2022 OITS International Conference on Information Technology (OCIT)最新文献_第2页

eSeiz 2.0: An IoMT Framework for Accurate Low-Latency Seizure Detection using Pulse Exclusion Mechanism eSeiz 2.0:一个使用脉冲排除机制进行准确低延迟癫痫检测的IoMT框架

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00030

Md. Abu Sayeed, Fatahi Nasrin, S. Mohanty, E. Kougianos

引用次数: 0

Diabetic Retinopathy Detection using an Improved ResNet 50-InceptionV3 and hybrid DiabRetNet Structures 使用改进的ResNet 50-InceptionV3和混合的DiabRetNet结构检测糖尿病视网膜病变

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00036

Payel Patra, Tripty Singh

{"title":"Diabetic Retinopathy Detection using an Improved ResNet 50-InceptionV3 and hybrid DiabRetNet Structures","authors":"Payel Patra, Tripty Singh","doi":"10.1109/OCIT56763.2022.00036","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00036","url":null,"abstract":"Diabetic Retinopathy (DR) could be a mortal eye ailment that happens in people who have the disease named diabetics which hurts mainly on retina and after a long duration, it may lead to visual lacking. Diabetic Retinopathy Detection (DRD) through the integration of state of the art Profound Proficiency styles. This research used dataset, which was obtained from Eye Foundation Hospital Bangalore and Narayana Netralaya Bangalore, In this paper authors designed the frameworks within the field of profound Convolutional Neural Networks (CNNs), which have demonstrated progressive changes in numerous areas of computer vision counting therapeutic imaging, and researchers bring their control to the conclusion of eye fundus images. This proposed outline is combination of three stages. To begin with, the fundus picture is pre-processed utilizing an intensity of normalised procedure and augmented method. 2nd, the pre-processed picture is input to distinctive foundations of the CNN architecture in arrange to extricate a point vector for the evaluating process. 3rd, a classification is utilized for DRD and decides its review (e.g., no DR, mild, severe, moderate, or Proliferative Diabetic Retinopa-thy). A trained model with Resnet50, Inception V3, VGG-19, DenseNet-121 and MobileNetV2 architectures will extricate the Indus images of the eye. The outcome is coming with amazing exactness of 93.79 percentile, which is better by 7% than earlier work, by utilizing several activation functions in the new DiabRetNet architecture.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123922393","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

Automate Descriptive Answer Grading using Reference based Models 使用基于参考的模型自动描述答案评分

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00057

M. Sayeed, Deepa Gupta

引用次数: 1

A YCbCr Model Based Shadow Detection and Removal Approach On Camouflaged Images 基于YCbCr模型的伪装图像阴影检测与去除方法

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00112

Isha Padhy, P. Kanungo, S. Sahoo

引用次数: 0

Missing Link Identification from Node Embeddings using Graph Auto Encoders and its Variants 基于图自动编码器及其变体的节点嵌入缺失链路识别

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00025

Binon Teji, Swarup Roy

{"title":"Missing Link Identification from Node Embeddings using Graph Auto Encoders and its Variants","authors":"Binon Teji, Swarup Roy","doi":"10.1109/OCIT56763.2022.00025","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00025","url":null,"abstract":"Graph representation learning recently has proven their excellent competency in understanding large graphs and their inner engineering for various downstream tasks. Link completion is an important computational task to guess missing edges in a network. The traditional methods extract local, pairwise information based on specific proximity statistics that are always ineffective in inferring missing links from a global topological perspective. Graph Convolutional Network (GCN) based em-bedding methods may be an effective alternative. In this work, we try to experimentally assess the power of GCN-based graph embedding techniques, namely Graph Auto Encoder (GAE) and its variants GraphSAGE, and Graph Attention Network (GAT) for link prediction tasks. Experimental results show that the GAE-based encoding methods are able to achieve superior results for predicting missing links in various real large-scale networks in comparison to traditional link prediction methods. Interestingly, our results reveal that the above techniques successfully recreate the original network with high true positive and negative rates. However, it has been observed that they produce many extra edges with an overall very high false positive rate.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126877838","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

Alcohol Consumption Rate Prediction using Machine Learning Algorithms 使用机器学习算法预测酒精消费量

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00026

Advait Singh, Vinay Singh, Mahendra Kumar Gourisaria, Ashish Sharma

引用次数: 0

Device Discovery Approaches in D2D Communication: A Survey D2D通信中的设备发现方法综述

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00080

Anusha Vaishnav, Amulya Ratna Swain, M. R. Lenka

{"title":"Device Discovery Approaches in D2D Communication: A Survey","authors":"Anusha Vaishnav, Amulya Ratna Swain, M. R. Lenka","doi":"10.1109/OCIT56763.2022.00080","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00080","url":null,"abstract":"As the world is moving forward to the Fifth Generation (5G) of wireless technology, the demand for efficient communication techniques has also increased. 5G provides a far higher level of performance than previous generations of wireless communication in terms of low latency, increased throughput, and increased spectral efficiency. In 5G, some companion technologies have been added to strengthen the communication efficiency among the users. Device-to-Device(D2D) communication is one of these technologies to be used for modern cellular networks like 5G. D2D technology allows devices to communicate with each other without the assistance of a base station. The primary benefits of D2D communication include increased spectrum, energy efficiency, reduced transmission delay, and improved system throughput. Along with these benefits, several technical challenges include device discovery, resource allocation, mode selection, interference management, privacy, and security. In this paper, we discuss one of the challenges and the primary aspect of D2D communication, i.e., Device Discovery. The device discovery process starts when the devices transmit a discovery signal to an intermediate device to enhance the communication process by connecting with that device. Finding a potential intermediate device that will not disrupt the communication channel can sometimes become challenging. The device discovery process cannot be overlooked as it is an important step that is required before the establishment of D2D communication as well as during the communication process. In other words, device discovery is one of the key building blocks of D2D-based networks. This paper thoroughly reviews most of the important device discovery techniques for D2D communication.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131753912","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

ELMVDP: extreme learning based virtual data position exploration and incorporation method for escalation of time series forecasting accuracy ELMVDP:基于极限学习的时间序列预测精度提升的虚拟数据位置探索与整合方法

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00034

S. Nayak, Satchidananda Dehuri, Sung-Bae Cho

{"title":"ELMVDP: extreme learning based virtual data position exploration and incorporation method for escalation of time series forecasting accuracy","authors":"S. Nayak, Satchidananda Dehuri, Sung-Bae Cho","doi":"10.1109/OCIT56763.2022.00034","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00034","url":null,"abstract":"Time series data are correlated in a nonlinear fashion which makes the future data prediction challenging. Particularly, the correlation among data at the fluctuation points is insignificant and it is hard to capture the underlaying nonlinearity at those points by conventional prediction systems. The accuracy of time series forecasting (TSF) is vastly influenced by the current and immediate past data rather by far away data points. This article proposes an extreme learning-based method for exploration of virtual data positions (ELMVDP) from the training data and incorporates them to the original time series to intensify the TSF accuracy of a single hidden layer neural network. Specifically, this method is useful for the time series having less volume of data which may not suffice to train a TSF model. The effectiveness of ELMVDP method is evaluated on time series available in the literature, compared with few similar deterministic and stochastic approaches, and observations from simulation studies show that ELMVDP method yields better predictions than others.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"256 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116876435","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

Estimation of Air Quality Index of Brajarajnagar and Talcher Industrial Region of Odisha State: A Higher Order Neural Network Approach 奥里萨邦布拉贾那格尔和塔尔彻工业区空气质量指数的高阶神经网络估计

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00042

Ch. Sanjeev Kumar Dash, A. K. Behera, S. Nayak, Satchidananda Dehuri, J. P. Mohanty

引用次数: 0

A Deep Learning approach for Emotion Based Music Player 基于情感的音乐播放器的深度学习方法

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00060

Prachi Vijayeeta, Parthasarathi Pattnayak

{"title":"A Deep Learning approach for Emotion Based Music Player","authors":"Prachi Vijayeeta, Parthasarathi Pattnayak","doi":"10.1109/OCIT56763.2022.00060","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00060","url":null,"abstract":"Deep Learning mechanisms can be leveraged for playing the type of music based on the emotions of an individual entity. This can be done by detecting the human facial expressions, color, posture, orientation, lightning, etc. An interface is designed which makes the system to analyze the possible variability of faces. The basic pre-requisite for emotion recognition is appropriate selection of facial features that helps in identifying the mood of a person. Traditionally, grouping songs into various playlist was manual interpreted that consumed lot of time and it was indeed a tedious task. However, the advent of Facial Expression Based Music System emphasizes an automatic creation of music playlist based on real time mental state of an individual. In this work we have employed Haar Cascade-CNN classifier and SVM classifier to detect the emotions in an image. Haar Cascade is a machine learning-based approach where a lot of positive and negative images are used to train the classifier. The learning algorithm keeps on training the input feature vector based on the image captured. The gray scale image of the face is used by the system to classify five basic emotions such as surprise, disgust, neutral, anger and happiness. The emotion classification is achieved by observing the parts of the face, like eyes, lips movement, etc. A comparative study of these two classifiers are conducted based on the trained datasets. This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"373 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124673252","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