Minufiya University; Faculty of Computers and InformationIJCI. International Journal of Computers and Information1687-78537120201001E-PROBCONS: Enhanced PROBCONS for Multiple Sequence Alignment1137826610.21608/ijci.2020.15523.1002ENEman MMohamedComputer Science Dept, Faculty of Computers and Information, Menoufia University, Egypt.HamdyMousaFaculty of Computer and Information Menoufia University0000-0001-9503-9124ArabiKeshkFaculty of Computers and Information, Menoufia University, EgyptJournal Article20190731Abstract— the perfect alignment between three or more sequences of protein, RNA or DNA is a very difficult task in Bioinformatics. There are many techniques for alignment of multiple sequences. Many techniques enlarge speed and do not have a concern with the accuracy of the resulting alignment. However, other techniques heighten accuracy and do not have a concern with the speed. The vital goals of any technique are (a) reducing memory and execution time requirements, and (b) increasing the accuracy of multiple sequence alignment on large-scale datasets. PROBCONS is a multiple protein sequence alignment (MPSA) tool that achieves the most expected accuracy, but it has a time-consuming problem. To solve this problem and enlarging the accuracy of the MPSA, E-PROBCONS is proposed to enhance PROBCONS tool. E- PROBCONS cluster the large multiple protein sequences into structurally similar protein sequences. Then PROBCONS MPSA tool will be performed in parallel on the Amazon Elastic Cloud (EC2). The proposed approaches are more suitable for large-scale data sets and short sequences. Comparing with algorithms (e.g., PROBCONS, KALIGN, and HALIGN I), provided more than 50% improvement in terms of average sum of pairs alignment scores (SPscores) and reduce the execution time for producing the alignment result. The proposed approaches are implemented on big data framework Hadoop Map-Reduce platform in order to improve the scalability with different protein datasets.https://ijci.journals.ekb.eg/article_78266_03b536d90d8495d3b8d381f971cbfbb4.pdfMinufiya University; Faculty of Computers and InformationIJCI. International Journal of Computers and Information1687-78537120201015Sentiment Analysis for Arabic Social Media143111880310.21608/ijci.2020.16170.1004ENManalZayedthe Faculty of Computers and Information, Department of Computer Science, Menoufia UniversityHamdiMousafaculty of computers and informationMohamedElmenshawyFaculty of Computers and InformationJournal Article20190922With the spread of social media services in Arabic societies, it leads to the explosive growth of Arabic posts, or comments. These services generate a huge volume of opinionated data on different topics such as politics and businesses. Analyzing valuable subjective information from data would assist in a better understanding and making decisions. Therefore, sentiment analysis coincides with social media networks and has become the most interesting research field in the sentiment analysis process. However, there are several challenges faced the sentiment analysis process. Arabic Sentiment analysis is indeed in its infantile stage and it has not obtained thoroughly attention wherein several challenges still need to address. Some of these challenges result from the complexity of Arabic natural language and other challenges result from social media platform itself. In this manuscript, we first study the impact of social media challenges on the challenges of Arabic language. Our findings show that such challenges add more complexities to the sentiment analysis process. Based on these findings, we review the contributed proposals, which give rise on analyzing Arabic social media data. Our review methodology is based on a set of criteria, which we propose to assess the advantages and limitations of these proposals. The interesting point here is to help researchers identify the social sentiment analysis problems along with a comprehensive survey on the sentiment analysis levels and classification approaches. Finally, we compare these proposals in terms of the average accuracy and suggest a new hybrid approach based on our findings.https://ijci.journals.ekb.eg/article_118803_309a4df86113b74b158b9f7959b59def.pdfMinufiya University; Faculty of Computers and InformationIJCI. International Journal of Computers and Information1687-78537120201015Classification and Prediction of Opinion Mining in Social Networks Data32418888410.21608/ijci.2020.26841.1015ENShaimaa MahmoudMohamedComputer Science Department, Faculty of Computers and Information,Menoufia University, Shebin Elkom 32511, EgyptMahmoudHussienFaculty of Computers and Information,
Menofia University, Egypt0000-0002-3742-7548ArabiKeshkFaculty of Computer and Information Menoufia UniversityJournal Article20200329opinion 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 Naïve 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.https://ijci.journals.ekb.eg/article_88884_4f9903b276481141630fd34161cca248.pdfMinufiya University; Faculty of Computers and InformationIJCI. International Journal of Computers and Information1687-78537120201015simple missing data estimation algorithm in wsn based on spatial and temporal correlation42549928610.21608/ijci.2020.26944.1016ENWalidAtwacomputer sciences department,faculty of computers and information, Menoufia universityAshrafBahgatFaculty of Computer and Information Menoufia UniversityMariemRefaiecomputer science faculty of computers and informations menofia universityJournal Article20200330we have a common problem in wireless sensor networks which is the missing data problem due to the nature of the wireless communication and the limited resources of the sensor nodes. This problem can't be ignored because it has a negative effect on the applications that use the sensor data. Estimating these missing data is important for the applications that concern with the sensor data. However, the traditional estimation techniques failed to be applied with the sensor data and the existing techniques have high computation complexity, high computation time, or low accuracy. So we introduce the simplified Spatial and Temporal Correlation (STC) estimation algorithm which uses the most related surrounding previous data to increase the accuracy of the estimation and reduce incremental error. The proposed algorithm utilizes the time correlation by using the closet data before the time of missing and utilizes the space correlation by using the data of the nearest sensor depending on the missing pattern. The experimental results show that our algorithm can reduce the error in the estimating process compared with the other algorithms in most of the missing patterns.https://ijci.journals.ekb.eg/article_99286_afbc8c902bf749e4fb3181b5a1c50a29.pdfMinufiya University; Faculty of Computers and InformationIJCI. International Journal of Computers and Information1687-78537120201001A New Coalition Formation Approach for Power Losses Reduction in Electrical Power Micro-Grids557111763810.21608/ijci.2020.35090.1024ENE.F.KelashBasic Engineering Sciences Department, Benha Faculty of Engineering, Benha University, Benha, EgyptOsamaAbdel-RaoufOperations Research and Decision Support Department, Faculty of Computers and Information, Menoufia University, Menoufia, EgyptHassan N.A.IsmailBasic Engineering Sciences Department, Benha Faculty of Engineering, Benha University, Benha, EgyptM. A.ElsisyBasic Engineering Sciences Department, Benha Faculty of Engineering, Benha University, Benha, EgyptJournal Article20200709In recent years, the technology of micro-grids (µGs) has gained a lot of interest. µGs utilize distributed generators (DGs) to locally meet the needs of consumers. This decentralized nature increases the efficiency of the system in addition to improving the electrical service reliability. In this paper, coalitional game theory is used to study the process of local power exchange among a set of µGs. A coalition forming (CF) algorithm which depends on the topology of the network is proposed. The presented algorithm is scalable i.e. can capture a substantial number of µGs which makes it applicable in real systems. Besides the macro-station (MS), each formed coalition comprises a set of µGs with a lack of power to purchase and a set of µGs that have an excess of power to sell. Within each coalition, quadratic programming (QP) is used to organize the transition of power among µGs and MS so as to reduce the power losses. To illustrate the significant impact of the presented procedure, a full numerical example is introduced. The analysis and simulation show that the proposed algorithm results in a decrease in the average power losses per bus, reaching up to 37.6% enhancement compared with the non-cooperative situation.https://ijci.journals.ekb.eg/article_117638_249bd5c2c753c5cf4bbfa2d0aa1bd787.pdfMinufiya University; Faculty of Computers and InformationIJCI. International Journal of Computers and Information1687-78537120201015A Trust-Management-Based Intrusion Detection System for Routing Protocol Attacks in Internet of Things728911034710.21608/ijci.2020.37546.1025ENMahmoudElbaradiecomputer science and math department faculty of science ,tanta university,tanta,egypt.Ashraf YousefElsisiFaculty of Computers and Information,
Menofia University, EgyptAnas Abd El AzizYousefcomputer science,facluty of information and computer ,sheben ,monifia0000-0002-5821-9035Journal Article20200728Abstract— The Internet of things is a pool of on-demand and configurable resources and services that are delivered across the usage of the internet. Providing privacy and security to protect their resources is considered a very challenging issue since the distributed architecture of the cloud makes it vulnerable to the intruders. To mitigate this issue, intrusion detection system plays an important role in detecting the attacks in the network. Intrusion detection system is a software or hardware component that implements monitoring and analysis processes of the system events or network activities. Once detecting any intrusion, an alert is raised to the administrator in order to take appropriate actions against such these intrusive events. In this paper an intrusion detection system is proposed for routing protocol for lossy and low power network attacks. The objective of the proposed system is to detect a variety of routing attacks namely sinkhole, selective forward and blackhole attacks. The detection algorithm uses trust management strategies that are based on a set of trust properties each of which is used for the detection of a specific type of routing attacks. The proposed attack detection algorithm was simulated using the Contiki Cooja simulator with centralized intrusion detection system placement strategy. The evaluation results show that in the proposed algorithm was able to detect the simulated attacks with 100% true positive detection rate in some scenarios.https://ijci.journals.ekb.eg/article_110347_f5d118eadceb332341b827b7cf14c49e.pdf