An Intelligent Classification System For Aggregate Based On Image Processing And Neural Network

Bentuk dan tekstur permukaan aggregat mempengaruhi kekuatan dan struktur konkrit. Secara tradisi, mesin pengayakan mekanikal dan pengukuran manual digunakan bagi menentukan kedua-dua saiz dan bentuk aggregat. Aggregate’s shape and surface texture immensely influence the strength and structure of the resulting concrete. Traditionally, mechanical sieving and manual gauging are used to determine both the size and shape of the aggregates

A novel adaptive schema to facilitates playback switching technique for video delivery in dense LTE cellular heterogeneous network environments

The services of the Video on Demand (VoD) are currently based on the developments of the technology of the digital video and the network’s high speed. The files of the video are retrieved from many viewers according to the permission, which is given by VoD services. The remote VoD servers conduct this access. A server permits the user to choose videos anywhere/anytime in order to enjoy a unified control of the video playback. In this paper, a novel adaptive method is produced in order to deliver various facilities of the VoD to all mobile nodes that are moving within several networks. This process is performed via mobility modules within the produced method since it applies a seamless playback technique for retrieving the facilities of the VoD through environments of heterogeneous networks. The main components comprise two servers, which are named as the GMF and the LMF. The performance of the simulation is tested for checking clients’ movements through different networks with different sizes and speeds, which are buffered in the storage. It is found to be proven from the results that the handoff latency has various types of rapidity. The method applies smooth connections and delivers various facilities of the VoD. Meantime, the mobile device transfers through different networks. This implies that the system transports video segments easily without encountering any notable effects.In the experimental analysis for the Slow movements mobile node handoff latency (8 Km/hour or 4 m/s) ,the mobile device’s speed reaches 4m/s, the delay time ranges from 1 to 1.2 seconds in the proposed system, while the MobiVoD system ranges from 1.1 to 1.5. In the proposed technique reaches 1.1026 seconds forming the required time of a mobile device that is switching from a single network to its adjacent one. while the handoff termination average in the MobiVoD reaches 1.3098 seconds. Medium movement mobile node handoff latency (21 Km/ hour or 8 m/s) The average handoff time for the proposed system reaches 1.1057 seconds where this implies that this technique can seamlessly provide several segments of a video segments regardless of any encountered problems. while the average handoff time for the MobiVoD reaches 1.53006623 seconds. Furthermore, Fast movement mobile node handoff latency (390 Km/ hour or 20 m/s). The average time latency of the proposed technique reaches 1.0964 seconds, while the MobiVoD System reaches to 1.668225 seconds

Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition

To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy

Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network

Landslide is one of the natural disasters that occur in Malaysia. Topographic factors such as elevation, slope angle, slope aspect, general curvature, plan curvature, and profile curvature are considered as the main causes of landslides. In order to determine the dominant topographic factors in landslide mapping analysis, a study was conducted and presented in this paper. There are three main stages involved in this study. The first stage is the extraction of extra topographic factors. Previous landslide studies had identified mainly six topographic factors. Seven new additional factors have been proposed in this study.They are longitude curvature, tangential curvature, cross section curvature, surface area, diagonal line length, surface roughness, and rugosity. The second stage is the specification of the weight of each factor using two methods. The methods are multilayer perceptron (MLP) network classification accuracy and Zhou’s algorithm. At the third stage, the factors with higher weights were used to improve the MLP performance. Out of the thirteen factors, eight factors were considered as important factors, which are surface area, longitude curvature, diagonal length, slope angle, elevation, slope aspect, rugosity, and profile curvature.Theclassification accuracy of multilayer perceptron neural network has increased by 3% after the elimination of five less important factors

Ranked Features Selection with MSBRG Algorithm and Rules Classifiers for Cervical Cancer

In this paper, an automatic three-phase cervical cancer diagnosis system is employed which includes feature extraction, feature selection followed by classification. Firstly, the modified seed-based region growing (MSBRG) algorithm is implemented for automatic segmentation and feature extraction using 500 cervical cancer cells. Processes to obtain the threshold values and the initial seed location are carried out automatically using moving k-mean (MKM) algorithm and invariant moment techniques. Secondly, eight attribute evaluators are applied for selecting and ranking the features, which are Correlation-based Feature Selection, Classifier Attribute Evaluator, Correlation Attribute Evaluator, Gain Ratio, Info Gain, OneR, ReliefF, and Symmetrical Uncertainty. Finally, the classification is compared based on five classifiers: Decision Table, JRip, OneR, PART, and ZeroR. The performance of the classifiers is evaluated using 3 test options: the training percentage splits (50% to 98%), the full training data and the cross validation (2-fold to 10-fold). The experimental results prove the capability of the MSBRG algorithm as an automatic feature extraction method. Furthermore, this paper proves the ability of the ranked feature selection methods to select important features of a cervical cell, and favors the Decision Table as the best classifier for cervical cancer classification

Ranked Features Selection with MSBRG Algorithm and Rules Classifiers for Cervical Cancer

In this paper, an automatic three-phase cervical cancer diagnosis system is employed which includes feature extraction, feature selection followed by classification. Firstly, the modified seed-based region growing (MSBRG) algorithm is implemented for automatic segmentation and feature extraction using 500 cervical cancer cells. Processes to obtain the threshold values and the initial seed location are carried out automatically using moving k-mean (MKM) algorithm and invariant moment techniques. Secondly, eight attribute evaluators are applied for selecting and ranking the features, which are Correlation-based Feature Selection, Classifier Attribute Evaluator, Correlation Attribute Evaluator, Gain Ratio, Info Gain, OneR, ReliefF, and Symmetrical Uncertainty. Finally, the classification is compared based on five classifiers: Decision Table, JRip, OneR, PART, and ZeroR. The performance of the classifiers is evaluated using 3 test options: the training percentage splits (50% to 98%), the full training data and the cross validation (2-fold to 10-fold). The experimental results prove the capability of the MSBRG algorithm as an automatic feature extraction method. Furthermore, this paper proves the ability of the ranked feature selection methods to select important features of a cervical cell, and favors the Decision Table as the best classifier for cervical cancer classification.</p

Gene Microarray Cancer Classification using Correlation Based Feature Selection Algorithm and Rules Classifiers

Gene microarray classification problems are considered a challenge task since the datasets contain few number of samples with high number of genes (features). The genes subset selection in microarray data play an important role for minimizing the computational load and solving classification problems. In this paper, the Correlation-based Feature Selection (CFS) algorithm is utilized in the feature selection process to reduce the dimensionality of data and finding a set of discriminatory genes. Then, the Decision Table, JRip, and OneR are employed for classification process. The proposed approach of gene selection and classification is tested on 11 microarray datasets and the performances of the filtered datasets are compared with the original datasets. The experimental results showed that CFS can effectively screen irrelevant, redundant, and noisy features. In addition, the results for all datasets proved that the proposed approach with a small number of genes can achieve high prediction accuracy and fast computational speed. Considering the average accuracy for all the analysis of microarray data, the JRip achieved the best result as compared to Decision Table, and OneR classifier. The proposed approach has a remarkable impact on the classification accuracy especially when the data is complicated with multiple classes and high number of genes

An Effective Self-Adaptive Policy for Optimal Video Quality over Heterogeneous Mobile Devices and Network Discovery Services

The Video on Demand (VOD) system is considered a communicating multimedia system that can allow clients be interested whilst watching a video of their selection anywhere and anytime upon their convenient. The design of the VOD system is based on the process and location of its three basic contents, which are: the server, network configuration and clients. The clients are varied from numerous approaches, battery capacities, involving screen resolutions, capabilities and decoder features (frame rates, spatial dimensions and coding standards). The up-to-date systems deliver VOD services through to several devices by utilising the content of a single coded video without taking into account various features and platforms of a device, such as WMV9, 3GPP2 codec, H.264, FLV, MPEG-1 and XVID. This limitation only provides existing services to particular devices that are only able to play with a few certain videos. Multiple video codecs are stored by VOD systems store for a similar video into the storage server. The problems caused by the bandwidth overhead arise once the layers of a video are produced. The replication codec of a similar video and the layering encodes require further bandwidth storage and transmissions. The main objective of this paper is to propose a mechanism that can adapt an optimal video service according to mobile devices features and specifications. Therefore, it can save the CPU and RAM to be used on the server side. This paper proposed a novel adaptive policy to deliver the services of the VOD for several heterogeneous mobile devices. Additionally, the emphasis on introducing an up-to-date policy for profiling a video and an advanced protocol, namely the DNDS protocol, which is significant to propose a technique that depends on the collected data that delivers the most effective VOD facilities through cellular devices. This protocol is created to function over the whole current proposed systems components. Moreover, the DNDS protocol is applied and performed between the mobile clients, media forwarders and main servers. The performance of the proposed system is validated by examining two factors that rely on the RAM and the CPU. It can be found to be proven from the results that the proposed server contains lesser overload as the CPUs and RAMs servers is not important for converting every on-line video for every ordered device profile

Early Diagnosis of Diabetes: A Comparison of Machine Learning Methods

Detection and management of diabetes at an early stage is essential since it is rapidly becoming a global health crisis in many countries. Predictions of diabetes using machine learning algorithms have been promising. In this work, we use data collected from the Pima Indians to assess the performance of multiple machine-learning approaches to diabetes prediction. Ages, body mass indexes, and glucose levels for 768 patients are included in the data set. The methods evaluated are Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting, and Neural Network. The findings indicate that the Logistic Regression and Neural Network models perform the best on most criteria when considering all classes together. The SVM, Random Forest, and Naive Bayes models also receive moderate to high scores, suggesting their strength as classification models. However, the kNN and Tree models show poorer scores on most criteria across all classes, making them less favorable choices for this dataset. The SGD, AdaBoost, and CN2 rule inducer models perform the poorest when comparing all models using a weighted average of class scores. The results of the study suggest that machine learning algorithms may help predict the onset of diabetes and for detecting the disease at an early stage