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Small Vehicle Damage Detection with Acceleration Spectrograms: An Autoencoder-Based Anomaly Detection Approach 12th

Small Vehicle Damage Detection with Acceleration Spectrogram...

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2023 International Conference on Smart Cities and Green ICT Systems and International Conference on Vehicle Technology and Intelligent Transport Systems

作者： Khan, Sara Faria, Bruno Ferreira, Andre Robert Bosch GmbH Renningen Germany Robert Bosch Car Multiemdia SA Braga Portugal

ISBN: (纸本)9783031709654;9783031709661

This paper presents a Machine Learning (ML) based methodology for a real-time small-vehicle damage detection system. While dents and scratches may seem trivial for individual car owners, they hold significant implications for insurance companies and car-rental/taxi service providers. In addition to these interested parties, car manufacturers and car service providers are also keen to obtain accurate vehicle damage information for vehicle behaviour analysis. Therefore, vehicle damage detection remains a key and challenging problem in the automotive research community. Our novel approach mainly uses spectrograms of inertial sensors like accelerometers which measure accurate vehicle acceleration. Inertial sensors are cost-effective and tailored for real-time data capture which makes them a competitive candidate for our choice. The designed system employs auto-encoders as automatic feature extractors from input acceleration spectrograms. These feature representations derived from sensor data are then classified into damage or non-damage categories using an anomaly detection approach. We achieved an impressive approximately 90% F1-score. The outcomes of this work contribute to the progress of intelligent damage detection systems. Furthermore, it assists in understanding the relationships between autoencoders and signal data in this context. Our approach can be used in many applications in the automotive sector, such as automated vehicle inspection, enhanced airbag response systems, and improving the safety of autonomous driving.

关键词： Accelerometer Autoencoders Anomaly detection Machine learning Damage detection

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A Novel Autonomous Bird Deterrent System for Crop Protection Using Drones 2nd

A Novel Autonomous Bird Deterrent System for Crop Protection...

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2nd International Conference on Agriculture-Centric Computation

作者： Devi, L. Nirmala Reddy, K. V. Subba Rao, A. Nageswar Osmania Univ Hyderabad Telangana India SLRDC Hindustan Aeronaut Ltd Hyderabad Telangana India

ISBN: (纸本)9783031744396;9783031744402

The major agriculture products of India include Rice, Wheat Maize, even though India has emerged as a major food production entity, there exist several challenges which makes the yields to reduce, due to lack of mechanization, soil erosion, effective marketing and bird flocking. Among these bird flocking is a major issue due to which the crop may get damaged in the final stage. Bird flocks cause major damage to the crops at ripening stage. Among several methods available, only a few are effective, but those methods are very expensive. To address this problem, this paper proposes a novel innovative, simple prototype which is designed for bird scaring based on Drone and Motion Detection Algorithms (DMDA). The proposed prototype has totally three modules;they are Bird Flocks Detection, Scaring Mechanism and Hexacopter. The first module is mounted on the top of Hexacopter and it consists of a High Resolution Camera Connected to Raspberry PI controller. Over the video captured, this module applies Temporal Difference based Motion Detection algorithm for Bird Flocks detection. Next, the Scaring unit is mounted at the bottom of Hexacopter and it consists of a loud speaker which is triggered through Raspberry PI controller upon the detection of bird flock. Finally, the Hexacopter is operated through Drone Pilot using a Flysky Controller. Extensive filed trials of proposed prototype shown satisfactory performance on different types of crops and also reduced the manual burden.

关键词： Bird Flocks Crop Hexacopter Motion Detection Loud Speaker

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Evaluating Adan vs. Adam: An Analysis of Optimizer Performance in Deep Learning 5th

Evaluating Adan vs. Adam: An Analysis of Optimizer Performan...

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5th International Symposium on Intelligent Computing Systems

作者： Ismail, Amelia Ritahani Azhary, Muhammad Zulhazmi Rafiqi Hitam, Nor Azizah Int Islam Univ Malaysia Kulliyyah Informat Commun Technol Kuala Lumpur Malaysia Higher Coll Technol Fac Comp Informat Syst Dubai U Arab Emirates

ISBN: (纸本)9783031829307;9783031829314

Choosing a suitable optimization algorithm in deep learning is essential for effective model development as it significantly influences convergence speed, model performance, and the success of the training process. Optimizers play an essential role in adjusting the model's parameters to minimize errors, assisting the learning process during the model development. With various optimization algorithms available, choosing the one that best suits the deep learning model and dataset can make a substantial difference in achieving optimal results. Adaptive Moment Estimation (Adam) and Adaptive Nesterov Accelerated Gradient (Adan), two well-known optimizers, are widely used in deep learning for their ability to handle large-scale data and complex models efficiently. While Adam is known for its balance between speed and reliability, Adan builds on this by incorporating momentum and lookahead mechanisms to enhance the model's performance. However, choosing the right optimizer for different tasks can be challenging, as each optimizer offers various advantages and disadvantages. This paper, therefore, explores the comparative effectiveness of Adam and Adan optimizers, analyzing their impact on convergence speed, model performance, and overall training success on different classifications tasks, which are image and text classifications. The results show that Adam performs better initially, but prone to overfitting. On the other hand, for image classification tasks, Adan provides more consistent optimisation across extended training periods. Based on these results, this paper aims to provide insights into the strengths and limitations of each optimizer, highlighting the importance of considering task-specific requirements when selecting an optimization algorithm for deep learning models.

关键词： Adam algorithm Adan algorithm Adaptive optimizer Deep Learning

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Comprehensive Water Quality Analysis and Prediction Using Ensemble Machine Learning Models 4th

Comprehensive Water Quality Analysis and Prediction Using En...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Sujatha, E. Ranjith, R. Malathi, K. Poongulali, E. Saveetha Engn Coll Autonomous Dept Comp Sci & Engn Chennai Tamil Nadu India Saveetha Engn Coll Autonomous Dept Artificial Intelligence & Machine Learning Chennai Tamil Nadu India

ISBN: (纸本)9783031837890;9783031837906

Ensuring water quality is crucial for public health and ecological balance. With increasing urbanization and industrialization, water sources are often contaminated, necessitating regular monitoring and predictive modelling. This study focuses on predicting water quality in Chennai's Puzhal Lake, Retteri Lake, and Chembarambakkam Lake using nine machine learning algorithms: Logistic Regression, Ridge Regression, Stochastic Gradient Descent (SGD) Classifier, Support Vector Classifier (SVC), Nu-Support Vector Classifier (NuSVC), Decision Tree, Random Forest Classifier, AdaBoost Classifier, and XGBoost Classifier. Water quality parameters such as pH, hardness, chloramines, sulphate, conductivity, organic carbon, trihalomethanes, turbidity, potability, Total Suspended Solids (TSS), Total Dissolved Solids (TDS), and Total Solids (TS) were measured using a water test kit. The dataset was used to train and evaluate the machine learning models based onWorld Health Organization (WHO) guidelines. Among the algorithms, the Random Forest Classifier achieved the highest accuracy with a score of 0.8179, followed by Support Vector Classifier (0.7132) and XGBoost Classifier (0.5925). A novel Ensemble Integration and Aggregation Algorithm (EIAA) was proposed to enhance predictive performance by aggregating the predictions of the individual models through majority voting and averaging methods. The EIAA demonstrated improved accuracy and reliability in predicting water quality. This study provides valuable insights into the most effective machine learning techniques for water quality prediction, contributing to improved water management and safety in Chennai.

关键词： Water Quality Prediction Machine Learning Ensemble Algorithm Chennai Lakes Environmental Monitoring

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Perceived Benefits and Challenges of Greek Managers Towards Enterprise information Systems and Industry 4.0 Technologies Adoption: The Role of Organizational and Personal Factors 5th

Perceived Benefits and Challenges of Greek Managers Towards ...

引用

5th 5th Olympus - International Conference on Supply Chain

作者： Stafyla, Amalia Nanos, Ioannis Int Hellen Univ Katerini Greece

ISBN: (纸本)9783031693502;9783031693519

The aim of this study is to explore the perceptions of managers towards the adoption and use of enterprise information systems and Industry 4.0 technologies in Greece, through a quantitative research in companies from various sectors of the economy. The results show that, although the use of enterprise information systems such as ERP, CRM etc. is quite widespread, the adoption of Industry 4.0 technologies is currently very low. However, most managers show intention to implement and adopt Industry 4.0 technologies and systems in the near future. Organizational and personal factors, such as awareness of Industry 4.0, seem to influence the perceived benefits and challenges of Industry 4.0 adoption and intention to use. Our study provides results which can be used by researchers to study and explore Industry 4.0 adoption in a national context and to question or validate results from similar empirical studies in international context and other countries.

关键词： Industry 4.0 Technology Adoption Enterprise information Systems ERP CRM Research Organizational Factors Personal Factors

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Deep Learning Algorithms for Colon Cancer Detection: A Comparative Study with Traditional Machine Learning Methods 4th

Deep Learning Algorithms for Colon Cancer Detection: A Compa...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Nabti, Ilhem Kouari, Zakaria Ferradji, Mohamed Abderraouf Merabet, Asma Univ Ferhat Abbes Setif 1 Setif Algeria Univ Larbi Ben Mhidi Oum El Bouaghi LIAOA Lab Oum El Bouaghi Algeria

ISBN: (纸本)9783031821523;9783031821530

This work aims to highlight the distinctions between deep learning models and traditional machine learning methods in colon cancer detection. Based on the LC25000 dataset, we analyze the performance of Support Vector Machine (SVM) and Random Forest (RF) compared to advanced deep learning models such as InceptionV3, VGG19, and EfficientNetV2. Our methodology involves a detailed analysis of each algorithm's Recall, F1 score, Precision, and accuracy. The results of this research show that deep learning algorithms, and EfficientNetV2 in particular, have outperformed traditional machine learning methods. These results highlight the need to enhance colon cancer detection accuracy by adopting deep learning technology in medical imaging diagnosis.

关键词： colon cancer detection deep learning machine learning SVM Random Forest VGG19 InceptionV3 EfficientNetV2

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Gamified Application for the Reinforcement of Mathematical Skills in Elementary Education 1

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Reyes Rogget, Marcelo Ruete Zuniga, David Caroca Navarro, Alejandro Leal Moraga, Danilo Maidana Gonzalez, Jean Paul Caselli Benavente, Nicolas Taramasco Toro, Carla Cruz Madariaga, Nicolas Univ Andres Bello Vina Del Mar Chile

ISBN: (数字)9783031832079

ISBN: (纸本)9783031832062;9783031832079

In response to the consistently low performance in mathematics among elementary students in Chile, a mobile application using gamification techniques was developed to enhance student engagement and academic achievement. This paper explores the implementation of this gamified app, designed to make mathematical learning both interactive and enjoyable. Leveraging elements typical of gaming, such as points and levels, the app aims to increase intrinsic motivation and participation within the classroom setting. The intervention included a group of students who used the app over a specified period, allowing for a pre and post evaluation of their mathematical skills and interest levels. Results showed an increase in mathematical performance and a modest increase on a Likert scale in students' interest towards mathematics. These findings suggest that gamification can serve as an educational tool to enhance learning and student engagement in mathematics.

关键词： Gamification Math Skills Math interest

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Digital Platform for Strengthening Accessibility of Tourist Destinations in the Pieria Region 5th

Digital Platform for Strengthening Accessibility of Tourist ...

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5th 5th Olympus - International Conference on Supply Chain

作者： Achillas, Charisios Aidonis, Dimitrios Folinas, Dimitrios Kostavelis, Ioannis Tsolakis, Naoum Triantafyllou, Dimitrios Nikolaou, Miltiadis Dimou, Vasileios Int Hellen Univ Katerini 60100 Greece Reg Cent Macedonia Thessaloniki 54110 Greece Pieria Org Tourism Dev & Promot Katerini 60100 Greece

ISBN: (纸本)9783031693502;9783031693519

Accessibility problems often prevent people with special needs from enjoying facilities and services in tourist destinations. It is an institutional obligation, as well as a social necessity, to "open the doors" of tourism for everyone, within the concept of tourism-for-all concept. Moreover, accessible tourism is considered an important economic opportunity for touristic destinations, taking also into account the ageing of population over the last decades. In order to strengthen the touristic brand of the Pieria Region and promote the area as an "Accessible Tourist Destination", an online platform has been developed under the responsibility of the Pieria Organization for Tourism Development and Promotion, with the scientific and technical support of the International Hellenic University. The platform provides complete information on accessibility infrastructure in respect to route accessibility (obstacles in pavements, ramps, etc.) accessibility issues for points of cultural and tourist interest (PoIs). In this paper, the key functionalities of the platform are presented through the case of Platamonas, Greece, where the platform has been tested and results have been validated.

关键词： Accessibility Tourism-for-All Pieria

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Lattices for Many-Logics Modal Logic: Constructions and Representations 4th

Lattices for Many-Logics Modal Logic: Constructions and Repr...

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4th International Conference on Artificial Intelligence Logic and Applications

作者： Freire, Alfredo Madeira, Alexandre Martins, Manuel A. Univ Brasilia Brasilia DF Brazil Univ Aveiro Dept Math CIDMA Aveiro Portugal

ISBN: (纸本)9789819603534;9789819603541

To reason about complex systems, logical tools and results provide a fundamental framework for studying regularities in such systems. However, standard approaches, though very successful, assume that transitions can occur only between states that behave according to a single logic. To reason about behaviors of some complex systems, it is worth considering different logics in different states. The issue is how to meaningfully communicate values in different lattices semantics of states' logics. In this paper, we propose a method, based on the direct product construction, to produce a natural common lattice background in which the relation between the original lattices can be represented.

关键词： computer circuits

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Ready for Digitization: Decision-Making Model for Low Code Development Platform Selection 4th

Ready for Digitization: Decision-Making Model for Low Code D...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Borissova, Daniela Dimitrova, Zornitsa Buhtiyarov, Nikolay Garvanova, Magdalena Garvanov, Ivan Tanev, Yasen Bulgarian Acad Sci Inst Informat & Commun Technol Sofia 1113 Bulgaria Univ Lib Studies & Informat Technol Sofia 1784 Bulgaria

ISBN: (纸本)9783031832062;9783031832079

In the ongoing digitalization process, low-code development platforms could be seen as promising solutions to help enterprises create their business applications in a short time. This is due to the use of different approaches such as visual modeling, model-driven development, rapid application development, and automatic code generation. On the market, there are plenty of vendors of low-code development platforms that propose a variety of products with similar parameters. In this regard, a mathematical model for evaluation and selection is proposed. This model considers three main groups of low-code development platforms assessment criteria. A characteristic feature of the model is that each group can be considered with a different degree of importance when forming the final decision. The applicability of the formulated decision-making model is demonstrated in a real example in the evaluation of five platforms according to 23 criteria distributed into 3 groups. The obtained results convincingly show the applicability of the proposed decision-making model in the assessment and evaluation of low-code development platforms.

关键词： Low-Code Development Platforms Decision-making Model for Selection

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