2022 International Conference on Knowledge Engineering and Communication Systems (ICKES)最新文献

{"title":"Breast Cancer Classification using Customized ResNet based Convolution Neural Networks","authors":"Nagaraja Rao Pamula Pullaiah, D. Venkatasekhar, P. Venkatramana","doi":"10.1109/ICKECS56523.2022.10060790","DOIUrl":"https://doi.org/10.1109/ICKECS56523.2022.10060790","url":null,"abstract":"Deep learning is the most frequently used tool in the classification of tumors in medical applications. In recent decades, many research works have been done on the Breast Imaging Reporting & Data System (BI-RADS) atlas based classification of Breast cancer. As reported in the existing research works, training the larger datasets is a challenging task. Therefore, a customized ResNet based Convolution Neural Network (cRN-CNN) with batch normalization is proposed in this manuscript for addressing the above mentioned issue. The proposed cRN-CNN method has the advantage of faster training and computationally effective for the classification of BIRADS atlas based MRI breast cancer records, where the proposed model's performance is superior compared to the conventional CNN model. The extensive experiments performed on the Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) dataset confirmed that the proposed cRN-CNN method achieved better classification results than the existing methods. In the proposed model, the deformation technique based on elastic deformation is also applied to increase the training size of data that helps to improve the outcomes of prediction up-to 99.80%, because of the efficient strategy of batch normalization as customization and elastic deformation.","PeriodicalId":171432,"journal":{"name":"2022 International Conference on Knowledge Engineering and Communication Systems (ICKES)","volume":"153 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120973567","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}

{"title":"Application of Data Mining Technology in Pet Wearable Device Data","authors":"Xingyue Feng, Jialu Yao, Tianquan Wen","doi":"10.1109/ICKECS56523.2022.10059748","DOIUrl":"https://doi.org/10.1109/ICKECS56523.2022.10059748","url":null,"abstract":"In the field of smart wearable devices, data analysis of pet wearable devices has always been a research hotspot. Online monitoring technology cannot solve the problem of data mining in pet wearable devices, and the ability of data mining is poor. Therefore, this paper proposes a mining algorithm for pet wearable devices and constructs a data mining model for pet wearable devices. Firstly, the big data theory is used to classify the data of pet wearable devices, and the data is divided according to the device type to which the data belongs, so as to reduce the complexity of the data in pet wearable devices. Then, the big data theory classifies pet wearable devices, forms the data domain of pet wearable devices, and comprehensively analyzes the data. MATLAB simulation results show that data mining technology is superior to online monitoring technology in accuracy, stability and calculation time under a certain amount of pet wearable equipment data. Under the condition of clear data processing standards, data mining technology can comprehensively analyze pet wearable devices and meet the data mining requirements of pet wearable devices.","PeriodicalId":171432,"journal":{"name":"2022 International Conference on Knowledge Engineering and Communication Systems (ICKES)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121172802","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}

{"title":"Deep Learning Algorithm Composition System Based on Music Score Recognition","authors":"Xiaochen Guo, Shihui Du","doi":"10.1109/ICKECS56523.2022.10060542","DOIUrl":"https://doi.org/10.1109/ICKECS56523.2022.10060542","url":null,"abstract":"With the development of computer science and music technology, algorithms have been widely studied and applied in the field of computer composition. The “computer generated art” evolved from this belongs to the category of algorithmic art. The creators can make the computer automatically generate and create music or assist the creators to complete music creation by writing programs and formulating relevant limiting rules. Music composition system based on deep learning algorithm of music score recognition is a method of creating deep neural network to recognize and classify music scores. The main idea behind this method is to use deep learning algorithm to generate features from the input data, and then use these features to classify music scores. The deep learning algorithm helps to identify patterns in the input data by using multi-layer artificial neurons or by training learning nodes. These layers may be stacked one after another to form a network with many hidden layers. In other words, this is an attempt to discover patterns in large data sets by using techniques such as clustering and analysis.","PeriodicalId":171432,"journal":{"name":"2022 International Conference on Knowledge Engineering and Communication Systems (ICKES)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126618324","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}

{"title":"Anomaly Detection in Surveillance Videos Using Deep Learning","authors":"K. Nithesh, Nikhath Tabassum, D. Geetha, R. Kumari","doi":"10.1109/ICKECS56523.2022.10059844","DOIUrl":"https://doi.org/10.1109/ICKECS56523.2022.10059844","url":null,"abstract":"One of the biggest studies on public safety and tracking that has sparked a lot of interest in recent years is deep learning approach. Current public safety methods are existent for counting and detecting persons. But many issues such as aberrant occurring in public spaces are seldom detected and reported to raise an automated alarm. Our proposed method detects anomalies (deviation from normal events) from the video surveillance footages using deep learning and raises an alarm, if anomaly is found. The proposed model is trained to detect anomalies and then it is applied to the video recording of the surveillance that is used to monitor public safety. Then the video is assessed frame by frame to detect anomaly and then if there is match, an alarm is raised.","PeriodicalId":171432,"journal":{"name":"2022 International Conference on Knowledge Engineering and Communication Systems (ICKES)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126247306","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}

{"title":"Fault Prediction System for Network Operation Status Based on Improved Clustering Algorithm","authors":"Jinkai Li, Bolong Wang, Ruishuang Bai, Yang Liang, Nan Jiang","doi":"10.1109/ICKECS56523.2022.10059945","DOIUrl":"https://doi.org/10.1109/ICKECS56523.2022.10059945","url":null,"abstract":"Cluster analysis algorithm is an essential unsupervised learning algorithm in the field of data mining and machine learning, which can quickly divide large data into different types according to data characteristics. After half a century of accumulation and precipitation, cluster analysis algorithms have achieved quite sufficient research results, including hierarchical clustering, grid-based clustering, and density-based CAs suitable for various application scenarios. The main purpose of this paper is to study the system design of network operating status (OS) fault prediction (FP) based on the improved clustering algorithm (ICA). This paper mainly evaluates the operation state of the distribution network based on the unbalanced data CA. The algorithm in this paper improves the iterative center reduction formula on the basis of the IT2FKM algorithm, and the calculation time required is slightly longer than the classic IT2FKM algorithm. However, with the increase of cluster imbalance, compared with other algorithms, the clustering performance of the proposed algorithm has been significantly improved. It can be seen from the experimental results that the improved IT2FKM algorithm proposed in this paper has strong adaptability when clustering in imbalanced data sets, and does not require too much computational cost.","PeriodicalId":171432,"journal":{"name":"2022 International Conference on Knowledge Engineering and Communication Systems (ICKES)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127812099","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}

{"title":"Deployment of Autonomous Vehicles in Agricultural and using Voronoi Partitioning","authors":"Shailendra Tahilyani, Surabhi Saxena, Dimitrios Alexios Karras, Shashi Kant Gupta, Chandra Kumar Dixit, Bhadrappa Haralayya","doi":"10.1109/ICKECS56523.2022.10060773","DOIUrl":"https://doi.org/10.1109/ICKECS56523.2022.10060773","url":null,"abstract":"There will be nine billion people on the planet by the year 2050, according to most estimates, thus agricultural output must rise steadily. In order to deal with the increasing population, agricultural chores must be mechanized and automated. The autonomous and safe navigation of ground robots has been one of the most difficult challenges in their development over the past decade. With the emergence of the “smart city” movement, automated vehicles have become a hot issue. Authorities and administrators are unprepared to deal with the anticipated disruption of autonomous vehicles, which might potentially replace traditional transportation. We don't yet know how new capabilities will disrupt existing systems and what policy solutions will be necessary to counteract this disruption. Autonomous vehicles have a variety of advantages and disadvantages that must be considered. One such challenge is its path planning. Autonomous vehicle path planning in dynamic situations is a critical yet difficult issue because of the limitations imposed by vehicle dynamics and the presence of other vehicles. Vehicle trajectories include a variety of operations, including lane holding, lane shift, ramp merging, and crossing the street at an intersection. In order to operate autonomously, a mobile platform must perform a variety of tasks such as localization and mapping, as well as motion control and path planning. We 'II look at a bunch of different ways path planning may be put to use in farming in this post. This paper, proposed a Voronoi based partitioning algorithm which is used to find the optimal path for the intelligent vehicles. The Minimum Spanning Tree is computed and the problem is posed as a Travelling Salesperson Problem. The solution for the problem is obtained by solving using various greedy approaches. The results are compared while using various algorithms for the results obtained through the proposed partitioning algorithm.","PeriodicalId":171432,"journal":{"name":"2022 International Conference on Knowledge Engineering and Communication Systems (ICKES)","volume":"239 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127600808","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}

{"title":"Innovative Calculation Method of Engineering Quantity Index and Reasonable Accuracy Index System for Power Communication Station Engineering","authors":"He Gao, Hongyang Zhang, Xiaoying Yang, Lele Cui, Qingbo Tu, Tao Tian, Piyush Kumar Pareek","doi":"10.1109/ICKECS56523.2022.10060531","DOIUrl":"https://doi.org/10.1109/ICKECS56523.2022.10060531","url":null,"abstract":"The existing electric power communication station project adopts the empirical valuation method to control the cost, but lacks the corresponding index system, which cannot reflect all the characteristics and special requirements of the new electric power communication project. Therefore, a more reasonable and accurate index system and method suitable for power communication station engineering are constructed by using the engineering quantity index of power communication station engineering. The combined weight model of Pearson correlation coefficient, grey correlation analysis method and Pareto analysis method is adopted to calculate and solve the weight value of the index system, and the defects of subj ective weighting method are abandoned. Take care of objectivity. The application of practical cases shows that the method has strong practicability, which provides comprehensive theoretical support and practical reference for further research on the precision index system of power communication station engineering, comparison and analysis of the effect of project group investment control, and mining of common problems.","PeriodicalId":171432,"journal":{"name":"2022 International Conference on Knowledge Engineering and Communication Systems (ICKES)","volume":"150 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133585411","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}

{"title":"U-shaped Transformers for 3D Lung Cancer Segmentation","authors":"H. K. Bhat, Aashish Mukund, S. Nagaraj, R. Prakash","doi":"10.1109/ICKECS56523.2022.10059627","DOIUrl":"https://doi.org/10.1109/ICKECS56523.2022.10059627","url":null,"abstract":"3-Dimensional (3D) image segmentation in medical images is essential for early detection and diagnosis of diseases. It also aids in effective monitoring and treatment preparation. Traditional methods of delineating the image manually requires anatomical knowledge and is error-prone, cumbersome and expensive. Deep learning methods, especially V-shaped Convolutional Neural Network (CNN) architectures have achieved state-of-the-art performance on 2-Dimensional (2D) clinical image data. However, when it comes to 3D medical images, they suffer from anisotropy which is non-homogeneity in all directions. This paper shows that conventional convolution-based networks are insufficient to accurately segment this kind of data and proposes a ‘U-shaped’ transformer-based network, leveraging the self-attention mechanism to achieve better segmentation results. The proposed model outperforms baseline convolution-based models in 3D lung cancer segmentation.","PeriodicalId":171432,"journal":{"name":"2022 International Conference on Knowledge Engineering and Communication Systems (ICKES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116887226","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}

{"title":"Big Data Association Rule Algorithm for Encryption of Accounting Data","authors":"Ailin Liu","doi":"10.1109/ICKECS56523.2022.10060031","DOIUrl":"https://doi.org/10.1109/ICKECS56523.2022.10060031","url":null,"abstract":"As the scale of enterprises continues to expand, the complexity of management within enterprises continues to increase and the amount of data generated is becoming larger and larger. For accounting information systems, how to ensure data security, visualize data and ensure analysis and mining of data are important issues for enterprises to consider. This paper will discuss the encryption of accounting information systems in relation to the actual situation such as the internal management of accounting systems in the era of big data (BD). In this paper, firstly, the existence of irreversible security risks in AD is analysed. Secondly, the data is analysed and mined, and the accounting data (AD) is encrypted through association rule algorithms (ARA). Finally, data information that occurs in the enterprise information system in the midst of financial activities and is even directly related to financial operations is extracted and processed for association analysis to improve the security and visualisation of the financial information system in the whole system. In this paper, we analyse the causes and solutions of the security risks in the process of acquiring data from the accounting system and put forward suggestions for improvement after analysing the encryption process of AD.","PeriodicalId":171432,"journal":{"name":"2022 International Conference on Knowledge Engineering and Communication Systems (ICKES)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117242811","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}

{"title":"Application of BD Analysis Technology in the Construction of WeChat Public Platform for Business English Learning","authors":"Jie Liu","doi":"10.1109/ICKECS56523.2022.10060323","DOIUrl":"https://doi.org/10.1109/ICKECS56523.2022.10060323","url":null,"abstract":"The emergence of new phenomena and new problems such as the increasingly complex foreign trade environment, the gradual increase in industry access thresholds, and the popularization of information technology have put forward higher requirements for foreign trade enterprises' talents. At present, the problems of narrow vision and weak market development ability caused by the weak BE ability of employees in foreign trade enterprises have seriously affected the survival and development of foreign trade enterprises. Based on the relevant theoretical research of BD analysis(BDA) technology, this paper analyzes the application of this technology in the construction of WeChat public platform(WCPP) for business English(BE) learning, analyzes its mechanism of action, and discusses the mechanism of BE knowledge system learning. The WCPP stands out among many new media because of the simplicity of operation, the convenience of real-time interaction, and the diversification of resource storage and sharing, providing a new teaching and learning platform for BE learning. This paper takes the research of WCPP as the starting point, and analyzes the application advantages of WCPP in BE learning by methods such as literature review, practice, and control experiments. The final results of the research show that the number of OAs of the WCPP for BE learning is 63, 82, 59, 41 and 75 respectively. The corresponding degrees of perfection in the construction of the WCPP for BE learning are 85.7%, 86.1%, 82.9%, 84.8% and 88.3%, respectively. It shows that the scheme has certain feasibility and popularization and application value.","PeriodicalId":171432,"journal":{"name":"2022 International Conference on Knowledge Engineering and Communication Systems (ICKES)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131395284","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}