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检索条件"任意字段=8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023"

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AIOps-Driven Enhancement of Log Anomaly Detection in Unsupervised Scenarios 8

AIOps-Driven Enhancement of Log Anomaly Detection in Unsuper...

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8th international conference on Big Data, Knowledge and Control systems engineering, BdKCSE 2023

作者： Dave, Daksh Sawhney, Gauransh Khut, Dhruv Nawale, Sahil Aggrawal, Pushkar Bhavathankar, Prasenjit Bits Pilani Dept. of Electrical Electronics Pilani India Sardar Patel Institute of Technology Information Technology Mumbai India Sardar Patel Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350313246

Artificial intelligence operations (AIOps) play a pivotal role in identifying, mitigating, and analyzing anomalous system behaviors and alerts. However, the research landscape in this field remains limited, leaving significant gaps unexplored. this study introduces a novel hybrid framework through an innovative algorithm that incorporates an unsupervised strategy. this strategy integrates Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs) and uses a custom loss function to substantially enhance the effectiveness of log anomaly detection. the proposed approach encompasses the utilization of both simulated and real-world datasets, including logs from SockShop and Hadoop Distributed File System (HDFS). the experimental results are highly promising, demonstrating significant reductions in pseudo-positives. Moreover, this strategy offers notable advantages, such as the ability to process logs in their raw, unprocessed form, and the potential for further enhancements. the successful implementation of this approach showcases a remarkable reduction in anomalous logs, thus un-equivocally establishing the efficacy of the proposed methodology. Ultimately, this study makes a substantial contribution to the advancement of log anomaly detection within AIOps platforms, addressing the critical need for effective and efficient log analysis in modern and complex systems. © 2023 ieee.

关键词： Anomaly detection

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CG-Net: A Novel End-to-end Framework for Fall Detection from Videos 8

CG-Net: A Novel End-to-end Framework for Fall Detection from...

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8th international conference on computer and Communication systems, ICCCS 2023

作者： Cheng, Yuning Chen, Gao Zhou, Qingfeng Liu, Chanzi Cai, Chuanbao School of Computer Science and Technology Dongguan University of Technology Dongguan China School of Electrical Engineering and Intelligentization Dongguan University of Technology Dongguan China Shen Zhen Everwin Precision Technology Co. LTD Shenzhen China

ISBN: (纸本)9781665456128

the convolutional neural network (CNN) is an effective tool for fall detection. It can successfully identify the fall action from images but still has the problem of predicting in videos. We propose a novel end-to-end framework named CNN-GCN-network (CG-Net) to address the problem, which can detect fall from videos directly. the proposed CG-Net consists of two modules: pose estimation and action recognition. the former is used to process videos to obtain joint points and the latter to extract features from joint points to detect falls. Furthermore, not only joint points, but also skeletons, are used for fall detection. According to the experimental results, our system achieves 99.3 % accuracy and 99.8 % recall on Le2i Fall Detection Dataset at the frame-level in videos, which provides higher fall detection performance compared with the existing CNN-based model. © 2023 ieee.

关键词： Fall detection

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Stock Trading Decision Method Based on Stop Loss Double threshold 8

Stock Trading Decision Method Based on Stop Loss Double Thre...

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8th international conference on computer and Communication systems, ICCCS 2023

作者： Wu, Jimmy Ming-Tai Ma, Shaowei Wang, Ke Yan, Huizhen Zhang, Qinghua College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781665456128

Artificial intelligence technology is widely used in stock market stock forecasting, which can help investors achieve 'buy at a low price and sell at a high price'. Many scholars are focusing on how to increase investors' returns and reduce their risks. this study takes stocks in the United States and Taiwan as the research objects, and historical data, futures, and options as the data set, in an attempt to find a less risky and more rewarding method of stock trading decisions. Long short-term memory neural networks (LSTM) are used to study stock price fluctuations. the Kelly criterion for fund management is used to calculate the optimal investment ratio, and a stop loss strategy based on double thresholds is added so that investors can sell within a certain period when the stock price falls to a certain range, thereby reducing investors' risks. the particle swarm optimization algorithm (PSO) is used to optimize the critical values of trading threshold and stop loss range. the experimental results show that the stop loss strategy based on double thresholds can effectively reduce investors' risk. the addition of leading indicators such as futures and options can increase the accuracy of stock prediction and increase the return of investors. © 2023 ieee.

关键词： Long short-term memory

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Spatial-Temporal Multimodal End-to-End Autonomous Driving 8

Spatial-Temporal Multimodal End-to-End Autonomous Driving

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8th international conference on computer and Communication systems, ICCCS 2023

作者： Yang, Lei Lei, Weimin School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781665456128

Autonomous driving requires precise perception of the surrounding environment, and considering the complementarity of sensor data, we propose an end-to-end model of spatial-temporal multimodal fusion using an attention mechanism. Our model uses a fusion of camera and light detection and ranging (LiDAR), which works as follows: (i) the spatial network performs spatial feature learning using images from the range view (RV) representation of LiDAR and red, blue, and green (RGB) images as input, followed by a parallel ResNet18 network for feature extraction and fusion through an attention mechanism;(ii) the temporal network performs the learning of the temporal dimension of spatial features, and uses the current spatial features from the spatial network and the historical spatial features to do attention learning, which enhances the features relevant to the autonomous driving task;(iii) finally, the model uses spatial-temporal features to select a different prediction branch by navigation instructions to perform regression of waypoints. Our model was trained and tested in the CARLA simulator, and experiments showed that it enabled to complete autonomous driving tasks in complex environments, achieving a success rate of 85%, especially with many dynamic objects. © 2023 ieee.

关键词： Deep neural networks

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Decentralized Optimization of Coupled Power-Transportation Network considering Environment-aware User Behavior 8

Decentralized Optimization of Coupled Power-Transportation N...

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8th international conference on computer and Communication systems, ICCCS 2023

作者： Cao, Zhiao Han, Yinghua Zhao, Qiang Wang, Jinkuan Zhao, Shuyi School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China College of Information Science and Engineering Northeastern University Shenyang China Taylor's University Faculty of Business and Law Subang Jaya Malaysia

ISBN: (纸本)9781665456128

the increasing popularity of electric vehicles (EVs) has led to a growing need for electrified transportation, which has further deepened the interconnectedness of power and transportation networks. To account for EV drivers' preferences in route selection, this paper proposes an environment-aware dispatch model that coordinates the integrated power and transportation network to achieve greater economic benefits and increased utilization of renewable energy. the model employs discrete choice models with an environmental factor to represent user behavior. the constructed user behavior models serve as the basis for the integrated optimal traffic-power flow (IOTPF) model, which aims to optimize the flow of both power and traffic while minimizing the total cost of EV travel and maximizing the use of renewable energy sources. the paper employs a decentralized optimization algorithm to solve the equilibrium flows of the IOTPF model. Numerical results illustrate how the environment-aware user behavior impacts travel costs and the use of renewable energy, demonstrating the effectiveness of the IOTPF model in coordinating the optimal strategy of the integrated power-transportation network. © 2023 ieee.

关键词： Electric load flow

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Detection of Deepfake Video using Deep Learning and MesoNet 8

Detection of Deepfake Video using Deep Learning and MesoNet

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Rebello, Lian Tuscano, Linnet Shah, Yashvi Solomon, Alvin Shrivastava, Varsha Sfit Computer Engineering Department Mumbai India

ISBN: (纸本)9798350396638

Fraudsters are increasingly using evidence tampering to evade criminal charges and the acquisition of personal data for identity-related offenses. Deepfake is one of the most common strategies used today for identity theft and reputation defamation. To prevent the spread of these crimes, we need a system that can tell the difference between real and deep fake videos. Deep Neural Networks will be used in our system to identify and mark films as legitimate or manipulated, as well as the altered sections, by running the video through our trained Sequence Model, which can detect any discrepancies or alterations as a sequence of frames. LSTM will be used for temporal sequence analysis, and CNN will be employed for frame feature extraction. MesoNet is a neural network built primarily to identify deep fakes, but it would also be used for other purposes. MesoNet manages the noise produced by low-quality video processing, which impedes analysis. DeepFakes jeopardizes facial recognition and internet content. this deception is risky and can be exploited to impersonate a legitimate user. Our approach will propose a temporal-aware method for automatically detecting deepfake videos. © 2023 ieee.

关键词： Long short-term memory

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A Systematic Study on Student Performance Prediction from the Perspective of Machine Learning and Data Mining Approaches 8

A Systematic Study on Student Performance Prediction from th...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Jha, Rashmi Management Education and Research Institute Computer Science & Engineering Janakpuri New Delhi India

ISBN: (纸本)9798350396638

there will be a lot of student-related data produced by a digital campus. the key to raising students' management skills is learning how to analyze and apply these data. In addition to helping schools improve school safety and provide early warning of potentially dangerous situations, the analysis of student behavior data can also be used to explain student behavior using real data, providing quantitative data support for scholarship & grant evaluation. In an effort to improve teaching and learning, predicting student performance within educational settings is a hotly debated topic among researchers. Educators and teachers could create appropriate teaching materials to assist students in studying in accordance with projected outcomes with the aid of effective prediction approaches and characteristics. Using algorithms for machine learning such as SVM, DT, Ensemble, & KNN, together with metrics for precision, accuracy, recall, or F1 score, the purpose of this study is to make a prediction about the performance of students. Support Vector Machines (SVM) obtained the value of 0.9987, 0.9948, 0.9931, and 0.9743, respectively, when compared to Ensembles, Decision Tree (DT), & KNN, for Accuracy, Precision, and Recall, and F1 score. © 2023 ieee.

关键词： Decision trees

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Different Categories of Feature Extraction and Machine Learning Classification Models Used for Hand Gesture Recognition systems: A Review 8

Different Categories of Feature Extraction and Machine Learn...

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8th ieee international conference for Convergence in Technology, I2CT 2023

作者： Dhiman, Richa Luthra, Pooja Singh, Nongmeikapam thoiba Chandigarh University Department of Computer Science Engineering Punjab India

ISBN: (纸本)9798350334012

Hand gestures are among the most common aspects of body expression that can be used for interactive communications. Hand movements can convey language information. Since they can facilitate interaction and provide a practical paradigm of interaction that can be applied in a number of situations, hand gestures are a fascinating issue for research. the HGR (Hand Gesture Recognition) system is used to recognise hand gestures. Feature extraction and classification are two stages that are crucial to the fundamental architecture of hand gesture recognition. In a hand gesture recognition system, feature extraction refers to techniques for establishing connections between two variables and accurately representing the information. the classifiers are used to classify the features of the images. Several feature extraction techniques and classifiers are mentioned in this paper. Hand gesture recognition is extensively used in SL (sign language), TV controlling, HCI (human computer interaction) systems, VR (virtual reality), games, etc. due to its creativity and rapidity of involvement. the various existing techniques for HGR are surveyed and analyzed. the analysis of existing techniques showed that there are some major issues with existing techniques, such as data augmentation and generalisation issues, reliability issues, limited datasets, Complex backgrounds degrade the performance of the existing techniques. the diversity and complexity of actions have a massive effect mostly on classification performance and accuracy throughout the purpose of selecting hand movements. In order to increase the accuracy of hand gesture recognition, machine learning methods can be applied. In this paper, various existing techniques are surveyed and compared with different parameters such as accuracy, precision, sensitivity, specificity, etc. © 2023 ieee.

关键词： Principal component analysis

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AI based Desktop Voice Assistant for Visually Impared Persons 8

AI based Desktop Voice Assistant for Visually Impared Person...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Sinha, Ankit Lal Muley, Hardik Ghosh, Jaydeep Sarode, Padmavati G.H.Raisoni College of Engineering and Management Department of Computer Science Pune India

ISBN: (纸本)9798350396638

the technologies are evolving rapidly day by day with the development of these technologies humans can perform their daily tasks easily. one of the most popular field of technology is artificial intelligence which is growing nowadays. Using artificial intelligence machines can recognize human behaviour and on the basis of that it can respond to the humans. desktop voice assistant is the best example of developing an artificial intelligence technology. these desktop voice assistants help humans to perform various tasks on desktop easily just by using their voice. the voice assistant uses speech recognition modules which is useful for recognizing and understanding human input voice and on the basis of user input command it gives the required input queries or performs the given task like opening and closing different applications, can search and send messages on whatsapp without using keyboard or mouse. In this paper artificial intelligence technology is used to create an desktop voice assistant which will be helpful for visually impaired and the people with disabilities. this desktop voice assistant can also be useful for normal people as it saves time and provide efficiency in doing our day to day tasks. © 2023 ieee.

关键词： Python

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A Low-Cost IoT based Navigation Assistance for Visually Impaired Person 8

A Low-Cost IoT based Navigation Assistance for Visually Impa...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Patil, Anurag Borse, Abhishek Borse, Anish Phad, Ajay Khiani, Simran G.H Raisoni College of Engineering and Management Computer Engineering Pune India

ISBN: (纸本)9798350396638

the Smart Shoe is an adaptive technology that can significantly improve the accessibility and comfort of living for individuals who are visually impaired. the proposed system presents a promising solution to address the challenges faced by visually impaired individuals in daily life. this system aims to assist visually impaired individuals with mobility and navigation by designing a Smart Shoe that can detect obstacles in their path. the shoe utilizes IoT technology and incorporates various sensors such as Ultrasonic, IR, Gyroscopic, and Accelerometer, along with a Microcontroller (ESP32), battery, and vibration motor. the shoe also has a battery that powers the system, making it independent of any external power source. When an obstacle is detected, the shoe sends a warning signal via voice command and vibration to the user via app. the app includes automated voice commands, which are sent to wireless headphones when an obstacle is detected. this feature makes it easier for visually impaired individuals to navigate and move around more independently. the system is portable, lightweight, easy to use, and affordable. this technology improves the accessibility and comfort of living for visually impaired individuals, enabling them to move around more safely and independently. © 2023 ieee.

关键词： Object detection

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