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Practical autoencoder based anomaly detection by using vector reconstruction error

Cybersecurity 2023年第2期6卷 1-13页

作者： Hasan Torabi Seyedeh Leili Mirtaheri Sergio Greco Department of Electrical and Computer Engineering Faculty of EngineeringKharazmi UniversityTehranIran Department of Informatics ModelingElectronics and System EngineeringUniversity of CalabriaArcavacataItaly.

Nowadays,cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the *** computing services are available through common internet protocols and network standards.n addition to the unique benefits of cloud computing,insecure communication and attacks on cloud networks cannot be *** are several techniques for dealing with network *** this end,network anomaly detection systems are widely used as an effective countermeasure against network *** anomaly-based approach generally learns normal traffic patterns in various ways and identifies patterns of *** anomaly detection systems have gained much attention in intelligently monitoring network traffic using machine learning *** paper presents an efficient model based on autoencoders for anomaly detection in cloud computing *** autoencoder learns a basic representation of the normal data and its reconstruction with minimum ***,the reconstruction error is used as an anomaly or classification *** addition,to detecting anomaly data from normal data,the classification of anomaly types has also been *** have proposed a new approach by examining an autoencoder's anomaly detection method based on data reconstruction *** the existing autoencoder-based anomaly detection techniques that consider the reconstruction error of all input features as a single value,we assume that the reconstruction error is a *** enables our model to use the reconstruction error of every input feature as an anomaly or classification *** further propose a multi-class classification structure to classify the *** use the CIDDS-001 dataset as a commonly accepted dataset in the *** evaluations show that the performance of the proposed method has improved considerably compared to the existing ones in terms of accuracy,recall,false-positive rate,and F1-score

关键词： Cloud Practical Anomaly detection Autoencoder Reconstruction error Machine learning

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Remaining Useful Life Prediction of Turbofan Engines based on CNN-LSTM Hybrid Model 3

Remaining Useful Life Prediction of Turbofan Engines based o...

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3rd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2024

作者： Wei, Jianfang Meng, Linggang Du, Junrong Yin, Zhifeng National Computer System Engineering Research Institute of China Beijing China

ISBN: (纸本)9798331506582

Good working condition of mechanical facility is necessary to ensure orderly industrial production, and remaining useful life (RUL) estimation of mechanical facility via current monitoring parameters is an important issue for condition-based maintenance. Deep learning has good capacity of nonlinear feature extraction, high dimension time series modeling and complex pattern recognition, and has good application prospect in the field of RUL prediction of mechanical facility. A hybrid model with CNN-LSTM structure for RUL prediction of mechanical facility is proposed, and a multivariate time series model with optimal length based sliding window is established to extract inner features. The method is validated on NASA's public turbofan engines dataset, achieving accurate remaining useful life prediction of turbofan engines. © 2024 IEEE.

关键词： Turbofan engines

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Comparative Analysis of Wearable and computer Vision-Based Methods for Human Torso Posture Estimation 9

Comparative Analysis of Wearable and Computer Vision-Based M...

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9th International Conference on Mathematics and computers in Sciences and Industry, MCSI 2024

作者： Muntean, Emanuel Leba, Monica University of Petrosani Department of System Control and Computer Engineering Petrosani Romania

ISBN: (纸本)9798350377224

This paper conducts a comparative analysis of human torso posture estimation methodologies, focusing on an inertial measurement unit (IMU) sensor coupled with an Arduino UNO as a wearable approach, and Kinect V2, utilizing OpenCV for posture analysis. The core objective of this study is to ascertain which method yields greater precision in the estimation of human torso posture. The IMU sensor, characterized by its wearability, provides the distinct advantage of unobtrusiveness and the capability to record motion across diverse environments. Conversely, Kinect V2 leverages computer vision techniques to derive posture estimations from visual data in real-time. Through comprehensive experimentation, this research evaluates the accuracy of both methodologies by juxtaposing their posture estimation outcomes. The findings of this investigation aim to significantly contribute to the enhancement of human posture estimation systems, with wide-ranging implications for health, sports, and ergonomics. © 2024 IEEE.

关键词： Ergonomics

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Sustainable Water Futures: Enhancing Efficiency And Conservation In Madinah AL Munawara

Journal of Applied Science and Engineering

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Journal of Applied Science and engineering 2024年第10期28卷 2027-2042页

作者： Syed, Toqeer Ali Naqash, Muhammad Tayyab Nawaz, Waqas Namoun, Abdallah Muhammad, Munir Azam Faculty of Computer and Information System Islamic University of Madinah Saudi Arabia Civil Engineering Department Faculty of Engineering Islamic University of Madinah Madinah42351 Saudi Arabia AI Center Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia Khwaja Fareed University of Engineering & Information Technology Faculty of Computer and Information System Punjab Rahim Yar Khan Pakistan

Water resource management worldwide faces significant challenges, including high consumption rates, scarcity, and ageing infrastructure. This paper proposes a comprehensive approach to address these issues through five key solutions. Advanced Metering Infrastructure (AMI) first facilitates real-time data collection and consumer awareness campaigns. Secondly, wastewater recycling and reuse are promoted by upgrading wastewater treatment plants and fostering public-private partnerships. Thirdly, reducing network losses is achieved through leak detection systems and regular maintenance. Fourthly, policy enhancement and regulatory frameworks are strengthened by implementing regulations for new developments and incentives for water-saving technologies. Lastly, community engagement and education initiatives are undertaken to promote sustainable water practices. This multifaceted strategy aims to improve water conservation, efficiency, and resilience of the water supply system. This paper proposes solutions that can be applied to the Madinah AL Munawara region of Saudi Arabia, an area of utmost importance, demonstrating the applicability and effectiveness of these solutions in addressing regional water management challenges. © The Author(’s).

关键词： Wastewater treatment

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Automatic segmentation, classification, and prediction of pelvic bone tumors using deep learning techniques

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Journal of engineering and Applied Science 2024年第1期71卷 214页

作者： Shouman, Mona Rahouma, Kamel Hussein Hamed, Hesham F. A. Computer and System Engineering Department Faculty of Engineering Minia University Minia Egypt Faculty of Engineering Minia University Minia Egypt College of Artificial Intelligence Russian University Cairo Egypt

MRI and CT images are widely utilized for detecting tumors in internal organs. However, accurately distinguishing tumors using these images only, poses a challenge. This demands human expertise to effectively mark tumors in a given MRI or CT image, which is time-consuming. In this study, MONAI Label and MONAI Dynamic U-Net (DynU-Net) were used to segment tumors in any given 3D CT image of the pelvic bone, aiming to assist radiologists further. This method helps to run the model without needing a GPU which is better than traditional approaches. In addition, a convolutional neural network (CNN) was used to classify the tumors as benign or malignant and to predict three grades of tumors (low, medium, and high). The use of CNN in classification and prediction gives higher results than other studies. A dataset of 178 3D CT picture images was employed to feed the networks with the help of Adam optimizer and Categorical cross-entropy. We employ a set of 3D CT scans because of their greater contrast and spatial resolution which is better used for pelvic bone tumors. The Dice similarity coefficient (DSC) and Hausdorff distance (HD) have been used to perform calculations in segmentation. The results show a DSC of 0.7660280 and an HD of 16.75480. A range of performance metrics, such as sensitivity, specification, and F1-score for classification and prediction methods, are used to evaluate the accuracy of the proposed system. The system has accuracy (99.4%) for classification and (97.8%) for prediction. These findings indicate that MONAI Label is effective for automatic segmentation of tumors in a given CT scan;with high accuracy. Moreover, CNN is useful for classification and prediction systems with high accuracy. However, achieving better results is possible with an abundance of training samples. © The Author(s) 2024.

关键词： computerized tomography

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2D Posture Estimation of Human Arm 9

2D Posture Estimation of Human Arm

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9th International Conference on Mathematics and computers in Sciences and Industry, MCSI 2024

作者： Rosca, Sebastian Daniel Leba, Monica Sibisanu, Remus University of Petrosani Department of System Control and Computer Engineering Petrosani Romania

ISBN: (纸本)9798350377224

Human pose estimation and automatic detection is a common task in Human-computer Interaction but especially more capitalized in human-robot interaction inspired from biological form of human anatomy to realize a biomimetic control. In this sense, using human pose estimation it can be realized the whole human body posture estimation using anatomical human joints detection as inputs for specialized algorithms to calculate it based on key points obtained from a person Kinematic tree using regression, generation or part relation as models to estimate the optimal position from video or even from image. In this paper we propose a solution to estimate of 2D human arm posture of a subject using image capture as input for advanced machine learning algorithms to determine the subject joints position used to control in biomimetic manner a simulation of human like robotic arm with six degrees of freedom as a computer-aided model in Matlab-Simulink based on forward kinematics. © 2024 IEEE.

关键词： MATLAB

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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike Information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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ARCHER:a ReRAM-based accelerator for compressed recommendation systems

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Frontiers of computer Science 2024年第5期18卷 147-160页

作者： Xinyang SHEN Xiaofei LIAO Long ZHENG Yu HUANG Dan CHEN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Modern recommendation systems are widely used in modern data *** random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and ***-based processing-in-memory(PIM)can resolve this problem by processing embedding vectors where they are ***,the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip,which induces off-chip accesses that may offset the PIM ***,we deploy the decomposed model on-chip and leverage the high computing efficiency of ReRAM to compensate for the decompression performance *** this paper,we propose ARCHER,a ReRAM-based PIM architecture that implements fully yon-chip recommendations under resource ***,we make a full analysis of the computation pattern and access pattern on the decomposed *** on the computation pattern,we unify the operations of each layer of the decomposed model in multiply-and-accumulate *** on the access observation,we propose a hierarchical mapping schema and a specialized hardware design to maximize resource *** the unified computation and mapping strategy,we can coordinatethe inter-processing elements *** evaluation shows that ARCHER outperforms the state-of-the-art GPU-based DLRM system,the state-of-the-art near-memory processing recommendation system RecNMP,and the ReRAM-based recommendation accelerator REREC by 15.79×,2.21×,and 1.21× in terms of performance and 56.06×,6.45×,and 1.71× in terms of energy savings,respectively.

关键词： recommendation system ReRAM processing-in-memory embedding layer

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Towards a robust solution to mitigate all content-based filtering draw-backs within a recommendation system

International Journal of Systematic Innovation

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International Journal of systematic Innovation 2023年第7期7卷 89-111页

作者： Stitini, Oumaima Kaloun, Soulaimane Bencharef, Omar Computer and system engineering laboratory Cadi Ayyad University Marrakesh Morocco

Recommendation systems deliver a method to simplify the user’s desire. Recommendation systems are now commonly used on the Internet. It helps suggest items in various categories, including e-commerce, medical, education, tourism, and industrial. The electronic commerce sector has taken a big place in our daily lives as an active research tool, which helps people find what they are looking for. This paper presents a new contribution based on the combination of different algorithms to find a suitable solution to all the drawbacks of content-based recommender systems. The main contribution of this research lies in how to solve each problem and move on to the next. This paper describes an Ideal Solution Mitigating Content Disadvantages based on Three Phases called ISMCD_3P. Experiments show that the algorithm can propose an appropriate solution to solve all the problems of content-based filtering. Experimentations operating on real datasets are used to estimate the efficacy of our strategy. © (2023). All Rights Reserved.

关键词： Content-based filtering drawbacks Limited content Over-specialization Serendipity Sparsity

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A Fully LTPS-TFT-Based Bi-Directional Biomedical Interface Circuit for Large-Area Healthcare Applications

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第12期3卷 526-532页

作者： Zhang, Hanbo Lou, Yuqing Zhang, Zhihang Li, Yongfu Rokhan, Fakhrul Wang, Guoxing Zhao, Jian Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication System Engineering Selangor Serdang43400 Malaysia

This paper proposed a fully LTPS-TFT-based bi-directional biomedical pixel interface with integrated a high linearity stimulator and a high-performance bio-potential sensing front-end circuit to amplify and digitize biological signals for high robustness data transmission. All circuits are designed and manufactured under a 3-μm LTPS process. We demonstrated the experiment in saline using the stimulator circuit to simulate stimulation inside the organism, achieving stimulation with different average currents under different duty cycles. The proposed front-end circuit has a gain of 40 dB. The referred input voltage noise of the entire signal chain is 86.2 μV/√Hz, and the ENOB is 9.13 bits at an input of 0.5 mVPP. Simultaneously conducting statistical testing on 20 chips has achieved a 1.05 dB SNDR standard deviation. Finally, we use a function signal generator to simulate the input signals of ECoG, ECG, and EOG, then use front-end circuits to read and reconstruct them to demonstrate that the proposed pixel interface can correctly read common biomedical signals for healthcare applications. © 2022 IEEE.

关键词： Image segmentation

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