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

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

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

Classifying The Shape Of Aggregate Using Hybrid Multilayered Perceptron Network.

In concrete production, shape of aggregate reflects the quality of concrete produced. The well-shaped aggregates are said to produce high quality concrete by reducing water to cement ratio. On the contrary, poor-shaped aggregates often require higher water to cement ratio in concrete production

Increased sporulation underpins adaptation of Clostridium difficile strain 630 to a biologically–relevant faecal environment, with implications for pathogenicity

Abstract Clostridium difficile virulence is driven primarily by the processes of toxinogenesis and sporulation, however many in vitro experimental systems for studying C. difficile physiology have arguably limited relevance to the human colonic environment. We therefore created a more physiologically–relevant model of the colonic milieu to study gut pathogen biology, incorporating human faecal water (FW) into growth media and assessing the physiological effects of this on C. difficile strain 630. We identified a novel set of C. difficile–derived metabolites in culture supernatants, including hexanoyl– and pentanoyl–amino acid derivatives by LC-MSn. Growth of C. difficile strain 630 in FW media resulted in increased cell length without altering growth rate and RNA sequencing identified 889 transcripts as differentially expressed (p < 0.001). Significantly, up to 300–fold increases in the expression of sporulation–associated genes were observed in FW media–grown cells, along with reductions in motility and toxin genes’ expression. Moreover, the expression of classical stress–response genes did not change, showing that C. difficile is well–adapted to this faecal milieu. Using our novel approach we have shown that interaction with FW causes fundamental changes in C. difficile biology that will lead to increased disease transmissibility

Antimicrobial Resistance in Escherichia coli

Multidrug resistance in Escherichia coli has become a worrying issue that is increasingly observed in human but also in veterinary medicine worldwide. E. coli is intrinsically susceptible to almost all clinically relevant antimicrobial agents, but this bacterial species has a great capacity to accumulate resistance genes, mostly through horizontal gene transfer. The most problematic mechanisms in E. coli correspond to the acquisition of genes coding for extended-spectrum β-lactamases (conferring resistance to broad-spectrum cephalosporins), carbapenemases (conferring resistance to carbapenems), 16S rRNA methylases (conferring pan-resistance to aminoglycosides), plasmid-mediated quinolone resistance (PMQR) genes (conferring resistance to [fluoro]quinolones), and mcr genes (conferring resistance to polymyxins). Although the spread of carbapenemase genes has been mainly recognized in the human sector but poorly recognized in animals, colistin resistance in E. coli seems rather to be related to the use of colistin in veterinary medicine on a global scale. For the other resistance traits, their cross-transfer between the human and animal sectors still remains controversial even though genomic investigations indicate that extended- spectrum β-lactamase producers encountered in animals are distinct from those affecting humans. In addition, E. coli of animal origin often also show resistances to other—mostly older—antimicrobial agents, including tetracyclines, phenicols, sulfonamides, trimethoprim, and fosfomycin. Plasmids, especially multiresistance plasmids, but also other mobile genetic elements, such as transposons and gene cassettes in class 1 and class 2 integrons, seem to play a major role in the dissemination of resistance genes. Of note, coselection and persistence of resistances to critically important antimicrobial agents in human medicine also occurs through the massive use of antimicrobial agents in veterinary medicine, such as tetracyclines or sulfonamides, as long as all those determinants are located on the same genetic elements

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