Smart job searching system based on information retrieval techniques and similarity of fuzzy parameterized sets

Job searching for the proper vacancy among several choices is one of the most important decision-making problems. The necessity of dealing with uncertainty in such real-world problems has been a long-term research challenge which has originated from different methodologies and theories. The main contribution of this work is to match the applicant curriculum vitae (CV) with the best available job opportunities based on certain criteria. The proposed job searching system (JSS) implements a series of approaches which can be broken down into segmentation, tokenization, part of speech, gazetteer, and fuzzy inference to extract and arrange the required data from the job announcements and CV. Moreover, this study designs a fuzzy parameterized structure to store such data as well as a measuring tool to calculate the degree of similarity between the job requirements and the applicant’s CV. In addition, this system analyses the computed similarity scores in order to get the optimal job opportunities for the job seeker in descending order. The performance evaluation of the proposed system shows high recall and precision percentages for the matching process. The results also confirm the viability of the JSS approach in handling the fuzziness that is associated with the problem of job searching

Smart detection of offensive words in social media using the soundex algorithm and permuterm index

Offensive posts in the social media that are inappropriate for a specific age, level of maturity, or impression are quite often destined more to unadult than adult participants. Nowadays, the growth in the number of the masked offensive words in the social media is one of the ethically challenging problems. Thus, there has been growing interest in development of methods that can automatically detect posts with such words. This study aimed at developing a method that can detect the masked offensive words in which partial alteration of the word may trick the conventional monitoring systems when being posted on social media. The proposed method progresses in a series of phases that can be broken down into a pre-processing phase, which includes filtering, tokenization, and stemming; offensive word extraction phase, which relies on using the soundex algorithm and permuterm index; and a post-processing phase that classifies the users’ posts in order to highlight the offensive content. Accordingly, the method detects the masked offensive words in the written text, thus forbidding certain types of offensive words from being published. Results of evaluation of performance of the proposed method indicate a 99% accuracy of detection of offensive words

Power Optimization in Mobile Robots Using a Real-Time Heuristic

Mobile robots typically run using finite energy resources, supplied by finite batteries. The limitation of energy resources requires human intervention for recharging the batteries. To reduce human intervention, this work focuses on coordinating power in a group of robots. A power optimization subroutine provides some sense of distribution of power by the control unit (CU). A variety of on-board sensors, actuators, and communication modules are controlled by a heuristic-based controller class allowing such components to conserve the current taken from the attached power source. Using the proposed approach, autonomous robots will be aware of their power system, especially regarding battery life. The new approach takes advantage of a heuristic function which uses evaluation values calculated at different times for the different robots. An experimental setup is applied on the team of robots. The optimization module is evaluated on each robot. The results show that the team of mobile robots consumes less energy and more efficient power regulation during their duties. Finally, the application of the proposed optimization technique in a distributed manner achieves good power saving figures when performing the particular task

Fuel Consumption Using OBD-II and Support Vector Machine Model

This paper presents a method to estimate gasoline fuel consumption using the onboard vehicle information system OBD-II (Onboard Diagnoses-II). Multiple vehicles were used on a test route so that their consumption can be compared. The relationships between fuel consumption and both of the engine speed are measured in RPM (revolutions per minute), and the throttle position sensor (TPS). The relationships are expressed as polynomial equations. The method which is composed of an SVM (support vector machine) classifier combined with Lagrange interpolation, is used to define the relationship between the two engine parameters and the overall fuel consumption. The relationship model is plotted using a surface fitting tool. In the experimental section, the proposed method is tested using the vehicles on a major highway between two cities in Jordan. The proposed model gets its sample data from the engine’s RPM, TPS, and fuel consumption. The method successfully has given precise fuel consumption with square root mean difference of 2.43, and the figures are compared with the values calculated by the conventional method

An Integrated Conceptual Model for m-Government Acceptance in Developing Countries: The Case Study of Jordan

Mobile information and communication technology changed how people and businesses can benefit from government public services at any time and from anyplace. The success or failure of mobile government services is becoming more dependent on satisfying the needs and the expectations of both citizens and business organizations. This paper reviews and analyses some existing empirical studies that examine m-Government acceptance in some developing countries. Then, a new integrated conceptual model for examining some important key factors that may affect m-Government acceptance in Jordan from user perspective was proposed.  An empirical test was conducted using a questionnaire to explore the effect of the following factors: Trust in mobile channel, trust in government, perceived usefulness, perceived ease of use, relative advantage, compatibility, complexity, service quality and user satisfaction on the behavioural intention to use m-Government applications. Finally, justification of the proposed integrated model and formulation of the associated hypotheses was conducted

An Integrated Conceptual Model for m-Government Acceptance in Developing Countries: The Case Study of Jordan

Mobile information and communication technology changed how people and businesses can benefit from government public services at any time and from anyplace. The success or failure of mobile government services is becoming more dependent on satisfying the needs and the expectations of both citizens and business organizations. This paper reviews and analyses some existing empirical studies that examine m-Government acceptance in some developing countries. Then, a new integrated conceptual model for examining some important key factors that may affect m-Government acceptance in Jordan from user perspective was proposed.  An empirical test was conducted using a questionnaire to explore the effect of the following factors: Trust in mobile channel, trust in government, perceived usefulness, perceived ease of use, relative advantage, compatibility, complexity, service quality and user satisfaction on the behavioural intention to use m-Government applications. Finally, justification of the proposed integrated model and formulation of the associated hypotheses was conducted

Effects of Replica Placement Algorithms on Performance of structured Overlay Networks

In DHT-based P2P systems, Replication-based content distribution and load balancing strategies consists of such decisions as which files should be replicated, how many replicas should be created and where to replicate them in order increase the system performance in the presence of non-uniform data and access distribution. There are many works on replica placement policies; however, the impact of system workload on different replica placement strategies is not well studied. We investigate this problem under the context of content addressable overlay networks. We compare a trace based replica placement algorithm with two of its variations, namely random placement and priority based placement under different workloads. Our experimental results show that the effect of replica placement policy is highly affected by the workload of the system, which indicates that an adaptive replica placement strategy is desirable for content distribution in an overlay network. 1