The Real Time System described herein was designed and implemented for the purpose of serving Univa c Management at all levels with an inquiry/response, updat e and data collection tool fitting the precise requirements o f the Company . The system was not intended to be offere d as a generalized software package . The system runs on the 1108 Computer installed a t Univac Headquarters in Blue Bell, Pennsylvania . Exec VII I Level 27 is currently employed . Further details relative t o the hardware configuration are provided by the attache d chart . There were several elements considered in the initia l design of the system . These elements may be divided int o three categories : User considerations, programming considerations, and operational considerations .

The real time system

Software Engineering (SE) is a discipline, concept, method that focus on all aspects related to software production. SE must adapt organized system and approach in building software. The objective of this article is to discuss needs elicitation in SE, so that the design can be done through utilizing appropriate tools and techniques based on the problem to be solved, restrictions and need that must be met, as well as resource availability. The method used in this article is literature review as a major source of problem analy-sis. In the next phase, the main topic of the article is clarified, validated, and verified by the model of the Focus Group Discussion (FGD). The respondents of FGD are lecturers of Informatics Department at UIN Sunan Gunung Djati Bandung. Requirements elici-tation is in-dept and comprehensive process of finding information from all stakeholders in relation to the built software. The result of requirements elicitation is the explanation from every stakeholder with natural language related to the will and interest of the built software. The mechanism of finding information can be done through observation, questionnaire, interview, and documentary study, which are then tested with data validation and verification in FGD. The product of requirements elicitation is the User Re-quirement Document (URD) that can be used as an early artifact in software development process. Furthermore, URD is used as a reference in tracking the suitability between the function of software and the needs of stakeholder. This article formulated analysis approach of SE need, so that it is easily modified, adaptive to growing software complexity, and connected with the analysis of hardware.

Requirements elicitation in software engineering

Simple Summary The automatic diagnosis of fish diseases is an important task in smart aquaculture, and plays a key role in detecting fish conditions, understanding disease signs, and improving fish welfare and health. This paper reviews the latest advances in intelligent fish disease diagnosis techniques for aquatic applications. To acquire high-quality images to improve the accuracy of fish disease diagnosis, the paper reviews, in the second paragraph, how image-processing techniques can be used to acquire high-quality images. The third paragraph of this paper reviews the methods of intelligent fish disease diagnosis, including expert system detection, camera image detection, microscope image detection, spectral image detection, fluorescence image detection, ultrasonic image detection, and sensor detection methods. Additionally, the advantages and disadvantages of each detection method are summarized, including whether they cause damage to the fish body. Finally, we put forward the prospect of the intelligent detection of fish diseases and summarize the methods that may be used in the intelligent diagnosis of fish disease. Abstract Aquatic products, as essential sources of protein, have attracted considerable concern by producers and consumers. Precise fish disease prevention and treatment may provide not only healthy fish protein but also ecological and economic benefits. However, unlike intelligent two-dimensional diagnoses of plants and crops, one of the most serious challenges confronted in intelligent aquaculture diagnosis is its three-dimensional space. Expert systems have been applied to diagnose fish diseases in recent decades, allowing for restricted diagnosis of certain aquaculture. However, this method needs aquaculture professionals and specialists. In addition, diagnosis speed and efficiency are limited. Therefore, developing a new quick, automatic, and real-time diagnosis approach is very critical. The integration of image-processing and computer vision technology intelligently allows the diagnosis of fish diseases. This study comprehensively reviews image-processing technology and image-based fish disease detection methods, and analyzes the benefits and drawbacks of each diagnostic approach in different environments. Although it is widely acknowledged that there are many approaches for disease diagnosis and pathogen identification, some improvements in detection accuracy and speed are still needed. Constructing AR 3D images of fish diseases, standard and shared datasets, deep learning, and data fusion techniques will be helpful in improving the accuracy and speed of fish disease diagnosis.

/pdf/advanced-techniques-for-the-intelligent-diagnosis-of-fish-37cjw6oy.pdf

Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review

This article aims to implement the algorithm model of k-Nearest Neighbor (k-NN) in analyzing, predicting, and classifying students who have potentials to get scholarships in universities. The k-NN algorithm works by making a prediction based on the closest data points between the old data history as training data and the new data as testing data. The data collected totals 1018 students with 24 scholarship receiver candidate students are used as the dataset for the test purposes. The attributes used in the prediction process are a semester, parents' income, number of family dependents, and Cumulative Grade Point Average. The distance calculation of the value from testing attribute to each training attribute uses Euclidean Distance equation, while the test of the model accuracy value is calculated using Confusion Matrix. The results of the simulation of the prediction model show that the determining factor of training data from both the number and the variation of different values can improve the performance of the k-NN algorithm with the best accuracy rate of 95.83 percent in predicting students who have the greatest chance of getting the scholarship.

https://iopscience.iop.org/article/10.1088/1757-899X/434/1/012039/pdf

The prediction of scholarship recipients in higher education using k-Nearest neighbor algorithm

In recent time, stock price prediction is an area of profound interest in the realm of fiscal market. To predict the stock prices, authors have proposed a technique by first calculating the sentiment scores through Naive Bayes classifier and after that neural network is applied on both sentiment scores and historical stock dataset. They have also addressed the issue of data cleaning using a Hive ecosystem. This ecosystem is being used for pre-processing part and a neural network model with inputs from sentiment analysis and historic data is used to predict the prices. It has been observed from the experiments that the accuracy level reaches above 90% in maximum cases, as well as it also provides the solid base that model will be more accurate if it trained with recent data. The intended combination of sentiment analysis and Neural networks is used to establish a statistical relationship between historic numerical data records of a particular stock and other sentimental factors which can affects the stock prices.

/pdf/stock-price-prediction-using-artificial-neural-model-an-fwm746t94h.pdf

Stock Price Prediction using Artificial Neural Model: An Application of Big Data

Many organisations (government of non-government) use websites to share information of new recruitment for the workers. This information overflows on thousands of sites with various attributes and criteria. However, this availability forms a complex puzzle in the selection process and lead to inefficient runtime. This study proposes a simple method for job searching simplification through a construction and collaboration of web scraping technique and classification using Naive Bayes on search engine. This study is resulting an effective and efficient application for users to seek a potential job that fit in with their interests.

https://iopscience.iop.org/article/10.1088/1757-899X/288/1/012038/pdf

Web Scraping and Naïve Bayes Classification for Job Search Engine

Indonesia is a country prone to the earthquake for Indonesia is located between three tectonic plates. Some natural disasters of an earthquake caused to have a big number of victims. This is due to the ineffectiveness of the current early warning system since it still uses conventional way. This study is aimed to create a system that can deliver direct information about earthquake to the people of easy-using media and provide direction to the nearest evacuation site to them. Method of system design applies prototype development method. The applied system uses Google Maps API as a tool to trace the user’s location and the evacuation site. In addition, the system will use firebase as a tool of notification delivery.

/pdf/early-warning-system-in-mobile-based-impacted-areas-q453cegmk7.pdf

Early warning system in mobile-based impacted areas

e-Government can be divided into several models, such as, Government-to-Citizen or Government-to-Customer (G2C), Government-to-Business (G2B), Government-to-Government (G2G), or Government-to-employees (G2E). Based on previous study, research on G2B model was not as many as the research on the other areas such as on G2C [12]. G2B faces business organization with complex rules. Many software applications have been developed however their utilization is low because most the applications could not meet the needs and expectation of the users or stakeholders. Therefore, it is necessary to build a specific assessment model to further assess the suitability of software with the needs and expectations from users, so that the quality of the software applications can be guaranteed, and their utilization can be increased. Our study proposes a software assessment model using the development and improvement of metrics products from ISO/IEC 9126 for e-Government in the G2B model. The model includes the assessments on functionality, reliability, usability and efficiency. The model is expected to help a government to assess the quality of a software application for a G2B model. A case study in a local government office was performed to validate the model. Our model has been executed systematically, and the results has evaluated. The results demonstrate that our model can be used to assess a software application for G2B model. However more application of the model should be performed to give results for further analysis.

Software assessment model using metrics products for e-Government in the G2B model

In software engineering, web applications are software that are accessed using a web browser through a network such as the Internet or intranet. Web applications are applications that can be relied on by users to do many useful activities. Despite the awareness of web application developers about safe programming practices, there are still many aspect in web applications that can be exploited by attacker. The development of web applications and the Internet causes the movement of information systems to use them as a basis. Security is needed to protect the contents of web applications that are sensitive and provide a safe process of sending data, therefore application security must be applied to all infrastructure that supports web applications, including the web application itself. Most organizations today have some kind of web application security program or try to build/ improve. But most of these programs do not get the results expected for the organization, are not durable or are not able to provide value continuously and efficiently and also cannot improve the mindset of developers to build/ design secure web applications. This research aims to develop a web application security scanner that can help overcome security problems in web applications.

pdf/security-scanner-for-web-applications-case-study-learning-e4awxggzhy.pdf

Security Scanner For Web Applications Case Study: Learning Management System

Ability to do problem solving will be greatly influenced by how the flow of thinking in decomposing a problem until it finds the root of the problem so that it can determine the best solution. There is currently a growing recognition around the world that all fields require a prerequisite ability, namely to think logically, in a structured manner, and use computational tools to rapidly model and visualize data. This ability is known as Computational Thinking (CT). In this study, the author applied the computational thinking key concept in a case study to train structured thinking in problem solving. Computational thinking key concept includes Decomposition, Pattern recognition, Abstraction, and lastly use algorithms when they design simple steps to solve problems. Based on our case study that has been model, the result shows us that Computational Thinking can be used to train structured thinking in problem solving in everyday life

/pdf/the-importance-of-computational-thinking-to-train-structured-4diopztjv2.pdf

The Importance of Computational Thinking to Train Structured Thinking in Problem Solving

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