bluuebunny/crossref_metadata_2025_split huggingface dataset:
Water Harvesting as a Technological Innovation and Greater Solving of Climatic Change Impact to Supply Fertigation
Authors: Bafdal Nurpilihan , Dwiratna Sophia , Suryadi Edy , Kendarto Dwi Rustam 
Published: March 2019

Water is one critical factor in the agriculture sector and becomes the first barrier in agriculture production. In the dry season, water availability without depending on the season expertise should be able to find the technological innovation that can be used to the farmers, especially in the rural areas. Global climate change caused more long dry season and will impact to decrease of water from rainfall and runoff. Rainfall and runoff harvesting it means capturing water from rainfall where it falls or capturing in and can be stored it the torrents and ponds and good enough for resources of fertigation. Fertigation engineering is the application of supplying irrigation and fertilizer to crops and engineering principles to the solution of water management problem. Research of water harvesting with cultivated of red oval cherry tomato and planted was carried out at the greenhouse located in Universitas Padjadjaran Campus, West Java Province, Indonesia from January to Mei 2017. The research method is descriptive analysis, and the red oval tomato planted it on the autopot with growth media uses mixed of charcoal husk and zeolite with ratio 9: 1 and 15 cm height. The amount of water harvesting from the green house’ rooftop is 20200,5 liter per year.The amount of water used by tomato plant used auto pot is 427,12 liter. The research shows that the water used of red oval tomato where for initial stage 66 liter; development stage 36,22 liter; midseason stage 310,04 liter and late seasons stage 14,86 liter; average total water used for red oval tomato at 8,21 liter/plant. The average yield of red oval tomato per plants using autopot where the lowest yield was 0.731 kg/plant while the highest yield of 1.648 kg/plant. The value of water use efficiency of red oval tomato is 10.kg/m3.
Physicochemical Properties of Egg Yolk Powder from Eggs of Different Types of Bird
Authors: Wei Lim Jing , Alkarkhi Abbas F.M. , Huda Nurul 
Published: March 2019

The goal of this study was to determine whether the physicochemical properties of egg yolk powders produced from eggs from different types of bird (Fighting chicken, Kampung chicken, Serama chicken, Leghorn chicken, Guineafowl, and Turkey) differ from each other and from those of commercial egg yolk powder. The powders were analysed to determine yield; proximate composition; colour, solubility; water holding capacity (WHC); and emulsion activity and stability. Egg yolk powders were prepared by separating the egg yolk manually followed by blast freezing and freeze drying. The weight of the egg ranged from 22.16 g for Serama to 66.25 g for Turkey. The lowest yield of egg yolk liquid was found in Leghorn egg (27.63) and highest in Serama egg (44.31). Egg yolk powder yield was also lowest for Leghorn eggs (12.85%) , followed by Turkey (15.85%), Guineafowl (16.22%), Kampung (16.48%), Fighting (16.62%), and the highest for Serama (18.92%). All parameters studied except WHC differed significantly (p < 0.05) among at least some of the different egg yolk powders. Egg yolk powder from Serama chicken had the highest protein content (40.77%), lowest fat content (51.96%), highest solubility (20.20 oBrix), and lowest WHC (79.78 %). Egg yolk powder from Fighting chicken had the highest emulsion activity (54.13%) and that from Leghorn chicken had the highest emulsion stability (48.41%). Egg yolk powder from Guineafowl had the highest yellowness intensity (72.21), whereas the value was lowest (35.84) for commercial egg yolk powder. In conclusion, physicochemical properties of egg yolk powder depend on the source of the eggs.
Segmentation of Carpal Bones Using Gradient Inverse Coefficient of Variation with Dynamic Programming Method
Authors: Jantan Sadiah , Muad Anuar Mikdad , Hussain Aini 
Published: March 2019

Segmentation of the carpal bones (CBs) especially for children above seven years old is a challenging task in computer vision mainly because of poor definitions of the bone contours and the occurrence of the partial overlapping of the bones. Although active contour methods are widely employed in image bone segmentation, they are sensitive to initialization and have limitation in segmenting overlapping objects. Thus, there is a need for a robust segmentation method for bone segmentation. This paper presents an automatic active boundary-based segmentation method, gradient inverse coefficient of variation, based on dynamic programming (DP-GICOV) method to segment carpal bones on radiographic images of children age 5 to 8 years old. A mapping procedure is designed based on a priori knowledge about the natural growth and the arrangement of carpal bones in human body. The accuracy of the DP-GICOV is compared qualitatively and quantitatively with the de-regularized level set (DRLS) and multi-scale gradient vector flow (MGVF) on a dataset of 20 images of carpal bones from University of Southern California. The presented method is capable to detect the bone boundaries fast and accurate. Results show that the DP-GICOV is highly accurate especially for overlapping bones, which is more than 85% in many cases, and it requires minimal user’s intervention. This method has produced a promised result in overcoming both issues faced by active contours method; initialization and overlapping objects.
Evaluation of Packet Tracer Application Effectiveness in Computer Design Networking Subject
Authors: Ambiyar - , Yondri Surfa , Irfan Dedy , Putri Meilani , Zaus Mahesi Agni , Islami Syaiful 
Published: March 2019

The dynamics of technological progress in various fields indirectly affect the world of education. One of the indications can be seen in the use of media as a learning tool. This study aims; 1) to investigate the effectiveness of the packet tracer learning media application, 2) to investigate the responses of the students to such applications, 3) to determine the appropriate media used in a learning process. The research is carried out in the subject of Computer Design Networking. The evaluation research is employed by using a mixed method of qualitative and quantitative approach. The quantitative analysis involved 58 students, while the qualitative analysis involved one productive teacher, one curriculum representative, and four students. Data are analysed by using quantitative and qualitative data analysis techniques. The results show that, the effectiveness of learning media with packet tracer is high i.e., 82.76%, which covers three aspects; software engineering, learning aspects, and display aspects. This means that this packet tracer learning media can be applied as a solution to the limitation of facilities and infrastructures of network practices by considering certain learning situations and conditions. The results indicate that this application is very useful to deal with the high cost of practising tools. It makes the students more enthusiastic and motivated during the learning process. Hence, by implementing this learning method, the interaction and the learning outcomes of the student can be increased.
Lexical Disambiguation (CKBD): A Tool to Identify and Resolve Semantic Conflicts Using Context Knowledge
Authors: Al Tahat Said , Ahmad Kamsuriah 
Published: March 2019

The schema matching process is a fundamental step in a schema integration system, and its quality impacts the overall performance of the system. Recently, a large number of schema matching approaches have been developed. Until today, the performance of schema matching is inherently uncertain and requires improvement. The most difficult task is inferring the real-world semantics of data from the information provided by schema labels in their representations. Usually, schemas with identical semantics are represented by different vocabularies and only their own designers can completely understand. A schema may contain synonyms and homonyms words. Therefore, it is necessary to understand how the schema elements are "presented"; it is often hard to get aware meaning associated with elements names, due to the semantic ambiguity of human language. Semantic ambiguity problem means the capability of being understood in two or more possible senses. Having more than one meaning for an individual schema element would cause confusion in interpretation of schema name. This may affect negatively on the matching result. Therefore, this paper aims to resolve this problem of semantic ambiguity and represent the intended meaning of the schema labels name, by introducing the CKBD (Context Knowledge-Based Disambiguation) approach. The CKBD is obtained by integrating two pieces of context knowledge: semantic domain and more frequency used into a disambiguation processor. Finally, the CKBD is implemented and is tested in a real dataset. The result is deeply grounded in the ability to detect schema name intended meaning.
FPGA Implementation of Hand-written Number Recognition Based on CNN
Authors: Giardino Daniele , Matta Marco , Silvestri Francesca , Spanò Sergio , Trobiani Valerio 
Published: March 2019

Convolutional Neural Networks (CNNs) are the state-of-the-art in computer vision for different purposes such as image and video classification, recommender systems and natural language processing. The connectivity pattern between CNNs neurons is inspired by the structure of the animal visual cortex. In order to allow the processing, they are realized with multiple parallel 2-dimensional FIR filters that convolve the input signal with the learned feature maps. For this reason, a CNN implementation requires highly parallel computations that cannot be achieved using traditional general-purpose processors, which is why they benefit from a very significant speed-up when mapped and run on Field Programmable Gate Arrays (FPGAs). This is because FPGAs offer the capability to design full customizable hardware architectures, providing high flexibility and the availability of hundreds to thousands of on-chip Digital Signal Processing (DSP) blocks. This paper presents an FPGA implementation of a hand-written number recognition system based on CNN. The system has been characterized in terms of classification accuracy, area, speed, and power consumption. The neural network was implemented on a Xilinx XC7A100T FPGA, and it uses 29.69% of Slice LUTs, 4.42% of slice registers and 52.50% block RAMs. We designed the system using a 9-bit representation that allows for avoiding the use of DSP. For this reason, multipliers are implemented using LUTs. The proposed architecture can be easily scaled on different FPGA devices thank its regularity. CNN can reach a classification accuracy of 90%.
Concreting Workmanship in Indonesia Study Case: Padang City, West Sumatra, Indonesia
Authors: Juliafad Eka , Rani Iskandar G. , Rifwan Fitra , P Yose Fajar 
Published: March 2019

This research gives information about the practice of concreting work in Reinforced Concrete (RC) building project in Indonesia. The study area is Padang City, which has suffered severe damages due to September 2009 Earthquake. The target interviewee is 100 builders who are constructing RC buildings. This interview together with observation was conducted by directly visiting the construction sites. While collecting information about the main factors which contribute to RC building quality, e.g., concrete mixing, compaction, concrete placing and curing, the information about the profile of the builders have been collected. The results show that the workers dominate the profile of builders in productive age with 30 years experiences on average. However, 90% of the builders yet experience any construction workshop. The concreting work, which is conducted by the builders, yet reaches the required standard. More than 50% of the builders produced concrete with the higher water-cement ratio, and almost 65% admitted that they add more water after concrete`s setting time is finished. There are 3common methods of concrete compaction which is used. 6% of the builders use a vibrator, 43% use the rodding method, 28% use hummer, and 8% choose to add more water to placed concrete to increase concrete workability. Meanwhile, more than 60% of the builders do not cure the placed concrete. The results show that construction practice that is conducted by the local builders is needed to be improved. The mistakes seem to be repeated over a generation since the source of learning of the builders is mainly from their senior builder (77.25%) and self-learning (2.5%). Only 7.5% that admitted they had experienced on construction workshop.
Prediction of Hourly Cooling Energy Consumption of Educational Buildings Using Artificial Neural Network
Authors: Jaber Alaa Abdulhady , Saleh Ahmed , Mohammed Ali Hussein Fouad 
Published: March 2019

Predicating the required building energy when it is in the design stage and before being constructed considers a crucial step for in charge people. Hence, the main aim of this research is to accurately forecast the needed building cooling energy per hour for educational buildings at University of Technology in Iraq. For this purpose, the feed forward artificial neural network (ANN) has been selected as an efficient technique to develop such a predication system. Firstly, the main building parameters have been investigated and then only the most important ones were chosen to be used as inputs to the ANN model. However, due to the long time period that is required to collect actual consumed building energy in order to be employed for ANN model training, the hourly analysis program (HAP), which is a building simulation software, has been utilized to produce a database covering the summer months in Iraq. Different training algorithms and range of learning rate values have been investigated, and the Bayesian regularization backpropagation training algorithm and 0.05 learning rate were found very suitable for precise cooling energy prediction. To evaluate the performance of the optimized ANN model, mean square error (MSE) and correlation coefficient (R) have been adopted. The MSE and R indices for the predication results proved that the optimized ANN model is having a high predication accuracy with 5.99*10-6 and 0.9994, respectively.
Wi-Fi Signals Database Construction using Chebyshev Wavelets for Indoor Positioning Systems
Authors: Narzullaev Anvar , Muminov Zahriddin , Ibragimov Gafurjan 
Published: March 2019

Nowadays fast and accurate positioning of assets and people is as a crucial part of many businesses, such as, warehousing, manufacturing and logistics. Applications that offer different services based on mobile user location gaining more and more attention. Some of the most common applications include location-based advertising, directory assistance, point-to-point navigation, asset tracking, emergency and fleet management. While outdoors mostly covered by the Global Positioning System, there is no one versatile solution for indoor positioning. For the past decade Wi-Fi fingerprinting based indoor positioning systems gained a lot of attention by enterprises as an affordable and flexible solution to track their assets and resources more effectively. The concept behind Wi-Fi fingerprinting is to create signal strength database of the area prior to the actual positioning. This process is known as a calibration carried out manually and the indoor positioning system accuracy highly depends on a calibration intensity. Unfortunately, this procedure requires huge amount of time, manpower and effort, which makes extensive deployment of indoor positioning system a challenging task. approach of constructing signal strength database from a minimal number of measurements using Chebyshev wavelets approximation. The main objective of the research is to minimize the calibration workload while providing high positioning accuracy. The field tests as well as computer simulation results showed significant improvement in signal strength prediction accuracy compared to existing approximation algorithms. Furhtermore, the proposed algorithm can recover missing signal values with much smaller number of on-site measurements compared to conventional calibration algorithm.
SMS-Based ICT Tool for Knowledge Sharing in Agriculture
Authors: Galeon Dario H. , Garcia Jr. Paul G. , Palaoag Thelma D. 
Published: March 2019

Agriculture is the world’s major industry with 60% of the global population depending on it. Being the major source of livelihood, agriculture is challenged by issues such as food shortage, food security problems, climate change affecting crop yield, land degradation, decrease of crop varieties and others. There is a huge amount of knowledge products generated by the government agencies, local universities, private civic groups, local government units and non-government organizations (NGOs) to address these issues however the information has difficulty reaching the intended farm practitioners as end-user. With this aforesaid challenge, a technology based information sharing and access among stakeholders such as agricultural knowledge experts (academe) and knowledge end-users (farmers) is a dire need. Information and Communications Technology (ICT) has always been an indispensable tool that can provide an environment where knowledge generators and knowledge users can use to exchange information any time and place. Retooling and scaling up the process how information and knowledge products is being accessed and shared is indeed a critical consideration. Using descriptive research and qualitative approach, this study determined an ICT tool, a managed SMS-based system to be highly acceptable to the identified stakeholders as an information sharing medium and model and is therefore recommended for use. The result of this research shall serve as an implementation guide for government, non-government organizations(NGOs), extension service providers, system integrators, researchers, and other related groups offering or planning to engage in similar service.
Artificial Intelligence in Diagnosing Tuberculosis: A Review
Authors: Meraj Syeda Shaizadi , Yaakob Razali , Azman Azreen , Mohd Rum Siti Nuralain , Ahmad Nazri Azree Shahrel 
Published: March 2019

Tuberculosis (TB) is among top ten causes of deaths worldwide. It is the single most cause of deaths by an infectious disease and is ranked 2nd only after the HIV/AIDS. In third world countries, the diagnosis of TB is done through conventional methods. To diagnostic results are obtain from conventional methods such as blood, culture, sputum and biopsies. They are tedious as well as take long time like 1-2 weeks or maybe evenmore. Therefore, to lower the detection time and raise the accuracy of diagnosis several researches have been carried out. In the past fifty years, due to the advanced and sophisticated technologies, in medical as well as computer science fields, have paved a way to utilize the essence of both the areas. In Artificial Intelligence (AI) various Machine Learning (ML) algorithms have furthered the interests in Computer-aided Detection (CADe) and Diagnosis (CADx) methods. These methodologies assist in medical field for diagnosing the diseases through clinical signs and symptoms as well as radiological images of the patient. They have been implemented for the diagnosis of TB. Advances in AI algorithms, has unveiled great promises in identifying the presence and absence of TB. As of late, many attempts have been made to formulate the strategies to increase the classification accuracy of TB diagnosis using the AI and machine learning approach. This review paper, aims to describes the diverse AI approaches employed in the diagnosis of TB.
Requirements Engineering (RE) Process for The Adaptation of The Hospital Information System (HIS)
Authors: Abd Malik Sofianiza , Nordin Azlin , Al-Ehaidib Reem Nasser 
Published: March 2019

Requirements Engineering (RE) process is an initial stage for any software development process. In this research, we adopted the RE process into a system adaptation software project for a Hospital Information System (HIS) in a Saudi Arabian hospital. Instead of developing from scratch, the adaptation of an existing system enables a working system to be configured into use in a similar situation in another organization, or use it in a different organization in a different geographical area. Nonetheless, the approach of systems adaptation has its own issues and challenges. This research considers investigating culture-specific behaviors when adapting a system to another environment. The research is motivated by the challenges faced by real-life cases of implementation of a HIS developed in South Korea at one of Kingdom of Saudi Arabian (KSA) hospital. The cultural behavioral requirements were identified using focus group technique and the outcomes were analyzed and represented using Unified Modelling Language (UML). The outcome of the research is validated through evaluation of feedback from a review session and academic experts. This work investigates cultural and behavioral features that have the potentials to support software engineers, who are considering new systematic techniques and approaches to improve systems adaptation processes, especially in HIS domain. This work could be used as a reference to guide developers to adapt similar systems in similar culture and environment settings. In addition to an adaptation to a specific environment, the outcome could also be useful for designing system for-reuse facet from the perspective of reusability.
Updated Goals, Operators, Methods, and Selection Rules (GOMS) with Touch Screen Operations for Quantitative Analysis of User Interfaces
Authors: Setthawong Pisal , Setthawong Rachsuda 
Published: March 2019

When developing applications, User Interfaces (UI) are considered as an important and integral component of any application. With badly designed UI, users are less likely to use the application, leading to low adoption rates and is not desirable in any application development setting. The process of developing good intuitive and stream-lined UI for users is complex, that requires many processes and experts from many fields to contribute. When evaluating potential UI designs, there are many attributes and features that could be examined either in a qualitative and/or quantitative standpoint. The Updated Goals, Operators, Methods, and Selection Rules (GOMS) model is an approach that has been used in the area. With the Keystroke Level (KLM) extension, it is possible to quantitatively estimate the time requirement or efficiency of UI for completing different tasks with minimal effort, and has been adopted in many GUI improvement projects. Due to the usefulness, extensions to the GOMS model had been proposed over the years including extensions to account for motion control interfaces. Though the GOMS model has been useful for quantitatively evaluating UI design, the main input device of modern smartphones and tablets are touch screens, which are different in nature when compared to traditional computer inputs. The differences leads to the point that GOMS model with KLM is ill-suited for touch screen based applications. This research paper addresses the issue by proposing extensions to the GOMS model to account for UI that are based on touch screen input devices.