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6th International Conference on Computing and Big Data, ICCBD 2023

作者： Zhang, Youyang Chen, Qiuxia Cui, Wenhua Yang, Jinfeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Institute of Applied Artificial Intelligence The Guang-dong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen China

ISBN: (纸本)9798350317008

To realize intelligent management in the Camellia oleifera fruit industry and foster the efficient growth of its supply chain, the accurate identification and detection of Camellia oleifera fruits are essential. However, in natural environments, these fruits exhibit challenges such as clustering, mutual occlusion, and obstruction by leaves. This paper proposes a Camellia oleifera fruits detection algorithm based on an improved YOLOv7 deep convolutional neural network, which is referred to as the SDCM network model, and add the function of counting the number of fruits. We introduce an enhancement to the backbone layer of the YOLOv7 model by incorporating a module called SDCM (Shortcut and Dilation Convolution based Module). This module enriches multiscale edge information, enabling the network to extract features more effectively, thus improving detection accuracy. The experimental results show that the detection accuracy of the SDCM network model is significantly improved compared with the original YOLOv7 model, and the detection performance of the model in this paper is superior compared with other classical detection models. By enhancing the detection accuracy of the model and adding the function of fruit counting, the model in this paper can provide more reliable and accurate detection performance and the function of predicting fruit yield for the development of the Camellia oleifera fruit industry, improve the production efficiency, and reduce the artificial resources. © 2023 IEEE.

关键词： Supply chains

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Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment

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computers, Materials & Continua 2022年第7期72卷 1157-1172页

作者： Ashit Kumar Dutta Jenyfal Sampson Sultan Ahmad T.Avudaiappan Kanagaraj Narayanasamy Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj 11942Saudi Arabia Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Department of Computer Applications J.J College of Arts and Science(Autonomous)Affiliated to Bharathidasan UniversityPudukkottai622422India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Intelligent Transportation Systems(ITS)have become a vital part in improving human lives and modern *** aims at enhancing road safety and environmental *** is a tremendous increase observed in the number of vehicles in recent years,owing to increasing *** vehicle has its own individual emission rate;however,the issue arises when the emission rate crosses a standard *** to the technological advances made in Artificial Intelligence(AI)techniques,it is easy to leverage it to develop prediction approaches so as to monitor and control air *** current research paper presents Oppositional Shark Shell Optimization with Hybrid Deep Learning Model for Air Pollution Monitoring(OSSOHDLAPM)in ITS *** proposed OSSO-HDLAPM technique includes a set of sensors embedded in vehicles to measure the level of *** addition,hybridized Convolution Neural Network with Long Short-Term Memory(HCNN-LSTM)model is used to predict pollutant level based on the data attained earlier by the *** HCNN-LSTM model,the hyperparameters are selected and optimized using OSSO *** order to validate the performance of the proposed OSSO-HDLAPM technique,a series of experiments was conducted and the obtained results showcase the superior performance of OSSO-HDLAPM technique under different evaluation parameters.

关键词： Deep learning air pollution environment monitoring internet of things intelligent transportation systems oppositional learning LSTM model

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ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging 26

ProMIL: Probabilistic Multiple Instance Learning for Medical...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Struski, Lukasz Rymarczyk, Dawid Lewicki, Arkadiusz Sabiniewicz, Robert Tabor, Jacek Zieliński, Bartosz Faculty of Mathematics and Computer Science Jagiellonian University Poland Faculty of Applied Computer Science University of Information Technology and Management Rzeszow Poland Department of Pediatric Cardiology and Congenital Heart Diseases Medical University of Gdansk Poland Ues Ltd. Doctoral School of Exact and Natural Sciences Jagiellonian University Poland

ISBN: (纸本)9781643684369

Multiple Instance Learning (MIL) is a weakly-supervised problem in which one label is assigned to the whole bag of instances. An important class of MIL models is instance-based, where we first classify instances and then aggregate those predictions to obtain a bag label. The most common MIL model is when we consider a bag as positive if at least one of its instances has a positive label. However, this reasoning does not hold in many real-life scenarios, where the positive bag label is often a consequence of a certain percentage of positive instances. To address this issue, we introduce a dedicated instance-based method called ProMIL, based on deep neural networks and Bernstein polynomial estimation. An important advantage of ProMIL is that it can automatically detect the optimal percentage level for decision-making. We show that ProMIL outperforms standard instance-based MIL in real-world medical applications. We make the code available. © 2023 The Authors.

关键词： Medical applications

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SIMPLE TEMPERATURE MODEL FOR HALF-BRIDGE MODULES IN DC-DC CONVERTERS 11

SIMPLE TEMPERATURE MODEL FOR HALF-BRIDGE MODULES IN DC-DC CO...

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11th International Conference on Power Electronics, Machines and Drives, PEMD 2022

作者： Kunstbergs, Noass Hinz, Hartmut Schofield, Nigel Marquina Sandoval, Mathias Diego Roll, Dennis Michalik, Robert Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt Germany Department of Engineering and Technology University of Huddersfield Huddersfield United Kingdom Faculty of Computer Science and Engineering Universidad de Cadiz Cadiz Spain

ISBN: (数字)9781839537189

ISBN: (纸本)9781839537189

Estimating junction temperature during the operation of power semiconductors is essential to avoid damage and to increase lifetime. However, published literature usually presents complex solutions to determine temperature rise. These require parameters that are not available in datasheets. Nevertheless, there is usually no additional information available for the design stage. This study introduces a simple method to determine device junction temperature of half-bridge modules using only datasheet parameters and the ambient temperature. This paper presents results from the proposed circuit design undertaken in the simulation environment PSIM and the simulation tool Semisel. The results are validated with a DC-DC converter operating up to 6.5 kW. The results show that the proposed model can predict temperature rise with a maximum deviation of 5.5%. © IET Conference Proceedings. All right reserved.

关键词： DC-DC converters

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Machine Learning-Based Solution for SMS Spam Detection Problem 2

Machine Learning-Based Solution for SMS Spam Detection Probl...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Shdefat, Ahmed Younes Sedky, Nouran M. Bialy, Zeina H. El Ahmed, Shahd Fathi, Hanaa Abdelminaam, Diaa Salama College of Engineering and Technology American University of the Middle East Egaila54200 Kuwait Applied Science Private University Applied Science Research Center Amman Jordan Misr International University Faculty of Computer Science Cairo Egypt Jadara University Jadara Research Center Irbid21110 Jordan Middle East University MEU Research Unit Amman11831 Jordan

ISBN: (纸本)9798350362633

The article describes a new method for image detection using machine learning, based on the development of sophisticated security systems tailored for examining digital media to identify and scrutinize altered images. Using deep learning models and advanced image processing techniques, the authors demonstrate high accuracy and efficiency i n detecting face modifications a nd malicious content within digital images. The authors illustrate the proposed technique by evaluating its performance on a unique dataset of unaltered and modified images. The method allows for improving detection accuracy by 93.99% and demonstrates robustness in real-time applications. The new method's effectiveness is confirmed by calculating precision and recall metrics. New research results develop advanced security solutions that can be used to enhance digital forensics, content authentication, and cybersecurity applications. Novelty and scientific contributions lie in integrating deep learning for real-time media analysis and enhancing data authenticity and integrity. © 2024 IEEE.

关键词： Adversarial machine learning

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The Role of Soft Skills in Engineering Education 53

The Role of Soft Skills in Engineering Education

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Fojcik, Marcin Fojcik, Martyna Katarzyna Ziebinski, Adam Pollen, Bjarte Kampen, Anne-Lena Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Førde Norway Volda University Collage Department of Humanities and Education Volda Norway Silesian University of Technology Department of Distributed Systems and Informatics Devices Gliwice Poland Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9798350336429

The rapid but uneven education development worldwide is creating many challenges. Changing conditions and rules in engineering fields such as programming, automation, and electronics are not always followed by changes in education. Employers expect potential candidates to be current with the latest domain competencies and soft skills. The idea is to hire candidates with both knowledge (theoretical and practical) and experience in reflection and collaboration skills. Universities can not always keep up with the development of technology and the adaptation of curricula because change happens so quickly. Therefore, upgrades are needed in engineering education. Focusing education solely on measurable knowledge and results is a simple but often insufficient approach to presenting the entire curriculum within the time frame of a course. Our study of about 150 engineering students in Poland and Norway included the role of students' experiences in hands-on activities, especially labs. The research aimed to determine what students expect, what influences their results, and what complications may arise. The study results show that students often struggle with time management, prioritizing, and understanding practical tasks. In addition, there is a difference between students with and without practical work experience. Our research showed that both groups of students did not achieve all their intended educational goals for various reasons. Therefore, adapting the current teaching system to the student's needs and requirements is important. © 2023 IEEE.

关键词： Bolognia process culture model engineering education laboratory teaching soft skills

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Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment

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computers, Materials & Continua 2022年第1期70卷 1247-1261页

作者： J.V.Anchitaalagammai T.Jayasankar P.Selvaraj Mohamed Yacin Sikkandar M.Zakarya Mohamed Elhoseny K.Shankar Department of Computer Science and Engineering Velammal College of Engineering and TechnologyMadurai625009India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Information Technology SRMInstitute of Science and TechnologyKattankulathur603203Tamil NaduIndia Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia Department of Mathematics College of ScienceKing Khalid UniversityP.O.Box 9004Abha61413Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt College of Computer Information Technology American University in the EmiratesDubai503000United Arab Emirates Department of Computer Applications Alagappa UniversityKaraikudiIndia

Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern *** generally refers to a network of gadgets linked via wireless network and communicates via *** management,especially energy management,is a critical issue when designing IoT *** studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT *** this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e.,EECBRM in IoT *** proposed EECBRM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance *** proposed EECBRMmodel was validated through a series of experiments and the results were verified under several *** model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network *** simulated,in comparison with other methods,EECBRM model yielded excellent results in a significant ***,the efficiency of the proposed model is established.

关键词： IoT environment clustering routing resource management energy efficiency

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KGIF: Optimizing Relation-Aware Recommendations with Knowledge Graph Information Fusion

KGIF: Optimizing Relation-Aware Recommendations with Knowled...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Jeon, Dong Hyun Sun, Wenbo Song, Houbing Herbert Liu, Dongfang Alvaro, Velasquez Xie, Yixin Chloe Niu, Shuteng Bowling Green State University Department of Computer Science United States University of Michigan Transportation Research Institute Ann Arbor United States University of Maryland Baltimore County Department of Information Systems United States Rochester Institute of Technology Department of Computer Engineering United States University of Colorado College of Engineering & Applied Science Boulder United States Kennesaw State University Department of Information Technology United States

ISBN: (纸本)9798350362480

While deep-learning-enabled recommender systems demonstrate strong performance benchmarks, many struggle to adapt effectively in real-world environments due to limited use of user-item relationship data and insufficient transparency in recommendation generation. Traditional collaborative filtering approaches fail to integrate multifaceted item attributes, and although Factorization Machines account for item-specific details, they overlook broader relational patterns. Collaborative knowledge graph-based models have progressed by embedding user-item interactions with item-attribute relationships, offering a holistic perspective on interconnected entities. However, these models frequently aggregate attribute and interaction data in an implicit manner, leaving valuable relational nuances *** study introduces the Knowledge Graph Attention Network with Information Fusion (KGIF), a specialized framework designed to merge entity and relation embeddings explicitly through a tailored self-attention mechanism. The KGIF framework integrates reparameterization via dynamic projection vectors, enabling embeddings to adaptively represent intricate relationships within knowledge graphs. This explicit fusion enhances the interplay between user-item interactions and item-attribute relationships, providing a nuanced balance between user-centric and item-centric representations. An attentive propagation mechanism further optimizes knowledge graph embeddings, capturing multi-layered interaction patterns. The contributions of this work include an innovative method for explicit information fusion, improved robustness for sparse knowledge graphs, and the ability to generate explainable recommendations through interpretable path visualization. The implementation and datasets for this study are publicly available1 © 2024 IEEE.

关键词： Knowledge graph

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Optimizing Blocked-Davidson Method based on Adaptive Algorithm using the New Sunway Supercomputer 2

Optimizing Blocked-Davidson Method based on Adaptive Algorit...

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2022 2nd Conference on High Performance Computing and Communication Engineering, HPCCE 2022

作者： Yang, Chaochao Liu, Anjun Liu, Weifeng Hou, Jie Guo, Meng Shandong Computer Science Center Qilu University of Technology China Jinan Key Laboratory of High Performance Industrial Software China Institute of Applied Physics and Computational Mathematics China Jinan Institute of Supercomputing Technology China

ISBN: (纸本)9781510663374

The Davidson method is a traditional large-scale iterative diagonalization method in computational quantum chemistry. The Blocked-Davidson method is a classical version of the Davidson method, which is an important part of VASP software, occupying 22.9% of the calculation time. When VASP is transplanted to the new Sunway supercomputer, it is incompatible with the many-core heterogeneous environment of Sunway processor. The Blocked-Davidson method does not use slave-core accelerators. This paper designs collaborative computing, double buffer, store variables in slave-core LDM, LDM Level 2 Access, and segmented transmission methods of Blocked-Davidson method based on Sunway many-core architecture. The test results showed that the function in the Blocked-Davidson method accelerated 30.65 times with a single core group in the silica test case. However, negative optimization of the Blocked-Davidson method happens in some test cases such as ZrTiAlVSiN5 and Hg50. Therefore, an adaptive algorithm of automatic selection of less time-consuming optimization paths is designed to solve the negative optimization. The maximum acceleration is 23.6 times compared with not using the adaptive algorithm. © 2023 SPIE.

关键词： Supercomputers

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Fault Diagnosis Method of Planetary Gearbox Based on Transfer Learning and EfficientNet 14

Fault Diagnosis Method of Planetary Gearbox Based on Transfe...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Li, Yin Gu, Yiliu Shi, Changwu Li, Ting School of Mechanical and Electrical Engineering Jiangxi University of Science and Technology Ganzhou China College of Computer Scienceand Electronic Engineering Hunan University Changsha China School of Automotive Jiangxi College of Applied Technology Ganzhou China

ISBN: (纸本)9798350301359

In the field of gear fault diagnosis, the traditional convolutional neural networks model has a single precision improvement method and has limitations. This paper proposes a planetary gearbox fault diagnosis model based on Gramian Angular Fields (GAF) image coding technology and TL-EfficientNet network model. The collected one-dimensional vibration signal is preprocessed and converted into GAF diagram. The EfficientNet is pre-trained on the ImageNet dataset, the trained feature weights are migrated to the EfficientNet network, and the samples are trained to complete the fault identification of the planetary gearbox. The experimental results show that the proposed method can effectively improve the fault diagnosis accuracy of planetary gearboxes, reaching 99.49%, and has faster convergence speed and better stability than a single method. At the same time, the TL+EfficientNet-B2 model has strong generalization and can be applied to other fault diagnosis with similar feature distribution data. © 2023 IEEE.

关键词： convolutional neural network fault diagnosis Gramian Angular Fields planetary gear box transfer learning

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