Search results for: frequent pattern mining

Authors: M. Velasco, D. While, P. Raju, J. Krasniewicz, A. Amini, L. Hernandez-Munoz

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Web service adaptation involves the creation of adapters that solve Web services incompatibilities known as mismatches. Since the importance of Web services adaptation is increasing because of the frequent implementation and use of online Web services, this paper presents a literature review of web services to investigate the main methods of adaptation, their theoretical underpinnings and the metrics used to measure adapters performance. Eighteen publications were reviewed independently by two researchers. We found that adaptation techniques are needed to solve different types of problems that may arise due to incompatibilities in Web service interfaces, including protocols, messages, data and semantics that affect the interoperability of the services. Although adapters are non-invasive methods that can improve Web services interoperability and there are current approaches for service adaptation; there is, however, not yet one solution that fits all types of mismatches. Our results also show that only a few research projects incorporate theoretical frameworks and that metrics to measure adapters’ performance are very limited. We conclude that further research on software adaptation should improve current adaptation methods in different layers of the service interoperability and that an adaptation theoretical framework that incorporates a theoretical underpinning and measures of qualitative and quantitative performance needs to be created.

Keywords: Web services adapters, software adaptation, web services mismatches, web services interoperability.

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Authors: Xun Ge, Jinjin Li, Ying Ge

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Adopting Zakowski-s upper approximation operator C and lower approximation operator C, this paper investigates granularity-wise separations in covering approximation spaces. Some characterizations of granularity-wise separations are obtained by means of Pawlak rough sets and some relations among granularitywise separations are established, which makes it possible to research covering approximation spaces by logical methods and mathematical methods in computer science. Results of this paper give further applications of Pawlak rough set theory in pattern recognition and artificial intelligence.

Keywords: Rough set, covering approximation space, granularitywise separation.

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Authors: A. Seham A. Elgamal, Reham H. Saleh

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Chronic kidney disease became a global health problem worldwide. Therefore, in order to maintain a patient’s life and improve the survival rate, hemodialysis is essential to replace the function of their kidneys. However, those patients may complain about multiple physical and psychological stressors due to the nature of the disease and the need for frequent hemodialysis sessions. So, those patients use various strategies to cope with the stressors related to their disease and the treatment procedures. Cross-sectional, descriptive study was carried out to achieve the aim of the study. A convenient sample including all adult patients was recruited for this study. Hemodialysis Stressors Scale (HSS) and Jalowiec Coping Scale (JCS) were used to investigate the stressors and coping strategies of 89 hemodialysis patients, at a governmental hospital (King Khalid Hospital-Jeddah). Results of the study revealed that 50.7% experienced physiological stressors and 38% experienced psychosocial stressors. Also, optimistic, fatalistic, and supportive coping strategies were the most common coping strategies used by the patients with mean scores (2.88 + 0.75, 2.87 + 0.75, and 1.82 + 0.71), respectively. In conclusion, being familiar with the types of stressors and the effective coping strategies of hemodialysis patients and their families are important in order to enhance their adaptation with chronic kidney diseases.

Keywords: Coping strategies, hemodialysis, physiological stressors, psychological stressors.

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Authors: R. Reka, R. S. D. Wahidabanu

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The aim of this paper is to propose a novel technique to guarantee Quality of Service (QoS) in a highly dynamic environment. The MANET changes its topology dynamically as the nodes are moved frequently. This will cause link failure between mobile nodes. MANET cannot ensure reliability without delay. The relay node is selected based on achieving QoS in previous transmission. It considers one more factor Connection Existence Period (CEP) to ensure reliability. CEP is to find out the period during that connection exists between the nodes. The node with highest CEP becomes a next relay node. The relay node is selected dynamically to avoid frequent failure. The bandwidth of each link changed dynamically based on service rate and request rate. This paper proposes Active bandwidth setting up algorithm to guarantee the QoS. The series of results obtained by using the Network Simulator (NS-2) demonstrate the viability of our proposed techniques.

Keywords: Bandwidth, Connection Existence Period (CEP), Mobile Adhoc Network (MANET), Quality of Service (QoS), Relay node.

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Authors: F. Pourhasanzade, S. H. Sabzpoushan

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The heart tissue is an excitable media. A Cellular Automata is a type of model that can be used to model cardiac action potential propagation. One of the advantages of this approach against the methods based on differential equations is its high speed in large scale simulations. Recent cellular automata models are not able to avoid flat edges in the result patterns or have large neighborhoods. In this paper, we present a new model to eliminate flat edges by minimum number of neighbors.

Keywords: Cellular Automata, Action Potential Simulation, Isotropic Pattern.

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Authors: Abdelhadi Lotfi, Abdelkader Benyettou

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In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorithm addresses one of the major drawbacks of PNN, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the network is compared against performance of standard PNN for different databases from the UCI database repository. Results show an important gain in network size and performance.

Keywords: Classification, probabilistic neural networks, network optimization, pattern recognition.

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Authors: Hamed Qahri Saremi, Gholam Ali Montazer

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With the proliferation of World Wide Web, development of web-based technologies and the growth in web content, the structure of a website becomes more complex and web navigation becomes a critical issue to both web designers and users. In this paper we define the content and web pages as two important and influential factors in website navigation and paraphrase the enhancement in the website navigation as making some useful changes in the link structure of the website based on the aforementioned factors. Then we suggest a new method for proposing the changes using fuzzy approach to optimize the website architecture. Applying the proposed method to a real case of Iranian Civil Aviation Organization (CAO) website, we discuss the results of the novel approach at the final section.

Keywords: Web content, Web navigation, Website system, Webusage mining.

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Authors: Kyoung-jae Kim, Hyunchul Ahn, Sangwon Jeong

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This study proposes a novel recommender system to provide the advertisements of context-aware services. Our proposed model is designed to apply a modified collaborative filtering (CF) algorithm with regard to the several dimensions for the personalization of mobile devices – location, time and the user-s needs type. In particular, we employ a classification rule to understand user-s needs type using a decision tree algorithm. In addition, we collect primary data from the mobile phone users and apply them to the proposed model to validate its effectiveness. Experimental results show that the proposed system makes more accurate and satisfactory advertisements than comparative systems.

Keywords: Location-based advertisement, Recommender system, Collaborative filtering, User needs type, Mobile user.

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Authors: Basheer M. Al-Maqaleh, Kamal K. Bharadwaj

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Automated discovery of hierarchical structures in large data sets has been an active research area in the recent past. This paper focuses on the issue of mining generalized rules with crisp hierarchical structure using Genetic Programming (GP) approach to knowledge discovery. The post-processing scheme presented in this work uses flat rules as initial individuals of GP and discovers hierarchical structure. Suitable genetic operators are proposed for the suggested encoding. Based on the Subsumption Matrix(SM), an appropriate fitness function is suggested. Finally, Hierarchical Production Rules (HPRs) are generated from the discovered hierarchy. Experimental results are presented to demonstrate the performance of the proposed algorithm.

Keywords: Genetic Programming, Hierarchy, Knowledge Discovery in Database, Subsumption Matrix.

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Authors: Aigerim Uyzbayeva, Valeriya Tyo, Nurlan Ibrayev

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Kazakhstan is currently one of the dynamically developing states in its region. The stable growth in all sectors of the economy leads to a corresponding increase in energy consumption. Thus country consumes significant amount of energy due to the high level of industrialisation and the presence of energy-intensive manufacturing such as mining and metallurgy which in turn leads to low energy efficiency. With allowance for this the Government has set several priorities to adopt a transition of Republic of Kazakhstan to a “green economy”. This article provides an overview of Kazakhstan’s energy efficiency situation in for the period of 1991- 2014. First, the dynamics of production and consumption of conventional energy resources are given. Second, the potential of renewable energy sources is summarised followed by the description of GHG emissions trends in the country. Third, Kazakhstan’ national initiatives, policies and locally implemented projects in the field of energy efficiency are described.

Keywords: Energy efficiency in Kazakhstan, greenhouse gases, renewable energy, sustainable development.

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Authors: Girija Chetty, Renuka Biswas

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In this paper, we propose novel algorithmic models based on information fusion and feature transformation in crossmodal subspace for different types of residue features extracted from several intra-frame and inter-frame pixel sub-blocks in video sequences for detecting digital video tampering or forgery. An evaluation of proposed residue features – the noise residue features and the quantization features, their transformation in cross-modal subspace, and their multimodal fusion, for emulated copy-move tamper scenario shows a significant improvement in tamper detection accuracy as compared to single mode features without transformation in cross-modal subspace.

Keywords: image tamper detection, digital forensics, correlation features image fusion

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Authors: Jheng-Cheng Chen, Chia-Jui Lai, Chang-Hsiung Tsai,

Abstract:

The crossed cube is one of the most notable variations of hypercube, but some properties of the former are superior to those of the latter. For example, the diameter of the crossed cube is almost the half of that of the hypercube. In this paper, we focus on the problem embedding a Hamiltonian cycle through an arbitrary given edge in the crossed cube. We give necessary and sufficient condition for determining whether a given permutation with n elements over Zn generates a Hamiltonian cycle pattern of the crossed cube. Moreover, we obtain a lower bound for the number of different Hamiltonian cycles passing through a given edge in an n-dimensional crossed cube. Our work extends some recently obtained results.

Keywords: Interconnection network, Hamiltonian, crossed cubes, prescribed edge.

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Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

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Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.

Keywords: IDS, DDoS, MLP, KDD.

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Authors: Ju Peng Poh, Jun Yu Charles Lee, Jonathan Chew Hoe Khoo

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The emergence of digital twin technology, a digital replica of physical world, has improved the real-time access to data from sensors about the performance of buildings. This digital transformation has opened up many opportunities to improve the management of the building by using the data collected to help monitor consumption patterns and energy leakages. One example is the integration of predictive models for anomaly detection. In this paper, we use the GAM (Generalised Additive Model) for the anomaly detection of Air Handling Units (AHU) power consumption pattern. There is ample research work on the use of GAM for the prediction of power consumption at the office building and nation-wide level. However, there is limited illustration of its anomaly detection capabilities, prescriptive analytics case study, and its integration with the latest development of digital twin technology. In this paper, we applied the general GAM modelling framework on the historical data of the AHU power consumption and cooling load of the building between Jan 2018 to Aug 2019 from an education campus in Singapore to train prediction models that, in turn, yield predicted values and ranges. The historical data are seamlessly extracted from the digital twin for modelling purposes. We enhanced the utility of the GAM model by using it to power a real-time anomaly detection system based on the forward predicted ranges. The magnitude of deviation from the upper and lower bounds of the uncertainty intervals is used to inform and identify anomalous data points, all based on historical data, without explicit intervention from domain experts. Notwithstanding, the domain expert fits in through an optional feedback loop through which iterative data cleansing is performed. After an anomalously high or low level of power consumption detected, a set of rule-based conditions are evaluated in real-time to help determine the next course of action for the facilities manager. The performance of GAM is then compared with other approaches to evaluate its effectiveness. Lastly, we discuss the successfully deployment of this approach for the detection of anomalous power consumption pattern and illustrated with real-world use cases.

Keywords: Anomaly detection, digital twin, Generalised Additive Model, Power Consumption Model.

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Authors: George Karkavitsas, Maria Rangoussi

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We present a genetic algorithm application to the problem of object registration (i.e., object detection, localization and recognition) in a class of medical images containing various types of blood cells. The genetic algorithm approach taken here is seen to be most appropriate for this type of image, due to the characteristics of the objects. Successful cell registration results on real life microscope images of blood cells show the potential of the proposed approach.

Keywords: Genetic algorithms, object registration, pattern recognition, blood cell microscope images.

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Authors: Maeng-Hwan Hyun, Dae-Man Do, Young-Bok Choi

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Around the world, there are frequent incidents of natural disasters, such as earthquakes, tsunamis, floods, and snowstorms, as well as manmade disasters such as fires, arsons, and acts of terror. These diverse and unpredictable adversities have resulted in a number of fatalities and injuries. If disaster occurrence can be assessed quickly and information such as the exact location of the disaster and evacuation routes can be provided, victims can promptly move to safe locations, minimizing losses. This paper proposes a behavior analysis method based on a nine degrees-of-freedom (9-DOF) sensor that is effective for the emergency rescue evacuation support system (ERESS), which is being researched with an objective of providing evacuation support during disasters. Based on experiments performed using the acceleration sensor and the gyroscope sensor in the 9-DOF sensor, data are analyzed for human behavior regarding stationary position, walking, running, and during emergency situation to suggest guidelines for system judgment. Using the results of the experiments performed to determine disaster occurrence, it was confirmed that the proposed method quickly determines whether a disaster has occurred.

Keywords: Behavior Analysis, Nine degrees-of-freedom sensor, Emergency rescue.

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Authors: Janet L. Kourik, Peter E. Maher

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To investigate the correspondence of theory and practice, a successfully implemented Knowledge Management System (KMS) is explored through the lens of Alavi and Leidner-s proposed KMS framework for the analysis of an information system in knowledge management (Framework-AISKM). The applied KMS system was designed to manage curricular knowledge in a distributed university environment. The motivation for the KMS is discussed along with the types of knowledge necessary in an academic setting. Elements of the KMS involved in all phases of capturing and disseminating knowledge are described. As the KMS matures the resulting data stores form the precursor to and the potential for knowledge mining. The findings from this exploratory study indicate substantial correspondence between the successful KMS and the theory-based framework providing provisional confirmation for the framework while suggesting factors that contributed to the system-s success. Avenues for future work are described.

Keywords: Applied KMS, education, knowledge management (KM), KM framework, knowledge management system (KMS).

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Authors: Youssef Khmou, Said Safi, Miloud Frikel

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Diffraction grating is periodic module used in many engineering fields, its geometrical conception gives interesting properties of diffraction and interferences, a uniform and periodic diffraction grating consists of a number of identical apertures that are equally spaced, in this case, the amplitude of intensity distribution in the far field region is generally modulated by diffraction pattern of single aperture. In this paper, we study the case of aperiodic diffraction grating with identical rectangular apertures where theirs coordinates are modeled by square root function, we elaborate a computer simulation comparatively to the periodic array with same length and we discuss the numerical results.

Keywords: Diffraction grating, interferences, amplitude modulation, laser.

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Authors: Randhir Singh Baghel

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In this work, long-term spatiotemporal changes in rainfall are investigated and assessed at the meteorological divisional level using whole-year data from Rajasthan, India. Data from each of the district's eight tehsils are studied to see how the rainfall pattern has altered over the last 10 years.  We primarily compare information from the Jaipur district in Rajasthan, India, at the tehsil level. We looked at the full year, and from January to December, there was constantly more rain than any other month.  Furthermore, we compare the research of annual and monthly rainfall. Havey rainfall is also shown for two months, July and August.

Keywords: Climate change, temperature, seasonal monsoons, rainfall variability.

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Authors: Nora Taleb, Sellami Mokhtar, Michel Simonet

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This work concerns the evolution and the maintenance of an ontological resource in relation with the evolution of the corpus of texts from which it had been built. The knowledge forming a text corpus, especially in dynamic domains, is in continuous evolution. When a change in the corpus occurs, the domain ontology must evolve accordingly. Most methods manage ontology evolution independently from the corpus from which it is built; in addition, they treat evolution just as a process of knowledge addition, not considering other knowledge changes. We propose a methodology for managing an evolving ontology from a text corpus that evolves over time, while preserving the consistency and the persistence of this ontology. Our methodology is based on the changes made on the corpus to reflect the evolution of the considered domain - augmented surgery in our case. In this context, the results of text mining techniques, as well as the ARCHONTE method slightly modified, are used to support the evolution process.

Keywords: Corpus, Evolution, Ontology

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Authors: Alireza Ansariyar, Mansoureh Jeihani

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Light Detection and Ranging (LiDAR) sensors is capable of recording traffic data including the number of passing vehicles and bicyclists, the speed of vehicles and bicyclists, and the number of conflicts among both road users. In order to collect real-time traffic data and investigate the safety of different road users, a LiDAR sensor was installed at Cold Spring Ln – Hillen Rd intersection in Baltimore city. The frequency and severity of collected real-time conflicts were analyzed and the results highlighted that 122 conflicts were recorded over a 10-month time interval from May 2022 to February 2023. By employing an image-processing algorithm, a safety Measure of Effectiveness (MOE) aims to identify critical zones for bicyclists upon entering each respective zone at the signalized intersection. Considering the trajectory of conflicts, the results of analysis demonstrated that conflicts in the northern approach (zone N) are more frequent and severe. Additionally, sunny weather is more likely to cause severe vehicle-bike conflicts.

Keywords: LiDAR sensor, Post Encroachment Time threshold, vehicle-bike conflicts, measure of effectiveness, weather condition.

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Authors: Qasim Zafar, Matthew Anderson, Esteban Garcia, Steven Drager

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Software failures can present an enormous detriment to people's lives and cost millions of dollars to repair when they are unexpectedly encountered in the wild. Despite a significant portion of the software development lifecycle and resources are dedicated to testing, software failures are a relatively frequent occurrence. Nevertheless, the evaluation of testing effectiveness remains at the forefront of ensuring high-quality software and software metrics play a critical role in providing valuable insights into quantifiable objectives to assess the level of assurance and confidence in the system. As the selection of appropriate metrics can be an arduous process, the goal of this paper is to shed light on the significance of software metrics by examining a range of testing techniques and metrics as well as identifying key areas for improvement. In doing so, this paper presents a method to compare the effectiveness of testing techniques with heterogeneous output metrics. Additionally, through this investigation, readers will gain a deeper understanding of how metrics can help to drive informed decision-making on delivering high-quality software and facilitate continuous improvement in testing practices.

Keywords: Software testing, software metrics, testing effectiveness, black box testing, random testing, adaptive random testing, combinatorial testing, fuzz testing, equivalence partition, boundary value analysis, white box testings.

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Authors: Laila Seada, Nouf Al Gharbi, Shaimaa Dawa

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Although skin cancers are prevalent worldwide, it is uncommon in Ha’il region in the Kingdom of Saudi Arabia, mostly non-melanoma sub-type. During a 4-year period from 2014 to 2017, out of a total of 120 cases of skin lesions, 29 non-melanoma cancers were retrieved from histopathology files obtained from King Khalid Hospital. As part of the study, all cases of skin cancer diagnosed during 2014 -2017 have been revised and the clinicopathological data recorded. The results show that Basal cell carcinoma (BCC) was the most common neoplasm (36%), followed by cutaneous lymphomas (mostly mycosis fungoides 25%), squamous cell carcinoma (SCC) (21%) and dermatofibrosarcoma protuberans (DFSP) (11%). Only one case of metastatic carcinoma was recorded. BCC nodular type was the most prevalent, with a mean age 57.6 years and mean size 2.73 cm. SCC was mostly grade 2, with mean size 1.9 cm and an older mean age of 72.3 cm. Increased size of lesion positively correlated with older age (p = 0.001). Non-melanoma skin cancer in Ha’il region is not frequently encountered. BCC is the most frequent followed by cutaneous T-cell lymphomas and SCC. The findings in this study were in accordance with other parts of, but much lower than other parts of the world.

Keywords: Non melanoma skin cancer, Hail Region, histopathology, BCC.

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Authors: Yueming Wang, Rendong Ying, Sumxin Jiang, Peilin Liu

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Unified Speech Audio Coding (USAC), the latest MPEG standardization for unified speech and audio coding, uses a speech/audio classification algorithm to distinguish speech and audio segments of the input signal. The quality of the recovered audio can be increased by well-designed orchestra/percussion classification and subsequent processing. However, owing to the shortcoming of the system, introducing an orchestra/percussion classification and modifying subsequent processing can enormously increase the quality of the recovered audio. This paper proposes an orchestra/percussion classification algorithm for the USAC system which only extracts 3 scales of Mel-Frequency Cepstral Coefficients (MFCCs) rather than traditional 13 scales of MFCCs and use Iterative Dichotomiser 3 (ID3) Decision Tree rather than other complex learning method, thus the proposed algorithm has lower computing complexity than most existing algorithms. Considering that frequent changing of attributes may lead to quality loss of the recovered audio signal, this paper also design a modified subsequent process to help the whole classification system reach an accurate rate as high as 97% which is comparable to classical 99%.

Keywords: ID3 Decision Tree, MFCC, Orchestra/Percussion Classification, USAC

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Authors: Baljit Singh Khehra, Amar Partap Singh Pharwaha

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Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.

Keywords: Clusters of Microcalcifications, Ductal Carcinoma in Situ, Least-Square Support Vector Machine, Particle Swarm Optimization.

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Authors: Mayank Singh, Mahendra P. Singh

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Present paper enumerates highlights of seasonal variation in floristic composition and ecological strategies for the management of ‘Gujar Tal’ at Jaunpur in tropical semi-arid region of eastern U.P. (India). Total composition of macrophytes recorded was 47 from 26 families with maximum 6 plant species of Cyperaceae from April, 2012 to March, 2013 at certain periodic intervals. Maximum number of plants (39) was present during winter followed by (37) rainy and (27) summer seasons. The distribution pattern depicted that maximum number of plants (27) was of marshy and swampy habitats usually transitional between land and water.

Keywords: Floristic, life form, management, weeds.

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Authors: Henrik Prinzhorn, Peter Nyhuis, Johannes Wagner, Peter Burggräf, Torben Schmitz, Christina Reuter

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Assembling large-scale products, such as airplanes, locomotives, or wind turbines, involves frequent process interruptions induced by e.g. delayed material deliveries or missing availability of resources. This leads to a negative impact on the logistical performance of a producer of xxl-products. In industrial practice, in case of interruptions, the identification, evaluation and eventually the selection of an alternative order of assembly activities (‘assembly alternative’) leads to an enormous challenge, especially if an optimized logistical decision should be reached. Therefore, in this paper, an innovative, optimization model for the identification of assembly alternatives that addresses the given problem is presented. It describes make-to-order, large-scale product assembly processes as a resource constrained project scheduling (RCPS) problem which follows given restrictions in practice. For the evaluation of the assembly alternative, a cost-based definition of the logistical objectives (delivery reliability, inventory, make-span and workload) is presented.

Keywords: Assembly scheduling, large-scale products, make-to-order, rescheduling, optimization.

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Authors: Lucia Hrebeňárová, Jarmila Žolnová, Veronika Palková

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Inclusive education of students with special educational needs has been on the increase in the Slovak Republic, facing many challenges. Preparedness of teachers for inclusive education is one of the most frequent issues; teachers lack skills when it comes to the use of effective instruction depending on the individual needs of students, improvement of classroom management and social skills, and support of inclusion within the classroom. Social support is crucial for the school success of students within inclusive settings. The aim of the paper is to analyse perception of the frequency and importance of peer social support by students with special educational needs in inclusive education. The data collection tool used was the Child and Adolescent Social Support Scale (CASSS). The research sample consisted of 953 fourth grade students – 141 students with special educational needs educated in an inclusive setting and 812 students of the standard population. No significant differences were found between the students with special educational needs and the students without special educational needs in an inclusive setting when it comes to the perception of frequency and importance of social support of schoolmates and friends. However, the perception of frequency and importance of a friend’s social support was higher than the perception of frequency and importance of a classmate’s social support in both groups of students.

Keywords: Inclusive education, peer social support, peer, student with special educational needs.

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Authors: Shigeaki Sakurai, Ryohei Orihara

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This paper proposes an improvement method of classification efficiency in a classification model. The model is used in a risk search system and extracts specific labels from articles posted at bulletin board sites. The system can analyze the important discussions composed of the articles. The improvement method introduces ensemble learning methods that use multiple classification models. Also, it introduces expressions related to the specific labels into generation of word vectors. The paper applies the improvement method to articles collected from three bulletin board sites selected by users and verifies the effectiveness of the improvement method.

Keywords: Text mining, Risk search system, Corporate reputation, Bulletin board site, Ensemble learning

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Authors: Mohanad Alhabo, Li Zhang, Naveed Nawaz

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In this paper, a non-cooperative game method is formulated where all players compete to transmit at higher power. Every base station represents a player in the game. The game is solved by obtaining the Nash equilibrium (NE) where the game converges to optimality. The proposed method, named Power Efficient Handover Game Theoretic (PEHO-GT) approach, aims to control the handover in dense small cell networks. Players optimize their payoff by adjusting the transmission power to improve the performance in terms of throughput, handover, power consumption and load balancing. To select the desired transmission power for a player, the payoff function considers the gain of increasing the transmission power. Then, the cell selection takes place by deploying Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). A game theoretical method is implemented for heterogeneous networks to validate the improvement obtained. Results reveal that the proposed method gives a throughput improvement while reducing the power consumption and minimizing the frequent handover.

Keywords: Energy efficiency, game theory, handover, HetNets, small cells.

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