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1st International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS)

作者： Bhosale, Karuna S. Nenova, Maria Iliev, Georgi Tech Univ Sofia Fac Telecommun Sofia Bulgaria

ISBN: (纸本)9781538677094

Nowadays the network security is the important topic to research. The Network Security from different types of attacks which is R2L, U2R, and DoS. It is very challenging tasks due to variety of research problems like noise, large data size, inefficient features selection method etc. Network Intrusion Detection System (IDS), as the basic security protection technique, is generally used limiting such malicious attacks. In this project, we are presenting the efficient IDS solution using filter based feature choice strategy. We are exhibiting the hybrid feature determination method. The Intrusion Detection System (IDS) examine the main part in distortion and ambushes in the framework. In this examination work, data mining methods unite with association rule features extraction and classifier. In this paper, we proposed filter based hybrid feature selection algorithm (HFSA), most relevant features are retained and used to construct classifiers for respective classes. In this system it is worked on the real time packets, which is captured using the Jpcap library. Along with HFSA method, first we are contributing approach of detecting the cyber-attack brute force by modifying the algorithms of HFSA and classification. For the classification we used the Naive bayes classifier. The performance of proposed method shows the efficiency compared to other methods.

关键词： Intrusion detection Data Mining Classifier feature selection algorithm communication networks

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A Framework for Privacy Information Protection on Android

A Framework for Privacy Information Protection on Android

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International Conference on Computing, Networking and Communications (ICNC)

作者： Jia, Peng He, Xiang Liu, Liang Gu, Binjie Fang, Yong Sichuan Univ Coll Elect & Informat Engn Chengdu Peoples R China

ISBN: (纸本)9781479969593

Permissions-based security model of Android increasingly shows its vulnerability in protecting users' privacy information. According to the permissions-based security model, an application should have the appropriate permissions before gaining various resources (including data and hardware) in the phone. This model can only restrict an application to access system resources without appropriate permissions, but can not prevent malicious accesses to privacy data after the application having obtained permissions. During the installation of an application, the system will prompt what permissions the application is requesting. Users have no choice but to allow all the requested permissions if they want to use the application. Once an application is successfully installed, the system is unable to control its behavior dynamically, and at this time the application can obtain privacy information and send them out without the acknowledgements of users. Therefore, there is a great security risk of the permissions-based security model. This paper researches on different ways to access users` privacy information and proposes a framework named PriGuard for dynamically protecting users' privacy information based on Binder communication interception technology and feature selection algorithm. Applications customarily call system services remotely by using the Binder mechanism, then access the equipment and obtain information through system services. By redirecting the Binder interface function of Native layer, PriGuard intercepts Binder messages, as a result, intercepting the application's Remote Procedure Call (RPC) for system services, then it can dynamically monitor the application's behaviors that access privacy information. In this paper, we collect many different types of benign Application Package File (APK) samples, and get the Application Programming Interface (API) calls of each sample when it is running. Afterwards we transform these API calls of each sample into feature

关键词： privacy protection binder RPC intercept android feature selection algorithm

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Prediction Model of Human Body Composition Based on Physiological Information Entropy 8

Prediction Model of Human Body Composition Based on Physiolo...

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8th International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Chen, Bo Gao, Xiu-e Zhang, Aiqiang Dalian Univ Coll Informat Engn Dalian Peoples R China

ISBN: (纸本)9781509000227

Body composition analysis can not only reflect the state of health, but also can play a role in disease prevention. Aiming at many influencing factors, complex modeling issues of the existing bioelectrical impedance analysis algorithms, this paper draws information entropy theory into modeling the human body composition for the first time, establishes entropy evaluation criteria of physiological characteristic parameters, puts forward feature selection algorithm based on physiological information entropy, selects a reasonable subset of features that can most effectively interpret body physiological information and have a minimal number of features to give the body composition prediction fitted model. Experimental results show that the algorithm can select the useful characteristic parameters and the fitted model improves the accuracy of body composition prediction.

关键词： body composition physiological information entropy feature selection algorithm fitted model

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Using PCA and LVQ Neural Network for Automatic Recognition of Five Types of White Blood Cells

Using PCA and LVQ Neural Network for Automatic Recognition o...

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32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

作者： Tabrizi, P. R. Rezatofighi, S. H. Yazdanpanah, M. J. Univ Tehran Sch Elect & Comp Engn CIPCE Tehran Iran

ISBN: (纸本)9781424441242

Designing an effective classifier has been a challenging task in the previous methods proposed in the literature. In this paper, we apply a combination of feature selection algorithm and neural network classifier in order to recognize five types of white blood cells in the peripheral blood. For this purpose, first nucleus and cytoplasm are segmented using Gram-Schmidt method and snake algorithm, respectively;second, three kinds of features are extracted from the segmented areas. Then the best features are selected using Principal Component Analysis (PCA). Finally, five types of white blood cells are classified using Learning Vector Quantization (LVQ) neural network. The performance analysis of the proposed algorithm is validated by an expert's classification results. The efficiency of the proposed algorithm is highlighted by comparing our results with those reported in a recent article which proposed a method based on the combination of Sequential Forward selection (SFS) as the feature selection algorithm and Support Vector Machines (SVM) as the classifier.

关键词： Gram-Schmidt method LVQ neural network PCA PCA neural network automatic cell recognition biomedical optical imaging blood cellular biophysics classification cytoplasm feature extraction feature extraction feature selection algorithm image classification image segmentation learning (artificial intelligence) learning vector quantization medical image processing neural nets nucleus peripheral blood principal component analysis principal component analysis segmentation sequential forward selection snake algorithm support vector machines support vector machines white blood cells

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Evaluation of the soil fertility for corn production (Zea Mays) using the multiple-criteria decision analysis (MCDA)

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MODELING EARTH SYSTEMS AND ENVIRONMENT 2020年第4期6卷 2251-2262页

作者： Mokarram, Marzieh Ghasemi, Mohammad Mehdi Zarei, Abdol Rassoul Shiraz Univ Coll Agr & Nat Resources Darab Dept Range & Watershed Management Shiraz Iran Fars Agr & Nat Resources Res & Educ Ctr Shiraz Iran Fasa Univ Fac Agr Dept Range & Watershed Management Nat Engn Fasa Iran

The ongoing water deficiencies in arid and semi-arid regions in conjunction with certain nutritional requirements highlight the importance of pinpointing the most optimal locations for cultivating agricultural products with the highest yield. Given the importance of this issue, this study proceeds to prepare soil fertility maps for corn production (Zea Mays L.) in Fars province, Iran. Initially, fuzzy membership functions (FMs) are employed to prepare fuzzy maps for each layer in the geographic information system (GIS), after which feature selection algorithms are deployed to designate the most relevant layers. The layers are then assigned specific weights obtained using a combination of analytic network process (ANP) and analytic hierarchy process (AHP) methods to prepare soil fertility maps. The input data consist of organic content (OC), phosphorus (P), potassium (K), iron (Fe), zinc (Zn), manganese (Mn), and copper (Cu). Inverse distance weighting (IDW) is utilized to procure interpolation maps for each layer. Thereafter, zoning maps are obtained using FMs. Ultimately, ANP and AHP models are once again deployed to generate the final overlayered fertility maps for corn production. The results show that combining the ANP method with feature selection (OC, K, Fe, and P) results in higher accuracy than solely applying the AHP method. Thus, incorporating feature selection and ANP methods with both inter and intra-group pair-wise comparison would result in more accurate fertility maps for corn production with lower costs and time complexity.

关键词： Corn production Geographic information system (GIS) Fuzzy map Analytic network process (ANP) Analytic hierarchy process (AHP) feature selection algorithm

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Unanticipated fault diagnosis of large telescopes based on IGWO-iForest 8

Unanticipated fault diagnosis of large telescopes based on I...

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Conference on Software and Cyberinfrastructure for Astronomy VIII

作者： Liu, Ruiqiang Xu, Lingzhe Deng, Zhuangzhuang Li, Yun Pan, Cong Yang, Shihai Chinese Acad Sci Nanjing Inst Astron Opt & Technol Nanjing 210042 Jiangsu Peoples R China Nanjing Inst Astron Opt & Technol CAS Key Lab Astron Opt & Technol Nanjing 210042 Jiangsu Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781510675261;9781510675254

A method for unanticipated fault diagnosis based on IGWO-iForest (Improved Grey Wolf Optimizer-Isolation Forest) is proposed to address various unpredictable problems faced by large telescopes in extreme environments. First, the random forest feature selection algorithm is used to identify the features of the original dataset and eliminate redundant features. Secondly, the differential evolution strategy is introduced into the GWO (Grey Wolf Optimizer) to improve the local search efficiency and accuracy, and the Levy flight strategy is introduced into the GWO to improve the global search ability of the algorithm. Then, the improved IGWO is used to optimize the parameters of the iForest model. Finally, the performance of the model is verified through data collected from a fault diagnosis and self-healing hardware-in-the-loop simulation platform. The experimental results show that the IGWO-iForest algorithm achieves a fault diagnosis accuracy of 99.1%, which demonstrates its higher sensitivity to a small number of unanticipated fault data compared with other anomaly detection algorithms, proving the effectiveness of this method in accurately diagnosing unanticipated faults in telescopes.

关键词： large telescope unanticipated failure isolated forest improved grey wolf optimization algorithm feature selection algorithm

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Evaluation of feature Extraction Techniques on Event-Related Potentials for Detection of Attention-Deficit/Hyperactivity Disorder

Evaluation of Feature Extraction Techniques on Event-Related...

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32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

作者： Castro-Cabrera, P. Gomez-Garcia, J. Restrepo, F. Moscoso, O. Castellanos-Dominguez, G. Univ Nacl Colombia Sede Manizales Colombia Univ Autonoma Manizales Caldas Colombia

ISBN: (纸本)9781424441242

Event-related potentials (ERPs) are one of the most informative and dynamic methods of monitoring cognitive processes, which are widely used in clinical research to deal a variety of psychiatric and neurological disorders as attention-deficit/hyperactivity disorder (ADHD). This work proposes an extraction and selection methodology for discriminating between normal and pathological patients with ADHD by using ERPs. Three different sets of features (morphological, wavelets, and nonlinear based) are analyzed, looking for the best classification accuracy. The results show that the wavelet features provided a good discriminative capability, but it improved by combining all the set of features and applying a feature selection algorithm, reaching a maximum accuracy rate of 91.3%.

关键词： ADHD detection attention deficit hyperactivity disorder behavioural sciences bioelectric potentials classification accuracy cognitive process monitoring electroencephalography event related potentials feature extraction feature extraction techniques feature selection algorithm medical disorders medical signal processing morphological features neurological disorders neurophysiology nonlinear features psychiatric disorders signal classification wavelet features

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Prediction of TNM stage in head and neck cancer using hybrid machine learning systems and radiomics features

Prediction of TNM stage in head and neck cancer using hybrid...

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Conference on Medical Imaging - Computer-Aided Diagnosis

作者： Salmanpour, Mohammadreza R. Hosseinzadeh, Mahdi Akbari, Azizeh Borazjani, Kasra Mojallal, Kasra Askari, Dariush Hajianfar, Ghasem Rezaeijo, Seyed Masoud Ghaemi, Mohammad M. Nabizadeh, Amir Hossein Rahmim, Arman Univ British Columbia Vancouver BC Canada BC Canc Res Inst Vancouver BC Canada Technol Virtual Collaborat TECVICO Corp Vancouver BC Canada Tarbiat Modares Univ Tehran Iran Hakim Sabzevari Univ Sab Zevar Iran Univ Tehran Tehran Iran Amirkabir Univ Technol Tehran Iran Shahid Beheshti Univ Med Dept Radiol Technol Tehran Iran Iran Univ Med Sci Rajaie Cardiovasc Med & Res Ctr Tehran Iran Ahvaz Jundishapur Univ Med Sci Sch Med Dept Med Phys Ahvaz Iran Kerman Univ Med Sci Kerman Iran IST Univ Lisbon P-1000029 Lisbon Portugal

ISBN: (数字)9781510649422

ISBN: (纸本)9781510649422;9781510649415

The tumor, node, metastasis (TNM) staging system enables clinicians to describe the spread of head-and-neck-squamous-cell-carcinoma (HNSCC) cancer in a specific manner to assist with the assessment of disease status, prognosis, and management. This study aims to predict TNM staging for HNSCC cancer via Hybrid Machine Learning Systems (HMLSs) and radiomics features. In our study, 408 patients were included from the Cancer Imaging Archive (TCIA) database, included in a multi-center setting. PET images were registered to CT, enhanced, and cropped. We created 9 sets including CT-only, PET-only, and 7 PET-CT fusion sets. Next, 215 radiomics features were extracted from HNSCC tumor segmented by the physician via our standardized SERA radiomics package. We employed multiple HMLSs, including 16 feature-extraction (FEA) + 9 feature selection algorithms (FSA) linked with 8 classifiers optimized by grid-search approach, with model training, fine-tuning, and selection (5-fold cross-validation;319 patients), followed by external-testing of selected model (89 patients). Datasets were normalized by z-score-technique, with accuracy reported to compare models. We first applied datasets with all features to classifiers only;accuracy of 0.69 +/- 0.06 was achieved via PET applied to Random Forest classifier (RFC);performance of external testing (similar to 0.62) confirmed our finding. Subsequently, we employed FSAs/FEAs prior to the application of classifiers. We achieved accuracy of 0.70 +/- 0.03 for Curvelet transform (fusion) + Correlation-based feature selection (FSA) + K-Nearest Neighbor (classifier), and 0.70 +/- 0.05 for PET + LASSO (FSA) + RFC (classifier). Accuracy of external testing (0.65 & 0.64) also confirmed these findings. Other HMLSs, applied on some fused datasets, also resulted in close performances. We demonstrate that classifiers or HMLSs linked with PET only and PET-CT fusion techniques enabled relatively low improved accuracy in predicting TNM stage. Meanwhile, th

关键词： prediction of TNM Stage hybrid machine learning system radiomics feature fusion techniques head and neck squamous cell carcinoma feature extraction algorithm feature selection algorithm classifier

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The Hemodynamic Parameters Values Prediction on the Non-Invasive Hydrocuff Technology Basis with a Neural Network Applying

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SENSORS 2022年第11期22卷 4229页

作者： Markuleva, Marina Gerashchenko, Mikhail Gerashchenko, Sergey Khizbullin, Robert Ivshin, Igor Penza State Univ Med Cybernet & Comp Sci Dept Penza 440026 Russia Kazan State Power Engn Univ Krasnoselskaya 51 Kazan 420066 Russia

The task to develop a mechanism for predicting the hemodynamic parameters values based on non-invasive hydrocuff technology of a pulse wave signal fixation is described in this study. The advantages and disadvantages of existing methods of recording the ripple curve are noted in the published materials. This study proposes a new hydrocuff method for hemodynamic parameters and blood pressure values measuring. A block diagram of the device being developed is presented. algorithms for processing the pulse wave contour are presented. A neural network applying necessity for the multiparametric feature space formation is substantiated. The pulse wave contours obtained using hydrocuff technology of oscillation formation for various age groups are presented. According to preliminary estimates, by the moment of the dicrotic surge formation, it is possible to judge the ratio of the heart and blood vessels work, which makes it possible to form an expanded feature space of significant parameters based on neural network classifiers. This study presents the characteristics accounted for creating a database for training a neural network.

关键词： blood pressure pressure pulse wave hemodynamic parameters hydrocuff technology feature selection algorithm multiparameter feature space machine learning

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Integrative artificial intelligence models for Australian coastal sediment lead prediction: An investigation of in-situ measurements and meteorological parameters effects

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022年 309卷 114711-114711页

作者： Bhagat, Suraj Kumar Tiyasha, Tiyasha Kumar, Adarsh Malik, Tabarak Jawad, Ali H. Khedher, Khaled Mohamed Deo, Ravinesh C. Yaseen, Zaher Mundher Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Ural Fed Univ Inst Nat Sci & Math Ekaterinburg 620002 Russia Univ Gondar Coll Med & Hlth Sci Sch Med Dept Biochem Gondar Ethiopia Univ Teknol MARA Fac Appl Sci Shah Alam 40450 Selangor Malaysia King Khalid Univ Coll Engn Dept Civil Engn Abha 61421 Saudi Arabia Mrezgua Univ Campus High Inst Technol Studies Dept Civil Engn Nabeul 8000 Tunisia Univ Southern Queensland Sch Math Phys & Comp Springfield Qld 4300 Australia Univ Southern Queensland Sch Math Phys & Comp USQs Adv Data Analyt Res Grp Toowoomba Qld 4350 Australia South Ural State Univ Inst Architecture & Construct Dept Urban Planning Engn Networks & Syst 76 Lenin Prospect Chelyabinsk 454080 Russia Asia Univ Coll Creat Design Taichung Taiwan Al Ayen Univ Sci Res Ctr New Era & Dev Civil Engn Res Grp Thi Qar 64001 Iraq Univ Teknol MARA Inst Big Data Analyt & Artificial Intelligence IB Kompleks Al Khawarizmi Shah Alam 40450 Selangor Malaysia

Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay's ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (T-min, T-max and T-avg ?), rainfall (R-n mm) and their interactions with the other batch HMs, are hypothesized to have high impact for the decision-making strategies to minimize the impacts of Pb. Three feature selection (FS) algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia. These FS algorithms were statistically evaluated using principal component analysis (PCA) Biplot along with the correlation metrics describing the statistical characteristics that exist in the input and output parameter space of the models. To ensure a high accuracy attained by the applied predictive artificial intelligence (AI) models i.e., XGBoost, support vector machine (SVM) and random forest (RF), an auto-hyper-parameter tuning process using a Grid-search approach was also implemented. Cu, Ni, Ce, and Fe were selected by all the three applied FS algorithms whereas the T-avg and R-n & nbsp;inputs remained the essential parameters identified by GA and Boruta. The order of the FS outcome was XGBoost > GA > Boruta based on the applied statistical examination and the PCA Biplot results and the order of applied AI predictive models was XGBoost-SVM > GA-SVM > Boruta-SVM, where the SVM model remained at the top performance among the other statistical metrics. Based on the Taylor diagram for model evaluation, the RF model was reflected only marginally different so overall, the proposed integrative AI model provided an evidence a robust and reliable predictive technique used for coastal sediment Pb prediction.

关键词： Artificial intelligence feature selection algorithm Sediment heavy metals Lead prediction Meteorological parameters

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