IJCI. International Journal of Computers and Information最新文献

{"title":"Analysis of Coronavirus Patients Flow in Hospitals: An Application of Queuing Theory","authors":"Walaa Abdellatief, Amira Abdelatey","doi":"10.21608/ijci.2021.90451.1057","DOIUrl":"https://doi.org/10.21608/ijci.2021.90451.1057","url":null,"abstract":"","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121876855","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}

{"title":"Detecting the Behaviour of COVID-19 Based On Parallel Approach of Sequential Rule Mining Algorithm","authors":"Nesma Youssef, Hatem Abdulkader, A. Abdelwahab","doi":"10.21608/ijci.2021.79097.1051","DOIUrl":"https://doi.org/10.21608/ijci.2021.79097.1051","url":null,"abstract":"The COVID-19 (Coronavirus) is a catastrophic disease, as it causes a global health crisis. Due to the nature of COVID-19, it spreads quickly among humans and infects millions of people within a few periods in the world. It is critical to detect the behaviour of COVID-19 and the speed of its mutating rapidly for better improvement of medications and assists patients in preventing the progression of the disease. This paper examines the discovery of additional information and interest patterns in COVID-19 genome sequences. An enhanced non-redundant sequential rule algorithm is mined from frequent closed dynamic bit vector and sequential generator patterns simultaneously. It speedily discovers nucleotide rules and predicts the next one after eliminating un-candidates' sequential patterns early. Almost all genotyping tests are partial, time-consuming, and involve multi-step processes. So, an efficient parallel approach is implemented by utilizing multicore processor architecture to produce the sequential rules in less time required. The experimental results show that; the proposed Parallel Non-Redundant Dynamic closed generator (PNRD-CloGen) algorithm performs well in terms of execution time, computational cost, and scalability. It has better performance, especially for large datasets and low minimum support values, as it takes around half the time as the competing algorithm. So, it helps to monitor the strain progression of COVID-19 sequentially and enhance clinical management.","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"474 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133018950","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}

{"title":"A Comparative Study for Outlier Detection Strategies Based On Traditional Machine Learning For IoT Data Analysis.","authors":"Amina elmahalawy, Hayam Mousa, Khalid Amin","doi":"10.21608/ijci.2021.91957.1059","DOIUrl":"https://doi.org/10.21608/ijci.2021.91957.1059","url":null,"abstract":"","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133342355","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}

{"title":"Performance Investigation of Features Extraction and Classification Approaches for Sentiment Analysis Systems","authors":"Raghdah Elnadree, A. El-Sisi, Walid Atwa","doi":"10.21608/ijci.2021.65578.1044","DOIUrl":"https://doi.org/10.21608/ijci.2021.65578.1044","url":null,"abstract":"Data pre-processing and feature extraction of micro-blogging data in sentiment analysis systems becomes an effective field of analysis. Object identification, negation expressions, sarcasm, outlines, misspellings are the major issues faced during sentiment analysis. So, data pre-processing in a sentiment analysis system is a conclusive step to improve data quality, raise the extraction, and classification of meaningful data. This paper presents a sentiment analysis system for performance investigation. Several pre-processing and feature extraction techniques are applied to optimize the sentiment analysis. Our system comprises three different components: data pre-processing, feature extraction, and sentiment analysis. The pre-processing and feature extraction approaches enhance the sentiment analysis system performance. We compare between different sentiment analysis approaches using a dataset of US Airlines from Twitter. Results show achieving high performance when using the Word2Vec approach with XGBoost and random forest classification algorithms. Also, the results show the classification technique, Naive Bayes is the lowest performance.","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128518048","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}

{"title":"Classification and Prediction of Opinion Mining in Social Networks Data","authors":"Shaimaa M. Mohamed, Mahmoud Hussien, A. Keshk","doi":"10.21608/ijci.2020.26841.1015","DOIUrl":"https://doi.org/10.21608/ijci.2020.26841.1015","url":null,"abstract":"opinion mining in social networks data considers one of the most significant and challenging tasks in our days due to the huge number of information that distributed each day. We can profit from these opinions by utilizing two significant procedures (classification and prediction). Although there is many researchers’ work at this point, it still needs improvement. Therefore, in this paper, we present a method to improve the accuracy of both processes. The improvement is done through cleaning the data set by converting all words to lower case, removing usernames, mentions, links, repeated characters, numbers, delete more than two spaces between words, empty tweets, punctuations and stop words, and converting all words like “isn't” to “is not”. we using both unigrams and bigrams as features. Our data set contains the user's feelings about distributed products, tweets labeled positive or negative, and each product rate from one to five. We implemented this work using different supervised machine learning algorithms like Naive Bayes, Support Vector Machine and MaxEntropy for the classification process, and Random Forest Regression, Logistic Regression, and Support Vector Regression for the prediction process. At last, we have accuracy in both processes better than existing works. In classification, we achieved an accuracy of 90% and in the prediction process, Support Vector Regression model is able to predict future product rate with a Mean Squared Error (MSE) of 0.4122, Logistic Regression model is able to predict with MSE of 0.4986 and Random Forest Regression model able to predict with MSE of 0.4770.","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"33 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141205046","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}

{"title":"A Novel Scalable and Effective Partitioning Approach for Big Data Reduction","authors":"M. Malhat, M. Elmenshawy, Hamdy M. Mousa, A. El-Sisi","doi":"10.21608/IJCI.2019.35122","DOIUrl":"https://doi.org/10.21608/IJCI.2019.35122","url":null,"abstract":"The continuous increment of data size makes the traditional instance selection methods ineffective to reduce big training datasets in a single machine. Recent approaches to solving this technical problem partition the training dataset into subsets prior to apply the instance selection methods into each subset separately. However, the performance of the applied instance selection methods to subsets is negatively affected, especially when the number of partitioned subsets is increased. In this work, we propose a novel scalable and effective automated partitioning approach, called overlapped distance-based class-balance partitioning. This approach distributes the training dataset instances to the partitioned subsets based on a given distance metric and ensures the equal representation of data classes into partitioned subsets. Moreover, the instances might be assigned to two subsets once they satisfy the dynamic threshold. We implement and test empirically the scalability and effectiveness of the proposed approach using condensed nearest neighbor method over eight standard datasets. The proposed approach is compared empirically and analytically with stratification partitioning approach and a non-overlapped version from our approach with respect to 1) the reduction rate, classification accuracy, and effectiveness metrics, and 2) the scalability aspect, where the number of subsets is increased. The comparison results demonstrate that our approach is more scalable and effective than other partitioning approaches with respect to these standard datasets.","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127329899","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}

{"title":"A Review of Open Information Extraction Techniques","authors":"Sakhawat Ali, Hamdy M. Mousa, M. Hussien","doi":"10.21608/IJCI.2019.35099","DOIUrl":"https://doi.org/10.21608/IJCI.2019.35099","url":null,"abstract":"Nowadays, massive amount of data flows all the time. Approximately between 20 or 30 percent of these data is text. This data is always organized in semi-structured text, which cannot be used directly. To make use of such huge amounts of textual data, there is a need to detect, extract, and structure the information conveyed through this data in a fast and scalable manner. This can be performed using Information Extraction Techniques. However, the task of information extraction is one of the main challenges in Natural Language Processing and there are limitations for its implementation on a large scale of data. Open Information Extraction (OIE) is an open-domain and relation-independent paradigm to perform information extraction in an unsupervised manner. This technique can lead to high-speed and scalable performance. The review of previous research proposals reveals that there are OIE experiments among different languages, such as English, Portuguese, Spanish, Vietnamese, Chinese, and Germany. This paper reviews the OIE techniques, compare their performance in some languages, and then integrates these results with the languages complexity levels to reveal the relationship between the suitable model and the language complexity level. \u0000Nowadays, massive amount of data flows all the time. Approximately between 20 or 30 percent of these data is text. This data is always organized in semi-structured text, which cannot be used directly. To make use of such huge amounts of textual data, there is a need to detect, extract, and structure the information conveyed through this data in a fast and scalable manner. This can be performed using Information Extraction Techniques. However, the task of information extraction is one of the main challenges in Natural Language Processing and there are limitations for its implementation on a large scale of data. Open Information Extraction (OIE) is an open-domain and relation-independent paradigm to perform information extraction in an unsupervised manner. This technique can lead to high-speed and scalable performance. The review of previous research proposals reveals that there are OIE experiments among different languages, such as English, Portuguese, Spanish, Vietnamese, Chinese, and Germany. This paper reviews the OIE techniques, compare their performance in some languages, and then integrates these results with the languages complexity levels to reveal the relationship between the suitable model and the language complexity level.Keywords—Open Information Extraction; Natural Language Processing","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133244435","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}

{"title":"Service Flow Management with deadline and budget Constraints using Genetic Algorithm in Heterogeneous Computing","authors":"A. Abdelhamed, Medhat A. Tawfik, A. Keshk","doi":"10.21608/IJCI.2019.35121","DOIUrl":"https://doi.org/10.21608/IJCI.2019.35121","url":null,"abstract":"The service flow management is one from the most challenges especially in heterogeneous environments which has several and various processors for computing. Service flow is used to explain services configuration process when service’s formation according to the precedence relations of configuration should be considered. Its management should take into account multi-objective constraints. The total execution time should not be completed after the specified time that leading to consider the deadline constraint into account. Also the cost minimization that is a critical issue shouldn’t be ignored. Obtaining the optimal management in a sensible time is so hard because there are many candidate with different processing power and price, constraints from the user and the precedence of heterogeneous services. In this paper, the service flow management problem is solved by a genetic algorithm that considers deadline and cost constraints. It focuses on the improvement of execution time to meet the deadline constraint and minimizes the execution cost according to the budget in heterogeneous computing. The results from the applied experiments proves that the proposed algorithm can be able to minimize total cost, and consolidate the execution time with the deadline constraint. It reach to a near-optimal solution in reasonable time. It outperforms the compared algorithms in the metric of Schedule Length Ratio (SLR), cost, risk ratio, speed up and completion time measurements.","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114397516","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}