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23rd International Conference on Computer and Information Technology (ICCIT)

作者： Bin Ashraf, Faisal Shafi, Md Shafiur Raihan Kabir, Md Rayhan Brac Univ Dept Comp Sci & Engn Dhaka Bangladesh Southeast Univ Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781665422444

Microbiomes, micro organisms living in a host environment, play significant r ole i n t he r egular a ctivities and abnormalities of the host. Researches throughout the world has found symbiotic relations between the human microbiomes and human physiology, immunity, diseases etc. Outstanding development in sequencing technology has paved the way to analyze large number of samples with reasonable cost. Applying supervised machine learning techniques on these data can help to find out the most i mportant microbiomes residing in the host environment and build a predictive model to classify unknown samples. In this study, we have applied different supervised classification a lgorithms a long w ith s ome e nsemble techniques to find a b etter p redictive model t o p redict t he t rait o f human host for the prognosis of colorectal cancer. Our study finds that, tree based classification a lgorithms w orks b est f or classifying the human microbiome data for colorectal cancer which are large, sparse and dispersed in nature. We have also identified important microbiomes that acts as a deciding factor behind colorectal cancer.

关键词： Human Microbiome Colorectal Cancer Prediction supervised algorithms

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Comparison Between Two Deep Learning Models for Temperature Prediction at Guayaquil 5th

Comparison Between Two Deep Learning Models for Temperature ...

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5th International Conference on Technologies and Innovation (CITI)

作者： Perez-Espinoza, Charles M. Sanchez-Guerrero, Johanna Samaniego-Cobos, Teresa Beltran-Robayo, Nuvia Univ Guayaquil Guayaquil Ecuador Univ Agr Ecuador Guayaquil Ecuador

ISBN: (纸本)9783030349899;9783030349882

Weather prediction is a subject that is constantly changing everywhere in the world because of the different methods that are applied. This study is done in the city of Guayaquil remembering that weather forecast has played a very important role for many people who belong to different fields of research because it needs to have a minimum margin of error in order to meet the different objectives of each researcher. This paper aims to find the best type of MLP or LSTM neural network model that has a lower margin of error when predicting the weather at a specific weather station in the aforementioned city. In order to assess the accuracy between these prediction models, the Euclidean estimation standard was used. With the results of this comparison, it is hoped to contribute to the prediction of the climate to be able to help not only the researchers but also the farmers, tourists, and people in general whose work depends on this topic.

关键词： Meteorology Deep learning Artificial intelligence Neural networks Guayaquil supervised algorithms

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A Review of Payment Card Fraud Detection Methods Using Artificial Intelligence 1

A Review of Payment Card Fraud Detection Methods Using Artif...

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

作者： Sengupta, Eishvak Jain, Naman Garg, Dhruv Choudhury, Tanupriya Amity Univ Noida Uttar Pradesh India UPES Dept Informat Sch Comp Sci Dehra Dun India

ISBN: (纸本)9781538677094

In the light of the twenty first century, payment cards like credit and debit cards are absolute basics in the global economic transactions. As these payment methods are popularising, the information associated with these are being accessed by a lot more people than required increasing the risk of a possible deceit. This paper presents a comprehensive and systematic review of artificial intelligence and machine learning algorithms and techniques for payment card fraud detection. This paper reviews that can be incorporated in the system according to specific needs.

关键词： Credit cards Artificial Intelligence supervised algorithms Genetic Algorithm Neural Network

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Phishing Websites Prediction Using Classification Techniques

Phishing Websites Prediction Using Classification Techniques

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International Conference on New Trends in Computing Sciences (ICTCS)

作者： Ibrahim, Dyana Rashid Hadi, Ali Hussein Princess Sumaya Univ Technol Dept Comp Sci Amman Jordan

ISBN: (纸本)9781538605271

Phishing is an important issue that faces the cyber security. This paper exploits the capabilities of classification techniques on Phishing Website Prediction (PWP), and introduces a methodology to protect users from the attackers. The blacklist procedure isn't a strong enough way to stay safe from the cybercriminals. Therefore, phishing website indicators have to be considered for this purpose, with the existence and usage of machine learning algorithms. Five different classification techniques have been used to evaluate their efficiency on (PWP) in terms of accuracy and the Relative Absolute Error (RAE) value for each one of them, with and without the feature selection process. WEKA tool was used for the implementation of these classifiers on a public dataset from NASA repository. The motivation behind this investigation is to employ a number of Data Mining (DM) algorithms for the prediction purpose of phishing websites and compare their effectiveness in terms of accuracy and RAE. Where DM classifiers have proved their goodness in this kind of problems.

关键词： data mining feature selection machine learning optimization algorithms phishing website prediction supervised algorithms

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Breast thermal images classification using optimal feature selectors and classifiers

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JOURNAL OF ENGINEERING-JOE 2016年第7期2016卷 237-248页

作者： Lashkari, AmirEhsan Pak, Fatemeh Firouzmand, Mohammad IROST Inst Elect Engn & Informat Technol Dept Biomed Engn Tehran Iran

In this study, a full automatic technique has been presented to assist physicians in early detection of breast cancer based on different degrees. First the region of interest is determined using full automatic operation and the quality of image is improved. Then, some features including statistical, morphological, frequency domain, histogram and grey-level co-occurrence matrix features are extracted from segmented right and left breasts. Subsequently, to achieve the best features and increase the accuracy of the proposed method, feature selectors such as minimum redundancy and maximum relevance, sequential forward selection, sequential backward selection, sequential floating forward selection, sequential floating backward selection and genetic algorithm have been used. Finally, to classify and TH labeling procedures, supervised learning techniques such as AdaBoost, support vector machine, nearest neighbor, Naive Bayes and probability neural network are applied and compared with each other. The results obtained on native database showed the significant performance of the proposed algorithm in comprising to the similar studies. The experimental results gave the best mean accuracy of 88.03% for only using 0 degrees image with combination of mRMR and AdaBoost and for combination of 3 degrees with combination of GA and AdaBoost.

关键词： biological organs image classification cancer medical image processing infrared imaging biomedical optical imaging biological tissues statistical analysis feature extraction image segmentation genetic algorithms learning (artificial intelligence) support vector machines full automatic technique supervised algorithms breast thermal image classification breast cancer detection cancerous mammary tissue sympathetic nervous system cold stimulus region of interest full automatic operation grey-level cooccurrence matrix features frequency domain statistical features morphological features frequency domain features histogram features sequential floating forward selection sequential floating backward selection genetic algorithm TH labelling procedures supervised learning techniques AdaBoost support vector machine k-nearest neighbours Naive Bayes ice testing native database probability neural network

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Catching WebSpam Traffic with Artificial Immune System (AIS) Classification Algorithm 7

Catching WebSpam Traffic with Artificial Immune System (AIS)...

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7th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Iqbal, Muhammad Abid, Malik Muneeb Ahmad, Muqeet Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu Sichuan Peoples R China Bahria Univ Dept Comp Sci Karachi Pakistan Southwest Jiaotong Univ Sch Transportat & Logist Chengdu Sichuan Peoples R China

ISBN: (纸本)9781467399043

Everyday a large number of internet users are being encountered with web spamming where the search engines produce false ranking to web sites due to the use of unethical methods of Search Engine Optimization (SEO). The objective of the paper is to identify the spam traffic by using Artificial Immune System (AIS) classification algorithm. The paper presents chi square method for attribute selection of different machine learning methods, including proposed biological inspired Artificial Immune System for Spam Classification (AISSC) method, which provides a solution for supervised classification problem. In order to show the efficiency of proposed algorithm results are compared and analyzed with well-known classifiers i.e., Naive Bayes and J48. Experimental work is performed on Webspam-uk-2007 dataset. The results of proposed method show prominent achievements with increase in number of features to train the model.

关键词： Web Spam Spam detection supervised algorithms AISSC

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Catching WebSpam Traffic with Artificial Immune System(AIS) Classification Algorithm

Catching WebSpam Traffic with Artificial Immune System(AIS) ...

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2016 IEEE 7th International Conference on Software Engineering and Service Science

作者： Muhammad Iqbal Malik Muneeb Abid Muqeet Ahmad School of Information Sciences and Technology Southwest Jiaotong University Department of Computer Science Bahria University School of Transporiation and Logistics Southwest Jiaotong University

Everyday a large number of internet users are being encountered with web spamming where the search engines produce false ranking to web sites due to the use of unethical methods of Search Engine Optimization(SEO).The objective of the paper is to identify the spam traffic by using Artificial Immune System(AIS) classification *** paper presents chi square method for attribute selection of different machine learning methods,including proposed biological inspired Artificial Immune System for Spam Classification(AISSC) method,which provides a solution for supervised classification *** order to show the efficiency of proposed algorithm results are compared and analyzed with wellknown classifiers i.e.,Naive Bayes and *** work is performed on Webspam-uk-2007 *** results of proposed method show prominent achievements with increase in number of features to train the model.

关键词： Web Spam Spam detection supervised algorithms AISSC

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An Experimental Study for some supervised Lexical Disambiguation methods of Arabic Language

An Experimental Study for some supervised Lexical Disambigua...

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4th International Conference on Information and Communication Technology and Accessibility (ICTA)

作者： Merhbene, Laroussi Zouaghi, Anis Zrigui, Mounir Univ Monastir Fac Sci Monastir LAtice Lab Monastir 5000 Tunisia Univ Sousse ISSAT sousse LAtice lab Sousse 4023 Tunisia

ISBN: (纸本)9781479927265

In this paper we propose an experimental study for some supervised algorithms to disambiguate arabic words. Due to the lack of linguistic data for the Arabic language, we work on non-annotated corpus and with the help of four annotators;we were able to annotate the different samples containing the ambiguous words. Since that, we test the naive bayes algorithm, the decision lists and the exemplar based algorithm. During the experimental study, we test the influence of the window size on the disambiguation quality, the derivation and the technique of smoothing for the (2n+1)-grams. We find that the exemplar based algorithm achieves the best rate of precision.

关键词： supervised algorithms Training data naive Bayes Decision list Exemplar based algorithm Window size

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