Seasonal vegetable plants are one of food commodity in Indonesia which contributed significantly in supporting the growth of national economy. Fuzzy Logic using Tahani Model is appropriate to analyze the harvested area, production, and yields of food commodity. It is based on query in fuzzy database. It is also can be proposed as recommendation for both government and entrepreneur in achieving in the target based on query. Target is main priority of food commodity. Fuzzy query is harvested area (medium), production (sufficient), and yield (normal). Non fuzzy query is commodity (export commodities), harvest (plants harvested several times), and form of product (fresh fruit). Result showed that eggplant (0.6587), tomato (0.6023), cucumber (0.5865), capsicum frutescens (0.2901), and capsicum annum (0.1581) as recommendation for priority of national food policy.

In the paper the deep hybrid system of computational intelligence with architecture adaptation for medical fuzzy diagnostics is proposed. This system allows to increase a quality of medical information processing under the condition of overlapping classes due to special adaptive architecture and training algorithms. The deep hybrid system under consideration can tune its architecture in situation when number of features and diagnoses can be variable. The special algorithms for its training are developed and optimized for situation of different system architectures without retraining of synaptic weights that have been tuned at previous steps. The proposed system was used for processing of three medical data sets (dermatology dataset, Pima Indians diabetes dataset and Parkinson disease dataset) under the condition of fixed number of features and diagnoses and in situation of its increasing. A number of conducted experiments have shown high quality of medical diagnostic process and confirmed the efficiency of the deep hybrid system of computational intelligence with architecture adaptation for medical fuzzy diagnostics.

Stock market prediction has become an attractive investigation topic due to its important role in economy and beneficial offers. There is an imminent need to uncover the stock market future behavior in order to avoid investment risks. The large amount of data generated by the stock market is considered a treasure of knowledge for investors. This study aims at constructing an effective model to predict stock market future trends with small error ratio and improve the accuracy of prediction. This prediction model is based on sentiment analysis of financial news and historical stock market prices. This model provides better accuracy results than all previous studies by considering multiple types of news related to market and company with historical stock prices. A dataset containing stock prices from three companies is used. The first step is to analyze news sentiment to get the text polarity using naïve Bayes algorithm. This step achieved prediction accuracy results ranging from 72.73% to 86.21%. The second step combines news polarities and historical stock prices together to predict future stock prices. This improved the prediction accuracy up to 89.80%.

With the evolution of World Wide Web (WWW) 2.0 and the emergence of many micro-blogging and social networking sites like Twitter, the internet has become a massive source of short textual messages called on-line micro-texts, which are limited to a few number of characters (e.g. 140 characters on Twitter). These on-line micro-texts are considered as real-time text streams. On-line micro-texts are extremely subjective; they contain opinions about various events, social issues, personalities, and products. However, despite being so voluminous in quantity, the qualitative nature of these micro-texts is very inconsistent. These qualitative inconsistencies of raw on-line micro-texts impose many challenges in sentiment analysis of on-line micro-texts by using the established methods of sentiment analysis of unstructured reviews. This paper presents many challenges and issues observed during sentiment analysis of On-line Micro-texts.

In this paper, we present the development of a decentralized mechanism for the resources control in a distributed computer system based on a network-centric approach. Intially, the network-centric approach was proposed for the military purposes, and now its principles are successfully introduced in the other applications of the complex systems control. Due to the features of control systems based on the network-centric approach, namely adding the horizontal links between components of the same level, adding the general knowledge control in the system, etc., there are new properties and characteristics. The concept of implementing of resource control module for a distributed computer system based on a network-centric approach is proposed in this study. We, basing on this concept, realized the resource control module and perform the analysis of its operation parameters in compare with resource control modules implemented on the hierarchical approach and on the decentralized approach with the creation of the communities of the computing resources. The experiments showed the advantages of the proposed mechanism for resources control in compare with the control mechanisms based on the hierarchical and decentralized approaches.

During past decades, several Meta-Heuristics were considered by researchers to solve Dynamic Vehicle Routing Problem.In this paper, Ant Colony Optimization integrated with Hybrid Immigrant Schemes methods are proposed for solving Dynamic Vehicle Routing Problem. Ant Colony Optimization with hybrid immigrant schemes methods namely HIACO-I, HIACO-II and HIACO-III focused on establishing the proper balance between intensification and diversification. The performance evaluation of the algorithms in which Random Immigrants and Elitism based Immigrants were hybridized in different proportions and added to Ant Colony Optimization algorithm showed that they had produced better results in many dynamic test cases generated from three Vehicle Routing Problem instances.

This paper analyzes the odd-even formula in Delhi using tweets posted on Twitter from December 2015 to August 2016. Twitter is a social network where users post their feelings, opinions and sentiments for any event using hashtags and mentions. The tweets posted publicly can be viewed by anyone interested. This paper transforms the unstructured tweets into structured information using open source libraries. Further objective is to build a model using Support Vector Machines (SVM) to classify unseen tweets on the same context. This paper collects tweets on this event under the hashtag “#oddeven formula”. This study explores four freely available resources in the form of Application Programming Interfaces (APIs)/Packages for labeling tweets for academic research. Four machine learning models using SVM multi-class classifier were built using the labels provided by the APIs/Packages. The performances of these four models are evaluated through standard evaluation metrics. The experimental results reveal that TextBlob and Pattern python packages outperformed Vivekn and Meaning Cloud APIs. This study may also help in decision making of this event to some extent.

Embedded Systems Engineering has grown in recent years to become an integral part of our daily living as it finds striking applications in various spheres of our lives. These range from Manufacturing, Electronic Health, Telecommunications, Construction and Robotics to numerous other fields. Primarily, Embedded Systems are usually a combination of selected electrical and electronic components functioning together under the direct control of a programmed controller. They serve fundamentally as additional units incorporated within already existing infrastructures with the sole aim of providing dedicated services to the larger infrastructure. Many of the controllers used operate on uniquely designed processor cores, instruction sets, and architecture profiles. This paper seeks to elucidate the application of the ARM Cortext-M3 processor based NXP LPC 1768 Microcontroller unit in the design and development of a Temperature Monitoring and Logging System. The write-up starts off with an overview of the principal ARM processor core families, architecture profiles, instruction sets and subsequently, demonstrates its utilization in the design of a Temperature Monitoring and Logging System. The paper shows how the NXP LPC 1768 Microcontroller Unit successfully serves as the brain of the temperature logger device through its standardized interfacing with a TMP102 temperature sensor using the Inter- Integrated Circuit (I2C) protocol. The Microcontroller is programmed using Embedded C while other unique functionalities of the ARM Cortex-M3 core such as Interrupt Handling and System Tick Timer efficiency are also explored.