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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning

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Biophysics Reports 2023年第2期9卷 57-66页

作者： Peng Chen Zhenlei Li Zhaolin Hong Haoran Zheng Rong Zeng School of Computer Science and Technology University of Science and Technology of China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Department of Systems Biology University of Science and Technology of China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences School of Life Science and Technology ShanghaiTech University

Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasetsbased semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles:(1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning;(2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality;(3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas(TCGA) and the Gene Expression Comprehensive Database(GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.

关键词： Deep learning Tumor type classification Cancer-specific biomarkers MSSL

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A Multi-Criteria Decision Support Tool for the Evaluation of Energy Storage systems: A Case Study in Battery Technology

A Multi-Criteria Decision Support Tool for the Evaluation of...

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International Conference on Information, Intelligence, systems and Applications (IISA)

作者： Georgios P. Trachanas Apostolos Arsenopoulos Theodoros Tsalidis Filippos Serepas Ourania Markaki Dimitrios Askounis Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

The increasing integration of renewable energy sources necessitates the deployment of efficient energy storage systems to ensure grid resilience, stability, and efficient operation. Selecting the optimal energy storage technology involves the evaluation across multiple criteria, reflecting technical, economic, and environmental dimensions, among others. This paper presents a decision support tool, based on an ensemble of Multi-Criteria Decision-Making methods, to rank energy storage technologies. These methods are renowned for their ability to handle conflicting criteria and provide clear, actionable insights for decision-makers. A detailed case study concerning battery systems is also included, evaluating the alternatives against seven technology and economic type criteria. The results demonstrate the efficacy of the employed tool in identifying the most suitable energy storage technologies, providing valuable guidance for stakeholders in the selection and development of energy storage solutions.

关键词： Renewable energy sources Costs Uncertainty Stability criteria Power system stability Nickel compounds Stakeholders Reliability Research and development Resilience

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On a novel tracking differentiator design based on iterative learning in a moving window

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Control Theory and Technology 2023年第1期21卷 46-55页

作者： Xiangyang Li Rafal Madonski Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510641China Energy and Electricity Research Center Jinan UniversityZhuhai 519070China Department of Electrical and Computer Science Cleveland State UniversityClevelandOH 44115USA

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative(PID).The practical challenge,however,is to extract such signals from noisy measurements and this difficulty is addressed first by *** in the form of linear and nonlinear tracking differentiator(TD).While improvements were made,TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the *** two approaches proposed in this paper start with the basic linear TD,but apply iterative learning mechanism to the historical data in a moving window(MW),to form two new iterative learning tracking differentiators(IL-TD):one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits;the other a serial IL-TD which is implementable digitally on any computer *** algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.

关键词： Tracking differentiator(TD) Iterative learning Iterative learning tracking differentiator(IL-TD) Active disturbance rejection control(ADRC)-Two-dimensional system(2-D system)

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Future Heat Risk Index: The Case of Attica Regions, Greece

Future Heat Risk Index: The Case of Attica Regions, Greece

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International Conference on Information, Intelligence, systems and Applications (IISA)

作者： Iason C. Dimitriou Georgios P. Trachanas Apostolos Arsenopoulos Elissaios Sarmas Decision Support Systems Laboratory School of Electrical & Computer Engineering National Technical University of Athens Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Extreme heat has become a common phenomenon especially the summer months. Dense urban environments suffer greatly from extreme heat events, which makes the everyday life of citizens difficult. For the more vulnerable population, such as the elderly, extreme heat waves can become dangerous for their health and even lethal. The study employs a Future Heat Risk Index to evaluate potential heat-related vulnerabilities under different climate change scenarios, particularly Representative Concentration Pathway 8.5 for the year 2040. Through the integration of climate change projections and spatial analysis, the research identifies areas at high risk of heat-related impacts and prioritizes mitigation strategies. Key findings underscore the importance of proactive planning and adaptation measures in enhancing resilience to extreme heat events, particularly in Mediterranean cities like those in Attica, Greece. The proposed mitigation strategies include implementation of vegetation enhancement and cold materials installation. The effects of both strategies are compared and discussed, highlighting the importance of incorporating climate change scenarios into decision-making processes to safeguard public health and well-being in a warming climate.

关键词： Heating systems Temperature sensors Climate change Prevention and mitigation Urban areas Decision making Vegetation mapping Planning Socioeconomics Resilience

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Quantitative comparison between reflection and transmission modes for the inspection of composite materials using pulsed thermography 45

Quantitative comparison between reflection and transmission ...

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Thermosense: Thermal Infrared Applications XLV 2023

作者： Alhammad, M. Avdelidis, N.P. Ibarra-Castanedo, C. Torbali, M.E. Zolotas, A. Maldague, X.P.V. School of Aerospace Transport and Manufacturing Cranfield University United Kingdom Universite Laval Computer Vision and Systems Laboratory Department of Electrical and Computer Engineering Quebec CityG1V 0A6 Canada

ISBN: (纸本)9781510661868

Composite materials have extensively been used in the aerospace industry. There are several inspection methods to ensure the safety of these composites;pulse thermography (PT) is one of the most promising ones. Both reflection and transmission modes of PT could be applied. However, few studies reveal the advantages and disadvantages of these two modes. This paper presents a quantitative comparison of pulsed thermal imaging in two modes of reflection and gradual heat transfer in carbon fibres. Experimental work was conducted on carbon fibre-reinforced plastic (CFRP) samples with different thicknesses and thermal images were recorded in both modes. Thermal images were further processed using statistical analysis and machine learning algorithms. Comparing the results from both modes, there is a marked improvement in the accuracy when the reflection mode is employed. © 2023 SPIE.

关键词： Nondestructive examination

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Design Optimisation of Electronic Nose with Multichannel Gas Sensor

Design Optimisation of Electronic Nose with Multichannel Gas...

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International Conference on Sensing Technology, ICST

作者： Yugal Pachori Richa Thakur Avinash Prashant D. Kulkarni Dinesh Singh Aditya Nigam Shubhajit Roy Chowdhury Biomedical Systems Lab School of Computing and Electrical Engineering Indian Institute of Technology Mandi Centre for Human Computer Interaction Indian Institute of Technology Mandi Multimedia Analytics Networks and Systems Lab School of Computing and Electrical Engineering Indian Institute of Technology Mandi

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

Human nose detects every kind of odors and our brain responds to every kind of smell that we are inhaling, so in the same way artificial nose or the electronic nose is a device that detects the smell and can enable the scope of digital smell technology. So, this paper proposes to optimize the design of an electronic nose that will eventually bring modification to the various digital platforms like internet, computers, mobile, television and tablets. We focused on design optimization of electronic nose which is built on a multichannel gas sensor array. The proposed electronic nose will enable the transmission or reception of smell over the internet and other digital platforms. The device can detect the smells of vapors coming out of volatile organic compounds. The sensing system is 3D printed in the model of a human nose. The sensing system works with an accuracy of detection of 90.45%. The response time of the electronic nose is 60 to 90 seconds, sometimes it is lesser than 60 seconds and it varies from substance to substance.

关键词： Volatile organic compounds Solid modeling TV Nose Electronic noses Three-dimensional printing Sensors Time factors Gas detectors Sensor arrays

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Currency Exchange Rate Forecasting Using Support Vector Classifiers 14

Currency Exchange Rate Forecasting Using Support Vector Clas...

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14th International Conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Koutsellis, Themistoklis Makarouni, Ioanna Choumas, Stylianos Ververidis, Christopher Papapolyzos, Thomas Bitsikas, Anastasios Doukas, Haris School of Electrical and Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece Hypertech Sa Athens Greece CAPITAL.GR Sa Athens Greece

ISBN: (纸本)9798350318067

Currency exchange markets are among the largest markets worldwide. Due to their decentrilised nature and the variation of the engaged stakeholders (i.e., from big funds and corporates to small enterprises and citizens), the currency exchange rate forecasting is a challenging task, crucial in decision making and democratisation of this domain. The main objective of this paper is to explore dependence patterns among various econometric time series, directly, or indirectly, related to currency exchange rate time series. Thereafter, on top of this dependence analysis, we deliver machine learning algorithms (e.g., support vector classifiers) to predict future values of currency exchange rates. Under this framework, the notions of time series autocorrelation and cross-correlation are utilised. However, both exhibit limitations because they cannot depict non-linear dependances. To tackle this caveat, we analyse the absolute values of the returns (i.e., percentage change) of the examined time series. The efficiency of the proposed procedure is demonstrated with eight currency exchange rate time series and eleven exogenous econometric time series related to the former. Finally, we trained eight support vector classifiers, one for each currency pair, all of which reached an accuracy level of approximately 80%. © 2023 IEEE.

关键词： Time series

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Securing the Future after Pager Bombs:Lifecycle Protection of Smart Devices via Blockchain Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2355-2358页

作者： Fei Lin Qinghua Ni Jing Yang Juanjuan Li Nan Zheng Levente Kovács Radu Prodan Mariagrazia Dotoli Qing-Long Han Fei-Yue Wang the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBei-jing 100049China IEEE Obuda University John von Neumann Faculty of InformaticsBecsi Str.96/bH-1034 BudapestHungary the Institute of Information Technology University of Klagenfurt9020 Klagenfurt am WortherseeAustria the Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190

THE Lebanese wireless device explosion incident has drawn widespread attention,involving devices such as pagers,walkie-talkies,and other common devices[1].This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment,signaling the onset of a new wave of"supply chain warfare"[2].

关键词： Smart cycle chains

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BECNN: Bias Field Estimation CNN Trained with a Dual Route Implicit Supervised Learning Strategy

BECNN: Bias Field Estimation CNN Trained with a Dual Route I...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Shuaizheng Feng, Chaolu Li, Wei Yang, Jinzhu Zhao, Dazhe Northeastern University School of Computer Science And Engineering Shenyang China Ministry of Education Northeastern University Key Laboratory of Intelligent Computing in Medical Image Shenyang China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China

ISBN: (纸本)9798350386226

Bias fields adversely affect various automatic analysis technologies. Therefore, bias field correction is essential. However, deep learning based methods encounter challenges in obtaining ground truth. Although existing methods attempt to address this problem by using training-free techniques or constructing datasets with approximations of the ground truth, the lack of task-oriented guidance, training instability, and inappropriate use of approximations still impact performance. Additionally, different approaches for bias field correction, i.e., estimating bias fields versus directly restoring clean images, exhibit different performances. However, there is no consensus on the best way, resulting in limited performance in some cases. To address these problems, we propose a bias field generation method to construct the dataset and provide task-oriented information. We then propose the concept of Equivalent of Residual Mapping (ERM) to analyze the advantages of estimating bias fields. According to ERM, we propose the Bias field Estimation Convolutional Neural Network (BECNN). Finally, we propose the Dual Route Implicit Supervised Learning (DRISL) strategy to balance the guidance derived from approximations of the ground truth with over-dependence on them. The proposed method is compared qualitatively and quantitatively with the correlated methods. Experiment results demonstrate that the proposed method performs effectively both on bias field estimation and correction. © 2024 IEEE.

关键词： Self-supervised learning

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