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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Introduction

Information Security and Cryptography

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Information Security and Cryptography 2020年 3-13页

作者： Alvim, Mário S. Chatzikokolakis, Konstantinos McIver, Annabelle Morgan, Carroll Palamidessi, Catuscia Smith, Geoffrey Computer Science Department Universidade Federal de Minas Gerais Belo Horizonte Brazil Department of Informatics and Telecommunications University of Athens Athens Greece Department of Computing Macquarie University SydneyNSW Australia School of Computer Science & Engineering University of New South Wales Trustworthy Systems Data61 CSIRO SydneyNSW Australia Inria Saclay and LIX École Polytechnique Institut Polytechnique de Paris Palaiseau France School of Computing & Information Sciences Florida International University MiamiFL United States

Protecting sensitive information from improper disclosure or corruption is a longstanding, fundamental goal of computer security: but it is one that is not currently being achieved well at all, as is evident from cont... 详细信息

关键词： Sensitive data

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Meta-Learning Based Adaptive Crop Price Prediction for Agriculture Application 5

Meta-Learning Based Adaptive Crop Price Prediction for Agric...

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5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Dhanasekaran, K. Ramprasath, M. Sathiyamoorthi, V. Poornima, N. Jayaraj, Irrai Anbu SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems TN Chennai Chengalpattu603 203 India Sona College of Technology TN Salem India CSL Australia Pty Ltd Australia

ISBN: (纸本)9781665435246

Dealing with unexpected changes in agricultural product prices that affect the farmers and the economic growth of a country is inevitable in the current day context. A unique approach is proposed for time-series prediction, which utilizes the power of crop price prediction and crop yield prediction of select crops to identify the relevant information with respect to the market prices, and crop yields. Many uncertain conditions such as climate changes, fluctuations in market, flooding, etc., cause problems to the agricultural process. In this work, the prices of selected essential crops analyzed for time-series prediction using meta-learning. The Self-Organized Map (S OM), LSTM (Long-Short Term Model), and this proposed Meta-Learning Based Adaptive Crop Price Prediction (MLACPP) was trained using crop price dataset, and crop yield dataset. Meta-learning function used in the approach utilizes the input, optimization-output, and task-related estimators for calculating meta-loss over multiple meta-network. The meta-learning with self-organizing capability learns a meta-network observing additional information from specialized support set. Moreover, this method deals with a task-oriented loss. Interestingly, it compares the current optimal-output of the optimization function g with the target-specific information. Experimental results show significant improvement in terms of prediction accuracy and cross-correlation entropy over the existing crop price prediction approaches. This research work provides the basis for making better decisions for price-fixing and crop yield maximization based on insights obtained from the dataset. © 2021 IEEE.

关键词： Crops

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Cloud computing based Smart Traffic Management System with Priority Switching for Health Care Services 2

Cloud Computing based Smart Traffic Management System with P...

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2nd International Conference on Applied Artificial Intelligence and computing, ICAAIC 2023

作者： Shiny, X.S. Asha Ravikumar, D. Chinnasamy, A. Hemavathi, S. Department of Computer Science and Engineering Telangana Hyderabad India Kings Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Chennai India Sri Sairam Engineering College Department of Computer Science Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665456302

Regular cities can be transformed into intelligent structures by leveraging information and communication technologies. Innovative city development could be significantly impacted by the Internet of Things paradigm, commonly called cloud computing. All urban locations have a lot of traffic. In this study, Internet-of- Things (IoT) based system is proposed for health care services to organize and to establish the traffic signaling and pick up a route under which road congestion can be administrated. Vehicle accident contraventions are identified by the traffic officers using an online service that is hierarchically carefully monitored or constrained. However, the suggested approach is generic and may be utilized in any major metropolis without losing its generality. Traffic lights linked to cameras in metropolitan areas can be upgraded by connecting to IoT. During a pandemic, this approach is precious. The police can easily regulate traffic from their homes using their cell phones and identify defaulters. The suggested technique aids in the separation of ambulance and rescue engines from ordinary traffic. © 2023 IEEE.

关键词： Internet of things

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd International Conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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Warts Disease Treatment Suggestion Using Images and Numerical data

Warts Disease Treatment Suggestion Using Images and Numerica...

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

作者： Daketi Yatin Challa Avinash D Hemavathi Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

To classify wart treatment methods, this research paper examines the effectiveness of using machine learning (ML) and deep learning algorithms in conjunction with numerical and image data. Human papillomavirus (HPV)–induced warts are a common dermatological concern. Several factors can affect the severity and spread of these lesions. Making use of both picture and numerical data, the study suggests a thorough method for classifying treatments. The paper shows that the suggested methodology is effective through thorough experimentation. The study achieves remarkable classification accuracy, specifically $91.67 \%$ for cryotherapy and $85 \%$ for immunotherapy datasets, by utilising machine learning and deep learning algorithms. Notably, accuracy rates of $76 \%$ for cryotherapy and $74 \%$ for immunotherapy are obtained by combining synthetic and raw data, demonstrating the potential benefits of integrating various data sources. The study adds a comprehensive framework that makes accurate classification of wart treatments possible. The model provides a comprehensive understanding of wart types and treatment outcomes by combining image analysis with numerical data. This creative method uses both quantitative and visual data to enable users to make well-informed decisions. All things considered, the study highlights the potential of AI and ML methods to improve the classification of wart treatments, offering dermatologists and other medical professionals a useful tool. This study is a major step towards more individualised and data- driven dermatological care strategies, which could lead to better patient outcomes and more effective treatments.

关键词： Deep learning Visualization Accuracy Machine learning algorithms Soft sensors Immunotherapy Classification algorithms

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Deep Learning-Based Emotion Recognition using CNN, LSTM for Multimodal Text, Speech, and Facial Analysis

Deep Learning-Based Emotion Recognition using CNN, LSTM for ...

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data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Talat Binti Firdou Mohammed Ashra A. V. Kalpan Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Emotion detection is a critical field in AI as well as computer vision, seeking to recognize or detect human emotions from facial expressions. Although this area has been greatly advanced through Convolutional Neural Networks (CNNs), current methods using only single-modal data often exhibit reduced accuracy. In this work, we confront these challenges with a hybrid deep learning method for disparate modalities of information such as speech, video, and text. We pre-process and then use CNN feature extractors for the FER2013 data set. The hybrid deep learning model, hence proposed, achieved a weighted accuracy of 79.3% on FER2013, overcoming the conventional unimodal methods. We are using transformers library with the pre-trained RoBERTa model for text. For speech emotion features, we are using TESS dataset then using a CNN and LSTM (Long Short-Term Memory) model. The CNN extracts feature from the speech, while the LSTM is used for temporal analysis to detect emotions. Once these multimodal features have been obtained, our work would be to fuse in order to get the final emotion recognition using Deep Neural Networks (DNNs). The process increases the precision and reliability of the recognition systems, helping to accumulate even more detailed intuition for different emotional states for a combined approach.

关键词： Deep learning Emotion recognition Accuracy Virtual assistants Speech recognition Feature extraction Convolutional neural networks Reliability Speech processing Long short term memory

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Analysing Several Hair and Noise Removal Techniques on a Skin Lesion during the Preprocessing Stage

Analysing Several Hair and Noise Removal Techniques on a Ski...

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

作者： S Praveena T. Veeramakali Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The cancer identified as melanoma is a highly serious and fast form of the disease, and it doesn’t have any of the typical symptoms of malignancy. Melanoma accounts for approximately seventy-five percent of all occurrences of skin cancer. In the scenario patients are given an accurate diagnosis of melanoma as soon as feasible, and have a greater probability of surviving the disease. The initial dermoscopy image preprocessing is a process that is both essential and challenging. However, the presence of hair is a common a barrier that arises during the dermoscopic examination of tiny skin lesions. among its most significant tasks of the preprocessing state involves the identification and removal of hair and other tiny particles. This paper presents a thorough analysis of several preprocessing approaches used for skin lesions. Particularly, it emphasizes on various methods for minimizing noise. The Dullrazor hair removal procedure and various morphological operators are explored in detail. Furthermore, it focuses on a wide variety of datasets and the fundamental aspects of preprocessing information. Ultimately, a thorough analysis of the different performance parameter values is provided.

关键词： Hair Deep learning Accuracy Noise Melanoma Skin Lesions

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Routing characteristics of ad hoc networks with unidirectional links

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Ad Hoc Networks 2006年第3期4卷 303-325页

作者： Jetcheva, Jorjeta G. Johnson, David B. Computer Science Department Carnegie Mellon University Pittsburgh PA 15213 United States Computer Science Department Rice University Houston TX 77005 United States Firetide Inc. ACM SIGMOBILE Sigma Xi Phi Beta Kappa Rice University Special Interest Group on Mobility of Systems Users Data and Computing ACM IEEE IEEE Computer Society IEEE Communications Society USENIX

Unidirectional links in an ad hoc network can result from factors such as heterogeneity of receiver and transmitter hardware, power control or topology control algorithms, or differing sources of interference or jamming. Previously proposed metrics for evaluating the difficulty of a unidirectional scenario are limited in scope and are frequently misleading. To be able to analyze ad hoc network routing protocol behavior in a complex networking environment, it is not sufficient to merely assign a single level of difficulty to a unidirectional network scenario;the many interrelated routing characteristics of these scenarios must be understood. In this paper, we develop a set of metrics for describing these characteristics, for example for characterizing routing scenarios in simulations, analysis, and testbed implementations. Based on these metrics, we perform a detailed simulation analysis of the routing characteristics of the three most common simulation models for generating unidirectional links in ad hoc networks: the random-power model, the two-power model, and the three-power model. Our findings enable protocol designers to better choose a set of network scenarios and parameters that truly explore a wide range of a routing protocol's behaviors in the presence of unidirectional links, and to better understand the complex interplay between routing mechanisms and network conditions. © 2004 Elsevier B.V. All rights reserved.

关键词： Telecommunication networks

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Comparison of the SOLA model and ARIMA for Cloud computing Resources Allocations

Comparison of the SOLA model and ARIMA for Cloud Computing R...

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International Multidisciplinary Information Technology and Engineering Conference (IMITEC)

作者： Sello Prince Sekwatlakwatla Vusumuzi Malele Department of Computer Science and Information Systems Unit for Data Science and Computing North-West University South Africa

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

Cloud computing solutions are becoming more and more popular as a way for organizations to improve productivity, save costs, and simplify procedures. The advantage of cloud services is that they enable users to store data remotely and access on-demand applications and services from a shared pool of configurable computing resources. Cloud computing resource allocation faces challenges in load balancing, effective resource management, and compliance with regulatory and legal requirements, requiring complex tools and techniques for optimal use. Most organizations are moving to cloud services and encouraging their clients to access services online. However, effective resource allocation is critical for improving performance and lowering costs in this area. Due to unpredictable network traffic in cloud computing, resource allocation is challenging, which causes customers to complain about application timeouts, delayed system times, and higher bandwidth use during peak hours. This entails allocating resources to various users and programs, such as memory, processing power, storage, and network bandwidth. In this regard, this study compares the ensemble method, which is stepwise Gaussian Linear Autoregressive (SGLA) and the individual method, which is autoregressive integrated moving average (ARIMA). The results show SGLA prediction accuracy increased with an average of 97.9%, and ARIMA prediction showed an accuracy of 71.5%. In this regard the ensemble method performed better than individual methods using the same datasets. The study recommends the ensemble method for the prediction and allocation of resources in cloud computing.

关键词： Productivity Cloud computing Accuracy Costs Organizations Bandwidth Telecommunication traffic Resource management Ensemble learning Servers

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