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16th EAI International Conference on Wireless Internet, WiCON 2023

作者： Dimitriou, Theodoros Skondras, Emmanouil Hitiris, Christos Gkola, Cleopatra Papapanagiotou, Ioannis S. Vergados, Dimitrios J. Fasoula, Georgia Koumantakis, Stratos Michalas, Angelos Vergados, Dimitrios D. Department of Informatics University of Piraeus Piraeus Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics University of Western Macedonia Kastoria Greece Internet Business Hellas Athens Greece MAS S.A. Athens Greece

ISBN: (纸本)9783031580529

Machine Learning (ML) is being successfully applied to ship engine management with proven economic and environmental benefits by engine performance optimization, timely fault detection and appropriate service planning. However, the data preparation for usage in ML algorithms provides several advantages including faster training and improved performance of the algorithm, improved visualization of the dataset, noise reduction, dataset simplification, avoidance of the curse of dimensionality and improved resource utilization. In this paper, two key techniques of the ML algorithms, that can be applied for data preparation and organization of ship engine data are studied, namely the dimensionality reduction and the data clustering. Dimensionality reduction involves the reduce of the number of input variables or features in a dataset, by retaining as much valuable information as possible. On the other hand, clustering ML techniques help to uncover insights and reduce data complexity through the organization of the data into clusters. Evaluation results demonstrate the usefulness of both techniques. © ICST Institute for computer Sciences, Social informatics and Telecommunications engineering 2024.

关键词： Engines

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Prediction of compressive strength of high-performance concrete using optimization machine learning approaches with SHAP analysis

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Journal of Building Pathology and Rehabilitation 2024年第2期9卷 1-20页

作者： Islam, Md Mahamodul Das, Pobithra Rahman, Md Mahbubur Naz, Fasiha Kashem, Abul Nishat, Mosaraf Hosan Tabassum, Nujhat Department of Informatics University of Oslo Oslo Norway Department of Civil Engineering Leading University Sylhet Bangladesh Department of Industrial and Systems Engineering Lamar University Beaumont United States Department of Computer Science University of Agriculture Faisalabad Faisalabad Pakistan Department of Civil and Environmental Engineering Shahjalal University of Science & amp Technology Sylhet Bangladesh Department of Civil and Environmental Engineering Islamic University Of Technology -IUT(OIC) Gazipur Bangladesh Department of Electrical and Computer Engineering North South University Dhaka Bangladesh

Forecasting the compressive strength of high-performance concrete (HPC) is crucial for its practical applications. However, conducting experimental tests for this purpose demands significant resources and time. In recent times, data-driven modeling, particularly machine learning, has become prominent in this field. Thus, this study employed machine learning methods, including Gradient Boosting, Random Forest, and CatBoost algorithms, to predict the compressive strength of high-performance concrete (HPC). Furthermore, interpretable algorithms like SHapley Additive exPlanations (SHAP) were utilized in this study to assess the contribution of input variables to predictions. The dataset includes parameters such as cement, blast furnace slag, fly ash, water, coarse aggregate, sand, and age as input variables, with compressive strength as the output variable for models development. The CatBoost model performance was superior to GBM and RF models, with R² values of 0.979 for the training stage and 0.959 for the testing stage. Moreover, it demonstrated minimal mean absolute errors (MAE) of 2.37 MPa and 3 MPa for compressive strength prediction in the training and testing stages, respectively. Furthermore, the SHAP analysis revealed that age, cement, and superplasticizer are the primary influencers in determining compressive strength. The machine learning models presented in this study exhibit promising potential for practical implementation in different engineering applications. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Compressive strength High-performance concrete Machine learning SHAP analysis

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Predicting Song Popularity Through Machine Learning and Sentiment Analysis on Social Networks 13th

Predicting Song Popularity Through Machine Learning and Sen...

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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Rompolas, Gerasimos Smpoukis, Athanasios Kafeza, Eleanna Makris, Christos Computer Engineering and Informatics Department University of Patras Patras26504 Greece College of Interdisciplinary Studies Zayed University Dubai144534 United Arab Emirates

ISBN: (纸本)9783031632266

In this paper, we delve into the intricate relationship between technology, music, and success. Our research focuses on leveraging the capabilities of machine learning algorithms to forecast the success and popularity of songs. By combining audio features, social media data, and emotion analysis, our machine learning-based model demonstrates its capability as a reliable tool for artists, amateurs, and music industry stakeholders to make informed and targeted decisions. Our analysis encompassed not only the inherent audio characteristics of each track but also the extent of audience engagement and emotional response of the fans. Through the integration of textual emotion analysis, we quantified the reactions of the audience and performed an emotion labelling, leveraging our social information to uncover the emotions surrounding each track’s release. The experimental results show that the feature data from which the classification algorithms were trained, unfolded to be qualitative and precise, while they also show that the use of the social media features improves the classifiers’ performance in predicting the popularity of the tracks. © IFIP International Federation for Information Processing 2024.

关键词： Emotion Recognition

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Private Blockchain-Based Platform for Cool Box Truck Based on LoRa Communication Protocol 1

Private Blockchain-Based Platform for Cool Box Truck Based o...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Prayudi, Agus Hakim, Oktafian Sultan Zainuddin, Muhammad Agus Sukaridhoto, Sritrusta Budiarti, Rizqi Putri Nourma Politeknik Elektronika Negeri Surabaya Department of Electrical Engineering Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Universitas Nadhlatul Ulama Surabaya Department of Information System Surabaya Indonesia

ISBN: (纸本)9798350365191

The Internet of Things utilizes software, embedded system, sensor, and network connectivity to control and monitor business processes such as supply chains. One of the implementations of IoT is LoRa (Long Range) which enables data transactions in long-range without using radio frequency. Currently, a combination of IoT and blockchain is able to increase the security level of IoT applications. Implementation of IoT in the supply chain has been done in several research by implementing blockchain and LoRa. Approximately 6.43% of the Gross Domestic Product (GDP) is attributed to logistics, with cold and hot logistics constituting around 11%. Among these, cold chain logistics, a specialized form, accounts for 9.7% of tonnage. Cold chain logistics, characterized by comprehensive temperature control, is a sophisticated system closely intertwined with advanced technology. Despite its complexity, there are still technological drawbacks leading to significant material losses on an annual basis. In this research, to overcome that problem, we proposed Blockchain-Based Monitoring System on Cool Box Trucks using LoRa. The proposed system implemented LoRa for data communication and Blockchain as decentralized databases. We simulated the LoRa environment by using FLoRa framework to obtain the optimal parameters such as Transmission Power (TP), Packet Delivery Ratio (PDR), and Spread Factor (SF) that used in TTGO T-Beam V1. © 2024 IEEE.

关键词： Trucks

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Design of Rule Translator for Temporal Rules Execution of BM-DEVS Model 15th

Design of Rule Translator for Temporal Rules Execution of BM...

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15th International Conference on computer Science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Ahn, Jungsub Cho, Taeho Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon16419 Korea Republic of

ISBN: (纸本)9789819724468

With the increasing complexity of systems, various studies are being conducted to accurately express and solve problems. Discrete Event System Specification (DEVS), one of the simulation theories, expresses a problem as a hierarchical model. The Behavior Monitoring-DEVS (BM-DEVS) theory is an extension of DEVS. It monitors the behavior of interest by expressing it as temporal rules in the model. In BM-DEVS, the defined temporal rules need to be converted into Büchi automata and applied to the model to evaluate the behavior expressed by the rules. To achieve this, modelers need to translate temporal rules into automata and verify these. This consumes a substantial amount of time. To solve this problem, this paper proposes a method to automatically translate temporal rules into automata for rule evaluation. In this proposed method, when temporal rules are input, structuring work is performed to match the rule structure and model structure. Moreover, a more convenient modeling environment is provided to modelers through the rule expansion function. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automata theory

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MTSS: Movie Trailers Surveillance System using Social Media Analytics and Public Mood 23

MTSS: Movie Trailers Surveillance System using Social Media ...

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4th ACM Workshop on Intelligent Cross-Data Analysis and Retrieval, ICDAR 2023

作者： Prokopiou, Ioannis Vikatos, Pantelis Chatzis, Christos Christodoulou, Christos Orfium Research Greece and Computer Engineering and Informatics Department Attiki Athens Greece Orfium Research Attiki Athens Greece

ISBN: (纸本)9798400701863

The movie industry is characterized by high levels of uncertainty due to the difficulties businesses have predicting sales and income since they depend on so many complex elements. Because of the significant movie industry's upfront costs, investors must make decisions based on accurate methodologies to estimate the success or returns of their investments. Due to social networks' widespread use in our daily lives, there is an unprecedented amount of publicly available posts containing emotional triggers that can be analyzed. In this paper, we showcase our movie trailers campaign surveillance system using social media analytics and public mood. The system provides an overall interactive dashboard to users and the capability for further process, analysis, and visualization of data in distinct customized reports. The proposed system also offers a configurable environment to gather metrics from heterogeneous social media platforms and extract advanced analytics through dedicated AI tools for sentiment and emotion extraction. This solution benefits the continuous surveillance of a movie trailer's popularity and supports the success of marketing campaigns. © 2023 ACM.

关键词： Light trailers

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Apple Scab Detection Using Transfer Learning and Deep Convolutional Network 7th

Apple Scab Detection Using Transfer Learning and Deep Convol...

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7th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Singh, Anupam Jain, Arjav Batra, Aryan Department of Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India Informatics Cluster School of Computer Science University of Petroleum & Energy Studies Uttarakhand Dehradun248007 India

ISBN: (纸本)9789819720309

Every year, diseases and pests inflict enormous economic losses on the apple industry. One of the significant challenges faced by the farmers is the identification of the various diseases, as the signs and symptoms of several illnesses could be very similar and be present simultaneously. Through this proposed work, we make an effort to offer accurate and timely detection of one such apple disease, i.e., apple scab, a fungal disease. The main reason for choosing apple scab and apple leaves scab detection as our work is due to the lack of research on the topic, and we wanted to take this opportunity to do something meaningful and help the agriculture industry in India. The first part of the work was data preprocessing and labeling. The datasets containing photographs of patch-affected apples and leaves of an apple tree are collected;however, there are hardly any public datasets that contain enough images for us to use and train our models with because the acquisition of these images is extremely time-consuming and has a component of probability;therefore, we decided that transfer learning (TF) would be a suitable training approach. Training deep neural networks from scratch on a small dataset can take a long time and may not converge to a good solution due to overfitting. Transfer learning allowed us to start with a pretrained model and fine-tune it on your specific task. This significantly reduced the training time and resource requirements. The convolutional neural network (CNN) on the collected dataset is the model used to categorize apples. End-to-end learning algorithms known as CNN automatically extract characteristics from raw photographs and learn complex features from them. To avoid our model overfitting, we would use data augmentation techniques like rotation, translation, and scaling. An experimental result demonstrates that using the proposed structure of CNN and transfer learning, the results are comparatively better than the pretrained deep learning mode

关键词： Losses

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An Ensemble of Bacterial Foraging, Genetic, Ant Colony and Particle Swarm Approach EB-GAP: A Load Balancing Approach in Cloud Computing

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Recent Advances in computer Science and Communications 2022年第5期15卷 693-699页

作者： Dewangan, Bhupesh Kumar Jain, Anurag Shukla, Ram Narayan Choudhury, Tanupriya Department of Informatics School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Virtualization School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science & Engineering School of Engineering OP Jindal University Raigarh India

Background: In the cloud environment, the satisfaction of service level agreement (SLA) is the prime objective. It can be achieved by providing services in a minimum time in an efficient manner at the lowest cost by efficiently utilizing the resources. This will create a win-win situation for both consumers and service providers. Through literature analysis, it has been found that the procedure of resource optimization is quite costly and time-consuming. Objectives: The research aims to design and develop an efficient load-balancing technique for the satisfaction of service level agreement and the utilization of resources in an efficient manner. Methods: To achieve this, the authors have proposed a new load-balancing algorithm named EBGAP by picking the best features from Bacterial Foraging, Genetic, Particle-Swarm, and AntColony algorithm. A fitness value is assigned to all virtual machines based on the availability of resources and load on a virtual machine. Results: A newly arrived task is mapped with the fittest virtual machine. Whenever a new task is mapped or left the system, the fitness value of the virtual machine is updated. In this manner, the system achieves the satisfaction of service level agreement, the balance of the load, and efficient utilization of resources. To test the proposed approach, the authors have used the real-time cloud environment of the amazon web service. In this, waiting time, completion time, execution time, throughput, and cost have been computed in a real-time environment. Conclusion: Through experimental results, it can be concluded that the proposed load balancing approach EB-GAP has outperformed other load balancing approaches based on relevant parameters. © 2022 Bentham Science Publishers.

关键词： Virtual machine

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Sequential Patch Analysis Framework for Lung Disease Classification 11th

Sequential Patch Analysis Framework for Lung Disease Classi...

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11th International Conference on Future Data and Security engineering, FDSE 2024

作者： Le, Kim-Ngoc T. Le, Duc-Tai Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of College of Computing and Informatics Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9789819604364

Deep neural network (DNN) models have shown promise in lung disease classification using spectrogram transformations of respiratory sounds. However, these models typically process entire images, which can be computationally expensive, and they require large datasets for effective training. With the largest available respiratory dataset, ICBHI, containing only 920 recordings, training a robust DNN becomes challenging. To address this issue, this paper introduces a simplified DNN framework that incorporates novel techniques such as patch splitting and transfer learning. Specifically, the proposed framework splits the spectrogram image into a sequence of fixed-size patches, effectively reducing computational demands. These patches are processed with the position embeddings by DNN encoders to capture acoustic characteristics, leveraging a pre-trained ImageNet model to make efficient use of the limited dataset. Evaluation experiments on the ICBHI dataset show that the proposed method improves the state-of-the-art 8-class classification accuracy by 1.91%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lung cancer

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An Evaluation of the Dual Vectors Based Solutions for Robot-World/Hand-Eye Calibration 1

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25th International Symposium on Measurement and Control in Robotics, ISMCR 2023

作者： Ganciu, Aura Burlacu, Adrian Department of Automatic Control and Applied Informatics Faculty of Automatic Control and Computer Engineering "Gheorghe Asachi" Technical University of Iași Iași Romania

ISBN: (数字)9783031510854

ISBN: (纸本)9783031510847

The hand-eye calibration problem represents a major challenge in robotics, arising from the widespread usage of robotic systems along with robot-mounted sensors. Briefly, consisting of estimating the position and orientation of a visual sensor (usually a camera attached to the robot’s end-effector) with respect to the robot itself, calibration is critical in tracking rigid bodies—a common task within mixed reality applications. Hand-eye calibration implies solving the problem formulated as AX = XB or AX = YB equations. The solution lies in computing the unknown homogeneous transformation matrices X and Y, given the A and B parameterizations of rigid body motions. We present the dual vectors based approaches for AX = XB and AX = YB calibration problems, making use of the isomorphism between the special Euclidean group and the orthogonal dual tensor group. Comparisons with state-of-the-art algorithms are used to assess our obtained results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Mixed reality

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