Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00028
P. Sahoo, D. K. Behera, J. Mohanty, C. S. K. Dash
Software product development is an indispensible part of the society we live in. In order to produce quality products economically, efficiently and within targeted completion date, estimation for development needs to be fairly precise. This work comes up with quite a viable estimation of the development efforts for the current day web applications. The modus operandi in this work collects facts existing in the Unified Modeling Language Sequence models generated for Object based systems. These facts, in combination with customized regression analysis programs specifically written for this work were used for the required estimation. To be specific: Decision Tree, Support Vector, Extreme Gradient Boosting and Bayesian Ridge Regression methods were used to estimate the efforts. The outcomes obtained by these methodologies, established its preciseness. As per the observations from experiments conducted, it was quite evident that the Bayesian Ridge Regression is providing the best accuracy compared to other Machine Learning models.
{"title":"Effort Estimation of Software products by using UML Sequence models with Regression Analysis","authors":"P. Sahoo, D. K. Behera, J. Mohanty, C. S. K. Dash","doi":"10.1109/OCIT56763.2022.00028","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00028","url":null,"abstract":"Software product development is an indispensible part of the society we live in. In order to produce quality products economically, efficiently and within targeted completion date, estimation for development needs to be fairly precise. This work comes up with quite a viable estimation of the development efforts for the current day web applications. The modus operandi in this work collects facts existing in the Unified Modeling Language Sequence models generated for Object based systems. These facts, in combination with customized regression analysis programs specifically written for this work were used for the required estimation. To be specific: Decision Tree, Support Vector, Extreme Gradient Boosting and Bayesian Ridge Regression methods were used to estimate the efforts. The outcomes obtained by these methodologies, established its preciseness. As per the observations from experiments conducted, it was quite evident that the Bayesian Ridge Regression is providing the best accuracy compared to other Machine Learning models.","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":"129069867","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/OCIT56763.2022.00061
Dipshikha Biswas, Suneel Nadipalli, B. Sneha, Deepa Gupta, J. Amudha
Natural Question Generation (NQG) is among the most popular open research problems in Natural Language Processing (NLP) alongside Neural Machine Translation, Open Domain Chatbots, etc. Among the many approaches taken up to solve this problem, neural networks have been deemed the benchmark in this particular research area. This paper aims at adopting a generator - evaluator framework in a neural network architecture to allow additional focus on the context of the content used for framing a question. The generator uses NLP architectures like transformers (T5) to generate a question given a context while the evaluator uses Reinforcement Learning (RL) to check the correctness of the generated question. The involvement of RL has improved the results (as shown in Table 2), and there is increased computational efficiency as the training is coupled with the policy of RL. This turns the problem into a reinforcement learning task and allows for the generation of a wide range of questions for the same context-answer pair. The given algorithm is tested on the benchmark dataset - SQuAD with BLEU score as the evaluation metric
{"title":"Natural Question Generation using Transformers and Reinforcement Learning","authors":"Dipshikha Biswas, Suneel Nadipalli, B. Sneha, Deepa Gupta, J. Amudha","doi":"10.1109/OCIT56763.2022.00061","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00061","url":null,"abstract":"Natural Question Generation (NQG) is among the most popular open research problems in Natural Language Processing (NLP) alongside Neural Machine Translation, Open Domain Chatbots, etc. Among the many approaches taken up to solve this problem, neural networks have been deemed the benchmark in this particular research area. This paper aims at adopting a generator - evaluator framework in a neural network architecture to allow additional focus on the context of the content used for framing a question. The generator uses NLP architectures like transformers (T5) to generate a question given a context while the evaluator uses Reinforcement Learning (RL) to check the correctness of the generated question. The involvement of RL has improved the results (as shown in Table 2), and there is increased computational efficiency as the training is coupled with the policy of RL. This turns the problem into a reinforcement learning task and allows for the generation of a wide range of questions for the same context-answer pair. The given algorithm is tested on the benchmark dataset - SQuAD with BLEU score as the evaluation metric","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"54 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":"114827562","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/OCIT56763.2022.00049
A. Dash, Puspanjali Mohapatra, N. Ray
The new coronavirus disease 2019 (COVID-19) pandemic completely changed individuals' daily lives and created economic disruption across the world. Many countries are using movement restrictions and physical distancing as their measures to slow down this transmission. Effective screening of COVID-19 cases is needed to stop the spreading of these diseases. In the first phases of clinical assessment, it was seen that patients with deformities in chest X-ray images show the signs of COVID-19 infection. Inspired from this, in this study, a novel framework is designed to detect the COVID-19 cases from chest radiography images. Here, a pre-trained deep convolutional neural network VGG-16 is used to extract discriminating features from the radiography images. These extracted features are given as an input to the Logistic regression classifier for automatic detection of COVID-19 cases. The suggested framework obtained a remarkable accuracy of 99.1% with a 100% sensitivity rate in comparison with other state-of-the-art classifier.
{"title":"A Transfer Learning Approach for Diagnosis of COVID-19 Cases from Chest Radiography Images","authors":"A. Dash, Puspanjali Mohapatra, N. Ray","doi":"10.1109/OCIT56763.2022.00049","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00049","url":null,"abstract":"The new coronavirus disease 2019 (COVID-19) pandemic completely changed individuals' daily lives and created economic disruption across the world. Many countries are using movement restrictions and physical distancing as their measures to slow down this transmission. Effective screening of COVID-19 cases is needed to stop the spreading of these diseases. In the first phases of clinical assessment, it was seen that patients with deformities in chest X-ray images show the signs of COVID-19 infection. Inspired from this, in this study, a novel framework is designed to detect the COVID-19 cases from chest radiography images. Here, a pre-trained deep convolutional neural network VGG-16 is used to extract discriminating features from the radiography images. These extracted features are given as an input to the Logistic regression classifier for automatic detection of COVID-19 cases. The suggested framework obtained a remarkable accuracy of 99.1% with a 100% sensitivity rate in comparison with other state-of-the-art classifier.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"46 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":"124411020","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/OCIT56763.2022.00055
M. Jena, Asit Patra, B. Sahoo, Satchidananda Dehuri
A regression tree is one of the most popular machine learning-based decision models. Unlike a decision tree it predicts a continuous value. Many regression models have emerged to handle the regression problems, where most of them faced difficulties while capturing the non-linear patterns. Some regression models are sensitive to outliers, like regression trees. In this paper, a hybrid regression model is proposed, which combines the features of regression tree and ridge regression to improve the performance of regression problem. In the proposed model, the leaf nodes of the regression tree are modified. Rather than storing the mean of the corresponding targeted output values, the proposed hybrid model stores the suitable tuples in its leaf nodes. When some predictor values are inserted, the control transfers to the corresponding leaf node, and ridge regression is applied to the leaf node to predict the required values. In this method, the threshold value plays a vital role in deciding the number of tuples in the leaf nodes, which further affects the time complexity and mean squared error. Extensive comparative analysis has been made by comparing the performance of the proposed model with other regression models using four real-world datasets. The experimental results show that the proposed method outperforms the regression tree and ridge regression when applied individually.
{"title":"Hybrid Regression Tree","authors":"M. Jena, Asit Patra, B. Sahoo, Satchidananda Dehuri","doi":"10.1109/OCIT56763.2022.00055","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00055","url":null,"abstract":"A regression tree is one of the most popular machine learning-based decision models. Unlike a decision tree it predicts a continuous value. Many regression models have emerged to handle the regression problems, where most of them faced difficulties while capturing the non-linear patterns. Some regression models are sensitive to outliers, like regression trees. In this paper, a hybrid regression model is proposed, which combines the features of regression tree and ridge regression to improve the performance of regression problem. In the proposed model, the leaf nodes of the regression tree are modified. Rather than storing the mean of the corresponding targeted output values, the proposed hybrid model stores the suitable tuples in its leaf nodes. When some predictor values are inserted, the control transfers to the corresponding leaf node, and ridge regression is applied to the leaf node to predict the required values. In this method, the threshold value plays a vital role in deciding the number of tuples in the leaf nodes, which further affects the time complexity and mean squared error. Extensive comparative analysis has been made by comparing the performance of the proposed model with other regression models using four real-world datasets. The experimental results show that the proposed method outperforms the regression tree and ridge regression when applied individually.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"29 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":"122732454","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/OCIT56763.2022.00102
A. Chandak, N. Ray
IoT devices in the smart home eases human life and they can be controlled from remote locations. Proper utilization of end devices and other resources is a basic requirements in smart home. In smart home for faster processing of data fog nodes are used. They are deployed to minimize processing delay and are most suitable when an appropriate number of resources are available. Resource provisioning refers to the optimal allocation of resources to improve resource utilization and response time. It also avoid situation where some fog node is overloaded and some are underloaded. Fog nodes are dynamic and they can leave or join the network anytime. In the same time any malicious fog node can also join the fog network and can tamper the data and other resources. In this article, an efficient resource provisioning mechanism for smart home is proposed. The proposed scheme uses an authentication mechanism in which fog nodes authenticate themselves before providing services. There are mainly two types of requests by IoT devices viz. data and computational. To improve response, it is necessary to categorize requests and allocate fog nodes in proportion of requests type. The proposed scheme assess the performance of adaptive resource provisioning with static and random provisioning based on makespan, average execution time, and response time.
{"title":"Adaptive Resource Provisioning for Smart Home Using Fog Computing","authors":"A. Chandak, N. Ray","doi":"10.1109/OCIT56763.2022.00102","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00102","url":null,"abstract":"IoT devices in the smart home eases human life and they can be controlled from remote locations. Proper utilization of end devices and other resources is a basic requirements in smart home. In smart home for faster processing of data fog nodes are used. They are deployed to minimize processing delay and are most suitable when an appropriate number of resources are available. Resource provisioning refers to the optimal allocation of resources to improve resource utilization and response time. It also avoid situation where some fog node is overloaded and some are underloaded. Fog nodes are dynamic and they can leave or join the network anytime. In the same time any malicious fog node can also join the fog network and can tamper the data and other resources. In this article, an efficient resource provisioning mechanism for smart home is proposed. The proposed scheme uses an authentication mechanism in which fog nodes authenticate themselves before providing services. There are mainly two types of requests by IoT devices viz. data and computational. To improve response, it is necessary to categorize requests and allocate fog nodes in proportion of requests type. The proposed scheme assess the performance of adaptive resource provisioning with static and random provisioning based on makespan, average execution time, and response time.","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":"123185602","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}
Long noncoding RNAs (lncRNA) have a vital role in tumor development. Variation in expressions of IncRNAs affect several target genes related to tumor initiation and development. Recent studies in Carcinogenesis have indicated the importance of IncRNA in cancer progression, diagnosis, and treatment. The purpose of our research is to identify the key cancer-related IncRNAs. It is considered a complex task to identify key IncRNAs in cancer with existing cancer data of tumor patients due to the high dimensionality nature of expression profiles. LncRNA expression profiles of 12309 IncRNAs and 2221 patients are gathered from TCGA. A Computational framework is proposed considering 5 cancer types (Bladder, Colon, Cervical, Liver, Head, and Neck) comprising four Machine learning classification models named K-Nearest Neighbor, Naive Bayes, Random Forest, and Support Vector Machine. An essential component in the framework is to use models along with the state-of-the-art Variance threshold, L1-based, and Tree-based feature selection algorithms for differential analysis. The study resulted in identifying 234 key IncRNAs capable of differentiating 5 cancer types. The capability of identified key IncRNAs is observed by the performance of classification models resulting in the highest 98.2% accuracy by SVM. Furthermore, the correlation analysis of 234 IncRNAs experimentally validated the results.
{"title":"Detecting Long Non-Coding RNAs Responsible for Cancer Development","authors":"Mitra Datta Ganapaneni, Kundhana Harshitha Paruchuru, J. Ambati, Mahesh Valavala, C.C Sobin","doi":"10.1109/OCIT56763.2022.00040","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00040","url":null,"abstract":"Long noncoding RNAs (lncRNA) have a vital role in tumor development. Variation in expressions of IncRNAs affect several target genes related to tumor initiation and development. Recent studies in Carcinogenesis have indicated the importance of IncRNA in cancer progression, diagnosis, and treatment. The purpose of our research is to identify the key cancer-related IncRNAs. It is considered a complex task to identify key IncRNAs in cancer with existing cancer data of tumor patients due to the high dimensionality nature of expression profiles. LncRNA expression profiles of 12309 IncRNAs and 2221 patients are gathered from TCGA. A Computational framework is proposed considering 5 cancer types (Bladder, Colon, Cervical, Liver, Head, and Neck) comprising four Machine learning classification models named K-Nearest Neighbor, Naive Bayes, Random Forest, and Support Vector Machine. An essential component in the framework is to use models along with the state-of-the-art Variance threshold, L1-based, and Tree-based feature selection algorithms for differential analysis. The study resulted in identifying 234 key IncRNAs capable of differentiating 5 cancer types. The capability of identified key IncRNAs is observed by the performance of classification models resulting in the highest 98.2% accuracy by SVM. Furthermore, the correlation analysis of 234 IncRNAs experimentally validated the results.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"117 20 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":"126410096","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/OCIT56763.2022.00084
Joydeb Dutta, Deepak Puthal, E. Damiani
Artificial Intelligence (AI) is gaining popularity in the Internet of Things (IoT) based application-based solution development. Whereas, Blockchain is become unavoidable in IoT for maintaining the end-to-end process in the decentralized approach. Combining these two current-age technologies, this paper details a brief comparative study with the implementations and further analyzes the adaptability of the AI-based solution in the Blockchain-integrated IoT architecture. This work focuses on identifying the of block data in the block validation stage using AI-based approaches. Several supervised, unsupervised, and semi-supervised learning algorithms are analyzed to determine a block's data sensitivity. It is identified that machine learning techniques can identify a block's data with very high accuracy. By utilizing this, the block's sensitivity can be identified, which can help the system to reduce the energy consumption of the block validation stage by dynamically choosing an appropriate consensus mechanism.
{"title":"AI-based Block Identification and Classification in the Blockchain Integrated IoT","authors":"Joydeb Dutta, Deepak Puthal, E. Damiani","doi":"10.1109/OCIT56763.2022.00084","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00084","url":null,"abstract":"Artificial Intelligence (AI) is gaining popularity in the Internet of Things (IoT) based application-based solution development. Whereas, Blockchain is become unavoidable in IoT for maintaining the end-to-end process in the decentralized approach. Combining these two current-age technologies, this paper details a brief comparative study with the implementations and further analyzes the adaptability of the AI-based solution in the Blockchain-integrated IoT architecture. This work focuses on identifying the of block data in the block validation stage using AI-based approaches. Several supervised, unsupervised, and semi-supervised learning algorithms are analyzed to determine a block's data sensitivity. It is identified that machine learning techniques can identify a block's data with very high accuracy. By utilizing this, the block's sensitivity can be identified, which can help the system to reduce the energy consumption of the block validation stage by dynamically choosing an appropriate consensus mechanism.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"18 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":"116448833","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/OCIT56763.2022.00072
Moupali Sen, Shreya V. Basu, A. Chatterjee, Anwesha Banerjee, Saheli Pal, Pritam Kumar Mukhopadhyay, Stobak Dutta, Arunabha Tarafdar
Unemployment is a circumstance which arises when people above a specific age are not engaged in any kind of activities which contribute to the economic welfare of the individual and country. Unemployment is becoming a rising concern which is making the daily life of people difficult. Unemployment causes poverty and depression among the citizens. Nowadays there are different opportunities in different sectors. But people are not aware of those opportunities. Different states are there where there is a lack of skilled labour whereas many states are there that have skilled labour but less opportunities. Another reason for unemployment since 2020 is the COVID-19 pandemic. We have selected this topic to spread awareness among the citizens. This work attempts to detect the states of India which are in serious need of increasing employment opportunities. We have applied the concept of Supervised Machine Learning algorithms to detect the states with the lowest employment rate. The data visualization gives a better picture of the trends in unemployment rate over years. There has been a use of different popular algorithms like Logistic Regression, Support Vector Machine, K-nearest neighbors (kNN) Algorithm and Decision Tree. In the end we have tried to find the algorithm which is going to give us more accuracy so that necessary steps can be taken for the employment of the eligible and deserving people.
{"title":"Prediction of Unemployment using Machine Learning Approach","authors":"Moupali Sen, Shreya V. Basu, A. Chatterjee, Anwesha Banerjee, Saheli Pal, Pritam Kumar Mukhopadhyay, Stobak Dutta, Arunabha Tarafdar","doi":"10.1109/OCIT56763.2022.00072","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00072","url":null,"abstract":"Unemployment is a circumstance which arises when people above a specific age are not engaged in any kind of activities which contribute to the economic welfare of the individual and country. Unemployment is becoming a rising concern which is making the daily life of people difficult. Unemployment causes poverty and depression among the citizens. Nowadays there are different opportunities in different sectors. But people are not aware of those opportunities. Different states are there where there is a lack of skilled labour whereas many states are there that have skilled labour but less opportunities. Another reason for unemployment since 2020 is the COVID-19 pandemic. We have selected this topic to spread awareness among the citizens. This work attempts to detect the states of India which are in serious need of increasing employment opportunities. We have applied the concept of Supervised Machine Learning algorithms to detect the states with the lowest employment rate. The data visualization gives a better picture of the trends in unemployment rate over years. There has been a use of different popular algorithms like Logistic Regression, Support Vector Machine, K-nearest neighbors (kNN) Algorithm and Decision Tree. In the end we have tried to find the algorithm which is going to give us more accuracy so that necessary steps can be taken for the employment of the eligible and deserving people.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"78 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":"133798152","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/OCIT56763.2022.00091
Anwesha Kashyap, Angshuman Jana
Over the decades and now-a-days the data-driven applications are playing a pivotal role in every aspect of our daily lives by providing an easy interface to store, access and process crucial data with the help of Database Management System (DBMS). However, it is always necessary to ensure the data integrity for every operation on a database. In this paper, we propose a novel framework for Structured Query Language (SQL), aiming at automatically and formally verifying integrity constraints in terms of enterprise policy specifications on data in the underlying database. To this aim, we extend the abstract interpretation theory to the case of structured query languages.
{"title":"Integrity Constraint Verification of Structured Query Language by Abstract Interpretation","authors":"Anwesha Kashyap, Angshuman Jana","doi":"10.1109/OCIT56763.2022.00091","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00091","url":null,"abstract":"Over the decades and now-a-days the data-driven applications are playing a pivotal role in every aspect of our daily lives by providing an easy interface to store, access and process crucial data with the help of Database Management System (DBMS). However, it is always necessary to ensure the data integrity for every operation on a database. In this paper, we propose a novel framework for Structured Query Language (SQL), aiming at automatically and formally verifying integrity constraints in terms of enterprise policy specifications on data in the underlying database. To this aim, we extend the abstract interpretation theory to the case of structured query languages.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"24 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":"115082577","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/OCIT56763.2022.00086
Narayan Nayak, B. Keswani, Dipak Ranjan Nayak, Pramod Sharma, A. G. Mohapatra, Ashish Khanna
In comparison to radio frequency and acoustic communication, underwater optical wireless communication (UOWC) has garnered greater attention recently due to its higher data rate and low latency. In this paper, we proposed underwater optical communication by using free-space optical communication (FSO) system to propagate the green light waves through the water. The application of three different modulation schemes, including quadrature phase shift keying(QPSK), dual-polarization quadrature phase shift keying (DP-QPSK), and 4-quadrature amplitude modulation (4-QAM) are designed not only to focus on the analysis of problems like attenuation, absorption, scattering, and turbulence but also investigates spectral efficiency. The Performance and physical aspects of the above modulation techniques with UOWC are studied and a comparison of multiple criteria, including the maximum quality factor, the minimum bit error rate (BER), and the eye diagram.
{"title":"Performance evaluation of DP-QPSK modulation for underwater optical wireless communication using a green light propagation","authors":"Narayan Nayak, B. Keswani, Dipak Ranjan Nayak, Pramod Sharma, A. G. Mohapatra, Ashish Khanna","doi":"10.1109/OCIT56763.2022.00086","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00086","url":null,"abstract":"In comparison to radio frequency and acoustic communication, underwater optical wireless communication (UOWC) has garnered greater attention recently due to its higher data rate and low latency. In this paper, we proposed underwater optical communication by using free-space optical communication (FSO) system to propagate the green light waves through the water. The application of three different modulation schemes, including quadrature phase shift keying(QPSK), dual-polarization quadrature phase shift keying (DP-QPSK), and 4-quadrature amplitude modulation (4-QAM) are designed not only to focus on the analysis of problems like attenuation, absorption, scattering, and turbulence but also investigates spectral efficiency. The Performance and physical aspects of the above modulation techniques with UOWC are studied and a comparison of multiple criteria, including the maximum quality factor, the minimum bit error rate (BER), and the eye diagram.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"140 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":"132400378","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：