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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Asif, Saara Uzair Akmal, Muhammad Koval, Leonid Knollmeyer, Simon Mathias, Selvine G. Grossmann, Daniel Ai Motion Technische Hochschule Ingolstadt Ingolstadt Germany Ai Motion Bavaria Technische Hochschule Ingolstadt Ingolstadt Germany Technische Hochschule Ingolstadt Faculty of Computer Science and Data Processing Ingolstadt Germany

ISBN: (纸本)9798350361230

In the manufacturing industry, automated optical inspection aims to improve the detection and classification of anomalies by utilizing artificial intelligence and computer vision techniques to enhance quality control processes and minimize production defects. However, this automated system faces significant challenges, particularly regarding the detection of anomalies due to predominance of normal instances over defected ones. Addressing this imbalance is crucial for effective real-time anomaly detection particularly in images captured by Airbag Sensors among other automotive parts. Earlier contributions in domain-specific fields commonly relied on traditional computer vision methods, while recent systems are increasingly using deep learning techniques. Utilizing various data augmentation techniques ensures a more balanced representation of anomalies in the dataset, thereby enhancing the accuracy of the detection process. Moreover, it also enhances the robustness and generalization of the anomaly detection model by exposing it to a more diverse range of instances during training. Such work has not been carried out to augment Airbag Sensor images for analysis through a deep learner. Accordingly, this paper introduces a framework that employs data augmentation techniques for Convolutional Neural Networks (CNNs). The proposed system, based on data augmentation and CNN, significantly improves the performance for anomaly detection in Airbag Sensor images with a classification accuracy on the unaugmented dataset being 53 % which improves to 90% with augmentation. © 2024 IEEE.

关键词： Convolutional neural networks

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CPAKE: Dynamic Batch Authenticated Key Exchange with Conditional Privacy 24th

CPAKE: Dynamic Batch Authenticated Key Exchange with Condit...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xiang, Axin Tian, Youliang Xiong, Jinbo Ying, Zuobin Peng, Changgen State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology Guiyang550025 China Industrial School of Joint Innovation Quanzhou Vocational and Technical University Quanzhou362268 China Faculty of Data Science City University of Macau 999078 China

ISBN: (纸本)9789819615247

Authenticated key exchange (AKE) needs to be designed for realizing point-to-multipoint secure communications in blockchain networks (BNet). However, since BNet is open, untrusted and decentralized, traditional certificateless AKE schemes are difficult to overcome two technical bottlenecks, i.e., private distribution of partial keys and accurate identification of anonymous traitors. This paper proposes a conditionally private and dynamic batch AKE scheme (CPAKE) in BNet. We first develop a privacy-preserving certificateless key generation algorithm to prevent the disclosure of nodes’ privacy during partial key distribution. Then, we design a simple anonymous traitor tracing mechanism against Byzantine adversaries to accurately identify and trace the traitor’s identity. Finally, we build a dynamic batch authentication protocol with decentralized node management to ensure the final validity of honest nodes. Moreover, performance analysis indicates that our scheme reduces the signing and batch-verifying time by approximately 193.10 ms and 10.93 ms respectively over state-of-the-art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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A Predictive Model for Preliminary Indication of Cardiovascular Disease

A Predictive Model for Preliminary Indication of Cardiovascu...

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2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022

作者： Arif, Ali Haider Bukhari, Faisal Fatima, Aimen Iqbal, Waheed University of the Punjab Faculty of Computing and Information Technology Department of Computer Science Lahore Pakistan University of the Punjab Faculty of Computing and Information Technology Department of Data Science Lahore Pakistan

ISBN: (数字)9781665459358

ISBN: (纸本)9781665459358

Executing medical diagnoses appropriately and effectively is critical due to the scarcity of available resources in the hospitals of developing countries. Diagnosis is also rendered by professional practice and expertise. Because of this, patients bear unnecessary and disproportionate care costs. The automated system is very beneficial for medical diagnosis. As in some cases, there could be a shortage of resource personnel, and not all doctors have experience in subspecialties. We needed studies of various available techniques and their comparisons for implementing these accurate automated systems. The current research, however, is an alternative precaution to this issue. A preliminary indication of cardiovascular disease using a predictive model is essential as it reduces the risk of developing the disease. The likelihood of having the cardiovascular disease can be predicted using multiple factors. One thousand sixty samples of individuals were taken from different hospitals in Punjab by using a questionnaire survey. Other scaling techniques were applied to the collected data. Various machine learning algorithms with hyperparameter tuning were applied to the collected data. As the results of the predictions were satisfactory, it would help predict cardiovascular disease and act as a critical public awareness to avoid future cardiac disease. © 2022 IEEE.

关键词： Diagnosis

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Interdisciplinary Research Scope for Electronic Nose

Interdisciplinary Research Scope for Electronic Nose

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Radha Wasudeo Wande Shiv Nath Chaudhari Rutuja Nitin Lanjewar Faculty of Engineering and Technology Artificial Intelligence and Data Science Maharastra India Faculty of Engineering and Technology Computer Science And Design Maharastra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The electronic nose (E-nose), a replica of the biological olfactory system, has emerged as a safe and useful instrument in nearly every industry, from pharmaceuticals to agriculture, food quality inspections, and more. E-noses imitate the human ability to identify volatile organic compounds (VOCs) through innovative chemical sensors, data acquisition, effective signal processing, and pattern recognition techniques. As a result, the collaboration between engineers specializing in material science and computer/electronic science has advanced the creation of novel sensors and materials, improved framework analysis, and raised the selectivity and sensitivity of E-nose systems. These advancements have made it possible for several industries, including healthcare, to monitor patients without requiring invasive procedures, as well as for real-time quality checks in agriculture, fresh food assurance, and medication quality assurance. E-nose technology has the potential to play a major role in improving safety, productivity, and environmental friendliness in these industries in the years to come. Considering the data regarding the current and future states of E-nose technology, its interdisciplinary application, and its focus on the technology’s advancement, its efficacy is highlighted as a crucial offer to fundamentally transform numerous sectors by providing fresh and efficient solutions.

关键词： Volatile organic compounds Accuracy Olfactory Transforms Signal processing Electronic noses Agriculture Real-time systems Biology Monitoring

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Development of Low-Cost Surgical Simulator for Neuroendoscopy using Unity3D and HTC VIVE 16

Development of Low-Cost Surgical Simulator for Neuroendoscop...

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16th International Conference on Open Source Systems and Technologies, ICOSST 2022

作者： Fatima, Aimen Bukhari, Faisal Iqbal, Waheed University of the Punjab Faculty of Computing and Information Technology Department of Computer Science Lahore Pakistan University of the Punjab Faculty of Computing and Information Technology Department of Data Science Lahore Pakistan

ISBN: (纸本)9781665464772

Neuroendoscopy is being utilized more frequently to treat brain tumors and hydrocephalus brought on by tumors. Neuroendoscopy is a minimally invasive operation that requires a lot of skills. Complete training is needed in hospitals to perform this procedure in the operating room. Simulators are necessary to train doctors due to the deficiency of time, resources, cost, and potential risks. With advancements in computer technology, it is becoming more common to simulate and plan surgery using a VR-based simulation. We have created and developed a surgical simulator to provide endoscopic training for the ETV procedure. A method to use multimedia capabilities for learning is to create a virtual 3D simulator using a game engine and render it using VR technology. The user-friendly collaborative environment of the Unity3D game engine offers good features. We have demonstrated how a simplex mesh depiction in a game-based simulator may effectively deform a soft body in actuality. We have successfully developed a method employing Position Based Dynamics to simulate the actual deformation of TM the cancerous brain. According to our study, the HTC Vive is a suitable VR technology for ETV training because it offers excellent precision, quick tactile feedback, a fast frame rate, and a wide field of vision. In the future, this practical tool might replace more costly training simulators. © 2022 IEEE.

关键词： Tumors

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Discovering multi-agent systems for resource-centric business process simulation

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Process science 2025年第1期2卷 1-26页

作者： Kirchdorfer, Lukas Blümel, Robert Kampik, Timotheus van der Aa, Han Stuckenschmidt, Heiner SAP Signavio Walldorf Germany Data and Web Science Group University of Mannheim Mannheim Germany Faculty of Computer Science University of Vienna Vienna Austria Department of Computing Science Umeå University Umeå Sweden

Business process simulation (BPS) is a powerful tool for estimating process performance across different scenarios, offering critical support for organizational process redesign and optimization. Traditional BPS approaches predominantly rely on a control-flow-first perspective by enriching a process model with simulation parameters. While these approaches seem suitable for capturing centrally orchestrated processes, such as those managed by workflow systems, they fall short of accurately reflecting real-world processes characterized by decentralized decision-making and distinct resource behaviors. To overcome this limitation, we propose AgentSimulator, a resource-first BPS approach that discovers a multi-agent system from an event log. By modeling the distinct behaviors and interaction patterns of individual resources, AgentSimulator effectively simulates the underlying process. Our approach automatically identifies whether resource behavior is rather orchestrated or autonomous, adapting to the specific decision-making structure of the process. Experimental results reveal that AgentSimulator achieves state-of-the-art simulation accuracy while ensuring high adaptability to various process types.

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Overlooked Aspects of data Governance: Workflow Framework for Enterprise data Deduplication

Overlooked Aspects of Data Governance: Workflow Framework fo...

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2023 International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2023

作者： Azeroual, Otmane Nikiforova, Anastasija Sha, Kewei Berlin Germany University of Tartu Faculty of Science and Technology Institute of Computer Science Chair of Software Engineering Tartu Estonia European Open Science Cloud Task Force 'Fair Metrics and Data Quality' Brussels Belgium University of Houston Clear Lake College of Science and Engineering Houston United States

ISBN: (纸本)9798350339291

data quality in companies is decisive and critical to the benefits their products and services can provide. However, in heterogeneous IT infrastructures where, e.g., different applications for Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), product management, manufacturing, and marketing are used, duplicates, e.g., multiple entries for the same customer or product in a database or information system, occur. There can be several reasons for this, but the result of non-unique or duplicate records is a degraded data quality. This ultimately leads to poorer, inefficient, and inaccurate data-driven decisions. For this reason, in this paper, we develop a conceptual data governance framework for effective and efficient management of duplicate data, and improvement of data accuracy and consistency in large data ecosystems. We present methods and recommendations for companies to deal with duplicate data in a meaningful way. © 2023 IEEE.

关键词： Ecosystems

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Metaheuristic Based Clustering with Deep Learning Model for Big data Classification

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computer Systems science & Engineering 2023年第1期44卷 391-406页

作者： R.Krishnaswamy Kamalraj Subramaniam V.Nandini K.Vijayalakshmi Seifedine Kadry Yunyoung Nam Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India Department of Biomedical Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatore641021India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600077India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two *** the same time,clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in ***-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial *** data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce *** this motivation,this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics(MR-HDBCC).The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big *** addition,the MR-HDBCC technique involves three distinct processes namely pre-processing,clustering,and *** proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)techni-que which is capable of detecting random shapes and diverse clusters with noisy *** improving the performance of the DBSCAN technique,a hybrid model using cockroach swarm optimization(CSO)algorithm is developed for the exploration of the search space and determine the optimal parameters for density based ***,bidirectional gated recurrent neural network(BGRNN)is employed for the classification of big *** experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.

关键词： Big data data classification clustering mapreduce dbscan algorithm

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Research on long and short-term social recommendation based on convolutional and gated recurrent units 29

Research on long and short-term social recommendation based ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Jia, Zihe Xue, Peng Dai, Zhiqiang Gao, Qian Zhang, Xiaomeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350330717

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users' long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data;secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier;finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved. © 2023 IEEE.

关键词： convolutional gated recurrent unit graph attention networks neighborhood recommendation systems social networks

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Transforming Ophthalmic Care: The Role of AI in Accurate Eye Disease Classification EDC

Transforming Ophthalmic Care: The Role of AI in Accurate Eye...

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National Conference on Radio science (NSRC)

作者： Samah A. Gamel Iman S. Alansari Safa’a S. Saleh Hatem A. Khater Faculty of Engineering Horus University Damietta Egypt Computer Science Department College of Computer Science and Engineering Taibah University Madinah Saudi Arabia Data Science Department Faculty of computer science and artificial intelligence Pharos University Alexandria Egypt

This research describes a unique strategy to classifying eye illnesses utilizing a Convolutional Neural Network (CNN) modification. The objective is to develop an automated system that accurately diagnoses and classifies eye diseases, leading to improved patient care and outcomes. A comprehensive dataset of eye images was collected from various sources and preprocessed to enhance quality and quantity. The proposed Eye Disease Classification (EDC) model was trained and optimized using well-known algorithms. The experimental findings illustrate the superiority of the suggested approach, achieving high precision ($95.63 \%$), recall (98.20%), F1-score (94.30%), and accuracy (94.50%), SVM, Decision Tree, KNN, and Random Forest are among the most often used classifiers, the results demonstrate the potential of the suggested technology to transform eye disease detection and therapy.

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