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Efficient CNN-based disaster events classification using UAV-aided images for emergency response application

Neural Computing and Applications 2024年第18期36卷 10599-10612页

作者： Bashir, Munzir Hubiba Ahmad, Musheer Rizvi, Danish Raza El-Latif, Ahmed A. Abd Department of Computer Engineering Jamia Millia Islamia New Delhi110025 India EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Kom32511 Egypt

Natural disasters can be unpredictable and catastrophic. Even after the event, the repercussions are prolonged due to the incompetence of disaster management strategies. To mitigate the effects of a natural hazard, disaster management teams have to rapidly come forth with innovative plans of action. To contain the damage that a disaster causes, the response time, and preparedness of a disaster management team is crucial. Having a forewarning about the nature of the disaster can prove to be beneficial for the management team. Remote/unreachable areas such as deep parts of the forests, far-flung rural areas, oceans and other hard to reach locations are at a higher risk of receiving poorer aid and response due to the lack of communication and connectivity with the rest of the world. Our paper provides a solution for this particular problem by suggesting UAV/Drones for surveillance and monitoring in these inaccessible, disaster struck areas. The drones not only monitor the situation but constantly click pictures and send them to a base station, where the images are used to collect insights about the type of disaster that has to be dealt with. The paper proposes a deep learning model based on feature concatenation for classification of disasters which can be deployed at the base station, and can receive data in the form of images from a UAV/drone hovering over the affected place. The proposed model is efficient and able to achieve a higher accuracy as compared to the leading CNN models and closely related recent works as well. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Human resource management

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EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection 24

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Yifan Xiang, Haolong Zhang, Xuyun Xu, Xiaolong Fan, Wei Zhang, Qin Qi, Lianyong School of Software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia University of Oxford Uffield Department of Women's & Reproductive Health United Kingdom College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering China

ISBN: (纸本)9798331506681

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods. © 2024 IEEE.

关键词： Anomaly Detection Efficient FR clustering Parallel Algorithm

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Mechanistic modeling of copper corrosions in data center environments

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Building Simulation 2024年第3期17卷 483-492页

作者： Rui Zhang Jianshun Zhang Roger Schmidt Jeremy L.Gilbert Department of Mechanical and Aerospace Engineering College of Engineering and Computer ScienceSyracuse UniversitySyracuseNY13244USA Buildings and Transportation Science Division Oak Ridge National LaboratoryOak RidgeTN37830USA SyracuseCoE-New York Center of Excellence in Environmental and Energy Systems Syracuse University727 E.Washington StreetSyracuseNY13244USA Department of Bioengineering Clemson UniversityClemson-Medical University of South Carolina Bioengineering ProgramCharlestonSC29425USA

Air-side economizers are increasingly used to take advantage of“free-cooling”in data centers with the intent of reducing the carbon footprint of ***,they can introduce outdoor pollutants to indoor environment of data centers and cause corrosion damage to the information technology *** evaluate the reliability of information technology equipment under various thermal and air-pollution conditions,a mechanistic model based on multi-ion transport and chemical reactions was *** model was used to predict Cu corrosion caused by Cl_(2)-containing pollutant *** also accounted for the effects of temperature(25℃and 28℃),relative humidity(50%,75%,and 95%),and *** also identified higher air temperature as a corrosion barrier and higher relative humidity as a corrosion accelerator,which agreed well with the experimental *** average root mean square error of the prediction was 13.7Å.The model can be used to evaluate the thermal guideline for data centers design and operation when Cl_(2)is present based on pre-established acceptable risk of corrosion in data centers’environment.

关键词： chloride pollutant mixture copper corrosion model data center temperature relative humidity synergistic effect

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Energy Storage Arbitrage and Peak Shaving in Distribution Grids Under Uncertainty

Energy Storage Arbitrage and Peak Shaving in Distribution Gr...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Tziovani, Lysandros Hadjidemetriou, Lenos Timotheou, Stelios University of Cyprus Kios Research and Innovation Center of Excellence Department of Electrical and Computer Engineering Nicosia Cyprus

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

Energy storage systems can provide peak shaving services in distribution grids to enable an increased penetration of renewable energy sources and load demand growth. Moreover, storage owners can make profits through energy arbitrage in electricity markets by buying energy when the price is low and selling when the price is high. This work considers the energy scheduling of a storage system integrated in a transformer substation to minimize the transformer power limit violations and maximize the arbitrage profits. However, uncertainty on the electricity price and net-load demand of the distribution grid may cause a mismatch between scheduled and actual operation, causing transformer power violations and reduced arbitrage profits. Towards this direction, a scenario-based stochastic optimization scheme to make uncertainty-aware decisions is developed, considering both net-load and price uncertainty. The corresponding scheme is formulated as a linear program that can be solved efficiently even under a large number of scenarios. The performance of the proposed scheme is evaluated and compared to the corresponding deterministic scheme using net-load profiles from a real distribution grid and price data from the Spanish dayahead electricity market. The developed scheme can be used for enhancing both the bidding and operating strategy of a storage system. © 2022 IEEE.

关键词： Profitability

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The Impact of Wireless Communication Networks on Wide Area Monitoring and Protection Applications 8

The Impact of Wireless Communication Networks on Wide Area M...

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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Asprou, Markos Akrytov, Antonis Hadjidemetriou, Lenos Charalambous, Charalambos Ciornei, Irina Ellinas, Georgios Panayiotou, Christos Kios Research and Innovation Center of Excellence University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

The fast deployment of the Phasor Measurement Units (PMUs), especially in the transmission level of the power systems, enables the development of wide area monitoring, protection and control (WAMPC) applications that enhance the situational awareness of the power system operator as well as the stability of the power system. Such applications are dependent on the communication network that supports the transfer of the PMU measurements to a central monitoring application or to a local protection application (situated in a substation). It is therefore of paramount importance to ensure the transfer of measurements with the least delay, while at the same time to ensure the integrity of the PMU measurements. In this work, the impact of using a wireless communication network for transferring the PMU measurements to the WAMPC applications is examined and the advantage of the 5G communication network over 4G and 3G in such real-time monitoring and control applications is demonstrated. © 2022 IEEE.

关键词： System stability

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification 7

A Novel Attention-based Explainable Deep Learning Framework ...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Muoka, Gladys W. Yi, Ding Ukwuoma, Chiagoziem Chima Martin, Moindi Duke Aydin, Ahmet Arif Al-Antari, Mugahed A. University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Chengdu University of Technology Sichuan College of Nuclear Technology and Automation Engineering Chengdu China Unicaf University Lilongwe Department of Computer Science Lilongwe Malawi Inönü University Faculty of Engineering Department of Computer Engineering Turkey Sejong University College of Software & Convergence Technology Daeyang AI Center Department of Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350383065

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study's primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics. © 2023 IEEE.

关键词： Optical tomography

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Stackelberg evolutionary games for cancer modeling and treatment 63

Stackelberg evolutionary games for cancer modeling and treat...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Romano, Chiara Borri, Alessandro Benedetto, Maria Domenica Di University of l'Aquila Computer Science and Mathematics Center of Excellence for Research Dews Department of Information Engineering Italy CNR-IASI Biomathematics Laboratory National Research Council of Italy Rome Italy

ISBN: (纸本)9798350316339

Stackelberg Evolutionary Game (SEG) theory models the interacting dynamics between a rational leader and a population of evolving followers, merging classical and evolutionary game theory. Although both methods are welldeveloped individually, the potential of SEG itself has not been appropriately recognized. Thus, in this paper we propose a novel eco-evolutionary model with resource mutualism and competition and we introduce a control framework based on SEGs, to steer the eco-evolutionary dynamics of followers at will. As a case study, we consider the treatment of cancer, where tumour cells are the followers that evolve in response to changes in the tumour micro-environment (and thus on available resources) and to the medical therapy, where the physician is the leader. An interesting aspect of this approach is that the objective function can be tailored according to the goal, e.g. jointly balancing tumour size, developed resistance, and toxicity of therapy, to ensure maximum quality of life for the patient. Simulations confirm the effectiveness and the potential of the proposed approach. © 2024 IEEE.

关键词： Lung cancer

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Energy Scheduling of Wind-Storage Systems Using Stochastic and Robust Optimization

Energy Scheduling of Wind-Storage Systems Using Stochastic a...

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

Energy storage systems (ESSs) is an emerging technology that enables increased and effective penetration of renewable energy sources into power systems. ESSs integrated in wind power plants can reduce power generation imbalances, occurring due to the deviation of day-ahead forecasted and actual wind generation. This work develops two-stage scenario-based stochastic and robust optimization schemes for the day-ahead energy scheduling of combined wind-storage systems, considering wind power uncertainty and the power balancing capability of the ESS. The set of scenarios used in the optimization schemes is appropriately selected from historical data of a real wind farm to closely match the day-ahead forecasted power production. Simulation results demonstrate the superiority of the stochastic and robust optimization schemes in terms of producer profits and power imbalances compared to the corresponding deterministic optimization scheme. The results also highlight that the two schemes offer a different trade-off between profits and power imbalances, making the stochastic and robust schemes more desirable for the producer and system operator, respectively. © 2022 IEEE.

关键词： Profitability

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Prequalification of Distribution Resources in a Coordinated Market Environment

Prequalification of Distribution Resources in a Coordinated ...

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Papayiannis, Ioannis Asprou, Markos Hadjidemetriou, Lenos Timotheou, Stelios Kios Research and Innovation Center of Excellence University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

The decentralization of power systems and the rapid deployment of renewable energy sources (RES) has upgraded the role of the Distribution System Operator (DSO) from a passive network observer to an active system operator. This requires effective collaboration and coordination with the Transmission System Operator (TSO) to avoid any congestion issues in the distribution grid. In this paper, the local market coordination scheme is considered, where DSO has priority to procure services from distributed energy resources (DERs) in an optimization-based separate local market. In this framework, a prequalification scheme is proposed and developed to enable the participation of DERs in central markets without risking the stability of the distribution system. The proposed prequalification scheme allows the DSO to detect and manage congested lines in the distribution grid preventing decisions by the TSO that can threaten the distribution system stability. Simulation results on the IEEE 33-bus system illustrate the effectiveness of the prequalification scheme in managing potential congestions in the distribution grid that might occur by the actions of the TSO. © 2022 IEEE.

关键词： Commerce

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Hyperspectral anomaly detection:a performance comparison of existing techniques

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International Journal of Digital Earth 2022年第1期15卷 2078-2125页

作者： Noman Raza Shah Abdur Rahman M.Maud Farrukh Aziz Bhatti Muhammad Khizer Ali Khurram Khurshid Moazam Maqsood Muhammad Amin iVISION Lab Electrical Engineering DepartmentInstitute of Space TechnologyIslamabadPakistan AI Enabling Technologies Lab Center of Excellence in Artificial IntelligenceBahria UniversityIslamabadPakistan Department of Electrical and Computer Engineering Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripurPakistan Avionics Engineering Department Institute of Space TechnologyIslamabadPakistan

Anomaly detection in Hyperspectral Imagery(HSI)has received considerable attention because of its potential application in several *** anomaly detection algorithms for HSI have been proposed in the literature;however,due to the use of different datasets in previous studies,an extensive performance comparison of these algorithms is *** this paper,an overview of the current state of research in hyperspectral anomaly detection is presented by broadly dividing all the previously proposed algorithms into eight different *** addition,this paper presents the most comprehensive comparative analysis to-date in hyperspectral anomaly detection by evaluating 22 algorithms on 17 different publicly available *** indicate that attribute and edge-preserving filtering-based detection(AED),local summation anomaly detection based on collaborative representation and inverse distance weight(LSAD-CR-IDW)and local summation unsupervised nearest regularized subspace with an outlier removal anomaly detector(LSUNRSORAD)perform better as indicated by the mean and median values of area under the receiver operating characteristic(ROC)***,this paper studies the effect of various dimensionality reduction techniques on anomaly *** indicate that reducing the number of components to around 20 improves the performance;however,any further decrease deteriorates the performance.

关键词： Anomaly detection algorithms hyperspectral imagery deep learning dimensionality reduction

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