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2024 IEEE International Conference on Big Data, BigData 2024

作者： Malysiak-Mrozek, Bozena Romanow, Dominik Grzesik, Piotr Benecki, Pawel Niyomugaba, Alexandre Habimana, Theodore Kostrzewa, Daniel Tokarz, Krzysztof Hung, Che-Lun Mrozek, Dariusz Silesian University of Technology Department of Distributed Systems and Informatics Devices Gliwice Poland Ines Ruhengeri - Institute of Applied Sciences Department of Computer Science Rwanda Silesian University of Technology Department of Applied Informatics Gliwice Poland Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland National Yang Ming Chiao Tung University Institute of Biomedical Informatics Taipei Taiwan

ISBN: (纸本)9798350362480

Fuzzy data processing enables data enrichment and increases data interpretation in industrial environments. In the cloud-based IoT data ingestion pipelines, fuzzy data processing can be implemented in several locations, closer to the IoT events gateways, stream processors, or the persistence layer before the data is visualized. Since Automated Guided Vehicles (AGV)-enabled manufacturing can produce vast amounts of data, the decision on the placement of the fuzzy data processing can be important for secondary processes performed on the enriched data, like the predictive maintenance inferencing. In this paper, we analyze two locations of fuzzy data processing in the cloud-based environment built for monitoring AGVs in smart factories - by formulating fuzzy queries against data streams on stream processing units and data at rest in a database. The querying scenarios cover fuzzy filtering with simple and complex criteria, fuzzy filtering through assignment to a linguistic variable, and joining data streams by representing joining attributes as fuzzy numbers. The experimental results show that querying the data stream can be more efficient and profitable in the scalable environment of many AGVs. However, the enrichment provided for the data at rest is also beneficial when gathering data for building future predictive maintenance models. © 2024 IEEE.

关键词： Data streams

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PADAAV: Enhancing Perception systems using GAN-generated Adversarial Augmented Domains for Autonomous Vehicles

PADAAV: Enhancing Perception Systems using GAN-generated Adv...

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2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Rawlley, Oshin Gupta, Shashank Birla Institute of Technology and Science Department of Computer Science & Information Systems Rajasthan Pilani India

ISBN: (纸本)9781665453813

In the field of autonomous vehicles (AV), it is crucial for the perceptual systems of the AVs to learn inter-domain adaptations in the absence of paired examples for detecting vehicular instances in unstructured real-world scenarios. One straightforward approach is to train the models directly on labeled synthetic datasets. However, this approach usually fails to achieve generality, owing to the domain bias between the real and fake databases of images. We therefore, propose a novel architecture that produces synthetic images based on cycle consistency, in the absence of labeled pair of images. We test the object detectors Detectron and SSD on four types of curated benchmark datasets to evaluate their robustness in detecting objects such as cars, bikes, and pedestrians on road. The four benchmark datasets contain a diversified set of image corruptions and a few variations are built using the proposed framework such as weather variations. The four datasets are PASCAL VOC and SYNTHIA dataset images, weather-translated images, variation-augmented images, and stylized renderings using Adain style transfer. After conducting extensive investigations, we observe a decreased classification loss when exposed to variable image quality. We also witness that augmenting the training datasets with variations in the wild aided in boosting the generalizing capability of the object detectors. The boost in performance is testified by the testing results showing better mAP. © 2023 IEEE.

关键词： Object detection

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GPU-Based Efficient Parallel Heuristic Algorithm for High-Utility Itemset Mining in Large Transaction Datasets (Extended Abstract) 40

GPU-Based Efficient Parallel Heuristic Algorithm for High-Ut...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Fang, Wei Jiang, Haipeng Lu, Hengyang Sun, Jun Wu, Xiaojun Lin, Jerry Chun-Wei Jiangnan University Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Department of Computer Science and Technology Wuxi214122 China Silesian University of Technology Department of Distributed Systems and IT Devices Poland

ISBN: (纸本)9798350317152

Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms still face the problem of long runtime and insufficient mining quality, especially for large transaction datasets with thousands to tens of thousands of items and up to millions of transactions. To solve these problems, a novel GPU-based efficient parallel heuristic algorithm for HUIM (PHA-HUIM) is proposed in this paper. The iterative process of PHA-HUIM consists of three main steps: the search strategy, fitness evaluation, and ring topology communication. The search strategy and ring topology communication are designed to run in constant time on GPU. The parallelism of fitness evolution helps to substantially accelerate the algorithm. To improve the mining quality, a multi-start strategy with an unbalanced allocation strategy is employed in the search process. Ring topology communication is adopted to maintain population diversity. A load balancing strategy is introduced to reduce the thread divergence to improve the parallel efficiency. The experimental results on nine large datasets show that PHA-HUIM outperforms state-of-the-art HUIM algorithms in terms of speedup performance, runtime, and mining quality. © 2024 IEEE.

关键词： Graphics processing unit

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Leveraging Safe and Secure AI for Predictive Maintenance of Mechanical Devices Using Incremental Learning and Drift Detection

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computers, Materials & Continua 2025年第6期83卷 4979-4998页

作者： Prashanth B.S Manoj Kumar M.V. Nasser Almuraqab Puneetha B.H Department of Information Science and Engineering Nitte Meenakshi Institute of TechnologyVisvesvaraya Technological UniversityBelagavi590018India Department of Information Science and Engineering Nitte Meenakshi Institute of TechnologyBangalore560064India Dubai Business School University of DubaiAcademic CityDubaiP.O.Box 14143United Arab Emirates Department of Computer Science and Business Systems Bapuji Institute of Engineering and TechnologyDavangere577004India

Ever since the research in machine learning gained traction in recent years,it has been employed to address challenges in a wide variety of domains,including mechanical *** of the machine learning models are built on the assumption of a static learning environment,but in practical situations,the data generated by the process is *** evolution of the data is termed concept *** research paper presents an approach for predictingmechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical *** method proposed here is applicable to allmechanical devices that are susceptible to failure or operational *** proposed method in this paper is equipped with the capacity to detect the drift in data generation and *** proposed approach evaluates the machine learning and deep learning models for their efficacy in handling the errors related to industrial machines due to their dynamic *** is observed that,in the settings without concept drift in the data,methods like SVM and Random Forest performed better compared to deep neural ***,this resulted in poor sensitivity for the smallest drift in the machine data reported as a *** this perspective,DNN generated the stable drift detection method;it reported an accuracy of 84%and an AUC of 0.87 while detecting only a single drift point,indicating the stability to performbetter in detecting and adapting to new data in the drifting environments under industrial measurement settings.

关键词： Incremental learning drift detection real-time failure prediction deep neural network proactive machine health monitoring

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Challenges and Initial Measurements on Communication and Localization for Mountain Bike Safety Applications

Challenges and Initial Measurements on Communication and Loc...

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Annual Conference on Wireless On Demand Network systems and Services (WONS)

作者： Michele Zucchelli Marcus Marx Max Hörmann Frank Kargl Michele Segata Department of Information Engineering and Computer Science University of Trento Italy Institute of Distributed Systems Ulm University Germany

ISBN: (数字)9783903176713

ISBN: (纸本)9798331522025

As of today, research in vulnerable road users (VRUs) applications is mainly focused on safety in urban road scenarios. There is little to be found in the literature with respect to VRUs in mountain areas, where mountain biking and hiking also present risks of collision. Here, it is not yet clear whether existing localization and communication technologies would provide sufficient performance in such harsh environments. In this work, we start answering this question by presenting the results of a measurement campaign which took place in a mountain area in Northern Italy during Summer 2024. With respect to localization, we show that global navigation satellite system (GNSS)-based localization alone often provides unreliable results due to vegetation and terrain. Trilateration with Bluetooth Low Energy (BLE) and beacons mounted at fixed positions performs well in some circumstances and can be used to enhance GNSS, however, we also observed many unclear effects that require further investigations. Concerning communication, the results indicate that both direct short range communications (DSRC) and cellular V2X (C-V2X) works fairly well in most cases, but terrain characteristics might induce packet losses or low signal quality, whereas instabilities in GNSS fixes might also cause C-V2X outages.

关键词： Location awareness Wireless communication Global navigation satellite system Roads Vegetation mapping Packet loss Communications technology Safety Bluetooth Low Energy Network systems

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Improved-Equalized Cluster Head Election Routing Protocol for Wireless Sensor Networks

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computer systems science & Engineering 2023年第1期44卷 845-858页

作者： Muhammad Shahzeb Ali Ali Alqahtani Ansar Munir Shah Adel Rajab Mahmood Ul Hassan Asadullah Shaikh Khairan Rajab Basit Shahzad Department of Computer Science Institute of Southern PunjabMultan5932Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Computer Skills Deanship of Preparatory YearNajran UniversityNajran61441Saudi Arabia Department of Engineering National University of Modern LanguagesIslamabad44000Pakistan

Throughout the use of the small battery-operated sensor nodes encou-rage us to develop an energy-efficient routing protocol for wireless sensor networks(WSNs).The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of *** routing protocols are available,but the issue is still *** is one of the most important techniques in the existing routing *** the clustering-based model,the important thing is the selection of the cluster *** this paper,we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining energy and the distance of the nodes in each ***,the bubble sort algorithm chose the two nodes with the maximum remaining energy in the cluster and chose a cluster head with a small *** proposed scheme performs hierarchal routing and direct routing with some energy *** simulation will be performed in MATLAB to justify its performance and results and compared with the ECHERP model to justify its ***,the simulations will be performed in two scenarios,gate-way-based and without gateway to achieve more energy-efficient results.

关键词： Bubble sort algorithm gateway energy thresholds wireless sensor networks

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Classification and Comprehension of Software Requirements Using Ensemble Learning

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computers, Materials & Continua 2024年第8期80卷 2839-2855页

作者： Jalil Abbas Arshad Ahmad Syed Muqsit Shaheed Rubia Fatima Sajid Shah Mohammad Elaffendi Gauhar Ali School of Computer Science and Technology Anhui UniversityHefei230039China School of Computing Sciences Pak Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science and IT University of LahoreLahore55150Pakistan Department of Computer Science Emerson UniversityPunjabMultan60000Pakistan EIAS(Emerging Intelligent Autonomous Systems)Data Science Lab Prince Sultan UniversityRiyad12435Saudi Arabia

The software development process mostly depends on accurately identifying both essential and optional ***,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional *** address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human ***,existing techniques often struggle with complex instructions and large-scale *** our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer *** results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT *** datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse *** findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

关键词： Ensemble learning machine learning non-functional requirements requirement engineering accuracy sliding window

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Auto-OPS: A Framework for Automated Optical Probing Simulation on GDS-II

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IEEE Embedded systems Letters 2025年第3期17卷 147-150页

作者： Flammarion, Paul Parvin, Sajjad Sill Torres, Frank Drechsler, Rolf Cyber-Physical Systems Group Bremen28359 Germany Institute of Computer Science University of Bremen Bremen28359 Germany Institute for the Protection of Maritime Infrastructures German Aerospace Center Bremerhaven27572 Germany Department of Cyber-Physical Systems Bremen28359 Germany

In this letter, for the first time, we propose a security evaluation framework, namely, Auto-OPS, that automates performing the optical probing (OP) attack in simulation on a full GDS-II design file. Auto-OPS empowers designers by automatically extracting the active regions geometry model of each logic cell in the standard cell library or custom-designed logic cells to evaluate the security robustness of a design. Auto-OPS enables scaling up of the current OP evaluation environments which rely on manual extraction of active regions which is an error-prone and cumbersome procedure. Additionally, we evaluated and demonstrated the performance of our framework on several benchmark circuits GDS-II files designed using an open-source 45-nm standard cell library. © 2009-2012 IEEE.

关键词： computer circuits

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Mystery shopping: Improving quality assurance of public transport services for people with restricted mobility using a prototypical mobile application 57

Mystery shopping: Improving quality assurance of public tran...

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57th Annual Hawaii International Conference on System sciences, HICSS 2024

作者： Akhgarnusch, Arschad Voß, Stefan Ge, Liping Department of Computer Science University of Hamburg Germany Institute of Information Systems University of Hamburg Germany Institute of Information Systems Leuphana University Lüneburg Germany

ISBN: (纸本)9780998133171

Due to legal regulations and the impending demographic aging of society, urban mobility service providers are increasingly confronted with the inclusive design of local public transport. Accessibility is a basic prerequisite for people with restricted mobility to be able to participate in society in a self-determined way. Quality standards are defined and reviewed in discussions and negotiations between transport companies, public transport authorities and stakeholders. These standards are the basis for the design of any infrastructure, vehicle fleet, information, and operation to promote accessibility. The aim of this paper is to determine how the review of the accessibility of public transport can be improved by means of mystery shopping using a prototypical app. The mobile application enables the cooperative evaluation of public transport services from the perspective of mobility-restricted users (e.g. those in wheelchairs) across the mobility chain. We subsume and extend relevant evaluation criteria and implement them in a mobile application to be used during field work while cooperating with the back office of a public transport company. The app is tested in both usability tests and a field test and in dialogue with the users;its correct functioning is confirmed. © 2024 IEEE computer Society. All rights reserved.

关键词： Fleet operations

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PDNet: A Novel Approach to Plant Disease Detection using CNN and Keras 6

PDNet: A Novel Approach to Plant Disease Detection using CNN...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Jain, Garima Shukla, Amita Chaudhary, Anamika Sharma, Nidhi Bisht, Sovers Singh Jain, Ankush Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Delhi India Department of Computer Science and Engineering Netaji Subhas University of Technology Delhi India

ISBN: (纸本)9798331522667

This research addresses the pressing global demand for food by leveraging cutting-edge deep learning techniques for automating plant disease detection. Focusing on tomato and potato leaf diseases, the study utilized the PlantVillage dataset to compare classical machine learning methods, such as Local Binary Patterns (LBP) combined with classifiers like kNN, SVM, and Extreme Learning Machines, against modern Convolutional Neural Network (CNN) architectures. These included MobileNet, DenseNet201, ResNet50, Inception V3, and VGG16/19. A custom-designed CNN model was developed with unique configurations implemented using Keras and TensorFlow. The model was tested for binary, 6-class, and 10-class classifications, achieving remarkable accuracy rates of 99.6%, 98.4%, and 97.2%, respectively. Notably, after 50 training epochs, the model achieved a peak accuracy of 99.78% for plant disease classification. A practical web-based application was also developed, integrating the best-performing model to facilitate real-time leaf disease detection and notify farmers via email. This approach demonstrates a significant advancement in sustainable agriculture by providing an efficient, accurate, and scalable solution for early disease detection, which is critical for improving crop yield and productivity. © 2025 IEEE.

关键词： Convolutional neural networks

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