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检索条件"任意字段=2023 3rd International Conference on Detection Technology and Intelligence System, DTIS 2023"

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Utilizing Blockchain and Deep Learning for Decentralized Discovery of Deceptive Practices in Healthcare Insurance

Utilizing Blockchain and Deep Learning for Decentralized Dis...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Nazeer Shaik Nihar Ranjan Kar Blessy Thankachan Amit Kumar Pathak Jagendra Singh Shelly Gupta Department of Computer Science Engineering SRIT-Autonomous Anantapur India School of Pharmacy University of Technology and Management Gopalpur India Department of Computer Applications JECRC University Jaipur India Department of Information Technology Raj Kumar Goel Institute of Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering (Artificial Intelligence) KIET Group of Institutions Ghaziabad India

To address the complex issues of detecting fraudulent practices in healthcare insurance, this research employs sophisticated machine learning, especially the Long Short-Term Memory (LSTM) model, to provide a complete framework for reliable fraud detection. The study meticulously examines performance metrics such as accuracy, precision, recall, and the F1 score by employing and evaluating the LSTM model across two distinct datasets-Dataset A (198810 samples) and Dataset B (434319 samples)-illuminating the model's capacity to detect fraudulent activities while minimizing misclassifications. The evaluation process, as shown by confusion matrices displayed as percentages, reveals the model's strengths and points up areas for improvement. This study makes an important contribution to the field of fraud detection by providing practical insights to strengthen healthcare insurance against misleading practices. This research proclaimers a paradigm shift by combining innovative methodologies, extensive dataset curation, and stringent evaluation, ushering in increased security, transparency, and efficacy in healthcare insurance fraud detection, ultimately fostering a future of resilient and precise fraud detection mechanisms.

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SP-MAE: Lidar Beam-Induced Domain Adaptation for 3D Object detection via Semantic Point Masked Autoencoders

SP-MAE: Lidar Beam-Induced Domain Adaptation for 3D Object D...

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Computer, Control and Robotics (ICCCR), international conference on

作者： Yaolin Liang Zhiming Tu Tao Wu College of Intelligence Science and Technology National University of Defense Technology Changsha China

In autonomous driving scenarios, lidar-based object detectors are widely used in 3D object detection. For the sake of safety redundancy, some self-driving vehicles will be equipped with multiple lidars with different beams, but training multiple lidar-based object detectors requires high costs. In our experiments, the performance of a lidar-based detector model trained on a high-beam lidar domain drops dramatically when transferred to a low-beam lidar domain. At present, there is no public 3D object detection dataset across-lidar-beams domains. In this paper, a 3D object detection dataset adapted across-lidar-beams domain is produced by the LGSVL simulator. The reason for the performance degradation of the lidar-based detectors across lidar-beams domains are mainly due to the large gap in the foreground object point count. To solve this problem, this paper proposed a semantic point generation method based on masked autoencoders, which can bridge the data gap between lidar beam-induced domains by generating more foreground semantic points so as to realize the migration of the detector model. In addition to solving the problem of lidar beam-induced domains migration, SP-MAE can also improve the performance of 3D lidar-based detectors. Experiments showed that the PVRCNN detector can improve 5.11 3D AP of pedestrian on the KITTI dataset after using SP-MAE.

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A Novel Logistic Regression-Based Fire detection Model Using IoT in Underground Coal Mines 3rd

A Novel Logistic Regression-Based Fire Detection Model Using...

引用

3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Thuppari, Chaitanya Jannu, Srikanth Edla, Damodar Reddy National Institute of Technology Farmagudi Goa Ponda403 401 India Vaagdevi Engineering College Telangana Warangal506 005 India

ISBN: (纸本)9789819708918

The environment of underground coal mines (UCMs) is vulnerable to many environmental problems and consequential endangerment. Among those problems, mine fire is a liable threat that causes the loss of lives of mine workers and other valuable infrastructure and resources in the mines. Therefore, continuous monitoring is very important for early detection of mine fire in UCMs. Internet of Things (IoT), nowadays, is widely used for continuous monitoring of the environment of UCMs. For the purpose of monitoring, deploy sensor nodes in the region of UCMs to sense the local information of the environment and transfer that information to the sink for further process. As the gathered information is indistinct, it is necessary to consider the information for taking precautions. Therefore, we propose a method to predict the occurrences of mine fire in UCMs by using logistic regression model. Then, we applied active learning (AL) and semi-supervised learning (SSL) methods on the dataset sequentially to train the model by dividing the dataset into training and testing datasets. The training dataset is used to train a model. On the other hand, the testing dataset is used to evaluate the trained model. The overall process is implemented at the sink instead of control stations. In case of any hazardous situation, the sink takes immediate and necessary actions based on the sensed data rather than the control station. The trained model is simulated using WEKA tool for the data of continuous monitoring on different hazard conditions. This model is more reliable and works effectively when compared to offline monitoring system in any kind of hazards situations in UCMs. The simulated results show the accuracy of the proposed method as 98% whereas the accuracy of linear regression and naive Bayes has been shown as 93% and 96%, respectively, that shows our proposed method outperforms over the existing techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Lt

关键词： Internet of things

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Advanced Manufacturing Process Development for Automation in the Industry using Artificial intelligence-Based system

Advanced Manufacturing Process Development for Automation in...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： D. Kumaravel Prashant Tomar Kodirova Dilshodkhan Tulanovna Sushant Sharma Krupal Prabhakar Pawar Pravin P Patil AMET University Chennai Tamil Nadu India Department of Mechanical Engineering Parul Institute of Technology Parul University Vadodara Gujarat India Candidate of Technical Sciences Docent Uzbekistan Department of Chemistry and Chemical Technology Fergana Polytechnic Institute Fergana Department Of Mechanical Engineering FET MRIIRS Manav Rachna International Institute of Research And Studies Faculty of Engineering and Technology Mechanical Engineering Rajiv Gandhi College Of Engineering India Department of Mechanical Engineering Graphic Era Deemed To Be University

The record of answer books and attendance (RABA) in any examination system is an unexplained but necessary component of educational institutions to register the presence and trace the unique number of answer sheets of a certain applicant. Manually writing in the RABA of pupils traditionally is time-consuming, difficult to manage, and prone to human mistakes. To address these issues, an AI-based system with facial recognition and a fingerprint-based biometric system of digital RABA is presented. In this work, an automated system for face recognition and fingerprint identification using biometrics is proposed, and the answer sheet barcode is subsequently scanned for RABA input using a camera module. Finally, all of these records are stored in a database and may be retrieved at any moment by the institution of the university. This approach provides a faster and more precise means of avoiding proxy attendance.

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Machine Unsupervised Abnormal Sound detection Based on Residual Autoencoder

Machine Unsupervised Abnormal Sound Detection Based on Resid...

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Communication technology and Information technology (ICCTIT), international conference on

作者： Yi Wei Yingxiong Nong Jian Pan Dong Xiao Zhe Li Department of Information Systems Project Management China Tobacco Guangxi Industrial Co. Ltd Nanning China Information Technology Architecture China Tobacco Guangxi Industrial Co. Ltd Nanning China Information System Construction and Operations Division China Tobacco Guangxi Industrial Co. Ltd Nanning China Information Systems Project Management Division Guangxi Branch China Telecom Corporation Limited Nanning China Network and Network Information Security Research Division China Tobacco Guangxi Industrial Co. Ltd Nanning China

To solve the low frequency, wide variety and changing problems in machine abnormal sound detection, we propose an unsupervised machine abnormal sound detection method based on residual autoencoder technology. This method can automatically learn and capture the characteristics of abnormal sounds without the need to mark a large number of abnormal samples in advance. First, Mel-frequency cepstral coefficients(MFCC) is used to construct the acoustic features of the normal sound, then residual autoencoder is used to reconstruct the acoustic features, and finally the anomaly fraction between the acoustic features reconstructed by the audio features to be measured and the acoustic features reconstructed by the normal sound is calculated. This method performs well in correctly identifying the abnormal sound.

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Implementation of Accident detection and Reporting system Using IOT

Implementation of Accident Detection and Reporting System Us...

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Innovative Practices in technology and Management (ICIPTM), international conference on

作者： R. Mohandas Aishwarya Vh Akshatha Shree S Anusha S Khushi J Department of Electrical and Electronics New Horizon College of Engineering Bengaluru India

The extension of transportation networks has sped up the speed of our lives. Car crashes cause a significant measure of death, property harm, and inestimable time, which is a significant worldwide medical condition. It is viewed as one of the essential executioners in the advanced world. The plan of a savvy mishap identification, area following, and cautioning framework that can detect mishaps as they happen is shrouded in this article. To find the mishap's definite area, a GPS device is utilized. The Worldwide Framework for Versatile (GSM) module sends a notification message that remembers a connection to the mishap's area for a Google guide to the nearby emergency clinic and police control focus. They can do whatever it may take to facilitate the salvage exertion by visiting the connection to find where the mishap is in the area.

关键词： GSM Medical conditions Law enforcement Transportation Modems Time measurement Internet

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Non-line-of-sight Human Vital Sign detection Method Based on Multipath Signals Fusion

Non-line-of-sight Human Vital Sign Detection Method Based on...

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Frontiers of Electronics, Information and Computation Technologies (ICFEICT), international conference on

作者： Xingyao Liu Jiahui Chen Shisheng Guo Jingjing Liang School of Information and Communication Engineering University of Electronic Science and Technology Chengdu China

This paper considers the problem of human vital sign detection in a non-line-of-sight scenario by exploiting a millimeter-wave radar system. Specifically, first, the multipaths induced by wall reflections and corner diffraction are separated in the range-angle domain. Then, a path association method is proposed to match different multipaths with different targets to obtain the locations of the targets. After estimating the targets’ range cells, the time series signs with respect to multipath are formed and used to estimate the respiration and heartbeat information of the human targets by exploiting the cross-power spectrum. Finally, the proposed method is verified to get high-precision location information and vital sign values by both simulations and experiments.

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Real-Time Threat detection in Critical Infrastructure with Machine Learning and Industrial Control system Data

Real-Time Threat Detection in Critical Infrastructure with M...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Atul Kumar Ishu Sharma Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The present study introduces a framework for the detection of threats in critical infrastructure in real-time. This framework leverages the capabilities of machine learning and utilizes data from Industrial Control systems (ICS). In order to evaluate the accuracy of threat identification, we conducted a comparative analysis of three well-known machine learning models: Logistic Regression, Random Forest, and Knearest Neighbors. Our study specifically emphasized the importance of precision as a key parameter in this assessment. The results of our study indicate that Logistic Regression has higher performance compared to the other models, emerging as the most effective method for effectively identifying possible hazards in real-time ICS data. This study highlights the effectiveness of Logistic Regression in strengthening security protocols within vital infrastructure, presenting a hopeful resolution to protect against rising risks and weaknesses.

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An Intrusion detection Framework with Optimized Feature Selection and Classification Combination Using Support Vector Machine

An Intrusion Detection Framework with Optimized Feature Sele...

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Electronic technology, Communication and Information (ICETCI), IEEE international conference on

作者： Luo Tianyao Huang Huadong Li Run Guangxi International Business Vocational College Nanning China

As intelligent devices and related technologies continue to develop, abnormal traffic detection has become a major issue in the field of internet security. Malicious attacks can have a negative impact on systems and cause a decline in computational performance. One of the technologies used to identify intruder activity and assess system security by sending out notifications is intrusion detection systems. In this endeavor, we employ the diversified NSL-KDD dataset for intrusion detection to introduce a novel feature selection and classification merging technique using support vector machines (SVM). The goal of this technique is to increase the capability of intrusion classification by considerably reducing the input feature set from the training data. The process of choosing significant input training features and discarding unimportant ones in supervised learning results in a feature subset that can produce greater classification accuracy. In our tests, we used the KDDTest+ and KDDTest-21 data sets’ various input feature subsets as input features for the SVM classifier. According to the experimental findings, the suggested technique obtains classification accuracy of 88.25% when using only KDDTest+ and 72.42% when using KDDTest-21.

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Computer Artificial intelligence technology in CAD Industrial Drafting and Design Platform system: A Review

Computer Artificial Intelligence Technology in CAD Industria...

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Data Science and Computer Application (ICDSCA), IEEE international conference on

作者： Qin Yonghong Oskar Hasdinor Hassan Shahriman Zainal Abidin Azhari Bin Md Hashim College of Creative Arts Universiti Teknologi MARA Shah Alam Selangor Darul Ehsan Malaysia Nanning Vocational and Technical College School of Art Design University of Nanning Nanning China

This paper discusses how a 3D drafting system is developed to accommodate complex human-machine interface characteristics. An object-oriented multi-level structured test case design method is proposed. An Oracle-based distributed project management database is constructed. A new 3D CAD surface model search method is given. A 3D CAD surface model retrieval algorithm based on distance-curvature shape distribution is proposed. The method can effectively solve the collaboration problem of multiple parallel programs. The designed software platform is proved to be complete, scalable and open through practical applications. Finally, it can effectively achieve human-to-human collaboration and resource sharing.

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