Security in Mission Critical Communication Systems

This chapter introduces two different algorithms to detect intrusions in mission critical communication systems to guarantee their security. The first algorithm is a classification algorithm which applies the concept of supervised learning. The second algorithm is a clustering algorithm which applies the concept of unsupervised learning. The algorithms detect intrusions using a set of detection rules that are structured in the form of decision trees. The algorithms are described in details and their results on well-known dataset are introduced. An enhancement for the J48algorithm is also introduced, where the decision tree for the algorithm is changed to a binary tree. The change enhances the complexity to reach a decision. The chapter includes a brief introduction about the security in Mission critical systems and the reason behind securing such systems. It introduces different methodologies that were introduced to detect intrusions in wireless communications.

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Application of Machine Learning in Animal Disease Analysis and Prediction

Current Bioinformatics ◽

10.2174/1574893615999200728195613 ◽

2020 ◽

Vol 15 ◽

Author(s):

Shuwen Zhang ◽

Qiang Su ◽

Qin Chen

Keyword(s):

Machine Learning ◽

Unsupervised Learning ◽

Supervised Learning ◽

Clustering Algorithm ◽

Principal Component ◽

Support Vector ◽

Animal Disease ◽

Human Beings ◽

Animal Diseases ◽

Disease Analysis

Abstract: Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers learn how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and understand its application prospect in animal diseases.

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Data Science Tools Application for Business Processes Modelling in Aviation

Advances in Computer and Electrical Engineering - Cases on Modern Computer Systems in Aviation ◽

10.4018/978-1-5225-7588-7.ch006 ◽

2019 ◽

pp. 176-190

Author(s):

Maryna Nehrey ◽

Taras Hnot

Keyword(s):

Decision Making ◽

Logistic Regression ◽

Unsupervised Learning ◽

Decision Trees ◽

Supervised Learning ◽

Data Science ◽

Business Processes ◽

Business Decision ◽

Logistic Regression Models ◽

Using Data

Successful business involves making decisions under uncertainty using a lot of information. Modern modeling approaches based on data science algorithms are a necessity for the effective management of business processes in aviation. Data science involves principles, processes, and techniques for understanding business processes through the analysis of data. The main goal of this chapter is to improve decision making using data science algorithms. There are sets of frequently used algorithms described in the chapter: linear, logistic regression models, decision trees as a classical example of supervised learning, and k-means and hierarchical clustering as unsupervised learning. Application of data science algorithms gives an opportunity for deep analyses and understanding of business processes in aviation, gives structuring of problems, provides systematization of business processes. Business processes modeling, based on the data science algorithms, enables us to substantiate solutions and even automate the processes of business decision making.

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Data Science Tools Application for Business Processes Modelling in Aviation

Research Anthology on Reliability and Safety in Aviation Systems, Spacecraft, and Air Transport ◽

10.4018/978-1-7998-5357-2.ch024 ◽

2021 ◽

pp. 617-631

Author(s):

Maryna Nehrey ◽

Taras Hnot

Keyword(s):

Decision Making ◽

Logistic Regression ◽

Unsupervised Learning ◽

Decision Trees ◽

Supervised Learning ◽

Data Science ◽

Business Processes ◽

Business Decision ◽

Logistic Regression Models ◽

Using Data

Successful business involves making decisions under uncertainty using a lot of information. Modern modeling approaches based on data science algorithms are a necessity for the effective management of business processes in aviation. Data science involves principles, processes, and techniques for understanding business processes through the analysis of data. The main goal of this chapter is to improve decision making using data science algorithms. There are sets of frequently used algorithms described in the chapter: linear, logistic regression models, decision trees as a classical example of supervised learning, and k-means and hierarchical clustering as unsupervised learning. Application of data science algorithms gives an opportunity for deep analyses and understanding of business processes in aviation, gives structuring of problems, provides systematization of business processes. Business processes modeling, based on the data science algorithms, enables us to substantiate solutions and even automate the processes of business decision making.

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Fuzzy Decision Trees

Encyclopedia of Decision Making and Decision Support Technologies ◽

10.4018/978-1-59904-843-7.ch044 ◽

2011 ◽

pp. 382-390

Author(s):

Malcolm J. Beynon

Keyword(s):

Decision Tree ◽

Decision Trees ◽

Binary Tree ◽

Fuzzy Decision ◽

Data Set ◽

Fuzzy Environment ◽

Id3 Algorithm ◽

Backtrack Algorithm ◽

The 1960S ◽

Fuzzy Decision Trees

The first (crisp) decision tree techniques were introduced in the 1960s (Hunt, Marin, & Stone, 1966), their appeal to decision makers is due in no part to their comprehensibility in classifying objects based on their attribute values (Janikow, 1998). With early techniques such as the ID3 algorithm (Quinlan, 1979), the general approach involves the repetitive partitioning of the objects in a data set through the augmentation of attributes down a tree structure from the root node, until each subset of objects is associated with the same decision class or no attribute is available for further decomposition, ending in a number of leaf nodes. This article considers the notion of decision trees in a fuzzy environment (Zadeh, 1965). The first fuzzy decision tree (FDT) reference is attributed to Chang and Pavlidis (1977), which defined a binary tree using a branch-bound-backtrack algorithm, but limited instruction on FDT construction. Later developments included fuzzy versions of crisp decision techniques, such as fuzzy ID3, and so forth (see Ichihashi, Shirai, Nagasaka, & Miyoshi, 1996; Pal & Chakraborty, 2001) and other versions (Olaru & Wehenkel, 2003).

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Prediksi Waktu Sandar Kapal Di Pelabuhan Batu Ampar, Kota Batam, Provinsi Kepulauan Riau

Jurnal Sistem Cerdas ◽

10.37396/jsc.v2i2.26 ◽

2019 ◽

Vol 2 (2) ◽

pp. 119-134

Author(s):

Saiful Rizal ◽

Candra Kurniawan ◽

Fahrur Rozi

Keyword(s):

Data Mining ◽

Decision Tree ◽

Decision Trees ◽

Supervised Learning ◽

Multiple Regression ◽

Analysis Of Variance ◽

Dwelling Time

Pelabuhan Batu Ampar merupakan pelabuhan barang terbesar di Kota Batam yang memiliki lalu lintas tertinggi baik untuk kegiatan ekspor maupun kegiatan impor. Waktu tunggu (dwelling time) masih menjadi masalah dalam layanan pelabuhan. Waktu tunggu merupakan salah satu indikator efisiensi pengelolaan pelabuhan. Rata-rata waktu tunggu pelabuhan Batu Ampar untuk kegiatan bongkar pada triwulan I-2015 adalah 7 hari, sedangkan kegiatan muatnya adalah 5 hari. Hal ini yang menjadikan kinerja pelabuhan Batu Ampar masih banyak dikeluhkan, sehingga berakibat banyaknya antrian kapal. Untuk itu, perlu dilakukan analisis guna menghasilkan model yang bisa memberikan gambaran waktu tunggu di pelabuhan dan melakukan evaluasi terhadap model analitik yang telah dibangun. Analisa data sekunder pelabuhan Batu Ampar menggunakan data mining. Metode data mining yang dilakukan menggunakan algoritma supervised learning, yaitu multiple regression dan decision trees. Tujuan umum dari multiple regression adalah untuk mempelajari lebih lanjut tentang hubungan antara beberapa variabel independen atau prediktor dan variabel dependen atau kriteria. Decision trees yang digunakan untuk eksplorasi data pelabuhan ini menggunakan klasifikasi. Klasifikasi decision trees dapat menemukan apakah data mengandung kelas objek yang dipisahkan dengan baik, sehingga kelas dapat diinterpretasikan secara bermakna dalam konteks teori substantif. Dua metode evaluasi model dilakukan untuk dua hasil permodelan yang dibangun. Uji Analysis of Variance (Anova) digunakan untuk evaluasi model multiple regression, sedangkan untuk model decision tree dievaluasi dengan confussion matrix. Hasil analisis data menunjukkan lamanya waktu kapal melakukan bongkar/muat dipengaruhi oleh tiga variabel yaitu jenis ekspedisi, bendera, dan volume. Dengan menggunakan regresi berganda maka dihasilkan model prediksi waktu sandar kapal. Hasil evaluasi model menunjukkan bahwa model yang dibuat signifikan. Dengan tingkat kepercayaan 95% model prediktif yang dibuat akan merepresentasikan nilai sebenarnya. Untuk decision tree, evaluasi menunjukkan model yang dibuat sudah fit, dengan presisi 84,50%.

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Data Classification Using Decision Trees J48 Algorithm for Text Mining of Business Data

Lahore Garrison University Research Journal of Computer Science and Information Technology ◽

10.54692/lgurjcsit.2021.0502210 ◽

2021 ◽

Vol 5 (2) ◽

pp. 73-81

Author(s):

Asif Yaseen

Keyword(s):

Text Mining ◽

Customer Satisfaction ◽

Decision Tree ◽

Decision Trees ◽

Supervised Learning ◽

Data Analytics ◽

Training Model ◽

Decision Tree Classification ◽

Class Labels ◽

Financial Transactions

The business industry is generating a lot of data on daily business deals and financial transactions. These businesses are generating intensive-data like they need customer satisfaction on top priority, fulfilling their needs, etc. In every step, Data is being produced. This Data has a great value that is hidden from regular users. Data analytics is used to unhide those values. In our project, we are using a business-related dataset that contains strings and their class (0 or 1). 0 or 1 denotes the positive or negative string labels. To analyze this data, we are using a decision tree classification algorithm (J48 exceptionally) to perform text mining (classification) on our target dataset. Text mining comes under supervised learning (type). In-text mining, generally, we use two datasets. One is used to train the model, and the second dataset is used to predict the missing class labels in the second dataset based on this training model generated using the first dataset.

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Mengenal Machine Learning Dengan Teknik Supervised Dan Unsupervised Learning Menggunakan Python

BINA INSANI ICT JOURNAL ◽

10.51211/biict.v7i2.1422 ◽

2020 ◽

Vol 7 (2) ◽

pp. 156

Author(s):

Endang Retnoningsih ◽

Rully Pramudita

Keyword(s):

Machine Learning ◽

Unsupervised Learning ◽

Supervised Learning ◽

Clustering Algorithm ◽

Training Data ◽

Abstract Machine ◽

Dbscan Clustering ◽

Learning Techniques ◽

Learning Technique ◽

Learning Programming

Abstrak: Machine learning merupakan sistem yang mampu belajar sendiri untuk memutuskan sesuatu tanpa harus berulangkali diprogram oleh manusia sehingga komputer menjadi semakin cerdas berlajar dari pengalaman data yang dimiliki. Berdasarkan teknik pembelajarannya, dapat dibedakan supervised learning menggunakan dataset (data training) yang sudah berlabel, sedangkan unsupervised learning menarik kesimpulan berdasarkan dataset. Input berupa dataset digunakan pembelajaran mesin untuk menghasilkan analisis yang benar. Permasalahan yang akan diselesaikan bunga iris (iris tectorum) yang memiliki bunga bermaca-macam warna dan memiliki sepal dan petal yang menunjukkan spesies bunga, dibutuhkan metode yang tepat untuk pengelompokan bunga-bunga tersebut kedalam spesiesnya iris-setosa, iris-versicolor atau iris-virginica. Penyelesaian digunakan Python yang menyediakan algoritma dan library yang digunakan membuat machine learning. Penyelesaian dengan teknik supervised learning dipilih algoritma KNN Clasiffier dan teknik unsupervised learning dipilih algoritma DBSCAN Clustering. Hasil yang diperoleh Python menyediakan library yang lengkap numPy, Pandas, matplotlib, sklearn untuk membuat pemrograman machine learning dengan algortima KNN memanggil from sklearn import neighbors termasuk teknik supervised, maupun DBSCAN memanggil from sklearn.cluster import DBSCAN termasuk teknik unsupervised learning. Kemampuan Python memberikan hasil output sesuai input dalam dataset menghasilkan keputusan berupa klasifikasi maupun klusterisasi. Kata kunci: DBSCAN, KNN, machine learning, python. Abstract: Machine learning is a system that is able to learn on its own to decide something without having to be repeatedly programmed by humans so that computers become smarter in learning from the experience of the data they have. Based on the learning technique, supervised learning can be distinguished using a dataset (training data) that is already labeled, while unsupervised learning draws conclusions based on the dataset. The input in the form of a dataset is used by machine learning to produce the correct analysis. The problem to be solved by iris flowers (iris tectorum), which has flowers of various colors and has sepals and petals that indicate the species of flowers, requires an appropriate method for grouping these flowers into iris-setosa, iris-versicolor or iris-virginica species. The solution is used by Python, which provides the algorithms and libraries used to make machine learning. The solution with the supervised learning technique was chosen by the KNN Clasiffier algorithm and the unsupervised learning technique was selected by the DBSCAN Clustering algorithm. The results obtained by Python provide a complete library of numPy, Pandas, matplotlib, sklearn to create machine learning programming with KNN algorithms calling from sklearn import neighbors including supervised techniques, and DBSCAN calling from sklearn.cluster import DBSCAN including unsupervised learning techniques. Python's ability to provide output according to the input in the dataset results in decisions in the form of classification and clustering. Keywords: DBSCAN, KNN, machine learning, python.

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Effective Performance Metrics for Multimedia Mission-critical Communication Systems

Annals of Emerging Technologies in Computing ◽

10.33166/aetic.2021.02.001 ◽

2021 ◽

Vol 5 (2) ◽

pp. 1-14

Author(s):

Ashraf Ali ◽

Andrew Ware

Keyword(s):

Performance Indicators ◽

Communication Systems ◽

Large Scale ◽

Performance Metrics ◽

Critical Systems ◽

Multimedia Services ◽

Large Scale Systems ◽

Effective Performance ◽

Commercial Communication ◽

Mission Critical

Mission-critical Communication Systems that are adaptable for use with the latest generation of multimedia services are crucial for system users. To determine the set of requirements that need to be hardcoded into such systems, a clear distinction between mission-critical and non-mission-critical systems is required. Moreover, the users of services provided by such systems are very different to those of current mobile commercial communication systems. These differences give rise to a set of challenges that need addressing to facilitate migration from existing systems to those now being proposed. One such challenge relates to the performance of the IP Multimedia Subsystem (IMS) registration process. This is a crucial consideration for mission-critical systems, particularly in large-scale systems where thousands or even millions of users may seek to access the system in disaster scenarios. This paper presents an evaluation of IMS and Session Initiation Protocol (SIP) performance metrics and Key Performance Indicators (KPIs). Moreover, it articulates a proposed study that will seek to address some of the challenges identified.

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Automated Development of Clinical Strategies Using Multistage Decision Analysis

Methods of Information in Medicine ◽

10.1055/s-0038-1635469 ◽

1986 ◽

Vol 25 (04) ◽

pp. 207-214 ◽

Cited By ~ 3

Author(s):

P. Glasziou

Keyword(s):

Decision Tree ◽

Decision Analysis ◽

Decision Trees ◽

Optimal Strategy ◽

Cholestatic Jaundice ◽

Clinical Strategies ◽

Simultaneous Study

SummaryThe development of investigative strategies by decision analysis has been achieved by explicitly drawing the decision tree, either by hand or on computer. This paper discusses the feasibility of automatically generating and analysing decision trees from a description of the investigations and the treatment problem. The investigation of cholestatic jaundice is used to illustrate the technique.Methods to decrease the number of calculations required are presented. It is shown that this method makes practical the simultaneous study of at least half a dozen investigations. However, some new problems arise due to the possible complexity of the resulting optimal strategy. If protocol errors and delays due to testing are considered, simpler strategies become desirable. Generation and assessment of these simpler strategies are discussed with examples.

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