is used for selecting the prominent *** main objective of this approach is to overcome the manual identification and classification of samples into different haemoprotozoan diseases in *** traditional laboratory approach of identification is time-consuming and requires human *** proposed methodology will help to identify and classify the heamoprotozoan disease in early stage without much of human ***/methodology/approach:LeNet-based Bi-LSTM model is used for the classification of pathology images into babesiosis,anaplasmosis,theileriosis and breast images classified into malignant or *** optimization-based super pixel clustering algorithm is used for segmentation once the normalization of histopathology images is *** edge information in the normalized images is considered for identifying the irregular shape regions of images,which are structurally ***,it is compared with another segmentationapproach circularHough Transform(CHT).The CHT is used toseparatethe *** Canny edge detection and gaussian filter is used for extracting the edges before sending to ***:The existing methods such as artificial neural network(ANN),convolution neural network(CNN),recurrent neural network(RNN),LSTM and Bi-LSTM model have been compared with the proposed hyperparameter optimization approach with LeNET and *** results obtained by the proposed hyperparameter optimization-Bi-LSTM model showed the accuracy of

关键词： Breast cancer detection Convolution neural network Deep neural network Haemoprotozoan disease detection Hyper-parameter optimization LeNet Bi-LSTM

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An Efficient ODR for computer Night Vision Using Multi-Scale Retinex Network-Aided Enhanced Images 4

An Efficient ODR for Computer Night Vision Using Multi-Scale...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Charles Prabu, V. Pandiaraja, P. Sathiyamoorthi, V. Durgadevi, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Government Polytechnic College Department of Computer Engineering Baisuhalli Tamilnadu Dharmapuri India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Campus Tamilnadu Chennai India

ISBN: (纸本)9798331508845

In the current scenario, recognizing various objects and tracking their movements in the real-time surveillance footage is the most difficult task. To detect objects, a combination of image processing and computer vision algorithms is utilized. computer-vision based automatic human activity recognition from surveillance video can be utilized for applications such as the identification of violent acts and the study of human behavior. Consequently, this work implements a new "Object Detection and Recognition (ODR)"model for computer night vision utilizing a deep learning technique. In order to improve the supplied input image, the combined images are first transmitted to the "Multi-scale Retinex (MSR)"model. The You Only Look Once Version 7 (YoloV7) model uses the improved image from MSR as input to identify and recognize things. After conducting experiments, the implemented ODR model on the ExDark Dataset obtained an efficiency of 94.8%. © 2024 IEEE.

关键词： Deep learning

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Multi-Convolutional Neural Network Based Crowd Counting Using VGG-19 Architecture

Multi-Convolutional Neural Network Based Crowd Counting Usin...

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International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

作者： John Pradeep, J. Raja Arumugham, M. Santhi, S. Kalaiselvi, S. Department of Information Technology National Engineering College Kovilpatti India Department of Computer Science and Engineering National Engineering College Kovilpatti India

ISBN: (纸本)9789819746491

Crowd counting has raised a major issue in computer vision with its extensive range of applications, like crowd management, public protection, and city development. The proposed model involves developing a method that can precisely determine the total number of crowds from a raw photograph captured from a random viewpoint and crowd density. A density map-based estimation that uses Multi-convolution Neural Network that is built on the VGG-19 architecture is developed that are trained to categorize the visual inputs with varying densities. The deeper architecture allows VGG-19 to learn more complex features from the input image, which is especially beneficial in crowd counting tasks where the input image contains a huge number of objects. The proposed crowd counting technique combines the VGG-19 architecture with the MCNN model to achieve accurate results. By using filters of different sizes, the MCNN model can handle input images of varying sizes or resolutions, while also accounting for variations in individuals or head sizes caused by viewpoint effects. This allows to accurately estimate crowd density without needing to identify the perspective map of the input image, making it highly versatile and operative for crowd counting tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Enabling Gesture-based Application Interaction on Head Mounted VR Display 7

Enabling Gesture-based Application Interaction on Head Mount...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Rishi Kalis, K. Rohith, V. Kalaiarasi, G. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

With the integration of smartphones, VR head-mounted displays that are affordable (HMDs) have made VR more accessible to the general public and increased its widespread use. However, due to their lack of interaction, these systems frequently have limitations. GestOnHMD, a technique for a pipeline for classifying gestures and gesture-based interaction, is presented in this study. It makes use of the gesture technique in smartphones to detect hand gestures, detecting 21 points on hand on VR headset. Initially, gesture-elicitation research is carried out using specific headset the Google Cardboard to detect the 21 hand points which detects the hand gestures and display the meaning of the hand gesture. Based on signal detectability and user choices, different kinds of gestures are selected to input the dataset. It uses the neural networks to map the hand points and the cam detects the hand gestures and recognizes it and the displays the meaning of each On the VR device's screen. Each gesture can be made specifically for the commands and its meaning by showing the hand gesture in front of the cam and storing it in the dataset. It can be used to read American Signal Language, etc. which makes people to understand hand gestures and might help in future exploring. The output is displayed on the Google Cardboard Virtual Reality device screen which is connected to the laptop. © 2023 IEEE.

关键词： Virtual reality

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An Interpretable Deep Learning Approach for Skin Cancer Categorization 26

An Interpretable Deep Learning Approach for Skin Cancer Cate...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Mahmud, Faysal Mahfiz, Md. Mahin Kabir, Md. Zobayer Ibna Abdullah, Yusha Ahsanullah University of Science & Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Skin cancer is a serious worldwide health issue, precise and early detection is essential for better patient outcomes and effective treatment. In this research, we use modern deep learning methods and explainable artificial intelligence (XAI) approaches to address the problem of skin cancer detection. To categorize skin lesions, we employ four cutting-edge pre-trained models: XceptionNet, EfficientNetV2S, InceptionResNetV2, and EfficientNetV2M. Image augmentation approaches are used to reduce class imbalance and improve the generalization capabilities of our models. Our models' decision-making process can be clarified because of the implementation of explainable artificial intelligence (XAI). In the medical field, interpretability is essential to establish credibility and make it easier to implement AI-driven diagnostic technologies into clinical workflows. We determined the XceptionNet architecture to be the best performing model, achieving an accuracy of 88.72%. Our study shows how deep learning and explainable artificial intelligence (XAI) can improve skin cancer diagnosis, laying the groundwork for future developments in medical image analysis. These technologies' ability to allow for early and accurate detection could enhance patient care, lower healthcare costs, and raise the survival rates for those with skin cancer. © 2023 IEEE.

关键词： Medical imaging

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Rule and Encryption based Secure Routing for Internet of Vehicle Networks 2

Rule and Encryption based Secure Routing for Internet of Veh...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Anand, M. Muthurajkumar, S. Selvi, M. Thangaramya, K. Svn, Santhosh Kumar Kannan, A. Anna University Madras Institute of Technolog Department of Computer Technology Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350365269

The Internet of Vehicles (IoV) has become one challenging communication technology in the current internet world. IoV enables real-time data exchange between vehicles, road infrastructures, and mobile communication devices and hence it helps in information exchange to vehicle drivers and the passengers. In IoV, Cybersecurity threats including Denial-of-Service (DoS) attacks are present and it compromises the security in communication among the vehicles and the passengers. Therefore, it is needed to develop a robust security mechanism that can protect the data communicated through routing in IoV. The inclusion of Encryption and Signature verification techniques in the routing algorithm will enhance the security. This paper proposes a Rule with Encryption-Oriented Secure Routing Protocol (REO-SRP) is proposed using clustering and RSA encryption with DSA Digital signature verification with rules for routing. Here, routing is performed after encryption of data and analysis of other vehicles using rules for reliable and secured data delivery. © 2024 IEEE.

关键词： Vehicles

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ItoV: Efficiently Adapting Deep Learning-Based Image Watermarking to Video Watermarking

ItoV: Efficiently Adapting Deep Learning-Based Image Waterma...

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2023 International Conference on Culture-Oriented science and technology, CoST 2023

作者： Ye, Guanhui Gao, Jiashi Wang, Yuchen Song, Liyan Wei, Xuetao Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350357998

Robust watermarking tries to conceal information within a cover imag e/video imperceptibly that is resistant to various distortions. Recently, deep learning-based approaches for image watermarking have made significant advancements in robustness and invisibility. However, few studies focused on video watermarking using deep neural networks due to the high complexity and computational costs. Our paper aims to answer this research question: Can well-designed deep learning-based image watermarking be efficiently adapted to video watermarking? Our answer is positive. First, we revisit the workflow of deep learning-based watermarking methods that leads to a critical insight: temporal information in the video may be essential for general computer vision tasks but not for specific video watermarking. Inspired by this insight, we propose a method named ITOV for efficiently adapting deep learning-based Image watermarking to Video watermarking. Specifically, ITOV merges the temporal dimension of the video with the channel dimension to enable deep neural networks to treat videos as images. We further explore the effects of different convolutional blocks in video watermarking. We find that spatial convolution is the influential primary component in video watermarking, and depthwise convolutions significantly reduce computational costs with negligible impact on performance. In addition, we propose a new frame loss to constrain that the watermark intensity in each video clip frame is consistent, significantly improving the invisibility. Extensive experiments show the superior performance of the adapted video watermarking method compared with the state-of-the-art methods on Kinetics-600 and Inter4K datasets, which demonstrates the efficacy of our method ITOV. © 2023 IEEE.

关键词： Image watermarking

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Enhanced Resource Allocation Strategy using Cox Regression for Wormhole Attack Mitigation in Routing Protocols 5

Enhanced Resource Allocation Strategy using Cox Regression f...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Praveenkumar, G.D. Ilango, M. Mohanasathiya, K.S. Naveen, N. Rakkimuthu, P. Kongu Engineering College-Erode Department of Computer Technology -UG India Government Arts and Science College Gudalur Department of Computer Science Tamilnadu The Nilgiris India VET Institute of Arts and Science Co-Education College- Erode Department of Computer Science India Excel Engineering college Department of Information Technology Tamilnadu India

ISBN: (纸本)9798350367171

MANETs, as self-configuring networks lacking a fixed infrastructure, are exceptionally vulnerable to a multitude of security threats. One such severe threat is the Wormhole Attack, where malicious nodes create a virtual link between two distant points in the network, creating a shortcut for communication. This deceptive behavior disrupts the routing protocols and leads to severe consequences, including data manipulation and network partitioning. The Wormhole Attack has had a significant impact on MANETs. This study stimulates COX Regression techniques that are used to calculate the regression coefficient for the association between mobile nodes to perform routing in the MANETs. Based on the calculation of residual energy, the delay and bandwidth are identified as optimal. Additionally, various mitigation techniques are proposed and evaluated to improve the network's security in the face of this sophisticated attack. According to the computation of residual energy, delay and bandwidth is detected as optimal one. Additionally, this study proposes and evaluates different mitigation techniques to enhance the network's security against this sophisticated attack. The proposed methods findings highlight the importance of proactive measures and vigilance in safeguarding MANETs from Wormhole Attacks. This helps to improve the rate of packet delivery with reduced latency and the lifetime of data transmission within the network. © 2024 IEEE.

关键词： Bandwidth

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Quantified emotion analysis based on design principles of color feature recognition in pictures

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Multimedia Tools and Applications 2024年第19期83卷 57243-57267页

作者： Chen, Chin-Ling Huang, Qing-Yang Zhou, Ming Huang, Der-Chen Liu, Ling-Chun Deng, Yong-Yuan School of Information Engineering Changchun Sci-Tech University Jilin Province Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung City413310 Taiwan Xiamen Academy of Arts and Design Fuzhou University Fujian Xiamen361024 China School of Computer and Information Engineering Xiamen University of Technology Fujian Xiamen361024 China Department of Computer Science and Engineering National Chung-Hsing University Taichung402 Taiwan

When people observe pictures, different pictures will generate different emotions, and the painters often convey emotional energy to the audience through the media. Through the effect of this emotional transfer, people get emotional satisfaction psychologically, and the author can better express the emotion he needs to express by using the principle. For those who do not know art appreciation, it is difficult to accurately describe the feelings brought by pictures and describe them accurately. It is also difficult to quickly and accurately find the picture of the corresponding feeling sought by oneself. This paper presents a quantitative picture emotion analysis method. Based on the analysis of the characteristics and colors of the pictures that arouse people's emotions, the classification planning is carried out in RGB space and HSV color space respectively. And the combination analysis of the color features with strong influence is carried out to select a more appropriate color space for the expression of human feelings. At the same time, the paper also discusses the effect of the black-and-white filling and the brightness saturation of the image. Finally, it makes use of the corresponding design principles to carry out a quantitative analysis of the emotion generated by it. The system performs a combined analysis of a picture from these directions and presents the results to the user. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Color

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Analyzing the Prediction Accuracy of Corn Leaf Diseases Using A Pre-Trained Network Model

Analyzing the Prediction Accuracy of Corn Leaf Diseases Usin...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Ashwini, C. Sellam, V. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396485

During corn's exploration and manufacturing phases, farmers have a complicated issue in accurately diagnosing corn crop infections. To solve this issue, this work provides a method for specific position three prevalent diseases of corn leaves: grey spot, leaf blight, and rusty depending on grouping and an upgraded deep learning method. Deep learning advancements paved the path for enhancing prediction accuracy as its significance is adopted over various research fields. Disease prediction and classification is time-consuming due to the lesser foreground and background intensity information. To identify three illnesses, first cluster reference images using the clustering technique, then input them into the enhanced deep network. The influences of various 'k' values on corn diagnostic techniques are investigated in this research. The trial findings show that the approach has the most significant identification impact on observations with the analytical prediction of corn disease, rust, and grey spot disease. Here, VGG-16 and ResNet18 likewise produce the best diagnostic findings, with an average diagnostic accuracy, respectively. The technique presented in this study has a diagnostic performance of 95% for the three corn diseases. It has a more substantial diagnostic impact than another four techniques and can be used to safeguard crops in the agricultural area. The simulation is done in the MATLAB 2020a environment, and various performance metrics like accuracy, precision, F1-score, recall and some other statistical measures are evaluated and compared with existing approaches. © 2023 IEEE.

关键词： Forecasting

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