Pub Date : 2021-08-25DOI: 10.1109/iccitm53167.2021.9677661
{"title":"[Copyright notice]","authors":"","doi":"10.1109/iccitm53167.2021.9677661","DOIUrl":"https://doi.org/10.1109/iccitm53167.2021.9677661","url":null,"abstract":"","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125867628","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 : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677657
Mohammed Shamsuldean Thanon, Raya Salim Al Rassam
Bayesian method is one of many methods inrroduced for estimating the parameters of the probability distributions, In this method the parameters of the distributions considered as random variables and has a probability distribution unlike other estimation methods. When estimating by the Bayesian method, the estimation is either directly or by using loss functions or using utility functions. The issue, however gets complicated as the number of estimated parameters increases, which makes the estimation process numerical because it is difficult to obtain analytical formulas. In our research, the method of estimating the parameters of the distributions has been generalized using the Lindley conjugate utility function with k parameters and that the parameters estimated in this way make the Lindley conjugate utility function the greatest possiblelity by obtaining the appropriate approximate optimal decisions, as this estimation method was clarified by applying it to the distribution of generalized gamma with three parameters and the estimators were found analytically.
{"title":"Generalizing the Conjugate Lindley's Utility Function to Estimate the Multi Parameter Distributions Parameters","authors":"Mohammed Shamsuldean Thanon, Raya Salim Al Rassam","doi":"10.1109/ICCITM53167.2021.9677657","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677657","url":null,"abstract":"Bayesian method is one of many methods inrroduced for estimating the parameters of the probability distributions, In this method the parameters of the distributions considered as random variables and has a probability distribution unlike other estimation methods. When estimating by the Bayesian method, the estimation is either directly or by using loss functions or using utility functions. The issue, however gets complicated as the number of estimated parameters increases, which makes the estimation process numerical because it is difficult to obtain analytical formulas. In our research, the method of estimating the parameters of the distributions has been generalized using the Lindley conjugate utility function with k parameters and that the parameters estimated in this way make the Lindley conjugate utility function the greatest possiblelity by obtaining the appropriate approximate optimal decisions, as this estimation method was clarified by applying it to the distribution of generalized gamma with three parameters and the estimators were found analytically.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125634510","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 : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677793
Wasnaa Hashm, Manaf Ahmed
The environmental or climatic change events are often represented by the spatial data, as well as in extreme case. So, taking into account the spatial features of these events is essential for any risk to be assessed. Most of the previous proposed spatial risk measures considered the dispersion of the loss function as the severity amount of the risk. This is because no spatial information can be provided by the expectation of this loss of function. In the present paper, we moved forward in developing the quantitative risk measures by proposing one combination between the spatial features and severity amount at the same time. Asymptotic behavior and its axiomatic properties have been well studied for this proposed spatial risk measure. A simulation study has been carried out to verify the theoretical results.
{"title":"A quantitative spatial Risk Measure for Extreme Events","authors":"Wasnaa Hashm, Manaf Ahmed","doi":"10.1109/ICCITM53167.2021.9677793","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677793","url":null,"abstract":"The environmental or climatic change events are often represented by the spatial data, as well as in extreme case. So, taking into account the spatial features of these events is essential for any risk to be assessed. Most of the previous proposed spatial risk measures considered the dispersion of the loss function as the severity amount of the risk. This is because no spatial information can be provided by the expectation of this loss of function. In the present paper, we moved forward in developing the quantitative risk measures by proposing one combination between the spatial features and severity amount at the same time. Asymptotic behavior and its axiomatic properties have been well studied for this proposed spatial risk measure. A simulation study has been carried out to verify the theoretical results.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131593599","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 : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677737
Munera A. Jabaar, S. N. Alsaad
Social media in the current technological era plays a major role in people's daily lives. Every day hundreds of thousands of images are circulated on social media applications such as WhatsApp, Instagram, Twitter, Facebook, and Snapchat. Photo is one of the most popular types of media that is shared among users on social media. It has become easy for small groups and even for individuals to edit and manipulate these images on a large scale in a very short time in such a way threatening the credibility of these images. In this paper, a detection system is implemented for verifying and classifying the content of social media images. The system adopted Deep Learning based on a convolutional neural network (CNN) to detect spliced images on WhatsApp. The images in dataset CASIA v2 (transformed to be appropriate for WhatsApp) are used for training and testing. The results point to an accuracy of 99.19% of training and 87.438% of testing.
{"title":"Detection of Spliced Images in Social Media Application","authors":"Munera A. Jabaar, S. N. Alsaad","doi":"10.1109/ICCITM53167.2021.9677737","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677737","url":null,"abstract":"Social media in the current technological era plays a major role in people's daily lives. Every day hundreds of thousands of images are circulated on social media applications such as WhatsApp, Instagram, Twitter, Facebook, and Snapchat. Photo is one of the most popular types of media that is shared among users on social media. It has become easy for small groups and even for individuals to edit and manipulate these images on a large scale in a very short time in such a way threatening the credibility of these images. In this paper, a detection system is implemented for verifying and classifying the content of social media images. The system adopted Deep Learning based on a convolutional neural network (CNN) to detect spliced images on WhatsApp. The images in dataset CASIA v2 (transformed to be appropriate for WhatsApp) are used for training and testing. The results point to an accuracy of 99.19% of training and 87.438% of testing.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121426647","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 : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677842
Muayad N. Abdullah, W. Bhaya
Nowadays, numerous web services with equivalent functionality have become available on the Internet. The Quality of Service (QoS) for web services seems to have an essential role when it comes to selecting the best web services. However, evaluating the user-side efficiently in terms of quality of web services has become a critical research topic. Predicting the QoS values of web services and the credibility of the values published by different users are major challenges in this area. A commonly used technique to predict QoS values of web services is collaborative filtering (CF). To address these critical challenges, a personalized QoS predicting technique is proposed for web services which depends on the reputation and location-based CF approach. Firstly, a set of untrusted users is identified through the Dirichlet probability distribution on the basis of the user's reputation, followed by processing the unreliable data contributed by untrusted users. Secondly, the users are clustered based on their geographic information to improve the neighborhood similarity computation. Finally, the similarity weights of neighboring users are used to predict unknown QoS values in each cluster. It has been observed that the proposed model realized a more favorable performance in terms of accuracy and efficiency as compared to other existing approaches. According to the matrix densities from 10% to 90%, the measures of MAE and RMSE for the response time attribute range from 0.47 to 0.30 and from 1.26 to 0.95, respectively, and the measures of MAE and RMSE for the throughput attribute range from 15.64 to 7.58 and from 50.50 to 34.15, respectively.
{"title":"Predicting QoS for Web Service Recommendations Based on Reputation and Location Clustering with Collaborative Filtering","authors":"Muayad N. Abdullah, W. Bhaya","doi":"10.1109/ICCITM53167.2021.9677842","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677842","url":null,"abstract":"Nowadays, numerous web services with equivalent functionality have become available on the Internet. The Quality of Service (QoS) for web services seems to have an essential role when it comes to selecting the best web services. However, evaluating the user-side efficiently in terms of quality of web services has become a critical research topic. Predicting the QoS values of web services and the credibility of the values published by different users are major challenges in this area. A commonly used technique to predict QoS values of web services is collaborative filtering (CF). To address these critical challenges, a personalized QoS predicting technique is proposed for web services which depends on the reputation and location-based CF approach. Firstly, a set of untrusted users is identified through the Dirichlet probability distribution on the basis of the user's reputation, followed by processing the unreliable data contributed by untrusted users. Secondly, the users are clustered based on their geographic information to improve the neighborhood similarity computation. Finally, the similarity weights of neighboring users are used to predict unknown QoS values in each cluster. It has been observed that the proposed model realized a more favorable performance in terms of accuracy and efficiency as compared to other existing approaches. According to the matrix densities from 10% to 90%, the measures of MAE and RMSE for the response time attribute range from 0.47 to 0.30 and from 1.26 to 0.95, respectively, and the measures of MAE and RMSE for the throughput attribute range from 15.64 to 7.58 and from 50.50 to 34.15, respectively.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129034192","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 : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677655
Murtaja S. Jalawkhan, Tareef K. Mustafa
Safety is the key to reliable civil aviation. In the airline industry, there is a growing emphasis on proactive safety management systems in order to improve the safety of current aviation operations. These systems utilize anomaly detection techniques to recognize and reduce the risk of accidents occurring. This work develops a new anomaly detection approach for commercial flight operations using routine operational data to enhance proactive safety management systems and utilizes data mining techniques to identify abnormal situations instantaneously during flights using real-life FDR (Flight Data Recorder) data. The Naïve Bayes classifier was used to detect normal and abnormal situations. This classifier was applied to a dataset of 100 flights and new abnormal situations could be recognized with a high probability of detection and a low probability of false alarm. The results strongly suggest that anomalies detected in a variety of flights can be recognized, which can help airlines with many different approaches, such as the deployment of predictive maintenance, the detection of early signs of performance divergence, safety support, and the training of staff accordingly.
{"title":"Anomaly Detection in Flight Data Using the Naïve Bayes Classifier","authors":"Murtaja S. Jalawkhan, Tareef K. Mustafa","doi":"10.1109/ICCITM53167.2021.9677655","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677655","url":null,"abstract":"Safety is the key to reliable civil aviation. In the airline industry, there is a growing emphasis on proactive safety management systems in order to improve the safety of current aviation operations. These systems utilize anomaly detection techniques to recognize and reduce the risk of accidents occurring. This work develops a new anomaly detection approach for commercial flight operations using routine operational data to enhance proactive safety management systems and utilizes data mining techniques to identify abnormal situations instantaneously during flights using real-life FDR (Flight Data Recorder) data. The Naïve Bayes classifier was used to detect normal and abnormal situations. This classifier was applied to a dataset of 100 flights and new abnormal situations could be recognized with a high probability of detection and a low probability of false alarm. The results strongly suggest that anomalies detected in a variety of flights can be recognized, which can help airlines with many different approaches, such as the deployment of predictive maintenance, the detection of early signs of performance divergence, safety support, and the training of staff accordingly.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130997239","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 : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677818
N. H. Alwash, A. N. Abdullah, Ali Talib Jawad, Noor S. Ali
The development of industrial infrastructure is considered the main requirement to grow the countries' economies. This development comes by making industrial systems more flexible, reliable, reducing manual works and improving automatic systems, and improving the communications system with effective monitoring and controlling system. These entire concepts make easy interactions with industrial devices, better productivity, and high-quality industrial products. As well as the Industrial Internet or Internet of Things (IoT) expression is circulation in wieldy industrial applications. In the present paper, we implemented the practical design of the industrial system that can monitor and control industrial motors operations in real time, which is considered one important industrial application. This industrial system is based on an IoT structure. We programmed our cloud server connection to connect a wirelessly with an Arduino microcontroller. This system have PID controller with PWM modulation and continuous controlling and monitoring any time around the world. The obtained results shows the stable operation of the system. It can a record of speed of motors and change of devices status in the industrial systems with the efficient and low-cost communication system.
{"title":"IoT Application-Specific for Supervising and Enquiring Industrial Devices","authors":"N. H. Alwash, A. N. Abdullah, Ali Talib Jawad, Noor S. Ali","doi":"10.1109/ICCITM53167.2021.9677818","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677818","url":null,"abstract":"The development of industrial infrastructure is considered the main requirement to grow the countries' economies. This development comes by making industrial systems more flexible, reliable, reducing manual works and improving automatic systems, and improving the communications system with effective monitoring and controlling system. These entire concepts make easy interactions with industrial devices, better productivity, and high-quality industrial products. As well as the Industrial Internet or Internet of Things (IoT) expression is circulation in wieldy industrial applications. In the present paper, we implemented the practical design of the industrial system that can monitor and control industrial motors operations in real time, which is considered one important industrial application. This industrial system is based on an IoT structure. We programmed our cloud server connection to connect a wirelessly with an Arduino microcontroller. This system have PID controller with PWM modulation and continuous controlling and monitoring any time around the world. The obtained results shows the stable operation of the system. It can a record of speed of motors and change of devices status in the industrial systems with the efficient and low-cost communication system.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128936234","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 : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677798
A. E. Mehyadin, Subhi R. M. Zeebaree, M. A. Sadeeq, Hanan M. Shukur, A. Alkhayyat, K. Sharif
One of the more significant recent major progress in computer science is the coevolution of deep learning and the Semantic Web. This subject includes research from various perspectives, including using organized information inside the neural network training method or enriching these networks with ontological reasoning mechanisms. By bridging deep learning and the Semantic Web, it is possible to enhance the efficiency of neural networks and open up exciting possibilities in science. This paper presents a comprehensive study of the closest previous researches, which combine the role of Deep Learning and the performance of the Semantic web, which ties together the Semantic Web and deep learning science with their applications. The paper also explains the adoption of an intelligent system in Semantic Deep Learning (SemDeep). As significant results obtained from previous works addressed in this paper, it can be notified that they focussed on real-time detection of phishing websites by HTML Phish. Also, the DnCNN, led by ResNet, achieved the best results, Res-Unit, UNet, and Deeper SRCNN, which recorded 88.5% SSIM, 32.01 percent PSNR 3.90 percent NRMSE.
{"title":"State of Art Survey for Deep Learning Effects on Semantic Web Performance","authors":"A. E. Mehyadin, Subhi R. M. Zeebaree, M. A. Sadeeq, Hanan M. Shukur, A. Alkhayyat, K. Sharif","doi":"10.1109/ICCITM53167.2021.9677798","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677798","url":null,"abstract":"One of the more significant recent major progress in computer science is the coevolution of deep learning and the Semantic Web. This subject includes research from various perspectives, including using organized information inside the neural network training method or enriching these networks with ontological reasoning mechanisms. By bridging deep learning and the Semantic Web, it is possible to enhance the efficiency of neural networks and open up exciting possibilities in science. This paper presents a comprehensive study of the closest previous researches, which combine the role of Deep Learning and the performance of the Semantic web, which ties together the Semantic Web and deep learning science with their applications. The paper also explains the adoption of an intelligent system in Semantic Deep Learning (SemDeep). As significant results obtained from previous works addressed in this paper, it can be notified that they focussed on real-time detection of phishing websites by HTML Phish. Also, the DnCNN, led by ResNet, achieved the best results, Res-Unit, UNet, and Deeper SRCNN, which recorded 88.5% SSIM, 32.01 percent PSNR 3.90 percent NRMSE.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128793765","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 : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677668
R. Ibrahim, F. Ramo
Nowadays, personality traits analysis has become one of the important things since international companies need to hire employees and be used in education and forensic verification. In this paper, three pre-trained models of deep learning were evaluated to classify an individual's personality traits from his signature after analyzing, processing, and labeling the data and dividing it into five categories according to the Big Five factor. The analysis is based on 6600 images divided into three groups (training, testing, and validation). Data Augmentation was used to overcome the lack of data and its imbalance. Also, transfer learning was used that represented by the three models (VGG16, Inception, and ResNet50), which work on the principle of freezing the first layers and updating the last layers to take advantage of the pre-trained weights and obtain the lowest error rate. Results showed that the ResNet-50 achieved the best classification accuracy with up to 99% and the lowest error rate with 0.0304. While the InceptionV3 model outperformed VGG16 in the training phase of 99%, but in the validation phase, the VGG16 provided the Highest accuracy of 98% and the least error of 0.1090.
{"title":"Classification of Personality Traits by Using Pretrained Deep Learning Models","authors":"R. Ibrahim, F. Ramo","doi":"10.1109/ICCITM53167.2021.9677668","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677668","url":null,"abstract":"Nowadays, personality traits analysis has become one of the important things since international companies need to hire employees and be used in education and forensic verification. In this paper, three pre-trained models of deep learning were evaluated to classify an individual's personality traits from his signature after analyzing, processing, and labeling the data and dividing it into five categories according to the Big Five factor. The analysis is based on 6600 images divided into three groups (training, testing, and validation). Data Augmentation was used to overcome the lack of data and its imbalance. Also, transfer learning was used that represented by the three models (VGG16, Inception, and ResNet50), which work on the principle of freezing the first layers and updating the last layers to take advantage of the pre-trained weights and obtain the lowest error rate. Results showed that the ResNet-50 achieved the best classification accuracy with up to 99% and the lowest error rate with 0.0304. While the InceptionV3 model outperformed VGG16 in the training phase of 99%, but in the validation phase, the VGG16 provided the Highest accuracy of 98% and the least error of 0.1090.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127165886","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 : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677868
S. Alhashemi, Asaad M. A. Alhossaini
Themain aim of this research is to present and study several basic characteristics of the idea of t-extending semimodules. The semimodule $F$ is said to be a t-extending semimodule if each t-closed sub-semimodule of $F$ is t-essential in a direct summand of $F$. Hence, the behavior of the t-extending semimodule is considered. In addition, the relationship between the t-essential (t-closed) and essential (closed) has been studied and investigated as well. Finally, in this work, there are a number of results related to the t-extending property, which is one of the generalizations of extending property, (every extending is t-extending, while the converse is not true).
{"title":"T-Extending Semimodule over Semiring","authors":"S. Alhashemi, Asaad M. A. Alhossaini","doi":"10.1109/ICCITM53167.2021.9677868","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677868","url":null,"abstract":"Themain aim of this research is to present and study several basic characteristics of the idea of t-extending semimodules. The semimodule $F$ is said to be a t-extending semimodule if each t-closed sub-semimodule of $F$ is t-essential in a direct summand of $F$. Hence, the behavior of the t-extending semimodule is considered. In addition, the relationship between the t-essential (t-closed) and essential (closed) has been studied and investigated as well. Finally, in this work, there are a number of results related to the t-extending property, which is one of the generalizations of extending property, (every extending is t-extending, while the converse is not true).","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"162 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125949670","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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