Pub Date : 2021-11-22DOI: 10.1109/ict4da53266.2021.9672225
M. Abegaz, Jordi Casademont Serra, Y. Negash
In this paper, we have proposed an augmented random access MAC protocol for cognitive radio network (CRN) called ACR-MAC. We have presented analytical framework for the proposed protocol, and developed closed form expressions for saturated throughput and average packet delay. The performance of the proposed protocol has been examined with the performance of cognitive radio MAC protocol which was developed based on the well-known distributed coordination function (DCF) which deploys two medium access mechanisms: the basic and RTS/CTS. The proposed MAC protocol shows promising performance in terms of saturated throughput and average packet delay over the conventional random access MAC protocol proposed for CRN.
{"title":"Augmented Random Access MAC Protocol for Cognitive Radio Network (ACR-MAC)","authors":"M. Abegaz, Jordi Casademont Serra, Y. Negash","doi":"10.1109/ict4da53266.2021.9672225","DOIUrl":"https://doi.org/10.1109/ict4da53266.2021.9672225","url":null,"abstract":"In this paper, we have proposed an augmented random access MAC protocol for cognitive radio network (CRN) called ACR-MAC. We have presented analytical framework for the proposed protocol, and developed closed form expressions for saturated throughput and average packet delay. The performance of the proposed protocol has been examined with the performance of cognitive radio MAC protocol which was developed based on the well-known distributed coordination function (DCF) which deploys two medium access mechanisms: the basic and RTS/CTS. The proposed MAC protocol shows promising performance in terms of saturated throughput and average packet delay over the conventional random access MAC protocol proposed for CRN.","PeriodicalId":371663,"journal":{"name":"2021 International Conference on Information and Communication Technology for Development for Africa (ICT4DA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134171414","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 : 2021-11-22DOI: 10.1109/ict4da53266.2021.9672215
Abate Tsegaye, Beakal Gizachew Assefa
The large-scale development of Internet of Things devices emerged a new computing environment called fog computing to reduce the makespan and cost spent on the cloud devices as a result of distant communication. However, unless the appropriate assignment of tasks is strictly allocated on an available resource of fog nodes, it results in wastage of resources and unachievable quality of service. In this paper, the balance of the most common conflicting objectives in task scheduling that is makespan and cost for the distributed fog-cloud environment is investigated. A novel hybrid squirrel search and invasive weed (HSSIW) algorithm is adapted to assign generated tasks from the Internet of Things(IoT) devices at appropriate fog and cloud nodes so that reduction in cost and makespan is assured. The proposed algorithm has been compared with three related state-of-the algorithms such as genetic algorithm (GA), particle swarm optimization algorithm (PSO), and squirrel search algorithm(SS). The experiment conducted on Cloudsim shows that the proposed algorithm reduces makespan 18% better than classic algorithms such as First Come First Serve(FCFS) and Short Job First(SJF) algorithms. Similarly, it has made a reduction in latency 4 % better than GA and PSO with optimal cost.
{"title":"HSSIW: Hybrid Squirrel Search and Invasive Weed Based Cost-Makespan Task Scheduling for Fog-Cloud Environment","authors":"Abate Tsegaye, Beakal Gizachew Assefa","doi":"10.1109/ict4da53266.2021.9672215","DOIUrl":"https://doi.org/10.1109/ict4da53266.2021.9672215","url":null,"abstract":"The large-scale development of Internet of Things devices emerged a new computing environment called fog computing to reduce the makespan and cost spent on the cloud devices as a result of distant communication. However, unless the appropriate assignment of tasks is strictly allocated on an available resource of fog nodes, it results in wastage of resources and unachievable quality of service. In this paper, the balance of the most common conflicting objectives in task scheduling that is makespan and cost for the distributed fog-cloud environment is investigated. A novel hybrid squirrel search and invasive weed (HSSIW) algorithm is adapted to assign generated tasks from the Internet of Things(IoT) devices at appropriate fog and cloud nodes so that reduction in cost and makespan is assured. The proposed algorithm has been compared with three related state-of-the algorithms such as genetic algorithm (GA), particle swarm optimization algorithm (PSO), and squirrel search algorithm(SS). The experiment conducted on Cloudsim shows that the proposed algorithm reduces makespan 18% better than classic algorithms such as First Come First Serve(FCFS) and Short Job First(SJF) algorithms. Similarly, it has made a reduction in latency 4 % better than GA and PSO with optimal cost.","PeriodicalId":371663,"journal":{"name":"2021 International Conference on Information and Communication Technology for Development for Africa (ICT4DA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115921411","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 : 2021-11-22DOI: 10.1109/ict4da53266.2021.9672230
A. Tonja, Michael Melese Woldeyohannis, Mesay Gemeda Yigezu
In this paper, we described an effort towards the development of parallel corpora for English and Ethiopian Languages, such as Wolaita, Gamo, Gofa, and Dawuro neural machine translation. The corpus is collected from the religious domain and to check the usability of the collected parallel corpora a bi-directional Neural Machine Translation experiments were conducted. The neural machine translation shows good results as a baseline experiment of BLEU score of 13.8 in Wolaita-English and 8.2 English-Wolaita machine translation. The Wolaita-English translation shows a better result than the other pairs of Ethiopian languages and the result of neural machine translation performs well when the amount of dataset increases, thus the amount of dataset has a great impact on the performance. Besides these, the morphological richness of Ethiopian language contributed to the low performance of neural machine translation when the Ethiopian language is used as the target language. Further, we are working on minimizing the effect of morphological richness through different morphological processing techniques in the translation of Ethiopian languages.
{"title":"A Parallel Corpora for bi-directional Neural Machine Translation for Low Resourced Ethiopian Languages","authors":"A. Tonja, Michael Melese Woldeyohannis, Mesay Gemeda Yigezu","doi":"10.1109/ict4da53266.2021.9672230","DOIUrl":"https://doi.org/10.1109/ict4da53266.2021.9672230","url":null,"abstract":"In this paper, we described an effort towards the development of parallel corpora for English and Ethiopian Languages, such as Wolaita, Gamo, Gofa, and Dawuro neural machine translation. The corpus is collected from the religious domain and to check the usability of the collected parallel corpora a bi-directional Neural Machine Translation experiments were conducted. The neural machine translation shows good results as a baseline experiment of BLEU score of 13.8 in Wolaita-English and 8.2 English-Wolaita machine translation. The Wolaita-English translation shows a better result than the other pairs of Ethiopian languages and the result of neural machine translation performs well when the amount of dataset increases, thus the amount of dataset has a great impact on the performance. Besides these, the morphological richness of Ethiopian language contributed to the low performance of neural machine translation when the Ethiopian language is used as the target language. Further, we are working on minimizing the effect of morphological richness through different morphological processing techniques in the translation of Ethiopian languages.","PeriodicalId":371663,"journal":{"name":"2021 International Conference on Information and Communication Technology for Development for Africa (ICT4DA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125585936","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 : 2021-11-22DOI: 10.1109/ict4da53266.2021.9672227
A. Genetu, Tesfa Tegegne
Sensitive information is a classified type of content that should not be disclosed to the public and that can harm the owner of the information if it is disclosed. To protect disclose of sensitive information first, it requires detecting the availability of sensitive information and its domain classification for further analysis. To the best of our knowledge, there is no work attempted for Amharic texts. Models developed for another language cannot be used for Amharic texts language because of morphology, grammar and semantics differences. To address these gaps, we have proposed a model for detecting and classifying personal sensitive information for Amharic texts. We have experimented with three deep learning algorithms: Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BI-LSTM) and Convolutional Neural Network (CNN) using 7.31K and 6.697K Amharic sentences for sensitivity detection and domain classification respectively. The accuracy of LSTM, BI-LSTM and CNN was 82%, 90% and 87% respectively for sensitivity classification and 88, 93, 89 respectively for domain classification.
{"title":"Designing Sensitive Personal Information Detection and Classification Model for Amharic Text","authors":"A. Genetu, Tesfa Tegegne","doi":"10.1109/ict4da53266.2021.9672227","DOIUrl":"https://doi.org/10.1109/ict4da53266.2021.9672227","url":null,"abstract":"Sensitive information is a classified type of content that should not be disclosed to the public and that can harm the owner of the information if it is disclosed. To protect disclose of sensitive information first, it requires detecting the availability of sensitive information and its domain classification for further analysis. To the best of our knowledge, there is no work attempted for Amharic texts. Models developed for another language cannot be used for Amharic texts language because of morphology, grammar and semantics differences. To address these gaps, we have proposed a model for detecting and classifying personal sensitive information for Amharic texts. We have experimented with three deep learning algorithms: Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BI-LSTM) and Convolutional Neural Network (CNN) using 7.31K and 6.697K Amharic sentences for sensitivity detection and domain classification respectively. The accuracy of LSTM, BI-LSTM and CNN was 82%, 90% and 87% respectively for sensitivity classification and 88, 93, 89 respectively for domain classification.","PeriodicalId":371663,"journal":{"name":"2021 International Conference on Information and Communication Technology for Development for Africa (ICT4DA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126587802","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 : 2021-11-22DOI: 10.1109/ict4da53266.2021.9672235
Esmael Mohammed, E. Alemneh
Software requirements which are significant for designing Software architecture are called architecturally significant requirements (ASR). If ASR is not correctly identified, the resulting architecture will not be good. Wrongly designed software can't achieve the desired goal and quality, and this eventually lead to the complete failure of the software. Due to the complex behaviors behind architectural requirements, identifying the correct requirement is complex even for experienced architects. Identification and classification of ASR using machine learning algorithms have been reported in the past. However, their work didn't include Non-functional requirements (NFR) which have more impact than the ordinary NFR that have little effect on the architecture. The significancy of NFR vary from system to system. In this study, we have built a machine learning model for the identification of architecturally significant non-functional requirements (ASNFR) for a real-time system from the SRS document. The proposed model used three machine learning techniques: support vectored machine (SVM), Naive Bayes (NB), and K-Nearest Neighbor (KNN) using feature extraction techniques TF-IDF and software engineering pre-trained word2vec model. Grid search cross-validation techniques are used to tune the optimal value of hyperparameters of algorithms. We have prepared our own dataset and used 10 fold stratified cross-validation for evaluating and comparing the model. ASNFR identification model predicts 88% accuracy using SVM with TF-IDF and 87% in NB and KNN using TF-IDF and it predicts 73%, 70%, and 75% using SVM, NB, and KNN with pre-trained word2vec respectively. SVM with TF-IDF outperforms the others for the identification of ASNFR.
{"title":"Identification of Architecturally Significant Non-Functional Requirement","authors":"Esmael Mohammed, E. Alemneh","doi":"10.1109/ict4da53266.2021.9672235","DOIUrl":"https://doi.org/10.1109/ict4da53266.2021.9672235","url":null,"abstract":"Software requirements which are significant for designing Software architecture are called architecturally significant requirements (ASR). If ASR is not correctly identified, the resulting architecture will not be good. Wrongly designed software can't achieve the desired goal and quality, and this eventually lead to the complete failure of the software. Due to the complex behaviors behind architectural requirements, identifying the correct requirement is complex even for experienced architects. Identification and classification of ASR using machine learning algorithms have been reported in the past. However, their work didn't include Non-functional requirements (NFR) which have more impact than the ordinary NFR that have little effect on the architecture. The significancy of NFR vary from system to system. In this study, we have built a machine learning model for the identification of architecturally significant non-functional requirements (ASNFR) for a real-time system from the SRS document. The proposed model used three machine learning techniques: support vectored machine (SVM), Naive Bayes (NB), and K-Nearest Neighbor (KNN) using feature extraction techniques TF-IDF and software engineering pre-trained word2vec model. Grid search cross-validation techniques are used to tune the optimal value of hyperparameters of algorithms. We have prepared our own dataset and used 10 fold stratified cross-validation for evaluating and comparing the model. ASNFR identification model predicts 88% accuracy using SVM with TF-IDF and 87% in NB and KNN using TF-IDF and it predicts 73%, 70%, and 75% using SVM, NB, and KNN with pre-trained word2vec respectively. SVM with TF-IDF outperforms the others for the identification of ASNFR.","PeriodicalId":371663,"journal":{"name":"2021 International Conference on Information and Communication Technology for Development for Africa (ICT4DA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121621416","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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