Intelligent policy recommendations on enterprise resource planning by the use of agent technology and data mining techniques

Enterprise Resource Planning systems tend to deploy Supply Chain Management and/or Customer Relationship Management techniques, in order to successfully fuse information to customers, suppliers, manufacturers and warehouses, and therefore minimize system-wide costs while satisfying service level requirements. Although efficient, these systems are neither versatile nor adaptive, since newly discovered customer trends cannot be easily integrated with existing knowledge. Advancing on the way the above mentioned techniques apply on ERP systems, we have developed a multi-agent system that introduces adaptive intelligence as a powerful add-on for ERP software customization. The system can be thought of as a recommendation engine, which takes advantage of knowledge gained through the use of data mining techniques, and incorporates it into the resulting company selling policy. The intelligent agents of the system can be periodically retrained as new information is added to the ERP. In this paper, we present the architecture and development details of the system, and demonstrate its application on a real test case.     

A wavelet-based Markov random field segmentation model in segmenting microarray experiments

In the present study, an adaptation of the Markov Random Field (MRF) segmentation model, by means of the stationary wavelet transform (SWT), applied to complementary DNA (cDNA) microarray images is proposed (WMRF). A 3-level decomposition scheme of the initial microarray image was performed, followed by a soft thresholding filtering technique. With the inverse process, a Denoised image was created. In addition, by using the Amplitudes of the filtered wavelet Horizontal and Vertical images at each level, three different Magnitudes were formed. These images were combined with the Denoised one to create the proposed SMRF segmentation model. For numerical evaluation of the segmentation accuracy, the segmentation matching factor (SMF), the Coefficient of Determination (r²), and the concordance correlation (p_c) were calculated on the simulated images. In addition, the SMRF performance was contrasted to the Fuzzy C Means (FCM), Gaussian Mixture Models (GMM), Fuzzy GMM (FGMM), and the conventional MRF techniques. Indirect accuracy performances were also tested on the experimental images by means of the Mean Absolute Error (MAE) and the Coefficient of Variation (CV). In the latter case, SPOT and SCANALYZE software results were also tested. In the former case, SMRF attained the best SMF, r², and p_c (92.66%, 0.923, and 0.88, respectively) scores, whereas, in the latter case scored MAE and CV, 497 and 0.88, respectively. The results and support the performance superiority of the SMRF algorithm in segmenting cDNA images.     

A multi scale theoretical study of Li+ interaction with carbon nanotubes

We investigated the effect of the curvature in lithium storage at single-walled carbon nanotubes, with both ab-initio and Molecular Dynamics simulations. Our results show that the carbon rings of nanotubes develop strong cation-pi- interactions with Li ion. These interactions result in positioning the alkali metal cation on top of a phenyl group. By using different types of carbon nanotubes it was revealed that the interaction is not affected by the type or the curvature of the nanotubes. Molecular Dynamics simulations of lithium intercalated nanotube bundles pointed at the fact that the cations remain attached to the nanotubes even at room temperature, with a maximum Li to C ratio of 1:2.1.     

Remote monitoring of electromagnetic signals and seismic events using smart mobile devices

This study presents the design and development of a novel mobile wireless system to be used for monitoring seismic events and related electromagnetic signals, employing smart mobile devices like personal digital assistants (PDAs) and wireless communication technologies such as wireless local area networks (WLANs), general packet radio service (GPRS) and universal mobile telecommunications system (UMTS). The proposed system enables scientists to access critical data while being geographically independent of the sites of data sources, rendering it as a useful tool for preliminary scientific analysis.     

Mining affective needs of automotive industry customers for building a mass-customization recommender system

Mass customization systems aim to receive customer preferences in order to facilitate personalization of products and services. Current online configuration systems are unable to efficiently identify real customer affective needs because they offer an excess variety of products that usually confuse customers. On the other hand, mining affective customer needs may result in recommender systems, which can enhance existing configuration systems by recommending initial configurations according to customer affective needs. This paper introduces a mass customization recommender system that exploits data mining techniques on automotive industry customer data aiming at revealing associations between user affective needs and the design parameters of automotive products. One key novelty of the presented approach is that it deploys the Citarasa engineering, a methodology that focuses on the provision of the appropriate characterizations on customer data in order to associate them with customer affective needs. Based on the application of classification techniques we build a recommendation engine, which is evaluated in terms of user satisfaction, tool’s effectiveness, usefulness and reliability among other parameters.     

Improving accuracy in astrocytomas grading by integrating a robust least squares mapping driven support vector machine classifier into a two level grade classification scheme

Grading of astrocytomas is an important task for treatment planning; however, it suffers from significantly great inter-observer variability. Computer-assisted diagnosis systems have been propose to assist towards minimizing subjectivity, however, these systems present either moderate accuracy or utilize specialized staining protocols and grading systems that are difficult to apply in daily clinical practice. The present study proposes a robust mathematical formulation by integrating state-of-art technologies (support vector machines and least squares mapping) in a cascade classification scheme for separating low from high and grade III from grade IV astrocytic tumours. Results have indicated that low from high-grade tumours can be correctly separated with a certainty as high as 97.3%, whereas grade III from grade IV tumours with 97.8%. The overall performance was 95.2%. These high rates have been a result of applying the least squares mapping technique to features prior to classification. A significant byproduct of least squares mapping is that the number of support vectors of the SVM classifiers dropped dramatically from about 80% when no mapping was used to less than 5% when mapping was used. The latter is a clear indication that the SVM classifier has a greater potential to generalize well to new data. In this way, digital image analysis systems for automated grading of astrocytomas are brought closer to clinical practice.     

A dual scheme for compression and restoration of sequentially transmitted images over Wireless Sensor Networks

A dual scheme for the compression and restoration of sequentially transmitted images over Wireless Sensor Networks (WSNs) is presented. These networks are characterized by low transmission rates, extended packet losses and limited power resources, factors that degrade the overall system performance. Especially in the cases of transmitting multimedia content, a considerable number of data packets are dropped, with a corresponding direct effect on the received image quality. For dealing with this loss of valuable information, various retransmission schemes have been proposed that act as quality of service modules. However, this approach increases energy consumption and inserts considerable time delays, due to the rise of the traffic load in the network. This article proposes a novel dual transmission scheme, targeting mainly the area of Multimedia WSNs (WMSN), that aims to decrease the overall traffic load introduced by the retransmission schemes, by performing image restoration for lost data packets, at the receiver side. The proposed novel dual scheme is based on: (a) the quad tree decomposition algorithm that is adopted for compressing the image data before transmission over the WSN, and (b) the fast image inpainting algorithm for restoring the effect of the missing data packets. The overall proposed scheme has been applied in multiple experimental studies that prove its efficacy.     

Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features

The aim of the present study was to design, implement and evaluate a software system for discriminating between metastatic and primary brain tumors (gliomas and meningiomas) on MRI, employing textural features from routinely taken T1 post-contrast images. The proposed classifier is a modified probabilistic neural network (PNN), incorporating a non-linear least squares features transformation (LSFT) into the PNN classifier. Thirty-six textural features were extracted from each one of 67 T1-weighted post-contrast MR images (21 metastases, 19 meningiomas and 27 gliomas). LSFT enhanced the performance of the PNN, achieving classification accuracies of 95.24% for discriminating between metastatic and primary tumors and 93.48% for distinguishing gliomas from meningiomas. To improve the generalization of the proposed classification system, the external cross-validation method was also used, resulting in 71.43% and 81.25% accuracies in distinguishing metastatic from primary tumors and gliomas from meningiomas, respectively. LSFT improved PNN performance, increased class separability and resulted in dimensionality reduction.