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Enhancing Network Intrusion Detection Model Using Machine Learning Algorithms

Computers, Materials & Continua 2021年第4期67卷 979-990页

作者： na.cy awada.la. awad Department of Computer and Information Systems Sadat Academy for Management SciencesCairo11742Egypt

After the digital revolution,large quantities of data have been generated with time through various *** networks have made the process of data analysis very difficult by detecting attacks using suitable *** Intrusion Detection Systems(IDSs)secure resources against threats,they still face challenges in improving detection accuracy,reducing false alarm rates,and detecting the unknown *** paper presents a framework to integrate data mining classification algorithms and association rules to implement network intrusion *** experiments have been performed and evaluated to assess various machine learning classifiers based on the KDD99 intrusion *** study focuses on several data mining algorithms such as;naïve Bayes,decision trees,support vector machines,decision tables,k-nearest neighbor algorithms,and artificial neural ***,this paper is concerned with the association process in creating attack rules to identify those in the network audit data,by utilizing a KDD99 dataset anomaly *** focus is on false negative and false positive performance metrics to enhance the detection rate of the intrusion detection *** implemented experiments compare the results of each algorithm and demonstrate that the decision tree is the most powerful algorithm as it has the highest accuracy(0.992)and the lowest false positive rate(0.009).

关键词： Intrusion detection association rule data mining algorithms KDD99

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A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

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Computers, Materials & Continua 2021年第4期67卷 51-71页

作者： Mohamed Elhoseny Mazin Abed Mohammed Salama A.Mostafa Karrar Hameed Abdulkareem Mashael S.Maashi Begonya Garcia-Zapirain Ammar awad Mutlag Marwah Suliman Maashi Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia College of Agriculture Al-Muthanna UniversitySamawah66001Iraq Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia eVIDA lab University of DeustoBilbao48007Spain Department of Pure Science Ministry of EducationGeneral Directorate of CurriculaBaghdadIraq Department of Medical laboratory Science Faculty of Applied Medical ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia

Heart disease(HD)is a serious widespread life-threatening *** heart of patients with HD fails to pump sufcient amounts of blood to the entire *** the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective *** methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related *** an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD ***,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with *** this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)*** MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD *** CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD *** evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation *** AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on ***,the AHDD system proposed herein has the highest capability to identify patients with *** system can be used by medical

关键词： Heart disease machine learning multi-agent feature wrapper model heart disease diagnosis HD cleveland datasets convolutional neural network

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Potentiality of Secondary Aquifers in Saudi Arabia: Evaluation of Groundwater Quality in Jubaila Limestone

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International Journal of Geosciences 2012年第1期3卷 71-80页

作者： Mohammed Tahir Hussein Mazin. M. Al Yousif Hussein S. awad SGSRC Department of Geology College of Science King Saud University Riyadh Saudi Arabia

Groundwater scarcity in arid regions may hinder development plans and cause many inconveniences for the population and authorities. Saudi Arabia has limited groundwater resources stored in the sedimentary sequence of the Arabian Shelf. Some of these resources were classified as major aquifers, secondary and minor aquifers, and some were considered as aquicludes. The Jubaila Limestone is one of the secondary aquifers of Saudi Arabia. The main purpose of this paper is to evaluate the groundwater resources of the Jubaila Limestone in Riyadh area, with emphasis on groundwater quality. Groundwater was found to occur in fractures and within solution openings of the Jubaila Limestone at depths which range between 19 and 210 m. The transmissivity value was 1.7 × 10–3 to 7.2 × 10–3 m2/s;the storage coefficient was of 1.3 × 10–4. The electrical conductivity for collected water samples ranged between 831 and 7670 μS/cm. The major ionic relationships were na > Ca > Mg and SO4Cl > HCO3. The groundwater evolves from naCl dominated at the southern end of the study area, into Ca, MgSO4 water in the north. The main chemical process responsible of this variation was found to be dissolution of anhydrite and gypsum. The groundwater was not found suitable for drinking purposes but can be used by livestock and for some agricultural purposes.

关键词： Saudi Arabia Riyadh Jubaila Limestone Groundwater Quality Dissolution

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天然化合物样品的分离和制备

天然化合物样品的分离和制备

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作者：古塞（GUSAI awad ALKRIM AHMED ALI）天津大学

学位级别：硕士

黄酮类化合物已广泛应用于多个重要领域。因为其的药用价值引起了许多研究人员和药剂师的兴趣。大多数植物品种中都含有黄酮类化合物,但其比例因品种不同而有一些差异。大多含有植物成分的食品中都含有黄酮类化合物。黄酮类化合物包括... 详细信息

黄酮类化合物已广泛应用于多个重要领域。因为其的药用价值引起了许多研究人员和药剂师的兴趣。大多数植物品种中都含有黄酮类化合物,但其比例因品种不同而有一些差异。大多含有植物成分的食品中都含有黄酮类化合物。黄酮类化合物包括羟基或甲氧基的替代基团,这些基团可能以单糖或二元糖中的乙醇酸的形式存在,能作为糖尿病替代物。黄酮类化合物结构中可用的单糖是葡萄糖、半乳糖、阿拉伯糖和木糖。黄酮类低酸化合物能溶于强碱,如氢氧化钠。黄酮类化合物含有较自由的羟基或极性的糖,能溶解在甲醇、乙醇、丙酮、水等极性溶剂中。含有大量甲氧基化合物的黄酮-3-醇和黄酮类化合物被溶解在乙醚和氯仿中。在实验室中已经采用了许多方法来制备或形成类黄酮结构。将人工合成的类黄酮化合物与天然类黄酮进行比较,用以研究其的性质。提取黄酮类化合物的方法有多种,乙醇(甲醇、乙醇两者的混合物)提取是最常用的提取方法之一,然后一种可选的方法是液-液萃取,用浓缩物除去酒精。萃取所使用的溶剂中,常用的是乙酸乙酯、正丁醇。在未对黄酮类化合物进行分离之前,大多数黄酮类化合物在色谱上是不直接可见的。因此,在先用Al Cl喷雾剂对黄酮类进行处理,并将其暴露于以NH为中心的洗剂烟雾,并利用紫外线(365 nm)进行照射,最后进行色谱分析。目前色谱的类型有色谱柱、薄层色谱、纸层析,高效液相色谱基本技术,分离的最佳应用和有机化合物的一般纯化。黄酮类化合物的结构研究依赖于其表面性质以及光谱的分析方法。紫外线辐射分析的优点在于快速且容易实现。事实上,少量的化合物就能够提供了该化合物完整的化学结构及其他信息。这项技术的基础取决于这样一个事实,即所有的黄酮类化合物在甲醇中都有一定的吸收光谱。添加的试剂包括:甲醇溶剂、na OH或甲醇钠、醋酸钠(na OAc)、硼酸和硼酸包含部分的盐酸(Al Cl和Al Cl+HCl)。核磁共振光谱(RMNH,RMNH)广泛应用于黄酮类化合物的研究和鉴别。具有独特芳香气味的挥发油在常温不分解的情况下是不可分割且易挥发。它有许多组分(芳香油,空灵油和精油)。植物是挥发性和固定性油的主要来源,这些油存在于植物的各个部分,这是一种单萜化合物。它在香水工业和医疗都有广泛的应用。虽然其原料是植物,但它是在实验室中提纯的。提取萜类化合物的方法很多(水蒸气蒸馏、水和水蒸气蒸馏、有机溶剂挥发)。水蒸气蒸馏是最常用的方法。广泛使用的分离萜类化合物的方法和色谱柱分离法(薄层法、柱法和高效液相色谱法),溶剂使用的色谱柱法是最合适的分离方法之一(溶剂:着色-氯仿或二氯甲烷,着色-丙酮或甲醇和正己烷-乙酸乙酯-丙酮)。

关键词：黄酮类化合物各种色谱类型有色谱柱核磁共振光谱(RMN1H,RMN13H)

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