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A Collaborators Recommendation Method Based on Multi-feature Fusion 1

3rd International Conference on Neural Computing for Advanced Applications, NCAA 2022

作者： Yuan, Qi Shao, Lujiao Zhang, Xinyu Yu, Xinrui Sun, Huiyue Ma, Jianghong Meng, Weizhi Gao, Xiao-Zhi Zhang, Haijun Departmant of Computer Science Harbin Institudte of Technology Shenzhen China Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark School of Computing University of Eastern Finland Kuopio Finland

ISBN: (数字)9789811961427

ISBN: (纸本)9789811961410

This research introduces a new collaborators recommendation model based on multi-feature fusion. Specifically, we use a tree structure to integrate scholar information and extract content features from a scholar tree by using a Tree2vector-CLE model. Then, from the heterogeneous academic network, we extract the meta-path feature between scholars, which quantifies the similarity and co-operation potential between scholars from a multidimensional perspective. By combining content and meta-path features, we reconstruct a co-authorship network. Finally, we use the network representation learning method to represent the nodes in the reconstructed co-authorship network where the top-k collaborators are recommended for the target scholar with the random walk strategy controlled by the meta-path feature weighting. Experimental results on a real dataset demonstrate that our proposed method is effective in the task of collaborators recommendation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning systems

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A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

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computer Systems science & Engineering 2023年第8期46卷 1789-1809页

作者： Hicham Moujahid Bouchaib Cherradi Oussama El Gannour Wamda Nagmeldin Abdelzahir Abdelmaboud Mohammed Al-Sarem Lhoussain Bahatti Faisal Saeed Mohammed Hadwan EEIS Laboratory ENSET of MohammediaHassan II University of CasablancaMohammedia28820Morocco STIE Team CRMEF Casablanca-SettatThe Provincial Section of El JadidaEl Jadida24000Morocco Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia College of Computer Science and Engineering Taibah UniversityMedina42353Saudi Arabia Department of Computer Science University of Sheba RegionMarib14400Yemen DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Information Technology College of ComputerQassim UniversityBuraydah 51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaiz6803Yemen

Due to the rapid propagation characteristic of the Coronavirus(COVID-19)disease,manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of ***,new automated diagnostic methods have been brought on board,particularly methods based on artificial intelligence using different medical data such as X-ray *** imaging,for example,produces several image types that can be processed and analyzed by machine and deep learning methods.X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging *** this paper,we propose a novel Convolutional Neural Network(CNN)model(COV2Net)that can detect COVID-19 virus by analyzing the X-ray images of suspected *** model is trained on a dataset containing thousands of X-ray images collected from different *** model was tested and evaluated on an independent *** order to approve the performance of the proposed model,three CNN models namely Mobile-Net,Residential Energy Services Network(Res-Net),and Visual Geometry Group 16(VGG-16)have been implemented using transfer learning *** experiment consists of a multi-label classification task based on X-ray images for normal patients,patients infected by COVID-19 virus and other patients infected with *** proposed model is empowered with Gradient-weighted Class Activation Mapping(Grad-CAM)and Grad-Cam++techniques for a visual explanation and methodology debugging *** finding results show that the proposed model COV2Net outperforms the state-of-the-art methods.

关键词： Artificial intelligence intelligent diagnostic systems decisionmaking COVID-19 convolutional neural network

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TAGE: Trustworthy Attribute Group Editing for Stable Few-shot Image Generation 16

TAGE: Trustworthy Attribute Group Editing for Stable Few-sho...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Zhang, Ruicheng Huang, Guoheng Huo, Yejing Yuan, Xiaochen Zhou, Zhizhen Wu, Shiting Zhong, Guo School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Faculty of Applied Sciences Macao Polytechnic University China Huizhou Boluo Power Supply Bureau Guangdong Power Grid Co. Ltd. 516001 China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou510006 China

ISBN: (纸本)9781510689251

Generative Adversarial Networks (GANs) have emerged as a prominent research focus for image editing tasks, leveraging the powerful image generation capabilities of the GAN framework to produce remarkable results. However, prevailing approaches are contingent upon extensive training datasets and explicit supervision, presenting a significant challenge in manipulating the diverse attributes of new image classes with limited sample availability. To surmount this hurdle, we introduce TAGE, an innovative image generation network comprising three integral modules: the Codebook Learning Module (CLM), the Code Prediction Module (CPM) and the Prompt-driven Semantic Module (PSM). The CPM module delves into the semantic dimensions of category-agnostic attributes, encapsulating them within a discrete codebook. This module is predicated on the concept that images are assemblages of attributes, and thus, by editing these category-independent attributes, it is theoretically possible to generate images from unseen categories. Subsequently, the CPM module facilitates naturalistic image editing by predicting indices of category-independent attribute vectors within the codebook. Additionally, the PSM module generates semantic cues that are seamlessly integrated into the Transformer architecture of the CPM, enhancing the model’s comprehension of the targeted attributes for editing. With these semantic cues, the model can generate images that accentuate desired attributes more prominently while maintaining the integrity of the original category, even with a limited number of samples. We have conducted extensive experiments utilizing the Animal Faces, Flowers, and VGGFaces datasets. The results of these experiments demonstrate that our proposed method not only achieves superior performance but also exhibits a high degree of stability when compared to other few-shot image generation techniques. © 2025 SPIE.

关键词： Image coding

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Crow Search Optimization to Identify Adversary Nodes in Wireless Networks 2

Crow Search Optimization to Identify Adversary Nodes in Wire...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Rajaram, Gnanajeyaraman Thangaraj, T. Stephen Packialatha, A. Deepa, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Applied Machine Learning Tamil Nadu Chennai India Paavai Engineering College Department of Computer Science and Engineering Tamil Nadu Namakkal India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350347579

Adversary node detection systems have become a vital component due to the malware attacks. These systems are very important for network security due to internet's widespread development and increased accessibility to global data systems. This paper presents a Crow Search Optimization Algorithm (CSOA) to detect the adversary node in a wireless network. The Rivest, Shamir, Adleman (RSA) algorithm transmits secure data at first. It uses the public key to encrypt the data and the private key to decrypt the data and the adversary nodes can't access or modify the real data. One of the significant problems is when an unknown attack occurs on the network due to the high volume of data while the detection's accuracy and false alarm rate decline. The proposed system aims to improve accuracy by identifying the adversary nodes using CSOA algorithm. From the results, it is observed that the CSOA mechanism reduces the loss ratio and improves throughput of the network. © 2023 IEEE.

关键词： Optimization

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Online Course Improvement Through GPT-4: Monitoring Student Engagement and Dynamic FAQ Generation

Online Course Improvement Through GPT-4: Monitoring Student ...

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IEEE Education Engineering (EDUCON)

作者： Oxana Lundström Neda Maleki Fredrik Ahlgren Department of Computer Science and Media Technology Applied loT Lab Linnaeus university Kalmar Sweden

ISBN: (数字)9798350394023

ISBN: (纸本)9798350394030

Artificial Intelligence (AI), specifically in language processing, is increasingly recognized as an invaluable educational tool. The Large Language Model (LLM) GPT, developed by OpenAI, is an advanced machine learning tool that utilizes deep learning for human-like text comprehension and generation. This study uses OpenAI's GPT-4 to enhance an online Internet of Things (loT) course at Linnaeus University. We analyzed 12,000+ messages on an online communication platform spanning four years. We compare traditional Natural Language Processing (NLP) techniques to Generative AI for understanding student feedback and issues, inspiring project ideas, and promoting student engagement. We provide a combined approach to monitor the sentiment or mood of the students' communications over the timeline of the course. Moreover, we show how to use LLM to refine the FAQ generation and decipher student feedback for course refinement. We demonstrate how to generate optimal prompts and prepare the data to apply LLMs effectively. Our research reinforces that strategic use of LLMs, like GPT-4, can revolutionize remote learning by lessening lecturer workload and boosting student satisfaction and engagement. Our future work aims to further leverage AI models across remote engineering education. One potential direction is developing an AI-powered bot for online platforms to facilitate real-time interaction, manage queries, encourage engagement, maintain FAQs, and enhance course outcomes.

关键词： Sentiment analysis Generative AI Mood Distance learning Large language models Real-time systems Internet of Things

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IoT-Driven Image Recognition for Microplastic Analysis in Water Systems using Convolutional Neural Networks 2

IoT-Driven Image Recognition for Microplastic Analysis in Wa...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Hasan, M D Ashfaqul Balasubadra, K. Vadivel, G. Arunfred, N. Ishwarya, M.V. Murugan, S. Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India R.M.D. Engineering College Kavaraipettai Department of Information Technology Tamil Nadu Chennai India University of Technology and Applied Science Department of Information Technology Dhofar Salalah Oman Srm Institute of Science and Technology Kattankulathur Faculty of Management Studies Tamil Nadu Chennai India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Microplastic pollution in water systems is growing, requiring novel detection and analysis methods. This research presents an Internet of Things (IoT)-driven image identification system using Convolutional Neural Networks (CNNs) to detect and quantify microplastics in water samples. The suggested method is more scalable and responsive due to IoT real-time data capture and remote monitoring of water infrastructure. An innovative CNN architecture for image processing allows the system to accurately identify micro plastics. The CNN model is trained and validated using a large dataset of micro plastic-containing water samples. The trained model can recognize various sizes, shapes, and colors of micro plastics, making it responsive to different environmental situations. The IoT architecture also allows image recognition modules in dispersed sensor nodes to cover water systems. Extensive studies prove the system can analyze vast amounts of image data quickly and reliably. Edge computing also minimizes latency and improves micro plastic analysis system responsiveness. The suggested IoT-driven image recognition method for continuous micro plastic pollution monitoring and evaluation in water systems seems promising. Scalability, realtime capabilities, and accuracy make it useful for environmental monitoring agencies and academics trying to reduce microplastics' influence on aquatic ecosystems. This system advances IoT applications in environmental and pollution management. © 2024 IEEE.

关键词： Large datasets

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Enhancing Urban Air Quality with Smart Vehicle Exhaust Management

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Operations Research Forum 2025年第2期6卷 1-24页

作者： Tyagi, Vaishali Dubey, Roshan Singh, Suraj Ram, Mangey Department of Applied Sciences & Humanities ABES Institute of Technology Uttar Pradesh Ghaziabad India Department of Computer Science & Engineering ABES Institute of Technology Uttar Pradesh Ghaziabad India Department of Mathematics Computer Science and Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India

Air pollution is the contamination of the indoor or outdoor environment by any chemical, physical, or biological agent that modifies the natural characteristics of the atmosphere. Burning gasoline and diesel fuel creates harmful byproducts like nitrogen dioxide, carbon monoxide, hydrocarbons, benzene, and formaldehyde. This study proposes a sensor-based model to control vehicular pollution by monitoring emission levels and preventing vehicles from operating if their pollution exceeds government standards. The proposed model consists of a smoke sensor, particulate sensors, carbon monoxide sensors, along with Arduino based microcontroller, data logger, LCD display, and GPRS modem. Using the block diagram, a mathematical model has been developed and some differential equations by using Markov process have been generated. This Markov-based model gives the information about the failure causes and working of the proposed system. Some reliability matrices like availability, mean time between failure (MTBF), and sensitivity analysis have been evaluated with the help of Laplace transform and illustrated with the help of graphs. Also, the failure cases of the system and its cost analysis have been done along with all the numerical calculations required for the successful operation of the model. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Air pollution Cost analysis Failure cases Markov process Reliability Sensor system

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Analysis of Feature Parameters and Their Time-Transition for Shockable Arrhythmia and Prediction of the Effect of Electrical Defibrillation 3

Analysis of Feature Parameters and Their Time-Transition for...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Noguchi, Shunta Yoshikawa, Yuta Okai, Takayuki Oya, Hidetoshi Rahman, Md. Masudur Yoshida, Minoru Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Tokyo City University Department of Computer Science Tokyo Japan Noakhali Science and Technology University Department of Information and Communication Engineering Bangladesh Kanagawa University Department of Applied Systems and Mathematics Kanagawa Japan

ISBN: (纸本)9798331540319

In this paper, we propose a prediction system of the effect of electrical defibrillation. In order to develop the proposed system, we firstly analyze from pre-shock (immediately before defibrillation) ECGs (ElectroCardioGrams) by using Gabor Wavelet Transform, Poincaré plot analysis, Spectral entropy, and feature parameters are extracted by those extracted analysis results, we focus on time-transition feature parameter. Next, effective feature parameters are selected based on χ2 -test. Finally, 'Effective' and 'Ineffective' for electrical defibrillation are classified by using SVM (Support Vector Machine) with three kernels (Linear, Gaussian, and Polynomial) and regularization. In this way, we shows the effectiveness of the proposed feature extraction methods and prediction system. © 2024 IEEE.

关键词： Support vector machines

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A Novel Method of Evaluating the Harmonic Power Analyzer's Performance for PC-Based Testing and Measurement Applications Related to Electromagnetic Compatibility 2

A Novel Method of Evaluating the Harmonic Power Analyzer's P...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Adeline Sneha, J. Wilfred Blessing, N.R. Palarimath, Suresh Poorana Senthilkumar, S. Manigandansekar Sutherlin Subitha, G. School of Computing Asia Pacific University of Technology & Innovation Kuala Lumpur Malaysia College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computing and Information Sciences-Salalah University of Technology and Applied Sciences Oman Dr. N. G. P Arts and Science College Department of Computer Applications Tamil Nadu Coimbatore India Newyork University Abudhabi United Arab Emirates Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Kanyakumari India

ISBN: (纸本)9798331542573

This paper discusses the methods for performance analysis of a PC-based harmonic power analyzer used in EMC testing and measuring applications. According to IEC 61000-3-2, this is an easy-to-use, reasonably priced way to assess how well a harmonics emission measurement system is performing in real time. There is no mention of any alternative approach or calibration method to verify the performance analysis in the IEC 61000-3-2 standard. Different vendor equipment is used to create a single harmonic emission monitoring setup. Thus, getting calibrating services is a highly difficult task that also requires a significant financial investment. Using this straightforward, low-cost analysis technique, it is possible to monitor the operation of the measurement setup locally and address all of these problems. Overall, the paper provides a comprehensive overview of the new approach to performance analysis of a PC-based harmonics power analyzer for EMC test and measurement applications, emphasizing the importance of a simple, cost-effective, and reliable method for maintaining measurement system performance. © 2024 IEEE.

关键词： Cost effectiveness

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Spline Approaches for Anomaly Detection in UAV-Based Aerial Surveillance

Spline Approaches for Anomaly Detection in UAV-Based Aerial ...

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IEEE International Conference on Actual Problems of Unmanned Air Vehicles Developments Proceedings (APUAVD)

作者： Anhelina Zhultynska Pylyp Prystavka Department of Applied Mathematics Faculty of Computer Science and Technology National Aviation University Kyiv Ukraine

ISBN: (数字)9798331534141

ISBN: (纸本)9798331534158

This article presents a new approach to detecting anomalies in data obtained from unmanned aerial vehicles using spline models. The relevance of the study is driven by the need for fast and accurate identification of anomalies in real time, which is critical for military intelligence and civilian monitoring. The proposed model is based on the use of momentary characteristics, such as average illumination intensity and standard deviation, which allows for effective data parameterization and detection of anomalous observations. The advantage of the spline model is its ability to adapt to complex and heterogeneous image textures, which significantly reduces the risk of missing critical anomalies. The model also demonstrates high robustness to variations in the data, making it a reliable tool for analyzing images in different lighting and landscape conditions. Experiments using forest, road, and car textures have shown that the spline model is able to accurately identify anomalies, which increases the efficiency of analysis and allows for a quick response to detected changes.

关键词： Adaptation models Analytical models Accuracy Surveillance Lighting Autonomous aerial vehicles Data models Real-time systems Splines (mathematics) Standards

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