Identifying major tasks and minor tasks within online reviews

© 2017 Elsevier B.V. Many e-commerce websites allow customers to provide reviews that reflect their experiences and opinions about products and services. Such published reviews, whether positive or negative, serve both the consumer and the business. Negative reviews can inform the merchant of issues that, when addressed, may improve the addressed aspect of the business and improve its online reputation. However, when the merchant fails to respond to customers’ concerns, the business faces potential loss of reputation. The Sentiminder system identifies major areas of customer concern, and specific concerns within each area. This helps the merchant to process a large body of reviews and find what needs to be addressed. In this paper we address the problems of quickly finding specific issues and specific comments that are consistently discussed in a negative way. Our technique drills down from the major task areas to more specific issues, assisting the user to accurately determine what issues need attention. The sentiment of reviews on the same topic can vary widely, so we maximize coherence over a variety of six different sentiment assessment techniques. We achieve from about 45% to 65% coherence. These suggestions are implemented in the Sentiminder, an online tool that creates schedules of optimal selections of tasks

E-Healthcare Knowledge Creation Platform Using Action Research

© 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. There has been a long discussion on knowledge creation in the health care environment. Recently, the action research approach is attracting considerable attention. Action research supports a learning process where collaboratively the healthcare stakeholders are cooperating to produce knowledge that will influence their practice. Usually physicians are involved in case study research where information is produced but it is not used to offer insights back to the community. In this paper we propose a healthcare learning platform (HLP) that enables members of the health multidisciplinary communities to collaborate, share up-to-date information and harvest useful evidence. In this e-health platform knowledge is created based on patient feedback, the dynamic creation of communities that involve the participation of several stakeholders and the creation of an action learning environment where problem identification, investigation and planning, action and reflection is a cycle that enables knowledge and experience to contribute to healthcare knowledge creation

Solving MAX-SAT Problem by Binary Biogeograph-based Optimization Algorithm

© 2019 IEEE. Several sensing problems in wireless sensor networks (WSNs) can be modeled to maximum satisfaction (MAX-SAT) or SAT problems. Also, MAX-SAT is an established framework for computationally expensive problems in other fields. There exist efficient algorithms to solve the MAX-SAT, which is an NP-hard problem. The reason for remodeling various sensing problems to MAX-SAT is to use these algorithms to solve challenging sensing problems. In this paper, we test a binary Biogeography-based (BBBO) algorithm for the MAX-SAT as an optimization problem with a binary search space. The original BBO is a swarm intelligence-based algorithm, which is well-tested for continuous (and nonbinary) integer space optimization problems, but its use for the binary space was limited. Since the exact algorithm to solve the MAX-SAT problem using moderate computing resources is not well-known; therefore, swarm intelligence based evolutionary algorithms (EAs) can be helpful to find better approximate solutions with limited computing resources. Our simulation results demonstrate the experimental exploration of the binary BBO algorithm against binary (enhanced fireworks algorithm) EFWA, discrete ABC (DisABC) and Genetic Algorithm (GA) for several classes of MAX-SAT problem instances

Dynamic Neural Network for Business and Market Analysis

© 2019, Springer Nature Switzerland AG. The problem of predicting nonlinear and nonstationary signals is complex since the physical law that controls them is unknown and it is complicated to be considered. In these cases, it is necessary to devise nonlinear models that imitate or learn the rules of behavior of the problem and can be developed based on historical data. For this reason, neural networks are useful tools to deal with this type of problem due to their nonlinearly and their capacity of generalizing. This paper aims at exploring various types of neural network architectures and study their performance with time series predictions. Predictions on two sets of data (of a very different nature) will be made using three neural networks including multilayer perceptrons, recurrent neural network and long-short term memory varying some important parameters: input neurons, epochs and the anticipation with which the predictions are made. Then, all results will be compared using standard metrics. As a conclusion, the influence of the type of series under study is more important than the parameters considered in what concerns the performance. The management of the memory in the networks is a key to its success in the prediction of S&P 500 and electrical power time series

Penetration and security of openssh remote secure shell service on raspberry Pi 2

© 2018 IEEE. This research presents a penetration testing approach to help secure OpenSSH service on Raspberry Pi 2. The study discusses a technique for penetrating Debian v7.1p2, installed on Raspberry Pi 2, using Kali Linux. We exploit the vulnerability found in SSH protocol exchange keys, which causes multiple CRLF injections in Raspberry Pi 2 Model B, allowing remote authenticated users to bypass intended shell-command restrictions via well crafted X11 data forwarding. We propose an innovative security model to solve the issues of allowing remote authentication access using SSH protocol exchange keys without affecting the encrypted protocols transmissions. We conclude with recommendations on how to securely mitigate MITM attacks using our secure proposed model