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Haptics-Enabled Surgical Training System with Guidance Using deep learning 9th

Haptics-Enabled Surgical Training System with Guidance Using...

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9th International Conference on Universal Access in Human-Computer Interaction (UAHCI) Held as Part of 17th International Conference on Human-Computer Interaction (HCI)

作者： Biglari, Ehren Feng, Marie Quarles, John Sako, Edward Calhoon, John Rodriguez, Ronald Feng, Yusheng Ctr Simulat Visualizat & Real Time Predict San Antonio TX USA Univ Texas San Antonio Dept Comp Sci San Antonio TX USA Univ Texas San Antonio Dept Cardiothorac Surg San Antonio TX USA Univ Texas Hlth Sci Ctr San Antonio Dept Urol San Antonio TX 78229 USA Univ Texas San Antonio Dept Mech Engn San Antonio TX USA

ISBN: (纸本)9783319206844;9783319206837

In this paper, we present a haptics-enabled surgical training system integrated with deep learning for characterization of particular procedures of experienced surgeons to guide medical residents-in-training with quantifiable patterns. The prototype of virtual reality surgical system is built for open-heart surgery with specific steps and biopsy operation. Two abstract surgical scenarios are designed to emulate incision and biopsy surgical procedures. Using deep learning algorithm (autoencoder), the two surgical procedures were trained and characterized. Results show that a vector with 30 real-valued components can quantify both surgical patterns. These values can be used to compare how a resident-in-training performs differently as opposed to an experienced surgeon so that quantifiable corrective training guidance can be provided.

关键词： Virtual surgical training system Haptic device Machine learning deep learning algorithm Autoencoder Motion tracking and quantification

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E-nose system based on ultra-low power single micro-LED gas sensor and deep learning

E-nose system based on ultra-low power single micro-LED gas ...

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IEEE Sensors Conference

作者： Lee, Kichul Cho, Incheol Park, Inkyu Korea Adv Inst Sci & Technol KAIST Dept Mech Engn Seoul South Korea Samsung Elect Co Ltd Suwon South Korea

ISBN: (纸本)9798350303872

Electronic nose (e-nose) technology utilizing chemoresistive sensors has gained substantial attention across various domains such as smart factory and personal health monitoring. The primary challenge associated with chemoresistive sensors lies in their susceptibility to cross-reactivity towards different gas species. To address this issue, we propose an innovative approach based on a photoactivated gas sensor integrated with micro-LED. By applying a pseudo-random voltage input to the LED, transient sensor responses for various gases are induced. Subsequently, we employ a deep learning to analyze the complex transient signals obtained, facilitating prediction of gas species and concentrations. In particular, the proposed e-nose system achieves high accuracy of gas type determination and concentration prediction for various target gases with only 0.5 mW by using only single sensor. Since it consumes less than one hundredth of the power of the existing e-nose systems, it has the advantage of being able to operate for a long time in battery-powered situation, and this advantage is expected to be utilized in combination with internet-of-things (IoT) technologies.

关键词： gas sensor electronic nose system micro-LED ultra-low power deep learning algorithm

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The Internet Technology for Defect Detection System with deep learning Method in Smart Factory 4

The Internet Technology for Defect Detection System with Dee...

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4th International Conference on Information Management (ICIM)

作者： Huang, Der-Chen Lin, Chun-Fu Chen, Chih-Yen Sze, Jyh-Rou Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung Taiwan Natl Appl Res Labs Instrument Technol Res Ctr Taipei Taiwan

ISBN: (纸本)9781538661475

The most obvious difference between the recent smart factory and the traditional automation factory is that the techniques about internet of thing (IoT) are introduced. The smart factory that employs IoT techniques intelligently manages the automated manufacturing equipment and automated defect detection equipment to improve production efficiency and quality significantly. The equipments used in the smart factory are manufacturing equipments, functional testing equipment and defect detection equipment. Defect detection equipment, mainly in the entire product manufacturing and packaging and functional testing process, the establishment of checkpoints, phased detection of semi-finished products to identify defective and defective products selected. As a result, the defective product no longer goes through all of the following processes, thus reducing costs and improving the yield of the final product shipped. In the development of classification algorithm, a huge breakthrough has been made in the deep learning algorithm in recent years. Therefore, in recent years, many studies have tried to apply deep learning algorithm to various fields. However, most of the current studies focus on the performance of the deep learning algorithm in their applications. Fewer studies research the environmental design for the system employing deep learning algorithm. For example, the network architecture for the system employing deep learning algorithm is less discussed by studies. Therefore, this study presents a architecture of smart factory which employing deep learning algorithm in defect detection system. Thereafter, this study presents the network architecture for the proposed smart factory. Finally, the internet technology for defect detection system with deep learning method in smart factory is presented.

关键词： smart factor internet of thing defect detection deep learning algorithm

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Classification and Detection of Various Structural Cracks Using deep learning Approach 10th

Classification and Detection of Various Structural Cracks Us...

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10th European Workshop on Structural Health Monitoring (EWSHM)

作者： Vundekode, Narasimha Reddy Kalapatapu, Prafulla Pasupuleti, Venkata Dilip Kumar Mahindra Univ Ecole Cent Coll Engn Hyderabad India

ISBN: (纸本)9783031072581;9783031072574

Concrete has evolved to be the most used construction material all over the world from minor to major structures. The accelerated deterioration of the structure can be reduced if the cracks are detected at their early stage, and it will be more effective if the crack type is also known. Development of crack and its propagation if unnoticed overtime can lead to minor to major damage. The proposed study targets to revolutionize the visual inspection process using deep learning (DL) algorithms. The most important part of training a DL algorithm is the data, hence the major challenge for the study is collection of various images consisting different types of cracks from the visual inspections carried manually during real-time projects. Overall, 3500 images were collected and out of which considering the computational complexity the proposed model was developed based on a 500-image sub dataset. The pre-trained DL model has demonstrated the desirable accuracies while no pre-trained model has dropped the accuracy. In practice, the paper also focuses on the current achievements and limitations of the existing methods and as well as opens lot of avenues for the upcoming researchers in the cross disciplinary fields of Structural and Computer Science.

关键词： Concrete crack detection Crack type deep learning algorithm Computer vision Structural health monitoring

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Evaluation of the deep learning-Based m-Health Application Using Mobile App Development and Assessment Guide 12

Evaluation of the Deep Learning-Based m-Health Application U...

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IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Estonilo, Christopher G. Festijo, Enrique D. Technol Inst Philippines Manila Grad Programs Manila Philippines Technol Inst Philippines Manila Manila Philippines

ISBN: (纸本)9781665483032

Diabetes mellitus is known as one of the fatal diseases and affects a large number of people all throughout the world. Apparently, a timely diagnosis of the condition is very important for effective treatment. A deep learning algorithm, which is a subset of machine learning, was utilized to predict diabetes mellitus. The predicting model with a 93% accuracy was deployed in the mobile-health (m- health) application called DMChecker. Nowadays, m-health solutions have evolved at an exponential rate with absolutely little oversight or regulation. Thus, the main purpose of this study was to evaluate the quality of the designs and development of the m-health application using a mobile app development and assessment guide or MAG. With a 4.68 overall weighted mean from the perceptions of the evaluators, the m-health application complied and addressed mostly the standard regulations.

关键词： diabetes mellitus m-health application deep learning algorithm predicting model DMChecker mobile app development and assessment guide MAG

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Next-Generation Skin Disease Diagnosis: deep learning Solution 1

Next-Generation Skin Disease Diagnosis: Deep Learning Soluti...

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1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Bhatt, Chandradeep Saini, Nancy Rathi, Garima Mishra, Pawan Kumar Graphic Era Hill University Cse Department Dehradun India Uttaranchal University Uttaranchal School of Computing Sciences Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350392111

Skin conditions are more prevalent than other illnesses. Fungal infections, bacterial infections, allergens, viruses, and other agents can cause skin problems. The development of photonics and laser-based medical technologies has accelerated the speed and accuracy of the analysis of skin conditions. Conversely, the cost of this kind of analysis is yet extremely high. As a result, image processing technology aids in the early development of automated dermatology screening systems. The process of characterizing traits is essential to the categorization of skin conditions. Computer vision may be used in a variety of ways to detect skin conditions. Skin conditions are widespread in deserts and hot climates. This paper advances the study of skin disorder recognition. We suggested using image processing to identify skin conditions. With this technique, the type of disease will be determined by image analysis applied to a digital image of the affected skin part. The method will function with a colored image as input. Next, use pretrained CNN to extract features from the image by resizing it. Following that, multiclass SVM will be used to categorize the features. Ultimately, the user will see the results, which include the disease's type, severity, and spread. © 2024 IEEE.

关键词： Alexnet deep learning algorithm eczema skin diseases detection

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Research on Building Energy-Saving Prediction Model of Building Air Conditioning Based on deep learning 2

Research on Building Energy-Saving Prediction Model of Build...

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2nd IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2024

作者： Qiu, Qingling Li, Min Wuhan Business University Wuhan China Wuhan Business University Institute of Environmental Pollution Control and Low Carbon Materials Wuhan China

ISBN: (纸本)9798350368208

Aiming at the problem of high energy consumption of building air conditioning system, this paper proposes an energy-saving prediction model construction system based on deep learning. The system uses the powerful feature extraction capability of deep learning algorithm and the running data of buildings to build a high-precision energy consumption prediction model. Through the analysis of many historical energy consumption data, the system can learn the complex relationship between building air conditioning energy consumption, environmental variables, and equipment status, and achieve accurate prediction of future energy consumption, to provide a scientific basis for the formulation of energy conservation strategies. In the algorithm design, this paper introduces the adaptive learning mechanism, so that the model can automatically adjust the prediction strategy according to the change of the environment, and improve the prediction accuracy and stability. In addition, the energy saving effect of the model under different working conditions is verified by energy consumption simulation experiments. The results show that the system can effectively reduce the energy consumption of the building air conditioning system while maintaining the indoor environment comfort. © 2024 IEEE.

关键词： adaptive algorithm air conditioning in buildings deep learning algorithm energy consumption simulation energy saving prediction model

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deep learning-based context aggregation network for tumor diagnosis 14

Deep learning-based context aggregation network for tumor di...

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14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Zhu, Lin Qu, Xinliang Wei, Shoushui Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China Shandong First Med Univ Affiliated Hosp 1 Jinan 250014 Shandong Peoples R China

ISBN: (纸本)9781665400046

Craniopharyngioma is a type of benign brain tumor but has severe biological malignant behavior. Whether the craniopharyngioma invades the surrounding brain tissue has important influence on making treatment plan and the prognosis of patients, so the accurate diagnosis of craniopharyngioma is a crucial step in the treatment processing. It is important to explore some methods for judging the invasiveness of craniopharyngioma preoperatively. Therefore, we proposed a context aggregation network (CA-2D Network) based on deep learning algorithm, which can diagnose the invasiveness of craniopharyngioma by judging the characteristics of head MRI images. The proposed CA-2D Network utilizes ResNet as the backbone, and has a context modeling block and feature aggregating head to correlate features from different slices, capture context information, and aggregate features for classification. The features extracted by the CA-2D Network yield area under the curve (AUC) values of 82.59% for the test set. As demonstrated in the results, the proposed CA-2D Network is promising.

关键词： Medical image analysis deep learning algorithm Computer-aided diagnosis

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Predicting Groundwater Quality with Advanced deep learning Models: Improving Environmental Management by Addressing Pollution Issues and Obtaining More Accurate Forecasts 8

Predicting Groundwater Quality with Advanced Deep Learning M...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Selvakumarasamy, K. Mahaveerakannan, R. Kumar, D. N.S Ravi Balamanigandan, R. Kumar, T. Rajesh Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai602105 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sathyabama Institute of Science and Technology School of Electronics Engineering Chennai India

ISBN: (纸本)9798350367904

To ensure clean water for consumption in the future, it is imperative to understand the pollutants and the quality of the existing groundwater. Controlling water pollution and enhancing water management both depend on the ability to predict the water's purity with high accuracy. Previous studies have shown that when it comes to deep learning models, extreme gradient boosting (XGBoost) performs better in terms of accuracy and precision than random forest (RF). This study explores the field of water quality assessment by creating a novel method that combines state-of-the-art deep learning algorithms such as artificial neural networks (ANN), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), gated recurrent units (GRU), convolutional neural networks (CNN), recurrent neural networks (RNN), and backpropagation neural networks (BPNN). Utilising deep learning techniques to enhance the index's prediction capacity and accuracy, as well as computing WQI utilising comprehensive water quality metrics, are our main research objectives. Through the use of deep learning models in conjunction with WQI, we hope to uncover patterns and connections in information on water quality that are not visible through conventional means. Plans for groundwater development will benefit from the study's findings, which will also enhance DL applications for better groundwater quality. © 2024 IEEE.

关键词： ANN Bi-LSTM BPNN CNN deep learning algorithm Forecast for Water Standards GRU LSTM RNN WAWQI

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IOT and deep learning Based Method for Detecting Anomalies in Power System Operation Status 6

IOT and Deep Learning Based Method for Detecting Anomalies i...

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6th International Conference on HVDC, HVDC 2024

作者： Gong, Ruibang Wang, Chengxuan Cheng, Junwen Zhang, Leiliang Chen, Qiuyue Dong, Bei Sun, Yize Electric Power Research Institute of State Grid Xinjiang Electric Power Co. Ltd. Urumqi China

ISBN: (纸本)9798350387759

The current conventional power system operation status anomaly detection method is mainly realized by mining anomaly data, which leads to poor monitoring accuracy due to the lack of effective fusion of anomaly data features. In this regard, an abnormal detection method of power system operation status based on IOT(Internet of Things technology) and deep learning is proposed. By cleaning and normalizing the operation status data to improve the data purity, and performing heterogeneous data fusion processing, the steps of anomaly detection are optimized by combining IOT technology and deep learning algorithm. The checkout results indicate that the algorithm constructed by proposed method has a high accuracy of anomaly component detection and a high detection accuracy. © 2024 IEEE.

关键词： deep learning algorithm detecting anomalies IOT power system

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