Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008368
Asma Z. Yamani, Khawlah Bajbaa, Reem Aljunaid
Cloud computing plays an important role in businesses' digital transformation as they offer easy-to-use services that save time and effort. Despite incredible features that are provided by cloud computing platforms, these platforms become the desirable target of attackers. This study aims to survey the literature for security threats related to web applications that have been developed using cloud computing services and then provide a set of recommendations to mitigate these threats. In this study, we first surveyed the literature for documented cases of threats faced while relying on cloud computing, then an online survey was sent to Computer Science students and web developers. The survey's questions were related to web threats whether they are aware of these threats or not and whether they have already applied any prevention measures for these threats. Then, a set of recommendations were provided that can help them to mitigate these threats. Finally, a tool was designed for generating security policies for the Broken Access Control threat for Firebase. Eighty-five responses were considered for this study. The average participants' awareness of all threats is 61 % despite 92% of participants having taken at least one security course. The main causes for neglecting to implement mitigation techniques was the lack of training and that developers are relying on the web services to provide security measures, then comes the process being time-consuming. The designed tool for mitigating Broken Access control showed promising results to ease the implementation of mitigation techniques. We conclude that due to the lack of awareness and negligence in implementing mitigation techniques, many present web apps may be compromised. Developing security tools for novice users, whenever possible, provides a solution for the main causes of the neglect to implement such measures and should be investigated further.
{"title":"Web Application Security Threats and Mitigation Strategies when Using Cloud Computing as Backend","authors":"Asma Z. Yamani, Khawlah Bajbaa, Reem Aljunaid","doi":"10.1109/CICN56167.2022.10008368","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008368","url":null,"abstract":"Cloud computing plays an important role in businesses' digital transformation as they offer easy-to-use services that save time and effort. Despite incredible features that are provided by cloud computing platforms, these platforms become the desirable target of attackers. This study aims to survey the literature for security threats related to web applications that have been developed using cloud computing services and then provide a set of recommendations to mitigate these threats. In this study, we first surveyed the literature for documented cases of threats faced while relying on cloud computing, then an online survey was sent to Computer Science students and web developers. The survey's questions were related to web threats whether they are aware of these threats or not and whether they have already applied any prevention measures for these threats. Then, a set of recommendations were provided that can help them to mitigate these threats. Finally, a tool was designed for generating security policies for the Broken Access Control threat for Firebase. Eighty-five responses were considered for this study. The average participants' awareness of all threats is 61 % despite 92% of participants having taken at least one security course. The main causes for neglecting to implement mitigation techniques was the lack of training and that developers are relying on the web services to provide security measures, then comes the process being time-consuming. The designed tool for mitigating Broken Access control showed promising results to ease the implementation of mitigation techniques. We conclude that due to the lack of awareness and negligence in implementing mitigation techniques, many present web apps may be compromised. Developing security tools for novice users, whenever possible, provides a solution for the main causes of the neglect to implement such measures and should be investigated further.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132968707","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 : 2022-12-04DOI: 10.1109/CICN56167.2022.10008350
C. Peña, Ciro Rodríguez, Israel Arellano Romero
The proposal of a facial recognition system to increase security, through facial recognition with multiple utilities such as facilitating the access of people with adequate protection measures in times of Covid-19, as well as security when seeking to hide their identity. The methodology considers the use of tools such as Python and OpenCV, as well as models such as Eigen Faces, Fisher Faces, and LBPH Faces, as units of analysis are considered photographs and portions of the video that capture facial expressions that then their patterns are trained with facial recognition algorithms. The results obtained show that the LBPH Faces obtained confidence values lower than 70, with a 95% certainty of recognition and a shorter recognition time, improving the accuracy of facial recognition, also with the increase of the data was achieved to improve the accuracy of recognition as well as improve confidence regarding the safety of people.
{"title":"Processing of Images Based on Machine Learning to Avoid Unauthorized Entry","authors":"C. Peña, Ciro Rodríguez, Israel Arellano Romero","doi":"10.1109/CICN56167.2022.10008350","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008350","url":null,"abstract":"The proposal of a facial recognition system to increase security, through facial recognition with multiple utilities such as facilitating the access of people with adequate protection measures in times of Covid-19, as well as security when seeking to hide their identity. The methodology considers the use of tools such as Python and OpenCV, as well as models such as Eigen Faces, Fisher Faces, and LBPH Faces, as units of analysis are considered photographs and portions of the video that capture facial expressions that then their patterns are trained with facial recognition algorithms. The results obtained show that the LBPH Faces obtained confidence values lower than 70, with a 95% certainty of recognition and a shorter recognition time, improving the accuracy of facial recognition, also with the increase of the data was achieved to improve the accuracy of recognition as well as improve confidence regarding the safety of people.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133329232","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 : 2022-12-04DOI: 10.1109/CICN56167.2022.10008273
Abdulkadir Saday, I. Ozkan
The robotic welding process is widely used in many industry sectors, and its use in production lines is becoming more common day by day. Obtaining a smooth weld seam in robot welding depends on the geometric structure of the welding path and the stability of the control loop. However, the weld path and the weld gap are usually not fixed, and their change negatively affects the automatic control. Programming the complex welding path by the operator may take more time than executing the task for some welding jobs. In addition, the variable weld gap negatively affects the weld quality in the constant control loop. This study proposes a system that provides a real-time definition of the weld path and its geometry on the embedded system to address this issue. The weld path image is captured using a camera, and the weld path is determined by image processing techniques using the embedded Linux operating system running on system-on-chip (SoC) hardware. The images captured through the Hard Processor System (HPS) unit are stored in memory, processed in the FPGA unit, and output by the HPS unit. Unprocessed SoC images and measurement images of weld pieces are presented with their values. When the values obtained from the processed weld path image are compared to manually measured path values, it is seen that the proposed system produces successful results.
{"title":"Robotic Welding Path Identification Using FPGA-Based Image Processing","authors":"Abdulkadir Saday, I. Ozkan","doi":"10.1109/CICN56167.2022.10008273","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008273","url":null,"abstract":"The robotic welding process is widely used in many industry sectors, and its use in production lines is becoming more common day by day. Obtaining a smooth weld seam in robot welding depends on the geometric structure of the welding path and the stability of the control loop. However, the weld path and the weld gap are usually not fixed, and their change negatively affects the automatic control. Programming the complex welding path by the operator may take more time than executing the task for some welding jobs. In addition, the variable weld gap negatively affects the weld quality in the constant control loop. This study proposes a system that provides a real-time definition of the weld path and its geometry on the embedded system to address this issue. The weld path image is captured using a camera, and the weld path is determined by image processing techniques using the embedded Linux operating system running on system-on-chip (SoC) hardware. The images captured through the Hard Processor System (HPS) unit are stored in memory, processed in the FPGA unit, and output by the HPS unit. Unprocessed SoC images and measurement images of weld pieces are presented with their values. When the values obtained from the processed weld path image are compared to manually measured path values, it is seen that the proposed system produces successful results.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133635092","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 : 2022-12-04DOI: 10.1109/CICN56167.2022.10008298
S. Mohammed
Many researchers use deep learning and technical indicators to forecast future stock prices. There are several hundred technical indicators and each one of them has a number of parameters. Finding the optimal combination of indicators with their optimal parameter values is very challenging. The aim of this work is to study if there is any benefit of feeding deep learning models with technical indicators instead of only feeding them with price and volume. After all, technical indicators are just functions of price and volume. Empirical studies done in this work using Saudi stocks show that deep learning models can benefit from technical indicators only if the right combination of technical indicators together with their right parameter values are used. The experimental results show that the right combination of technical indicators can improve the forecasting accuracy of deep learning modules. They also showed that using the wrong combination of indicators is worse than using no indicator even if they were assigned the best parameter values.
{"title":"The Validity of Using Technical Indicators When forecasting Stock Prices Using Deep Learning Models: Empirical Evidence Using Saudi Stocks","authors":"S. Mohammed","doi":"10.1109/CICN56167.2022.10008298","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008298","url":null,"abstract":"Many researchers use deep learning and technical indicators to forecast future stock prices. There are several hundred technical indicators and each one of them has a number of parameters. Finding the optimal combination of indicators with their optimal parameter values is very challenging. The aim of this work is to study if there is any benefit of feeding deep learning models with technical indicators instead of only feeding them with price and volume. After all, technical indicators are just functions of price and volume. Empirical studies done in this work using Saudi stocks show that deep learning models can benefit from technical indicators only if the right combination of technical indicators together with their right parameter values are used. The experimental results show that the right combination of technical indicators can improve the forecasting accuracy of deep learning modules. They also showed that using the wrong combination of indicators is worse than using no indicator even if they were assigned the best parameter values.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129852751","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 : 2022-12-04DOI: 10.1109/CICN56167.2022.10008346
Kanda Alamer, Abdulaziz Aldribi
One of the most rapidly spreading areas of infor-mation technology is cloud computing. However, this raises sig-nificant security issues that entice burglars. This paper presents a machine learning-based framework for intrusion classification for cloud computing networks. It offers new capabilities derived from cloud network flow. By dividing the flow into windows of time, a method known as the Riemann Chunking Scheme computes these features. After experimenting with this dataset, we have extracted 40 features that best describe the problem of anomaly classification and improve the accuracy of the study on multilayer perceptron for anomaly classification in cloud network traffic
{"title":"Intrusion Classification for Cloud Computing Network: A Step Towards an Intelligent Classification System","authors":"Kanda Alamer, Abdulaziz Aldribi","doi":"10.1109/CICN56167.2022.10008346","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008346","url":null,"abstract":"One of the most rapidly spreading areas of infor-mation technology is cloud computing. However, this raises sig-nificant security issues that entice burglars. This paper presents a machine learning-based framework for intrusion classification for cloud computing networks. It offers new capabilities derived from cloud network flow. By dividing the flow into windows of time, a method known as the Riemann Chunking Scheme computes these features. After experimenting with this dataset, we have extracted 40 features that best describe the problem of anomaly classification and improve the accuracy of the study on multilayer perceptron for anomaly classification in cloud network traffic","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130975920","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 : 2022-12-04DOI: 10.1109/CICN56167.2022.10008279
Muhammad Noman Saeed, Ahmad Mufarreh Al Mufarreh, K. M. Noaman, Muhammad Arshad, Atiq Rafiq Shaikh
Higher education across the world during the COVID pandemic changes its knowledge delivery mode from on-campus studies to off-campus studies, i.e. E-Learning. The e-education provider must be competent in order to create a robust learning environment that can handle the difficulties facing teachers, students, and system administrators at this rapid pace of change. The system administrator needs to improve the network connectivity, bandwidth etc. for providing seamless connectivity for E-Learning alongside their campus network services. The challenge of providing smooth services for e-learning is sometimes hurdled the other network services for the campus and therefore the management and administrator suggest deploying the e-learning services on the cloud and setting apart the campus network services. This will solve the problem of available network limits can face by the institute due to the limited amount of hardware and bandwidth issues. Furthermore, the cloud deployment reduces the capital as well as the recurring cost of running the services. This paper will focus to address the problem defined above and providing Amazon Web Services (AWS) based cost-effective cloud architecture for OpenedX based learning solutions. This study is expected to demonstrate a technological solution for the process of implementing a cloud-based LMS.
{"title":"JUX - A Cloud Hosted Learning Management System Based on OpenedX","authors":"Muhammad Noman Saeed, Ahmad Mufarreh Al Mufarreh, K. M. Noaman, Muhammad Arshad, Atiq Rafiq Shaikh","doi":"10.1109/CICN56167.2022.10008279","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008279","url":null,"abstract":"Higher education across the world during the COVID pandemic changes its knowledge delivery mode from on-campus studies to off-campus studies, i.e. E-Learning. The e-education provider must be competent in order to create a robust learning environment that can handle the difficulties facing teachers, students, and system administrators at this rapid pace of change. The system administrator needs to improve the network connectivity, bandwidth etc. for providing seamless connectivity for E-Learning alongside their campus network services. The challenge of providing smooth services for e-learning is sometimes hurdled the other network services for the campus and therefore the management and administrator suggest deploying the e-learning services on the cloud and setting apart the campus network services. This will solve the problem of available network limits can face by the institute due to the limited amount of hardware and bandwidth issues. Furthermore, the cloud deployment reduces the capital as well as the recurring cost of running the services. This paper will focus to address the problem defined above and providing Amazon Web Services (AWS) based cost-effective cloud architecture for OpenedX based learning solutions. This study is expected to demonstrate a technological solution for the process of implementing a cloud-based LMS.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131223004","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 : 2022-12-04DOI: 10.1109/CICN56167.2022.10008376
A. Mohammad, Subankan Karunakaran, Mithushankar Panchalingam, R. Davidrajuh
During drilling operations for oil and gas, swab and surge pressure occur while tripping in and out of a wellbore. High tripping speed can lead to fracturing the well's formation, whereas low tripping speed can increase non-productive time and cost. Hence, there is a need to predict surge/swab pressure accurately. Several analytical and machine learning models have already been developed to predict surge/swab pressure. However, these existing models use numerical calculations to generate the data. This paper explored four supervised machine learning models, i.e., Linear Regression, XGBoost, Feedforward Neural Network (FFNN), and Long-Short-Term Memory (LSTM). In this study, actual field data from the Norwegian Continental Shelf provided by an Exploration & Production company is utilized to develop the four machine learning models. The results indicated that XGBoost was the best-performing model with an R2-score of 0.99073. Therefore, this trained model can be applied during a tripping operation to regulate tripping speed where repetitive surge/swab pressure calculation is needed.
{"title":"Prediction of Downhole Pressure while Tripping","authors":"A. Mohammad, Subankan Karunakaran, Mithushankar Panchalingam, R. Davidrajuh","doi":"10.1109/CICN56167.2022.10008376","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008376","url":null,"abstract":"During drilling operations for oil and gas, swab and surge pressure occur while tripping in and out of a wellbore. High tripping speed can lead to fracturing the well's formation, whereas low tripping speed can increase non-productive time and cost. Hence, there is a need to predict surge/swab pressure accurately. Several analytical and machine learning models have already been developed to predict surge/swab pressure. However, these existing models use numerical calculations to generate the data. This paper explored four supervised machine learning models, i.e., Linear Regression, XGBoost, Feedforward Neural Network (FFNN), and Long-Short-Term Memory (LSTM). In this study, actual field data from the Norwegian Continental Shelf provided by an Exploration & Production company is utilized to develop the four machine learning models. The results indicated that XGBoost was the best-performing model with an R2-score of 0.99073. Therefore, this trained model can be applied during a tripping operation to regulate tripping speed where repetitive surge/swab pressure calculation is needed.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"69 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113933096","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 : 2022-12-04DOI: 10.1109/CICN56167.2022.10008274
Lulwah M. Al-Harigy, H. Al-Nuaim, N. Moradpoor, Zhiyuan Tan
Cyberbullying is a widespread problem that has only increased in recent years due to the massive dependence on social media. Although, there are many approaches for detecting cyberbullying they still need to be improved upon for more accurate detection. We need new approaches that understand the context of the words used in cyberbullying by generating different representations of each word. In addition. there is a large amount of unlabelled data on the Internet that needs to be labelled for a more accurate detection process. Even though multiple methods for annotating datasets exists, the most widely used are still manual approaches, either using experts or crowdsourcing. However, The time needed and high cost of labor for manually annotation approaches result in a lack of annotated social network datasets for training a robust cyberbullying detector. Automated approaches can be relied upon in labelling data, such as using the Self-Supervised Learning (SSL) model. In this paper, we proposed two main parts. The first part is proposing a model of parallel BERT + Bi-LSTM used for detecting cyberbullying terms. The second part is utilizing Contrastive Self-Supervised Learning (a form of SSL) to augment the training set from unlabeled data using a small portion of another manually annotated dataset. Our proposed model that used deep pre-trained contrastive self-supervised learning for detecting cyberbullying using augmented datasets achieved a performance of (0.9311) using macro average F1 score. This result shows our model outperformed the baseline models - the top three teams in the competition SemEval-2020 Task 12.
{"title":"Deep Pre-trained Contrastive Self-Supervised Learning: A Cyberbullying Detection Approach with Augmented Datasets","authors":"Lulwah M. Al-Harigy, H. Al-Nuaim, N. Moradpoor, Zhiyuan Tan","doi":"10.1109/CICN56167.2022.10008274","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008274","url":null,"abstract":"Cyberbullying is a widespread problem that has only increased in recent years due to the massive dependence on social media. Although, there are many approaches for detecting cyberbullying they still need to be improved upon for more accurate detection. We need new approaches that understand the context of the words used in cyberbullying by generating different representations of each word. In addition. there is a large amount of unlabelled data on the Internet that needs to be labelled for a more accurate detection process. Even though multiple methods for annotating datasets exists, the most widely used are still manual approaches, either using experts or crowdsourcing. However, The time needed and high cost of labor for manually annotation approaches result in a lack of annotated social network datasets for training a robust cyberbullying detector. Automated approaches can be relied upon in labelling data, such as using the Self-Supervised Learning (SSL) model. In this paper, we proposed two main parts. The first part is proposing a model of parallel BERT + Bi-LSTM used for detecting cyberbullying terms. The second part is utilizing Contrastive Self-Supervised Learning (a form of SSL) to augment the training set from unlabeled data using a small portion of another manually annotated dataset. Our proposed model that used deep pre-trained contrastive self-supervised learning for detecting cyberbullying using augmented datasets achieved a performance of (0.9311) using macro average F1 score. This result shows our model outperformed the baseline models - the top three teams in the competition SemEval-2020 Task 12.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125240131","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 : 2022-12-04DOI: 10.1109/CICN56167.2022.10008329
Bikash Sadhukhan, S. Mukherjee, R. Samanta
The analysis of global temperature trends at various regional and temporal dimensions has received considerable interest from the scientific community over the past century due to the growing awareness of the effects of climate change on the earth. The objective of this research is to determine and analyse the trend of monthly fluctuations in land and ocean temperatures around the world. This was accomplished by scraping a database maintained by the National Oceanic and Atmospheric Administration (NOAA) for monthly global land and ocean temperature anomaly data between 1881 and 2020. This study uses the Mann-Kendall trend test and Sen's estimator for slope to examine the global impact of climate change by comparing the trends of global land temperature anomalies, global ocean temperature anomalies, and their combined global land and ocean temperature anomaly records. The substantial magnitude of several statistical parameters demonstrates that the temperature anomalies (land, ocean, and combined) have significantly increased during the previous five decades, mostly as a consequence of strong anthropogenic sources. This necessitates the development of a proper action plan to limit global warming and the design of policies to reduce the elements that are likely to have detrimental effects on the climate on a local and global scale.
{"title":"A Study of Global Temperature Anomalies and their Changing Trends due to Global Warming","authors":"Bikash Sadhukhan, S. Mukherjee, R. Samanta","doi":"10.1109/CICN56167.2022.10008329","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008329","url":null,"abstract":"The analysis of global temperature trends at various regional and temporal dimensions has received considerable interest from the scientific community over the past century due to the growing awareness of the effects of climate change on the earth. The objective of this research is to determine and analyse the trend of monthly fluctuations in land and ocean temperatures around the world. This was accomplished by scraping a database maintained by the National Oceanic and Atmospheric Administration (NOAA) for monthly global land and ocean temperature anomaly data between 1881 and 2020. This study uses the Mann-Kendall trend test and Sen's estimator for slope to examine the global impact of climate change by comparing the trends of global land temperature anomalies, global ocean temperature anomalies, and their combined global land and ocean temperature anomaly records. The substantial magnitude of several statistical parameters demonstrates that the temperature anomalies (land, ocean, and combined) have significantly increased during the previous five decades, mostly as a consequence of strong anthropogenic sources. This necessitates the development of a proper action plan to limit global warming and the design of policies to reduce the elements that are likely to have detrimental effects on the climate on a local and global scale.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121983088","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 : 2022-12-04DOI: 10.1109/CICN56167.2022.10008380
Saiman Quazi, Sarhan M. Musa
Text classification is one of the important fields in Natural Language Processing (NLP). It assigns text documents into at least two categories in the domain by submitting and deriving a set of features to describe each document and to select the correct category for each one for a set of pre-defined tags or categories based on content. It is even used in several real-life applications such as engineering, science, and marketing and it can be quite effective in addressing problems with labeled data. There are certain Deep Learning (DL) algorithms that can be handy in categorizing text data such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes. This paper illustrates how the text in each document is reviewed and grouped into different sets through the above-mentioned techniques. That way, it will determine which method is best suited for higher accuracy and what possible problems the deep learning model faces using text classification and categorization so that new solutions can be invented to resolve these issues without interfering with the processes in the future.
{"title":"Text Classification and Categorization through Deep Learning","authors":"Saiman Quazi, Sarhan M. Musa","doi":"10.1109/CICN56167.2022.10008380","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008380","url":null,"abstract":"Text classification is one of the important fields in Natural Language Processing (NLP). It assigns text documents into at least two categories in the domain by submitting and deriving a set of features to describe each document and to select the correct category for each one for a set of pre-defined tags or categories based on content. It is even used in several real-life applications such as engineering, science, and marketing and it can be quite effective in addressing problems with labeled data. There are certain Deep Learning (DL) algorithms that can be handy in categorizing text data such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes. This paper illustrates how the text in each document is reviewed and grouped into different sets through the above-mentioned techniques. That way, it will determine which method is best suited for higher accuracy and what possible problems the deep learning model faces using text classification and categorization so that new solutions can be invented to resolve these issues without interfering with the processes in the future.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122361937","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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