Search results for: Information Extraction

Authors: Laura Lukmanto, Harya Widiputra, Lukas

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Studies in economics domain tried to reveal the correlation between stock markets. Since the globalization era, interdependence between stock markets becomes more obvious. The Dynamic Interaction Network (DIN) algorithm, which was inspired by a Gene Regulatory Network (GRN) extraction method in the bioinformatics field, is applied to reveal important and complex dynamic relationship between stock markets. We use the data of the stock market indices from eight countries around the world in this study. Our results conclude that DIN is able to reveal and model patterns of dynamic interaction from the observed variables (i.e. stock market indices). Furthermore, it is also found that the extracted network models can be utilized to predict movement of the stock market indices with a considerably good accuracy.

Keywords: complex dynamic relationship, dynamic interaction network, interactive stock markets, stock market interdependence.

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Authors: Jun-Ho Park, Kwang-Woo Nam, Seung-Mo Hong, Tae-Heon Moon, Sang-Ho Lee, Youn-Taik Leem

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The aim of this study was to build ‘Ubi-Net’, a decision-making support system for systematic establishment in U-City planning. We have experienced various urban problems caused by high-density development and population concentrations in established urban areas. To address these problems, a U-Service contributes to the alleviation of urban problems by providing real-time information to citizens through network connections and related information. However, technology, devices, and information for consumers are required for systematic U-Service planning in towns and cities where there are many difficulties in this regard, and a lack of reference systems. Thus, this study suggests methods to support the establishment of sustainable planning by providing comprehensive information including IT technology, devices, news, and social networking services (SNS) to U-City planners through intelligent searches. In this study, we targeted Smart U-Parking Planning to solve parking problems in an ‘old’ city. Through this study, we sought to contribute to supporting advances in U-Space and the alleviation of urban problems.

Keywords: Design and decision support system, smart U-parking planning, social network analysis.

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Authors: Francois T. Pronk, Steven J. Hulshoff

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A study of various turbulent inflow generation methods was performed to compare their relative effectiveness for LES computations of turbulent boundary layers. This study confirmed the quality of the turbulent information produced by the family of recycling and rescaling methods which take information from within the computational domain. Furthermore, more general inflow methods also proved applicable to such simulations, with a precursor-like inflow and a random inflow augmented with forcing planes showing promising results.

Keywords: Boundary layer, Flat plate, Inflow modeling, LES

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Authors: Ji Young Song, Soo Jung Hu, Hyunjin Joo, Joung Boon Hwang, Mi Ok Kim, Shin Jung Kang, Dae Hyun Cho

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Multi-residue analysis method for penicillins was developed and validated in bovine muscle, chicken, milk, and flatfish. Detection was based on liquid chromatography tandem mass spectrometry (LC/MS/MS). The developed method was validated for specificity, precision, recovery, and linearity. The analytes were extracted with 80% acetonitrile and clean-up by a single reversed-phase solid-phase extraction step. Six penicillins presented recoveries higher than 76% with the exception of Amoxicillin (59.7%). Relative standard deviations (RSDs) were not more than 10%. LOQs values ranged from 0.1 and to 4.5 ug/kg. The method was applied to 128 real samples. Benzylpenicillin was detected in 15 samples and Cloxacillin was detected in 7 samples. Oxacillin was detected in 2 samples. But the detected levels were under the MRL levels for penicillins in samples.

Keywords: Penicillins, livestock product, Multi-residue analysis, LC/MS/MS

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Authors: Xiao Chen, Xiaoying Kong, Min Xu

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This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification.

Keywords: Vehicle classification, signal processing, road traffic model, magnetic sensing.

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Authors: Piyush Samant, Ravinder Agarwal

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Complementary and Alternative Medicine (CAM) techniques are quite popular and effective for chronic diseases. Iridology is more than 150 years old CAM technique which analyzes the patterns, tissue weakness, color, shape, structure, etc. for disease diagnosis. The objective of this paper is to validate the use of iridology for the diagnosis of the diabetes. The suggested model was applied in a systemic disease with ocular effects. 200 subject data of 100 each diabetic and non-diabetic were evaluated. Complete procedure was kept very simple and free from the involvement of any iridologist. From the normalized iris, the region of interest was cropped. All 63 features were extracted using statistical, texture analysis, and two-dimensional discrete wavelet transformation. A comparison of accuracies of six different classifiers has been presented. The result shows 89.66% accuracy by the random forest classifier.

Keywords: Complementary and alternative medicine, Iridology, iris, feature extraction, classification, disease prediction.

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Authors: A. Azadeh, Z. Raoofi

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Natural gas, as one of the most important sources of energy for many of the industrial and domestic users all over the world, has a complex, huge supply chain which is in need of heavy investments in all the phases of exploration, extraction, production, transportation, storage and distribution. The main purpose of supply chain is to meet customers’ need efficiently and with minimum cost. In this study, with the aim of minimizing economic costs, different levels of natural gas supply chain in the form of a multi-echelon, multi-period fuzzy linear programming have been modeled. In this model, different constraints including constraints on demand satisfaction, capacity, input/output balance and presence/absence of a path have been defined. The obtained results suggest efficiency of the recommended model in optimal allocation and reduction of supply chain costs.

Keywords: Cost Approach, Fuzzy Theory, Linear Programming, Natural Gas Supply Chain.

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Authors: M. Kalamani, S. Valarmathy, M. Krishnamoorthi

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In this paper, Fuzzy C-Means clustering with Expectation Maximization-Gaussian Mixture Model based hybrid modeling algorithm is proposed for Continuous Tamil Speech Recognition. The speech sentences from various speakers are used for training and testing phase and objective measures are between the proposed and existing Continuous Speech Recognition algorithms. From the simulated results, it is observed that the proposed algorithm improves the recognition accuracy and F-measure up to 3% as compared to that of the existing algorithms for the speech signal from various speakers. In addition, it reduces the Word Error Rate, Error Rate and Error up to 4% as compared to that of the existing algorithms. In all aspects, the proposed hybrid modeling for Tamil speech recognition provides the significant improvements for speechto- text conversion in various applications.

Keywords: Speech Segmentation, Feature Extraction, Clustering, HMM, EM-GMM, CSR.

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Authors: Ahsan Bin Tufail, Ali Abidi, Adil Masood Siddiqui, Muhammad Shahzad Younis

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An early and accurate detection of Alzheimer's disease (AD) is an important stage in the treatment of individuals suffering from AD. We present an approach based on the use of structural magnetic resonance imaging (sMRI) phase images to distinguish between normal controls (NC), mild cognitive impairment (MCI) and AD patients with clinical dementia rating (CDR) of 1. Independent component analysis (ICA) technique is used for extracting useful features which form the inputs to the support vector machines (SVM), K nearest neighbour (kNN) and multilayer artificial neural network (ANN) classifiers to discriminate between the three classes. The obtained results are encouraging in terms of classification accuracy and effectively ascertain the usefulness of phase images for the classification of different stages of Alzheimer-s disease.

Keywords: Biomedical image processing, classification algorithms, feature extraction, statistical learning.

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Authors: Hasan Karal, Yasemin Aydin, Ömer Faruk Ursavas

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Primary studies are being carried out in Turkey for expanding information and communication technologies (ICT) aided instruction activities. Subject of the present study is to identify whether those studies achieved their goals in the application. Information technologies (IT) formative teachers in the primary schools, and academicians in the faculties of education were interviewed to investigate the process and results of implementing computer-aided instruction methods whose basis is strengthened in theory. Analysis of the results gained from two separate surveys demonstrated that capability of the teachers in elementary education institutions for carrying into effect computer-aided instruction and technical infrastructure has not been established for computer-aided instruction practices yet. Prospective teachers must be well-equipped in ICT to duly fulfill requirements of modern education and also must be self-confident. Finally, scope and intensity of the courses given in connection with teaching of the ICT in faculties of education needs to be revised.

Keywords: Information and Communication Technologies, Teacher, Education

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Authors: Antonino Greco, Nadia Mammone, Francesco Carlo Morabito, Mario Versaci

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Artifact rejection plays a key role in many signal processing applications. The artifacts are disturbance that can occur during the signal acquisition and that can alter the analysis of the signals themselves. Our aim is to automatically remove the artifacts, in particular from the Electroencephalographic (EEG) recordings. A technique for the automatic artifact rejection, based on the Independent Component Analysis (ICA) for the artifact extraction and on some high order statistics such as kurtosis and Shannon-s entropy, was proposed some years ago in literature. In this paper we try to enhance this technique proposing a new method based on the Renyi-s entropy. The performance of our method was tested and compared to the performance of the method in literature and the former proved to outperform the latter.

Keywords: Artifact, EEG, Renyi's entropy, kurtosis, independent component analysis.

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Authors: S. Pancucci

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Professional development is the focus of this study. It reports on questionnaire data that examined the perceived effectiveness of the Train the Trainer model of technology professional development for elementary teachers. Eighty-three selected teachers called Information Technology Coaches received four half-day and one after-school in-service sessions. Subsequently, coaches shared the information and skills acquired during training with colleagues. Results indicated that participants felt comfortable as Information Technology Coaches and felt well prepared because of their technological professional development. Overall, participants perceived the Train the Trainer model to be effective. The outcomes of this study suggest that the use of the Train the Trainer model, a known professional development model, can be an integral and interdependent component of the newer more comprehensive learning community professional development model.

Keywords: change, education, learning community, professional development, school improvement, technology coach, Train the Trainer.

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Authors: Kohtaroh Miyamoto

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As enterprise computing becomes more and more complex, the costs and technical challenges of IT system maintenance and support are increasing rapidly. One popular approach to managing IT system maintenance is to prepare and use a FAQ (Frequently Asked Questions) system to manage and reuse systems knowledge. Such a FAQ system can help reduce the resolution time for each service incident ticket. However, there is a major problem where over time the knowledge in such FAQs tends to become outdated. Much of the knowledge captured in the FAQ requires periodic updates in response to new insights or new trends in the problems addressed in order to maintain its usefulness for problem resolution. These updates require a systematic approach to define the exact portion of the FAQ and its content. Therefore, we are working on a novel method to hierarchically structure the FAQ and automate the updates of its structure and content. We use structured information and the unstructured text information with the timelines of the information in the service incident tickets. We cluster the tickets by structured category information, by keywords, and by keyword modifiers for the unstructured text information. We also calculate an urgency score based on trends, resolution times, and priorities. We carefully studied the tickets of one of our projects over a 2.5-year time period. After the first 6 months we started to create FAQs and confirmed they improved the resolution times. We continued observing over the next 2 years to assess the ongoing effectiveness of our method for the automatic FAQ updates. We improved the ratio of tickets covered by the FAQ from 32.3% to 68.9% during this time. Also, the average time reduction of ticket resolution was between 31.6% and 43.9%. Subjective analysis showed more than 75% reported that the FAQ system was useful in reducing ticket resolution times.

Keywords: FAQ System, Resolution Time, Service Incident Tickets, IT System Maintenance.

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Authors: Motonobu Hattori

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In neural networks, when new patterns are learned by a network, the new information radically interferes with previously stored patterns. This drawback is called catastrophic forgetting or catastrophic interference. In this paper, we propose a biologically inspired neural network model which overcomes this problem. The proposed model consists of two distinct networks: one is a Hopfield type of chaotic associative memory and the other is a multilayer neural network. We consider that these networks correspond to the hippocampus and the neocortex of the brain, respectively. Information given is firstly stored in the hippocampal network with fast learning algorithm. Then the stored information is recalled by chaotic behavior of each neuron in the hippocampal network. Finally, it is consolidated in the neocortical network by using pseudopatterns. Computer simulation results show that the proposed model has much better ability to avoid catastrophic forgetting in comparison with conventional models.

Keywords: catastrophic forgetting, chaotic neural network, complementary learning systems, dual-network

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Authors: Jie Zhang, Qianyu Guo, Tieyi Zhang, Zhiyong Feng, Xiaohong Li

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The widespread popularity of mobile devices and the development of artificial intelligence (AI) have led to the widespread adoption of deep learning (DL) in Android apps. Compared with traditional Android apps (traditional apps), deep learning based Android apps (DL-based apps) need to use more third-party application programming interfaces (APIs) to complete complex DL inference tasks. However, existing methods (e.g., FlowDroid) for detecting sensitive information leakage in Android apps cannot be directly used to detect DL-based apps as they are difficult to detect third-party APIs. To solve this problem, we design DLtrace, a new static information flow analysis tool that can effectively recognize third-party APIs. With our proposed trace and detection algorithms, DLtrace can also efficiently detect privacy leaks caused by sensitive APIs in DL-based apps. Additionally, we propose two formal definitions to deal with the common polymorphism and anonymous inner-class problems in the Android static analyzer. Using DLtrace, we summarize the non-sequential characteristics of DL inference tasks in DL-based apps and the specific functionalities provided by DL models for such apps. We conduct an empirical assessment with DLtrace on 208 popular DL-based apps in the wild and found that 26.0% of the apps suffered from sensitive information leakage. Furthermore, DLtrace outperformed FlowDroid in detecting and identifying third-party APIs. The experimental results demonstrate that DLtrace expands FlowDroid in understanding DL-based apps and detecting security issues therein.

Keywords: Mobile computing, deep learning apps, sensitive information, static analysis.

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Authors: Khairul Akram Zainol Ariffin, Ahmad Kamil Mahmood, Jafreezal Jaafar, Solahuddin Shamsuddin

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Random Access Memory (RAM) is an important device in computer system. It can represent the snapshot on how the computer has been used by the user. With the growth of its importance, the computer memory has been an issue that has been discussed in digital forensics. A number of tools have been developed to retrieve the information from the memory. However, most of the tools have their limitation in the ability of retrieving the important information from the computer memory. Hence, this paper is aimed to discuss the limitation and the setback for two main techniques such as process signature search and process enumeration. Then, a new hybrid approach will be presented to minimize the setback in both individual techniques. This new approach combines both techniques with the purpose to retrieve the information from the process block and other objects in the computer memory. Nevertheless, the basic theory in address translation for x86 platforms will be demonstrated in this paper.

Keywords: Algorithms, Digital Forensics, Memory Analysis, Signature Search.

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Authors: Zeinab Ghasemi, S. Amirhassan Monadjemi, Abbas Vafaei

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This paper presents a comparative analysis of a new unsupervised PCA-based technique for steel plates texture segmentation towards defect detection. The proposed scheme called Variance Based Component Analysis or VBCA employs PCA for feature extraction, applies a feature reduction algorithm based on variance of eigenpictures and classifies the pixels as defective and normal. While the classic PCA uses a clusterer like Kmeans for pixel clustering, VBCA employs thresholding and some post processing operations to label pixels as defective and normal. The experimental results show that proposed algorithm called VBCA is 12.46% more accurate and 78.85% faster than the classic PCA.

Keywords: Principal Component Analysis; Variance Based Component Analysis; Defect Detection; Texture Segmentation.

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Authors: V. Masilamani, Kamala Krithivasan

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We study the problem of reconstructing a three dimensional binary matrices whose interiors are only accessible through few projections. Such question is prominently motivated by the demand in material science for developing tool for reconstruction of crystalline structures from their images obtained by high-resolution transmission electron microscopy. Various approaches have been suggested to reconstruct 3D-object (crystalline structure) by reconstructing slice of the 3D-object. To handle the ill-posedness of the problem, a priori information such as convexity, connectivity and periodicity are used to limit the number of possible solutions. Formally, 3Dobject (crystalline structure) having a priory information is modeled by a class of 3D-binary matrices satisfying a priori information. We consider 3D-binary matrices with periodicity constraints, and we propose a polynomial time algorithm to reconstruct 3D-binary matrices with periodicity constraints from two orthogonal projections.

Keywords: 3D-Binary Matrix Reconstruction, Computed Tomography, Discrete Tomography, Integral Max Flow Problem.

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Authors: Adil AL-Rammahi

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In this short paper, new properties of transition matrix were introduced. Eigen values for small order transition matrices are calculated in flexible method. For benefit of these properties applications of these properties were studied in the solution of Markov's chain via steady state vector, and information theory via channel entropy. The implemented test examples were promised for usages.

Keywords: Eigen value problem, transition matrix, state vector, information theory.

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Authors: Bolarinwa, K. K., Oyeyinka, R. A.

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Relevant agricultural information disseminator (extension agent) ratio of 1:3500 farm families which become a menace to agricultural production capacity in developing countries necessitate this study. Out of 4 zones in the state, 24 extension agents in each zone, 4 extension agents using cell phones and 120 farmers using cell phone and 120 other farmers not using cell phone were purposively selected to give 240 farmers that participated in the research. Data were collected using interview guide and analysized using frequency, percentage and t-test.. Frequency of contact with agricultural information centers revealed that cell phone user farmers had greater means score of X 41.43 contact as against the low mean X19.32 contact recorded by farmers receiving agricultural information from extension agents not using cell phone and their production was statistically significant at P < 0.05. Usage of cell phone increase extension agent contact and increase farmers- production capacity.

Keywords: Cell phone, contact, extension agents and production.

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Authors: Ngoc Dang Khoa Nguyen, Imran Ali

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The cloud customer relationship management (CRM) has emerged as an innovative tool to augment the customer satisfaction and performance of banking systems. Cloud CRM allows to collect, analyze and utilize customer-associated information and update the systems, thereby offer superior customer service. Cloud technologies have invaluable potential to ensure innovative customer experiences, successful collaboration, enhanced speed to marketplace and IT effectiveness. As such, many leading banks have been attracted towards adoption of such innovative and customer-driver solutions to revolutionize their existing business models. Chief Information Officers (CIOs) are already implemented or in the process of implementation of cloud CRM. However, many organizations are still reluctant to take such initiative due to the lack of information on the factors influencing its implementation. This paper, therefore, aims to delve into the strategies, benefits and challenges intertwined in the implementation of cloud CRM in banking sector and provide reliable solutions.

Keywords: Banking sector, cloud computing, cloud CRM, strategy.

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Authors: Abbas Moshref Razavi, Rodina Ahmad

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As a primitive assumption, if a new information system is able to remind users their old work habits, it should have a better opportunity to be accepted, adopted and finally, utilized. In this paper some theoretical concepts borrowed from psychodynamic theory e.g. ego defenses are discussed to show how such resemblance can be made without necessarily affecting the performance of the new system. The main assertion is a new system should somehow imitate old work habits, not literally, but through following their paces in terms of the order of habitual tensional states including stimulation, defensive actions and satisfactions.

Keywords: information Systems, users' habits, psychodynamic

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Authors: Meng-Lung Lin, Qiubing Wang, Fujun Sun, Tzu-How Chu, Yi-Shiang Shiu

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The normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) derived from the moderate resolution imaging spectroradiometer (MODIS) have been widely used to identify spatial information of drought condition. The relationship between NDVI and NDMI has been analyzed using Pearson correlation analysis and showed strong positive relationship. The drought indices have detected drought conditions and identified spatial extents of drought. A comparison between normal year and drought year demonstrates that the amplitude analysis considered both vegetation and moisture condition is an effective method to identify drought condition. We proposed the amplitude analysis is useful for quick spatial assessment of drought information at a regional scale.

Keywords: NDVI, NDMI, Drought, remote sensing, spatialassessment.

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Authors: G. T. Balakayeva, K. K. Nurlybayeva, M. B. Zhanuzakov

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In the proposed work, we have developed software and designed a software architecture for the implementation of enterprise business processes. The proposed software has a multi-level architecture using a domain-specific tool. The developed architecture is a guarantor of the availability, reliability and security of the system and the implementation of business processes, which are the basis for effective enterprise management. Automating business processes, automating the algorithmic stages of an enterprise, developing optimal algorithms for managing activities, controlling and monitoring, reducing risks and improving results help organizations achieve strategic goals quickly and efficiently. The software described in this article can connect to the corporate information system via two methods: a desktop client and a web client. With an appeal to the application server, the desktop client program connects to the information system on the company's work PCs over a local network. Outside the organization, the user can interact with the information system via a web browser, which acts as a web client and connects to a web server. The developed software consists of several integrated modules that share resources and interact with each other through an API. The following technology stack was used during development: Node js, React js, MongoDB, Ngnix, Cloud Technologies, Python.

Keywords: Algorithms, document processing, automation, integrated modules, software architecture, software design, information system.

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Authors: Mousa Mojarrad, Amir Masoud Rahmani, Mehrab Mohebi

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Detection, feature extraction and pose estimation of people in images and video is made challenging by the variability of human appearance, the complexity of natural scenes and the high dimensionality of articulated body models and also the important field in Image, Signal and Vision Computing in recent years. In this paper, four types of people in 2D dimension image will be tested and proposed. The system will extract the size and the advantage of them (such as: tall fat, short fat, tall thin and short thin) from image. Fat and thin, according to their result from the human body that has been extract from image, will be obtained. Also the system extract every size of human body such as length, width and shown them in output.

Keywords: Analysis of Image Processing, Canny Edge Detection, Human Body Recognition, Measurement, Pose Estimation, 2D Human Dimension.

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Authors: Murat Koklu, Yavuz Unal

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Data mining can be called as a technique to extract information from data. It is the process of obtaining hidden information and then turning it into qualified knowledge by statistical and artificial intelligence technique. One of its application areas is medical area to form decision support systems for diagnosis just by inventing meaningful information from given medical data. In this study a decision support system for diagnosis of illness that make use of data mining and three different artificial intelligence classifier algorithms namely Multilayer Perceptron, Naive Bayes Classifier and J.48. Pima Indian dataset of UCI Machine Learning Repository was used. This dataset includes urinary and blood test results of 768 patients. These test results consist of 8 different feature vectors. Obtained classifying results were compared with the previous studies. The suggestions for future studies were presented.

Keywords: Artificial Intelligence, Classifiers, Data Mining, Diabetic Patients.

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Authors: C. W. Hsu, C. N. Liang, L. Y. Ke, F. Y. Huang

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Many accidents were happened because of fast driving, habitual working overtime or tired spirit. This paper presents a solution of remote warning for vehicles collision avoidance using vehicular communication. The development system integrates dedicated short range communication (DSRC) and global position system (GPS) with embedded system into a powerful remote warning system. To transmit the vehicular information and broadcast vehicle position; DSRC communication technology is adopt as the bridge. The proposed system is divided into two parts of the positioning andvehicular units in a vehicle. The positioning unit is used to provide the position and heading information from GPS module, and furthermore the vehicular unit is used to receive the break, throttle, and othersignals via controller area network (CAN) interface connected to each mechanism. The mobile hardware are built with an embedded system using X86 processor in Linux system. A vehicle is communicated with other vehicles via DSRC in non-addressed protocol with wireless access in vehicular environments (WAVE) short message protocol. From the position data and vehicular information, this paper provided a conflict detection algorithm to do time separation and remote warning with error bubble consideration. And the warning information is on-line displayed in the screen. This system is able to enhance driver assistance service and realize critical safety by using vehicular information from the neighbor vehicles.KeywordsDedicated short range communication, GPS, Control area network, Collision avoidance warning system.

Keywords: Dedicated short range communication, GPS, Control area network, Collision avoidance warning system.

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Authors: Rusyaizila Ramli, Nasriah Zakaria, Putra Sumari

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Privacy issues commonly discussed among researchers, practitioners, and end-users in pervasive healthcare. Pervasive healthcare systems are applications that can support patient-s need anytime and anywhere. However, pervasive healthcare raises privacy concerns since it can lead to situations where patients may not be aware that their private information is being shared and becomes vulnerable to threat. We have systematically analyzed the privacy issues and present a summary in tabular form to show the relationship among the issues. The six issues identified are medical information misuse, prescription leakage, medical information eavesdropping, social implications for the patient, patient difficulties in managing privacy settings, and lack of support in designing privacy-sensitive applications. We narrow down the issues and chose to focus on the issue of 'lack of support in designing privacysensitive applications' by proposing a privacy-sensitive architecture specifically designed for pervasive healthcare monitoring systems.

Keywords: Human Factors, Pervasive Healthcare, PrivacyIssues

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Authors: Laszlo Vari

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In the digital age, the spread of the mobile world and the nature of the cyberspace, offers many new opportunities for the prevalence of the fundamental right to free expression, and therefore, for free speech and freedom of the press; however, these new information communication technologies carry many new challenges. Defamation, censorship, fake news, misleading information, hate speech, breach of copyright etc., are only some of the violations, all of which can be derived from the harmful exercise of freedom of expression, all which become more salient in the internet. Here raises the question: how can we eliminate these problems, and practice our fundamental freedom rightfully? To answer this question, we should understand the elements and the characteristic of the nature of freedom of expression, and the role of the actors whose duties and responsibilities are crucial in the prevalence of this fundamental freedom. To achieve this goal, this paper will explore the European practice to understand instructions found in the case-law of the European Court of Human rights for the rightful exercise of freedom of expression.

Keywords: Collision of rights, European case-law, freedom opinion and expression, media law, freedom of information, online expression

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Authors: Maria Cecilia G. Cantos, Lorena W. Rabago, Bartolome T. Tanguilig III

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This paper describes a conducive and structured information exchange environment for the students of the College of Computer Studies in Manuel S. Enverga University Foundation in. The system was developed to help the students to check their academic result, manage profile, make self-enlistment and assist the students to manage their academic status that can be viewed also in mobile phones. Developing class schedules in a traditional way is a long process that involves making many numbers of choices. With Hill Climbing Algorithm, however, the process of class scheduling, particularly with regards to courses to be taken by the student aligned with the curriculum, can perform these processes and end up with an optimum solution. The proponent used Rapid Application Development (RAD) for the system development method. The proponent also used the PHP as the programming language and MySQL as the database.

Keywords: Hill climbing algorithm, integrated system, mobile web-based, student information system.

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