APPLICATION OF GROUP TECHNOLOGY FOR PRODUCTION TIME ESTIMATION

The development of industry, transport, communications and technology results in tougher competition, i.e. the process of globalization takes place. Small and medium enterprises (SMEs), which are greatly affected by this process, are expected to accept or to refuse the received offer in a very short period of time. Consequently, it is impossible for a small enterprise to create a technological process in order to calculate production times. The idea of the authors of this paper is to develop a system of part classification by means of group technologies. The classifier is used to classify parts into similar groups. So, if we have a new part, we can find a similar part and adopt its technological process to the new part. In the next step, an application to enable the production time estimation, and consequently the production cost estimation for each family of parts will be developed

On The Evaluation of Model Based Approaches for Applications in Affective Computing

Automatic recognition of emotion has a huge potential in several applications. In order to address such potential, researchers from diverse fields are collaborating together to build systems capable of recognizing human emotion. As a preliminary step towards such systems, many works are being done to automatically detect facial expressions. A technique generally termed as ``Model Based Technique\u27\u27 has gained significant attention among the researchers for its utility in detecting facial expressions.However, methods currently used for evaluation of the performance of such systems have several flaws and inefficiencies. Due to these inefficient evaluation methods, it becomes difficult to compare among the systems from their literary descriptions. In this thesis, origins of such flaws are analyzed and efforts have been made to derive some solutions. As a part of this endeavor, a Three Level Evaluation (TLE) model has been proposed. In addition, some new and efficient assessment metrics have been suggested that can make faithful comparison of the systems

Mining Event Logs to Support Workflow Resource Allocation

Workflow technology is widely used to facilitate the business process in enterprise information systems (EIS), and it has the potential to reduce design time, enhance product quality and decrease product cost. However, significant limitations still exist: as an important task in the context of workflow, many present resource allocation operations are still performed manually, which are time-consuming. This paper presents a data mining approach to address the resource allocation problem (RAP) and improve the productivity of workflow resource management. Specifically, an Apriori-like algorithm is used to find the frequent patterns from the event log, and association rules are generated according to predefined resource allocation constraints. Subsequently, a correlation measure named lift is utilized to annotate the negatively correlated resource allocation rules for resource reservation. Finally, the rules are ranked using the confidence measures as resource allocation rules. Comparative experiments are performed using C4.5, SVM, ID3, Na\"ive Bayes and the presented approach, and the results show that the presented approach is effective in both accuracy and candidate resource recommendations.Comment: T. Liu et al., Mining event logs to support workflow resource allocation, Knowl. Based Syst. (2012), http://dx.doi.org/ 10.1016/j.knosys.2012.05.01

Optimizing the Failure Prediction in Deep Learning

  Avatars are computer-generated digital representations that people may use in the Predicting issues with software systems built from modules is the focus of this research. This data collection was used as a reference in order to accomplish this objective. The evaluation framework for reusable software components is provided by this research. The dataset of factors that play a role in the decision-making process has been run through the PSO algorithm. The primary objective is to provide a clever and time-saving method of choosing components. After filtering for ideal values, the dataset is utilized to train a deep learning model. Accuracy measurements including recall value, precision, and F1 score will be used to evaluate the effectiveness of the optimized component selection model. This research is significant because it provides a high-performance and accurate solution to a major problem in predicting. We have done our best to estimate the number of lines of code, the complexity, the design complexity, the projected time, the difficulty, the intelligence, and the efforts required. A model for discovering mistakes has been developed after the dataset was filtered to account for the ideal value. By keeping just the most crucial characteristics and getting rid of all optimized data, we have made the model more trustworthy. &nbsp

A Review of Leak Detection Systems for Natural Gas Pipelines and Facilities

Pipelines facilities, used for the transportation of natural gas in large quantities to homes and industries, remain the best economic, most reliable and safest mode of transport of energy. Despite these numerous advantages, gas pipelines have been enmeshed in various accidents and thefts, nonetheless this could be reduced if properly maintained and pipelines can last indefinitely without leaks. Pipelines are susceptible to leakages and rupture accidents as a result of age, corrosion, material defects, operational errors or other reasons. Pipeline failures may be caused intentionally (e.g. vandalism) or unintentionally (e.g. device/material failure and corrosion), which may result into irreversible damages such as financial losses, human casualties, ecological disaster and extreme environmental pollution. Leakages in natural gas facilities and installations require three vital aspects, namely: Gas Leakage Prevention, Gas Leakage Detection and Gas Leakage Mitigation. Many Gas Leak Detection methods are used for pipeline integrity management and especially for minimizing gas leakage. The performance of these methods depends on the approaches, operational conditions and pipeline networks. Also, there are some essential requirements and guidelines which must be met before we can consider any leak detection system suitable for production solutions, including sensitivity, reliability, accuracy and robustness. The attempt of this study is to carry out a critical review of these models, to ascertain the best model(s) applicable to natural gas leak detection. Keywords: Gas Leak Detection System, Leak Location, Leak Size DOI: 10.7176/JETP/13-2-02 Publication date: April 30th 202

Intelligent Advanced User Interfaces for Monitoring Mental Health Wellbeing

It has become pressing to develop objective and automatic measurements integrated in intelligent diagnostic tools for detecting and monitoring depressive states and enabling an increased precision of diagnoses and clinical decision-makings. The challenge is to exploit behavioral and physiological biomarkers and develop Artificial Intelligent (AI) models able to extract information from a complex combination of signals considered key symptoms. The proposed AI models should be able to help clinicians to rapidly formulate accurate diagnoses and suggest personalized intervention plans ranging from coaching activities (exploiting for example serious games), support networks (via chats, or social networks), and alerts to caregivers, doctors, and care control centers, reducing the considerable burden on national health care institutions in terms of medical, and social costs associated to depression cares