Pub Date : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088296
Usman Alhaji Abdurrahman, Lirong Zheng, Abdulrauf Garba Sharifai, I. D. Muraina
Due to its limited capacity, working memory can become overloaded with extra activities that do not directly contribute to learning. According to cognitive load theory, working memory overload reduces task performance. Thus, monitoring the individual's current mental workload is essential to avoid dealing with the effects of cognitive overload. Heart rate and pupil dilation are two important metrics that can appropriately be measured at a low cost. These two signals have been generated to classify the participants' cognitive load levels in this study. Ninety-eight (98) participants volunteered in the studies, and we assessed their cognitive workloads using psychophysiological measurements generated during the experiment and performance characteristics obtained from the virtual driving system. The driving system continuously monitored the subjects' driving performance parameters, including heart rate and pupil dilation. The experiment involved driving tasks in a virtual environment, and some popular machine learning algorithms have been applied for user classification. Data analysis of the signals reveals that the heart rate and pupil dilation could appropriately be used to determine the cognitive workload of the individuals. Also, using multimodal data fusion, the accuracy of the cognitive load classification can be improved.
{"title":"Heart Rate and Pupil Dilation As Reliable Measures of Neuro-Cognitive Load Classification","authors":"Usman Alhaji Abdurrahman, Lirong Zheng, Abdulrauf Garba Sharifai, I. D. Muraina","doi":"10.1109/ASSIC55218.2022.10088296","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088296","url":null,"abstract":"Due to its limited capacity, working memory can become overloaded with extra activities that do not directly contribute to learning. According to cognitive load theory, working memory overload reduces task performance. Thus, monitoring the individual's current mental workload is essential to avoid dealing with the effects of cognitive overload. Heart rate and pupil dilation are two important metrics that can appropriately be measured at a low cost. These two signals have been generated to classify the participants' cognitive load levels in this study. Ninety-eight (98) participants volunteered in the studies, and we assessed their cognitive workloads using psychophysiological measurements generated during the experiment and performance characteristics obtained from the virtual driving system. The driving system continuously monitored the subjects' driving performance parameters, including heart rate and pupil dilation. The experiment involved driving tasks in a virtual environment, and some popular machine learning algorithms have been applied for user classification. Data analysis of the signals reveals that the heart rate and pupil dilation could appropriately be used to determine the cognitive workload of the individuals. Also, using multimodal data fusion, the accuracy of the cognitive load classification can be improved.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126742767","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088295
Sudeep Sharma, S. Monika, S. Prasad, Kiran Dasari, Shivani Kamaganikuntla
In educational institutes, taking attendance on each day is mandatory. The conventional way of taking attendance is by calling every individual with their names or by signing on some sheet. This method might cause some errors and consume more time, which makes this method inefficient. Giving proxy to other student is one of the major drawbacks in the traditional way. So, to avoid all this, we came up with RFID (Radio Frequency Identification) based low-cost attendance recording system. In this system, we use RFID technology to take attendance, where each student is issued with an RFID tag. Each tag is contained with some unique information about the student. When the tag is placed near the reader, the reader reads the information from the tag and sends it to the Arduino board. The controller checks for the data and compares with the data base. If the tag is valid, the controller marks the student as present and opens the classroom door for entry. The main components of the proposed prototype hardware setup are Arduino uno, RFID tags, tag reader, servo motor, IR (Infra-Red) sensor and LCD (Liquid Crystal Display).
{"title":"RFID Based Low Cost Attendance Recording and Proxy Avoidance System","authors":"Sudeep Sharma, S. Monika, S. Prasad, Kiran Dasari, Shivani Kamaganikuntla","doi":"10.1109/ASSIC55218.2022.10088295","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088295","url":null,"abstract":"In educational institutes, taking attendance on each day is mandatory. The conventional way of taking attendance is by calling every individual with their names or by signing on some sheet. This method might cause some errors and consume more time, which makes this method inefficient. Giving proxy to other student is one of the major drawbacks in the traditional way. So, to avoid all this, we came up with RFID (Radio Frequency Identification) based low-cost attendance recording system. In this system, we use RFID technology to take attendance, where each student is issued with an RFID tag. Each tag is contained with some unique information about the student. When the tag is placed near the reader, the reader reads the information from the tag and sends it to the Arduino board. The controller checks for the data and compares with the data base. If the tag is valid, the controller marks the student as present and opens the classroom door for entry. The main components of the proposed prototype hardware setup are Arduino uno, RFID tags, tag reader, servo motor, IR (Infra-Red) sensor and LCD (Liquid Crystal Display).","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127915997","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}
The EEG is a structured and dependable approach for analysing epileptic seizures and capturing brain electrical activity. The physical effort of clinicians diagnosing epilepsy is decreased through automatic epilepsy screening employing data-driven algorithms. The latest algorithms are skewed toward signal processing or DL, each with its own set of benefits and drawbacks. The proposed hybrid framework is developed by hybridizing a feature extraction module, and deep transformer model. The fourier transform is utilized for the effective feature extraction, and deep transformer model is used for the seizure prediction. The proposed framework can interpret the hidden features to naturally select the interesting fields in EEG data for strong predictions. The proposed framework is validated using CHB-MIT database, and the performance is compared with different benchmark models. The proposed model achieved an average sensitivity of 95.2% with a false positive rate of 0.02, which is better compared to other comparative models. The proposed framework achieved excellent results on the test datasets, and can be used as a promising tool for the hospitals for examining the patients.
{"title":"A hybrid deep transformer model for epileptic seizure prediction","authors":"Saketh Maddineni, Shivani Janapati, Vishalteja Kosana, Kiran Teeparthi","doi":"10.1109/ASSIC55218.2022.10088398","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088398","url":null,"abstract":"The EEG is a structured and dependable approach for analysing epileptic seizures and capturing brain electrical activity. The physical effort of clinicians diagnosing epilepsy is decreased through automatic epilepsy screening employing data-driven algorithms. The latest algorithms are skewed toward signal processing or DL, each with its own set of benefits and drawbacks. The proposed hybrid framework is developed by hybridizing a feature extraction module, and deep transformer model. The fourier transform is utilized for the effective feature extraction, and deep transformer model is used for the seizure prediction. The proposed framework can interpret the hidden features to naturally select the interesting fields in EEG data for strong predictions. The proposed framework is validated using CHB-MIT database, and the performance is compared with different benchmark models. The proposed model achieved an average sensitivity of 95.2% with a false positive rate of 0.02, which is better compared to other comparative models. The proposed framework achieved excellent results on the test datasets, and can be used as a promising tool for the hospitals for examining the patients.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128796385","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}
Graph neural networks(GNN) are a special variant of neural networks which help in dealing with unstructured data such as graph data. The advent of the GNN has helped in dealing with problems in different domains, especially in the domain of Natural Language Processing(NLP). In NLP, GNNs are used to implement tasks such as text classification which has a wide variety of applications. There are two ways to represent the text data using GNN namely, Inductive and transductive. In this paper, we apply the approach of the inductive model using different variants of GNN. We observed that the GAT variant gave better performance compared to other variants. Moreover, we observed that the complexity of the model and the dataset size influences the entropy of the output.
{"title":"Comparative Study of Inductive Graph Neural Network Models for Text Classification","authors":"Saran Pandian, Uttkarsh Chaurasia, Shudhanshu Ranjan, Shefali Saxena","doi":"10.1109/ASSIC55218.2022.10088315","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088315","url":null,"abstract":"Graph neural networks(GNN) are a special variant of neural networks which help in dealing with unstructured data such as graph data. The advent of the GNN has helped in dealing with problems in different domains, especially in the domain of Natural Language Processing(NLP). In NLP, GNNs are used to implement tasks such as text classification which has a wide variety of applications. There are two ways to represent the text data using GNN namely, Inductive and transductive. In this paper, we apply the approach of the inductive model using different variants of GNN. We observed that the GAT variant gave better performance compared to other variants. Moreover, we observed that the complexity of the model and the dataset size influences the entropy of the output.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"2006 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127335693","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088330
Devang Jagdale, Sukrut Bidwai, Tejashvini R. Hiremath, Neil Bhutada, S. Bhingarkar
River networks are widely observed and scrutinized for various purposes, which incorporate determining the terrestrial positions of water bodies, examining the gauge levels of the river, predicting river flows, and conserving sustainable energy resources as a consequence of Global warming. Extraction of these River networks on digital imagery systems are executed by various segmentation and machine learning model integration. In this paper, distinct datasets are used from Kaggle and Google Earth Engine, Segmentation methods such as Image segmentation, gray scaling, enhancement, global thresholding, and Deep Learning UNet Architecture are integrated with contemplation of extracting river networks from satellite images which result in achieving 80.98 % dice score for the developed UNet Model. Hence, these developed techniques can further be used for river extraction from satellite images. And can be applied to various semantic segmentation detection datasets.
{"title":"Extraction of River Networks from Satellite Images using Image Processing & Deep Learning Techniques","authors":"Devang Jagdale, Sukrut Bidwai, Tejashvini R. Hiremath, Neil Bhutada, S. Bhingarkar","doi":"10.1109/ASSIC55218.2022.10088330","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088330","url":null,"abstract":"River networks are widely observed and scrutinized for various purposes, which incorporate determining the terrestrial positions of water bodies, examining the gauge levels of the river, predicting river flows, and conserving sustainable energy resources as a consequence of Global warming. Extraction of these River networks on digital imagery systems are executed by various segmentation and machine learning model integration. In this paper, distinct datasets are used from Kaggle and Google Earth Engine, Segmentation methods such as Image segmentation, gray scaling, enhancement, global thresholding, and Deep Learning UNet Architecture are integrated with contemplation of extracting river networks from satellite images which result in achieving 80.98 % dice score for the developed UNet Model. Hence, these developed techniques can further be used for river extraction from satellite images. And can be applied to various semantic segmentation detection datasets.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130969391","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088364
K. Anand, A. Jena, Tanisha Choudhary
Software Testing is an essential activity in the development process of a software product. A defect-free software is the need of the hour. Identifying the defects as early as possible is critical to avoid any disastrous consequences in the later stages of development. Software Defect Prediction (SDP) is a process of early identification of defect-prone modules. Lately, software defect prediction coupled with machine learning techniques has gained momentum as it significantly brings down maintenance costs. Feature selection (FS) plays a very significant role in a defect prediction model's efficiency; hence, choosing a suitable FS method is challenging when building a defect prediction model. This paper evaluates six filter-based FS techniques, four wrapper-based FS techniques, and two embedded FS techniques using four supervised learning classifiers over six NASA datasets from the PROMISE repository. The experimental results strengthened that FS techniques significantly improve the model's predictive performance. From our experimental data, we concluded that SVM based defect prediction model showed the best performance among all other studied models. We also observed that Fisher's score, a filter-based FS technique, outperformed all other FS techniques studied in this work.
{"title":"Performance Analysis of Feature Selection Techniques in Software Defect Prediction using Machine Learning","authors":"K. Anand, A. Jena, Tanisha Choudhary","doi":"10.1109/ASSIC55218.2022.10088364","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088364","url":null,"abstract":"Software Testing is an essential activity in the development process of a software product. A defect-free software is the need of the hour. Identifying the defects as early as possible is critical to avoid any disastrous consequences in the later stages of development. Software Defect Prediction (SDP) is a process of early identification of defect-prone modules. Lately, software defect prediction coupled with machine learning techniques has gained momentum as it significantly brings down maintenance costs. Feature selection (FS) plays a very significant role in a defect prediction model's efficiency; hence, choosing a suitable FS method is challenging when building a defect prediction model. This paper evaluates six filter-based FS techniques, four wrapper-based FS techniques, and two embedded FS techniques using four supervised learning classifiers over six NASA datasets from the PROMISE repository. The experimental results strengthened that FS techniques significantly improve the model's predictive performance. From our experimental data, we concluded that SVM based defect prediction model showed the best performance among all other studied models. We also observed that Fisher's score, a filter-based FS technique, outperformed all other FS techniques studied in this work.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131087453","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088357
K. Rani, Mohammad Arshad, A. Sangeetha
Potholes are considered the most dangerous part of road accidents. They should be spotted and fixed before they become an issue. Being aware of their existence can help prevent road accidents. Potholes are an unavoidable obstacle faced by all Indian drivers, especially when it rains. Techniques have been implemented to solve this problem, from manual answering to specialists to the utilization of vibration-based sensors. In any case, these strategies have a few downsides, for example, high arrangement costs, risk during recognition, the main idea is to detect and notify possible potholes without human intervention and using the YOLO algorithm. YOLO is an acronym for the term “You Only Look Once”. A calculation distinguishes and perceives various articles in a picture (continuously). Object detection in YOLO is performed as a regression problem and provides the class probability of detected images. It is to degree of execution included Real-time responsiveness and location accuracy using image sets. An image set is recognized by running a convolutional neural network (CNN) on multiple dip locators. After collecting a set of $mathbf{720}times mathbf{720}$ pixel resolution images capturing different types of potholes in characteristic road conditions, the set is divided into subsets for preparation, testing and approval. It'll show potholes in genuine time, and the pothole will be highlighted with boxes, as seen in real-time question discovery frameworks. The YOLO algorithm uses a convolution neural network (CNN) to detect objects in real time. CNN is used to simultaneously predict different class probabilities and bounding boxes.
{"title":"Pothole Detection Using YOLO (You Only Look Once) Algorithm","authors":"K. Rani, Mohammad Arshad, A. Sangeetha","doi":"10.1109/ASSIC55218.2022.10088357","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088357","url":null,"abstract":"Potholes are considered the most dangerous part of road accidents. They should be spotted and fixed before they become an issue. Being aware of their existence can help prevent road accidents. Potholes are an unavoidable obstacle faced by all Indian drivers, especially when it rains. Techniques have been implemented to solve this problem, from manual answering to specialists to the utilization of vibration-based sensors. In any case, these strategies have a few downsides, for example, high arrangement costs, risk during recognition, the main idea is to detect and notify possible potholes without human intervention and using the YOLO algorithm. YOLO is an acronym for the term “You Only Look Once”. A calculation distinguishes and perceives various articles in a picture (continuously). Object detection in YOLO is performed as a regression problem and provides the class probability of detected images. It is to degree of execution included Real-time responsiveness and location accuracy using image sets. An image set is recognized by running a convolutional neural network (CNN) on multiple dip locators. After collecting a set of $mathbf{720}times mathbf{720}$ pixel resolution images capturing different types of potholes in characteristic road conditions, the set is divided into subsets for preparation, testing and approval. It'll show potholes in genuine time, and the pothole will be highlighted with boxes, as seen in real-time question discovery frameworks. The YOLO algorithm uses a convolution neural network (CNN) to detect objects in real time. CNN is used to simultaneously predict different class probabilities and bounding boxes.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122637835","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088338
Desmond Kong Ze Fong, Vinesha Selvarajah, M. S. Nabi
The InterPlanetary File System is a protocol and peer-to-peer network for storing and sharing data in a distributed file system. IPFS uses content-addressing to uniquely identify each file in a global namespace connecting all computing devices. The distributed system would allow data users to upload and locate their content to the IPFS blockchain network as well as sharing it peer-to-peer resulting in high-speed content loading or bandwidth relying on the network and data usage either user want to upload or download through IPFS based application. Each content uploaded to the IPFS would be unique and identified based on the content identity (CID) that would be assigned once the file is uploaded. The paper also discusses the usage of centralized and decentralized storage system as well as secure and practical storage management. The paper also indicates that the traditional storage management do not guarantee the possibility of data loss or data redundancy. Therefore, multi-authentication toward IPFS based storage would be implemented for data user to locate their data on IPFS blockchain.
{"title":"Secure Server Storage Based IPFS through Multi-Authentication","authors":"Desmond Kong Ze Fong, Vinesha Selvarajah, M. S. Nabi","doi":"10.1109/ASSIC55218.2022.10088338","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088338","url":null,"abstract":"The InterPlanetary File System is a protocol and peer-to-peer network for storing and sharing data in a distributed file system. IPFS uses content-addressing to uniquely identify each file in a global namespace connecting all computing devices. The distributed system would allow data users to upload and locate their content to the IPFS blockchain network as well as sharing it peer-to-peer resulting in high-speed content loading or bandwidth relying on the network and data usage either user want to upload or download through IPFS based application. Each content uploaded to the IPFS would be unique and identified based on the content identity (CID) that would be assigned once the file is uploaded. The paper also discusses the usage of centralized and decentralized storage system as well as secure and practical storage management. The paper also indicates that the traditional storage management do not guarantee the possibility of data loss or data redundancy. Therefore, multi-authentication toward IPFS based storage would be implemented for data user to locate their data on IPFS blockchain.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124405362","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}
Software development is rapidly growing at a global level. It requires a lot of technical knowledge and a well-structured skill set. Due to these and other factors, software development projects contain elements of uncertainty. Risks can also be defined as the possibility of the occurrence of an event which may either have a positive or negative impact on the result. Risk management is one of the most important functions of management strategies. In project management, risk management plays an important role in preventing and mitigating risks that have the potential to adversely affect the specified outcomes. Artificial neural network-based training algorithm is used to predict the risks in agile software development project. They help project managers in decision making and predicting the risks involved in the projects with respect to the resources, techniques and other constraints involved.
{"title":"Software Vulnerabilities Detection in Agile Process using graph method and Deep Neural Network","authors":"Devesh Kumar Srivastava, Rishabh Makhija, Aarushi Batta","doi":"10.1109/ASSIC55218.2022.10088314","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088314","url":null,"abstract":"Software development is rapidly growing at a global level. It requires a lot of technical knowledge and a well-structured skill set. Due to these and other factors, software development projects contain elements of uncertainty. Risks can also be defined as the possibility of the occurrence of an event which may either have a positive or negative impact on the result. Risk management is one of the most important functions of management strategies. In project management, risk management plays an important role in preventing and mitigating risks that have the potential to adversely affect the specified outcomes. Artificial neural network-based training algorithm is used to predict the risks in agile software development project. They help project managers in decision making and predicting the risks involved in the projects with respect to the resources, techniques and other constraints involved.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129027312","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088356
D. S. Dev, Aneervan Ray, Josh Austin
Exchange rate forecasting has proven challenging for players like traders and professionals in this current financial industry. Econometric and statistical models are often utilized in the analysis and forecasting of foreign exchange rate. Governments, financial organizations, and investors prioritize analyzing the future behaviour of currency pairs because this analyzing technique is being utilized to understand a country's economic status and to make a decision on whether to do any transactions of goods from that country. Several models are used to predict this kind of time-series with adequate accuracy. However, because of the random nature of these time series, strong predicting performance is difficult to achieve. During the Covid-19 situation, there is a drastic change in the exchange rate worldwide. This paper examines the behaviour of Australia's (AUD) daily foreign exchange rates against the US Dollar from January 2016 to December 2020 and forecasts the 2021 exchange rate using the ARIMA model. For better accuracy, technical indicators such as Interest Rate Differential, GDP Growth Rate and Unemployment Rate are also taken into account. In exchange rate forecasting, there are various types of performance measures based on which the accuracy of the forecasted result is computed. This paper examines seven performance measures and found that the accuracy of the forecasted results is adequate with the actual data.
{"title":"ARIMA Model based Time Series Modelling and Prediction of Foreign Exchange Rate against US Dollar","authors":"D. S. Dev, Aneervan Ray, Josh Austin","doi":"10.1109/ASSIC55218.2022.10088356","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088356","url":null,"abstract":"Exchange rate forecasting has proven challenging for players like traders and professionals in this current financial industry. Econometric and statistical models are often utilized in the analysis and forecasting of foreign exchange rate. Governments, financial organizations, and investors prioritize analyzing the future behaviour of currency pairs because this analyzing technique is being utilized to understand a country's economic status and to make a decision on whether to do any transactions of goods from that country. Several models are used to predict this kind of time-series with adequate accuracy. However, because of the random nature of these time series, strong predicting performance is difficult to achieve. During the Covid-19 situation, there is a drastic change in the exchange rate worldwide. This paper examines the behaviour of Australia's (AUD) daily foreign exchange rates against the US Dollar from January 2016 to December 2020 and forecasts the 2021 exchange rate using the ARIMA model. For better accuracy, technical indicators such as Interest Rate Differential, GDP Growth Rate and Unemployment Rate are also taken into account. In exchange rate forecasting, there are various types of performance measures based on which the accuracy of the forecasted result is computed. This paper examines seven performance measures and found that the accuracy of the forecasted results is adequate with the actual data.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115859764","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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