Project management information systems have their proven position as an effective tool for achieving project management success in terms of the successful realization of the project regarding time, cost and quality. Recent research results have indicated that quality of project management information system output information is positively and significantly related to project management information system application and project management factors and revealed the empirical support. However, getting the reporting quality of the project status report, monthly generated from the project management information system based on the information timely maintained by the project managers, responsible for ERP implementation up to the satisfactory level at any time, can be problematic without having a systematic approach implemented. This article is to discuss how the continuous quality improvement based on the plan-do-check-act cycle was conducted on the reporting quality of the project status report from project management information system generated by the project managers, for achieving project management success in ERP projects implemented by a solution provider for their customers in the various industries in Japan. The results of the study indicate that the continuous improvement on the reporting quality of project management information system was found to be effective in achieving quality of project management information system output information to help managers in decision making, planning, organizing and controlling the project. It was also found to be effective in positively influencing achievement of project management success in terms of respecting the time, cost and quality.

This research is based on the determination of the parameters of the PID and fractional-order PID controllers designed for quarter-car suspension system. Initially, without considering the active suspension structure, the performance of the passive suspension system under different wheel load index is presented by using the transfer function of the system. Then, by adding a wheel-load, the classical PID controller is designed and applied to the current controlled hydraulic actuator as a part of active suspension system. The parameters of this controller are determined by three heuristic optimization algorithms; Particle Swarm Optimization (PSO), Differential Evolution (DE) and Gravitational Search Algorithm (GSA). As the second part of this study after evaluating the performance of classical PID controller, fractional-order PID controller is designed and applied to the problem to improve the performance of the classical PID controller. Similarly, the parameters of this controller are also obtained by using the same optimization algorithms. In the paper, for modeling the road, instead of sinusoidal (road with hill) or random changes, a saw tooth signal is preferred as a relatively harder condition. Implementation results are showed that the performance of the fractional-order PID controller is much better that PID controller and also instead of relatively complex and expensive controller, it is possible to use fractional-order PID controller for the problem.

With the widespread usage of social media in our daily lives, user reviews emerged as an impactful factor for numerous fields including understanding consumer attitudes, determining political tendency, revealing strengths or weaknesses of many different organizations. Today, people are chatting with their friends, carrying out social relations, shopping and following many current events through the social media. However social media limits the size of user messages. The users generally express their opinions by using emoticons, abbreviations, slangs, and symbols instead of words. This situation makes the sentiment classification of social media texts more complex. In this paper a sentiment classification model for Twitter messages is proposed to overcome this difficulty. In the proposed model first the short messages are expanded with BabelNet which is a concept network. Then the expanded and the original form of the messages are included in an ensemble learning model. Consequently we compared our ensemble model with traditional classification algorithms and observed that the F-measure value is increased.

The paper presents the theory, design, usage aspects of data wrangling process used in data ware housing and business intelligence. Data wrangling is defined as an art of data transformation or data preparation. It is a method adapted for basic data management which is to be properly processed, shaped, and is made available for most convenient consumption of data by the potential future users. A large historical data is either aggregated or stored as facts or dimensions in data warehouses to accommodate large adhoc queries. Data wrangling enables fast processing of business queries with right solutions to both analysts and end users. The wrangler provides interactive language and recommends predictive transformation scripts. This helps the user to have an insight of reduction of manual iterative processes. Decision support systems are the best examples here. The methodologies associated in preparing data for mining insights are highly influenced by the impact of big data concepts in the data source layer to self-service analytics and visualization tools.

Crypto ransomware has earned an infamous reputation in the malware landscape and its sound sends a lot of shivers to many despite being a new entrant. The media has not helped matters even as the myths and inaccuracies surrounding crypto ransomware continue to deepen. It’s been purported that once crypto ransomware attacks, the victim is left with no option but to pay in order to retrieve the encrypted data, and that without a guarantee, or risk losing the data forever. Security researchers are inadvertently thrown into a cat-and-mouse chase to catch up with the latest vices of the aforesaid in order to provide data resilience. In this paper, we debunk the myths surrounding loss of data via a crypto ransomware attack. Using a variety of crypto ransomware samples, we employ reverse engineering and dynamic analysis to evaluate the underlying attack structures and data deletion techniques employed by the ransomware. Further, we expose the data deletion techniques used by ransomware to prevent data recovery and suggest how such could be countered. From the results, we further present observed sandbox evasion techniques employed by ransomware against both static and dynamic analysis in an effort to obfuscate its operations and subsequently prevent data recovery. Our analyses have led us to the conclusion that no matter how devastating a crypto ransomware attack might appear, the key to data recovery options lies in the underlying attack structure and the implemented data deletion methodology.

Enormous increase for vehicles in the megacities, with limited parking creates a serious issue. In order to handle the issue, many cities have adopted the guided parking as a part of Intelligent Transportation System (ITS). The current ITS is continuously evolving to incorporate the required issues. ITS communicates among vehicles and parking facilities and shares the information of interest. Thereafter ITS employs dynamic information obtained from vehicles for guiding the parking. In the current work, authors have suggested two functions for parking guidance in this study. Using these functions, central server uses this dynamic information obtained from sensory networks and uses the same to suggest parking to the driver. The driver, upon receiving the suggestion, in turn may reserve the suggested parking or may choose to decline the suggestion based on his personal experience. The proposed approach considers various parameters to evaluate effectiveness of the guided parking. During simulation, these parameters have been demonstrated and it is observed that the proposed system outperforms the existing system in literature.

Psychological issues in the world are exponentially growing and the treatment gap is also comparatively high. The main reason would be the shortage of expertise and time-consuming in conventional diagnose process. The main objective of this research is to lower the mental issues treatment gap of professionals or apprentices in the field by creating a virtual expert system to assist psychiatrists. This system diagnoses most common mental disorders such as Depression Disorder, Anxiety Disorder, and Dementia. The proposed expert system can communicate with patients, to identify the current state of the illness. During the conversation, a standard questionnaire is given for the disease verification purpose. The experienced mental health professionals can use this expert system to assist in diagnosing process and the apprentices of the psychology can use this expert system as a training asset.

Cloud computing is service based technology on internet which facilitates users to access plenty of resources on demand from anywhere and anytime in a metered manner i.e. pay per usage without paying much heed to the maintenance and implementation details of application. As cloud technology is evolving day by day it is being confronted by numerous challenges, such as time and cost under deadline constraints. Research work done so far mainly focused on reducing cost as well as execution time. In order to minimize cost and execution time previously existing workflow scheduling model known as predict earliest finish time is used. In this research work we have proposed a new PEFT genetic algorithm approach to further reduce the execution time on this model. A strategy is developed to let GA focus on to optimize chromosomes objective to get best suitable mutated children. After obtaining a feasible solution, the genetic algorithm focuses on optimizing the execution time. Experimental results show that our algorithm can find better solution within lesser time.