Amyloid PET-Positive Predictability of Machine Learning Algorithm Based on MDS-OAβ Levels

Abstract Background: The Multimer Detection System-Oligomeric amyloid-β (MDS-OAβ) level is a valuable blood-based biomarker for Alzheimer’s disease (AD). We used machine learning algorithms trained using multi-center datasets to examine whether blood MDS-OAβ values can predict AD-associated changes in the brain.Methods: A logistic regression model using TensorFlow (ver. 2.3.0) was applied to data obtained from 163 participants (amyloid positron emission tomography [PET]-positive and -negative findings in 102 and 61 participants, respectively). Algorithms with various combinations of features (MDS-OAβ levels, age, gender, and anticoagulant type) were tested 50 times on each dataset. Results: The predictive accuracy, sensitivity, and specificity values of blood MDS-OAβ levels for amyloid PET positivity were 78.16±4.97%, 83.87±9.40%, and 70.00±13.13%, respectively.Conclusions: The findings from this multi-center machine learning-based study suggest that MDS-OAβ values may be used to predict amyloid PET-positivity.

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An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽

10.3390/s21020656 ◽

2021 ◽

Vol 21 (2) ◽

pp. 656

Author(s):

Xavier Larriva-Novo ◽

Víctor A. Villagrá ◽

Mario Vega-Barbas ◽

Diego Rivera ◽

Mario Sanz Rodrigo

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

High Performance ◽

Learning Algorithm ◽

Detection System ◽

Machine Learning Algorithms ◽

Statistical Characteristics ◽

Detection Techniques ◽

Traffic Characteristics ◽

Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

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Hybridization of Machine Learning Algorithm in Intrusion Detection System

Handbook of Research on Machine and Deep Learning Applications for Cyber Security - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-5225-9611-0.ch008 ◽

2020 ◽

pp. 150-175

Author(s):

Amudha P. ◽

Sivakumari S.

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Classification Accuracy ◽

Intrusion Detection System ◽

Learning Algorithm ◽

Detection System ◽

Principal Component ◽

Machine Learning Algorithms ◽

Feature Selection Technique ◽

Efficient Manner

In recent years, the field of machine learning grows very fast both on the development of techniques and its application in intrusion detection. The computational complexity of the machine learning algorithms increases rapidly as the number of features in the datasets increases. By choosing the significant features, the number of features in the dataset can be reduced, which is critical to progress the classification accuracy and speed of algorithms. Also, achieving high accuracy and detection rate and lowering false alarm rates are the major challenges in designing an intrusion detection system. The major motivation of this work is to address these issues by hybridizing machine learning and swarm intelligence algorithms for enhancing the performance of intrusion detection system. It also emphasizes applying principal component analysis as feature selection technique on intrusion detection dataset for identifying the most suitable feature subsets which may provide high-quality results in a fast and efficient manner.

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Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks

Advances in Fuzzy Systems ◽

10.1155/2020/8581202 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Peter Appiahene ◽

Yaw Marfo Missah ◽

Ussiph Najim

Keyword(s):

Machine Learning ◽

Random Forest ◽

Decision Tree ◽

Banking Sector ◽

Banking Industry ◽

Predictive Accuracy ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

Machine Learning Algorithm ◽

And Performance

The financial crisis that hit Ghana from 2015 to 2018 has raised various issues with respect to the efficiency of banks and the safety of depositors’ in the banking industry. As part of measures to improve the banking sector and also restore customers’ confidence, efficiency and performance analysis in the banking industry has become a hot issue. This is because stakeholders have to detect the underlying causes of inefficiencies within the banking industry. Nonparametric methods such as Data Envelopment Analysis (DEA) have been suggested in the literature as a good measure of banks’ efficiency and performance. Machine learning algorithms have also been viewed as a good tool to estimate various nonparametric and nonlinear problems. This paper presents a combined DEA with three machine learning approaches in evaluating bank efficiency and performance using 444 Ghanaian bank branches, Decision Making Units (DMUs). The results were compared with the corresponding efficiency ratings obtained from the DEA. Finally, the prediction accuracies of the three machine learning algorithm models were compared. The results suggested that the decision tree (DT) and its C5.0 algorithm provided the best predictive model. It had 100% accuracy in predicting the 134 holdout sample dataset (30% banks) and a P value of 0.00. The DT was followed closely by random forest algorithm with a predictive accuracy of 98.5% and a P value of 0.00 and finally the neural network (86.6% accuracy) with a P value 0.66. The study concluded that banks in Ghana can use the result of this study to predict their respective efficiencies. All experiments were performed within a simulation environment and conducted in R studio using R codes.

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MALGRA: Machine Learning and N-Gram Malware Feature Extraction and Detection System

Electronics ◽

10.3390/electronics9111777 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1777

Author(s):

Muhammad Ali ◽

Stavros Shiaeles ◽

Gueltoum Bendiab ◽

Bogdan Ghita

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Detection System ◽

Malware Detection ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Detection Methods ◽

Normal Operation ◽

Analysis Technique ◽

N Gram

Detection and mitigation of modern malware are critical for the normal operation of an organisation. Traditional defence mechanisms are becoming increasingly ineffective due to the techniques used by attackers such as code obfuscation, metamorphism, and polymorphism, which strengthen the resilience of malware. In this context, the development of adaptive, more effective malware detection methods has been identified as an urgent requirement for protecting the IT infrastructure against such threats, and for ensuring security. In this paper, we investigate an alternative method for malware detection that is based on N-grams and machine learning. We use a dynamic analysis technique to extract an Indicator of Compromise (IOC) for malicious files, which are represented using N-grams. The paper also proposes TF-IDF as a novel alternative used to identify the most significant N-grams features for training a machine learning algorithm. Finally, the paper evaluates the proposed technique using various supervised machine-learning algorithms. The results show that Logistic Regression, with a score of 98.4%, provides the best classification accuracy when compared to the other classifiers used.

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Different aspects of Alzheimer’s disease-related amyloid β-peptide pathology and their relationship to amyloid positron emission tomography imaging and dementia

Acta Neuropathologica Communications ◽

10.1186/s40478-019-0837-9 ◽

2019 ◽

Vol 7 (1) ◽

Cited By ~ 3

Author(s):

Dietmar Rudolf Thal ◽

Alicja Ronisz ◽

Thomas Tousseyn ◽

Ajeet Rijal Upadhaya ◽

Karthikeyan Balakrishnan ◽

...

Keyword(s):

Positron Emission Tomography ◽

Amyloid Β ◽

Emission Tomography ◽

Neuritic Plaque ◽

Amyloid Β Peptide ◽

Amyloid Pet ◽

Amyloid Positron Emission Tomography ◽

Positron Emission ◽

Plaque Pathology ◽

Topographical Distribution

AbstractAlzheimer’s disease (AD)-related amyloid β-peptide (Aβ) pathology in the form of amyloid plaques and cerebral amyloid angiopathy (CAA) spreads in its topographical distribution, increases in quantity, and undergoes qualitative changes in its composition of modified Aβ species throughout the pathogenesis of AD. It is not clear which of these aspects of Aβ pathology contribute to AD progression and to what extent amyloid positron emission tomography (PET) reflects each of these aspects. To address these questions three cohorts of human autopsy cases (in total n = 271) were neuropathologically and biochemically examined for the topographical distribution of Aβ pathology (plaques and CAA), its quantity and its composition. These parameters were compared with neurofibrillary tangle (NFT) and neuritic plaque pathology, the degree of dementia and the results from [18F]flutemetamol amyloid PET imaging in cohort 3. All three aspects of Aβ pathology correlated with one another, the estimation of Aβ pathology by [18F]flutemetamol PET, AD-related NFT pathology, neuritic plaques, and with the degree of dementia. These results show that one aspect of Aβ pathology can be used to predict the other two, and correlates well with the development of dementia, advancing NFT and neuritic plaque pathology. Moreover, amyloid PET estimates all three aspects of Aβ pathology in-vivo. Accordingly, amyloid PET-based estimates for staging of amyloid pathology indicate the progression status of amyloid pathology in general and, in doing so, also of AD pathology. Only 7.75% of our cases deviated from this general association.

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Comparative study on machine learning algorithms for early fire forest detection system using geodata.

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i5.pp5507-5513 ◽

2020 ◽

Vol 10 (5) ◽

pp. 5507

Author(s):

Zouiten Mohammed ◽

Chaaouan Hanae ◽

Setti Larbi

Keyword(s):

Machine Learning ◽

Forest Fire ◽

Forest Fires ◽

Learning Algorithm ◽

Detection System ◽

Learning Algorithms ◽

Spatial Prediction ◽

Machine Learning Algorithms ◽

Geographical Information ◽

Deep Learning Algorithm

Forest fires have caused considerable losses to ecologies, societies and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, a competitive spatial prediction model for automatic early detection of wild forest fire using machine learning algorithms can be proposed. This model can help researchers to predict forest fires and identify risk zonas. System using machine learning algorithm on geodata will be able to notify in real time the interested parts and authorities by providing alerts and presenting on maps based on geographical treatments for more efficacity and analyzing of the situation. This research extends the application of machine learning algorithms for early fire forest prediction to detection and representation in geographical information system (GIS) maps.

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Hoax News Classification using Machine Learning Algorithms

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b3753.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 3938-3944

Keyword(s):

Machine Learning ◽

Social Media ◽

Learning Algorithm ◽

Detection System ◽

Machine Learning Algorithms ◽

Training Data ◽

Stochastic Gradient Descent ◽

Support Vector ◽

The Impact ◽

F Measure

Hoax news on social media has had a dramatic effect on our society in recent years. The impact of hoax news felt by many people, anxiety, financial loss, and loss of the right name. Therefore we need a detection system that can help reduce hoax news on social media. Hoax news classification is one of the stages in the construction of a hoax news detection system, and this unsupervised learning algorithm becomes a method for creating hoax news datasets, machine learning tools for data processing, and text processing for detecting data. The next will produce a classification of a hoax or not a Hoax based on the text inputted. Hoax news classification in this study uses five algorithms, namely Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression, Stochastic Gradient Descent, and Neural Network (MLP). These five algorithms to produce the best algorithm that can use to detect hoax news, with the highest parameters, accuracy, F-measure, Precision, and recall. From the results of testing conducted on five classification algorithms produced shows that the NN-MPL algorithm has an average of 93% for the value of accuracy, F-Measure, and Precision, the highest compared to five other algorithms, but for the highest Recall value generated from the algorithm SVM which is 94%. the results of this experiment show that different effects for different classifiers, and that means that the more hoax data used as training data, the more accurate the system calculates accuracy in more detail.

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Routing Attacs pada Internet Of Things Berbasis Smart Intrution Detecion System

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020721926 ◽

2020 ◽

Vol 7 (2) ◽

pp. 329

Author(s):

Eka Lailatus Sofa ◽

Subiyanto Subiyanto

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Internet Of Things ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Learning Algorithm ◽

Detection System ◽

Machine Learning Algorithms ◽

Machine Learning Algorithm ◽

Routing Attacks

Internet of Things (IoT) telah memasuki berbagai aspek kehidupan manusia, diantaranya smart city, smart home, smart street, dan smart industry yang memanfaatkan internet untuk memantau informasi yang dibutuhkan. Meskipun sudah dienkripsi dan diautentikasi, protokol jaringan <a title="IPv6" href="https://en.wikipedia.org/wiki/IPv6">IPv6</a> over Low-Power Wireless <a title="Personal area network" href="https://en.wikipedia.org/wiki/Personal_area_network">Personal Area Networks</a> (6LoWPAN) yang dapat menghubungkan benda-benda yang terbatas sumber daya di IoT masih belum dapat diandalkan. Hal ini dikarenakan benda-benda tersebut masih dapat terpapar oleh routing attacks yang berasal dari jaringan 6LoWPAN dan internet. Makalah ini menyajikan kinerja Smart Intrusion Detection System berdasarkan Compression Header Analyzer untuk menganalisis model routing attacks lainnya pada jaringan IoT. IDS menggunakan compression header 6LoWPAN sebagai fitur untuk machine learning algorithm dalam mempelajari jenis routing attacks. Skenario simulasi dikembangkan untuk mendeteksi routing attacks berupa selective forwarding attack dan sinkhole attack. Pengujian dilakukan menggunakan feature selection dan machine learning algorithm. Feature selection digunakan untuk menentukan fitur signifikan yang dapat membedakan antara aktivitas normal dan abnormal. Sementara machine learning algorithm digunakan untuk mengklasifikasikan routing attacks pada jaringan IoT. Ada tujuh machine learning algorithm yang digunakan dalam klasifikasi antara lain Random Forest, Random Tree, J48, Bayes Net, JRip, SMO, dan Naive Bayes. Hasil percobaan disajikan untuk menunjukkan kinerja Smart Intrusion Detection System berdasarkan Compression Header Analyzer dalam menganalisis routing attacks. Hasil evaluasi menunjukkan bahwa IDS ini dapat mendeteksi antara serangan dan non-serangan. AbstractInternet of Things (IoT) has entered various aspects of human life including smart city, smart home, smart street, and smart industries that use the internet to get the information they need. Even though it's encrypted and authenticated, Internet protocol <a title="IPv6" href="https://en.wikipedia.org/wiki/IPv6">IPv6</a> over Low-Power Wireless <a title="Personal area network" href="https://en.wikipedia.org/wiki/Personal_area_network">Personal Area Networks</a> (6LoWPAN) networks that can connect limited resources to IoT are still unreliable. This is because these objects can still be exposed to attacks from 6LoWPAN and the internet. This paper presents the performance of an Smart Intrusion Detection System based on Compression Header Analyzer to analyze other routing attack models on IoT networks. IDS uses a 6LoWPAN compression header as a feature for machine learning algorithms in learning the types of routing attacks. Simulation scenario was developed to detect routing attacks in the form of selective forwarding and sinkhole. Testing is done using the feature selection and machine learning algorithm. Feature selection is used to determine significant features that can distinguish between normal and abnormal activities. While machine learning algorithm is used to classify attacks on IoT networks. There were seven machine learning algorithms used in the classification including Random Forests, Random Trees, J48, Bayes Net, JRip, SMO, and Naive Bayes. Experiment Results to show the results of the Smart Intrusion Detection System based on Compression Header Analyzer in analyzing routing attacks. The evaluation results show that this IDS can protect between attacks and non-attacks.

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Network Intrusion Detection Systems Design: A Machine Learning Approach

10.5753/sbrc.2019.7413 ◽

2019 ◽

Cited By ~ 1

Author(s):

Manuel Gonçalves da Silva Neto ◽

Danielo G. Gomes

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Learning Algorithm ◽

Detection System ◽

Systems Design ◽

Machine Learning Algorithms ◽

Intrusion Detection Systems ◽

Machine Learning Techniques ◽

Detection Systems ◽

Network Intrusion

With the increasing popularization of computer network-based technologies, security has become a daily concern, and intrusion detection systems (IDS) play an essential role in the supervision of computer networks. An employed approach to combat network intrusions is the development of intrusion detection systems via machine learning techniques. The intrusion detection performance of these systems depends highly on the quality of the IDS dataset used in their design and the decision making for the most suitable machine learning algorithm becomes a difficult task. The proposed paper focuses on evaluate and accurate the model of intrusion detection system of different machine learning algorithms on two resampling techniques using the new CICIDS2017 dataset where Decision Trees, MLPs, and Random Forests on Stratified 10-Fold gives high stability in results with Precision, Recall, and F1-Scores of 98% and 99% with low execution times.

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Intelligent system of English composition scoring model based on improved machine learning algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189235 ◽

2020 ◽

pp. 1-11

Author(s):

Jie Liu ◽

Lin Lin ◽

Xiufang Liang

Keyword(s):

Machine Learning ◽

Evaluation System ◽

Intelligent System ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Assessment System ◽

English Composition ◽

Region Extraction ◽

Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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