2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)最新文献_第5页

Deep Learning Features Restoration and Regional Longitudinal Fitting of Computed Tomography Images using Convolution Neural Network 基于卷积神经网络的深度学习特征恢复与区域纵向拟合

2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2022-11-10 DOI: 10.1109/I-SMAC55078.2022.9987413

R. Krishnaswamy, A. Titus, G. Gengalakshmi., S. Srinivasan, J. Manikandan

{"title":"Deep Learning Features Restoration and Regional Longitudinal Fitting of Computed Tomography Images using Convolution Neural Network","authors":"R. Krishnaswamy, A. Titus, G. Gengalakshmi., S. Srinivasan, J. Manikandan","doi":"10.1109/I-SMAC55078.2022.9987413","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987413","url":null,"abstract":"Positron Emission Tomography (PET) is suggested for its high potential Deep Learning (DL) diagnostic imaging with a profound learning approach. The network training is done using clear images but reconstructing the low resolution images using Poisson operation. In training the Convolutional Neural Networks (CNN) at a default noise level, a major issue for their generic applicability is the noise level discrepancy. The noise level varies considerably in each iteration reduces the overall efficiency. The results and measured efficiency loss in different noise environments with various noise levels due to inadequate current trials is also presented. To fix this problem, a local linear fitting function is represented before improving the image quality. It indicates that the resulting approach is resilient to noise levels despite the network being educated at a fixed noise level. The proposed protocol is demonstrated to exceed traditional approaches based on total variance and penalty by mean and standard deviation via simulations and trials.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126509895","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

Software Design of 3D Animation Scene based on Virtual Image Modeling Algorithm 基于虚拟图像建模算法的三维动画场景软件设计

2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2022-11-10 DOI: 10.1109/I-SMAC55078.2022.9987341

Juanjuan Luo

引用次数: 0

Stock Price Prediction using HFTSF Algorithm 基于HFTSF算法的股价预测

2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2022-11-10 DOI: 10.1109/I-SMAC55078.2022.9987378

C. Latha, S. Bhuvaneswari, K. Soujanya

{"title":"Stock Price Prediction using HFTSF Algorithm","authors":"C. Latha, S. Bhuvaneswari, K. Soujanya","doi":"10.1109/I-SMAC55078.2022.9987378","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987378","url":null,"abstract":"Forecasting is still a potential area of research, particularly in the stock market. Any forecasting model must overcome the subjective nature of the factors that affect market oscillation. Current fuzzy models have made an effort throughout the years to improve financial market forecasting accuracy. The fuzzy returns of the phenomena under study contribute to reducing the subjective nature of the financial market, particularly with respect to the effect of human emotions. These are based on large part on fuzzy sets. Fuzzy sets, on the other hand, may not fully satisfy or characterize the ambiguity of the data since they are unable to depict the level of neutrality of time series. Existing fuzzy inference systems’ reliance on a univariate framework is another important and crucial shortcoming. However, the time series that are part of a prediction problem frequently interact with one another. Given these factors, it is important to create a hybrid fuzzy system for a time series prediction issue that is built on fresh fuzzy sets and a collection of fuzzy logic relations. In this context, this research suggests a hybrid fuzzy time-series forecasting model (HFTSF) on the Standard & Poor Bombay Stock Exchange Information Technology (S& P BSE IT) index, for the prediction of time-series data. This model boosts the chances of getting better forecasts. The validation techniques such as root mean square error, mean square error, and mean absolute error were used in terms of validating the predicting outcomes.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131594874","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

Hybrid Cluster Head Selection Approach for Node Lifetime Enhancement in Wireless Sensor Networks 无线传感器网络中节点寿命增强的混合簇头选择方法

2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2022-11-10 DOI: 10.1109/I-SMAC55078.2022.9987316

C. Padmavathy, V. Akshaya, R. Menaha, S. Raja

{"title":"Hybrid Cluster Head Selection Approach for Node Lifetime Enhancement in Wireless Sensor Networks","authors":"C. Padmavathy, V. Akshaya, R. Menaha, S. Raja","doi":"10.1109/I-SMAC55078.2022.9987316","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987316","url":null,"abstract":"Node lifetime is an important factor in wireless sensor networks as the entire lifetime of the network depends on the individual nodes. Researchers pay more attention towards enhancement of node lifetime through various deployment models. Rather than concentrating over node deployment, efficient clustering, data aggregation in wireless sensor networks enhances the node and network lifetime, minimize the energy utilization, reduces network congestion and identifies an optimal route for better load balancing. Clustering approaches considers the parameters like residual energy of node, communication range, distance between node and sink. Specifically, cluster head selection and replacement is a crucial part in clustering which directly relates to energy management of network. Considering these facts, an energy efficient clustering approach to enhance node lifetime through hybrid adaptive neuro fuzzy inference system (ANFIS) is proposed in this research work. Conventional models are compared with proposed hybrid approach to demonstrate the superior performance.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128683785","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

Machine Learning Approaches for Electronic Design Automation in IC Design Flow 集成电路设计流程中电子设计自动化的机器学习方法

2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2022-11-10 DOI: 10.1109/I-SMAC55078.2022.9987302

M. P. Varghese, T. Muthumanickam

引用次数: 0

Deep Reinforcement Learning for Energy Efficient Routing and Throughput Maximization in Various Networks 基于深度强化学习的各种网络中高效路由和吞吐量最大化

2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2022-11-10 DOI: 10.1109/I-SMAC55078.2022.9987395

V. Mohanavel, M. Tamilselvi, G. Ramkumar, R. Prabu, G. Anitha

{"title":"Deep Reinforcement Learning for Energy Efficient Routing and Throughput Maximization in Various Networks","authors":"V. Mohanavel, M. Tamilselvi, G. Ramkumar, R. Prabu, G. Anitha","doi":"10.1109/I-SMAC55078.2022.9987395","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987395","url":null,"abstract":"Large bandwidth and more mobility are only two reasons why wireless and mobile networks are fast overtaking wired ones as the preferred mode of connectivity. Heterogeneous networks refer to systems that consist of many independent networks, each of which has its own unique set of protocols and characteristics. Due to their density and complexity, such dense small-cell heterogeneous networks currently consume a lot of power; thus, in order to tackle climate change, we require power information security. A Modified Deep Reinforcement Learning (MDRL) approach may offer an on-demand automated approach with short inference time for NP-hard network communication problems including radio resource distribution, identification, and battery preservation. We examine the DRL algorithm’s applicability to a multi-objective issue. A paradigm for hopeful nonlinear assistance that is founded on the entertainer paradigm and explores repeatedly for potential answers to the multiobjective issue we have given. Throughput and energy savings achieved by our algorithm are equivalent to those of currently used approaches, according to the findings of our tests.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122588092","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: 5

Application of Image Saturation Enhancement Algorithm based on OpenGL Aided Design System 基于OpenGL辅助设计系统的图像饱和度增强算法的应用

2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2022-11-10 DOI: 10.1109/I-SMAC55078.2022.9987418

Yi-Heng Mao, Man Zhang

引用次数: 0

LoRa-Powered Energy-Effcient Object Detection Mechanism in Edge Computing Nodes 基于lora的边缘计算节点节能目标检测机制

2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2022-11-10 DOI: 10.1109/I-SMAC55078.2022.9987393

Anshul Jindal, Jiby Mariya Jose, S. Benedict, M. Gerndt

{"title":"LoRa-Powered Energy-Effcient Object Detection Mechanism in Edge Computing Nodes","authors":"Anshul Jindal, Jiby Mariya Jose, S. Benedict, M. Gerndt","doi":"10.1109/I-SMAC55078.2022.9987393","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987393","url":null,"abstract":"The ongoing accomplishments in the decades-long realization of computer vision have infused new dimensions in various research areas such as smart mobility, smart healthcare, education, finance, and so forth. Research works relating to automated object detection, deep learning-assisted data pipelines, and energy-efficient end-to-end solutions have enabled newer perceptions among researchers, albeit the existence of challenges. This paper proposes an object detection system using energy-efficient Long Range (LoRA) communication media on edge nodes such as Raspberry Pi, Coral DevBoard, and Nvidia Jetson Nano. The proposed approach utilized energy-efficient methods to collaboratively offload object detection-related tasks such as capturing images, training images, and inferring objects across a compendium of computing nodes using LoRA. In addition, this research study has attempted to reveal the inference capabilities of images on three different edge nodes. The proposed work has achieved a power difference of at least 1.2 watts during the inference period of the deep learning models without challenging the prediction accuracy with respect to the base model.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126557822","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

Resource Allocation and Information Exchange of Cognitive user Connectivity with Minimal Interference using Simulation Analysis 基于仿真分析的最小干扰认知用户连接的资源分配与信息交换

2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2022-11-10 DOI: 10.1109/I-SMAC55078.2022.9987352

J. M. Sahayaraj, K. Gunasekaran, S. Verma, P. Ramesh, G. Murugesan

{"title":"Resource Allocation and Information Exchange of Cognitive user Connectivity with Minimal Interference using Simulation Analysis","authors":"J. M. Sahayaraj, K. Gunasekaran, S. Verma, P. Ramesh, G. Murugesan","doi":"10.1109/I-SMAC55078.2022.9987352","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987352","url":null,"abstract":"The purpose of this research is to develop protocols for the underutilized channels of primary user usage with scant transmission and minimal interference associating with the secondary users. This has been achieved with large scale fading channels using discrete time queues. Flow level analysis has been made by appropriate queuing model and packet level analysis has done with NS2 simulator. CRTTP uses the channel selection procedure based on utilization, throughput and minimal drop rate whereas the response time denotes whether to increment or decrement transmission power control. Cognitive Radio based Temporal Transmission Protocol Single Channel (CRTTP-SC) denies transmission if sustainable routing parameter does not by cognitive user. Cognitive Radio based Temporal Transmission Protocol Single Channel Receiver Capacity (CRTTP-SCRC). CRTTP-SCRC protocol calculates the channel utilization, drop rate and receiver capacity after which it determines whether to prolong transmission or to refrain from transmission. Cognitive Radio based Temporal Transmission Protocol Multiple Channel (CRTTP MC) uses exponential distribution with inter-arrival time of packets with appropriate transmission power assigned to each channel. Cognitive Radio based Temporal Transmission Protocol Multiple Channel Collision Avoidance (CRTTP-MCCA) assigning hyper exponential distribution with inter arrival time of packets for optimizing the usage of lesser utilized channel. Comparison has been done with simulations for single channel protocols of CRTTP and multiple channel protocols of CRTTP.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128002305","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

Construction of an Intelligent Platform based on the Perspective of IoT Data Analysis 基于物联网数据分析视角的智能平台构建

2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Pub Date : 2022-11-10 DOI: 10.1109/I-SMAC55078.2022.9987348

Yihang Wang

引用次数: 0