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International Conference on Electrical Engineering, computer science and Informatics (EECSI)

作者： Shashi Shekhar Kumar Ashutosh Kumar Ritesh Chandra Sonali Agarwal Mohammad Syafrullah Krisna Adiyarta Dept. of Information Technology IIIT Allahabad Prayagraj India Program of Master of Computer Science Universitas Budi Luhur Indonesia Budi Luhur Indonesia

The large volume of data processing is always challenging for real-time applications. These applications need an optimal framework for handling large scale data and correlating these streams in real time to make better decision making. Complex event processing has emerged as a novel methodology for handling event streams based on atomic events or complex events to find useful patterns by predefined *** play a major role in these systems and the streams are matched with rules created through a decision tree and machine learning classifier algorithms. In this research work, we propose a complex event processing based framework for rule extraction as well as a comparative analysis of rule-based classifier algorithms for automatic extraction of rules, and since event rules are based on human expertise, sometimes they fail due to a static approach, therefore there is a need for an automatic rule extraction *** comparative analysis is performed using a case study of an air quality dataset that outperformed traditional approaches to rule extraction for stream data. Decision tree fetched most number of rules with an accuracy of 99%.The classifier’s learning rate show how efficiently the rule are fetched.

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Leveraging regression models for rule based complex event processing

Leveraging regression models for rule based complex event pr...

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International Conference on Electrical Engineering, computer science and Informatics (EECSI)

作者： Shashi Shekhar Kumar Ashutosh Kumar Ritesh Chandra Sonali Agarwal Mohammad Syafrullah Krisna Adiyarta Dept.of Information Technology IIIT Allahabad Prayagraj India Program of Master of Computer Science Universitas Budi Luhur Indonesia Budi Luhur Indonesia

In recent times, IoT devices have surged enormously, which creates a lot of raw data that is dynamic in nature, and processing it in real time to find useful information is challenging. Complex event processing involves the analysis of large volumes of real-time data to identify patterns and events of interest. These events are formed based on a predefined set of rules, and since rules are created by domain experts and for dynamic data, there is a requirement for a robust model that can eliminate manual intervention for rule generation. In this paper, to help the domain experts, a regression-based model is proposed so that more accurate decision-making can be performed by finding more robust event patterns. For regression-based rule implementation, three models are compared: logistic regression, ridge regression, and support vector machine. The models are trained using an IoT temperature dataset and tested using a synthetically generated dataset with the same set of *** ridge regression performed best among all the models, with an accuracy, precision, recall, and f1 score of 99% 97% 96% 95% among all. The entire experiment was carried out with the apache flink ecosystem and pattern API.

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Learning Linear Embeddings for Non-Linear Network Dynamics with Koopman Message Passing

arXiv

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arXiv 2023年

作者： Yeh, King Fai Flood, Paris Redman, William Liò, Pietro Department of Computer Science and Technology University of Cambridge United Kingdom Interdepartmental Graduate Program in Dynamical Neuroscience University of California Santa Barbara United States

Recently, Koopman operator theory has become a powerful tool for developing linear representations of non-linear dynamical systems. However, existing data-driven applications of Koopman operator theory, including both traditional and deep learning approaches, perform poorly on nonlinear network dynamics problems as they do not address the underlying geometric structure. In this paper we present a novel approach based on Koopman operator theory and message passing networks that finds a linear representation for the dynamical system which is globally valid at any time step. The linearisations found by our method produce predictions on a suite of network dynamics problems that are several orders of magnitude better than current state-of-the-art techniques. We also apply our approach to the highly non-linear training dynamics of neural network architectures, and obtain linear representations which can generate network parameters with comparable performance to networks trained by classical optimisers. © 2023, CC BY.

关键词： Network architecture

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Evaluating Conveyor Belt Health With Signal Processing Applied to Inertial Sensing

Evaluating Conveyor Belt Health With Signal Processing Appli...

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2023 Symposium on Internet of Things, SIoT 2023

作者： Matos, Saulo Neves Coletti, Otavio Ferracioli Filho, Fernando Ugucioni De Carvalho, Ricardo C. C. L. Pinto, Thomas V. B. De Barros, Luiz G. D. Ranieri, Caetano Mazzoni Lopes, Bruno Eduardo Ueyama, Jo Pessin, Gustavo Institute of Mathematics and Computer Science University of Sao Paulo Brazil Sao Carlos School of Engineering University of Sao Paulo Brazil Vale Institute of Technology Ouro Preto Brazil Universidade Federal of Minas Gerais Graduate Program in Electrical Engineering MG Belo Horizonte Brazil Vale -SA Vitoria Brazil

ISBN: (纸本)9798350329438

Conveyor belts are commonly used in the mining industry for efficient material transport. However, they are prone to failures such as idler anomalies, belt tears, and misalignment. Current monitoring systems only evaluate the conveyor or idle belt's condition at the installation point. This study proposes a method to detect faults in conveyor belts, specifically idler anomalies and belt deviation, using two IMUs installed at the lateral extremities of the belt to evaluate its health along its route. In a reduced-scale conveyor belt, we conducted experiments by collecting gyroscopes and accelerometer data in various scenarios, including normal conditions and anomaly situations. We modified the idlers' physical structure or changed the belt tracks to create these scenarios. We used similarity analysis techniques to compare the data collected and performed statistical hypothesis analysis to observe the influence of the anomaly scenarios on the gyroscope and accelerometer data. We used Dynamic Time Warping (DTW) for gyroscope data to compare ground truth with anomaly scenarios. For the acceleration data, we performed vibration analysis by converting the time domain data to the frequency using Fast Fourier Transform (FFT) and evaluating the difference in spectral density between the reference and anomaly data. Statistical hypothesis analysis revealed that anomaly scenar-ios influenced the gyroscope x-axis and z-axis more. According to the spectral density analysis of the accelerometer, it was revealed that belt misalignment on the y-axis may be detected. © 2023 IEEE.

关键词： Belt conveyors

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Double-SP Method to Enhance Privacy and Security of Smart City Applications 10

Double-SP Method to Enhance Privacy and Security of Smart Ci...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Bahbouh, Nour Mahmoud Hakami, Abdulelah Alhussain Hassan Aljohani, Yaser Mohammed Sen, Adnan Ahmed Abi Alkhodre, Ahmad Alsughayyir, Aeshah Granada University Department of Information and Communication Sciences Granada Spain Saudi Electronic University Al-Madinah Saudi Arabia YasRef Program Yanbu Saudi Arabia Computing and Information Technology Islamic University Al-Madinah Saudi Arabia Islamic University Faculty of Computer and Information Systems Al-Madinah Saudi Arabia College of Computer Science and Engineering Taibah University Al-Madinah Saudi Arabia

ISBN: (纸本)9789380544472

Smart cities promise a lot of well-being to their users in all areas of life through millions of applications and services. Smart services rely heavily on collecting data and the preferences of users. But on the dark side, the users' information is posed to threat and penetration during transmission or stored far in the clouds. Depending on encryption only is not sufficient if the attacker has strong resources or if the attacker is the service provider (SP) itself. In addition, changing data before sending is not a practical solution in many systems because of the adverse impact on the quality of a main service. This research presented a new idea to address the issue of protection. The proposed method enhanced the privacy and security of users' data without affecting the accuracy of service. The core of the suggested solution relies on a knowledge base of services that are managed by experts. Also, the solution depends on fog nodes to measure the level of security and privacy of users' queries without delay. Moreover, the fog nodes manage contact with SPs. Finally, the proposed method divided the SP into two, one for user queries and the other for user data. The simulation and analytical discussion on a practical case in smart cities demonstrated the superiority of the proposed approach over previous methods in the level of protection by maintaining the quality of services, and the resistance to attacks. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Fog

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Design an Adaptive Virtual Reality Game to Promote Elderly Health

Design an Adaptive Virtual Reality Game to Promote Elderly H...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Hsiao-Hui Li Jian-Jie Lian Yuan-Hsun Liao Bachelor's Degree Program in Chain Store Management Tainan University of Technology Tainan Taiwan Department of Computer Science Tunghai University Taichung Taiwan

The elderly population in cities worldwide is experiencing significant growth each year, primarily due to advancements in medical technology and increased life expectancy. Consequently, the health of the elderly has become a paramount concern. To lead healthy and fulfilling lives, the elderly must engage in more active lifestyles. However, many activities that promote well-being, such as fishing, golfing, or hiking, may be restricted due to dangerous outdoor environments that pose a risk to their safety. This presents a critical challenge that needs to be addressed. To tackle this issue, our research combines two cutting-edge technologies, namely virtual reality (VR) and artificial intelligence (AI), to develop an adaptive VR game specifically designed for elderly individuals seeking to enhance their overall health. VR, as an innovative electronic device, offers an immersive experience that allows users to engage with 3D 360-degree environments through VR glasses, providing a lifelike sensation. By leveraging the highly interactive nature of VR, users can partake in various movements and activities to effectively promote their health without encountering any physical risks. Furthermore, we have incorporated AI into the game to provide real-time guidance to users and dynamically adjust the game content as needed. This AI integration enables personalized advice based on the user's current situation, allowing the game to adapt to their specific health promotion goals. Through AI's analytical capabilities, the game content can be continuously optimized to better align with the user's objectives and ensure an engaging and effective experience. By combining VR and AI, our research aims to create a safe and immersive virtual environment for elderly individuals, enabling them to actively participate in activities that contribute to their well-being. This novel approach addresses the challenges faced by the elderly in accessing health-promoting activities, providing them wit

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The Efficient Traffic Sign Detection and Recognition for Taiwan Road Using YOLO Model with Hybrid Dataset

The Efficient Traffic Sign Detection and Recognition for Tai...

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International Conference on Applied System Innovation (ICASI)

作者： Taweelap Suwattanapunkul Lung-Jen Wang International Master Program in Information Technology and Applications National Pingtung University Taiwan Department of Computer Science and Information Engineering National Pingtung University Taiwan

According to the trend of worldwide car sales have grown up, this cause may increase accidents on the road due to human error. The self-driverless car has been developed to solve this problem. One task of the self-driverless car is traffic sign detection and recognition (TSDR), which will help drivers notify the traffic sign installed on the road in advance. Taiwan roads have specific traffic signs, and no Taiwan traffic sign public dataset is available. In this paper, our proposed object detection method was experimentally performed using YOLOv5s6 and YOLOv8s models on three different datasets, as Tsinghua-Tencent 100K (TT100k), the self-created Taiwan traffic sign (TWTS), and the hybrid dataset, which combine the traffic scenes between TT100k and TWTS dataset. The output results from each dataset and each model, which is trained on the same parameter, will be compared to validate the proposed method. The experiment results’ comparison of the hybrid dataset between YOLOv5s6 and YOLOv8s models display the results of the mAP@.5 is about 65% and 76.2%, respectively, which means the performance of the YOLOv8s is higher than the YOLOv5s6 when using hybrid dataset.

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The Social Media Implications on the Sales of Business Products

The Social Media Implications on the Sales of Business Produ...

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Smart Data Intelligence (ICSMDI), International Conference on

作者： Elvina Jacia Gabriel Dyan Amelia Ifan Ford Lumban Gaol Tokuro Matsuo Department of Information System Bina Nusantara University Alam Sutera Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan

The use of technology nowadays does not feel strange. Everyday people who use technology for their daily needs, starting with each other, seeking knowledge, can even earn income by doing business using technology. Social media is a tool that can be used by people to become with each other by creating, sharing, and exchanging information virtually through a network. However, in the current era, the notion of social media is not only about exchanging information, but also to support business products. The problem is how to compete tightly in this modern era in increasing the selling value of business products. The positive impact of social media for sales in business products are huge such as, increase sales, increase product promotion, greater market distribution. On the other side, the negative impact also takes part such as, the more competitors, the products compete fiercely in the market. This research aims to identify more the impact of social media on sales of business products that focused on business, benefit, and factor. The research method that we used is quantitative which focuses on the number of samples of respondents to support the research results and the results are in the form of a graphic that shows the answers of the respondents to the questionnaire.

关键词： Graphics Freeware Video on demand Social networking (online) Multimedia Web sites Media Internet telephony

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Modelling e-waste management data in smart cities 2

Modelling e-waste management data in smart cities

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2nd Sustainable Cities Latin America Conference, SCLA 2021

作者： De Novais, Marcos Pereira De Almeida Xavier, Victor Xavier, Lucia Helena Hwang, Junseok Seoul National University Technology Management Economics and Policy Program Siheung-si Korea Republic of University of Rio de Janeiro Program in Computer Science and Systems Engineering Rio de Janeiro Brazil Centre for Mineral Technology Ministry of Science Technology and Inovation Rio de Janeiro Brazil

ISBN: (纸本)9781665437684

The smart cities concept is strongly related to energy efficiency and low carbon patterns, what that refers to meeting the sustainability criteria established by regulatory mechanisms. The Smart City Index (SCI) data can reach a higher quality of living by observing aspects as mobility, health, safety, transparency, smart management, and many others, like the waste management. Thus, this set of indicators was used to evaluate the e-waste management as an emergent challenge in urban cities, providing both, the generation of waste from the intense consumption of electronic devices, as well meeting the questions arising from urbanization from the integration of digital solutions. This study proposes a model of analysis using simple linear regression and k-mean clustering as correlation methods. The findings suggested correlation between the SCI ranking, the E-waste generated, and the economic situation of the entire set of smart cities. Nevertheless, it was found weak correlation for the best rated cities. The results provided the perception that it is possible to identify correlation from the Smart City Index and aspects from the e-waste management system, enabling the discussion over the importance of next smart city indexes and given a new perspective to the e-waste management. © 2021 IEEE.

关键词： Environmental management

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NNSplit-SØREN: Supporting the model implementation of large neural networks in a programmable data plane

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computer Networks 2023年 222卷

作者： Li, David Chunhu Maulana, Muhamad Rizka Chou, Li-Der Information Technology and Management Program Ming Chuan University Taoyuan City333321 Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan City320317 Taiwan

Embedding neural network (NN) models in the data plane is one of the very promising and attractive ways to leverage the computational power of computer network switches. This method became possible with the advent of the P4 language controlling the programmable data plane. However, most data planes today have some constraints, such as a limited set of operations and limited memory size. The computational cost of training large-scale NNs is high. In addition, while complex large-scale NN architectures are often used to improve prediction accuracy, they affect the functional performance of the data plane because of factors such as numerous input parameters and complex model design. Therefore, determining how to reduce the performance cost incurred by implementing large NN models in the data plane is a critical issue that needs to be addressed. This research proposes a technique called Neural Network Split (NNSplit) to solve the performance problems of embedding a large NN in a data plane by splitting the NN layers across multiple data planes. To support layer splitting, a new protocol called SuppORting ComplEx Computation in the Network (SØREN) is also proposed. The SØREN protocol header carries the activation value and bridges the NN layers in all switches. A multi-class classification use case of network traffic is used as the context for the experimental analysis. Experimental results show that compared to non-splitting NN architectures, NNSplit can reduce memory usage by nearly 50% and increase network traffic throughput with the cost of a 14% increase in round-trip time. In addition, when the SØREN protocol is encapsulated into data packets, the average processing time of the switch is 773 µs, which has very little impact on the processing time of the packets. Experimental results also show that the proposed NNSplit–SØREN can support large NN models on the data plane with a small performance cost. © 2022 Elsevier B.V.

关键词： Cost reduction

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