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Multi-Path Restorative Pre-Training with Localized Adaptive Graph Convolution for 2D Tooth Segmentation 4

Multi-Path Restorative Pre-Training with Localized Adaptive ...

引用

4th international conference on Industrial Automation, Robotics and Control engineering, IARCE 2024

作者： Wang, Haibin Li, Yang Liu, Li Beihang University Department of Automation Science and Electrical Engineering Beijing China

ISBN: (纸本)9798350380323

Segmenting teeth in 2D oral CT images is crucial for diagnosing dental conditions. However, many current models for 2D tooth segmentation depend solely on end-to-end segmentation loss during training, which can fail to capture the image's intrinsic features. Additionally, neighboring regions may interfere with tooth segmentation, leading to less-than-optimal results. To address these challenges, we introduce a novel framework named Multi-Path Restorative Pre-training with Localized Adaptive Graph Convolution (MPRP-LAGC) for 2D tooth segmentation. the MPRP utilizes two paths - one for image reconstruction and another for tooth edge reconstruction - as auxiliary tasks for pretraining the segmentation model. this enables the model to better capture both global image characteristics and the specific features of tooth edges. After dividing the feature maps to correspond to the tooth, maxillary, and mandibular regions, dynamic convolution is applied to emphasize the distinctions between the tooth area and surrounding tissues in the feature space. Experiments on one publicly available 2D oral CT dataset show that the proposed method surpasses current state-of-the-art models and accurately segments the boundaries of teeth, particularly where they meet the maxilla and mandible. © 2024 IEEE.

关键词： Image segmentation

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AI2DS: Advanced Deep Autoencoder-Driven Method for Intelligent Network Intrusion Detection Systems 4

AI2DS: Advanced Deep Autoencoder-Driven Method for Intellige...

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4th IEEE international conference on artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Rahman, Md Habibur Martinez, Leo Mishra, Avdesh Nijim, Mais Goyal, Ayush Hicks, David Texas A&M University-Kingsville Department of Electrical Engineering and Computer Science KingsvilleTX78363 United States

ISBN: (纸本)9798331518882

Network security is a growing concern as digital infrastructure expands, and traditional measures struggle against modern cyber threats. With the increasing complexity of attacks, there is a need for more adaptive and intelligent solutions. this research introduces AI2DS (Advanced Deep Autoencoder-Driven Method for Intelligent Network Intrusion Detection Systems), an autoencoder-based architecture that enhances security by identifying deviations from normal network behavior. the model is trained on normal data, using reconstruction errors to detect anomalies through adaptive thresholding. By simplifying attack classification into a single 'intrusion' class, AI2DS demonstrates high accuracy and broad applicability. the model shows average improvements of 6.61%, 31.11, 11.46%, and 16.91 in Precision, Recall, F1 Score, and Accuracy, respectively, over state-of-the-art methods, with potential for real-time application and future advancements. © 2025 IEEE.

关键词： Network intrusion

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A Novel Approach to Mitigating Traffic Issues through Advanced Congest Detection and Adaptive Signal Control Using AI 4

A Novel Approach to Mitigating Traffic Issues through Advanc...

引用

4th international conference on Intelligent Technologies, CONIT 2024

作者： Priya, G. Vishnu Gowri, B.Shyamala Rajalakshmi, B. Dilli Kumar, L. Janagan, Us Sudhir, Rv Easwari Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9798350349900

this paper presents a novel approach to mitigating traffic issues through the development of an AI- driven intelligent traffic congestion identificationand signal switching system. the proposed system aims to improve traffic flow and reduce congestion by employing advanced congestion detection techniques and adaptive signal control mechanisms. the system utilizes state-of-the-art AI algorithms, such as machine learning and deep learning, to accurately identify congested areas in real-time. By analyzing various traffic parameters, includingtraffic flow, speed, and density, the system can effectively detect and predict congestion patterns. Once a congested area is identified, the system employs adaptive signal control strategies to optimize traffic signal timings and prioritize the flow of traffic in congested zones. this is achieved by dynamically adjusting signal timings based on the current traffic conditions, such as the number of vehicles waiting at each intersection and the expected delay. the system also integrates historicaltraffic data to further enhance congestion predictionaccuracy and signal switching decisions. Experimental results demonstrate the effectiveness of the proposed system in reducing traffic congestion and improving overall traffic performance. the AI-driven intelligent traffic congestion identification and signal switching system presented in this paper offer a promising and innovative solution to address traffic issues. © 2024 IEEE.

关键词： Traffic congestion

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Deep Learning-Based Classification of Brain Tumours on MRI Images Using EfficientNetB0 4

Deep Learning-Based Classification of Brain Tumours on MRI I...

引用

4th international conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Kumar, Aditya Nelson, Leema Arumugam, Deepak Chitkara University Department of Computer Science and Engineering Punjab Rajpura India Saveetha University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387490

this study presents an innovative approach to brain tumour classification utilising MRI images and deep learning techniques, specifically the EfficientNetB0 architecture. the methodology involves meticulous dataset curation, comprehensive data preprocessing, and the design of an efficient model architecture. the model achieves a notable accuracy of 98% through rigorous training and evaluation procedures, demonstrating its proficiency in distinguishing between various types of brain tumours, including glioma, meningioma, pituitary tumours, and non-tumour samples. the results reveal commendable precision and recall rates, affirming the model's ability to accurately identify pathological conditions from MRI scans. By employing state-of-the-art deep learning methodologies, this study contributes significantly to the advancement of automated brain tumour diagnostics, offering a reliable tool for clinicians to expedite the diagnosis process and improve patient outcomes. the demonstrated accuracy underscores the potential of deep learning algorithms in enhancing medical imaging analysis, paving the way for more efficient and precise diagnostic systems in clinical practice. Overall, this study underscores the transformative impact of artificial intelligence in medical imaging and highlights the promising prospects for integrating deep learning models into routine healthcare practices for improved patient care and outcomes. © 2024 IEEE.

关键词： Deep learning

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Research on architecture and key technologies of urban traffic digital twin system 4

Research on architecture and key technologies of urban traff...

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2024 4th international conference on Intelligent Traffic Systems and Smart City, ITSSC 2024

作者： Xu, Xuedong Sun, Wei School of Automotive Engineering CIT Changchun130012 China College of Automotive Engineering Jilin University Changchun130012 China School of Art& Design CIT Changchun130012 China

ISBN: (纸本)9781510686304

Digital twin cities are important carriers for realizing urban digital governance and developing the digital economy. In response to the unclear definition of the scope of urban transportation digital twins, the urgent need to deepen the architectural model, and the difficulty in integrating cross-domain heterogeneous *** the technical needs of transportation digital twins, defines the concept of transportation scenarios, proposes a technical architecture for urban transportation digital twins, and studies key technologies such as digital identification and coding of transportation twins, unified coordinate systems, and construction of transportation scenario information models. It also conducts application demonstrations combined with urban expressway traffic control, promoting the implementation of digital twin cities in the transportation field and improving the level of intelligent transportation construction. © 2025 SPIE.

关键词： Urban transportation

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Optimal Integration of Active and Reactive Power DGs in Distribution Network via a Novel Multi-Objective Intelligent Technique 4

Optimal Integration of Active and Reactive Power DGs in Dist...

引用

4th IEEE international conference in Power engineering Applications, ICPEA 2024

作者： Abdullah, Azlina Musirin, Ismail Othman, Muhammad Murtadha Rahim, Siti Rafidah Abdul Shaaya, Sharifah Azwa Senthil Kumar, A.V. Universiti Tenaga Nasional Department of Electrical & Electronics Engineering Selangor Kajang43000 Malaysia Universiti Teknologi MARA School of Electrical Engineering College of Engineering Selangor Shah Alam40450 Malaysia Universiti Malaysia Perlis Faculty of Electrical Engineering Technology Kampus Pauh Putra Perlis Arau02600 Malaysia Hindusthan College of Arts and Science City Campus Nava India Avinashi Road Coimbatore India

ISBN: (纸本)9798350344578

this work introduces a novel approach called the Multi-Objective Integrated Immune Moth Flame Evolutionary programming (MO-IIMFEP) algorithm. this algorithm aims to determine the optimal sizes and positions for Type III distributed generators (DGs) that generate both active and reactive power. the objectives involve reducing overall losses in the distribution system while adhering to voltage restrictions and taking into account the cost limitations connected with the installation of DG. MO-IIMFEP overcomes the constraints of traditional Evolutionary programming (EP) and Moth Flame Optimization (MFO), particularly in effectively handling local optima. Fuzzy logic is employed in MO-IIMFEP to determine the best solution to compromise conflicting goals, as obtained from the non-dominated Pareto solutions. the efficacy of MOIIMFEP in identifying optimal solutions for multi-objective problems is demonstrated through comprehensive assessments conducted on the 118-Bus Radial Distribution Systems (RDS), comparing it against MO-EP and MO-MFO. the results underscore the strategic benefits of DG installation in sustaining voltage levels, reducing power losses, and minimizing total operating costs for power suppliers. © 2024 IEEE.

关键词： Multiobjective optimization

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Enhancing Solar Energy Harvesting Efficiency: A Dual-Axis Solar Tracking System 4

Enhancing Solar Energy Harvesting Efficiency: A Dual-Axis So...

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4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Ravikumar, S. Eugene Berna, I. Vijay, K. Pradeep, R. Babu, R. R&d Institute of Science and Technology Vel Tech Rangarajan Dr.Sagunthala Department of Computer Science and Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of College of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307757

the photovoltaic effect has permanently transformed the landscape of renewable sources by making it possible to convert solar energy into useable power. this concept is based on solar photovoltaic (PV) cells, but current PV technology limits us to capturing just a small percentage of the sun's energy at any given time. However, productivity can be greatly increased by using solar monitoring equipment. Creating and validating a low-priced active dual-axis solar tracking system is a breeze if you follow the steps outlined in this article. the best orientation for solar panels is perpendicular to the sun because their production is directly related to the amount of light they receive. Conventional solar panels have trouble keeping up with the ever-changing ideal angle for sunlight absorption and energy gathering. By tracing the sun's movement across the sky, solar trackers can triple the effectiveness of solar panels. this technological advancement is expected to lead to an improvement in energy generation and overall efficiency. this state-of-the-art solar tracker is powered by LEDs and an Arduino Uno. these systems work together to follow the sun's motion and maintain the solar panels facing the appropriate way. To ensure that the solar panels receive the most amount of sunshine possible, the system will automatically adjust their angle. Data processing and analysis in LabVIEW demonstrated the improved efficiency of the novel architecture. Researchers discovered a remarkable 13.44% boost in energy production compared to conventional fixed solar PV systems. © 2024 IEEE.

关键词： Solar panels

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Smart Clothing for Elderly Patients with Dementia 4

Smart Clothing for Elderly Patients with Dementia

引用

4th IEEE international conference on Information Technology, Big Data and artificial Intelligence, ICIBA 2024

作者： Du, Xinchen Ma, Hao Li, Ruiying Sun, Jiayin Beijing Institute of Fashion Technology School of Fashion Art and Engineering Beijing China

ISBN: (纸本)9798350363586

Aiming at the current increase in the number of demented elderly people, which brings about the problem of increased pressure on medical care, this paper designs a smart nursing garment with fecal incontinence detection for demented elderly patients. the intelligent garment is functionally divided into control module, temperature and humidity detection module, wireless communication monitor and power module, etc., and the APP detection function is realized on the cell phone terminal. At the same time, this paper enhances the comfort and ease of wearing and taking off of the smart clothing, as well as the acceptance of dementia elderly patients through the style design, structure design and color design of the clothing, and the virtual effect of the smart clothing is demonstrated. through the smart clothing function test, when the dementia elderly patients are incontinent, the cell phone APP has temperature and humidity display, prompting the caregiver to change clothes in time to realize the intelligent nursing function. © 2024 IEEE.

关键词： Nursing

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Machine Learning Approach for Detection of Speech Emotions for RAVDESS Audio Dataset 4

Machine Learning Approach for Detection of Speech Emotions f...

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4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Rochlani, Yogesh R. Raut, Anjali B. H. V. P. m'S College of Engineering and Technology Department of Computer Science and Engineering Maharashtra Amravati India

ISBN: (纸本)9798350343670

the most effective technique to convey one's thoughts and actions to another is through the use of emotion. Emotion recognition from one's own voice is the most urgently needed technology right now. Emotions are attached to everything that a person owns. Every customer's feelings might provide light on what they really need from the customer service professional. Since human communication relies heavily on the ability to read one another's emotions, speech emotion recognition is crucial for understanding another person's inner workings. the time and frequency-based acoustic characteristics integrated with machine learning classifiers that can categorize emotions to enhance the performance of the proposed intelligent system. this research makes use of the audio records available in the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). Emotion is gleaned from speech via temporal and spectral characteristics. three machine learning classifiers, the SVM, DT, and RF classifiers, are then trained on the retrieved data. the RF classifier achieved 88.78% higher accuracy rate than the state-of-the-art approaches. © 2024 IEEE.

关键词： Classification Decision Tree Emotion recognition Mel frequency cepstral coefficients Pitch Random Forest Classifier Short term energy Support Vector Machine

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Log Anomaly Detection Method Based On Multi-Feature Graph Embedding 4

Log Anomaly Detection Method Based On Multi-Feature Graph Em...

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4th international conference on Electronic Information engineering and Computer Communication, EIECC 2024

作者： Liu, JiZhao Li, Le ZhongYuan University of Technology School of Computer Science Henan Zhengzhou China

ISBN: (纸本)9798331534622

Modern software systems generate a large amount of logs during runtime, which reflect the system's operational status. the reliability of system services relies on automatic log anomaly detection. Researchers have proposed anomaly detection methods based on log features and sequence structures. However, existing methods, while utilizing various log features and structured information, still do not exploit the implicit feature information of logs and fail to fully capture the structural information of log sequences when fusing contextual event features of logs. therefore, we propose an anomaly detection method based on multi-feature graph embedding, LogMFG, which fully extracts explicit, implicit, parameter, and semantic features of logs using a defined multi-feature extraction method for log messages. Log messages are represented as graph nodes with multiple feature attributes. Finally, GTN (Graph Transformer Network) is utilized to effectively fuse multi-feature information of log events with the global structure of log sequences for anomaly detection in log sequences. Experimental results on public datasets HDFS and BGL show that LogMFG outperforms eight log anomaly detection methods, with an anomaly log detection F1 score higher than 0.9992, and surpasses the state-of-the-art LogGT algorithm in offline log anomaly detection. © 2024 IEEE.

关键词： Graph embeddings

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