2022 International Conference on Connected Systems & Intelligence (CSI)最新文献_第3页

Deep Learning Classification of Fetal Cardiotocography Data with Differential Privacy 基于差分隐私的胎儿心脏造影数据深度学习分类

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9924087

Ashish Kumar Lal, S. Karthikeyan

{"title":"Deep Learning Classification of Fetal Cardiotocography Data with Differential Privacy","authors":"Ashish Kumar Lal, S. Karthikeyan","doi":"10.1109/CSI54720.2022.9924087","DOIUrl":"https://doi.org/10.1109/CSI54720.2022.9924087","url":null,"abstract":"Cardiotocography (CTG) is a continuous recording of the fetal heart rate (FHR) obtained from an ultrasound transducer placed on the mother's abdomen. In common practice, obstetricians visually inspect the CTG signal to monitor the condition of the fetus's heart. This manual inspection is not reliable as it is prone to human error and biases. To overcome these short-comings, researchers had developed various AI-based diagnosis models for the automatic classification of CTG data. A few recent research had reported that neural network outperforms other machine learning models. Despite the advancements in automatic classification techniques, the adoption of these AI models has not been widespread due to the requirement for privacy of the patient record. The medical institutions are unwilling to share or publish these records, due to ethical and legal reasons. This discourages the deployment of such AI models and consequently hinders active and collaborative research work. To alleviate the privacy breach concern, we used a deep privacy-preserving CTG data classification model by adopting Differential Privacy (D P) framework. DP has widely been accepted as the gold standard of privacy guarantee. As privacy comes at an additional cost of slight downgrade in the model's performance. To mitigate this performance degradation, we have proposed a two stage binary classification which improves the model performance while maintaining the same privacy guarantee. The experimental results show that an improved performance of the proposed model with accuracy increased to 0.91 from 0.89 with E = 10 of (E,6)- Differential Privacy.","PeriodicalId":221137,"journal":{"name":"2022 International Conference on Connected Systems & Intelligence (CSI)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126436306","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}

引用次数: 1

Zone Based Selective Neighbours to Mitigate Flooding & Reliable Routing for WSN 基于区域的选择性邻居减轻洪水和可靠路由的WSN

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9924006

Poonam T. Agarkar, M. Chawhan, K. Kulat, P. Hajare

{"title":"Zone Based Selective Neighbours to Mitigate Flooding & Reliable Routing for WSN","authors":"Poonam T. Agarkar, M. Chawhan, K. Kulat, P. Hajare","doi":"10.1109/CSI54720.2022.9924006","DOIUrl":"https://doi.org/10.1109/CSI54720.2022.9924006","url":null,"abstract":"Growing demand in wireless Mobile Adhoc Networks due to their immense potential and effectiveness in varied applications had motivated researchers to control and optimize various parameters for efficient routing in mobile environment. The rapidly changing environment of the network due to mobility of nodes results in unpredictable topology and thus introduces primary challenge to design better and an efficient routing protocol. The network in such case lacks fixed infrastructure and common head centric design. MANET routing schemes use flooding to propagate packets in the network resulting retransmissions, collisions, and congestions which significantly degrade the performance of the network. Using GPS, knowing the geographical position of the sensor nodes, the protocol performance can be improved while reducing the number of retransmissions. The proposed work is a future extension of efficient flooding based on selective neighbours where the surrounding region limiting the transmission range is divided into eight quadrants or zones with the source at the centre called zone based selective neighbours (ZBSN). Flooding based on selective four neighbours suffers from few selection of hoping or forwarding nodes when the node density is low and does not meet the selection criteria around the source or forwarder node and may miss the chance of approaching the destination or requires large number of hops. This neighbour selection scheme uses the modified approach of the Adhoc on Demand Distance Vector (AODV) protocol to reduce the flooding of Route request (RREQ) packets, control the network overall traffic, improves link stability and the residual energy reducing the overheads by 24%.","PeriodicalId":221137,"journal":{"name":"2022 International Conference on Connected Systems & Intelligence (CSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130428914","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}

引用次数: 1

Social Loss Analysis Approach of Traffic Congestion from Traffic Flow Monitoring in India 印度交通流监测中交通拥堵的社会损失分析方法

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9924071

T. Tsuboi

{"title":"Social Loss Analysis Approach of Traffic Congestion from Traffic Flow Monitoring in India","authors":"T. Tsuboi","doi":"10.1109/CSI54720.2022.9924071","DOIUrl":"https://doi.org/10.1109/CSI54720.2022.9924071","url":null,"abstract":"This paper describes new traffic congestion analysis method challenge by social loss calculation based on actual traffic flow monitoring data in India. This actual monitoring data is collected in one of typical major city of India, where it is Ahmedabad city in Guj arat state and one of most growing cities in India. Traffic congestion is always big issues in developing counties like in India. Therefore, it is important to understand how much this traffic congestion generates impact to their life. There are several ways to measure social impact such as waste of time for commute, un-necessary fuel consumption, and air pollution and so on. In this paper, it takes a social loss which is calculated as the gap between the traffic demand and the capacity which means infrastructure supply. This social loss has potential for impact parameter of traffic congestion. In order to calculate the social loss, it is necessary to have clear traffic parameter about the traffic demand data, traffic supply data, and infrastructure cost for traffic improvement. It is not easy to get those data especially tin developing countries. This paper shows how to get traffic parameter from the actual monitoring data and how to calculate traffic social loss from this data. And as summary, this calculated social loss is valid especially for comparing the traffic congestion condition in each location. This new traffic congestion analysis by social loss is authorized by the traffic flow theory but it is the first time to bring new method for the developing countries where there are so many unknown traffic flow parameter","PeriodicalId":221137,"journal":{"name":"2022 International Conference on Connected Systems & Intelligence (CSI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129615143","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}

引用次数: 0

Indian Stock Market Forecasting using Prophet Model 利用先知模型预测印度股市

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9924117

Anjana V Nair, Jayasree Narayanan

引用次数: 0

EAC: Energy-Aware Caching Scheme for Internet of Things using ICN EAC:基于ICN的物联网能源感知缓存方案

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9924012

Geetu Dhawan, A. P. Mazumdar, Y. Meena

{"title":"EAC: Energy-Aware Caching Scheme for Internet of Things using ICN","authors":"Geetu Dhawan, A. P. Mazumdar, Y. Meena","doi":"10.1109/CSI54720.2022.9924012","DOIUrl":"https://doi.org/10.1109/CSI54720.2022.9924012","url":null,"abstract":"The Internet of Things (IoT) envisions millions of devices communicating with one another. Its ubiquitous nature leads to an early dissipation of device energy, making it a hot topic for IoT research. There has been an increased interest in content-centric IoT as it facilitates critical features to constrained devices. In IoT, interest and data packet transmissions consume the majority of node energy. As the existing IoT schemes focus primarily on the node's sleep cycle, the number of transmissions, clustering, and energy harvesting, they fail to account for the size and popularity of incoming content, which increase the volume of data exchanged. Therefore, an energy-efficient strategy that considers all the parameters of constrained devices to reduce power consumption is of great importance. In this article, we proposed an energy-aware caching scheme (EAC) that caches data based on the size of incoming content while simultaneously taking into account the content's popularity and freshness. Energy-constrained IoT devices can benefit from the proposed EAC scheme, which takes into account the trade-off between content size and popularity. The experimental results show that the proposed scheme has a higher caching hit rate than the Centrally Controlled Caching (CCC) and No-Cache schemes.","PeriodicalId":221137,"journal":{"name":"2022 International Conference on Connected Systems & Intelligence (CSI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125525750","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}

引用次数: 0

CNN Based Automatic Segmentation of Scaling in 2-D Psoriasis Skin Images 基于CNN的二维牛皮癣皮肤图像缩放自动分割

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9924079

N. Kasthuri, R. Ramyea, D. Jeffrin, N. K. Chitrasena, K. Divveshwari

{"title":"CNN Based Automatic Segmentation of Scaling in 2-D Psoriasis Skin Images","authors":"N. Kasthuri, R. Ramyea, D. Jeffrin, N. K. Chitrasena, K. Divveshwari","doi":"10.1109/CSI54720.2022.9924079","DOIUrl":"https://doi.org/10.1109/CSI54720.2022.9924079","url":null,"abstract":"In India, over 3 to 4 % of people are affected by chronic proliferative disease known as Psoriasis. The patchy rashes, small scaling spots, cracked skin, itching, burning or soreness are the common signs and symptoms of psoriasis. These signs and symptoms vary depending on the type of psoriasis. The various types of psoriasis are plague psoriasis, nail psoriasis, Guttate psoriasis, inverse, pustular and erythrodermic psoriasis. These psoriasis affects the skin and, in some cases, the fungal infections will trigger the disease. To evaluate psoriasis severity, various methods are used to monitor the therapeutic response. In this paper, Principal Component Analysis (PCA) and rigid transformations are used for the automatic segmentation of psoriasis. Convolutional Neural Network (CNN) which comprises of convolutional layer, ReLU activation layer, max pooling layer and fully connected feed forward network are used for the classification of skin images. The feature map is extracted from the input images by convolution operation. These feature maps are obtained to train the neural network model to classify the images. The performance metric of the model is calculated after training the model with input images and the model performance varies depending on the type of images. The accuracy, specificity, sensitivity, F1 score are determined to find the best model for evaluation.","PeriodicalId":221137,"journal":{"name":"2022 International Conference on Connected Systems & Intelligence (CSI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127198981","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}

引用次数: 0

Improved GA-PI Technique for Non-Linear Dynamic Modelling of a UAV 基于改进GA-PI技术的无人机非线性动力学建模

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9924088

P. Priya, Sushma Kamlu

引用次数: 0

Delay-aware Intelligent Task Offloading Strategy in Vehicular Fog Computing 车辆雾计算中的延迟感知智能任务卸载策略

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9924066

Indranil Sarkar, Sanjay Kumar

{"title":"Delay-aware Intelligent Task Offloading Strategy in Vehicular Fog Computing","authors":"Indranil Sarkar, Sanjay Kumar","doi":"10.1109/CSI54720.2022.9924066","DOIUrl":"https://doi.org/10.1109/CSI54720.2022.9924066","url":null,"abstract":"In the era of Internet of Things (loT), data offloading become a promising and crucial strategy to improve the overall system performance and also to provide quality-of-service (QOS). In this context, recently fog computing has gained a lot of interests from the industry as well as academia. In this paper, we propose a delay-aware task offloading strategy in mobile fog-based network. We consider several moving vehicles in a one-way road out of which some vehicles act as client vehicles and some of them act as mobile fog nodes. Individual fog nodes allocate its available resources to the the requesting client vehicles in its proximity. However, because of the dynamic nature of the vehicular environment, it is difficult to develop a scheme that can decide how to allocate the computing resources to the local on-board CPU or to the neighbouring fog nodes. In this regards, the paper propose a deep reinforcement learning based intelligent task offloading for vehicles in motion (ITOVM) policy, considering the vehicle mobility and communication bandwidth constraints, to minimize the overall latency of the network. The proposed IOTVM policy is formulated as the Markov decision process (MDP) which is solved by the concept of deep Q network (DQN). Finally, extensive simulation results demonstrate the efficacy and performance enhancement of the proposed approach compared to several baseline algorithms.","PeriodicalId":221137,"journal":{"name":"2022 International Conference on Connected Systems & Intelligence (CSI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130125238","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}

引用次数: 1

BERT-LSTM for Fake News Detection on Facebook Using SVD 基于SVD的Facebook假新闻检测BERT-LSTM

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9923970

S. T. S., P. Sreeja

引用次数: 0

Text Localization and Script Identification in Natural Scene Images and Videos 自然场景图像和视频的文本定位与脚本识别

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9924044

Chandana Udupa, Anusha Upadhyaya, Basanagoud S. Patil, S. Seeri, Prakashgoud Patil, P. Hiremath

{"title":"Text Localization and Script Identification in Natural Scene Images and Videos","authors":"Chandana Udupa, Anusha Upadhyaya, Basanagoud S. Patil, S. Seeri, Prakashgoud Patil, P. Hiremath","doi":"10.1109/CSI54720.2022.9924044","DOIUrl":"https://doi.org/10.1109/CSI54720.2022.9924044","url":null,"abstract":"Text detection and its script identification in a natural scene image/video has attracted the attention of many researchers over the recent years due to its application in the de-sign of computer vision devices for usage by the visually impaired people, global tourists travelling in unfamiliar tourist places, etc. to facilitate them to understand the textual information displayed on sign boards, bill boards, public notice boards, etc., the objective of the proposed method is detection and localization of multilingual text in a natural scene video image and its corresponding script identification. The texts in three languages, namely, English, Hindi and Kannada, are considered. In the proposed method, CNN based YOLOv5 is used for text detection and localization in real-time videos of natural scene and it is also trained for script identification. The YOLOv5 performance is found to yield an accuracy higher than otherobject detection algorithms. The proposed model is trained witha custom dataset containing video images of natural scenes and istested for different scenarios like texts in different backgrounds, fonts, orientations, resolutions, and disturbances in the images. The experimental results demonstrate the effectiveness and robustness of the proposed method. The performance comparison is done with other methods in the literature.","PeriodicalId":221137,"journal":{"name":"2022 International Conference on Connected Systems & Intelligence (CSI)","volume":"250 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117171914","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}

引用次数: 0