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Beta Distribution Weighted Fuzzy C-Ordered-Means Clustering
Get PDFThe fuzzy C-ordered-means clustering (FCOM) is a fuzzy clustering algorithm that enhances robustness and clustering accuracy through the ordered mechanism based on fuzzy C-means (FCM). However, despite these improvements, the FCOM algorithm’s effectiveness remains unsatisfactory due to the significant time cost incurred by its ordered operation. To address this problem, an investigation was conducted on the ordered weighted model of the FCOM algorithm leading to proposed enhancements by introducing the beta distribution weighted fuzzy C-ordered-means clustering (BDFCOM). The BDFCOM algorithm utilises the properties of the Beta distribution
to weight sample features, thus not only circumventing the time cost problem of the traditional ordered mechanism but also reducing the influence of noise. Experiments were conducted on six UCI datasets to validate the effectiveness of the BDFCOM, comparing its performance against seven other clustering algorithms using six evaluation indices. The results show that compared to the average of the other seven algorithms, BDFCOM improves about 15 percent on F1-score, 11 percent on Rand Index, 13 percent on Adjusted Rand Index, 3 percent on Fowlkes-Mallows Index and 16 percent on Jaccard Index. For the other two ordered mechanism FCM algorithms, the time consumption was also reduced by 90.15 percent on average. The proposed algorithm, which designs a new way of feature weighting for ordered mechanisms, advances the field of ordered mechanisms. And, this paper provides a new method in the application field where there is a lot of noise in the datase
An Embedded Machine Learning-Based Spoiled Leftover Food Detection Device for Multiclass Classification
Get PDFFood waste’s negative environmental repercussions are causing it to become a global concern. Several studies have examined the factors influencing food waste behaviour and management. This work was motivated by the lack of previous research on machine learning and electronic noses to detect contamination from leftover cooked food. This work proposes using machine learning algorithms and electronic nose technology to recognise and forecast the contamination in leftover cooked food. After five days of storage, the freshness of cooked leftovers was evaluated using an electronic nose combined with machine learning algorithms. Most food samples used in this work were from Malaysian’s leftover lunch and dinner dishes. Four (4) gas sensors—MQ- 2, MQ-136, MQ-137, and MQ-138—are used in developing the electronic nose to identify the presence of gas in the food sample. The data from the gas sensors was analysed using machine learning methods, namely Random Forest, k-nearest Neighbors, Support Vector Machine, and Linear Discriminant Analysis. Based on the results, a multi-classification technique yielded a greater accuracy rate in classifying and identifying the level of contamination in the cooked food leftovers, with average accuracy ranging from 90 percent to 100 percent. In conclusion, the work demonstrates a novel method for using machine learning algorithms to classify, identify, and predict the contamination level of leftover cooked food, contributing to reducing food waste generated primarily by Malaysian
E-Hailing Services in Malaysia: A Snapshot of Legal Issues and Risks
Get PDFThe rise of e-hailing services such as Grab and MyCar has overshadowed public transportation in Malaysia. E-hailing is a transport service that allows passengers to book a trip in real-time using virtual devices such as mobile phones. E-hailing platforms are predicted to grow tremendously as consumers continue to demand them for their convenience, speed, and cost-saving benefits. Beginning in October 2019, Malaysian e-hailing operators have been subject to control-oriented laws similar to those imposed on taxi drivers. This article highlights the regulatory framework governing e-hailing services in Malaysia and the legal issues and risks that e-hailing services face. It employs qualitative legal research, emphasising the study of law and the use of web, print, scholarly, and government statistics. The data were structured using theme analysis to generate a discussion narrative regarding e-hailing services in Malaysia, taking into consideration multiple viewpoints on the regulatory framework, legal concerns, and risks of this industry. It is found that despite the existence of essential laws that legalise e-hailing services, it has been discovered that legal concerns concerning legal status, safety, privacy, and liability persist in Malaysia's e-hailing services. On the one hand, there is a genuine urgency to ensure users' safety, which must not be suffocated by harsh and overbearing laws. As a result, understanding legal reactions to e-hailing services and exploring innovative approaches to handle growing legal difficulties is critical
