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2nd International Conference of the IEEE-Nigeria-Computer-Chapter (NigeriaComputConf)

作者： Gana, Noah Ndakotsu Abdulhamid, Shafi'I Muhammad Fed Univ Technol Minna Dept Cyber Secur Sci Minna Nigeria

ISBN: (纸本)9781728107134

Exponential growth experienced in Internet usage have pave way to exploit users of the Internet, phishing attack is one of the means that can be used to obtained victim confidential details unwittingly across the Internet. A high false positive rate and low accuracy has been a setback in phishing detection. In this research RandomForest, SysFor, SPAARC, RepTree, RandomTree, LMT, ForestPA, JRip, PART, NNge, OneR, AdaBoostMl, RotationForest, LogitBoost, RseslibKnn, LibSVM, and BayesNet were employed to achieve the comparative analysis of machine classifier. The performance of the classifier algorithms were rated using Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operation Characteristics Area, Root Relative Squared Error False Positive Rate and True Positive Rate using WEKA data mining tool. The research revealed that quit a number of classifiers also exist which if properly explored will yield more accurate results for phishing detection. RondomForest was found to be an excellent classifier that gives the best accuracy of 0.9838 and a false positive rate of 0.017. The comparative analysis result indicates the achievement of low false positive rate for phishing classification which suggest that anti-phishing application developer can implement the machine learning classification algorithm that was discovered to be the best in this study to enhance the feature of phishing attack detection and classification.

关键词： Phishing Attack classification algorithm Accuracy Performance metric RandomForest

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T-DFNN: An Incremental Learning algorithm for Intrusion Detection Systems

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IEEE ACCESS 2021年 9卷 154156-154171页

作者： Data, Mahendra Aritsugi, Masayoshi Kumamoto Univ Grad Sch Sci & Technol Kumamoto 8608555 Japan Brawijaya Univ Dept Comp Sci Malang 65145 Indonesia Kumamoto Univ Fac Adv Sci & Technol Kumamoto 8608555 Japan

Machine learning has recently become a popular algorithm in building reliable intrusion detection systems (IDSs). However, most of the models are static and trained using datasets containing all targeted intrusions. If new intrusions emerge, these trained models must be retrained using old and new datasets to classify all intrusions accurately. In real-world situations, new threats continuously appear. Therefore, machine learning algorithms used for IDSs should have the ability to learn incrementally when these new intrusions emerge. To solve this issue, we propose T-DFNN. T-DFNN is an algorithm capable of learning new intrusions incrementally as they emerge. A T-DFNN model is composed of multiple deep feedforward neural network (DFNN) models connected in a tree-like structure. We examined our proposed algorithm using CICIDS2017, an open and widely used network intrusion dataset covering benign traffic and the most common network intrusions. The experimental results showed that the T-DFNN algorithm can incrementally learn new intrusions and reduce the catastrophic forgetting effect. The macro average of the F1-score of the T-DFNN model was over 0.85 for every retraining process. In addition, our proposed T-DFNN model has some advantages in several aspects compared to other models. Compared to the DFNN and Hoeffding tree models trained with a dataset containing only the latest targeted intrusions, our proposed T-DFNN model has higher F1-scores. Moreover, our proposed T-DFNN model has significantly shorter training times than a DFNN model trained using a dataset containing all targeted intrusions. Even though several factors can affect the duration of the training process, the T-DFNN algorithm shows promising results in solving the problem of ever-evolving network intrusion variants.

关键词： Machine learning algorithms Training Computational modeling Deep learning Machine learning Support vector machines Task analysis Network intrusion detection incremental learning catastrophic forgetting deep learning classification algorithm

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Learning Brain Computer Interface model for Amyotrophic Lateral Sclerosis (ALS) people using Random Forest - Linear Discriminant Analysis (RF-LDA) algorithm 19

Learning Brain Computer Interface model for Amyotrophic Late...

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19th IEEE-India-Council International Conference (INDICON)

作者： Kumar, Arun S. Kannur, Anil Akash, C. K. Koushik, Venkatanath P. S. Kushal, S. Anwar, C. Shaik Nagarjuna Coll Engn & Technol Informat Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9781665473507

As modern technology grows and the study of the human brain makes it easier to understand its complex activity, we are moving toward the acknowledgment of imagination. It is not a matter of comfort for various paralytics. It's the potential to tweak devices with a single thought. The improvement of the Brain Computer Interface may be the genuinely unambiguous movement of the last few years. Brain Computer Interface is a prompt correspondence channel between the mind and an electrical device. It is the new advancement in HCI. There are numerous things to investigate like Asynchronous method, small signal to noise ratio (SNR) and different noise sources are amongst the greatest challenges in EEG-based BCI application. Challenges are inherent in translating any new technology to practical and useful clinical applications and BCIs are no exception. We discuss the potential uses and users of BCI systems and address some of the limitations and challenges facing the field. In this paper we propose a new algorithm which combines LDA with Random forest algorithm which sub-divides the dataset and apply LDA algorithm to each subdivision and vote for the final output. Experimental results shows that our algorithm enhance the accuracy in printing the target letter.

关键词： Brain Computer Interface Signal Processing Feature Extraction classification algorithm

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Machine Learning classification algorithms for Adware in Android Devices: A Comparative Evaluation and Analysis 15

Machine Learning Classification Algorithms for Adware in And...

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15th International Conference on Electronics, Computer and Computation (ICECCO)

作者： Ndagi, Joseph Yisa Alhassan, John K. Fed Univ Technol Dept Cyber Secur Sci Minna Nigeria

ISBN: (数字)9781728151601

ISBN: (纸本)9781728151601

Exponential growth experienced in Internet usage has paved the way to exploit users of the Internet, a phishing attack is one of the means that can he used to obtained victim confidential details unwittingly across the Internet. A high false positive rate and low accuracy have been a setback in phishing detection. In this research 17 different supervised learning techniques such as RandomForest, Systematically Developed Forest (SysFor), Spectral Areas and Ratios Classifier (SPAARC), Reduces Error Pruning Tree (RepTree), RandomTree, Logic Model Tree (LMT), Forest by Penalizing Attributes (ForestPA), JRip, PART, Nearest Neighbor with Generalization (NNge), One Rule (OneR), AdaBoostM1, RotationForest, LogitBoost, RseslibKnn, Library for Support Vector Machine (LibSVM), and BayesNet were employed to achieve the comparative analysis of machine classifier. The performance of the classifier algorithms was rated using Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operation Characteristics Area, Root Relative Squared Error False Positive Rate and True Positive Rate using WEKA data mining tool. The research revealed that quite several classifiers also exist which if properly explored will yield more accurate results for phishing detection. RandomForest was found to be an excellent classifier that gives the best accuracy of 0.9838 and a false positive rate of 0.017. The comparative analysis result indicates the achievement of low false-positive rate for phishing classification which suggests that anti-phishing application developer can implement the machine learning classification algorithm that was discovered to be the best in this study to enhance the feature of phishing attack detection and classification.

关键词： Adware Attack classification algorithm Accuracy Performance metric RandomForest

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Speech feature selection and emotion recognition based on weighted binary cuckoo search

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ALEXANDRIA ENGINEERING JOURNAL 2021年第1期60卷 1499-1507页

作者： Zhang, Zicheng Nanjing Univ Sch Informat Management Nanjing 210023 Peoples R China Jiangsu Key Lab Data Engn & Knowledge Serv Nanjing 210023 Peoples R China

In this paper, a hybrid system is proposed for speech emotion recognition (SER). The algorithm in this paper adopts a two-stage design concept. In the first stage, we use the ensemble learning model random forest algorithm to obtain the importance of each feature. We use Emo- DB for experimental comparison and find that the combination of the logistic regression algorithm and the WBCS algorithm achieves best results. Cross-training method is used to ensure the features adapt to various situations. Under 100 training sets, the sentiment classification results are satisfactory. The proposed method is more accurate than state-of-the-art intelligent optimization dimensionality reduction algorithms. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.

关键词： classification algorithm Weighted binary cuckoo search Feature dimensionality reduction Speech emotion recognition

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A Comparison of classification algorithms Based on The Number of Features

A Comparison of Classification Algorithms Based on The Numbe...

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第三十九届中国控制会议

作者： Peichen Xiong Wei Ping Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University School of Information Engineering College of Science and Technology Ningbo University

As the most widely known data mining algorithm, classification algorithms have attracted wide attention. K-Nearest Neighbor(KNN) algorithm and decision tree algorithm are the two widely known algorithms in classification algorithms. Sometimes, people not sure how to choose the suitable to solve the classification problems. In this paper, we establish KNN algorithm model and decision tree ID3 algorithm model to analyze the accuracy of the two algorithms in the same data set with different number of features. Through the learning curve and cross validation, we find ID3 algorithm is better than KNN algorithm, and when the number of feature increased the accuracy of KNN is increasing while ID3 is decreased.

关键词： KNN Decision Tree ID3 classification algorithm

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Investigating the Impacts of Weather and Personalisation on Asthma Exacerbations using Machine Learning 2022

Investigating the Impacts of Weather and Personalisation on ...

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11th International Conference on Software and Computer Applications, ICSCA 2022

作者： Haque, Radiah Ho, Sin-Ban Chai, Ian Abdullah, Adina Faculty of Computing and Informatics Multimedia University Malaysia Faculty of Medicine University of Malaya Malaysia

ISBN: (纸本)9781450385770

This paper reports the results of investigating the impacts of weather and personalisation on asthma exacerbations. Additionally, several machine learning classification algorithms were applied and evaluated to develop an intelligent mHealth solution that can offer accurate personalised estimations of asthma exacerbations based on weather and demography and provide tailored precautionary guidelines for selfcare to each user. Consequently, the proposed solution will enhance asthma self-management by assisting patients with chronic asthma to plan their daily outdoor activities and limit exposure to weather triggers, thus controlling their asthma exacerbations. © 2022 ACM.

关键词： Machine learning Neural networks Personalisation Asthma self-management classification algorithm

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A review of land use/land cover change mapping in the China-Central Asia-West Asia economic corridor countries

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Big Earth Data 2021年第2期5卷 237-257页

作者： Amin Naboureh Jinhu Bian Guangbin Lei Ainong Li Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengduChina University of Chinese Academy of Sciences BeijingChina

Large-scale projects,such as the construction of railways and highways,usually cause an extensive Land Use Land Cover Change(LULCC).The China-Central Asia-West Asia Economic Corridor(CCAWAEC),one key large-scale project of the Belt and Road Initiative(BRI),covers a region that is home to more than 1.6 billion *** numerous studies have been conducted on strategies and the economic potential of the Economic Corridor,reviewing LULCC mapping studies in this area has not been *** study provides a comprehensive review of the recent research progress and discusses the challenges in LULCC monitoring and driving factors identifying in the study *** review will be helpful for the decision-making of sustainable development and construction in the Economic *** this end,350 peer-reviewed journal and conference papers,as well as book chapters were analyzed based on 17 attributes,such as main driving factors of LULCC,data collection methods,classification algorithms,and accuracy assessment *** was observed that:(1)rapid urbanization,industrialization,population growth,and climate change have been recognized as major causes of LULCC in the study area;(2)LULCC has,directly and indirectly,caused several environmental issues,such as biodiversity loss,air pollution,water pollution,desertification,and land degradation;(3)there is a lack of well-annotated national land use data in the region;(4)there is a lack of reliable training and reference datasets to accurately study the long-term LULCC in most parts of the study area;and(5)several technical issues still require more attention from the scientific ***,several recommendations were proposed to address the identified issues.

关键词： Land use change land cover change China-Central Asia-West Asia Economic Corridor accuracy assessment reference data classification algorithm

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A Measurement System to Monitor Postural Behavior: Strategy Assessment and classification Rating

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2020年第10期69卷 8020-8031页

作者： Ando, Bruno Marletta, Vincenzo Baglio, Salvatore Crispino, Ruben Mostile, Giovanni Dibilio, Valeria Nicoletti, Alessandra Zappia, Mario Univ Catania Dept Elect Elect & Informat Engn DIEEI I-95125 Catania Italy AOU Policlin Vittorio Emanuele Clin Neurol I-95125 Catania Italy

Postural instability is a symptom of disorders like ear diseases, high blood pressure, diabetes, psychiatric disorders, and neurodegenerative diseases like Parkinson and one of the main causes of falls. A continuous monitoring of the patient's postural stability while performing the daily activities would be of strategic interest for the doctors to gain continuous information on the user pathology or its progression. This article focuses on the experimental assessment of a wearable inertial device aimed at the continuous monitoring of the user's postural sway, which exploits a novel postural sway classification algorithm providing a classification index. Two performance keys have been proposed: 1) to assess the postural sway monitoring strategy and 2) to rate the reliability of the classification outcome. Experiments reported in this article have been performed involving "healthy" users miming typical instability dynamics under the supervision of the neurologists. In the case of study addressed through this article, a full classification success has been obtained with an overall reliability of about 70%.

关键词： Monitoring Biomedical monitoring Aging Reliability Neurological diseases classification algorithm continuous monitoring inertial sensors neurological diseases postural instability reliability index (RI) stabilogram

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Arguments around weighting the first year in degree classification algorithms

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Perspectives: Policy and Practice in Higher Education 2019年第1期23卷 24-27页

作者： O’Neill, Michael Chemistry Department University of Hull Hull United Kingdom

The UK’s HE system is mired in public debate around ‘grade inflation’, and there is substantial pressure to address the perceived devaluation of degrees through blunt policy measures such as modifying classification algorithms. Policy-makers should be aware of the impact of their actions upon students’ learning;this article frames some of the neglected pedagogical arguments in concise syllogistic forms, focusing particularly on the issues around weighting the first year of a traditional UK bachelor’s degree classification algorithm. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： classification algorithm first year transition into HE

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