Halal logistics legal framework: Malaysia perspective

Halal logistics is one of the profitable industries in Malaysia with demand from local and international clients. Though there are regulations and standards protecting this industry, the enforcement on these two demands attention and further study as there are still reports of abuse of the Halal logo due to failure to segregation of halal and non-halal products during the logistics chains. Currently, there are very limited literature review on enforcement of halal logistics. Hence, this research seeks to discover the critical factors for effective enforcement of halal logistics in Malaysia. Applying a qualitative method, semi-structured interviews were conducted with stakeholders of the industry. Besides that, references were made to previous publication, case laws and legal documents. The interviews were recorded, transcribed, coded and reconciled. By using Nvivo software (12 Plus), the researcher coded the transcriptions and identify the themes and sub-themes. The findings discovered the following elements as critical within the industry: First the source of laws applicable in halal logistics, the duties and responsibilities of legal agencies and the due process (legal proceedings). This research will focus on the critical elements which are the sources of laws and the due process. The result shows that with regards to the sources of law, all participants agreed criminal laws are applicable in Halal logistics cases. However, only the academician and Halal logistics operator agreed civil laws should be included. Moreover, they also concurred that inclusive application of civils laws should also extend legal exposure to the halal logistics operators in order to manage the activities and avoid mistakes and cross-contamination

Study of keyword extraction techniques for electric double-layer capacitor domain using text similarity indexes: An experimental analysis

Keywords perform a significant role in selecting various topic-related documents quite easily. Topics or keywords assigned by humans or experts provide accurate information. However, this practice is quite expensive in terms of resources and time management. Hence, it is more satisfying to utilize automated keyword extraction techniques. Nevertheless, before beginning the automated process, it is necessary to check and confirm how similar expert-provided and algorithm-generated keywords are. This paper presents an experimental analysis of similarity scores of keywords generated by different supervised and unsupervised automated keyword extraction algorithms with expert-provided keywords from the electric double layer capacitor (EDLC) domain. The paper also analyses which texts provide better keywords such as positive sentences or all sentences of the document. From the unsupervised algorithms, YAKE, TopicRank, MultipartiteRank, and KPMiner are employed for keyword extraction. From the supervised algorithms, KEA and WINGNUS are employed for keyword extraction. To assess the similarity of the extracted keywords with expert-provided keywords, Jaccard, Cosine, and Cosine with word vector similarity indexes are employed in this study. The experiment shows that the MultipartiteRank keyword extraction technique measured with cosine with word vector similarity index produces the best result with 92% similarity with expert-provided keywords. This study can help the NLP researchers working with the EDLC domain or recommender systems to select more suitable keyword extraction and similarity index calculation techniques

Comparison of document similarity algorithms in extracting document keywords from an academic paper

The idea of this study is to validate a list of keywords derived from a scientific article by a domain expert from years of knowledge with prominent document similarity algorithms. For this study, a list of handcrafted keywords generated by Electric Double Layer Capacitor (EDLC) experts are chosen, and relevant documents to EDLC are considered for the comparison. Then, different similarity calculation algorithms were employed in different settings on the documents such as using the whole texts of the documents, selecting the positive sentences of the documents, and generating similarity score with automatically extracted keywords from the documents. The experiment’s outcome provides us with findings that the machine-generated keywords are mostly similar to the curated list by the domain experts. This study also suggests the preferable algorithms for similarity calculation and automated key-phrase extraction for the EDLC domain