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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Neha Swaroop Mohanty Deepak Das D. Anandhasilambarasan T. Yawanikha Swati Bula Patil School of Management CV Raman Global University Bhubaneswar India Management Cv Raman Global University Bhubaneswar India Agricultural Engineering Karunya Institute of Science and Technology Coimbatore India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Department of Information Technology Karpagam Institute of Technology Coimbatore India IT Vishwakarma Institute of Information Technology Pune India

Ingenious frugal and cost-effective irrigation techniques have developed to meet the world's largest population's precious water needs. In other respects, water usage must be moderate sufficiently to focus on saving the limited supply of sparkling flavored. Due to improper watering techniques, a large amount of freshwater was squandered. We used a clever strategy that was qualified to use taxonomy to determine 50% of the choice, and the remaining 50% was dependent on the inertial sensor's values. The ultimate ruling, known as the output of a computer intelligence method, is derived from the ontological judgement as well as the measured values combined (KNN). Besides that, a Microsoft's new server is added among the secondary GSM as well as the primary IoT server. subsystem. This approach is going to lessen latencies but also prevent the IoT server from being overloaded with data acquisition. This method combines the IoT devices with a wide range of devices to nimbly track all incoming data, evaluate it from the end device, transport only a select few data sets towards the primary Wireless sensor computer, and then broadcast the outcome on such any android phone.

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A Liver Segmentation Algorithm with Interactive Error Correction for Abdominal CT Images: A Preliminary Study 4th

A Liver Segmentation Algorithm with Interactive Error Correc...

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4th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2021

作者： Nayantara, P. Vaidehi Kamath, Surekha Manjunath, K.N. Rajagopal, K.V. Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Radiodiagnosis and Imaging Kasturba Medical College Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9783030925994

An automatic method for segmenting the liver from the portal venous phase of abdominal CT images using the K-Means clustering method is described in this paper. We have incorporated an interactive technique for correcting the errors in the liver segmentation results using power law transformation. The proposed method was validated on abdominal CT volumes of fifteen patients obtained from Kasturba Medical College, Manipal. The average values of the various standard evaluation metrics obtained are as follows: Dice coefficient = 0.9361, Jaccard index = 0.8805, volumetric overlap error = 0.1195, absolute volume difference = 4.048%, average symmetric surface distance = 1.7282 mm and maximum symmetric surface distance = 38.039 mm. The quantitative and qualitative results obtained in our preliminary work show that the K-Means clustering technique along with power law transformation is effective in producing good liver segmentation outputs. As future work, we will attempt to automate the power law transformation technique. © 2021, IFIP International Federation for Information Processing.

关键词： K-means clustering

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CoMiX: Cross-Modal Fusion with Deformable Convolutions for HSI-X Semantic Segmentation

arXiv

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arXiv 2024年

作者： Zhang, Xuming Gu, Xingfa Tian, Qingjiu Bruzzone, Lorenzo International Institute for Earth System Science Nanjing University Nanjing210023 China Key Laboratory of Computational Optical Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Department of Information Engineering and Computer Science University of Trento Trento38050 Italy

Pixel-wise semantic segmentation of hyperspectral images (HSIs) enables detailed and accurate analysis of complex scenes, benefiting various applications. Improving HSI semantic segmentation by exploiting complementary information from a supplementary data type (here denoted as X-modality) is promising but challenging due to differences in imaging sensors, image content, and resolution. Current cross-model fusion techniques have limitations in enhancing modality-specific and modality-shared information, as well as in capturing dynamic information interactions and fusion between different modalities. In response, this study proposes CoMiX, a cross-modal fusion framework with deformable convolutions (DCNs) for pixel-wise HSI-X semantic segmentation. CoMiX is an asymmetric encoder-decoder architecture designed to extract, calibrate, and fuse information from HSI and X modalities. Its pipeline includes an encoder with two parallel and interacting backbones and a lightweight all-multilayer perceptron (ALL-MLP) decoder. Within the encoder, four stages are deployed, each incorporating 2D DCN blocks for the X-model to adapt to geometric variations, and 3D DCN blocks for HSIs to adaptively aggregate spatial-spectral features. Additionally, each stage includes a Cross-Modality Feature enhancement and eXchange (CMFeX) module and a feature fusion module (FFM). CMFeX is designed to exploit spatial and spectral correlations from both modalities to recalibrate and enhance modality-specific and modality-shared features while adaptively exchanging complementary information between them. Outputs from CMFeX are fed into the FFM for fusion and passed to the next stage for further information learning. Finally, the outputs from each FFM are integrated by the ALL-MLP decoder for final prediction. Extensive experiments demonstrate that our CoMiX achieves superior performance and generalizes well to various multimodal recognition tasks. The CoMiX code will be released. Copyright © 2024, Th

关键词： Semantic Segmentation

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Spatiotemporal omics of life energy: Towards medicine of frequencies and terahertz drugs

MedComm - Biomaterials and Applications

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MedComm - Biomaterials and Applications 2024年第1期3卷 71-71页

作者： Wang, Li-Ping Gao, Feng Zhao, Zhou-Xiang Liu, Shi-hao Xu, Rui-Song Guo, Jun Zhang, Feng Quantum Biomedicine Lab University of Chinese Academy of Sciences Wenzhou Institute Wenzhou China Key Laboratory of Optical Technology and Instrument for Medicine Ministry of Education School of Optical-Electrical and Computer-Engineering University of Shanghai for Science and Technology Shanghai China

The quantum mechanisms how life efficiently utilizes energy and transmits information remain unclear yet. Frequency medicine, an emerging cross-discipline of both quantum mechanics and biomedicine, is a promising turning point for biomaterials and medicine developing from the matter level to the energy level. Recognizing the pivotal role of molecular vibrational frequencies in resonant energy coupling and transmission underscores the potential of frequency medicine to precisely regulate biomaterial vibrations, influencing interactions and reactions in living organisms. At present, scientists have unveiled sophisticated phototherapeutics;nevertheless, their advancement necessitates the precise mapping of the life energy. In contrast to genomics, proteomics, and metabolomics studied on the matter level, omics of frequencies related to medicine should be on the energy level. Herein, starting from the history of frequency medicine, and followed by the introduction of vibrational strong coupling applications in life sciences, we emphasize the significance, necessity, and urgency of studying spatiotemporal omics of medicine frequencies. By decoding the energy atlas of life, we can acquire profound insights into the quantum mechanisms that govern life processes from an energy standpoint. We anticipate that the integration of biomaterials with spatiotemporal frequency omics related to medical research will contribute significantly to advancing the goals of precision medicine, potentially revolutionizing pharmaceuticals, such as terahertz drugs. © 2024 The Authors. MedComm – Biomaterials and Applications published by John Wiley & Sons Australia, Ltd on behalf of Sichuan International Medical Exchange & Promotion Association (SCIMEA).

关键词： frequency medicine resonant energy transfer spatiotemporal omics terahertz drugs vibrational strong coupling

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Channel Cycle Time: A New Measure of Short-Term Fairness

Channel Cycle Time: A New Measure of Short-Term Fairness

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IEEE Conference on Wireless Communications and Networking

作者： Pengfei Shen Yulin Shao Haoyuan Pan Lu Lu Yonina C. Eldar Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization CAS Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Internet of Things for Smart City University of Macau Macau S.A.R Department of Electrical and Computer Engineering University of Macau Macau S.A.R. College of Computer Science and Software Engineering Shenzhen University China Weizmann Institute of Science Rehovot Israel

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

This paper puts forth a new metric, dubbed channel cycle time (CCT), to measure the short-term fairness of Communication networks. CCT characterizes the average duration between two consecutive successful transmissions of a user, during which all other users successfully accessed the channel at least once. In contrast to existing short-term fairness measures, CCT provides more comprehensive insight into the transient dynamics of communication networks, with a particular focus on users' delays and jitter. To validate the efficacy of our approach, we analytically characterize the CCTs for two classical commu-nication protocols: slotted Aloha and CSMA/CA. The analysis demonstrates that CSMA/CA exhibits superior short-term fairness over slotted Aloha. Beyond its role as a measurement metric, CCT has broader implications as a guiding principle for the design of future communication networks by emphasizing factors like fairness, delay, and jitter in short-term behaviors.

关键词： Jitter Media Access Protocol Time measurement Real-time systems Heterogeneous networks Delays Communication networks

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CNN-based Deep Learning Approach for MRI-based Brain Tumor Detection

CNN-based Deep Learning Approach for MRI-based Brain Tumor D...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Kanagaraj Narayanasamy Elangovan Sathish Kumar L M Maragatharajan D. Deepa Department of Computer Science Karpagam Academy of Higher Education Coimbatore TamilNadu India Department of DSBS SRM Institute of Science and Technology Kattankulathur TamilNadu India School of Computing Science and Engineering VIT Bhopal University Kotrikalan MP India Department of CSE Kongu Engineering College Perundurai Tamil Nadu India

Conventional brain tumor prediction method is a time-consuming and inaccurate process that involves manually evaluating a large number of MRI (magnetic resonance imaging) scan pictures in search of signs of brain cancer. This might have an effect on the patient's medical care. It may take a long time to analyse a large volume of scan data sets comprising brain pictures. Normal brain tissue and brain cancer tissue seem quite similar under the microscope, making it difficult to distinguish between the two during segmentation. A very accurate automated method for detecting cancer is crucial. This research study has developed a novel method that uses an algorithm to analyse 2D MRI human brain scans and detect brain Tumors. Combining conventional classification algorithms with deep learning methods, the approach makes use of a convolutional neural network (CNN). The model was successfully trained using a dataset of magnetic resonance imaging (MRI) scan images, including Tumors of varied sizes, locations, and intensities. Here, a SVM classifier (Support Vector Machine) and several activation methods are also used to check the results, including Softmax, RMSProp, and sigmoid. Our research showed that a CNN could attain an accuracy of 99.87%, which is higher than the present state-of-the-art findings. The CNN -based approach is meant to aid doctors in the precise identification of brain cancers in MRI scans. This innovation might greatly accelerate the pace at which patients are helped.

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Information-density Masking Strategy for Masked Image Modeling

Information-density Masking Strategy for Masked Image Modeli...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： He Zhu Yang Chen Guyue Hu Shan Yu Brainnetome Center National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences(CASIA) School of Future Technology University of Chinese Academy of Sciences(UCAS) School of Computer Science and Engineering Nanyang Technological University School of Artificial Intelligence University of Chinese Academy of Sciences(UCAS)

Recent representation learning approaches mainly fall into two paradigms: contrastive learning (CL) and masked image modeling (MIM). Combining these two methods may boost the performance, but its learning process still heavily depends on the random masking strategy. We conjecture that the random masking may hinder learning the comprehensive relationship between concept and visual patches. To overcome these limitations, we propose an information-density masking (IDM) strategy for general visual transformers. Specifically, the IDM mask out the visual patches according to their activation values of attention maps. To obtain the attention maps before the reconstruction, a self-supervised training framework CAMAE is further proposed. In addition, in order to reduce the redundancy among different attention maps, we introduce a pattern-learning balance (PLB) sampling to adaptively adjust the learning progress in different attention spaces. Extensive experiments indicate that our method efficiently retains more comprehensive visual characteristics and achieves state-of-the-art performance.

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A Study on Efficacy of Modified Electrodes Using Carbon and Graphene-Based Quantum Dots for Dopamine Detection

A Study on Efficacy of Modified Electrodes Using Carbon and ...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： S. Karthikeyan K. E. Lakshmiprabha Parthasarathy P R. Krishnakumar S. Shargunam Department of Mechanical Engineering Jai Shriram Engineering College Tiruppur Karpaga Vinayaga College of Engineering and Technology Chennai Tamilnadu India Department of ECE CMR Institute of Technology Bengaluru Karnataka India Department of EEE Sri Ramakrishna Engineering College Coimbatore Tamilnadu India Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Srivilliputhur Tamilnadu

Due to their high conductivity, minimal cytotoxicity, and optoelectronic features, carbon-based (CQD) quantum dots have been increasingly used into electrochemical sensors for neurotransmitter detection in recent years. This is because these sensors were created in response to a need for more sensitive and less priced electrodes. Due to the widespread use of monoamine neurotransmitters (NTs) and the strong electro-catalytic activity of carbon-based and graphene-based quantum dots (CQDs and GQDs), a thorough investigation of the efforts undertaken to create sensors for NTs is warranted. The desirable chemical characteristics of glassy carbon electrodes (GCEs) make them a popular choice for use in the production of electrochemical sensors. Some of these features include impermeability to chemicals and liquids and pore size on a tiny scale. GCE quantum dots, a type of carbon-based quantum dot, were the most frequently used to modify electrodes for the identification of monoamine NTs, possibly because of these properties. The primary objective of this present study is to provide a concise overview of recent research on electrodes modified with nanomaterials, which have shown efficacy in the electrochemical detection of dopamine with notable sensitivity and selectivity.

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Multimodal Dual Emotion with Fusion of Visual Sentiment for Rumor Detection

arXiv

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arXiv 2022年

作者： Wang, Ge Tan, Li Shang, Ziliang Liu, He School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China Chongqing Academy of Educational Sciences Chongqing400015 China

In recent years, rumors have had a devastating impact on society, making rumor detection a significant challenge. However, the studies on rumor detection ignore the intense emotions of images in the rumor content. This paper verifies that the image emotion improves the rumor detection efficiency. A Multimodal Dual Emotion feature in rumor detection, which consists of visual and textual emotions, is proposed. To the best of our knowledge, this is the first study which uses visual emotion in rumor detection. The experiments on real datasets verify that the proposed features outperform the state-of-the-art sentiment features, and can be extended in rumor detectors while improving their performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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Alternative Role of Motif B in Template Dependent Polymerase Inhibition

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Chinese Journal of Chemical Physics 2022年第3期35卷 407-412,I0001页

作者： Xueying Luo Tiantian Xu Xin Gao Lu Zhang State Key Laboratory of Structural Chemistry Fujian Institute of Research on the Structure of MatterChinese Academy of SciencesFuzhou 350002China University of Chinese Academy of Sciences Beijing 100049China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900King-dom of Saudi Arabia KAUST Computational Bioscience Research Center(CBRC) King Abdullah University of Science and TechnologyThuwal 23955-6900Kingdom of Saudi Arabia Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry Xiamen 361005China

Severe acute respiratory syndrome coronavirus 2(SARS-Co V-2) relies on the central molecular machine RNA-dependent RNA polymerase(Rd Rp) for the viral replication and transcription. Remdesivir at the template strand has been shown to effectively inhibit the RNA synthesis in SARS-Co V-2 Rd Rp by deactivating not only the complementary UTP incorporation but also the next nucleotide addition. However, the underlying molecular mechanism of the second inhibitory point remains unclear. In this work, we have performed molecular dynamics simulations and demonstrated that such inhibition has not directly acted on the nucleotide addition at the active site. Instead, the translocation of Remdesivir from +1 to-1 site is hindered thermodynamically as the posttranslocation state is less stable than the pre-translocation state due to the motif B residue G683. Moreover, another conserved residue S682 on motif B further hinders the dynamic translocation of Remdesivir due to the steric clash with the 1′-cyano substitution. Overall,our study has unveiled an alternative role of motif B in mediating the translocation when Remdesivir is present in the template strand and complemented our understanding about the inhibitory mechanisms exerted by Remdesivir on the RNA synthesis in SARS-Co V-2 Rd Rp.

关键词： SARS-CoV-2 RNA-dependent RNA polymerase Inhibitory mechanism Nu-cleotide analog Molecular dynamics simulation

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