By simulating and analyzing the spatial spillover effect between different regions, the spatial correlation of carbon emission efficiency can be studied. Understanding how carbon emission behaviors interact and transmit across regions will help to develop more comprehensive and accurate carbon emission management strategies. Therefore, a new evolutionary simulation method for spatial spillover effect of carbon emission efficiency is proposed in this study. The PSO-PFCM clustering algorithm was improved to detect the overflow of carbon emission efficiency. The main characteristics of the multidimensional spatial spillover effect of carbon emission efficiency were selected, the multidimensional spatial feature mapping model was constructed, and the level of spillover effect was judged to complete the analysis of the evolution of the spatial spillover effect of carbon emission efficiency. The experimental results show that the proposed method has shorter abnormal detection time of carbon emission data spill and higher evolutionary accuracy of spatial spillover effect of carbon emission efficiency.
{"title":"Evolution simulation of spatial spillover effect of carbon emission efficiency based on improved PSO-PFCM clustering algorithm","authors":"Yufeng Chai, Yuehua Li, Bing Cai, Liang Han, Jiangbo Sha","doi":"10.1117/12.3032126","DOIUrl":"https://doi.org/10.1117/12.3032126","url":null,"abstract":"By simulating and analyzing the spatial spillover effect between different regions, the spatial correlation of carbon emission efficiency can be studied. Understanding how carbon emission behaviors interact and transmit across regions will help to develop more comprehensive and accurate carbon emission management strategies. Therefore, a new evolutionary simulation method for spatial spillover effect of carbon emission efficiency is proposed in this study. The PSO-PFCM clustering algorithm was improved to detect the overflow of carbon emission efficiency. The main characteristics of the multidimensional spatial spillover effect of carbon emission efficiency were selected, the multidimensional spatial feature mapping model was constructed, and the level of spillover effect was judged to complete the analysis of the evolution of the spatial spillover effect of carbon emission efficiency. The experimental results show that the proposed method has shorter abnormal detection time of carbon emission data spill and higher evolutionary accuracy of spatial spillover effect of carbon emission efficiency.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the problems that the convenient household formaldehyde gas detector could not realize simultaneous multipoint detection, excessive warning and the remote monitoring, a kind of indoor formaldehyde gas detection system with wireless AD hoc networking capability was designed. With the Zigbee module CC2530 of TI Company as the core, it adopted the Zigbee network ad-supported capability to realize real-time collection of air quality in multiple rooms at the same time, and uploaded the detected data to Aliyun Internet of Things platform to realize the remote monitoring function. The experimental results show that the system can simultaneously detect the formaldehyde concentration in multiple test points. When the formaldehyde concentration exceeds the preset concentration threshold, the system can make sound and light alarm and release the data to the Internet of Things platform. The remote Internet of Things platform stores the formaldehyde data for PC and mobile terminal to access and check. The system can meet the requirements of indoor formaldehyde concentration detection.
针对便捷式家用甲醛气体检测仪无法实现多点同时检测、超标预警和远程监控的问题，设计了一种具有无线AD hoc组网能力的室内甲醛气体检测系统。该系统以 TI 公司的 Zigbee 模块 CC2530 为核心，采用 Zigbee 网络广告支持能力，实现多个房间同时进行空气质量的实时采集，并将检测到的数据上传到阿里云物联网平台，实现远程监控功能。实验结果表明，该系统可同时检测多个检测点的甲醛浓度。当甲醛浓度超过预设浓度阈值时，系统可发出声光报警，并将数据发布到物联网平台。远程物联网平台存储甲醛数据，供 PC 和移动终端访问和检查。该系统可满足室内甲醛浓度检测的要求。
{"title":"Design of indoor formaldehyde multipoint real-time monitoring and alarm system","authors":"Xuexiao Chen","doi":"10.1117/12.3032076","DOIUrl":"https://doi.org/10.1117/12.3032076","url":null,"abstract":"Aiming at the problems that the convenient household formaldehyde gas detector could not realize simultaneous multipoint detection, excessive warning and the remote monitoring, a kind of indoor formaldehyde gas detection system with wireless AD hoc networking capability was designed. With the Zigbee module CC2530 of TI Company as the core, it adopted the Zigbee network ad-supported capability to realize real-time collection of air quality in multiple rooms at the same time, and uploaded the detected data to Aliyun Internet of Things platform to realize the remote monitoring function. The experimental results show that the system can simultaneously detect the formaldehyde concentration in multiple test points. When the formaldehyde concentration exceeds the preset concentration threshold, the system can make sound and light alarm and release the data to the Internet of Things platform. The remote Internet of Things platform stores the formaldehyde data for PC and mobile terminal to access and check. The system can meet the requirements of indoor formaldehyde concentration detection.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the continuous impact of haze weather, Xianyang city's air quality has ranked in the bottom three of the province for three consecutive years. This has led to an urgent need to improve air quality. Haze pollution prediction is of great practical significance. By timely and accurate prediction of haze pollution, the government and relevant institutions can take necessary measures to improve air quality and protect the ecosystem. Although the traditional RNN and LSTM models can effectively capture the time sequence information in the haze data over the years for prediction, it is still difficult to achieve accurate prediction due to the complexity of haze prediction. In this study, 8769 pieces of heterogeneous data were successfully collected using multi-source big data acquisition technology. A series of pre-processing operations, including data conversion and dimensionality reduction, were performed on different data such as AQI, PM2.5, PM10, SO2, NO2, CO and O3. The method of big data fusion and deep learning is adopted to integrate haze data and discover hidden rules and trends in it. Finally, based on FEDformer model and STL time series decomposition method, the prediction model was established in this study, which achieved significant improvement in both short - and long-term time series prediction problems.
{"title":"Research on haze prediction method of Xianyang City based on STL decomposition and FEDformer","authors":"Yanan Cao, Qian Zhou, Jinglei Tang, Zhenhong Liu","doi":"10.1117/12.3031964","DOIUrl":"https://doi.org/10.1117/12.3031964","url":null,"abstract":"Due to the continuous impact of haze weather, Xianyang city's air quality has ranked in the bottom three of the province for three consecutive years. This has led to an urgent need to improve air quality. Haze pollution prediction is of great practical significance. By timely and accurate prediction of haze pollution, the government and relevant institutions can take necessary measures to improve air quality and protect the ecosystem. Although the traditional RNN and LSTM models can effectively capture the time sequence information in the haze data over the years for prediction, it is still difficult to achieve accurate prediction due to the complexity of haze prediction. In this study, 8769 pieces of heterogeneous data were successfully collected using multi-source big data acquisition technology. A series of pre-processing operations, including data conversion and dimensionality reduction, were performed on different data such as AQI, PM2.5, PM10, SO2, NO2, CO and O3. The method of big data fusion and deep learning is adopted to integrate haze data and discover hidden rules and trends in it. Finally, based on FEDformer model and STL time series decomposition method, the prediction model was established in this study, which achieved significant improvement in both short - and long-term time series prediction problems.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to solve the problems of insufficient computational power and high power consumption of deep learning hardware, the use of deep learning in the field of hardware design is thoroughly investigated, focusing on the design and validation of a hardware gas pedal for Convolutional Neural Networks (CNNs) for target detection. The completeness of the design is ensured by implementing a hardware gas pedal with high computational parallelism using the Verilog HDL language and functional testing using the Universal Verification Methodology UVM. Through module level and system level verification. The experiments confirm the effectiveness of the hardware gas pedal in improving the computational efficiency of the target detection algorithm, contributing valuable insights to the research in the field of deep learning and chip design.
{"title":"Advanced deep-learning-based chip design enabling algorithmic and hardware architecture convergence","authors":"Hedi Qu, Danqing Ma, Zongqing Qi, Ni Zhu","doi":"10.1117/12.3032069","DOIUrl":"https://doi.org/10.1117/12.3032069","url":null,"abstract":"In order to solve the problems of insufficient computational power and high power consumption of deep learning hardware, the use of deep learning in the field of hardware design is thoroughly investigated, focusing on the design and validation of a hardware gas pedal for Convolutional Neural Networks (CNNs) for target detection. The completeness of the design is ensured by implementing a hardware gas pedal with high computational parallelism using the Verilog HDL language and functional testing using the Universal Verification Methodology UVM. Through module level and system level verification. The experiments confirm the effectiveness of the hardware gas pedal in improving the computational efficiency of the target detection algorithm, contributing valuable insights to the research in the field of deep learning and chip design.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the advancement of mobile cloud computing technology, the demand for collaborative operation and maintenance technology is constantly increasing. Therefore, this article proposes a model that combines multi feature collaborative knowledge graph and blockchain technology to achieve secure and trustworthy collaborative operation and maintenance computing in cross domain environments. The model focuses on addressing data privacy and security issues, and improving the accuracy of collaborative operations. By introducing a multi feature collaborative knowledge graph, secure fusion of multi-source feature data can be achieved. Meanwhile, design a blockchain based trust verification mechanism to ensure the traceability of anonymous data sources, prevent data tampering, and ensure data authenticity. In addition, an adaptive recommendation algorithm based on MKGCN is proposed, which utilizes multi feature collaborative knowledge graph data to achieve secure and accurate collaborative computing. The experimental results show that this method improves the accuracy of recommendation calculation while ensuring privacy and security, promoting the development and practical application of cross domain operation and maintenance computing technology.
{"title":"Research on secure and trustworthy cross domain collaborative computing methods for data","authors":"Xinlin Liu, Jian Zhang, Wei-Ping Deng","doi":"10.1117/12.3032067","DOIUrl":"https://doi.org/10.1117/12.3032067","url":null,"abstract":"With the advancement of mobile cloud computing technology, the demand for collaborative operation and maintenance technology is constantly increasing. Therefore, this article proposes a model that combines multi feature collaborative knowledge graph and blockchain technology to achieve secure and trustworthy collaborative operation and maintenance computing in cross domain environments. The model focuses on addressing data privacy and security issues, and improving the accuracy of collaborative operations. By introducing a multi feature collaborative knowledge graph, secure fusion of multi-source feature data can be achieved. Meanwhile, design a blockchain based trust verification mechanism to ensure the traceability of anonymous data sources, prevent data tampering, and ensure data authenticity. In addition, an adaptive recommendation algorithm based on MKGCN is proposed, which utilizes multi feature collaborative knowledge graph data to achieve secure and accurate collaborative computing. The experimental results show that this method improves the accuracy of recommendation calculation while ensuring privacy and security, promoting the development and practical application of cross domain operation and maintenance computing technology.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advancements in graph neural networks (GNNs) have prompted diverse research endeavors focused on utilizing GNNs for anomaly detection. The fundamental concept revolves around harnessing the inherent expressive capabilities of GNNs to acquire meaningful node representations, aiming to distinguish between anomalous and normal nodes in the embedding space. However, prior methods have often employed simple readout modules (such as sum, mean, or max functions) for subgraph aggregation, failing to fully exploit subgraph information. In response to this limitation, we propose an anomaly detection application algorithm called “Graph Contrastive Learning Network with Adaptive Readouts” (GNAR), tailored specifically for Graph Anomaly Detection (GAD) tasks. Through extensive experiments on three famous public datasets, we consistently observe that GNAR outperforms baseline methods.
{"title":"GNAR: graph contrastive learning networks with adaptive readouts for anomaly detection","authors":"changcheng wan, Suixiang Gao","doi":"10.1117/12.3031986","DOIUrl":"https://doi.org/10.1117/12.3031986","url":null,"abstract":"Recent advancements in graph neural networks (GNNs) have prompted diverse research endeavors focused on utilizing GNNs for anomaly detection. The fundamental concept revolves around harnessing the inherent expressive capabilities of GNNs to acquire meaningful node representations, aiming to distinguish between anomalous and normal nodes in the embedding space. However, prior methods have often employed simple readout modules (such as sum, mean, or max functions) for subgraph aggregation, failing to fully exploit subgraph information. In response to this limitation, we propose an anomaly detection application algorithm called “Graph Contrastive Learning Network with Adaptive Readouts” (GNAR), tailored specifically for Graph Anomaly Detection (GAD) tasks. Through extensive experiments on three famous public datasets, we consistently observe that GNAR outperforms baseline methods.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Studying the theme of emergencies is of great significance to the emergency management of subsequent public opinion. In order to solve the problem that LDA ignores the context semantics, and the topic distribution is biased towards high-frequency words, Bert is introduced. LDA-Bert public opinion topic mining model is proposed. First, LDA is used to select candidate words; Then, Bert is used to construct candidate word vectors and topic vectors with context semantics, and the topic keywords are filtered twice by cosine similarity calculation; Finally, the corresponding public opinion response strategies are proposed through the subject mining results of different life cycles. Taking the "Xi'an epidemic" as an example, the experiment proved that the model can effectively extract theme keywords, providing a strong basis for the follow-up analysis of theme changes at different stages of public opinion.
{"title":"LDA-Bert based public opinion subject mining analysis of emergencies","authors":"Tiantian Liu, XIAO-FENG Gu","doi":"10.1117/12.2679263","DOIUrl":"https://doi.org/10.1117/12.2679263","url":null,"abstract":"Studying the theme of emergencies is of great significance to the emergency management of subsequent public opinion. In order to solve the problem that LDA ignores the context semantics, and the topic distribution is biased towards high-frequency words, Bert is introduced. LDA-Bert public opinion topic mining model is proposed. First, LDA is used to select candidate words; Then, Bert is used to construct candidate word vectors and topic vectors with context semantics, and the topic keywords are filtered twice by cosine similarity calculation; Finally, the corresponding public opinion response strategies are proposed through the subject mining results of different life cycles. Taking the \"Xi'an epidemic\" as an example, the experiment proved that the model can effectively extract theme keywords, providing a strong basis for the follow-up analysis of theme changes at different stages of public opinion.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123769418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on the uncertain characteristics of emergency material demand, this paper uses interval number to represent the uncertainty of emergency material demand and considers the two objectives of rescue cost and time to establish an optimization model of emergency logistics facility location and route. The interval constraint theory is used to transform the interval uncertainty into the definite number, and the multi-objective is transformed into a single objective by the non-dimensional linear weighting method. The genetic algorithm is designed to solve the model, and the effectiveness of the model and algorithm is demonstrated by a practical example.
{"title":"Study on emergency logistics location-route problem under uncertain demand","authors":"Qirong Li","doi":"10.1117/12.2679063","DOIUrl":"https://doi.org/10.1117/12.2679063","url":null,"abstract":"Based on the uncertain characteristics of emergency material demand, this paper uses interval number to represent the uncertainty of emergency material demand and considers the two objectives of rescue cost and time to establish an optimization model of emergency logistics facility location and route. The interval constraint theory is used to transform the interval uncertainty into the definite number, and the multi-objective is transformed into a single objective by the non-dimensional linear weighting method. The genetic algorithm is designed to solve the model, and the effectiveness of the model and algorithm is demonstrated by a practical example.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122519426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The purpose of cutting material to length is to cut the material into finished products according to customer needs and minimize the amount of surplus material. To address this problem, we propose a model for extensions to the one-dimensional bin packing problem involving multiple length restrictions, which considers that all finished products have different lengths, and all length requirements are loaded into the finished products to achieve the optimization objectives of minimizing the quantity of the finished material and minimizing the remaining length of the finished material. The model is solved by a hybrid intelligent algorithm consisting of a genetic algorithm integrated with fit algorithms. The effectiveness of the hybrid intelligent algorithm is theoretically validated and experimentally verified using actual data and specific numerical examples.
{"title":"An intelligent algorithm integrated with fit algorithms for solving the one-dimensional bin packing problem under multiple length restrictions","authors":"Kancheng Huang, Yueming Dai","doi":"10.1117/12.2679127","DOIUrl":"https://doi.org/10.1117/12.2679127","url":null,"abstract":"The purpose of cutting material to length is to cut the material into finished products according to customer needs and minimize the amount of surplus material. To address this problem, we propose a model for extensions to the one-dimensional bin packing problem involving multiple length restrictions, which considers that all finished products have different lengths, and all length requirements are loaded into the finished products to achieve the optimization objectives of minimizing the quantity of the finished material and minimizing the remaining length of the finished material. The model is solved by a hybrid intelligent algorithm consisting of a genetic algorithm integrated with fit algorithms. The effectiveness of the hybrid intelligent algorithm is theoretically validated and experimentally verified using actual data and specific numerical examples.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122010076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visual object tracking is a traditional task in computer vision, which has developed with several decades. With the development of machine learning, Correlation Filter (CF) has been proposed with satisfying performance and very high framerate. Though the CF framework has numerous strengths in this task, the tracker is fragile to miss the target in several scenes, including extreme illumination, target occlusion and deformation. Recently, thermal modality, which detects the target’s temperature, is robust to the night scenes and can provide a precise target contour. In this paper, we propose a CF based tracker with decision fusion strategy for visible-thermal (RGB-T) tracking. First, we introduce multi-modal KCF trackers as our baseline. Then, we design a decision fusion method considering the Peak-to-Side Rate (PSR) of the score maps, thereby achieving an adaptive fusing those modalities and avoiding model’s heterogeneity. In the experiments, our tracker has validated on the public dataset, namely GTOT. Compared with two uni-modality trackers, the proposed tracker with real-time speed has shown superior results on both target localization and scale estimation.
{"title":"RGB-T object tracking with adaptive decision fusion","authors":"Yida Bai, Ming Yang","doi":"10.1117/12.2679107","DOIUrl":"https://doi.org/10.1117/12.2679107","url":null,"abstract":"Visual object tracking is a traditional task in computer vision, which has developed with several decades. With the development of machine learning, Correlation Filter (CF) has been proposed with satisfying performance and very high framerate. Though the CF framework has numerous strengths in this task, the tracker is fragile to miss the target in several scenes, including extreme illumination, target occlusion and deformation. Recently, thermal modality, which detects the target’s temperature, is robust to the night scenes and can provide a precise target contour. In this paper, we propose a CF based tracker with decision fusion strategy for visible-thermal (RGB-T) tracking. First, we introduce multi-modal KCF trackers as our baseline. Then, we design a decision fusion method considering the Peak-to-Side Rate (PSR) of the score maps, thereby achieving an adaptive fusing those modalities and avoiding model’s heterogeneity. In the experiments, our tracker has validated on the public dataset, namely GTOT. Compared with two uni-modality trackers, the proposed tracker with real-time speed has shown superior results on both target localization and scale estimation.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121169838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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