2022 OITS International Conference on Information Technology (OCIT)最新文献_第10页

Impact of Organisational Commitment and Job Satisfaction on Employee Efficiency in Transformational Leadership 变革型领导中组织承诺和工作满意度对员工效率的影响

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00070

S. Samanta, P. Mallick, Jyotiranjan Gochhayat

{"title":"Impact of Organisational Commitment and Job Satisfaction on Employee Efficiency in Transformational Leadership","authors":"S. Samanta, P. Mallick, Jyotiranjan Gochhayat","doi":"10.1109/OCIT56763.2022.00070","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00070","url":null,"abstract":"Job satisfaction is one of the most significant indicators of organizational effectiveness. This paper focuses on discussions about the leading variables of transformational leadership and their impact on the ability of mid-career executives to perform their jobs. Human resources are an essential asset in developing and achieving the goals of government organizations. This study analyzes the impact of leadership on employee job satisfaction and observes the impact of corporate culture on employee job satisfaction. The study used primary data from a survey of 245 employees at the Indian Institutions of the Maros Devices' Work Unit as samples. Structural Equation Modeling applications were used to examine the research data (SEM). The proposed method describes the variable features of leadership, job efficiency organizational culture, job satisfaction, and inspiration among workers of the Regional Education Service Maros. The main goal of verification research is to examine the validity of a hypothesis that is executed in the field through data collection. The results of this study suggest that leadership has an impact on employee job satisfaction. The analysis and validation of this case via this work improve the existing idea. The obtained results show that organizational learning and transformational leadership have no substantial effect on employee efficiency, both intrinsically and extrinsically by job satisfaction. Employee efficiency is significantly influenced by job happiness. Because the analytical approach utilized is a structural equation model (SEM), which is based on the concept and theory of the partial least squares (PLS) program package, the findings are accurate. Transformational leadership has a direct and considerable effect on work happiness and corporate engagement, according to the findings of this study.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"82 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":"129213515","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

Somali Extractive Text Summarization 索马里语摘录文本摘要

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00063

Ahmed Iman Seid, Abdiqani Abdullahi Abdisalan, Mustafe Mohamed Abdulahi, Shantipriya Parida, S. Dash

引用次数: 1

An IOT Solution for Cattle Health Monitoring and Tracking 用于牛健康监测和跟踪的物联网解决方案

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00101

Subhra Debdas, Anjali Behera, Atri Bandyopadhyay, Subhranil Karmakar, Ayushi Subhadarshini

引用次数: 2

Analysis of Multi-Class Weather Classification using deep learning models and machine learning classifiers 基于深度学习模型和机器学习分类器的多类天气分类分析

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00050

Silky Goel, Snigdha Markanday, Shlok Mohanty

引用次数: 1

Symptoms Prediction of Tuberculosis using Soft Computing Technique 应用软计算技术预测肺结核症状

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00071

Radhanath Hota, Sachikanta Dash, Sujogya Mishra, Sipali Pradhan, P. Pattnaik

引用次数: 1

Detection of Lung Cancer Using CT-Scan Image - Deep Learning Approach 基于ct扫描图像的肺癌检测——深度学习方法

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00014

Jashasmita Pal, Subhalaxmi Das, Jogeswar Tripathy

{"title":"Detection of Lung Cancer Using CT-Scan Image - Deep Learning Approach","authors":"Jashasmita Pal, Subhalaxmi Das, Jogeswar Tripathy","doi":"10.1109/OCIT56763.2022.00014","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00014","url":null,"abstract":"Cancer is a disease that comes in many forms and is the largest cause of death worldwide for men and women alike. Early detection of cancer has the highest chance of saving a person's life. Some of the procedures used to diagnose cancer include CT scans, bone scans, MRIs, PET (Positron Emission Tomography), ultrasound, and X-rays. Cancers such as lung cancer are among the deadliest worldwide, killing approximately five million people every year. This chapter focuses on lung cancer detection. The diagnosis of Cancer is usually a very difficult task in the biomedical and the bioinformatics field. Now, computed tomography (CT) scans can provide useful information for lung cancer diagnosis. In recent advances, deep learning approaches have improved to outperform humans in some tasks like classifying objects in images and also predicting better accuracy. Therefore, these techniques have been utilized in this model for the treatment of cancerous conditions. We detect lung cancer nodules from a given input and classify cancer as Adenocarcinoma, Large Cell Carcinoma, or Squamous Cell Carcinoma in our research. To detect the location of lung nodules, researchers used revolutionary deep learning approaches. In this paper basically, we used three deep learning case studies to diagnose lung cancer such as VGG16, INCEPTIONV3 and RESNET50 and also, we are discussing various measures for evaluating the performance of our model to get better accuracy. SS","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"1969 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":"129973007","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

The Blood Boon 血的恩惠

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/ocit56763.2022.00077

Bharath Kumar Nangunuri, G. Sripriya, K. Avinash, M. RamaChandraRao

{"title":"The Blood Boon","authors":"Bharath Kumar Nangunuri, G. Sripriya, K. Avinash, M. RamaChandraRao","doi":"10.1109/ocit56763.2022.00077","DOIUrl":"https://doi.org/10.1109/ocit56763.2022.00077","url":null,"abstract":"“Blood” is one of the most important necessities in our lives. The number of blood donors in our country is very small compared to other countries. Our project proposes a new and efficient way to overcome such contours. The average blood donation volume is 470 ml per person, which is only 8% of adults. In this paper, we are describing how people can use our website. Through this website, anyone interested in blood donation can register in the same way as the organization they want to register on this site. For example, with the tap of a button, donors will be prompted to enter personal details such as name, phone number, age, weight, date of birth, blood type, and address. In the event of a blood need, GPS can help you find a nearby blood donor. When the user of the website enters the required blood type, nearby donors are automatically displayed and an alert notification message is sent to the donor. If the first donor is not available, it will automatically search for the next donor in the queue. When the donor accepts the receiver's request, the receiver can directly contact the nearby donor. When the donor donates blood, the donor details will be automatically deleted for the next 3 months.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"74 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":"130823087","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

Neural Machine Translation for Kashmiri to English and Hindi using Pre-trained Embeddings 使用预训练嵌入的克什米尔语到英语和印地语的神经机器翻译

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00053

Shailashree K. Sheshadri, Deepa Gupta, M. Costa-jussà

{"title":"Neural Machine Translation for Kashmiri to English and Hindi using Pre-trained Embeddings","authors":"Shailashree K. Sheshadri, Deepa Gupta, M. Costa-jussà","doi":"10.1109/OCIT56763.2022.00053","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00053","url":null,"abstract":"Neural Machine Translation (NMT) is one of the advanced approaches of Machine Translation (MT) that has recently gained popularity. A significant amount of parallel corpus is required to achieve a sound translation system, but most languages have a deficit worldwide. Many SoTA NMT systems are available for low-resource langauges that are developed using transfer learning, knowledge transfer, and zero-shot learning mechanisms. Most Indic languages fall into low-resource and zero-resource due to the non-availability of rich parallel and monolingual corpora. Though many Indian border languages have social and economic significance, they lack resources and automated machine translation systems. Kashmiri, one such Indian border language, belongs to the zero-resource category with limited corpora and no significant translation system. This paper uses pre-trained word embeddings to create the first NMT system specifically for Kashmiri-English and Kashmiri-Hindi translation. mBPE pre-trained word embeddings for Kashmiri language are used to develop the NMT system. A pre-trained word embedding model shows +2.58 BLEU improvisation in comparison to Vanilla NMT.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"14 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":"132154953","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

Software Fault Prediction Using Machine Learning Models 使用机器学习模型的软件故障预测

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00041

Ayushi Kundu, Priyanka Dutta, Kunal Ranjit, Sthitaprajna Bidyadhar, Mahendra Kumar Gourisaria, Himansu Das

{"title":"Software Fault Prediction Using Machine Learning Models","authors":"Ayushi Kundu, Priyanka Dutta, Kunal Ranjit, Sthitaprajna Bidyadhar, Mahendra Kumar Gourisaria, Himansu Das","doi":"10.1109/OCIT56763.2022.00041","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00041","url":null,"abstract":"In recent years, computers have great role to the society for their reliability which becoms a key essential in day to day life. The role of software and its captious function in computer system for some certain software has appeared as important achievement for certain infrastructure. Exploitation of system perspective which recognise the importance of software that characterized the current state of fault identification research work as it contributes to the reliability of computer systems. In general, different classification algorithms like K-Nearest Neighbors (KNN), Decision Tree (DT), Naive Bayes (NB), Radial Basis Function Support Vector Machine (RBF-SVM), (L-SVM), Polynomial Support Vector Machine (P-SVM), Adaboost, and Random Forest (RF) have been considered to determine classification performance to evaluate the accuracy of classification with ten number of fault-tolerance datasets. In most of the cases, it is noticed that the nature of data have great impact in the performance of the classification algorithm. The evaluation of several performance measures of all the above ML classification algorithms have been analyzed for ten number of fault-tolerance datasets. It is also observed that the classifier Adaboost gives better result as compared to rest of the classification algorithms.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"339 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":"132076710","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: 2

A Reinforced Active Learning Sampling for Cybersecurity NER Data Annotation 网络安全NER数据标注的强化主动学习抽样

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI: 10.1109/OCIT56763.2022.00066

Smita Srivastava, Deepa Gupta, Biswajit Paul, S. Sahoo

{"title":"A Reinforced Active Learning Sampling for Cybersecurity NER Data Annotation","authors":"Smita Srivastava, Deepa Gupta, Biswajit Paul, S. Sahoo","doi":"10.1109/OCIT56763.2022.00066","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00066","url":null,"abstract":"A vast majority of cybersecurity data comes in the form of unstructured textual data and needs to be annotated proficiently to train supervised machine learning models. The critical question is how much and which subset of data should be annotated for better model performance under budget constraints. Though most of the Machine Learning (ML) research focuses on learning better models using annotated datasets, this paper focuses on data annotation, specifically on suitable subset selection with an emphasis on Named Entity Recognition (NER) for cybersecurity. The proposed method provides an active learning based sampling strategy to select minimal yet most informative samples from a large set. Further, reinforcement learning is combined with the active learning approach to automate the process of sampling. The results on the auto-labelled cyber-NER dataset indicate that the cyber-NER model with Reinforced Active Learning (RAL) based sampling increases F1-Score by +2-7% and reduces compute time by 90% compared to random sampling based subset selection. Further, the proposed RAL approach achieved an 80% reduction in sample size and, consequently, annotation cost with comparable accuracy to that of complete selection.","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":"128696911","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