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Driver Assistance System, based on the Convolutional Neural Network 16

Driver Assistance System, based on the Convolutional Neural ...

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16th Electrical engineering Faculty Conference, BulEF 2024

作者： Filipova-Petrakieva, Simona Yanev, Petar Dept. Fundamentals of Electrical Engineering Technical University of Sofia Sofia Bulgaria Dept. Computer Systems Technical University of Sofia Sofia Bulgaria

ISBN: (纸本)9798350391671

The continuously rising number of deaths due to car accidents on the roads is a critical problem nowadays. To reduce the number of risky situations on the road, it is necessary to increase the attention of drivers when driving motor vehicles. In this context, an advisory system to assist driving is proposed in this paper. The mathematical model uses a convolutional neural network to recognize traffic signs and detect real road situations. It is implemented using Python in the Jupyter Notebook environment. The following libraries were used: Tensorflow (Tensors and Keras libraries, for machine learning and deep learning, respectively), Tkinter (GUI library), Pillow (image processing library), matplotlib (data visualization library), NumPy (library for performing complex computations), and Pandas (data analysis and processing library). The developed application is installed on the onboard vehicle computer. The detected sign is displayed in an appropriate form on the automobile dashboard. Simulations of real traffic situations are implemented in the system. All this will facilitate driving and help drivers with less experience acquire the necessary driving reactions. © 2024 IEEE.

关键词： Convolutional neural networks

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Medicinal Plants Recognition Using Deep Learning

Medicinal Plants Recognition Using Deep Learning

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2023 International Conference on Multimedia Computing, Networking and Applications, MCNA 2023

作者： Sharrab, Yousef Al-Fraihat, Dimah Tarawneh, Monther Sharieh, Ahmad Isra University Dept. of Computer Science Amman Jordan Isra University Dept. of Software Engineering Amman Jordan The University of Jordan Dept. of Computer Science Amman Jordan

ISBN: (纸本)9798350339277

The use of medicinal plants has been a longstanding practice in traditional medicine worldwide. Accurately identifying medicinal plants is crucial for determining their medicinal properties and potential applications. However, it can be a challenging task due to the complexity of their appearance. Variations in growth stage, lighting, and imaging conditions can make classification challenging, which limits the application of traditional methods for plant identification. This paper proposes a deep learning-based approach that uses a convolutional neural network (CNN) based on the VGG-16 model. With a dataset of 25,686 images, the CNN is capable of learning and representing complex features in images, enabling it to recognize and classify medicinal plants with high accuracy. The proposed approach can efficiently classify plants with different growth stages, lighting conditions, and imaging settings, providing a reliable tool for plant identification. We achieved an impressive recognition rate of 98%, demonstrating the feasibility of using deep learning techniques for accurate plant classification. The proposed approach has enormous potential for providing healthcare professionals and herbal medicine researchers with a reliable tool for identifying herbal plants. the study represents an essential advancement in the use of deep learning techniques for medicinal plant recognition, overcoming the challenges posed by their complex appearance. The proposed approach has farreaching implications and can significantly impact the field of herbal medicine research, enabling researchers and healthcare professionals to identify and classify medicinal plants more accurately © 2023 IEEE.

关键词： Classification (of information)

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Movement Attentive Graph Embedding for Improving Next Point of Access Prediction 18

Movement Attentive Graph Embedding for Improving Next Point ...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Kang, Honggu Kim, Taesoo Yang, Huigyu Choo, Hyunseung Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Superintelligence Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798350331011

Next Point-of-Attachment (PoA) prediction is a deep learning-based approach to provide seamless connectivity in the mobile networks from heterogenous and dense user access. Existing recurrent neural models utilize time series characteristics to extract and learn the sequential association changes occurs between all Base Stations (BSs). Since the user moves from certain BS to its adjacent BS, the deployments of these BSs are a crucial feature for improving prediction accuracy. To this end, we propose a Graph Embedding - Long Short-Term Memory (GE-LSTM) model using Recurrent Neural Network (RNN) and Graph Neural Network (GNN) jointly. The time-series pattern of the user movement is extracted by LSTM, and the movement between adjacent BSs is emphasized as GNN through GE. The joint property of the proposed model is implemented through mixture-of-experts approach from GNN and LSTM. The performance is evaluated along with the RNN families and Transformer models, furthermore, it explores the significance of the graph-based mobility embeddings. In our experiment, the proposed model outperforms other SOTA models by achieving the maximum accuracy 94%. © 2024 IEEE.

关键词： Forecasting

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A Systematic Review on Pattern-based GUI Testing of Android and Web Apps: State-of-the-Art, Taxonomy, Challenges and Future Directions 25

A Systematic Review on Pattern-based GUI Testing of Android ...

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25th International Multi Topic Conference, INMIC 2023

作者： Kousar, Ambreen Khan, Saif Ur Rehman Hussain, Shahid Abdul Basit Ur Rahim, M. Wang, Wen-Li Ibrahim, Naseem Comsats University Islamabad Dept of Computer Science Islamabad Pakistan Shifa Tameer-e-Millat University Dept. of Software Engineering Islamabad Pakistan Penn State University Behrend School of Computer Science and Software Engineering Pennsylvania16563 United States California State University Dept. of Computer Engineering and Computer Science Long Beach United States Penn State Erie Dept. of Computer Science and Software Engineering United States

ISBN: (纸本)9798350317701

Graphical User Interface (GUI) designing and testing of different applications is significant from a user point of view. GUI testing ensures that the user interface of apps is visually appealing, user-friendly, and enhances user satisfaction. Pattern-Based GUI Testing (PBGT) is a new model-based testing approach that aims to enhance reusability while reducing the workload associated with modeling user interfaces and testing applications. The objective of conducting a Systematic Literature Review (SLR) on pattern-based GUI testing is to consolidate existing knowledge and provide insights for improving the effectiveness of PBGT methods. The objectives of conducting an SLR are the identification of current state-of-the-art approaches and tools in the targeted research context. To accomplish the mentioned objectives, A hybrid methodology combining Kitchenham's SLR and PRISMA guidelines was used to conduct this SLR. We performed a keyword-based search on well-known databases IEEE, ACM, Science Direct, Wiley, and Springer Link to determine the potential studies published in the last 10 years. Following a comprehensive systematic analysis, we identified a total of twenty-four studies employing varied approaches for pattern-based GUI testing. Additionally, we identified 11 tools that have been used and proposed in PBGT for Android and web apps and made a taxonomy of tools. We also identified challenges and research directions. The implications of this study are significant. The current study is helpful for researchers intending to work in pattern-based GUI testing. The comprehensive knowledge about the current state-of-the-art Tools, techniques, and approaches will help practitioners to effectively apply them in industry and enable researchers to extend their work in this context in future research. © 2023 IEEE.

关键词： Graphical user interfaces

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Monitoring and Forecasting Quality Indicators of Predough Production in the Bakery Industry 5

Monitoring and Forecasting Quality Indicators of Predough Pr...

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5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Zhyltsov, Andrii Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Electromagnetic Systems The Institute of Electrodynamics The National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350384499

The work demonstrates the process of developing a machine learning model for a system for monitoring and controlling the quality of predough at a bakery. This system will provide continuous analysis of the predough parameters, such as temperature, humidity, lifting force and titranic acidity, which will ensure timely detection of deviations in the quality of the predough and avoid production problems. In addition, it will help automate the quality control process and ensure the stability of production processes. Analysis and forecasting of dough parameters will improve the quality of produced bread or other bakery products. Management based also on the predicted values of technological indicators of the predough will allow the enterprise to avoid unnecessary costs of materials and energy and increase economic efficiency. As part of the study, seven machine learning models were created and reviewed, including three linear and four varieties of binary decision trees. An analysis of the best model - a regression decision tree - was carried out, confirming its effectiveness and the validity of the resulting hierarchical structure, in particular, the prediction of important indicators of predough with a high degree of reliability, which indicates its importance in practical applications in production. The created model can be used to monitor and control product quality in real time, allowing operators to quickly identify and correct any deviations in process performance. Also, by analyzing changes in the variables of the predough production process, it is possible to identify precursors of possible production accidents or deviations in product quality, which will allow timely measures to be taken to avoid them. ©2024 IEEE.

关键词： Bakeries

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Design of Array Microstrip Antenna with Circular Polarization for WBAN/WIMAX 26

Design of Array Microstrip Antenna with Circular Polarizatio...

引用

2024 IEEE International Conference on Automation/26th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2024

作者： Ahumada, Cristian Kaschel, Hector Osorio-Comparan, Roman Cordero, Sergio Universidad Técnica Federico Santa María Dept. Electrotechnics and Computer Valparaiso Chile Dept. of Electrical Engieneering Santiago Chile Dept. of Computer Systems Engineering Ciudad de México Mexico

ISBN: (纸本)9798350378115

This article presents the design of a array microstrip antenna with circular polarization for the evaluation of the specific absorption rate (SAR) in human tissues. The analysis focuses on a single element and a two element microstrip patch, each designed at 2.5 GHz. Both antennas were crafted on a microstrip substrate, sporting a thickness of 1.6 mm and a dielectric constant of 4.3. An evaluation was conducted that contrasted the efficiency of the engineered antenna array with that of the standalone antenna, with particular emphasis on VSWR (Voltage Standing Wave Radio), gain, and directionality, was designed, simulated, and optimized using CST Microwave Studio (MWS) software. Furthermore, the SAR level was scrutinized under flat conditions within the human tissue representation. © 2024 IEEE.

关键词： Circular polarization

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Comparative analysis of OFDM in AWGN & Local Acoustic Channel 1

Comparative analysis of OFDM in AWGN & Local Acoustic Channe...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Ahmed, Areeb Siddqui, Aish Khan, Rehan Nabi, Ghulam Ali, Murtaza Mohammad Ali Jinnah University Dept. Electrical and Computer Systems Engineering Karachi Pakistan Islamabad Pakistan

ISBN: (纸本)9798350373042

In this research study, a comparative analysis is presented that analyze the Orthogonal Frequency Division Multiplexing (OFDM) in two distinct communication systems Additive White Gaussian Noise (AWGN) and a Local Acoustic Channel. This study incorporates insights from the BELLHOP model to enhance understanding of sound propagation in dept. of water and guide the adaptation of OFDM for optimized communication. From simulated experiments, evaluate the performance of OFDM is assessed in the presence of local acoustic channel and AWGN, the important parameters like signal-to-noise ratio (SNR) and bit error rate (BER). The system of BELLHOP provides valuable context in the comparative analysis, highlighting the result of environmental factors on the performance of OFDM. The study not only contributes to the ongoing efforts in advancing underwater communication technique but also emphasizes the significance of tailored approach's in different underwater scenarios, ultimately having the way for more robust and efficient communication systems beneath the waves. Through a comparative analysis between AWGN and a local acoustic channel, results show that the system achieves a BER of 10-3 at SNR of 10 in local acoustic channel, while in the AWGN, a similar BER is achieved at SNR of 16. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Analyzing Different Protocols of Information Granularity Distribution to Improve Consistency of Fuzzy Preference Relations in Decision-Making

Analyzing Different Protocols of Information Granularity Dis...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Gonzalez-Quesada, Juan Carlos Perez, Ignacio Javier Morente-Molinera, Juan Antonio Alonso, Sergio Herrera-Viedma, Enrique Cabrerizo, Francisco Javier University of Granada Dept. of Computer Science and Ai Granada Spain University of Granada Dept. of Software Engineering Granada Spain

ISBN: (纸本)9781665430654

A fundamental principle of Granular Computing is that of an information granularity distribution and its optimization process. It has been used in system modelling to elevate a numerical model to its granular counterpart, which is more in rapport with reality. For example, in decision-making with fuzzy preference relations, it has been applied to enhance the existing numerical consistency improvement procedures. However, even though different protocols of information granularity distribution have been proposed, only the one based on a uniform and symmetric distribution and the one based on a symmetric but non-uniform distribution have been considered. Given that there exist others, this study aims to analyze how we can take advantage of all of them to improve the consistency of the fuzzy preference relations. Some numerical experiments are also completed to show the performance of these protocols. © 2023 IEEE.

关键词： consistency decision-making fuzzy preference relation information granularity

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Mitigating YouTube Recommendation Polarity using BERT and K-Means Clustering 10

Mitigating YouTube Recommendation Polarity using BERT and K-...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Ahmad, Zoheb Quasim, Danish Garg, Shreeya Kumar, Vinod Delhi Technological University Dept. of Software Engineering New Delhi India Delhi Technological University Dept. of Computer Engineering New Delhi India Iiit Delhi Dept. of Computer Engineering and Bioscience New Delhi India

ISBN: (纸本)9789380544472

YouTube's recommendation system is famous for its success in maintaining high retention rates. The cause of its success is its ability to learn and predict an individual user's preferences appropriately. An unintended consequence, however, is that users get stuck in what is known as their own 'echo chambers' when dealing with and feeding users back their preferences. These echo chambers can cause increasing perspective bias within users, making it difficult for users to understand differing opinions. This work aims to prepare a model that counteracts YouTube's recommendation system by forcefully exposing users to content from varying viewpoints. The SSKA pipeline (Suno Sabki, Karo Apni) is a complementary deep learning model that involves Natural Language Processing (NLP) and K-Means clustering. It utilizes modern software libraries such as the YouTube API (Application Programming Interface) for data collection and was trained and tested on a varied set of users. The results prove that the model is successful in decreasing the bias recommendation by exposing users to the content of varying opinions and helping them break away from their echo chambers. The proposed methodology of explicitly exposing users to the content of varying opinions can positively impact local societies and the global community. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： K-means clustering

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Decomposition Algorithm for Security-Constrained Unit Commitment with AC Optimal Power Flows

Decomposition Algorithm for Security-Constrained Unit Commit...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Renl, Weihang Li, Mingze Fan, Lei Guan, Yongpei University of Florida Dept. of Industrial and Systems Engineering FL United States University of Houston Dept. of Electrical and Computer Engineering TX United States University of Houston Engineering Technology TX United States

ISBN: (纸本)9798331541125

This paper presents a comprehensive approach to solving the security-constrained unit commitment with alternating current power flows (SCUC-ACPF) problem in contemporary power systems. We introduce algorithms that decompose the problem into subproblems suitable for specialized solvers, so that the large-scale mixed-integer nonlinear program-ming SCUC-ACPF problem can be solved. In case studies, we validate the efficiency and effectiveness of our algorithms on synthetic and industry-scale power system networks. © 2025 IEEE.

关键词： Lagrange multipliers

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