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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mohammed, Murtuza Ali Mohammed, Vazeer Ali Ramakrishnan, Rakesh Mohammed, Mehmood Ali Logeshwaran, J. Maheswaran, S. Lewis University Department of Computer & Information System IL United States University of the Cumberlands Department of Information Technology Union CityCA United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350335095

computer vision methods utilizing transfer learning can offer a promising approach for automated eye cancer diagnosis with three-dimensional dosimetry imaging. The latest advances in AI have encouraged researchers to use deep learning models for automated cancer classification. This paper presents a novel approach using three-dimensional dosimetry imaging for automated ocular cancer classification using transfer learning. The approach uses a combination of dataset augmentation methods. The approach is adapted from the popular Inception V3 CNN architecture. Two different feature extraction approaches, namely mean and maximum pooling, are compared while extracting features from the output of the CNN layers. The extracted features are fed to the classifier which uses the SVM with a linear kernel as its decision system. The effectiveness of this method is evaluated on a set of 20 healthy ocular images and 26 suspicious ocular images. The overall classification accuracy achieved with this approach is 94.85%. The results obtained demonstrate that this approach has a promising potential for automated ocular cancer classification with an appropriate transfer learning model. © 2023 IEEE.

关键词： Classification (of information)

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Traffic Light Control Using Reinforcement Learning 2

Traffic Light Control Using Reinforcement Learning

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2nd IEEE International Conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Masfequier Rahman Swapno, S.M. Nuruzzaman Nobel, S.M. Ramachandra, A.C. Islam, Md Babul Haque, Rezaul Rahman, Mohammad Mominur Bangladesh University of Business and Technology Dept of CSE Dhaka Bangladesh Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India UNICAL Dept. of Computer Modeling Electronic and System Engineering Rende Italy East West University Dept of CSE Dhaka1212 Bangladesh Hamad Bin Khalifa University College of Science and Engineering Qatar

ISBN: (纸本)9798350317558

Nowadays, one of the biggest issues in urban areas is traffic. This problem wastes important time and contributes to air and sound pollution. This affects people's general quality of life in addition to posing health dangers. Our study attempts to mitigate these problems, effectively cutting down on wait times and delays. Our Reinforcement Learning method creates intelligent agents that can adjust traffic lights at crossings instantly. Our objective is to minimize delays, reduce congestion, reduce travel times, improve safety, and improve traffic flow. We implemented the Deep Q Learning algorithm which activities yield the greatest benefits under various traffic scenarios. Our model can the sequence time since the Green signal (GS) lasts 10 seconds and the Red signal (RS) lasts 5 seconds. The waiting period is shortened by 50% as a result. This study suggests reinforcement learning may improve traffic signal controller synchronization and urban traffic congestion. This novel method may improve transport efficiency and sustainability. © 2024 IEEE.

关键词： Action Deep Q Learning GS Model Q Value Rewards RS State Traffic signal vehicles Waiting Time

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Energy Aware Secure Cyber-Physical systems with Clustered Wireless Sensor Networks

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computers, Materials & Continua 2022年第9期72卷 5499-5513页

作者： Masoud Alajmi Mohamed K.Nour Siwar Ben Haj Hassine Mimouna Abdullah Alkhonaini Manar Ahmed Hamza Ishfaq Yaseen Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical *** greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems(CPES).At the same time,the rising ubiquity of wireless sensor networks(WSN)in several application areas makes it a vital part of the design of *** security and energy efficiency are the major challenging issues in CPES,this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms(EASCPSMA).The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion *** EASCPSMA technique encompasses two main stages namely improved fruit fly optimization algorithm(IFFOA)based clustering and optimal deep stacked autoencoder(OSAE)based intrusion ***,the optimal selection of stacked autoencoder(SAE)parameters takes place using root mean square propagation(RMSProp)*** extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying *** simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.

关键词： Intrusion detection system metaheuristics stacked autoencoder deep learning cyber physical energy systems clustering wsn

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An innovation prediction of DNA damage of melanoma skin cancer patients using deep learning 14

An innovation prediction of DNA damage of melanoma skin canc...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Ramakrishnan, Rakesh Mohammed, Mehmood Ali Mohammed, Murtuza Ali Mohammed, Vazeer Ali Logeshwaran, J. Maheswaran, S. University of the Cumberlands Department of Information Technology Union CityCA United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States Lewis University Department of Computer & Information System IL United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350335095

There are now a variety of intriguing options for the study of genetic data thanks to recent developments in artificial neural networks and deep learning. In this study, we use a deep learning-based prediction model to find possible DNA damage in individuals with melanoma skin cancer. We create a convolutional neural network (CNN) model to forecast the DNA damage susceptibility of melanoma cancer cells using a publically available genome sequencing dataset. This model preprocesses the genomic data, extracts features, and categorises them. Comparing the results of our CNN model with those of a traditional logistic regression model, we find that our CNN reported superior performance in identifying differences between healthy and cancerous samples with an accuracy of nearly 96%. The model can be used to augment the standard clinical diagnosis of melanoma, which only uses visual assessment and histology. By intervening sooner, clinicians can put forward more personalized and informed plans of care and surveillance for each melanoma patient, reducing medical costs and improving the quality of patient diagnosis. © 2023 IEEE.

关键词： Neural network models

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Power-to-Liquid fuel export potential from Chile to Europe on the pathway to carbon neutrality 23

Power-to-Liquid fuel export potential from Chile to Europe o...

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23rd Wind and Solar Integration Workshop, WIW 2024

作者： Jansen, Lukas Duque Pérez, Estefanía Häckner, Benedikt Pfennig, Maximilian Fraunhofer Institute of Energy Economics and Energy System Technology IEE Joseph-Beuys-Str. 8 Kassel34117 Germany Department of Electrical Engineering and Computer Science University of Kassel Wilhelmshöher Allee 73 Kassel34121 Germany

ISBN: (纸本)9781837242689

Power-to-Liquid (PtL) fuels will be vital in achieving carbon neutrality. This study uses a energy system optimization model to analyze the market-based export potential of PtL fuels from Chile on the path to 2050. The sector-integrated energy system model minimizes the of capacity expansion in five-year steps. Our analysis shows that under the assumptions made, Chile is able to phase out the use of fossil resources and significantly export Fischer-Tropsch fuels in 2030, 2040 and 2050 at competitive marginal costs. The study focuses on the impact of export volumes, CO2 budgets, and expansion limits for 2030, 2040, and 2050. The model results show that the production of PtL enables the connection of level 1 renewable resources in remote, off-grid regions in Chile to global demand centres. The achievable export volumes and corresponding costs are discussed from a European perspective. Finally, it is presented that the necessary expansion rates for Chile's wind and solar power plants must be significantly higher than historical values in all cases. © Energynautics GmbH.

关键词： Budget control

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Impact of fast frequency response on renewable energy source curtailment and load shedding

Impact of fast frequency response on renewable energy source...

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CIRED 2024 Vienna Workshop

作者： Savvas Panagi Antonis Lazari Vrahimis Koutsoloukas Petros Aristidou Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Transmission System Operator of Cyprus Cyprus

ISBN: (数字)9781837241224

system operators in low-inertia power systems often have to curtail renewable energy sources (RES) and employ strict under-frequency load shedding (UFLS) schemes to ensure frequency security after an event leading to loss of generation. This approach limits the maximum RES penetration in a system and results to the loss of load. To tackle these problems, fast frequency response (FFR) schemes can be used to limit the frequency Nadir after a disturbance and decrease the need for RES curtailments and UFLS. This article provides insights on the interaction between the Kinetic Energy (KE), Frequency Containment Reserves (FCR), and Nadir after a disturbance, which are the driving mechanisms leading to RES curtailment. Then, it analyses the impact of FFR on the Nadir and its ability to alleviate RES curtailment problems. The low-inertia, islanded, Cyprus dynamic model is used to quantify the results and showcase the impact on a real system.

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Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM

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Tsinghua Science and technology 2025年第1期30卷 68-86页

作者： Yahang Qin Zhenni Li Shengli Xie Bo Li Ming Liu Victor Kuzin School of Automation Guangdong University of Technologyand also with Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete ManufacturingGuangzhou 510006China School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Information Processing and System Integration of IoTMinistry of EducationGuangzhou 510006China Department of Electronic and Computer Engineering Hong Kong University of Science and TechnologHong Kong 999077China Academician of Russian Engineering Academy Moscow Moscow 125009Russia

Multipath signal recognition is crucial to the ability to provide high-precision absolute-position services by the BeiDou Navigation Satellite system(BDS).However,most existing approaches to this issue involve supervised machine learning(ML)methods,and it is difficult to move to unsupervised multipath signal recognition because of the limitations in signal *** by an autoencoder with powerful unsupervised feature extraction,we propose a new deep learning(DL)model for BDS signal recognition that places a long short-term memory(LSTM)module in series with a convolutional sparse autoencoder to create a new autoencoder ***,we propose to capture the temporal correlations in long-duration BeiDou satellite time-series signals by using the LSTM module to mine the temporal change patterns in the time ***,we develop a convolutional sparse autoencoder method that learns a compressed representation of the input data,which then enables downscaled and unsupervised feature extraction from long-duration BeiDou satellite series ***,we add an l_(1/2) regularizer to the objective function of our DL model to remove redundant neurons from the neural network while ensuring recognition *** tested our proposed approach on a real urban canyon dataset,and the results demonstrated that our algorithm could achieve better classification performance than two ML-based methods(e.g.,11%better than a support vector machine)and two existing DL-based methods(e.g.,7.26%better than convolutional neural networks).

关键词： convolutional sparse autoencoder BeiDou Navigation Satellite system(BDS) long short-term memory(LSTM) multipath classification

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LineShield - A Generalized LiDAR Pipeline for Automated Vegetation Encroachment Detection on Powerlines

LineShield - A Generalized LiDAR Pipeline for Automated Vege...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Aziz Al-Najjar Marzieh Amini James R. Green Felix Kwamena System and Computer Engineering Department Carleton University Ottawa Canada School of Information Technology Carleton University Ottawa Canada

ISBN: (数字)9798350356830

ISBN: (纸本)9798350356847

Vegetation encroachment on the powerline poses significant risks to the reliability and safety of the power infrastructure. Many LiDAR-Based methods have tried to address this problem, yet these methods lack scalability and generality across different LiDAR collection methods with varying resolutions and data sizes. This paper presents LineShield, a generalized LiDAR-based pipeline for automated vegetation encroachment detection on powerlines. The pipeline addresses these challenges on both airborne and mobile LiDAR datasets. Key components: clustering with DBSCAN for accurate powerline segmentation, PCA-based alignment for standardizing orientation, sliding window traversal for efficient processing of large datasets, voxel downsampling for reducing data complexity, and proximity-based severity classification for prioritizing interventions. Experimental results demonstrate the pipeline’s adaptability across three datasets, namely, ECLAIR, DALES, and Toronto-3D, achieving up to 97.4% detection accuracy and maintaining performance in both urban and rural environments. The approach also provides detailed reporting to help maintenance teams prioritize interventions.

关键词： Laser radar Accuracy Three-dimensional displays Scalability Pipelines Vegetation Real-time systems Maintenance Safety Monitoring

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Container lifecycle-aware scheduling for serverless computing

Container lifecycle-aware scheduling for serverless computin...

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作者： Wu, Song Tao, Zhiheng Fan, Hao Huang, Zhuo Zhang, Xinmin Jin, Hai Yu, Chen Cao, Chun National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

Elastic scaling in response to changes on demand is a main benefit of serverless computing. When bursty workloads arrive, a serverless platform launches many new containers and initializes function environments (known as cold starts), which incurs significant startup latency. To reduce cold starts, platforms usually pause a container after it serves a request, and reuse this container for subsequent requests. However, this reuse strategy cannot efficiently reduce cold starts because the schedulers are agnostic of container lifecycle. For example, it may ignore soon available containers or evict soon needed containers. We propose a container lifecycle-aware scheduling strategy for serverless computing, CAS. The key idea is to control distribution of requests and determine creation or eviction of containers according to different lifecycle phases of containers. We implement a prototype of CAS on OpenWhisk. Our evaluation shows that CAS reduces 81% cold starts and therefore brings a 63% reduction at 95th percentile latency compared with native scheduling strategy in OpenWhisk when there is worker contention between workloads, and does not add significant performance overhead. © 2021 John Wiley & Sons Ltd.

关键词： Containers

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Studying of an Adaptive Unscented Kalman Filter as a Target Tracking Algorithm with Matlab Simulation 2

Studying of an Adaptive Unscented Kalman Filter as a Target ...

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2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Ammar, Abdussalam Masaud Ellafi, Amira Youssef Bozed, Kenz Amhmed Zerek, Amer R. Faculty of Electronic Technology Tripoli Libya Benghazi University Faculty of Information Technology Department of Computer System Design Libya Zawia University Faculty of Engineering EE Department Zawia Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

The radar is considered as one of tracking application which we need to tracking unknown targets. This application is using a kalman filter. Also the extended and unscented kalman filter has been created to deal with nonlinear systems such as multidimensional radar tracking. If wideband radar is used, multiple return signals may be obtained by scattering on a single target in a single pulse. It is important to monitor multiple dispersions on an undefined number of artifacts in the specific problem considered, with a standard wideband radar model, whose motion involves a nonlinear rotational velocity component. The measurements used are scattered range, range-rate (in analogy to Doppler), nonlinear functions of the scattered position and velocity, with parameters in the state-space model. In order to evaluate the scene and observe its progression over time, the tracker design uses a Kalman Filter type to model the nonlinear dynamics and measurement items and a multi-pronged association scheme. © 2022 IEEE.

关键词： Mean square error

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