Multi-Aras Rangkaian Neural untuk Pengecaman Warna

Keperluan sistem pengecaman warna secara automatik dalam industri, aplikasi secara komersil, mahupun pertanian telah menjadi semakin penting. Contohnya seperti pengkodan warna dalam pembuatan barangan elektrik, kesesuaian ton warna dalam menyamak kulit binatang dan dalam industri cat, pengecaman warna sebagai bantuan bagi yang buta atau buta-warna dan pengecaman sebagai parameter yang boleh dipercayai bagi pengecaman objek dalam robotik. Contoh yang lebih khusus ialah pengkelasan warna berlian, pengawalan kualiti bagi pembuatan kertas warna dan penggredan buah-buahan berdasarkan warna. Kaedah multi-aras rangkaian neural digunakan untuk mengecam warna secara automatik. Data yang mewakili warna diimbas menggunakan Minolta Chroma Meter yang berupaya menukarkan warna kepada nilai. Ia menyediakan lima sistem wama bagi pengukuran kromatisiti iaitu CIE Yxy, L*a*b*, L*C*Ho, Hunter Lab dan XYZ. Hanya sistem warna L *a*b* yang digunakan bagi kajian ini. Pada awal kajian, dua jenis rangkaian neural digunakan iaitu backpropagation (BP) dan counterp ropagation (CPN). Sebanyak 100 data (warna) digunakan sebagai pengujian. Hasilnya didapati dalam masa yang singkat, CPN telah mencapai 100% pengecaman data yang dilatih dan data yang tidak dilatih berbanding dengan BP yang hanya mencapai 49% pengecaman bagi data dilatih dan 48% bagi data tidak dilatih. Apabila bilangan data ditambah kepada 808, proses latihan memerlukan ruang ingatan yang besar, masa pembelajaran yang lebih lama dan peratus pengecaman kurang memuaskan. Bagi menyelesaikan masalah tersebut, gabungan dua rangkaian CPN telah dibangunkan. Hasilnya peratus pengecaman bertambah baik berbanding kajian awal dengan 99% pengecaman bagi data yang dilatih dan data yang tidak dilatih

An investigation of back-propagation neural network on university selection

Processing thousands of applications can be a challenging task, especially when the applicant does not consider the university requirements and their qualification, while in some cases, the selection officer may face difficulties in deciding if more than one candidate has the same qualification for a limited vacancy of a particular program. In this paper, we present an investigation on university selection using back-propagation neural network to assist the selection officer in selecting eligible applicants based on SPM results. The experiments have shown the back-propagation method produced better performance with the average more than 90% accuracy for student selection across all of sets of the test data

Recognition of food with monotonous appearance using speeded-up robust feature (SURF)

Food has become one of the most photographed objects since the inceptions of smart phones and social media services. Recently, the analysis of food images using object recognition techniques have been investigated to recognize food categories. It is a part of a framework to accomplish the tasks of estimating food nutrition and calories for health-care purposes. The initial stage of food recognition pipeline is to extract the features in order to capture the food characteristics. A local feature by using SURF is among the efficient image detector and descriptor. It is using fast hessian detector to locate interest points and haar wavelet for descriptions. Despite the fast computation of SURF extraction, the detector seems ineffective as it obviously detects quite a small volume of interest points on the food objects with monotonous appearance. It occurs due to 1) food has texture-less surface 2) image has small pixel dimensions, and 3) image has low contrast and brightness. As a result, the characteristics of these images that were captured are clueless and lead to low classification performance. This problem has been manifested through low production of interest points. In this paper, we propose a technique to detect denser interest points on monotonous food by increasing the density of blobs in fast hessian detector in SURF. We measured the effect of this technique by performing a comparison on SURF interest points detection by using different density of blobs detection. SURF is encoded by using Bag of Features (BoF) model and Support Vector Machine (SVM) with linear kernel adopted for classification. The findings has shown the density of interest point detection has prominent effect on the interest points detection and classification performance on the respective food categories with 86% classification accuracy on UEC100-Food dataset

Device verification and compatibility for heterogeneous semantic IoT systems

Interoperability has always been ambiguous for heterogeneous IoT device management. A lot work done in the past focused on the protocol management and to the extent of application inter-operation. Yet, semantic gap in ensuring federated message exchange among heterogeneous IoT devices remain as the significant challenge. In this paper, we illustrate device compatibility using semantic rule between multiple devices in a heterogeneous IoT system. The proposed approach is SWRL based on the light weight IoT ontologies which was modeled and also correspond to segregation of devices based on device compatibility. SWRL was deployed for verifying the compatibility between multiple devices using the semantic rule engine based on bespoke parameters. Performance evaluation was carried out with execution time of transferring axioms of ontology to rules engine, execution time of rules in the engine and the time taken for axioms reflected to the ontology between all device instances defined in the ontology

An extended ID3 decision tree algorithm for spatial data

Utilizing data mining tasks such as classification on spatial data is more complex than those on non-spatial data. It is because spatial data mining algorithms have to consider not only objects of interest itself but also neighbours of the objects in order to extract useful and interesting patterns. One of classification algorithms namely the ID3 algorithm which originally designed for a non-spatial dataset has been improved by other researchers in the previous work to construct a spatial decision tree from a spatial dataset containing polygon features only. The objective of this paper is to propose a new spatial decision tree algorithm based on the ID3 algorithm for discrete features represented in points, lines and polygons. As in the ID3 algorithm that use information gain in the attribute selection, the proposed algorithm uses the spatial information gain to choose the best splitting layer from a set of explanatory layers. The new formula for spatial information gain is proposed using spatial measures for point, line and polygon features. Empirical result demonstrates that the proposed algorithm can be used to join two spatial objects in constructing spatial decision trees on small spatial dataset. The proposed algorithm has been applied to the real spatial dataset consisting of point and polygon features. The result is a spatial decision tree with 138 leaves and the accuracy is 74.72%

Modeling forest fires risk using spatial decision tree

Forest fires have long been annual events in many parts of Sumatra Indonesia during the dry season. Riau Province is one of the regions in Sumatra where forest fires seriously occur every year mostly because of human factors both on purposes and accidently. Forest fire models have been developed for certain area using the weightage and criterion of variables that involve the subjective and qualitative judging for variables. Determining the weights for each criterion is based on expert knowledge or the previous experienced of the developers that may result too subjective models. In addition, criteria evaluation and weighting method are most applied to evaluate the small problem containing few criteria. This paper presents our initial work in developing a spatial decision tree using the spatial ID3 algorithm and Spatial Join Index applied in the SCART (Spatial Classification and Regression Trees) algorithm. The algorithm is applied on historic forest fires data for a district in Riau namely Rokan Hilir to develop a model for forest fires risk. The modeling forest fire risk includes variables related to physical as well as social and economic. The result is a spatial decision tree containing 138 leaves with distance to nearest river as the first test attribute

Overview of cross site request forgery and client-side protection

As long as internet and web application are a part of our lives to let us to live as easy as we moved like: online market, online bank, online shop and many more, it take attention of malicious to take an advantage of our easy life. Lately there are many types of attacks on web application but so far mostly focused Cross Site Scripting and SQL injection attacks. However there is less attention to prevent Cross Site Request. Cross Site Request Forgery permits malicious to make a request on behalf of user without his/her knowledge. The attack used the authentication between the target website and user through the internet browser. In this paper we would present how Cross Site Request forgery attack works. In additional we present our approach to mitigate Cross Site Request forgery by PCSRF Framework (Prevent Cross Site Request forgery) on Firefox. We propose client side protection. We had experimental test of our framework functionality. From 134 numbers of attacks which contains Post, Get and other methods, we successfully managed to prevent over 79% of attack through three different test sections

An extended ID3 decision tree algorithm for spatial data

Utilizing data mining tasks such as classification on spatial data is more complex than those on non-spatial data. It is because spatial data mining algorithms have to consider not only objects of interest itself but also neighbours of the objects in order to extract useful and interesting patterns. One of classification algorithms namely the ID3 algorithm which originally designed for a non-spatial dataset has been improved by other researchers in the previous work to construct a spatial decision tree from a spatial dataset containing polygon features only. The objective of this paper is to propose a new spatial decision tree algorithm based on the ID3 algorithm for discrete features represented in points, lines and polygons. As in the ID3 algorithm that use information gain in the attribute selection, the proposed algorithm uses the spatial information gain to choose the best splitting layer from a set of explanatory layers. The new formula for spatial information gain is proposed using spatial measures for point, line and polygon features. Empirical result demonstrates that the proposed algorithm can be used to join two spatial objects in constructing spatial decision trees on small spatial dataset. The proposed algorithm has been applied to the real spatial dataset consisting of point and polygon features. The result is a spatial decision tree with 138 leaves and the accuracy is 74.72%

Spatial data mining application in forest fire assessment in tropical peat areas

Forest fires are considered a potential hazard that causes physical, biological, and environmental losses. Recent forest fires in tropical peat areas have created atmospheric haze and transboundary pollution. Identifying high fire hazard areas in tropical peat areas can help in forest fire management and reduce atmospheric haze pollution. With the advancement of computer technology, data mining techniques and tools can be used to assess areas with the potential for high hazard to forest fires. This work explores spatial data mining techniques for predicting occurrence of hotspots. The study area was conducted in Rokan Hilir district in Riau Province in Indonesia where peat fires occur during the dry season. The spatial dataset containing spread of hotspots, land cover, rivers, roads, city centers, and peatland was used with socio-economic factors and weather factors. The results showed that spatial decision trees for predicting hotspots had higher accuracy compared to those not using spatial data mining techniques. This study shows the potential of spatial data mining techniques in forest fire hazard assessment in tropical peat areas

A Decision Tree Based on Spatial Relationships for Predicting Hotspots in Peatlands

Predicting hotspot occurrence as an indicator of forest and land fires is essential in developing an early warning system for fire prevention.  This work applied a spatial decision tree algorithm on spatial data of forest fires. The algorithm is the improvement of the conventional decision tree algorithm in which the distance and topological relationships are included to grow up spatial decision trees. Spatial data consist of a target layer and ten explanatory layers representing physical, weather, socio-economic and peatland characteristics in the study area Rokan Hilir District, Indonesia. Target objects are hotspots of 2008 and non-hotspot points.  The result is a pruned spatial decision tree with 122 leaves and the accuracy of 71.66%.  The spatial tree has produces higher accuracy than the non-spatial trees that were created using the ID3 and C4.5 algorithm. The ID3 decision tree has accuracy of 49.02% while the accuracy of C4.5 decision tree reaches 65.24%