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Advancements in open source, free integrated library management system (LMS) for cataloging, circulation, flexible reporting and automated library services especially in academic communities has gained extreme importance. The purpose of this study is to provide solution to a distinct problem about automatic generation of multiple copies for unique titles leading to title mismatch and duplication in biblio-records related to university collection of books. The aim of this paper is to provide solution to generate the unique titles report in any large size university library using KOHA, without loss of accession history or empirical data. This paper also demonstrates the smooth transition from one library software to KOHA.

The case university is considered here as a giant entity having huge collection of reading material, along with multiple institutes affiliations. The study demonstrates a step-by-step trial-and-error method involving several iterations detecting root cause, implementing corrective actions and finally resolving the problem of data redundancy and duplication of records. Currently, KOHA’s user manual does not provide any solution to this problem. The authors believe that this paper will enable various practitioners of KOHA-LMS toward understanding and appreciating the quality of library information/records being managed in delivering quality services to all its users and stakeholders. The methodology used in this work is KOHA’s open access platform, and the existing LMS, for generating unique titles report. The Microsoft’s Excel format, pivot table approach, Libsuite software, SQL queries for KOHA, databases, cloud-based system platform, etc. approaches are used to successfully achieve the unique title report of print books in the university library.

This paper provides the solution about how to generate a complete and correct unique title report for all print books of the university. The preventive measures related to generation of unique titles when influx of new books or adding new institute(s) under the university are required.

The focus of the work discussed here is limited to generating correct report of unique titles using KOHA related to only print books of a university having multiple institutes affiliated to it.

This paper gives a constructive solution for generation of the unique titles report using KOHA, practically useful for any university or to the institute who wish to use KOHA, one of the open source software used worldwide for libraries.

This paper fulfills an identified need to study how to generate unique titles report related to print books of the university library. To the best of the authors’ knowledge, there exists no such case study from available knowledge base/literature on the topic of interest and particularly focusing on the multiple copies data redundancy problem of KOHA-LMS.

To reduce the electricity consumption in our homes, a first step is to make the user aware of it. Reading a meter once in a month is not enough, instead, it requires real-time meter reading. Smart electricity meter (SEM) is capable of providing a quick and exact meter reading in real-time at regular time intervals. SEM generates a considerable amount of household electricity consumption data in an incremental manner. However, such data has embedded load patterns and hidden information to extract and learn consumer behavior. The extracted load patterns from data clustering should be updated because consumer behaviors may be changed over time. The purpose of this study is to update the new clustering results based on the old data rather than to re-cluster all of the data from scratch.

This paper proposes an incremental clustering with nearness factor (ICNF) algorithm to update load patterns without overall daily load curve clustering.

Extensive experiments are implemented on real-world SEM data of Irish Social Science Data Archive (Ireland) data set. The results are evaluated by both accuracy measures and clustering validity indices, which indicate that proposed method is useful for using the enormous amount of smart meter data to understand customers’ electricity consumption behaviors.

ICNF can provide an efficient response for electricity consumption patterns analysis to end consumers via SEMs.

Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.