Abstract
With the recent development of weblogs and social networks, many supplier industries share their data on different websites and weblogs. Even the Small-to-Medium sized enterprises (SMEs) in the manufacturing sector (as well as non-manufacturing sector) are rapidly strengthening their web presence in order to improve their visibility, customer reachability and remain competitive in the global market. Our study aims to classify data into various groups so that users can identify the most appropriate content based on their choice at any given time. To classify and characterize manufacturing suppliers in supply chain through their capability narratives and textual portfolios obtained from websites of such suppliers online source portals for testing and Naive Bayes and support vector machine (SVM) Classification method at term-level for classification has been used. The performance of the proposed classifier was tested experimentally based on the standard metrics such as precision, recall, and F-measure.

Abstract
Recent studies have shown that the early detection of neurodegenerative diseases (such as Parkinson) can significantly improve the effectiveness of treatments that increase quality of life, reducing the costs associated with the disease. In this paper, the proposed methodology consists in detecting early signs of Parkinson's disease through speech, with the presence of background noise. The approach uses machine learning algorithms and signal processing techniques to correctly distinguish between healthy controls and Parkinson's disease patients. In order to detect early signs of the disease, a database with patients at different stages of the Parkinson's disease is used. The learning algorithms were optimized for generalization and accuracy. An analysis of the results obtained from the proposed methodology show potential uses of machine learning algorithms in biomedical applications to detect early signs of Parkinson's disease.

Abstract
In this paper, the unrelated parallel machine scheduling problem considering machine-dependent and job sequence-dependent setup times is addressed. This problem involves the scheduling of n jobs on m unrelated machines with setup times in order to minimize the makespan. The Simulated Annealing algorithm is used to solve four sets of small scheduling problems, from the literature, on two unrelated machines: the first one has six jobs, the second has seven jobs and the third and fourth has eight and nine jobs, respectively. The results seem promising when compared with other methods referred in literature. © 2020, Springer Nature Switzerland AG.

Abstract
This paper aims to provide a solution to a problem shared by online marketing platforms. Many of these platforms are exploited by spammers to ease their job of distributing spam. This can lead to platforms domains being black-listed by ISP’s, which translates to lower deliverability rates and consequently lower profits. Normally, platforms try to counter the problem by using rule-based systems, which require high-maintenance and are not easily editable. Additionally, since analysis occurs when a contact database is imported, the regular approach of judging messages’ contents directly is not an effective solution, as those do not yet exist. The proposed solution, a machine-learning based system for the classification of contact database’s importations, tries to surpass these aforementioned systems by making use of the capabilities introduced by machine-learning technologies, namely, reliability in regards to classification and ease of maintenance. Preliminary results show the legitimacy of this approach, since various algorithms can be successfully applied to it. The most proficient of the ones applied being Ada-boost and Random-forest. © 2020, Springer Nature Switzerland AG.

Abstract
In this paper a scheduling meta-model is proposed for supporting hybrid collaboration, regarding machine-machine and human-machine scheduling interactions, based on a scheduling ontology. The utilization of the proposed scheduling ontology-based meta-model is illustrated through an example, which is further analysed, and some main features and advantages of each kind of collaborative interaction are discussed. © 2020, Springer Nature Switzerland AG.

Abstract