International Journal of Advanced Research in Artificial Intelligence最新文献

{"title":"Category Decomposition Method for Un-Mixing of Mixels Acquired with Spaceborne Based Visible and Near Infrared Radiometers by Means of Maximum Entropy Method with Parameter Estimation Based on Simulated Annealing","authors":"K. Arai","doi":"10.14569/IJARAI.2013.020410","DOIUrl":"https://doi.org/10.14569/IJARAI.2013.020410","url":null,"abstract":"Category decomposition method for un-mixing of mixels (Mixed Pixels) which is acquired with spaceborne based visible to near infrared radiometers by means of Maximum Entropy Method (MEM) with parameter estimation based on Simulated Annealing: SA is proposed. Through simulation studies with spectral characteristics of the ground cover targets which are derived from spectral library and actual remote sensing satellite imagery data, it is confirmed that the proposed method works well.","PeriodicalId":323606,"journal":{"name":"International Journal of Advanced Research in Artificial Intelligence","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128911031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Simulated Multiagent-Based Architecture for Intrusion Detection System","authors":"O. Adebukola, Ajayi Bamidele, A. Taofik","doi":"10.14569/IJARAI.2013.020406","DOIUrl":"https://doi.org/10.14569/IJARAI.2013.020406","url":null,"abstract":"In this work, a Multiagent-based architecture for Intrusion Detection System (MIDS) is proposed to overcome the shortcoming of current Mobile Agent-based Intrusion Detection System. MIDS is divided into three major phases namely: Data gathering, Detection and the Response phases. The data gathering stage involves data collection based on the features in the distributed system and profiling. The data collection components are distributed on both host and network. Closed Pattern Mining (CPM) algorithm is introduced for profiling users’ activities in network database. The CPM algorithm is built on the concept of Frequent Pattern-growth algorithm by mining a prefix-tree called CPM-tree, which contains only the closed itemsets and its associated support count. According to the administrator’s specified thresholds, CPM-tree maintains only closed patterns online and incrementally outputs the current closed frequent pattern of users’ activities in real time. MIDS makes use of mobile and static agents to carry out the functions of intrusion detection. Each of these agents is built with rule-based reasoning to autonomously detect intrusions. Java 1.1.8 is chosen as the implementation language and IBM’s Java based mobile agent framework, Aglet 1.0.3 as the platform for running the mobile and static agents. In order to test the robustness of the system, a real-time simulation is carried out on University of Agriculture, Abeokuta (UNAAB) network dataset and the results showed an accuracy of 99.94%, False Positive Rate (FPR) of 0.13% and False Negative Rate (FNR) of 0.04%. This shows an improved performance of MIDS when compared with other known MA-IDSs.","PeriodicalId":323606,"journal":{"name":"International Journal of Advanced Research in Artificial Intelligence","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122689539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index","authors":"A. Sheta, Sara Ahmed, Hossam Faris","doi":"10.14569/IJARAI.2015.040710","DOIUrl":"https://doi.org/10.14569/IJARAI.2015.040710","url":null,"abstract":"Obtaining accurate prediction of stock index sig-nificantly helps decision maker to take correct actions to develop a better economy. The inability to predict fluctuation of the stock market might cause serious profit loss. The challenge is that we always deal with dynamic market which is influenced by many factors. They include political, financial and reserve occasions. Thus, stable, robust and adaptive approaches which can provide models have the capability to accurately predict stock index are urgently needed. In this paper, we explore the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVM) to build prediction models for the S&P 500 stock index. We will also show how traditional models such as multiple linear regression (MLR) behave in this case. The developed models will be evaluated and compared based on a number of evaluation criteria.","PeriodicalId":323606,"journal":{"name":"International Journal of Advanced Research in Artificial Intelligence","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122932755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Information-Theoretic Measure for Face Recognition: Comparison with Structural Similarity","authors":"A. F. Hassan, Z. M. Hussain, Dong Cai-lin","doi":"10.14569/IJARAI.2014.031102","DOIUrl":"https://doi.org/10.14569/IJARAI.2014.031102","url":null,"abstract":"Automatic recognition of people faces is a challenging problem that has received significant attention from signal processing researchers in recent years. This is due to its several applications in different fields, including security and forensic analysis. Despite this attention, face recognition is still one among the most challenging problems. Up to this moment, there is no technique that provides a reliable solution to all situations. In this paper a novel technique for face recognition is presented. This technique, which is called ISSIM, is derived from our recently published information - theoretic similarity measure HSSIM, which was based on joint histogram. Face recognition with ISSIM is still based on joint histogram of a test image and a database images. Performance evaluation was performed on MATLAB using part of the well-known AT&T image database that consists of 49 face images, from which seven subjects are chosen, and for each subject seven views (poses) are chosen with different facial expressions. The goal of this paper is to present a simplified approach for face recognition that may work in real-time environments. Performance of our information - theoretic face recognition method (ISSIM) has been demonstrated experimentally and is shown to outperform the well-known, statistical-based method (SSIM).","PeriodicalId":323606,"journal":{"name":"International Journal of Advanced Research in Artificial Intelligence","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116396060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Zernike Moment Feature Extraction for Handwritten Devanagari (Marathi) Compound Character Recognition","authors":"K. Kale, P. Deshmukh, S. V. Chavan, M. Kazi, Y. Rode","doi":"10.14569/IJARAI.2014.030110","DOIUrl":"https://doi.org/10.14569/IJARAI.2014.030110","url":null,"abstract":"Compound character recognition of Devanagari\u0000script is one of the challenging tasks since the characters are complex\u0000in structure and can be modified by writing combination of\u0000two or more characters. These compound characters occurs 12 to\u000015% in the Devanagari Script. The moment based techniques are\u0000being successfully applied to several image processing problems\u0000and represents a fundamental tool to generate feature descriptors\u0000where the Zernike moment technique has a rotation invariance\u0000property which found to be desirable for handwritten character\u0000recognition. This paper discusses extraction of features from\u0000handwritten compound characters using Zernike moment feature\u0000descriptor and proposes SVM and k-NN based classification system.\u0000The proposed classification system preprocess and normalize\u0000the 27000 handwritten character images into 30x30 pixels images\u0000and divides them into zones. The pre-classification produces three\u0000classes depending on presence or absence of vertical bar. Further\u0000Zernike moment feature extraction is performed on each zone.\u0000The overall recognition rate of proposed system using SVM and\u0000k-NN classifier is upto 98.37%, and 95.82% respectively.","PeriodicalId":323606,"journal":{"name":"International Journal of Advanced Research in Artificial Intelligence","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125919703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FlexRFID: A Security and Service Control Policy- Based Middleware for Context-Aware Pervasive Computing","authors":"Mehdia Ajana El Khaddar, Mhammed Chraibi, H. Harroud, M. Boulmalf, M. Elkoutbi, A. Maach","doi":"10.14569/IJARAI.2014.031004","DOIUrl":"https://doi.org/10.14569/IJARAI.2014.031004","url":null,"abstract":"Ubiquitous computing targets the provision of seamless services and applications by providing an environment that involves a variety of devices having different capabilities. The design of applications in these environments needs to consider the heterogeneous devices, applications preferences, and rapidly changing contexts. RFID and WSN technologies are widely used in today’s ubiquitous computing. In Wireless Sensor Networks, sensor nodes sense the physical environment and send the sensed data to the sink by multi-hops. WSN are used in many applications such as military and environment monitoring. In Radio Frequency Identification, a unique ID is assigned to a RFID tag which is associated with a real world object. RFID applications cover many areas such as Supply Chain Management (SCM), healthcare, library management, automatic toll collection, etc. The integration of both technologies will bring many advantages in the future of ubiquitous computing, through the provision of real-world tracking and context information about the objects. This will increase considerably the automation of an information system. In order to process the large volume of data captured by sensors and RFID readers in real time, a middleware solution is needed. This middleware should be designed in a way to allow the aggregation, filtering and grouping of the data captured by the hardware devices before sending them to the backend applications. In this paper we demonstrate how our middleware solution called FlexRFID handles large amount of RFID and sensor scan data, and executes applications’ business rules in real time through its policy-based Business Rules layer. The FlexRFID middleware provides easy addition and removal of hardware devices that capture data, as well as uses the business rules of the applications to control all its services. We demonstrate how the middleware controls some defined healthcare scenarios, and deals with the access control security concern to sensitive healthcare data through the use of policies. We propose hereafter the design of FlexRFID middleware along with its evaluation results.","PeriodicalId":323606,"journal":{"name":"International Journal of Advanced Research in Artificial Intelligence","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116596191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Sentiment Analysis Methods and Identifying Scope of Negation in Newspaper Articles","authors":"S. Padmaja, S. Fatima, Sasidhar Bandu","doi":"10.14569/IJARAI.2014.031101","DOIUrl":"https://doi.org/10.14569/IJARAI.2014.031101","url":null,"abstract":"Automatic detection of linguistic negation in free text is a demanding need for many text processing applications including Sentiment Analysis. Our system uses online news archives from two different resources namely NDTV and The Hindu. While dealing with news articles, we performed three subtasks namely identifying the target; separation of good and bad news content from the good and bad sentiment expressed on the target and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. In this paper, our main focus was on evaluating and comparing three sentiment analysis methods (two machine learning based and one lexical based) and also identifying the scope of negation in news articles for two political parties namely BJP and UPA by using three existing methodologies. They were Rest of the Sentence (RoS), Fixed Window Length (FWL) and Dependency Analysis (DA). Among the sentiment methods the best F-measure was SVM with the values 0.688 and 0.657 for BJP and UPA respectively. On the other hand, the F measures for RoS, FWL and DA were 0.58, 0.69 and 0.75 respectively. We observed that DA was performing better than the other two. Among 1675 sentences in the corpus, according to annotator I, 1,137 were positive and 538 were negative whereas according to annotator II, 1,130 were positive and 545 were negative. Further we also identified the score of each sentence and calculated the accuracy on the basis of average score of both the annotators.","PeriodicalId":323606,"journal":{"name":"International Journal of Advanced Research in Artificial Intelligence","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116640471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visualization of Link Structures and URL Retrievals Utilizing Internal Structure of URLs Based on Brunch and Bound Algorithms","authors":"K. Arai","doi":"10.14569/IJARAI.2012.010803","DOIUrl":"https://doi.org/10.14569/IJARAI.2012.010803","url":null,"abstract":"Method for visualization of URL link structure and URL retrievals using internal structure of URLs based on brunch and bound method is proposed. Twisting link structure of URLs can be solved by the proposed visualization method. Also some improvements are observed for the proposed brunch and bound based method in comparison to the conventional URL retrieval methods.","PeriodicalId":323606,"journal":{"name":"International Journal of Advanced Research in Artificial Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116798148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of K-Means Algorithm for Efficient Customer Segmentation: A Strategy for Targeted Customer Services","authors":"C. P. Ezenkwu, S. Ozuomba, C. Kalu","doi":"10.14569/IJARAI.2015.041007","DOIUrl":"https://doi.org/10.14569/IJARAI.2015.041007","url":null,"abstract":"The emergence of many business competitors has engendered severe rivalries among competing businesses in gaining new customers and retaining old ones. Due to the preceding, the need for exceptional customer services becomes pertinent, notwithstanding the size of the business. Furthermore, the ability of any business to understand each of its customers’ needs will earn it greater leverage in providing targeted customer services and developing customised marketing programs for the customers. This understanding can be possible through systematic customer segmentation. Each segment comprises customers who share similar market characteristics. The ideas of Big data and machine learning have fuelled a terrific adoption of an automated approach to customer segmentation in preference to traditional market analyses that are often inefficient especially when the number of customers is too large. In this paper, the k-Means clustering algorithm is applied for this purpose. A MATLAB program of the k-Means algorithm was developed (available in the appendix) and the program is trained using a z-score normalised two-feature dataset of 100 training patterns acquired from a retail business. The features are the average amount of goods purchased by customer per month and the average number of customer visits per month. From the dataset, four customer clusters or segments were identified with 95% accuracy, and they were labeled: High-Buyers-Regular-Visitors (HBRV), High-Buyers-Irregular-Visitors (HBIV), Low-Buyers-Regular-Visitors (LBRV) and Low-Buyers-Irregular-Visitors (LBIV).","PeriodicalId":323606,"journal":{"name":"International Journal of Advanced Research in Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117107949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Method for Traffic Flow Estimation using On- dashboard Camera Image","authors":"K. Arai, S. R. Sentinuwo","doi":"10.14569/IJARAI.2014.030204","DOIUrl":"https://doi.org/10.14569/IJARAI.2014.030204","url":null,"abstract":"This paper presents the method to estimate the traffic flow on the urban roadway by using car’s on-dashboard camera image. The system described, shows something new which utilizes only road traffic photo images to get the information about urban roadway traffic flow automatically.","PeriodicalId":323606,"journal":{"name":"International Journal of Advanced Research in Artificial Intelligence","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131192243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}