Search results for: Binary Tree Classifier

Authors: K. Sivasubramanian, K. B. Jayanthi

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Floorplanning plays a vital role in the physical design process of Very Large Scale Integrated (VLSI) chips. It is an essential design step to estimate the chip area prior to the optimized placement of digital blocks and their interconnections. Since VLSI floorplanning is an NP-hard problem, many optimization techniques were adopted in the literature. In this work, a music-inspired Harmony Search (HS) algorithm is used for the fixed die outline constrained floorplanning, with the aim of reducing the total chip area. HS draws inspiration from the musical improvisation process of searching for a perfect state of harmony. Initially, B*-tree is used to generate the primary floorplan for the given rectangular hard modules and then HS algorithm is applied to obtain an optimal solution for the efficient floorplan. The experimental results of the HS algorithm are obtained for the MCNC benchmark circuits.

Keywords: Floor planning, harmony search, non-slicing floorplan, very large scale integrated circuits.

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Authors: Yang Zhang, Jian He

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Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper presents sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for CHD prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network.

Keywords: Bi-LSTM, CHD, coronary heart disease, ECG, electrocardiogram, ResNet, sliding window.

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Authors: Srinivasan Latha, M. R. Christhu Raj, Rajeev Sukumaran

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Active learning strategies have completely rewritten the concept of teaching and learning. Academicians have clocked back to Socratic approaches of questioning. Educators have started implementing active learning strategies for effective learning with the help of tools and technology. As Generation-Y learners are mostly visual, engaging them using visualization techniques play a vital role in their learning process. The facilitator has an important role in intrinsically motivating the learners using different approaches to create self-learning interests. Different visualization techniques were used along with lectures to help students understand and appreciate the concepts. Anonymous feedback was collected from learners. The consolidated report shows that majority of learners accepted the usage of visualization techniques was helpful in understanding concepts as well as create interest in learning the course. This study helps to understand, how the use of visualization techniques help the facilitator to engage learners effectively as well create and intrinsic motivation for their learning.

Keywords: Visualization techniques, concept maps, mind maps, argument maps, flowchart, tree diagram, problem solving.

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Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

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Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: Artificial Neural Network, Data Mining, Electroencephalogram, Epilepsy, Feature Extraction, Seizure Detection, Signal Processing.

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Authors: Ayat E. Ali, Christeen R. Aziz, Merna A. Helmy, Mohammed M. Malek, Sherif H. El-Gohary

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Goal: Purpose of the project was to make a plastic surgery prediction by using pre-operative images for the plastic surgeries’ patients and to show this prediction on a screen to compare between the current case and the appearance after the surgery. Methods: To this aim, we implemented a software which used data from the internet for facial skin diseases, skin burns, pre-and post-images for plastic surgeries then the post- surgical prediction is done by using K-nearest neighbor (KNN). So we designed and fabricated a smart mirror divided into two parts a screen and a reflective mirror so patient's pre- and post-appearance will be showed at the same time. Results: We worked on some skin diseases like vitiligo, skin burns and wrinkles. We classified the three degrees of burns using KNN classifier with accuracy 60%. We also succeeded in segmenting the area of vitiligo. Our future work will include working on more skin diseases, classify them and give a prediction for the look after the surgery. Also we will go deeper into facial deformities and plastic surgeries like nose reshaping and face slim down. Conclusion: Our project will give a prediction relates strongly to the real look after surgery and decrease different diagnoses among doctors. Significance: The mirror may have broad societal appeal as it will make the distance between patient's satisfaction and the medical standards smaller.

Keywords: K-nearest neighbor, face detection, vitiligo, bone deformity.

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Authors: Marco Actis Grande, Somlak Wannarumon

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This paper proposes the numerical simulation of the investment casting of gold jewelry. It aims to study the behavior of fluid flow during mould filling and solidification and to optimize the process parameters, which lead to predict and control casting defects such as gas porosity and shrinkage porosity. A finite difference method, computer simulation software FLOW-3D was used to simulate the jewelry casting process. The simplified model was designed for both numerical simulation and real casting production. A set of sensor acquisitions were allocated on the different positions of the wax tree of the model to detect filling times, while a set of thermocouples were allocated to detect the temperature during casting and cooling. Those detected data were applied to validate the results of the numerical simulation to the results of the real casting. The resulting comparisons signify that the numerical simulation can be used as an effective tool in investment-casting-process optimization and casting-defect prediction.

Keywords: Computer fluid dynamic, Investment casting, Jewelry, Mould filling, Simulation.

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Authors: Naghmeh Moradpoor Sheykhkanloo

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Thousands of organisations store important and confidential information related to them, their customers, and their business partners in databases all across the world. The stored data ranges from less sensitive (e.g. first name, last name, date of birth) to more sensitive data (e.g. password, pin code, and credit card information). Losing data, disclosing confidential information or even changing the value of data are the severe damages that Structured Query Language injection (SQLi) attack can cause on a given database. It is a code injection technique where malicious SQL statements are inserted into a given SQL database by simply using a web browser. In this paper, we propose an effective pattern recognition neural network model for detection and classification of SQLi attacks. The proposed model is built from three main elements of: a Uniform Resource Locator (URL) generator in order to generate thousands of malicious and benign URLs, a URL classifier in order to: 1) classify each generated URL to either a benign URL or a malicious URL and 2) classify the malicious URLs into different SQLi attack categories, and a NN model in order to: 1) detect either a given URL is a malicious URL or a benign URL and 2) identify the type of SQLi attack for each malicious URL. The model is first trained and then evaluated by employing thousands of benign and malicious URLs. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed approach.

Keywords: Neural Networks, pattern recognition, SQL injection attacks, SQL injection attack classification, SQL injection attack detection.

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Authors: Hyun-Woo Cho

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The early diagnostic decision making in industrial processes is absolutely necessary to produce high quality final products. It helps to provide early warning for a special event in a process, and finding its assignable cause can be obtained. This work presents a hybrid diagnostic schmes for batch processes. Nonlinear representation of raw process data is combined with classification tree techniques. The nonlinear kernel-based dimension reduction is executed for nonlinear classification decision boundaries for fault classes. In order to enhance diagnosis performance for batch processes, filtering of the data is performed to get rid of the irrelevant information of the process data. For the diagnosis performance of several representation, filtering, and future observation estimation methods, four diagnostic schemes are evaluated. In this work, the performance of the presented diagnosis schemes is demonstrated using batch process data.

Keywords: Diagnostics, batch process, nonlinear representation, data filtering, multivariate statistical approach

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Authors: Abdenour Bounsiar, Edith Grall, Pierre Beauseroy

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In this paper we are interested in classification problems with a performance constraint on error probability. In such problems if the constraint cannot be satisfied, then a rejection option is introduced. For binary labelled classification, a number of SVM based methods with rejection option have been proposed over the past few years. All of these methods use two thresholds on the SVM output. However, in previous works, we have shown on synthetic data that using thresholds on the output of the optimal SVM may lead to poor results for classification tasks with performance constraint. In this paper a new method for supervised classification with rejection option is proposed. It consists in two different classifiers jointly optimized to minimize the rejection probability subject to a given constraint on error rate. This method uses a new kernel based linear learning machine that we have recently presented. This learning machine is characterized by its simplicity and high training speed which makes the simultaneous optimization of the two classifiers computationally reasonable. The proposed classification method with rejection option is compared to a SVM based rejection method proposed in recent literature. Experiments show the superiority of the proposed method.

Keywords: rejection, Chow's rule, error-reject tradeoff, SupportVector Machine.

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Authors: M. Vogiatzis, K. Perakis

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Spatial-explicit and up-to-date land use/land cover information is fundamental for spatial planning, land management, sustainable development, and sound decision-making. In the last decade, many satellite-derived land cover products at different spatial, spectral, and temporal resolutions have been developed, such as the European Copernicus Land Cover product. However, more efficient and detailed information for land use/land cover is required at the regional or local scale. A typical Mediterranean basin with a complex landscape comprised of various forest types, crops, artificial surfaces, and wetlands was selected to test and develop our approach. In this study, we investigate the improvement of Copernicus Land Cover product (CLC2018) using Landsat 8 and Sentinel-2 pixel-based classification based on all available existing geospatial data (Forest Maps, LPIS, Natura2000 habitats, cadastral parcels, etc.). We examined and compared the performance of the Random Forest classifier for land use/land cover mapping. In total, 10 land use/land cover categories were recognized in Landsat 8 and 11 in Sentinel-2A. A comparison of the overall classification accuracies for 2018 shows that Landsat 8 classification accuracy was slightly higher than Sentinel-2A (82,99% vs. 80,30%). We concluded that the main land use/land cover types of CLC2018, even within a heterogeneous area, can be successfully mapped and updated according to CLC nomenclature. Future research should be oriented toward integrating spatiotemporal information from seasonal bands and spectral indexes in the classification process.

Keywords: land use/land cover, random forest, Landsat-8 OLI, Sentinel-2A MSI, Corine land cover

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Authors: Farrukh Shahzad

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The rich Islamic resources related to religious text, Islamic sciences, and history are widely available in print and in electronic format online. However, most of these works are only available in Arabic language. In this research, an attempt is made to utilize these resources to create interactive web applications in Arabic, English and other languages. The system utilizes the Pattern Recognition, Knowledge Management, Data Mining, Information Retrieval and Management, Indexing, storage and data-analysis techniques to parse, store, convert and manage the information from authentic Arabic resources. These interactive web Apps provide smart multi-lingual search, tree based search, on-demand information matching and linking. In this paper, we provide details of application architecture, design, implementation and technologies employed. We also presented the summary of web applications already developed. We have also included some screen shots from the corresponding web sites. These web applications provide an Innovative On-line Learning Systems (eLearning and computer based education).

Keywords: Islamic resources, Muslim scholars, hadith, narrators, history, fiqh.

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Authors: Chan-Sik Kim, Jong-Seong Kim, Se Won Lee, Hoo-Gon Choi

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In rapidly changing market environment, firms are investing a lot of time and resources into new product development (NPD) projects to make profit and to obtain competitive advantage. However, failure rate of NPD projects is becoming high due to various internal and external risks which hinder successful NPD projects. To reduce the failure rate, it is critical that risks have to be managed effectively and efficiently through good strategy, and treated by optimal responses to minimize risk cost. Four strategies are adopted to handle the risks in this study. The optimal responses are characterized by high reduction of risk costs with high efficiency. This study suggests a framework to decide the optimal responses considering the core risks, risk costs, response efficiency and response costs for successful NPD projects. Both binary particles warm optimization (BPSO) and multi-objective particle swarm optimization (MOPSO) methods are mainly used in the framework. Although several limitations exist in use for real industries, the frame work shows good strength for handling the risks with highly scientific ways through an example.

Keywords: NPD projects, risk cost, strategy, optimal responses, Particle Swarm Optimization.

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Authors: Sun Woo Lee, Tae Bum Lee, Yoon Hwa Park, Yoo Jeong Kim

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Cervical dentinal hypersensitivity (CDH) affects 8-30% of adults and nearly 85% of perio-treated patients. Various treatment schemes have been applied for treating CDH, among them being fluoride application, laser irradiation, and, recently, bioglass. The purpose of this study was to investigate the influence of bioglass, copper-bromide (Cu-Br) laser irradiation and their combination on dentinal tubule occlusion as a potential dentinal hypersensitivity treatment for CDH. 45 human dentin surfaces were organized into three equal groups: group A received Cu-Br laser only; group B received bioglass only; group C received bioglass followed by Cu-Br laser irradiation. Specimens were evaluated with regard to dentinal tubule occlusion under environmental scanning electron microscope. Treatment modality significantly affected dentinal tubule occlusion (p<0.001). Groups B and C scored higher dentinal tubule occlusion than group A. Binary logistic regression showed that bioglass application significantly (p<0.001) contributed to dentinal tubule occlusion, compared with other variables. Under the conditions used herein and within the limitations of this study, bioglass application, alone or combined with Cu-Br laser irradiation, is a superior method for producing dentinal tubule occlusion, and may lead to an effective treatment modality for CDH.

Keywords: Bioglass, Cu-Br laser, cervical dentinal hypersensitivity, dentinal tubule occlusion.

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Authors: Christina A. van Hal, Xiaoqian Jiang, Luyao Chen, Yan Chu, Robert D. Jolly, Yaobin Lin, Jitian Zhao, Kang Lin Hsieh

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The COVID-19 Literature Summary App, now live on the university website, was created for the primary purpose of enabling academicians and clinicians to quickly sort through the vast array of recent coronavirus publications by topics of interest. Multiple methods of summarizing and sorting the manuscripts were created. A summary page introduces the application function and capabilities, while an interactive map provides daily updates on infection, death, and recovery rates. A page with a pivot table allows publication sorting by topic, with an interactive data table that allows sorting topics by columns, as wells as the capability to view abstracts. Additionally, publications may be sorted by the medical topics they cover. We used the CORD-19 database to compile lists of publications. The data table can sort binary variables, allowing the user to pick desired publication topics, such as papers that describe COVID-19 symptoms. The application is primarily designed for use by researchers but can be used by anybody who wants a faster and more efficient means of locating papers of interest. 

Keywords: COVID-19, literature summary, information retrieval, snorkel

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Authors: Kamaldeep Joshi, Rajkumar Yadav, Sachin Allwadhi

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Image steganography is the best aspect of information hiding. In this, the information is hidden within an image and the image travels openly on the Internet. The Least Significant Bit (LSB) is one of the most popular methods of image steganography. In this method, the information bit is hidden at the LSB of the image pixel. In one bit LSB steganography method, the total numbers of the pixels and the total number of message bits are equal to each other. In this paper, the LSB method of image steganography is used for watermarking. The watermarking is an application of the steganography. The watermark contains 80*88 pixels and each pixel requirs 8 bits for its binary equivalent form so, the total number of bits required to hide the watermark are 80*88*8(56320). The experiment was performed on standard 256*256 and 512*512 size images. After the watermark insertion, histogram analysis was performed. A noise factor (salt and pepper) of 0.02 was added to the stego image in order to evaluate the robustness of the method. The watermark was successfully retrieved after insertion of noise. An experiment was performed in order to know the imperceptibility of stego and the retrieved watermark. It is clear that the LSB watermarking scheme is robust to the salt and pepper noise.

Keywords: LSB, watermarking, salt and pepper, PSNR.

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Authors: Cristina Vatamanu, Doina Cosovan, Dragoş Gavriluţ, Henri Luchian

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In the past few years, the amount of malicious software increased exponentially and, therefore, machine learning algorithms became instrumental in identifying clean and malware files through (semi)-automated classification. When working with very large datasets, the major challenge is to reach both a very high malware detection rate and a very low false positive rate. Another challenge is to minimize the time needed for the machine learning algorithm to do so. This paper presents a comparative study between different machine learning techniques such as linear classifiers, ensembles, decision trees or various hybrids thereof. The training dataset consists of approximately 2 million clean files and 200.000 infected files, which is a realistic quantitative mixture. The paper investigates the above mentioned methods with respect to both their performance (detection rate and false positive rate) and their practicability.

Keywords: Detection Rate, False Positives, Perceptron, One Side Class, Ensembles, Decision Tree, Hybrid methods, Feature Selection.

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Authors: Xu Han, Shanxiong Chen, Shiyu Zhu, Xiaoyu Lin, Fujia Zhao, Dingwang Wang

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Character detection is an important issue for character recognition of ancient Yi books. The accuracy of detection directly affects the recognition effect of ancient Yi books. Considering the complex layout, the lack of standard typesetting and the mixed arrangement between images and texts, we propose a character detection method for ancient Yi books based on connected components and regressive character segmentation. First, the scanned images of ancient Yi books are preprocessed with nonlocal mean filtering, and then a modified local adaptive threshold binarization algorithm is used to obtain the binary images to segment the foreground and background for the images. Second, the non-text areas are removed by the method based on connected components. Finally, the single character in the ancient Yi books is segmented by our method. The experimental results show that the method can effectively separate the text areas and non-text areas for ancient Yi books and achieve higher accuracy and recall rate in the experiment of character detection, and effectively solve the problem of character detection and segmentation in character recognition of ancient books.

Keywords: Computing methodologies, interest point, salient region detections, image segmentation.

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Authors: Kevin Fong-Rey Liu, Jia-Shen Chen, Han-Hsi Liang, Cheng-Wu Chen, Yung-Shuen Shen

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The purpose of determining impact significance is to place value on impacts. Environmental impact assessment review is a process that judges whether impact significance is acceptable or not in accordance with the scientific facts regarding environmental, ecological and socio-economical impacts described in environmental impact statements (EIS) or environmental impact assessment reports (EIAR). The first aim of this paper is to summarize the criteria of significance evaluation from the past review results and accordingly utilize fuzzy logic to incorporate these criteria into scientific facts. The second aim is to employ data mining technique to construct an EIS or EIAR prediction model for reviewing results which can assist developers to prepare and revise better environmental management plans in advance. The validity of the previous prediction model proposed by authors in 2009 is 92.7%. The enhanced validity in this study can attain 100.0%.

Keywords: Environmental impact assessment review, impactsignificance, fuzzy logic, data mining, classification tree.

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Authors: Sule Yucelbas, Gulay Tezel, Cuneyt Yucelbas, Seral Ozsen

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In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural network (PSO-NN) classifier systems. The main purpose of this study is to evaluate the performance of hybrid AIS-ANN and PSO-ANN classifiers with regard to the ANN and AIS. For this purpose, the normal sinus rhythm (NSR), atrial premature contraction (APC), sinus arrhythmia (SA), ventricular trigeminy (VTI), ventricular tachycardia (VTK) and atrial fibrillation (AF) data for each of the RR intervals were found. Then these data in the form of pairs (NSR-APC, NSR-SA, NSR-VTI, NSR-VTK and NSR-AF) is created by combining discrete wavelet transform which is applied to each of these two groups of data and two different data sets with 9 and 27 features were obtained from each of them after data reduction. Afterwards, the data randomly was firstly mixed within themselves, and then 4-fold cross validation method was applied to create the training and testing data. The training and testing accuracy rates and training time are compared with each other.

As a result, performances of the hybrid classification systems, AIS-ANN and PSO-ANN were seen to be close to the performance of the ANN system. Also, the results of the hybrid systems were much better than AIS, too. However, ANN had much shorter period of training time than other systems. In terms of training times, ANN was followed by PSO-ANN, AIS-ANN and AIS systems respectively. Also, the features that extracted from the data affected the classification results significantly.

Keywords: AIS, ANN, ECG, hybrid classifiers, PSO.

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Authors: Insah Bhurtah, P. Clarel Catherine, K. M. Sunjiv Soyjaudah

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When using modern Code Division Multiple Access (CDMA) in mobile communications, the user must be able to vary the transmission rate of users to allocate bandwidth efficiently. In this work, Orthogonal Variable Spreading Factor (OVSF) codes are used with the same principles applied in a low-rate superorthogonal turbo code due to their variable-length properties. The introduced system is the Variable Rate Superorthogonal Turbo Code (VRSTC) where puncturing is not performed on the encoder’s final output but rather before selecting the output to achieve higher rates. Due to bandwidth expansion, the codes outperform an ordinary turbo code in the AWGN channel. Simulations results show decreased performance compared to those obtained with the employment of Walsh-Hadamard codes. However, with OVSF codes, the VRSTC system keeps the orthogonality of codewords whilst producing variable rate codes contrary to Walsh-Hadamard codes where puncturing is usually performed on the final output.

Keywords: CDMA, MAP Decoding, OVSF, Superorthogonal Turbo Code.

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Authors: Somayeh Timarchi, Keivan Navi

Abstract:

Efficient modulo 2n+1 adders are important for several applications including residue number system, digital signal processors and cryptography algorithms. In this paper we present a novel modulo 2n+1 addition algorithm for a recently represented number system. The proposed approach is introduced for the reduction of the power dissipated. In a conventional modulo 2n+1 adder, all operands have (n+1)-bit length. To avoid using (n+1)-bit circuits, the diminished-1 and carry save diminished-1 number systems can be effectively used in applications. In the paper, we also derive two new architectures for designing modulo 2n+1 adder, based on n-bit ripple-carry adder. The first architecture is a faster design whereas the second one uses less hardware. In the proposed method, the special treatment required for zero operands in Diminished-1 number system is removed. In the fastest modulo 2n+1 adders in normal binary system, there are 3-operand adders. This problem is also resolved in this paper. The proposed architectures are compared with some efficient adders based on ripple-carry adder and highspeed adder. It is shown that the hardware overhead and power consumption will be reduced. As well as power reduction, in some cases, power-delay product will be also reduced.

Keywords: Modulo 2n+1 arithmetic, residue number system, low power, ripple-carry adders.

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Authors: Syed Fahad Tahir, Asifullah Khan, Abdul Majid, Anwar M. Mirza

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In this paper, we present an innovative scheme of blindly extracting message bits from an image distorted by an attack. Support Vector Machine (SVM) is used to nonlinearly classify the bits of the embedded message. Traditionally, a hard decoder is used with the assumption that the underlying modeling of the Discrete Cosine Transform (DCT) coefficients does not appreciably change. In case of an attack, the distribution of the image coefficients is heavily altered. The distribution of the sufficient statistics at the receiving end corresponding to the antipodal signals overlap and a simple hard decoder fails to classify them properly. We are considering message retrieval of antipodal signal as a binary classification problem. Machine learning techniques like SVM is used to retrieve the message, when certain specific class of attacks is most probable. In order to validate SVM based decoding scheme, we have taken Gaussian noise as a test case. We generate a data set using 125 images and 25 different keys. Polynomial kernel of SVM has achieved 100 percent accuracy on test data.

Keywords: Bit Correct Ratio (BCR), Grid Search, Intelligent Decoding, Jackknife Technique, Support Vector Machine (SVM), Watermarking.

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Authors: M. A. Sedghamiz, S. Raeissi

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This study presents three different approaches to estimate bubble point pressures for the binary system of CO2 and ethyl palmitate fatty acid ethyl ester. The first method involves the Peng-Robinson (PR) Equation of State (EoS) with the conventional mixing rule of Van der Waals. The second approach involves the PR EOS together with the Wong Sandler (WS) mixing rule, coupled with the UNIQUAC GE model. In order to model the bubble point pressures with this approach, the volume and area parameter for ethyl palmitate were estimated by the Hansen group contribution method. The last method involved the Peng-Robinson, combined with the Wong-Sandler method, but using NRTL as the GE model. Results using the Van der Waals mixing rule clearly indicated that this method has the largest errors among all three methods, with errors in the range of 3.96-6.22%. The PR-WS-UNIQUAC method exhibited small errors, with average absolute deviations between 0.95 to 1.97 percent. The PR-WS-NRTL method led to the least errors, where average absolute deviations ranged between 0.65-1.7%.

Keywords: Bubble pressure, Gibbs excess energy model, mixing rule, CO2 solubility, ethyl palmitate.

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Authors: Abdelkarim M. Ertiame, D. W. Yu, D. L. Yu, J. B. Gomm

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In this paper, a robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor, when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics, and using the weighted sum-squared prediction error as the residual. The Recursive Orthogonal Least Squares algorithm (ROLS) is employed to train the model to overcome the training difficulty of the independent mode of the network. Then, another RBFNN is used as a fault classifier to isolate faults from different features involved in the residual vector. Several actuator and sensor faults are simulated in a nonlinear simulation of the reactor in Simulink. The scheme is used to detect and isolate the faults on-line. The simulation results show the effectiveness of the scheme even the process is subjected to disturbances and uncertainties including significant changes in the monomer feed rate, fouling factor, impurity factor, ambient temperature, and measurement noise. The simulation results are presented to illustrate the effectiveness and robustness of the proposed method.

Keywords: Robust fault detection, cascade control, independent RBF model, RBF neural networks, Chylla-Haase reactor, FDI under closed-loop control.

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Authors: Snezhana G. Gocheva-Ilieva, Maya P. Stoimenova

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Ambient air pollution with fine particulate matter (PM10) is a systematic permanent problem in many countries around the world. The accumulation of a large number of measurements of both the PM10 concentrations and the accompanying atmospheric factors allow for their statistical modeling to detect dependencies and forecast future pollution. This study applies the classification and regression trees (CART) method for building and analyzing PM10 models. In the empirical study, average daily air data for the city of Pleven, Bulgaria for a period of 5 years are used. Predictors in the models are seven meteorological variables, time variables, as well as lagged PM10 variables and some lagged meteorological variables, delayed by 1 or 2 days with respect to the initial time series, respectively. The degree of influence of the predictors in the models is determined. The selected best CART models are used to forecast future PM10 concentrations for two days ahead after the last date in the modeling procedure and show very accurate results.

Keywords: Cross-validation, decision tree, lagged variables, short-term forecasting.

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Authors: Vesile Evrim, Aliyu Awwal

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By the evolvement in technology, the way of expressing opinions switched direction to the digital world. The domain of politics, as one of the hottest topics of opinion mining research, merged together with the behavior analysis for affiliation determination in texts, which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 were constituted by Linguistic Inquiry and Word Count (LIWC) features were tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that the “Decision Tree”, “Rule Induction” and “M5 Rule” classifiers when used with “SVM” and “IGR” feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “Function”, as an aggregate feature of the linguistic category, was found as the most differentiating feature among the 68 features with the accuracy of 81% in classifying articles either as Republican or Democrat.

Keywords: Politics, machine learning, feature selection, LIWC.

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Authors: Govindaraju.M, Ganeshkumar.R.S, Suganthi.P, Muthukumaran.V.R, Visvanathan.P

Abstract:

The present study was conducted to investigate the response of plants exposed to lignite-based thermal power plant emission. For this purpose, five plant species were collected from 1.0 km distance (polluted site) and control plants were collected from 20.0 km distance (control site) to thermal power plant. The common tree species Cassia siamea Lamk., Polyalthia longifolia. Sonn, Acacia longifolia (Andrews) Wild., Azadirachta indica A.Juss, Ficus religiosa L. were selected as test plants. Photosynthetic pigments changes (chlorophyll a, chlorophyll b and carotenoids) and rubisco enzyme modifications were studied. Reduction was observed in the photosynthetic pigments of plants growing in polluted site and also large sub unit of the rubisco enzyme was degraded in Azadirachta indica A. Juss collected from polluted site.

Keywords: Air pollution, Lignite-based thermal power plant, Photosynthetic pigments, Rubisco enzyme.

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Authors: M. Mehani, N. Salhi, T. Valeria, S. Ladjel

Abstract:

Red River Gum (Eucalyptus camaldulensis) is a tree of the genus Eucalyptus widely distributed in Algeria and in the world. The value of its aromatic secondary metabolites offers new perspectives in the pharmaceutical industry. This strategy can contribute to the sustainable development of our country. Preliminary tests performed on the essential oil of Eucalyptus camendulensis showed that this oil has antibacterial activity vis-à-vis the bacterial strains (Enterococcus feacalis, Enterobacter cloaceai, Proteus microsilis, Escherichia coli, Klebsiella pneumonia, and Pseudomonas aeruginosa) and antifungic (Fusarium sporotrichioide and Fusarium graminearum). The culture medium used was nutrient broth Muller Hinton. The interaction between the bacteria and the essential oil is expressed by a zone of inhibition with diameters of MIC indirectly expression of. And we used the PDA medium to determine the fungal activity. The extraction of the aromatic fraction (essentially oilhydrolat) of the fresh aerian part of the Eucalyptus camendulensis was performed by hydrodistillation. The average essential oil yield is 0.99%. The antimicrobial and fungal study of the essential oil and hydrosol showed a high inhibitory effect on the growth of pathogens.

Keywords: Essential oil, Eucalyptus camendulensis, bacteria and Fungi.

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Authors: Ildar Akhmadullin, Mayank Tyagi

Abstract:

The subject of this paper is the design analysis of a single well power production unit from low enthalpy geothermal resources. A complexity of the project is defined by a low temperature heat source that usually makes such projects economically disadvantageous using the conventional binary power plant approach. A proposed new compact design is numerically analyzed. This paper describes a thermodynamic analysis, a working fluid choice, downhole heat exchanger (DHE) and turbine calculation results. The unit is able to produce 321 kW of electric power from a low enthalpy underground heat source utilizing n-Pentane as a working fluid. A geo-pressured reservoir located in Vermilion Parish, Louisiana, USA is selected as a prototype for the field application. With a brine temperature of 126 , the optimal length of DHE is determined as 304.8 m (1000ft). All units (pipes, turbine, and pumps) are chosen from commercially available parts to bring this project closer to the industry requirements. Numerical calculations are based on petroleum industry standards. The project is sponsored by the Department of Energy of the US.

Keywords: Downhole Heat Exchangers, Geothermal Power Generation, Organic Rankine Cycle, Refrigerants, Working Fluids.

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Authors: Tessy Badriyah, Jim S. Briggs, Dave R. Prytherch

Abstract:

It is well known that Logistic Regression is the gold standard method for predicting clinical outcome, especially predicting risk of mortality. In this paper, the Decision Tree method has been proposed to solve specific problems that commonly use Logistic Regression as a solution. The Biochemistry and Haematology Outcome Model (BHOM) dataset obtained from Portsmouth NHS Hospital from 1 January to 31 December 2001 was divided into four subsets. One subset of training data was used to generate a model, and the model obtained was then applied to three testing datasets. The performance of each model from both methods was then compared using calibration (the χ2 test or chi-test) and discrimination (area under ROC curve or c-index). The experiment presented that both methods have reasonable results in the case of the c-index. However, in some cases the calibration value (χ2) obtained quite a high result. After conducting experiments and investigating the advantages and disadvantages of each method, we can conclude that Decision Trees can be seen as a worthy alternative to Logistic Regression in the area of Data Mining.

Keywords: Decision Trees, Logistic Regression, clinical outcome, risk of mortality.

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