Volume 4, Issue 5

The importance of programming skills has increased with advances in information and communication technology (ICT). However, the di ? culty of learning programming is a major problem for novices. Therefore, we propose a logic error detection algorithm based on structure patterns, which are an index of similarity based on abstract syntax trees, and error degree, which is a measure of appropriateness for feedback. We de?ne structure patterns and error degree and present the proposed algorithm. In addition, we develop a Logic Error Detector (LED) Application Programming Interface (API) based on the proposed algorithm. An implementation of the proposed algorithm is used in experiments using actual data from an e-learning system. The results show that the proposed algorithm can accurately detect logic errors in many programs solving problems in the Introduction to Programming set.

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All forms of skin cancer are becoming widespread. These forms of cancer, and melanoma in particular, are insidious and aggressive and if not treated promptly can be lethal to humans. Effective treatment of skin lesions depends strongly on the timeliness of the diagnosis: for this reason, artificial vision systems are required to play a crucial role in supporting the diagnosis of skin lesions. This work offers insights into the state of the art in the field of melanoma image classification. We include a numerical section where a preliminary analysis of some classification techniques is performed, using color and texture features on a data set constituted by plain photographies, to which no pre-processing technique has been applied. This is motivated by the necessity to open new horizons in creating self-diagnosis systems for accessible skin lesions, due also to a huge innovation of cameras, smartphones technology and wearable devices.

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Current work demonstrates how open-source optimization library ”Extremum” (OSOL Extremum) can be used to build feedback controller of a satellite. Proposed software was developed to as an attempt to eliminate current problems that are present in scientific area: black-box effect (i.e. there is no opportunity to explore source code, modify it, or simply verify), no code reuse (i.e. implemented procedures are accessible only within software that includes it), limitated application of modern optimization algorithms (i.e. number of optimization algorithms increases but most of them were verified only on synthetic tests). All of them lead to so-called reproducibility crisis.

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In this paper, we present a new Somewhat Homomorphic Encryption (SHE) scheme using computation over complex numbers. We then use Bootstrapping technique to make the scheme Fully Homomorphic (FH) and supports unbounded number of circuit depth. In addition to its homomorphic properties and security level, a main characteristic of the proposed new scheme is its simplicity as it is merely based on addition and multiplication operations over complex numbers. The new scheme is implemented under Python using SAGEMath library and evaluated. Then a crypt-analysis based on Approximate GCD problem is done. A comparison with the BGV, a well known Fully Homomorphic Encryption (FHE) scheme, shows that this new scheme is an e ion scheme.

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In this paper, we present a new Somewhat Homomorphic Encryption (SHE) scheme using computation over complex numbers. We then use Bootstrapping technique to make the scheme Fully Homomorphic (FH) and supports unbounded number of circuit depth. In addition to its homomorphic properties and security level, a main characteristic of the proposed new scheme is its simplicity as it is merely based on addition and multiplication operations over complex numbers. The new scheme is implemented under Python using SAGEMath library and evaluated. Then a crypt-analysis based on Approximate GCD problem is done. A comparison with the BGV, a well known Fully Homomorphic Encryption (FHE) scheme, shows that this new scheme is an e ion scheme.Great efforts have been shown in the literature to improve the efficiency of photovoltaic (PV) systems. In this workpaper, authors present the development of a computational system based on free software that allows to estimate with an accuracy superior to 99.9 percent the performance of the PV array connected to a current inverter block, from the physical models behind. For this purpose, a solar radiation measures was made in the laboratory, and also voltage, current and temperature tests over the whole system, adding a storage interface and serial data transmission port to drive and process the data from a computer. Once the system was tested and calibrated in laboratory, the I-V experimental curves of the PV system were obtained in real time, in order to define the most important parameters: short-circuit current, open-circuit voltage, maximum current and voltage, in a natural environment. Those parameters were the input data to the computational tool based on Scilab, which allow estimate the performance of PV system described, solving the non-linear equations of the physical models used. The performance results were compared with a similar computational tool developed in Matlab, as well as with a commercial solar simulator used by the manufacturer of PV module. A highly accurate computational system based in Scilab was obtained allows to evaluate the efficiency and the filling factor of an array for di evels in real time.

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We present findings on classifying the class of executable code using convolutional, re- current neural networks by creating images from only the .text section of executables and dividing them into standard-size windows, using minimal preprocessing. We achieve up to 98.24% testing accuracy on classifying 9 types of malware, and 99.50% testing accuracy on classifying malicious vs. benign code. Then, we find that a recurrent network may not entirely be necessary, opening the door for future neural network architectures.

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In Latin America, there is a need for better security systems in different national or sectional votes, which is why this study was carried out, to show a proposal for a security model that helps to obtain better results and to this purpose has been selected information on the database for different Latin American countries, it has also been considered descriptive aspects and with a quantitative approach, to provide an objective livelihood. It has been intended to identify several security requirements that give confidence to citizens. Currently in Latin America studies continue to be carried out on technological systems for electronic voting and their real effectiveness in different countries, and then improve or implement them. Not all have come to implement it completely but in part due precisely to the demands it demands, also because of the limited information and because there are few related studies on these issues. The growth of technology and its easy access has allowed it to be used in different areas and politics is no exception, which is why different modalities of electronic voting have been implemented so that they can reduce some failures that are maintained in the traditional voting system. In conclusion, it is noted that voters need to know better about the new systems applicable to counting, it encourages individual reinsurance, which does not reveal the identity of the voter, as well as transparency, easy access information and auditability. Awareness is suggested about the use of electronic voting and the benefits it brings without also excluding negative factors because there is not totally secure and reliable digital voting system. Citizen participation must go hand in hand with political decision-making, so that processes are transparent, and results are protected. With all these criteria it can be noted that it is necessary to consider a comprehensive electronic security model that guarantees that reliability and integrity of the secret election vote, bearing in mind that the purpose is to reduce and prevent errors.

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3D-printing technologies have greatly influenced the field of fabrication of medical devices. In particular, Fused Deposition Modeling 3D printing has emerged as one the most popular and most promising technologies for fabricating upper-limb prostheses. Over the last years, a variety of types and designs of 3D-printed hand prostheses have been created and are commercially available. However, there are no standards or established procedures for testing these devices. Available information regarding their long-term performance and functionality is very limited. This paper presents a case study of mechanical testing methods applied to a specific design of an upper-limb prosthesis. The device and its subassemblies were subjected to flexion test in hyperextension and abduction conditions, fatigue/wear test, and tensile test. The experimental results are presented and examined. Testing procedures, adaptations and recommendations are described and discussed to demonstrate ways of generating reliable data that serve for comparison among different hand prostheses designs.

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Perishable inventory management contributes simultaneously to society and the economy, by reducing food wastage and capitalizing on the freshness of goods. For this reason, countless mathematical models have been developed for their effectiveness and cost-efficient management. Yet, the majority of these models can only optimize systems for a limited time frame, allowing for small gains in operations management, but failing to change the recurring patterns in inventory levels. System dynamics (SD) modelling shifts emphasizes these patterns and the recurring decisions that make them. Moreover, the framework has generated insight for other supply chain cases that could not have been derived from a short-term perspective. Thus, the current study now seeks to apply the SD framework in modelling perishable inventory systems, in designing policies that benefit the environment and the economy by reducing waste production and increasing the viability of goods reaching the customer. In particular, it evaluates the impact of opposing issuance policies (i.e. First-In-First-Out (FIFO) and Last-In-First-Out (LIFO)) on perishables to demonstrate the potential of SD in improving perishable inventory management. The simulated results share the sentiments of optimization models, that FIFO will ultimately generate less wastes and incur less material costs. Yet, the simulations also reveal implementing FIFO will result in larger fluctuations in inventory levels, which imply greater inconsistency in age-based quality. These suggest that LIFO would be preferable for quality-sensitive products, while FIFO would be preferable for cases sensitive to waste production. The current study demonstrates the efficiency of system dynamics in generating insight beyond that which can be derived from the existing mathematical models. Future studies may likewise extend this approach in the evaluation of policies on the use of technology in perishable inventory systems, which are the prevalent in present literature.

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Physical computing is important for ICT (information and communication technology) Education and other informatics education such as software education since physical computing can provide learning-by-doing education for students. It is also a strong tool to increase students’ programming ability using various type of physical computing tools like a robot. In physical computing, it is necessary to have evaluation metrics for learners in order to test students’ progresses and environmental safety, etc. However, in the literature, there is little work for evaluation metrics for learners. In this paper, for the proposed evaluation metrics for learners developed in the previous research, the usefulness and validity are discussed and justified with statistical analysis. The final 23 evaluation metric will be very useful in actual physical computing education.

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The demand for renewable energy sources such as waste pyrolysis plastic oil (WPPO), ethanol biofuel with high oxygen content is increasing globally. This study used blending of conventional diesel oil, waste plastic pyrolysis oil, ethanol and 2-ethyl hexyl nitrate (EHN) as additive. The purpose was to improve combustion, the ignition quality, and performance and emission characteristics of WPPO as an alternative source of energy. As an additive EHN reduces emissions of CO, CO2, UHC, NOX, and PM. On the other hand, the inclusion of ethanol purposed to improve the viscosity, increase the oxygen content of the blends, and increase miscibility of WPPO. The study utilized the following ratios, 50/WPPO25/E25, 60/WPPO20/E20, 70/WPPO15/E15, 80/WPPO10/E10 and 90/WPPO5/E5 for conventional diesel (CD), WPPO and ethanol and EHN respectively. The ratio of the additive was determined by the percentage method based on the total quantity of the blended fuel and was calculated as 0.001 %. A complete Miscibility was observed to avoid phase separation during the study and experimentation, for the blended ratios of WPPO. The testing for performance and emission characteristics was conducted on a fixed bed, water-cooled, single cylinder diesel generator engine. The results were compared to ASTM standards and discussed using tables and figures. The results conclusively show very close densities of 792 kg/m3, 963 kg/m3, 825 kg/m3 for WPPO, ethanol, EHN respectively, which are close to CD fuel at 845 kg/m3. Increased blend ratio decreased BSFC, 50 % engine load blend 80/WPPO10/E10 had values of 0.043 g/kW.h compared to full load with 0.041 g/kW.h. The highest brake thermal efficiency was by blend 90/WPPO5/E5 at 25 % engine load with a value of 26.5 % compared to 19 % at full engine load. Increase in the blend ratio and engine load decreased CO emissions up to 75 % engine load. For example, blend 90/WPPO5/E5 had a value of 0.035 % carbon emissions compared to 0.055 % at 25 % engine load. Therefore, conclusively WPPO blends can be alternative fuel with or without major engine modification.

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Recently, there is an enormous research on the smart campus concept due to the revolution of the IoT technologies. The motivation of this paper is to: reinforce the safety on campus, reduce the cost, and take one step forward toward a University smart campus. In this paper, we are not only proposing a framework that would act as an instantaneous responder, but we also provide a glimpse of the evolving research on smart campus. In addition, we explore the challenges, and highlight the future work regarding this on-the-spot responder system.

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This paper considers active disturbance rejection control (ADRC) as a new control approach for pH neutralization process. The pH process is challenging to control due to its sensitivity to disturbance, nonlinear dynamics, and time-varying characteristics. Special attention is paid to the impact of disturbances typically exists in the chemical process. First, the pH neutralization process nonlinear model is presented, then the active disturbance rejection control is described briefly and a pH neutralization process closed-loop control system is designed on its bases, and investigated with simulation. More robust and better disturbance rejection performances are achieved compared to the control technique of feedback linearization (FL).

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The most important biological factors, mainly determining the economic efficiency of milk production, are the productivity potential and the level of viability of cows. The aim of the work is to predict the bio-economic effects in a heterogeneous population of dairy cows taking into account the decrease in the length of productive life with increase in milk productivity. In the considered model situation, with an increase in the productivity of cows, the total profit per cow per year increases, while the profitability of production may decline. The developed algorithms are supposed to be used for solving the problems of optimizing the management of the dairy cow herd and planning technological development in animal husbandry.

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Clustering is a branch of data mining which involves grouping similar data in a collection known as cluster. Clustering can be used in many fields, one of the important applications is the intelligent text clustering. Text clustering in traditional algorithms was collecting documents based on keyword matching, this means that the documents were clustered without having any descriptive notions. Hence, non-similar documents were collected in the same cluster. The key solution for this problem is to cluster documents based on semantic similarity, where the documents are clustered based on the meaning and not keywords. In this research, fifty papers which use semantic similarity in different fields have been reviewed, thirteen of them that are using semantic similarity based on document clustering in five recent years have been selected for a deep study. A comprehensive literature review for all the selected papers is stated. A comparison regarding their algorithms, used tools, and evaluation methods is given. Finally, an intensive discussion comparing the works is presented.

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Trade credit is an important cost reduction tool in the inventory management. The effect of trade credit is studied on the integrated system for sharing the cost benefits realized due to the permissible delay. Credit term factor is introduced to divide the cost benefits between the retailer and the supplier. The various costs in the inventory model are subjected to the same inflation rate. This research paper revisits EOQ model for remanufacturing process under green supply chain with the permissible delay available to the retailer. Numerical examples prove that the optimal re-ordering schedule exists and is unique. Also sensitivity analysis is performed on certain parameters to ascertain their logical implications.

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An optimization method using a semi-analytical approach is detailed in this paper to design a wideband multilayer meander-line polarizer. It consists of the transmission line equivalent circuit study to find the best values of the shunt components that allow the maximum transmission and a 90 phase difference between two orthogonal components over the entire bandwidth, followed by a simulated annealing algorithm to optimize the polarizer’s dimensions by converging the values of the shunt components found earlier, to some rigorous empirical formulas of the equivalent admittances.
To validate the proposed method, a wideband (6-18 GHz) four layer’s meander-line polarizer is optimized and manufactured. The polarizer prototype is stacked to a horn antenna operating at the same frequency band and tested. The experimental results verify that the linear waves of the horn antenna alone were converted to circular ones when the multilayer meander-line polarizer is stacked to it.

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Due to the recent development of information and communication technology, such as sensor technology, IoT technology, and smart technology, interest in maker education is increasing. Maker education offers students experience-based education that allows them to make their own results, giving them various interests and motivations. Recently, various research results on maker education have been under way. However, there is little research work on the teaching aids selection criteria for maker education. The purpose of this study is to justify the teaching aids selection standards that are developed in the previous research works. In the previous research work, 14 selection standards were developed in a total of eight areas. In this work, through rigorous statistical analysis, justification of the 14 standards are verified. The findings are expected to help a lot in future policy proposals and related research on maker education.

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The aim of this article was the identification of cardiovascular diseases, after applying Katz and Higuchi fractal algorithms on 4 databases of ECG signals downloaded from the Physionet website: heart failure (HF), hypertension (H), ischemic heart disease (IHD) and normal sinus rhythm (NSR). For this purpose, initially the ECG signals passed through a filtering stage using a Butterworth high pass filter of order 6 and 0.5Hz of cutoff frequency, in order to cancel variations of the baseline. The fractal algorithms were applied independently for each database. For such application, all signals were standardized with a total of 100,000 data, and for the calculation of each fractal dimension (FD) a frame equal to 10,000 with an overlap of 1,000 was used in a first stage; in a second stage, a frame equal to 1,000 with an overlap of 100 was used. Thus, the results showed that the Higuchi algorithm, in general, has a better performance compared with the Katz algorithm. These results refer to the observation of the variance of the FD averages, which are shown in Table 4. For example, in the cardiovascular disease Arterial Hypertension, the Higuchi algorithm presented 0.0093 of variance compared with the algorithm of Katz that only reached 0.0352. Complementarily, we used the Principal Component Analysis (PCA) to show graphically the differences between algorithms, so the components related to the Higuchi algorithm were presented in a less dispersed and grouped form, making possible their differentiation, mainly when the second analysis stage was carried out.

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The Universidad Nacional Tecnologica de Lima Sur (UNTELS), like all universities, has as one of its objectives the training of professionals with skills that the changing working world requires. One way to establish the relevance of what UNTELS offers to its students is to know relevant information from graduates that entails, to their authorities, establish policies for continuous improvement of educational quality and improve the training of new professionals. In this context, the purpose of this research project is to collect information from graduates of the Professional Careers of Systems Engineering, Environmental Engineering, Mechanical and Electrical Engineering, Electronic Engineering and Communications, Business Administration from the National Technological University of South Lima of the second academic semester of the year 2017; To evaluate the satisfaction with regard to their professional training, receive a physical questionnaire with questions aimed at knowing their professional competencies, teaching staff, environments, library services, laboratory services, computer center services, administrative service of the Faculty and Professional School, University support services and personal and social attitudes that support decision-making for the improvement of educational quality at the National Technological University of South Lima.

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The purpose of this research is to develop an android application named “TANYA MEOW” which can be used by cat owners to detect the diseases that their cats suffer based on visible symptoms. The cat owners need to answer questionnaire to detect what disease is suffering by their cat. The result of the disease is also accompanied by the first treatment that cat owners can do to treat their cats. In addition, this application also has a feature that provides information, tips and tricks in cat care. The research methods used to analyze the development of this application are literature study through books, journals and e-books. Furthermore, observation, distribution of questionnaires for data collection and interviews to ensure data accuracy. We also make comparisons with applications that have similar concepts and features similar to our application. The research method used for the development of this application is waterfall that has five stages, requirements definition, system and software design, implementation and unit testing, integration and system testing and operation and maintenance. The results obtained from this writing show that the owner of the cat can feel the ease in detecting and knowing the disease from their cats who are sick, also getting knowledge on how to take care of cats in a good way.

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Plants are living organisms that can hear and recognize the environment around them but cannot communicate to inform their needs. Thus, in the past, humans thought that it was impossible to communicate with plants. However, in this modern era, humans can be able to communicate with these plants. In this paper, we propose a model that can interact (Chat) with plants cultivated in the automated farm system based on Internet of Things (IoT) and Fuzzy Logic. According to the communication of plants and humans, we apply a chatbot algorithm for sending/receiving messages between users and automated smart farming. The messages are processed by the natural language processing (NLP) to parse and interpret the meaning of the conversation. The experimented plant in this paper is orchid, namely Dendrobium Sonia (Bomjo). The result from the evaluation shows that the average accuracy (Harmonic mean) of een the user and the orchid is equal to 0.71, the precision and recall 0.75 and 0.6 respectively.

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This paper is an extension of research work originally presented in 2018 IEEE fifth International Congress on Information Science and Technology (CiSt). The research consists on developing method to diagnose electrical defects affecting wind turbine doubly-fed induction generator DFIG which constitutes a crucial part of wind energy conversion chain. First off all, we create a model of a non-defected wind conversion system based on mathematical equations introduced in Matlab Simulink. Then, we apply an indirect vector control stator field orientation in order to increase wind energy performance. With the aim of diagnosing the defects attacking wind turbine generator, we propose a method based on grouping of fast Fourier transform spectral analysis and Lissajous curves performed to generator stator and rotor currents. This diagnosis technique is applied to wind turbine in normal operation (non-defected generator) in order to have a reliable reference data for asynchronous generator behaviour. However, connected to the grid, wind turbine generator is affected by various faults occurring in electrical power networks. Therefore, the diagnosis method is applied also to a defected generator. Considering diversity of grid defects, we deal in the current paper with open stator supplying phases and open rotor feeding phases due to rotor side converter legs opening. Indeed, this diagnosis method allows diagnosing generator defects type and severity by comparing the resulting frequency spectrum analysis and Lissajous curves under abnormal condition operating to reference data obtained in case of non-defected generator. So, our proposed method contributes to DFIG defects identification and anticipation.
The simulations had been accomplished using Matlab Simulink. These results proved the efficiency and effectiveness of the proposed DFIG diagnosis method for wind energy conversion chain.

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This research focussed on assessing the impact of using upgraded biogas from chicken waste on the performance of a gasoline generator. In the study, the characteristics of raw biogas were determined and a biogas upgrading device was developed before assessing the generator performance on upgraded biogas. A Geotech GA 2000 plus gas analyser was used for biogas characterization and a slightly modified Elemax SH 2900 gasoline generator (5 HP) was used. The findings revealed that biogas from chicken waste comprised of 57% CH4 and 41% CO2 with small amounts of H2S, O2 and NH4. During biogas upgrading, the highest solvent to biogas flow rate used was 0.80. NaOH excelled with 93.0% CO2 reduction followed by KOH at 82.5% and lastly water at 63.0%. Using upgraded biogas improved the generator’s brake thermal efficiency from 14.2% for raw biogas to 17.6% for upgraded biogas. A locally fabricated scrubbing device significantly upgraded the biogas quality.

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Data mining is recognized as an effective technique for extracting and retrieving valuable information or decision from the vast available data. Because of the nature of the functionality of medical centers and hospitals, their data centers contain a collection of valuable information about their patients. By properly processing these data, different applications can be developed to utilize them. These applications could participate in predicting and diagnosing particular diseases. Two prime diseases realized to impact the overall health of society are heart diseases and diabetes. The presented work intends to develop and test a software application that helps doctors and practitioners predict the emergence of noncommunicable diseases (NCDs) such as diabetes and heart diseases. The application applies the predictive data mining model to the medical records which are collected from the Bahrain Defense Force Hospital (BDFH). The BDFH doctors evaluated the application and executed it on actual patients. The results obtained are accurately matching the expectation of doctors in BDFH. All kinds of risks are categorized appropriately according to the defined categories. As a conclusion, this application can help doctors in making proper decisions toward patient health risks. In addition, data mining is more supportive for the health sector and is essential for exploring the knowledge to be used in the health care sector.

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This paper presents a solution for question answering system for Vietnamese language by integrating diacritics restoration and question classification via deep learning approach. It could be said that this will be the first research integrating two phases into Vietnamese question answering system. Question classification has a critical role in the question answering system. However if the question has too many missing diacritics, this will make the classification extremely more difficult. In this paper, both automatic insertion of diacritics and question classification tasks are built to rely on deep learning approach. For diacritics restoration task, we apply the Encoder-Decoder LSTM model. The result of the first step will be the input of question classification. We use pre-train word embeddings in the Bidirectional LSTM model for Vietnamese question classification. The deep learning approach for both tasks is powerful and highly accurate model. By integrating diacritics restoration and question classification into Vietnamese question answering system – ICTbot of Binh Duong Department of Information and Communications Support System – it has produced remarkably positive results; thus proves the practicability of this proposed system.

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Manufacturing System (MS) sizing is a crucial task to complete in order to obtain the desired MS performance and efficiency. It involves selecting the required number of resources from each used type in a given planning horizon. In fact, different approaches coupling simulation/optimization tools have been developed to solve this issue and evaluate the MS performance. One of these approaches is the Simulation Expert System Approach (SESA). Unfortunately, the application domain of this approach is limited in sizing only the production resources (machines and labor) but neglects the material handling system (MHS) components. Moreover, omitting the transferring problem is not viable in the real world due to its importance in each shop floor. Thus, the aim of this paper is to describe the evolution of SESA, then, to check if the simulation optimization tools used in SESA are still relevant. This paper also investigates the importance of incorporating MHS in this approach and finally proposes some improvement opportunities for SESA including the tackling of the MHS fleet sizing problem. In fact, the wide literature review performed in this research indicates that SESA is still a pertinent approach but it must be improved. Therefore, it is expected that SESA improvement opportunities proposed in this work will greatly assist industrialists in enhancing the overall MS performance, providing a significant productivity increase and a minimization of the total production costs.

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Research is dedicated to fuzzy reconstructions in the diachronic associative linguistics, and encompasses several simulation practices. The principles of an associative memory model and fuzzy computing with words are adopted in the working methodology. The evolved mathematical model reanimates the older patterns of Scottish associative map by means of fuzzy logic. The compiled fuzzy associative fields are connected by epidigmatic relations represented by fuzzy associative word structures in a fuzzy associative map constituting the grounds of the proposed thesaurus. The simulation of the historical associative thesaurus enables extended associative scrutiny: tracing associative antipodes, reconstructing an associative experiment for any map associate, finding words’ similarities and distances, as well as associative differentiating and stratifying the Older Scottish lexis. The special interest represents the fuzzy associative differential, an electronic tool for estimating the lexis through its stable syntagmatic associates. All the simulation processes are fuzzy initiated and quantified. The proposed practice in its final configuration has no precedents, representing the novelty of theoretical interpretation and empirical application. The proposed terminology is mainly introduced by the researchers.

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In this work of technological development and research is presented the work done in the ITSAO to contribute to the use of clean energy such as photovoltaics and the implementation of activities to reduce electricity consumption by the technological community, the monitoring of electricity consumption is made on a monthly basis and an annual report is prepared in which the consumption monitoring and the reduction percentage thereof are shown by tables and graphs. The students of electronic Engineering carry out activities of optoelectronics and sustainable development where they intervene with the design and installation of photovoltaic systems that are part of their academic formation. It has a photovoltaic system that produces 2.16 kW-h / d and another will be installed to have photovoltaic energy in the tables of the ITSAO planters; these actions are part of the operational control related to the energy of the Integral Management System (SGI).

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Short-term load forecasting (STLF) plays an important role in building business strategies, ensuring reliability and safe operation for any electrical system. There are many different methods, including: regression models, time series, neural networks, expert systems, fuzzy logic, machine learning and statistical algorithms used for short-term forecasts. However, the practical requirement is how to minimize the forecast errors to prevent power shortages or wastage in the electricity market and limit risks.
The paper proposes a method of short-term load forecasting by constructing a Standardized Load Profile (SLP) based on the past electrical load data, combining machine learning algorithms Support Regression Vector (SVR) to improve the accuracy of short-term forecasting algorithms.

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This paper presents the use of Electromagnetic simulation tools such as ADS and CST in order to practice designing the RF front-end transceiver for a continuous-wave radar. Teaching the microwave engineering course based on the conventional education methods results in students who may not be able to apply their theoretical knowledge to design the microwave components. This work introduces a project-based education which is expected to guide the undergraduate students toward the design and simulation of the most important active and passive microwave components such as oscillator, power amplifier, Cantenna, low noise amplifier, mixer, filter and power divider. This project can be added to the curriculum of the microwave engineering course to achieve a balance between the theoretical and practical knowledge.

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Dyslexia has a lack of ability which causes other disturbances to social-emotional development. Based on an interview with the Chair of the Indonesian Dyslexia Association, increasing self-awareness is one form of dyslexia therapy to reduce that problem. Therefore there is a need for increasing self-awareness to solve that problem, whereas such technology the closest has not been fit with dyslexic children need. Then this study tries to focus on designed and developed CISELexia (Computer-Based Method for Improving Self-Awareness in Children with Dyslexia) technology using a gamification approach. It aims to help dyslexic children improve their competencies by increasing their self-awareness. Based on the implementation results CISELexia can increase the child’s self-awareness state according to the rubric questionnaire given for about 10%.

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This study investigated thermoelectric cooling system performances to cool the water flowing inside a water block. The experiments employed two identical thermoelectrics model TEC2-25408 with an overall dimension of 40 mm x 40 mm x 6.4 mm. The cold side of the thermoelectric was affixed to the top surface of the water block, while the hot side of the thermoelectric was attached to the heat pipe. The mass flow rates of the water were varied, i.e. 1 g/s, 1.5 g/s, and 2 g/s. Calibrated K-type thermocouples with an uncertainty of ± 0.5°C were used for measuring the temperatures. The experiment was observed for about 5 hours. All data were recorded using the data logger DAQ MX 9714 NI that was connected to the PC with the LabView program. The results show that raising the mass stream rate increases the cooling capacity and the COP. However, the effect of the mass stream rate on the water temperature at the outlet is unclear. In general, the thermoelectric has less advantage for cooling flowing water.

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The problem of planning and location of loading/unloading (L/U) areas attracting interest from several researchers. This is mainly because of the heterogeneity of information and the great complexity of logistics flows. Among the projects of the urban logistics in Morocco, the L/U areas represent an essential importance in the deliveries to the centers of the agglomerations. In light of this context, this work proposes a methodology for planning and location of L/U spaces for urban freight vehicles taking a commercial street in Morocco as a case study. Therefore, the proposed framework details the different steps of planning of L/U areas since the elaboration of the commercial map of the study area until the phase of sizing and location. Then, we study the applicability of this system, making a detailed proposal of the physical arrangements will be put in place, and the principle of spatial and temporal organization of deliveries, aiming at the best match between available supply and demand.

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TransJakarta is one of the methods to reduce congestion in Jakarta. However, the number of TransJakarta users compared to number of private vehicle users is very small, only 24% of the total population in Jakarta. The purpose of this research is to know public opinions about TransJakarta whether positive or negative by doing sentiment analysis about TransJakarta based on the opinion of Twitter, as Twitter is one of media to express its many users to express their opinions about an individual or an instance.
Data is retrieved from Twitter using the R-Studio application by utilizing the “TwitteR” library, then pre-processing and stored in a database. Next step is labelling the data using Sengon Lexicon and will be trained and tested using the Convolutional Neural Network algorithm. There are three CNN architectural models to be tested, namely VGG, ResNet, and GoogleNet. The designed VGG consists of 16 layers, ResNet 34 layers, and GoogleNet 22 layers. After the data are trained and tested, the results will be evaluated using Confusion Matrix to get the best F-Score. The results showed that among the three architectural models that were tested, the Resnet 34 layers architecture model gave the best F-Score of 98.11%, better compared to VGG which had the highest F-Score value of 96.74% and GoogleNet of 96.80%.

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Anaphora resolution is one of the old problems in Natural Language Processing. It is the process of identifying the antecedent of an anaphoric expression in a natural language text. Most of the NLP applications such as text summarization, question answering, information extraction, machine translation etc. require the successful resolution of anaphors. In this paper, we propose a methodology for the resolution of pronominal anaphors present in Malayalam text document. The proposed methodology is a hybrid architecture employing machine learning and rule-based techniques. In our study, we have used a deep level tagger developed using a machine learning based algorithm. The deep level tagger provides detailed information regarding the number and gender of nouns in a text document. The morphological features of the language are effectively utilized for the computational analysis of Malayalam text. Despite using less amount of linguistic features, our system provides better results which can be utilized for higher level NLP tasks such as question answering,text summarization, machine translation, etc.

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This paper proposed a frequency bandwidth required for the Control and Non-Payload Communications (CNPC) in autonomous operations of the Maritime Autonomous Surface Ship (MASS). The issue was to seek a radio spectrum to meet future demand for safe operations of autonomous ship at sea. As described in Document 5B/841 of ITU-R SG, safe navigation of autonomous ships relies on communications for command and control. Based on the operations required for the safe navigation of the MASS, the total bit rates were calculated. The spectrum requirements were obtained for command and control communications in operating the MASS safely. As a result, in terms of bandwidth, transmit rate, and transmit distance, state of the art satellite communication system was suitable for a safe operation of the MASS.

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Thermo-acoustic technology is very potential to be applied to convert heat into another energy source. The complete thermoacoustic engine construction requires a resonator connected to a core where there is a heat exchanger. The thermoacoustic core has a function as an acoustic power generator while the resonator functions as a storage of acoustic power which will shift the amplitude or phase of oscillating pressure or volume flow rate. Experimental testing was carried out to obtain the performance of a quarter wave length of standing wave thermoacoustic engine (SWTE). The resonator is made of a 2 inch Sch 40 stainless steel pipe with a length of 390 mm. The results of this experiment showed that the measured pressure amplitude was 4800 kPa with a working frequency of 138 HZ and produced an acoustic power of 22.85 watts. The parameters of this experimental test are used for the simulation program. The acoustic power generated from the simulation is 20.13 watts, 18.67 watts and 17.82 watts respectively for the length of the resonator 390 mm, 780 mm and 1170 mm.

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Wireless sensor networks (WSNs) are considered to be the currently flourishing scientific domain, thereby found to be applicable in numerous industrial and domestic applications. As per the mathematical results in Pulse-coupled oscillator (PCO), it has been predicted that, numerous iterations are needed for convergence, leading to increased power consumption. Biologically inspired solutions are greatly applicable for recovering coverage issues and efficient routing processes. In Hybrid energy efficient distributed clustering (HEED), to find a node with lowest communication cost, large number of iterations is needed, thereby leading to larger time duration for finding such node, and this is considered as the prevalent drawback, resulting in significant power consumption. In Optical low energy adaptive clustering hierarchy (O-LEACH), the cluster head selection is based on randomness, resulting in easy cluster failure. Hence, for cases, where power minimization and higher network lifetime is to be achieved to a larger extent, the existing strategies shall not be applicable, due to few restrictions. In this paper, a priority incorporated zone based distributed clustering algorithm, the Better Integrated and Optimized Low Energy Adaptive Clustering Hierarchy (BIO-LEACH) has been proposed for heterogeneous WSN. The methodology of this distributed clustering algorithm possesses three distinct features. First, the given clustering area will be divided in to different clusters and each cluster will be assigned with priority. The cluster which is highly sensitive and which needs frequent data recording will be given highest priority. The clusters in which the priority is assigned, takes multiple sensing and communication even in one cycle. But, the clusters where priority is not assigned, only one sensing and data transmission will be allowed for one cycle. Second, the clusters possessing priorities will have one cluster head (CH) and two supporting cluster head (SCH), but the clusters that do not have priorities will have only one cluster head. Third, the clusters possessing priorities will be possessing cluster nodes more than that of the clusters without priorities, so as to avoid cluster failure. Simulation results have been done to evaluate the performance of the proposed algorithm in terms of number of cluster head selection, amount of energy consumed and number packets received.

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This paper examines application of linear general model predictive control (LGMPC). The stability of the LGMPC is proven by means of a demonstration of a theorem stating a sufficient and constructive condition. Lower bounds conditions are found for one of these matrices and then a system with saturation is taken into consideration. The conditions can be interpreted through physical aspects. The results obtained were tested by means of computer simulations and an example with a water recovery process is considered.

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Predicting trainees’ final academic results in the early stage of programming class is a significant mission in the field of learning analytics. Performing exercises in programming class is hard and it takes a lot of time for trainees. For this reason, careful support with trainees are offered in many classes through classroom assistants (CAs). Even with CAs’ assistances, managing a programming class is difficult. Because each trainee’s coding activity is different from another’s, even when each of them is solving the same exercise. Classroom assistants with little teaching experience have difficulty for understanding the unique features of trainee’s coding activity. We have employed data mining to analyze trainees’ coding activities to distinguish those various features. The objective of this research is to present such behavioral features of trainees to CAs to enrich their assistance for the trainees. In order to investigate the timing of guidance, we conjectured the academic results from the chronicle record of coding activities.

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Hadoop has certain issues that could be taken care to execute the job efficiently. These limitations are due to the locality of the data in the cluster, allocation of the jobs, scheduling of the tasks and resource allocations in Hadoop. Execution in the mapreduce remains a challenge in terms of efficiency. So, an improved Hadoop architecture that takes care of the computation time has been discussed. The improved architecture addresses the communication issues with the task trackers, inefficient clean up task, heartbeat function. Comparing with native Hadoop, the improved Hadoop reduces the total time taken for running the reducer tasks. The performance of the improved system using optimization serves better in the computation time.

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Health Information Technology is playing a key role in healthcare. Specifically, the use of electronic health records has been found to bring about most significant improvements in healthcare quality, mainly as relates to patient management, healthcare delivery and research support. Health record systems adoption has been promoted in many countries to support efficient, high quality integrated healthcare. The objective of this work is the implementation of an Electronic Health Record system based on a relational database. The system architecture is modular and based on the concentration of specific pathology related parameters in one module, therefore the system can be easily applied to different pathologies. Several examples of its application are described. It is intended to extend the system integrating genomic data.

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In this research work, we proposed a technique to decrease the complexity of 2-D Matrix Pencil (MP) for the direction of arrival (DOA) evaluation by combining two perpendicular arrays: The first one is the uniform linear array (ULA) and the second one is a uniform circular array (UCA). This special structure for the 2D Matrix Pencil pipe to a powerful methodology for real-time implementation on a digital signal processor, while the MP used to correlated and uncorrelated sources with the presence of a white noise. The obtained results show that proposed MP method gives better results at the level of the precision and the estimation of the DOA compared to the published measure.

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Panjang Island is one of the islands which is categorized as a small island and is located in Jepara District, Jepara Regency, Central Java Province. The problem faced by Panjang Island is erosion, which increasingly erodes the shoreline and begins to damage buildings on the beach. A solution that is effective enough to overcome erosion or changes in the coastline is to build coastal shelters to reduce wave energy and increase sediment supply. The purpose of this study is to study the exact location of the submerged breakwater from aspects of wave parameters. The method used is wave forecasting and numerical wave model scenarios for submerged breakwater structures. The results showed wave height forecasting results in the western season (December, January, and February) is 2.66 meters, in the first transition season (March, April, May) is 2.14 meters, at east season (September, October, December) 1.45 meters, in the second transition season (June, July, August) is 2.26 meters. The model simulation results with a scenario based on the plan show the most effective damping in the east season with a reduction of 52.26%.

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Knowing about different events in several Latin American countries, as well as in Ecuador, in relation to data alteration, in the different sectional voting processes, it has been necessary to carry out a security analysis provided by the systems of choice in the different sectional governments of the country. The objective of this study is to analyze information security policies, their processes, standards and encryption methods, for the voting processes of the National Electoral Council of Ecuador (CNE). In this study a qualitative, deductive and exploratory research approach was applied, which allowed an analysis of the articles in reference. The articles of law that the Constitution has stipulated were reviewed, since the country manages a traditional system, which is carried out through several stages or phases, but which in turn uses technological tools. During these stages there are risks that can harm the sectional candidates, in such a way that a review of the Ecuadorian reality is essential, which implies the commitment of their actors, working as a team with specialists in the different areas, as well as compliance with public policies. It is concluded that the prevention, processes and policies proposed by the entity are based on the pillars of information security of the ISO 27001 standard, it is also pertinent that the cryptography implemented by the CNE with the hash code is in constant integration with new more robust and secure cryptographic methods providing greater security, integrity and confidentiality avoiding alterations in the data entered in the electoral process.

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Since lighting constitutes an important part of the total electricity consumption of a country, the design and implementation of more efficient interior lighting systems are one of the feasible strategies to reduce energy consumption. With this purpose, in recent years several studies have considered the use of LED lighting systems. However, its implementation at a large scale is still unaffordable for some organizations like academic institutions. In this context, this research presents a strategy to replace the current fluorescent-based lighting system to LED technology in a building of a higher education institution in Ecuador, and thus contribute to reducing energy consumption in campus facilities. The appropriate LED lamp alternative was selected from those available in the Ecuadorian market through AHP multicriteria decision-making method. The improvement proposal included an annual schedule for the progressive replacement of fluorescent lamps, based on the economic savings that would be generated every year after its implementation. The proposal was technically validated by simulation in DIAlux 4.12 and its economic feasibility was demonstrated by estimating the net present value and the equivalent uniform annual cost. With this strategy, a total of 2595 fluorescent tubes installed in the 215 building light points, can be replaced with LED technology in three years, with an initial investment of 4949.10 USD and generate an energy saving of 47.36%.

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With the progress of IoT, everything is going to be connected to the network. It will bring us a lot of benefits however some security risks will be occurred by connecting network. To avoid such problems, it is indispensable to strengthen security more than now. We focus on personal authentication as one of the security.
As a security enhancement method, we proposed a method to carry out numeral identification and personal authentication using numerals written in the air with Leap motion sensor. In this paper, we also focus on proper handling of aerial input numerals to verify whether the numerals written in the air are helpful for authentication. We collect numerals 0 to 9 from five subjects, then apply three pre-processing to these data, learn and authenticate them by CNN (convolutional neural network) which is a method of machine learning. As a result of learning, an average authentication accuracy was 92.4%. This result suggests that numerals written in the air are possible to carry out personal authentication and it will be able to construct a better authentication system.

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In recent years, deep learning has been widely used for time series prediction. Deep learning model that is most often used for time series prediction is LSTM. LSTM is widely used because of its excellence in remembering very long sequences. However, doing training on models that use LSTM requires a long time. Trying from one model to another model that use LSTM will take a very long time, thus a method is needed for optimizing hyperparameter to get a model with a small RMSE. This research proposed Artificial Bee Colony (ABC) as a method in optimizing hyperparameter for models that use LSTM. ABC is a metaheuristic method that mimics the behavior of bee colonies in foraging. Optimized hyperparameter in this research consisted of sliding window size, number of LSTM units, dropout rate, regularizer, regularizer rate, optimizer and learning rate. In this research the proposed method called as ABC-LSTM. Bitcoin prices historical data was used as the dataset for evaluating the prediction of the models. The best ABC-LSTM model resulted best RMSE of 189.61 compared to model that use LSTM without optimization resulted best RMSE of 236.17. This result showed that ABC-LSTM model outperformed models that use LSTM without optimization.

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A control of the greenhouse when it is connected to the photovoltaic generator is described in this paper. The greenhouse is modulated by differential equations while the temperature and humidity are regulated using the fuzzy logic controller. The photovoltaic system can supply maximum power for a specified voltage and output current. In addition, the fuzzy logic method is used to follow the maximum power point (MPPT) of a photovoltaic system. Our system consists of a solar module, a DC/DC converter with MPPT control, a DC/AC inverter and a three-phase induction motor. In addition, a direct torque control (DTC) of the induction machine which is utilized to control the ventilation flow rate. The state of control is displayed through the simulation results performed with Matlab / Simulink.

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In the past decades, various ideas have proposed for reconductoring of overhead lines to enhance power transmission capacity considering growing energy demand. Conventional method of reconductoring is use to take couple of weeks but availability of shutdown for a prolong period is a major constraint in line uprating. Emergency restoration structure (ERS) is effective to deliver such challenging task within hardly 4-5 hours line shutdown only. This quick restoration and easiness is mobility features are making ERS versatile for restoration even after any natural disasters. In this article, a case study of 132 kV restorations is described as a conceptual proof of concepts. Successful execution of this project resolved a 400 kV transmission line hindrance between Atur and Indira Puram since March 2017 and was likely to remain idle due to a 132 kV single circuit transmission line crossing its route.

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Anomaly detection aims at identification of suspicious items, observations or events by differing from most of the data. Intrusion Detection, Fault Detection, and Fraud Detection are some of the various applications of Anomaly Detection. The Machine learning classifier algorithms used in these applications would greatly affect the overall efficiency. This work is an extension of our previous work ERCRTV: Ensemble of Random Committee and Random Tree for Efficient Anomaly Classification using Voting. In the current work, we propose SDMR a simple Feature Selection Technique to select significant features from the data set. Furthermore, to reduce the dimensionality, we use PCA in the pre-processing stage. The EKMC (Ensemble of kNN using MetaCost) with ten-fold cross validation is then applied on the pre-processed data. The performance of EKMC is evaluated on UNSW_NB15 and NSL KDD data sets. The results of EKMC indicate better detection rate and prediction accuracy with a lesser error rate than other existing methods.

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In Big Data, several solution providers offer distributions to handle this large amount of data. Given the variety of these solutions, we are working to provide universal meta-modeling for all layers of a Big Data system in order to address the issue of interoperability and portability between these solutions. As part of our continuous efforts to standardize concepts in Big Data world, we apply in this paper techniques related to Model-Driven Engineering “MDE” to propose a meta-model for the security layer in Big Data. This meta-model with the others that we have already proposed for the other layers of the Big Data system, will be used as a platform-independent according to Model-Driven Architecture pattern, which describes the structures of Big Data layers independently of any specific platform.

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