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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Urvashi Sugandh Swati Nigam Manju Khari Department of Computer Science Faculty of Mathematics and Computing Banasthali Vidyapith Banasthali India School of computing Science Engineering Galgotias University Greater Noida Uttar Pradesh India School of computer and System Sciences Jawaharlal Nehru University New Delhi India

An increasing population demands more food, but the available resources are shrinking. The agriculture sector in India is facing several challenges such as climate change, water scarcity, soil degradation, and declining farm profitability. To address these challenges, there is a growing need for the adoption of smart agriculture practices that leverage technology. This paper presents an overview of the ecosystem of technologies for smart agriculture and their potential to improve the efficiency and profitability of Indian farmers. The paper explores the role of various technologies such as precision agriculture, IoT, artificial intelligence, drones, and robotics in improving crop yields, reducing input costs, and enhancing overall farm management. Additionally, the paper discusses the challenges that need to be overcome to ensure the successful adoption of smart agriculture technologies in India. These include the lack of awareness and access to technology, inadequate infrastructure, and limited availability of financing. The paper concludes that the ecosystem of technologies for smart agriculture holds significant promise for Indian farmers to overcome the challenges and improve the efficiency and profitability of their farms.

关键词： Smart agriculture Farming Blockchains Climate change Ecosystems Energy efficiency Soil measurements Drones Statistics Water resources Profitability Food products

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MULTITASK LEARNING FOR IMPROVED LATE MECHANICAL ACTIVATION DETECTION OF HEART FROM CINE DENSE MRI

arXiv

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arXiv 2022年

作者： Xing, Jiarui Wang, Shuo Bilchick, Kenneth C. Epstein, Frederick H. Patel, Amit R. Zhang, Miaomiao Department of Electrical and Computer Engineering University of Virginia United States Department of Computer Science University of Virginia United States Department of Biomedical Engineering University of Virginia Health System United States School of Medicine University of Virginia Health System United States

The selection of an optimal pacing site, which is ideally scar-free and late activated, is critical to the response of cardiac resynchronization therapy (CRT). Despite the success of current approaches formulating the detection of such late mechanical activation (LMA) regions as a problem of activation time regression, their accuracy remains unsatisfactory, particularly in cases where myocardial scar exists. To address this issue, this paper introduces a multi-task deep learning framework that simultaneously estimates LMA amount and classify the scar-free LMA regions based on cine displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging (MRI). With a newly introduced auxiliary LMA region classification sub-network, our proposed model shows more robustness to the complex pattern cause by myocardial scar, significantly eliminates their negative effects in LMA detection, and in turn improves the performance of scar classification. To evaluate the effectiveness of our method, we tests our model on real cardiac MR images and compare the predicted LMA with the state-of-the-art approaches. It shows that our approach achieves substantially increased accuracy. In addition, we employ the gradient-weighted class activation mapping (Grad-CAM) to visualize the feature maps learned by all methods. Experimental results suggest that our proposed model better recognizes the LMA region pattern. © 2022, CC BY.

关键词： Magnetic resonance imaging

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Dynamic Searchable Scheme with Forward Privacy for Encrypted Document Similarity

Dynamic Searchable Scheme with Forward Privacy for Encrypted...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Mustafa A. Al Sibahee Chengwen Luo Jin Zhang Yijing Huang Zaid Ameen Abduljabbar National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen PR China College of Computer Science and Software Engineering Shenzhen University Shenzhen PR China Computer Technology Engineering Department Iraq University College Basrah Iraq Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Shenzhen Institute Huazhong University of Science and Technology Shenzhen PR China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Document retrieval plays an essential role in many real-world applications especially when the data storage is outsourced. Due to the great advantages offered by cloud computing, clients tend to outsource their personal data to remote servers maintained by external service providers. This raises serious privacy concerns about outsourced data because such providers are usually considered untrusted entities. The majority of previous schemes of document similarity search share the same limitation: they focus mainly on static collections. Dynamic searchable schemes (DSE) allow adding or removing documents at the expense of more leakage than static schemes. To thwart certain attacks, DSE schemes should support forward privacy property, which ensures that newly added documents cannot be related to previously issued search queries. We design and implement dynamic secure similarity search schemes with forward privacy for textual documents utilizing simhash method for hamming similarity. Our scheme provides an efficient search time and a sufficient level of privacy. To show the practicality of our proposed scheme, we performed excremental results with large document collections.

关键词： Privacy Data privacy Cloud computing Memory Search problems Cryptography Servers

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Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network

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computers in Biology and Medicine 2024年 174卷 108146-108146页

作者： Khan, Siraj Sajjad, Muhammad Abbas, Naveed Escorcia-Gutierrez, José Gamarra, Margarita Muhammad, Khan Department of Computer Science Islamia College University Peshawar25120 Pakistan Department of Computational Science and Electronics Universidad de la Costa CUC Barranquilla080002 Colombia Department of System Engineering Universidad del Norte Puerto Colombia081007 Colombia Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that play a pivotal role in human health and are vital indicators of diseases such as malaria, leukemia, AIDS, and other viral infections. WBCs detection and classification in blood smears offers insights to pathologists, aiding diagnosis across medical conditions. Traditional techniques, including manual counting, detection, classification, and visual inspection of microscopic images by medical professionals, pose challenges due to their labor-intensive nature. However, traditional methods are time consuming and sometimes susceptible to errors. Here, we propose a high-performance convolutional neural network (CNN) coupled with a dual-attention network that efficiently detects and classifies WBCs in microscopic thick smear images. The main aim of this study was to enhance clinical hematology systems and expedite medical diagnostic processes. In the proposed technique, we utilized a deep convolutional generative adversarial network (DCGAN) to overcome the limitations imposed by limited training data and employed a dual attention mechanism to improve accuracy, efficiency, and generalization. The proposed technique achieved overall accuracy rates of 99.83%, 99.35%, and 99.60% for the peripheral blood cell (PBC), leukocyte images for segmentation and classification (LISC), and Raabin-WBC benchmark datasets, respectively. Our proposed approach outperforms state-of-the-art methods in terms of accuracy, highlighting the effectiveness of the strategies employed and their potential to enhance diagnostic capabilities and advance real-world healthcare practices and diagnostic systems. © 2024 Elsevier Ltd

关键词： Classification (of information)

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Comparative Analysis of Epileptic Seizure Prediction: Exploring Diverse Pre-Processing Techniques and Machine Learning Models

Comparative Analysis of Epileptic Seizure Prediction: Explor...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Md. Simul Hasan Talukder Rejwan Bin Sulaiman Gunjan Chhabra Suman Avdhesh Yadav Rezaul Haque Md Babul Islam Keshav Kaushik Nuclear Safety Security and Safegurad Division Bangladesh Atomic Energy Regulatory Authority Dhaka Bangladesh School of Computer Science and Technology Northumbria University Newcastle UK Dept. of CSE Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Amity School of Engineering & Technology Noida India Dept of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Modeling Electronic and System Engineering (DIMES) University of Calabria Calabria Italy School of Computer Science University of Petroleum and Energy Studies Dehradun India

Precise prediction and detection of epilepsy, a common neurological condition marked by sporadic and repeated seizures, is necessary for efficient management and patient care. To do accurate and reliable seizure detection, machine learning (ML) on electroencephalogram (EEG) recordings is a good choice because of its capacity to provide insightful information about brain activity during seizures. In this work, we do a thorough comparative analysis of five machine learning methods for the prediction of epileptic seizures using EEG data: Random Forest (RF), Decision Tree (DT), Extra Trees (ET), Logistic Regression (LR), and Gradient Boosting (GB). Firstly, to assure the quality of the data and enable precise model training, the dataset underwent extensive preprocessing, which included cleaning, normalization, treatment of outliers, and oversampling. These preprocessing techniques significantly enhanced the models' performance. In our study, we evaluate the accuracy of each model; the LR classifier obtains 56.95% accuracy, while both GB and DT achieve 97. 17% accuracy. Outperforming the findings of previous investigations, the ET model showed the best performance with an accuracy of 99.29%. Achieving 98.99% accuracy was another accomplishment for the RF model. Our results imply that the ET model not only performed better than the other models in the comparison analysis but also more effectively than the most advanced outcomes from earlier research. Regarding the accurate and dependable prediction of epileptic seizures using EEG data, the ET model is a solid option due to its remarkable performance.

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An Ensemble Learning Method Based on Deep Neural and Pca-Based Svm Network for Baggage Threat and Smoke Recognition

An Ensemble Learning Method Based on Deep Neural and Pca-Bas...

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Advances in science and engineering Technology International Conferences (ASET)

作者： Abdelfatah Hassan Ahmed Muaz Al Radi Naoufel Werghi Department of Electrical Engineering and Computer Sciences Center for Cyber-Physical System (C2PS) Khalifa University of Science and Technology Abu Dhabi United Arab Emirates

Ensemble learning methods are emerging as one of the most widely used image classification and object recognition techniques. They have the advantage of achieving higher classification and recognition performance compared to single models while being easy to implement in various applications. This work proposes an ensemble-based classification network that leverages the recognition performance of both threat and smoke detection tasks. The method utilizes ensemble learning of a deep Convolutional Neural Network (CNN) combined with a Principal Component Analysis (PCA)-based Support Vector Machine (SVM) classifier. Comparisons to several single model classifiers and other state-of-the-art methods and the proposed methods were carried out. The proposed method showed superior performance in comparison for the problems of baggage X-ray imagery classification and smoke recognition.

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Efficient Model-Based OPC via Graph Neural Network

Efficient Model-Based OPC via Graph Neural Network

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Electronics Design Automation (ISEDA), International Symposium of

作者： Shuyuan Sun Xuelian Chen Fan Yang Bei Yu Shang Li Xuan Zeng State Key Lab of ASIC & System School of Microelectronics Fudan University China Cogenda Inc China Department of Computer Science and Engineering The Chinese University of Hong Kong China

As feature size continues to shrink and light source wavelengths remain unchanged, the optical diffraction effects seriously degrade chip yield. Optical proximity correction (OPC) has become an essential step for chip manufacturability. However, OPC cannot achieve satisfactory mask correction results in an affordable number of iterations on some layouts, delaying the chip design cycle. In this paper, we propose to use the Graph Neural Network (GNN) to predict the initial mask correction and speed up model-based OPC. We design a graph model to represent the chip layout, with segments represented as graph nodes and the diffraction effects between them represented as graph edges. The GNN is used to obtain the embedding of the segment, and we predict the shift value based on its output. The proposed GNN-based predictor enhances the original OPC to find a good correction solution for full-chip-size masks in fewer iterations. compared with the original OPC method, experimental results on 55nm and 32nm full chip designs show that the proposed GNN-based OPC method reduces the number of iterations up to 75% and the acquired mask of comparable quality.

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The Effect of Data Augmentation on the Accuracy of Fish Species Classification Using Deep Learning

The Effect of Data Augmentation on the Accuracy of Fish Spec...

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International Conference on Informatics and Computing (ICIC)

作者： Ellya Helmud Catur Edi Widodo Oky Dwi Nurhayati Doctor Information System Postgraduate Diponegoro University Semarang Indonesia Faculty of Information Technology Information Systems Study Program Atma Luhur Institute of Science and Business Pangkalpinang Indonesia Physics Department Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of $\mathbf{3 0 1 0}$ fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of $95 \%$ with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of $94.56 \%$ with a loss value of $\mathbf{0. 1 7 9 4}$. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance.

关键词： Deep learning Training Accuracy Computational modeling computer architecture Fish Data augmentation Data models Convolutional neural networks Image classification

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Domain-Adaptive 3D Medical Image Synthesis: An Efficient Unsupervised Approach

arXiv

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arXiv 2022年

作者： Hu, Qingqiao Li, Hongwei Bran Zhangy, Jianguo Research Institute of Trustworthy Autonomous System Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Department of Computer Science Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland

Medical image synthesis has attracted increasing attention because it could generate missing image data, improving diagnosis and benefits many downstream tasks. However, so far the developed synthesis model is not adaptive to unseen data distribution that presents domain shift, limiting its applicability in clinical routine. This work focuses on exploring domain adaptation (DA) of 3D image-to-image synthesis models. First, we highlight the technical difference in DA between classification, segmentation and synthesis models. Second, we present a novel efficient adaptation approach based on 2D variational autoencoder which approximates 3D distributions. Third, we present empirical studies on the effect of the amount of adaptation data and the key hyper-parameters. Our results show that the proposed approach can significantly improve the synthesis accuracy on unseen domains in a 3D setting. The code is publicly available at https://***/WinstonHuTiger/2D-VAE-UDA-for-3D-sythesis. © 2022, CC BY-NC-SA.

关键词： Diagnosis

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DF-SSmVEP: Dual Frequency Aggregated Steady-State Motion Visual Evoked Potential Design with Bifold Canonical Correlation Analysis

arXiv

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arXiv 2022年

作者： Karimi, Raika Mohammadi, Arash Asif, Amir Benali, Habib Department of Electrical and Computer Engineering Concordia University Montreal Canada Concordia Institute for Information System Engineering Concordia University Montreal Canada Department of Electrical Engineering and Computer Science York University Toronto Canada

Recent advancements in Electroencephalography (EEG) sensor technologies and signal processing algorithms have paved the way for further evolution of Brain computer Interfaces (BCI) in several practical applications ranging from rehabilitation systems to smart consumer technologies. When it comes to Signal Processing (SP) for BCI, there has been a surge of interest on Steady-State motion-Visual Evoked Potentials (SSmVEP), where motion stimulation is utilized to address key issues associated with conventional light-flashing/flickering. Such benefits, however, come with the price of having less accuracy and less Information Transfer Rate (ITR). In this regard, the paper focuses on the design of a novel SSmVEP paradigm without using resources such as trial time, phase, and/or number of targets to enhance the ITR. The proposed design is based on the intuitively pleasing idea of integrating more than one motion within a single SSmVEP target stimuli, simultaneously. To elicit SSmVEP, we designed a novel and innovative dual frequency aggregated modulation paradigm, referred to as the Dual Frequency Aggregated steady-state motion Visual Evoked Potential (DF-SSmVEP), by concurrently integrating "Radial Zoom" and "Rotation" motions in a single target without increasing the trial length. Compared to conventional SSmVEPs, the proposed DF-SSmVEP framework consists of two motion modes integrated and shown simultaneously each modulated by a specific target frequency. The paper also develops a specific unsupervised classification model, referred to as the Bifold Canonical Correlation Analysis (BCCA), based on two motion frequencies per target. The corresponding covariance coefficients are utilized as extra features improving the classification accuracy. The proposed DF-SSmVEP is evaluated based on a real EEG dataset and the results corroborate its superiority. The proposed DF-SSmVEP outperforms its counterparts and achieved an average ITR of 30.7±1.97 and an average accuracy of 92.5

关键词： Brain computer interface

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