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International Conference on Data science and Network engineering, ICDSNE 2023

作者： Upadhyay, Alka Naaz, Sameena Thakur, Vinay Ansari, Iffat Rehman Department of Computer Science and Technology School of Engineering Sciences and Technology Jamia Hamdard New Delhi India New Delhi India Electronics Engineering Section Faculty of Engineering and Technology University Women’s Polytechnic A.M.U.UP Aligarh202002 India

ISBN: (纸本)9789819967544

The development in software, hardware and communication technologies has made the broadcasting of sensory data collected from various devices very easy and simple. Interconnected devices through Internet technology form the Internet of Things (IoT). Applying intelligent methods for the analysis of this big data is the key which develops smart IoT applications. The world today has become increasingly dependent on digitized data which raises various security concerns and the need for advanced and reliable security technologies to deal with the increasing number of cyber-attacks. The work depicted in this paper makes use of machine learning techniques to detect cyber-attacks using the UNSW-NB15 data set and the KDD CUP 1999 dataset. Decision Tree, k-means clustering, multi-layer perception (MLP), Naive Byes and Random Forest classifier are the algorithms used in this work in order to find higher level information about the data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Tomato Disease Classification Using CNN 4th

Tomato Disease Classification Using CNN

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Archanaa, N. Daniel, V. Vifert Jenuben Divya, S. Raja, Kalpana Oviya, I.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Sciences Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States

ISBN: (纸本)9789819736898

Plant disease detection using various machine learning techniques is a very popular research area. Many promising results have been obtained, but only a few specific applications can still make farmers’ lives easier. This research focuses on the important task of identifying tomato diseases at an early stage to prevent their spread and protect crop yields more broadly, emphasizing the idea that disease prevention will more important than treatment. Leveraging the capabilities of convolutional neural networks (CNN), we developed a model that diagnoses eight distinct tomato diseases using only their images. Diseases listed include bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, two-spotted spider mite, target spot, and tomato yellow leaf curl virus. An integral part of our approach involves sophisticated image prepossessing and enhancement techniques, ensuring data quality and model robustness. Feature extraction plays a key role in transforming raw image data into recognizable patterns. The architecture of the CNN has been meticulously designed, consisting of multiple layers with a notable focus on the ReLU activation function and other relevant hyper-parameters. Our main dataset comes from PlantVillage, known for synthesizing rich and diverse plant images. After rigorous training and validation, the CNN model showed exceptional accuracy of 96.88% in tomato disease identification. However, challenges such as overfitting, image quality variability, and class imbalances were encountered but were resolved through strategic data augmentation, careful CNN overlay. In summary, our research presents a practical solution for farmers and agricultural entities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fruits

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Recent progress in modelling and simulation technologies of energy efficiency and environmental quality in the built environment

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Building Simulation 2022年第7期15卷 1175-1176页

作者： Wenhui Ji Yanping Yuan Liangliang Sun Xudong Zhao School of Mechanical Engineering Southwest Jiaotong UniversityNo.111North Section 1Second Ring RoadChengdu 610031China University of Hull School Engineering and Computer ScienceKingston Upon Hull HU67RXN HumbersideUK

The International Conference for Global Chinese Academia on Energy and Built Environment(ICCEBE)is a premier international event covering a broad range of topics on clean energy utilization,energy efficiency and environmental quality in the built *** conference aims to provide a forum to share cutting-edge achievements in the built environment,and to promote academic collaboration and cooperation among worldwide Chinese scholars.

关键词： energy modelling collaboration

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Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks

arXiv

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arXiv 2025年

作者： Orfanoudakis, Stavros Palensky, Peter Vergara, Pedro P. Section Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

Maintaining grid stability amid widespread electric vehicle (EV) adoption is vital for sustainable transportation. Traditional optimization methods and Reinforcement Learning (RL) approaches often struggle with the high dimensionality and dynamic nature of real-time EV charging, leading to sub-optimal solutions. To address these challenges, this study demonstrates that combining Large Language Models (LLMs), for sequence modeling, with Graph Neural Networks (GNNs), for relational information extraction, not only outperforms conventional EV smart charging methods, but also paves the way for entirely new research directions and innovative solutions. © 2025, CC BY.

关键词： Charging stations

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IT-Security Risk Based Approach for Secure Operation of Distributed Data Platforms in Supply Chains

IT-Security Risk Based Approach for Secure Operation of Dist...

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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Voß, Marvin Kallisch, Jonas Runge, Maxim Theus, Tobias Niemann, Karl-Heinz Wunck, Christoph Faculty I - Electrical Engineering and Information Technology University of Applied Sciences and Arts Hannover Germany Department of Engineering Section Electrical Engineering and Computer Science University of Applied Sciences Leer Emden Germany Faculty of Mathematics Informatics and Statistics Ludwig Maximilian University Munich Germany

ISBN: (纸本)9798350386097

This paper presents the development of a secure data platform designed to enhance operational efficiency and to facilitate cross-company collaboration within the manufacturing supply chain. The platform is designed to ease secure information exchange while maintaining the sovereignty of each participant, leveraging standardized security features and advanced technologies such as federated learning. Security measures are based on a comprehensive risk analysis, which methodically identifies, assesses, and mitigates potential threats. Moreover, federated learning models that are not transparent are utilized. This allows data owners to maintain control over sensitive information within the consortium. The described approach markedly enhances both security and operational collaboration across the industry, representing a crucial stride towards more integrated and efficient supply chain management in manufacturing. © 2024 IEEE.

关键词： Risk analysis

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A setup for live AI support in interventional radiology

A setup for live AI support in interventional radiology

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作者： Komposch, Jan Malzacher, Till Baumgärtner, Timo Braun, Michael Roßkopf, Johannes Franz, Alfred M. Schmitz, Bernd Institute for Computer Science Ulm University of Applied Sciences Ulm Germany Neuroradiology Section District Hospital Guenzburg Guenzburg Germany Institute for Medical Engineering and Mechatronics Ulm University of Applied Sciences Ulm Germany

Artificial intelligence (AI) has the potential to support time-critical stroke treatment. In a previous study we demonstrated the feasibility of deep learning based automatic classification for thrombus detection during thrombectomies, a catheter-guided procedure to remove occlusions of cerebral vessels. However, this method has yet to be tested during a live intervention. In this work, we present a setup to integrate AI based support in an angiography suite. A classification PC was connected to the angiography by means of a real-time video connection as well as a research interface for control signals. We found that video conversion in real-time does not affect the classification result in comparison to offline classification of DICOM data. Analyzing 50 video streams of previous cases, the system could classify digital-subtraction angiography (DSA) sequences within 13.3 seconds on average. This processing time can further be reduced to an average of 7.9 seconds with GPU acceleration. Additionally, the system successfully classified two DSA sequences acquired during live thrombectomy, identifying the presence of thrombi in less than 5 seconds. So far, the classification result has only been displayed in the control room of the angiography suite to demonstrate feasibility. In the outlook, however, we also discuss how the result can be displayed directly on the angiography screen. © 2024 by Walter de Gruyter Berlin/Boston.

关键词： Radiology

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A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Model for Classifying DNA Barcodes 1

A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Mo...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Yadav, Abhivyakti Divya, S. Oviya, I.R. Raja, Kalpana Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Medicine Yale University Section for Biomedical Informatics and Data Science New HavenCT United States

ISBN: (纸本)9798350342796

DNA Barcodes, which are particular fragments derived from brief sections of DNA (such as mitochondrial, nuclear, and plastid sequences), can be used to identify organisms from the major life kingdoms. In addition to supporting conventional taxonomic techniques, DNA barcoding is a potent tool that advances our knowledge of species diversity and their ecological functions. On a variety of organisms, the use of this approach for species categorization has been successful. In this paper, we examine how DNA barcoding has been used to classify species based on DNA barcodes as well as other related research that has been done over the years on the subject. After experimenting with a number of deep learning models, we have propose a Variational Auto Encoder + Feed Forward Neural Network workflow for classifiying species using DNA barcodes. The models have been assessed on the basis of performance factors including accuracy, recall, and precision. COI, rbcL, matK, and ITS are the specific gene sections that have been identified as barcodes. For both simulated and real datasets, the model can attain an average accuracy of greater than 95 percent. This DNA barcoding approach has the ability to simplify DNA barcode-based species identification and serve as a tool for species categorization. © 2023 IEEE.

关键词： DNA

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Parameter estimation of nonlinear systems: dwarf mongoose optimization algorithm with key term separation principle

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16921-16931页

作者： Mehmood, Khizer Chaudhary, Naveed Ishtiaq Khan, Zeshan Aslam Cheema, Khalid Mehmood Raja, Muhammad Asif Zahoor Department of Electrical and Computer Engineering International Islamic University Islamabad44000 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Electronic Engineering Fatima Jinnah Women University Rawalpindi46000 Pakistan

In recent years, swarm intelligence-based metaheuristics are extensively explored for optimizing challenging and stiff nonlinear problems. This research explores the identification of the input nonlinear output error (NOE) model through a swarm intelligence-based metaheuristic called dwarf mongoose optimization algorithm (DMOA). Further, the key term separation method (KTSM) based identification model is presented to address the issue of over-parameterization. The DMOA performance is deeply examined for various generations, population sizes, and signal-to-noise ratios. The comparative analyses of the DMOA with the recent counterpart through observations of non-parametric statistical tests confirmed the effectiveness of the DMOA for NOE identification. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Nonlinear systems

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Classification of Breast Cancer using a Novel Neural Network-based Architecture 14

Classification of Breast Cancer using a Novel Neural Network...

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

作者： Mishra, Priti Khare, Sangita Singh, Tripty Nair, Rekha R Sharma, Mansi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dayananda Sagar University School of Engineering Department of Computer Applications Bengaluru India Cloud Computing Sw Research Section Future Computing Research Division Korea Republic of

ISBN: (纸本)9798350335095

Breast cancer is currently one of the deadliest types of cancer, and the death rate has considerably grown as a result of a lack of knowledge about the disease, its symptoms, and prevention techniques. Therefore, early detection at a nearly stage is crucial and vital in order to limit the progression of cancer. Malignant and benign breast cancer are the two further subtypes. In order to categorise the different forms of breast cancer, an automated system with logistic regression and neural network is proposed. The classification of the breast cancer data utilises the DNN with various levels of processing. The Digital Database for Screening Mammography (DDMS) dataset was used in the proposed study on the Kaggle platform. The dataset was divided into various train-test splits. On the basis of accuracy, ROC AUC cuve the system's performance is evaluated. The outcome demonstrates that proposed-2 outperforms in a comparable sense with a training accuracy of 98.99% and testing accuracy of 98.83%. © 2023 IEEE.

关键词： Diseases

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Structural RC computer aided intelligent analysis and computational performance via experimental investigations

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Structural engineering and Mechanics 2024年第3期90卷 253-261页

作者： Huang, Y.C. Lulios, TuMuli Chang, Chu-Ho Noor, M. Nasir Shao, Jen-Chung Chiu, Chien-Liang Lee, Tsair-Fwu Wang, Renata Information Technology Faculty Duy Tân University 254 Nguyen Van Linh Danang Viet Nam Princess Margaret Hospital Funafuti Island Private Mail Bag Tuvalu College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan College of Maritime National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Medical Physics and Informatics Laboratory of Electronics Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Information and Research Center of China 1/9 Road: 2 Block: D Section: 15 Dhaka1216 Bangladesh

This research explores a new finite element model for the free vibration analysis of bi-directional functionally graded (BDFG) beams. The model is based on an efficient higher-order shear deformation beam theory that incorporates a trigonometric warping function for both transverse shear deformation and stress to guarantee traction-free boundary conditions without the necessity of shear correction factors. The proposed two-node beam element has three degrees of freedom per node, and the inter-element continuity is retained using both C1 and C0 continuities for kinematics variables. In addition, the mechanical properties of the (BDFG) beam vary gradually and smoothly in both the in-plane and out-of-plane beam’s directions according to an exponential power-law distribution. The highly elevated performance of the developed model is shown by comparing it to conceptual frameworks and solution procedures. Detailed numerical investigations are also conducted to examine the impact of boundary conditions, the bi-directional gradient indices, and the slenderness ratio on the free vibration response of BDFG beams. The suggested finite element beam model is an excellent potential tool for the design and the mechanical behavior estimation of BDFG structures. © 2024 Techno-Press, Ltd.

关键词： Boundary conditions

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