Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00014
Jashasmita Pal, Subhalaxmi Das, Jogeswar Tripathy
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
{"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}
Pub Date : 2022-12-01DOI: 10.1109/ocit56763.2022.00077
Bharath Kumar Nangunuri, G. Sripriya, K. Avinash, M. RamaChandraRao
“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.
{"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}
Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00070
S. Samanta, P. Mallick, Jyotiranjan Gochhayat
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.
{"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}
Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00066
Smita Srivastava, Deepa Gupta, Biswajit Paul, S. Sahoo
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.
{"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}
Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00063
Ahmed Iman Seid, Abdiqani Abdullahi Abdisalan, Mustafe Mohamed Abdulahi, Shantipriya Parida, S. Dash
Somali is an Afro-asiatic language of the Cushitic family. Somali is one the most spoken languages in the Horn of Africa. It is the national language of Somalia, Official language in Ethiopia and Northern Kenya. It is also the most widely spoken language in Djibouti. Somali is also spoken by the Somalis in the diaspora. Somali is considered to be a morphologically complicated language with limited corpus and datasets. In this paper, we have scrapped paragraphs from various Somali sources and summarized the text using Extractive Text Summarization Techniques to create an extractive text summarization for Somali language.
{"title":"Somali Extractive Text Summarization","authors":"Ahmed Iman Seid, Abdiqani Abdullahi Abdisalan, Mustafe Mohamed Abdulahi, Shantipriya Parida, S. Dash","doi":"10.1109/OCIT56763.2022.00063","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00063","url":null,"abstract":"Somali is an Afro-asiatic language of the Cushitic family. Somali is one the most spoken languages in the Horn of Africa. It is the national language of Somalia, Official language in Ethiopia and Northern Kenya. It is also the most widely spoken language in Djibouti. Somali is also spoken by the Somalis in the diaspora. Somali is considered to be a morphologically complicated language with limited corpus and datasets. In this paper, we have scrapped paragraphs from various Somali sources and summarized the text using Extractive Text Summarization Techniques to create an extractive text summarization for Somali language.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"52 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":"128902373","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}
Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00081
A. K. Dalai, B. Sahoo
Device fingerprinting involves identifying devices based on attributes provided by their configuration and usage. In this work a Deep Neural Network (DNN) architecture is designed for device fingerprinting. DNN is fed Inter Arrival Time (IAT) and Transmission Time (TT) of preprocessed wireless network traffic. The DNN consists of multiple Convolution Neural Networks (CNN), Rectified Linear Units (ReLU), and maximum pooling layers. As a final step, two fully connected layers, a softmax layer and a classification layer, are applied to classify devices. To evaluate the proposed model, two benchmark datasets, SIGCOMM-2004 and SIGCOMM-2008, were used. Using only 200 frames, it can accurately fingerprint 74 devices in SIGCOMM-2004 and 48 devices in SIGCOMM-2008 with accuracy of 97.04% and 97.70% respectively. The experimental results indicate that the proposed method is more efficient, since it requires fewer frames and produces a higher level of accuracy.
设备指纹是指根据设备的配置和使用情况提供的属性来识别设备。在这项工作中，为设备指纹识别设计了一个深度神经网络(DNN)架构。DNN以预处理后的无线网络流量的Inter - Arrival Time (IAT)和Transmission Time (TT)为馈源。深度神经网络由多个卷积神经网络(CNN)、整流线性单元(ReLU)和最大池化层组成。作为最后一步，使用两个完全连接的层(softmax层和分类层)对设备进行分类。为了评估所提出的模型，使用了两个基准数据集，SIGCOMM-2004和SIGCOMM-2008。仅使用200帧，就能准确识别SIGCOMM-2004中的74个器件和SIGCOMM-2008中的48个器件，准确率分别为97.04%和97.70%。实验结果表明，该方法所需帧数更少，精度更高，效率更高。
{"title":"Device Fingerprinting in Wireless Networks using Deep Learning","authors":"A. K. Dalai, B. Sahoo","doi":"10.1109/OCIT56763.2022.00081","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00081","url":null,"abstract":"Device fingerprinting involves identifying devices based on attributes provided by their configuration and usage. In this work a Deep Neural Network (DNN) architecture is designed for device fingerprinting. DNN is fed Inter Arrival Time (IAT) and Transmission Time (TT) of preprocessed wireless network traffic. The DNN consists of multiple Convolution Neural Networks (CNN), Rectified Linear Units (ReLU), and maximum pooling layers. As a final step, two fully connected layers, a softmax layer and a classification layer, are applied to classify devices. To evaluate the proposed model, two benchmark datasets, SIGCOMM-2004 and SIGCOMM-2008, were used. Using only 200 frames, it can accurately fingerprint 74 devices in SIGCOMM-2004 and 48 devices in SIGCOMM-2008 with accuracy of 97.04% and 97.70% respectively. The experimental results indicate that the proposed method is more efficient, since it requires fewer frames and produces a higher level of accuracy.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"27 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":"127260333","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}
Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00050
Silky Goel, Snigdha Markanday, Shlok Mohanty
Extreme weather detection in huge datasets is a difficult task for researchers studying climate change. Current algorithms for detecting severe weather are reliant on human experience in classifying occurrences using arbitrary physical thresholds. On the same dataset, numerous competing approaches frequently yield wildly dissimilar findings. Understanding the trends and potential effects of such weather conditions depends on accurate categorization of severe events in climate simulations and observational data archives. In this paper, deep learning techniques are used as an alternate tool for identifying extreme weather occurrences. From labelled data, deep neural networks can develop high-level representations of a wide range of patterns. In this work, we have created a deep convolutional neural network (CNN) classification system. Our deep CNN system detects extreme events with VGG19 model and logistic regression classifier with 98.5% accuracy.
{"title":"Analysis of Multi-Class Weather Classification using deep learning models and machine learning classifiers","authors":"Silky Goel, Snigdha Markanday, Shlok Mohanty","doi":"10.1109/OCIT56763.2022.00050","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00050","url":null,"abstract":"Extreme weather detection in huge datasets is a difficult task for researchers studying climate change. Current algorithms for detecting severe weather are reliant on human experience in classifying occurrences using arbitrary physical thresholds. On the same dataset, numerous competing approaches frequently yield wildly dissimilar findings. Understanding the trends and potential effects of such weather conditions depends on accurate categorization of severe events in climate simulations and observational data archives. In this paper, deep learning techniques are used as an alternate tool for identifying extreme weather occurrences. From labelled data, deep neural networks can develop high-level representations of a wide range of patterns. In this work, we have created a deep convolutional neural network (CNN) classification system. Our deep CNN system detects extreme events with VGG19 model and logistic regression classifier with 98.5% accuracy.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"17 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":"125900037","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}
Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00053
Shailashree K. Sheshadri, Deepa Gupta, M. Costa-jussà
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.
{"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}
Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00100
Samirit Saha, M. Beena
The study focuses on the techniques and applications of green computing in various fields of human life such as biomedical systems and networking, is explored in detail. The influence of green computing, and the possible developments and improvements in its' mode of operation has also been gone over through in this paper. The considerable developments in green computing and the impact it has on the long-term trajectory that various disciplines, such as networking, computing, and artificial intelligence, are going through right now, make it an important area of research and study.
{"title":"Power Cognizant Optimization Techniques for Green Cloud Systems","authors":"Samirit Saha, M. Beena","doi":"10.1109/OCIT56763.2022.00100","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00100","url":null,"abstract":"The study focuses on the techniques and applications of green computing in various fields of human life such as biomedical systems and networking, is explored in detail. The influence of green computing, and the possible developments and improvements in its' mode of operation has also been gone over through in this paper. The considerable developments in green computing and the impact it has on the long-term trajectory that various disciplines, such as networking, computing, and artificial intelligence, are going through right now, make it an important area of research and study.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"175 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":"133528720","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}
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.
{"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}
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