2023 IEEE 8th International Conference for Convergence in Technology (I2CT)最新文献_第7页

PQTBA: Priority Queue based Token Bucket Algorithm for congestion control in IoT network PQTBA:基于优先队列的物联网网络拥塞控制令牌桶算法

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126166

A. P, Vimala H S, J Shreyas

{"title":"PQTBA: Priority Queue based Token Bucket Algorithm for congestion control in IoT network","authors":"A. P, Vimala H S, J Shreyas","doi":"10.1109/I2CT57861.2023.10126166","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126166","url":null,"abstract":"The Internet of Things connects millions of devices in the areas of smart cities, e-health, transportation, and the military to fulfill a variety of human needs. To offer these services, a large amount of data must be transmitted to the IoT network servers. But the node processing power, buffer size, and server capacity limitations on IoT networks have a negative influence on throughput, latency, and energy consumption. Additionally, the IoT network’s performance is decreased by congestion caused by the high network traffic that results from the high volume of data. In order to handle congestion challenges in IoT networks, unique congestion control strategies—such as the queue management strategy—must be created. In this study, a novel Priority Queue-based Token Bucket Algorithm (PQTBA) is suggested as a means of controlling congestion in IoT networks. The PQTBA uses a preemptive/non-preemptive technique with a discretionary rule to categorize network traffic into priority groups in accordance with real-time requirements. Our proposed work performs con-siderably better than the most recent techniques in terms of throughput, packet loss ratio, and energy consumption.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125581063","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

Eye Health Monitoring System 眼健康监测系统

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126343

Krishi Godhani, Adit Patel, Harsh Shah, Achal Mehta, Devlina Adhikari

引用次数: 0

Implementation of stochastic computing in activation functions using stochastic arithmetic components 利用随机算法组件实现激活函数中的随机计算

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126491

P. Ashok, B. T. Sundari

引用次数: 0

An Automated Approach for Accurate Detection and Classification of Kiwi Powdery Mildew Disease 猕猴桃白粉病准确检测与分类的自动化方法研究

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126126

Ankit Bansal, Rishabh Sharma, Vikrant Sharma, A. Jain, V. Kukreja

{"title":"An Automated Approach for Accurate Detection and Classification of Kiwi Powdery Mildew Disease","authors":"Ankit Bansal, Rishabh Sharma, Vikrant Sharma, A. Jain, V. Kukreja","doi":"10.1109/I2CT57861.2023.10126126","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126126","url":null,"abstract":"Powdery mildew is a fungal disease that affects kiwi fruit plants and leads to a reduction in yield and quality. Early detection and classification of the disease can help farmers to take necessary measures to curb the transmission of the infection. In this research, we conducted binary and multi-classification of kiwi powdery mildew disease (KPMD) using 12000 images of kiwi fruit. The binary classification was done using two classes, healthy and inf, while multi-classification was done using four different classes of powdery mildew. An integrated CNN and LSTM model was developed for multi-classification, which resulted in an accuracy of 92.14% in binary classification and 95.91% in multi-classification. The results were analyzed based on various performance parameters, and the proposed model demonstrated encouraging results in terms of accuracy for both detection and classification. The analysis also revealed that the proposed model had the highest accuracy for detecting powdery mildew in its terminal severity level. This research provides a useful tool for the early detection and classification of kiwi powdery mildew disease, which can assist farmers in preventing the spread of the disease and improving crop yield and quality.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131757884","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

Vocals - An App for Vocally Impaired using NLP Conversational Model 声音-一个应用程序的声音受损使用NLP会话模型

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126416

Prashasthi K Kanchan, Sahana S, Shreya K Loni, R. Raksha, T. Babu

引用次数: 0

Facilitating Leucocyte Count Using Deep Learning: A Paradigm Shift 使用深度学习促进白细胞计数:范式转变

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126331

Kshitij G. Saha, Dhruv Jyoti Garodia, Prarthana Kalal, Devang Saraogi, V. R. Badri Prasad

{"title":"Facilitating Leucocyte Count Using Deep Learning: A Paradigm Shift","authors":"Kshitij G. Saha, Dhruv Jyoti Garodia, Prarthana Kalal, Devang Saraogi, V. R. Badri Prasad","doi":"10.1109/I2CT57861.2023.10126331","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126331","url":null,"abstract":"White blood cell classification is a task that is given paramount importance in the field of pathology in order to accurately diagnose a plethora of ailments and diseases. Blood cell classification is essential for the calculation of the differential count of blood cells in a particular blood sample. Physicians derive a medical prognosis based on blood reports in which differential count plays a key role. To reduce the cost and difficulty of this task as well as to improve accuracy in cell classification, image processing and deep learning have proven to be viable alternatives to conventional pathological methods. To tackle this problem through image classification, we have successfully conducted a comparative study of accuracy metrics of various Convolution Neural Network models including the very popular Alexnet, VGG, ResNet, Inception, Densenet and EfficientNet. With multiple methods of preprocessing the dataset and deploying these well-known image classification models, the most accurate and computationally inexpensive deep learning model was identified for WBC classification.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134570081","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

Analysis of differences in EEG Signal features between Visual Imagery and Perception 视觉意象与感知脑电信号特征差异分析

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126204

Shiyona Dash, Deepjyoti Kalita, K. B. Mirza

{"title":"Analysis of differences in EEG Signal features between Visual Imagery and Perception","authors":"Shiyona Dash, Deepjyoti Kalita, K. B. Mirza","doi":"10.1109/I2CT57861.2023.10126204","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126204","url":null,"abstract":"Recent research works have increasingly focused on gaining a better understanding of visual perception from brain activity. This work was partially motivated by functional Magnetic Resonance Imaging (fMRI) based studies on the neurobiology of \"mental images\" and Brain-Computer Interface (BCI) devices. The ultimate objective is to recreate thoughts from brain activity using generative AI models. It is crucial to extract and enumerate the differences between visual perception (when a stimulus is present) and visual imagery (the recall of the stimulus after that) by the brain. In this work, we determine that it is possible to detect changes in brain activity due to differences in Visual Perception and Imagery even while using EEG signal features recorded with limited channels. The first step in this process was doing a spatiotemporal-based feature estimation on the EEG data for seven people across all channels and trials. Results indicate that Alpha Band power, an essential characteristic in the posterior electrodes and indicating a parieto-occipital origin, significantly differed across the different channels.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131294182","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

Recommendation System using NLP and Collaborative Filtering 基于NLP和协同过滤的推荐系统

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126261

Dhvani Shah, Chinmay Shokeen, Shantanu Khanzode, P. Kale, Divyaprabha Kn

引用次数: 0

Antipodal Vivaldi Antenna with Rectangular Loaded Slots for Radar and Tracking Applications 雷达和跟踪用矩形加载槽对映维瓦尔第天线

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126308

A. T. Reddy, K. Premchand, A. J. Rani, Ch. Navadeep, B. Aravind

引用次数: 0

A Novel approach to Handle Imbalanced Dataset in Machine Learning 机器学习中一种处理不平衡数据集的新方法

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126309

Taj Sapra, Shubhama, S. Meena

引用次数: 0