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An Improvised Approach Using YOLOv3 Architecture for Digital Panoramic Teeth Recognition and Classification 46

An Improvised Approach Using YOLOv3 Architecture for Digital...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Krishnappa, Veena Divya Jatti, Anand Rajasree, P.M. Vidya, M.J. Joshi, Revan Kumar Renumadhavi, C.H. Padmaja, K.V. Subramanya, K.N. Krishnamoorthy, Adharsh Rv College of Engineering Dept. of Electronics and Instrumentation Engineering Bengaluru India Rv University School of Computer Science & Engineering Bangalore India Dapm Rv Dental College and Hospital Department of Oral Medicine and Radiology Bengaluru India Rv College of Engineering Department of Industrial Engineering and Management Bengaluru India

ISBN: (纸本)9798350371499

Tooth loss may occur due to a lack of access to diagnostic imaging and other dental radiographs, despite the fact that these images are vital for treating oral health issues. For better teeth recognition and classification networks, a new model based on YOLOv3 is suggested. A smaller convolution layer and architectural deepening for improved feature extraction are two examples of how the model improves upon the YOLOv3 model for better metrics. A reduction in convolution layers allows for fast recognition and the introduction of the network architecture. A validation/test dataset is used to assess the model's performance, with the help of the Radiology department at Bengaluru's DAPM RV Dental College and Ho *** Relevance - When it comes to training artificial intelligence systems, radiologists are indispensable for producing accurate labels. These systems are vital for learning and dependable use in clinical areas. According to the research, artificial intelligence systems may one day be able to detect periodontal issues from digital Panoramic data. © 2024 IEEE.

关键词： Clinical research

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Modeling Neural Circuit of Lower Urinary Tract

Modeling Neural Circuit of Lower Urinary Tract

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Midwest Symposium on Circuits and systems (MWSCAS)

作者： Hunter Bushnell Isabel Banks Vinay Guntu Gregory Glickert Satish S. Nair Dept. of Electrical Engineering & Computer Science University of Missouri Columbia MO USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

We report a biophysical network model of the mammalian lower urinary tract including the neural circuit that controls its closed loop function. The model was able to reproduce the firing rates and bladder dynamics during a normal fill and void cycle. We were then able to adapt the model to produce the changes and effects of spinal cord injury, which causes severe dysfunction in micturition function and control.

关键词： Adaptation models Firing Circuits and systems Biological system modeling Neural circuits Bladder Spinal cord injury

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Automating Document Classification in the Financial Markets: A Comparative Study of Simple Models Versus Complex Models

Automating Document Classification in the Financial Markets:...

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International Conference on Advanced systems and Electric Technologies (IC_ASET)

作者： Houda Atmani Mohamed Tarik Moutacalli dept. of Mathematics Computer Science and Engineering University of Quebec at Rimouski Quebec Canada

ISBN: (数字)9798350384895

ISBN: (纸本)9798350384901

As the volume of textual data continues to surge, the demand for automated techniques to classify and analyze large text datasets intensifies. This article addresses the critical task of text classification, particularly in the context of financial market regulation, where efficient document analysis is paramount. Our study conducts a comparative analysis employing both simple learning models and complex language models to automate and enhance document classification. By leveraging a diverse dataset from a prominent online platform, meticulous preprocessing techniques, and rigorous Out of Sample testing, we aim to evaluate the generalizability of our models across various contexts and datasets, including the complexities and biases inherent in real so-cial media data. Results indicate that the Random Forest emerges as a standout performer among simple models, achieving a low error rate of 3.755% and a notable False Negative Rate (PI0) of 3.154 %. Conversely, among complex models, the Bidirectional Encoder Representations from Transformers (BERT) is identified as the preferred choice, displaying a superior balance of a low error rate of 2.843% and a minimal PI0 of 2% at a threshold of 0.9. This research not only provides insights into effective resource prioritization for businesses but also acknowledges the challenges of working with real social media data, emphasizing the importance of capturing and addressing the intricacies and biases present in such datasets. Future avenues of exploration may include advanced data balancing methods, sophisticated preprocessing techniques, and alternative classification models to further enrich document classification research in dynamic social media contexts and beyond.

关键词： Analytical models Error analysis Social networking (online) Text categorization Data models Regulation Complexity theory

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Active Control for Resonance Mitigation in Electric Drive Applications

Active Control for Resonance Mitigation in Electric Drive Ap...

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International Symposium on Industrial Electronics (INDEL)

作者： Emelie Berglund Johan Borg Jonny Johansson Dept. of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

ISBN: (数字)9798350352320

ISBN: (纸本)9798350352337

The use of inverter-based motor controllers that can generate fast voltage level transitions is increasing in the field of electric drive systems because of their ability to achieve higher efficiency. However, the fast voltage transitions, dv/dt's, are prone to generate bearing currents and cause overvoltages that may harm the electric motor. An LC filter applied between the inverter and the motor terminals can reduce the dv/dt's. However, the LC filter will cause overvoltage due to resonance but this can be overcome by introducing an extra pulse at each pulse-width modulation (PWM) transition. The timing of this extra pulse is crucial for suppressing the resonance, but due to nonlinearities in a real system, the time instants may vary over time and are thus in need of control. In this study, an analytical expression for the optimal pulse location and duration is derived and we propose a control algorithm that continuously calculates the optimal pulse switch time instants based on the measured output voltage. The control algorithm can handle sudden system disturbances and a Monte Carlo simulation is performed to evaluate the performance of the algorithm. This hybrid filter successfully reduces dv/dt's at the motor terminals while simultaneously eliminating overvoltages due to the filter resonance.

关键词： Voltage measurement Monte Carlo methods Heuristic algorithms Filtering algorithms Pulse width modulation Resonance Inverters Timing Surges Voltage control

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GreenLight: Green Traffic Signal Control using Attention-based Reinforcement Learning on Fog Computing Network

GreenLight: Green Traffic Signal Control using Attention-bas...

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International Green Computing Conference

作者： Chengyu Tang Sanjeev Baskiyar Dept. of Computer Science and Software Engineering Auburn University Auburn Alabama USA

ISBN: (数字)9798331507862

ISBN: (纸本)9798331507879

Traffic congestion is a pervasive problem causing severe environmental and economic issues. In recent years, traffic signal control using reinforcement learning (RL) has come a long way. Most existing studies focus on using distributed agents with data exchange among neighbors, which, however, increases network complexity and usage and still suffers from the lack of broader coordination. Meanwhile, the attention mechanism has achieved tremendous success, and advances in vehicle-to-infrastructure (V2I) communications have enabled real-time collections of granular data. However, integrating these technologies into traffic signal control remains under-explored. Therefore, we present GreenLight, a forward-thinking and eco-friendly traffic signal control framework that can be applied to V2I-equipped fog computing environments. For a large urban area, traffic signals are divided into clusters, each coordinated by a fog node with an RL agent. Intra-cluster indexed self-attention is applied to extract context-aware features that the fog-residing RL agent utilizes to determine the proper signal control command. Results of simulation experiments using both synthetic and real-world scenarios show that the presented framework yields lower waiting time, emissions, and fuel consumption compared to baseline methods, indicating its potential for next-generation transportation systems.

关键词： Vehicle-to-infrastructure Green products Urban areas Transportation Reinforcement learning Feature extraction Real-time systems Fuels Traffic congestion Edge computing

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Deep Learning Based Segmentation of Weed Images in crop fields using U-Net

Deep Learning Based Segmentation of Weed Images in crop fiel...

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Advancement in Technology (ICONAT), International Conference for

作者： K. Jairam Naik Dept. of Computer Science & Engineering National Institute of Technology Raipur Raipur India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Weeds are problems for crop yields because they exert harmful effects on the yields by competing with cultivated crops for necessary resources like nutrients, sunlight, and soil. As a result, continual attempts are being made to create safer and more effective herbicide formulations. For precision weeding and to reduce pesticide pollution, segmenting weeds in photos is extremely important. However, weeds and crops are comparable in the field setting, making it challenging to precisely segregate weed from intricate field photos. A deep learning-based system for segmenting weeds from photos was proposed in this paper. This algorithm can distinguish between weeds and crops and soil in photos. Utilizing a condensed U-net, this semantic segmentation method was created. An image augmentation strategy was suggested because image tagging for the semantic segmentation of weeds is challenging. The classification algorithms used in the modified U-Net are Support Vector Machine (SVM), Random Forrest (RF) and Decision Tree (DT). These were also used for predicting the weed part in a sample image before their segmentation. The threshold accuracy for segmenting the image using to be around 51.85% with a segmentation time of approximately 01.02 ms whereas our modified U-Netis obtained 91.03% accuracy and the segmentation time of 50.35ms. The results showed the modified U-Net successfully differentiated weeds from images with numerous other plants. This study seeks to demonstrate that the weed-associated image segmentation technique is effectively identifying and separating weeds in intricate field conditions.

关键词： Training Support vector machines Radio frequency Image segmentation Accuracy Pollution Semantic segmentation Training data Soil Classification algorithms

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Applications and Approaches of Sentiment Analysis: A Current Review

Applications and Approaches of Sentiment Analysis: A Current...

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IEEE Recent Advances in Intelligent Computational systems (RAICS)

作者： Sandra G Salaja Silas Elijah Blessing Rajsingh Dept. of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

Limitless amount of data is loaded in every moment in social media platforms in the forms of audio, videos, texts, and photos. They are rich sources of information, which has immense value. Hence extraction of information from them is inevitable. Many methods are employed for this and the popular among them to mine the information is sentiment analysis. To materialize the objectives of this study, a systematic review of articles published from 2013 to 2023 was done. Our study reviewed the relevance of sentiment analysis in social media by probing the different approaches followed, the platforms used and its applications, as its main objectives. Its results show that majority of studies applied machine learning approach or hybrid method for executing sentiment analysis.

关键词： Sentiment analysis Social networking (online) Voting Decision making Machine learning Disaster management Fraud Stakeholders Forecasting Meteorology

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Fair and Efficient Congestion Management for Low Voltage Distribution Networks

Fair and Efficient Congestion Management for Low Voltage Dis...

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IEEE International Energy Conference and Exhibition (EnergyCon)

作者： Aswin Ramesh Vadavathi Gerwin Hoogsteen Johann Hurink dept. of Electrical Engineering Mathematics and Computer Science University of Twente Enschede the Netherlands

The widespread increase of photovoltaic panels in the distribution grid often leads to overvoltage. Traditional voltage regulation techniques are unfair for prosumers at the sensitive part of the grid due to larger power curtailment. Consequently, there has been considerable research on implementing fair curtailment strategies for congestion management. However, existing fairness-incorporated curtailment approaches yield varying results based on changes in the grid topology and integration of renewable energy sources. Therefore, this paper introduces proportional voltage fairness that is independent of the above-mentioned network properties and inherently distinguishes regions for fair curtailment. Additionally, there is a need to consider the underutilisation of the grid while adhering to a fair curtailment scheme. Thus this paper proposes a control algorithm incorporating proportional voltage fairness and gives the opportunity to specify the trade-off between fairness and the total power curtailed. Further, a comparative analysis with respect to state-of-the-art notions of fairness is performed.

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Enhanced Detection of DoS/DDoS Attacks in SDN Using Ensemble and Hybrid CNN-LSTM Models

Enhanced Detection of DoS/DDoS Attacks in SDN Using Ensemble...

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Computing, Applications and systems (COMPAS), IEEE International Conference on

作者： Maharin Afroj Khan Md Sadbin Rifat Md. Saifur Rahman Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Software-Defined Networking (SDN) faces significant challenges in mitigating Attacks known as denial of service (DoS) and distributed DDoS due to its centralized control plane architecture. This centralization makes the control plane an attractive target for attackers, highlighting the need for effective detection and mitigation strategies. In this study, we propose three innovative methodologies to enhance the accuracy of detecting DoS/DDoS attacks in SDN environments. Our first methodology employs an ensemble model with a voting classifier, which achieves a detection accuracy of 97.4%. This approach leverages multiple weak learners to enhance the robustness of detection. The second methodology utilizes an ensemble with a stacking technique, attaining a superior accuracy of 99%, demonstrating the efficacy of integrating multiple base models in a hierarchical fashion. The third approach introduces a novel hybrid model that blends LSTM networks with CNN, achieving a competitive accuracy of 98.51%. A comparative analysis of these methods reveals that, while all three significantly enhance detection rates, the stacking ensemble technique outperforms the others. This finding underscores the potential of advanced ensemble techniques in effectively addressing the dynamic and evolving cyber threats within SDN environments. Our study offers valuable insights into strengthening SDN infrastructures against DoS/DDoS attacks, contributing to the ongoing efforts to secure modern network architectures.

关键词： Logistic regression Accuracy Prevention and mitigation Stacking Network architecture Robustness Real-time systems Ensemble learning Software defined networking Long short term memory

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Impact of Automated Driving under Virtual Traffic Signals on Traffic Resilience of Urban Road Networks

Impact of Automated Driving under Virtual Traffic Signals on...

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International Conference on Transportation and Development 2024: Transportation Safety and Emerging Technologies, ICTD 2024

作者： Zhang, Yankai Chen, Jiajing Liang, Bo Xu, Zihuan Mo, Lei Zhang, Zhao Bian, Ruijie Key Laboratory of Intelligent Transportation Technology and System Ministry of Education School of Transportation Science and Engineering Beihang Univ. Beijing China Louisiana Transportation Research Center Dept. of Civil and Environmental Engineering Louisiana State University Baton RougeLA United States

ISBN: (纸本)9780784485514

With the rapid development of autonomous driving technology, it is imperative to study the impact of this technology on the resilience of urban road transportation networks. This study focuses on virtual traffic signal control (VTS) as a representative of autonomous driving technology and proposes a multi-dimensional resilience evaluation system to compare the impacts of three different signal-timing strategies on the resilience of urban traffic networks under various scenarios. Simulation results demonstrate that the average vehicle speed under VTS control is 38.1% and 40.1% higher than the other two control modes, respectively. In addition, the VTS approach significantly reduces delays and minimizes fuel consumption compared to the other two approaches. Moreover, this paper delves into the intrinsic mechanisms by which VTS enhances traffic resilience. It highlights the importance of integrating autonomous driving technology into traffic strategies to achieve carbon emissions reduction and promote sustainable transportation. The network resilience enhanced by VTS enables cities to effectively respond to unexpected events, thereby ensuring smooth traffic flow and safety. This research provides valuable insights into existing knowledge and promotes further research in the field of traffic resilience. © ASCE.

关键词： Autonomous vehicles

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