Pub Date : 2020-11-28DOI: 10.1109/ACIT50332.2020.9300104
R. Hodrob, Mahmoud Obaid, Abdulsalam Mansour Abdulsalam Mansour, Ali Sawahreh, Mokhles Naghnagheah, Shatha AbuShanab
the reliance of an electrocardiogram system in heart patients is paramount. It is essential to note that electrocardiography is a medical tactic used by doctors to assess the blood circulatory systems of their patients. In this paper we proposed a novel health care based Internet of Things (IoT) system that measures the ECG signal using Healthy-pi HAT, the system measures vital signs and ECG using Raspberry-pi, after that, the data will be sent to a cloud system to analyze it and the data will be saved in the database system. The proposed system has an android app used by the customer, and a web application used by the doctor. Likewise, the framework will advise the nearest clinical center if there should be an occurrence of the patient's unexpected wellbeing disintegration, an SMS for the patient location using GPS will be sent. The way toward investigating the ECG signal depends on extricating its highlights, this will be performed by perusing the P-wave, QRS-complex, and T-wave, these are the three principle waves that ECGs comprise, and a large number of arrhythmia diagnosing depends on these qualities.
{"title":"An IoT Based Healthcare using ECG","authors":"R. Hodrob, Mahmoud Obaid, Abdulsalam Mansour Abdulsalam Mansour, Ali Sawahreh, Mokhles Naghnagheah, Shatha AbuShanab","doi":"10.1109/ACIT50332.2020.9300104","DOIUrl":"https://doi.org/10.1109/ACIT50332.2020.9300104","url":null,"abstract":"the reliance of an electrocardiogram system in heart patients is paramount. It is essential to note that electrocardiography is a medical tactic used by doctors to assess the blood circulatory systems of their patients. In this paper we proposed a novel health care based Internet of Things (IoT) system that measures the ECG signal using Healthy-pi HAT, the system measures vital signs and ECG using Raspberry-pi, after that, the data will be sent to a cloud system to analyze it and the data will be saved in the database system. The proposed system has an android app used by the customer, and a web application used by the doctor. Likewise, the framework will advise the nearest clinical center if there should be an occurrence of the patient's unexpected wellbeing disintegration, an SMS for the patient location using GPS will be sent. The way toward investigating the ECG signal depends on extricating its highlights, this will be performed by perusing the P-wave, QRS-complex, and T-wave, these are the three principle waves that ECGs comprise, and a large number of arrhythmia diagnosing depends on these qualities.","PeriodicalId":193891,"journal":{"name":"2020 21st International Arab Conference on Information Technology (ACIT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123865476","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 : 2020-11-28DOI: 10.1109/ACIT50332.2020.9300080
Samson Otieno Ooko, Jamila Kadam'manja, Marie Grace Uwizeye, Dagmawi Lemma
The use of Internet of Things (IoT) is fast growing and more objects need a connection to the internet to extend their capabilities. 6LoWPAN introduces the IPv6 usage to connect IEEE 802.15.4 networks providing a large address space to enable more devices to connect to the internet. Despite the advantages, there are also privacy and security issues that need to be mitigated. This review outlines the 6LoWPAN network architecture, the protocol stack, and its advantages and applications. The security and privacy issues have also been analyzed with solutions and recommendations given.
{"title":"Security Issues in IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN): A Review","authors":"Samson Otieno Ooko, Jamila Kadam'manja, Marie Grace Uwizeye, Dagmawi Lemma","doi":"10.1109/ACIT50332.2020.9300080","DOIUrl":"https://doi.org/10.1109/ACIT50332.2020.9300080","url":null,"abstract":"The use of Internet of Things (IoT) is fast growing and more objects need a connection to the internet to extend their capabilities. 6LoWPAN introduces the IPv6 usage to connect IEEE 802.15.4 networks providing a large address space to enable more devices to connect to the internet. Despite the advantages, there are also privacy and security issues that need to be mitigated. This review outlines the 6LoWPAN network architecture, the protocol stack, and its advantages and applications. The security and privacy issues have also been analyzed with solutions and recommendations given.","PeriodicalId":193891,"journal":{"name":"2020 21st International Arab Conference on Information Technology (ACIT)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124286286","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}
Information and Communication Technologies (ICT) make the educational process more efficient by facilitating the imparting of information. Recently, many research and studies asserted that the use of ICT will significantly improve the outcomes of the educational process. Therefore, Saudi Arabia's government invested massively to integrate ICT in the education sector, but the implementation has been disappointing and not achieving the desired goals. Therefore, this study aims to explore the main factors behind the lack of ICT utilization in Saudi schools. Furthermore, the results of this study and the recommendations set forth in this paper will contribute towards the successful utilization of ICT in the education sector of Saudi Arabia and other Arab countries. Data have been collected by conducting semistructured questionnaires and interviews with teachers working at secondary schools in Madinah city. Then, these data have been analyzed by using qualitative and quantitative data analysis methods. The study reveals that there are four main factors behind the lack of ICT utilization, which are: lack of infrastructure and technical support, lack of ICT management and strategies, lack of ICT training courses, and negative teachers' attitudes and beliefs towards utilization of ICT. In conclusion, we provide some recommendations that contribute to overcoming these factors and promoting the full usage of ICT in the educational process.
{"title":"Investigating the Lack of Utilization of Information and Communication Technologies in Saudi Schools","authors":"Nouf Aljuhani, Elaaf Aljohani, Raghad Alharbi, Rasha Almutairi, Maram Meccawy","doi":"10.1109/ACIT50332.2020.9300058","DOIUrl":"https://doi.org/10.1109/ACIT50332.2020.9300058","url":null,"abstract":"Information and Communication Technologies (ICT) make the educational process more efficient by facilitating the imparting of information. Recently, many research and studies asserted that the use of ICT will significantly improve the outcomes of the educational process. Therefore, Saudi Arabia's government invested massively to integrate ICT in the education sector, but the implementation has been disappointing and not achieving the desired goals. Therefore, this study aims to explore the main factors behind the lack of ICT utilization in Saudi schools. Furthermore, the results of this study and the recommendations set forth in this paper will contribute towards the successful utilization of ICT in the education sector of Saudi Arabia and other Arab countries. Data have been collected by conducting semistructured questionnaires and interviews with teachers working at secondary schools in Madinah city. Then, these data have been analyzed by using qualitative and quantitative data analysis methods. The study reveals that there are four main factors behind the lack of ICT utilization, which are: lack of infrastructure and technical support, lack of ICT management and strategies, lack of ICT training courses, and negative teachers' attitudes and beliefs towards utilization of ICT. In conclusion, we provide some recommendations that contribute to overcoming these factors and promoting the full usage of ICT in the educational process.","PeriodicalId":193891,"journal":{"name":"2020 21st International Arab Conference on Information Technology (ACIT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124292402","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 : 2020-11-28DOI: 10.1109/ACIT50332.2020.9300113
S. Eletter, Tahira Yasmin, G. Elrefae, H. Aliter, Abdullah Elrefae
Heart failure (HF) is a significant and chronic health disease. Nevertheless, despite the high mortality rate and associated costs, it can be managed. Emerging technologies such as artificial intelligence, big data, and internet of things offer advantages for the management of HF. Using the medical records of HF patients, five machine learning algorithms - deep learning (DL), generalized linear models (GLM), naïve base (NB), random forest (RF), and support vector machines(SVM) were used to build classifiers to predict HF. The results indicate that machine learning algorithms are effective tools for classifying the medical records of HF patients. GLM and SVM can potentially be utilized together to predict HF with high classification accuracy.
心力衰竭(HF)是一种重要的慢性疾病。然而，尽管死亡率和相关费用很高，但它是可以控制的。人工智能、大数据、物联网等新兴技术为高频管理提供了优势。采用深度学习(DL)、广义线性模型(GLM)、naïve base (NB)、随机森林(RF)、支持向量机(SVM)等5种机器学习算法，构建HF分类器进行预测。结果表明，机器学习算法是对心衰患者病历进行分类的有效工具。GLM和SVM可以共同用于高频预测，分类精度较高。
{"title":"Building an Intelligent Telemonitoring System for Heart Failure: The Use of the Internet of Things, Big Data, and Machine Learning","authors":"S. Eletter, Tahira Yasmin, G. Elrefae, H. Aliter, Abdullah Elrefae","doi":"10.1109/ACIT50332.2020.9300113","DOIUrl":"https://doi.org/10.1109/ACIT50332.2020.9300113","url":null,"abstract":"Heart failure (HF) is a significant and chronic health disease. Nevertheless, despite the high mortality rate and associated costs, it can be managed. Emerging technologies such as artificial intelligence, big data, and internet of things offer advantages for the management of HF. Using the medical records of HF patients, five machine learning algorithms - deep learning (DL), generalized linear models (GLM), naïve base (NB), random forest (RF), and support vector machines(SVM) were used to build classifiers to predict HF. The results indicate that machine learning algorithms are effective tools for classifying the medical records of HF patients. GLM and SVM can potentially be utilized together to predict HF with high classification accuracy.","PeriodicalId":193891,"journal":{"name":"2020 21st International Arab Conference on Information Technology (ACIT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125010950","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 : 2020-11-28DOI: 10.1109/ACIT50332.2020.9300069
S. Hamandi, A. M. Rahma, R. Hassan
Presently, the extraction of robust facial features is becoming very effective for accurate face recognition especially for smart security surveillance systems. This paper investigates three different moment invariants techniques for robust facial features extraction and then determine how each one of these moments is affected by whether the face image was thermal or on a greyscale with the proposal of a hybrid technique that deals with the robust descriptors of each method. This hybrid technique has improved the results and gave robust facial features for face identification. A feed-forward neural network is trained with these moments' features where the recognized faces are classified to one of the basic faces of IRIS and CARL face datasets which achieved high accuracy reaching 98.1% for thermal images and 81.2% for grey images.
{"title":"A New Hybrid Shape Moment Invariant Techniques for Face Identification in Thermal and Visible Visions","authors":"S. Hamandi, A. M. Rahma, R. Hassan","doi":"10.1109/ACIT50332.2020.9300069","DOIUrl":"https://doi.org/10.1109/ACIT50332.2020.9300069","url":null,"abstract":"Presently, the extraction of robust facial features is becoming very effective for accurate face recognition especially for smart security surveillance systems. This paper investigates three different moment invariants techniques for robust facial features extraction and then determine how each one of these moments is affected by whether the face image was thermal or on a greyscale with the proposal of a hybrid technique that deals with the robust descriptors of each method. This hybrid technique has improved the results and gave robust facial features for face identification. A feed-forward neural network is trained with these moments' features where the recognized faces are classified to one of the basic faces of IRIS and CARL face datasets which achieved high accuracy reaching 98.1% for thermal images and 81.2% for grey images.","PeriodicalId":193891,"journal":{"name":"2020 21st International Arab Conference on Information Technology (ACIT)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123402912","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 : 2020-11-28DOI: 10.1109/ACIT50332.2020.9300106
Sayed Ahmed, A. Kotb, Ehab Samir, M. Moustafa, E. Farg, Ahmed Abd Elhay, S. Arafat
Recently, the Egyptian government benefits from the digital transformation by developing different platforms to help manage and monitor the routine process. Rural land management is one of the critical issues that could significantly improve the economy. As far as we know, rural land management affects farmers and may cause agricultural land loss and waste of irrigation resources. Many attempts have been investigated to address this issue in Egypt. Therefore, we design and implement a WebGIS decision support system for rural land management in Wadi El-Natrun valley (WNDSS), El-Beheria governorate. The proposed WNDSS was developed using the client/server model and contains several functions, including data extraction, statistical analysis, and visualization via an interactive map. RESTful Web Service Application Programming Interfaces (APIs) were utilized as the communication interface between client and server. Finally, we use the browser to get the data by predefined API and to present the rural farms with Google Map API and jQuery JavaScript library. The online system provides several tools for governorate managers to (a) precise survey data, (b) powerful change detection suite, (c) Normalized Difference Vegetation Index (NDVI) multi-temporal,(d) multi-date land use land cover information, (e) statistics reporting tools, (f) tabular or spatial query for associated data.
{"title":"A WebGIS Decision Support System for Wadi El Natrun Rural Land Management","authors":"Sayed Ahmed, A. Kotb, Ehab Samir, M. Moustafa, E. Farg, Ahmed Abd Elhay, S. Arafat","doi":"10.1109/ACIT50332.2020.9300106","DOIUrl":"https://doi.org/10.1109/ACIT50332.2020.9300106","url":null,"abstract":"Recently, the Egyptian government benefits from the digital transformation by developing different platforms to help manage and monitor the routine process. Rural land management is one of the critical issues that could significantly improve the economy. As far as we know, rural land management affects farmers and may cause agricultural land loss and waste of irrigation resources. Many attempts have been investigated to address this issue in Egypt. Therefore, we design and implement a WebGIS decision support system for rural land management in Wadi El-Natrun valley (WNDSS), El-Beheria governorate. The proposed WNDSS was developed using the client/server model and contains several functions, including data extraction, statistical analysis, and visualization via an interactive map. RESTful Web Service Application Programming Interfaces (APIs) were utilized as the communication interface between client and server. Finally, we use the browser to get the data by predefined API and to present the rural farms with Google Map API and jQuery JavaScript library. The online system provides several tools for governorate managers to (a) precise survey data, (b) powerful change detection suite, (c) Normalized Difference Vegetation Index (NDVI) multi-temporal,(d) multi-date land use land cover information, (e) statistics reporting tools, (f) tabular or spatial query for associated data.","PeriodicalId":193891,"journal":{"name":"2020 21st International Arab Conference on Information Technology (ACIT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115065480","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 : 2020-11-28DOI: 10.1109/ACIT50332.2020.9300084
Bilal I. Sowan, N. Matar, Firas Omar, Mohammad Alauthman, Mohammed Eshtay
A class decomposition is one of the possible solutions and the most important factors of success for the improvement of classification performance. The idea is to transform a dataset by categorizing each class label into groups or clusters. Thus, the transformation is done concerning data characteristics and similarities. This paper proposed a hybrid model for a class decomposition by the integration of gap statistic, k-means clustering algorithm, and Naive Bayes classifier. The model is based on clustering validity using gap statistic for enhancing the classifier performance. The model works by dividing each dataset into several subsets regarding its class labels. After that, the clustering validity using gap statistic is employed for estimating the optimal number of clusters for each subset that belong to a particular class label. The estimated number of clusters is used then as an input parameter for the k-means clustering algorithm for relabeling the data objects with a new class label in each subset. Every data object is allocated to each of the clusters generated by the k-means clustering algorithm, which consider it as the new class label. The proposed model integrates the class decomposition approach with Naive Bayes classifier to compare the performance of the proposed model under several classification measures. The model is validated and evaluated by employing different real-world datasets collected from the UCI machine learning repository. The experimental results show that a significant improvement in classification accuracy and F-measure when the class decomposition is applied. Also, the experiments indicate that using a class decomposition is not appropriate for all datasets.
{"title":"Evaluation of Class Decomposition based on Clustering Validity and K-means Algorithm","authors":"Bilal I. Sowan, N. Matar, Firas Omar, Mohammad Alauthman, Mohammed Eshtay","doi":"10.1109/ACIT50332.2020.9300084","DOIUrl":"https://doi.org/10.1109/ACIT50332.2020.9300084","url":null,"abstract":"A class decomposition is one of the possible solutions and the most important factors of success for the improvement of classification performance. The idea is to transform a dataset by categorizing each class label into groups or clusters. Thus, the transformation is done concerning data characteristics and similarities. This paper proposed a hybrid model for a class decomposition by the integration of gap statistic, k-means clustering algorithm, and Naive Bayes classifier. The model is based on clustering validity using gap statistic for enhancing the classifier performance. The model works by dividing each dataset into several subsets regarding its class labels. After that, the clustering validity using gap statistic is employed for estimating the optimal number of clusters for each subset that belong to a particular class label. The estimated number of clusters is used then as an input parameter for the k-means clustering algorithm for relabeling the data objects with a new class label in each subset. Every data object is allocated to each of the clusters generated by the k-means clustering algorithm, which consider it as the new class label. The proposed model integrates the class decomposition approach with Naive Bayes classifier to compare the performance of the proposed model under several classification measures. The model is validated and evaluated by employing different real-world datasets collected from the UCI machine learning repository. The experimental results show that a significant improvement in classification accuracy and F-measure when the class decomposition is applied. Also, the experiments indicate that using a class decomposition is not appropriate for all datasets.","PeriodicalId":193891,"journal":{"name":"2020 21st International Arab Conference on Information Technology (ACIT)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124602870","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 : 2020-11-28DOI: 10.1109/ACIT50332.2020.9300092
Marwa Meissa, Saber Benharzallah, L. Kahloul, O. Kazar
The core idea of IoT is the connectivity of real-world devices to the Internet, which allows them to expose their functionalities in APIs ways, communicate to other entities, and flow their data over internet. With the massive growth of connected IoT devices, the number of APIs have also increased. Thus, led up to overload information problem, which is making APIs selection more and more difficult for devices owners and users. Therefore, this paper propose web APIs recommendation framework in IoT environment based on social relationships. The main purpose is providing a novel Recommendation method, which enable to discover APIs and provide relevant suggestion for users. The proposed hybrid algorithm is combined content-based filtering and collaborative filtering techniques to improve the accuracy of rating prediction. Finally, experiments are conducted to evaluate the performance of recommendation.
{"title":"Social-aware Web API Recommendation in IoT","authors":"Marwa Meissa, Saber Benharzallah, L. Kahloul, O. Kazar","doi":"10.1109/ACIT50332.2020.9300092","DOIUrl":"https://doi.org/10.1109/ACIT50332.2020.9300092","url":null,"abstract":"The core idea of IoT is the connectivity of real-world devices to the Internet, which allows them to expose their functionalities in APIs ways, communicate to other entities, and flow their data over internet. With the massive growth of connected IoT devices, the number of APIs have also increased. Thus, led up to overload information problem, which is making APIs selection more and more difficult for devices owners and users. Therefore, this paper propose web APIs recommendation framework in IoT environment based on social relationships. The main purpose is providing a novel Recommendation method, which enable to discover APIs and provide relevant suggestion for users. The proposed hybrid algorithm is combined content-based filtering and collaborative filtering techniques to improve the accuracy of rating prediction. Finally, experiments are conducted to evaluate the performance of recommendation.","PeriodicalId":193891,"journal":{"name":"2020 21st International Arab Conference on Information Technology (ACIT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133662889","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 : 2020-11-28DOI: 10.1109/ACIT50332.2020.9300081
Maryam Al-Janabi, A. Altamimi
Malicious software, commonly known as malware, is one of the most harmful threats developed by cyber attackers to intentionally cause damage or gaining access to computer systems. Malware has evolved over the years and comes in all shapes with different types and functions depending on the goals of the developer. Virus, Spyware, Bots, and Ransomware are just some examples of malware. While those described above found themselves causing issues by accident, however, they all share one thing in common, harming the system. As a response, many infection treatments and detecting methods have been proposed. The signature-based methods are currently utilized to delete malware; however, these methods cannot provide accurate detection of zero-day attacks and polymorphic viruses. Contrarily, the use of machine learning-based detection has been recognized as one of the most modern and notable methods. Specifically, these methods can be categorized based on their analysis technique into static, dynamic, or hybrid. The purpose of this work was to provide a survey that determines the best features extraction and classification methods that result in the best accuracy in detecting malware. Moreover, a review of representable research papers in this topic is represented with a detailed tabular comparison between them based on their accuracy in detecting malware. Among these methods, the J48 algorithm and Hybrid analysis outperformed the others with the accuracy of 100% in detecting malware in the Windows system. On the other hand, the same accuracy has been achieved in the Android system when employing the Decision Tree algorithm through Dynamic analysis. We believe that this study performs a base for further research in the field of malware analysis with machine learning methods.
{"title":"A Comparative Analysis of Machine Learning Techniques for Classification and Detection of Malware","authors":"Maryam Al-Janabi, A. Altamimi","doi":"10.1109/ACIT50332.2020.9300081","DOIUrl":"https://doi.org/10.1109/ACIT50332.2020.9300081","url":null,"abstract":"Malicious software, commonly known as malware, is one of the most harmful threats developed by cyber attackers to intentionally cause damage or gaining access to computer systems. Malware has evolved over the years and comes in all shapes with different types and functions depending on the goals of the developer. Virus, Spyware, Bots, and Ransomware are just some examples of malware. While those described above found themselves causing issues by accident, however, they all share one thing in common, harming the system. As a response, many infection treatments and detecting methods have been proposed. The signature-based methods are currently utilized to delete malware; however, these methods cannot provide accurate detection of zero-day attacks and polymorphic viruses. Contrarily, the use of machine learning-based detection has been recognized as one of the most modern and notable methods. Specifically, these methods can be categorized based on their analysis technique into static, dynamic, or hybrid. The purpose of this work was to provide a survey that determines the best features extraction and classification methods that result in the best accuracy in detecting malware. Moreover, a review of representable research papers in this topic is represented with a detailed tabular comparison between them based on their accuracy in detecting malware. Among these methods, the J48 algorithm and Hybrid analysis outperformed the others with the accuracy of 100% in detecting malware in the Windows system. On the other hand, the same accuracy has been achieved in the Android system when employing the Decision Tree algorithm through Dynamic analysis. We believe that this study performs a base for further research in the field of malware analysis with machine learning methods.","PeriodicalId":193891,"journal":{"name":"2020 21st International Arab Conference on Information Technology (ACIT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133782891","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 : 2020-11-28DOI: 10.1109/ACIT50332.2020.9300094
Hassanin M. Al-Barhamtoshy, R. Younis
The study aims to use artificial intelligent model to accelerate multi-disciplinary sciences such as biology and physics in scientific discoveries to predict protein structure based on its genetic sequences. This paper presents an intelligent model to correct error sequences of the DNA. Therefore, dataset in genome structure will be used to predict error corrections in DNA sequences of proteins. Accordingly, Nucleus library and TensorFlow are integrated and used for these corrections. To correct sequence errors of DNA, three types of errors: insert spurious base, delete of base, and substitute one base by another. The paper will implement a computational deep neural network based on CNN with TensorFlow to correct such DNA sequence errors.
{"title":"DNA Sequence Error Corrections based on TensorFlow","authors":"Hassanin M. Al-Barhamtoshy, R. Younis","doi":"10.1109/ACIT50332.2020.9300094","DOIUrl":"https://doi.org/10.1109/ACIT50332.2020.9300094","url":null,"abstract":"The study aims to use artificial intelligent model to accelerate multi-disciplinary sciences such as biology and physics in scientific discoveries to predict protein structure based on its genetic sequences. This paper presents an intelligent model to correct error sequences of the DNA. Therefore, dataset in genome structure will be used to predict error corrections in DNA sequences of proteins. Accordingly, Nucleus library and TensorFlow are integrated and used for these corrections. To correct sequence errors of DNA, three types of errors: insert spurious base, delete of base, and substitute one base by another. The paper will implement a computational deep neural network based on CNN with TensorFlow to correct such DNA sequence errors.","PeriodicalId":193891,"journal":{"name":"2020 21st International Arab Conference on Information Technology (ACIT)","volume":"339 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134296096","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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