This is a guidebook that uses some of the same material as the CD-ROM of similar name (Faust & Gulledge, Harmful Marine Dinoflagellates). It is aimed at ‘the researcher, instructor and the student’. A brief introduction is followed by coverage of species in alphabetical order. For each species the following sections are given: Species overview, Taxonomical description, Nomenclatural types, Thecal plate description, Morphology and structure, Reproduction, Ecology, Toxicity, Species comparison and Habitat and locality. This information is reasonably detailed and covers 1–2 pages depending on the organism. All the illustratons are given at the end of the volume in a series of 48 plates (one per species). Given the large number of potentially harmful marine dinoflagellates, the book is limited in scope as it covers only 48 species from 12 genera (one quarter of which belong to the genus Prorocentrum). I believe the word ‘Identifying’ in the title is a little misleading. For someone with limited knowledge of dinoflagellates, with an unknown organism in front of them, accessing the information might be problematic, given the alphabetical arrangement of descriptions without any simple guide even to genera. Flicking through the plates is an option, but not a practise, I would wish to encourage in students. Neither are there any species descriptions of morphologically similar organisms that might be confusing or lead to incorrect identifications by the inexperienced observer. The section given on species comparison is useful in this respect but is not accompanied by illustrations to assist ‘identification’. The illustrations (for those using this for identification) are of mixed quality. For example the illustrations for Alexandrium acatenella (and some other Alexandrium species) would be of limited assistance compared to some of the Dinophysis and Prorocentrum species that are illustrated excellently. I found the arrangement of the text somewhat perverse with regard to cyst descriptions. Where these are given in the text, they are included in the section on Ecology, (rather than in either the section entitled Taxonomic description or the section on Reproduction). I found a few errors in the text (e.g. comparison of Alexandrium minutum and A. tamarense). Also, as is inevitable in this fast-moving field, parts of the material given are incorrect because of recent developments – most seriously the taxonomy of the Gymnodinium/Gyrodinium species is out dated. If you treat this volume as a Guidebook, the broad, succinct, coverage (from taxonomy to ecology) will make it a useful initial starting point for learning something quickly of an unfamiliar species. There are plenty of references given to consult for more detailed information. Faust, MA, Gulledge RA (2002) Harmful Marine Dinoflagellates. World Biodiversity Database CDROM Series, ISBN 90-75000-21-9.

Identifying Harmful Marine Dinoflagellates

Purpose The purpose of this paper is to see how critical and vital artificial intelligence (AI) and big data are in today’s world. Besides this, this paper also seeks to explore qualitative and theoretical perspectives to underscore the importance of AI and big data applications in multi-sectoral scenarios of businesses across the world. Moreover, this paper also aims at working out the scope of ontological communicative perspectives based on AI alongside emphasizing their relevance in business organizations that need to survive and sustain with a view to achieve their strategic goals. Design/methodology/approach This paper attempts to explore the qualitative perspectives to build a direction for strategic management via addressing the following research questions concerned with assessing the scope of ontological communicative perspectives in AI relevant to business organizations; exploring benefits of big data combined with AI in modern businesses; and underscoring the importance of AI and big data applications in multi-sectoral scenarios of businesses in today’s world. Employing bibliometric analysis along with NVivo software to do sentiment analysis, this paper attempts to develop an understanding of what happens when AI and big data are combined in businesses. Findings AI and big data have tremendous bearing on modern businesses. Because big data comprises enormous information of diverse sorts, AI-assisted machines, tools and devices help modern businesses process it quickly, efficiently and meaningfully. Therefore, business leaders and entrepreneurs need to focus heavily on ontological and communicative perspectives to deal with diverse range of challenges and problems particularly in the context of recent crises caused by COVID-19 pandemic. Research limitations/implications There is hardly any arena of human activity wherein AI and big data are not relevant. The implication of this paper is that of combining both well so that we may find answers to the difficult and challenging multi-sectoral scenarios concerning not just businesses but life at large. Moreover, automated tools based on AI such as natural language processing and speech to text also facilitate meaningful communication at various levels not just in business organizations but other fields of human activities as well. Social implications This paper has layered social implications, as it conceptually works out as to how strategically we may combine AI and big data to benefit modern business scenarios dealing with service providers, manufacturers, entrepreneurs, business leaders, customers and consumers. All the stakeholders are socio-culturally and contextually rooted/situated, and that is how this study becomes socially relevant. Originality/value This paper is an original piece of research and has been envisioned in view of the challenging business scenarios across the world today. This paper underscores the importance of strategically combining AI and big data, as they have enormous bearing on modern businesses. The insights arrived at in this paper have implications for business leaders and entrepreneurs across the globe who could focus more on ontological and communicative perspectives of AI combined with Big Data to deal with diverse range of challenges and problems that modern businesses have been facing particularly in recent times. 

Artificial intelligence and big data: ontological and communicative perspectives in multi-sectoral scenarios of modern businesses


Purpose
The purpose of this paper is to see how critical and vital artificial intelligence (AI) and big data are in today’s world. Besides this, this paper also seeks to explore qualitative and theoretical perspectives to underscore the importance of AI and big data applications in multi-sectoral scenarios of businesses across the world. Moreover, this paper also aims at working out the scope of ontological communicative perspectives based on AI alongside emphasizing their relevance in business organizations that need to survive and sustain with a view to achieve their strategic goals.


Design/methodology/approach
This paper attempts to explore the qualitative perspectives to build a direction for strategic management via addressing the following research questions concerned with assessing the scope of ontological communicative perspectives in AI relevant to business organizations; exploring benefits of big data combined with AI in modern businesses; and underscoring the importance of AI and big data applications in multi-sectoral scenarios of businesses in today’s world. Employing bibliometric analysis along with NVivo software to do sentiment analysis, this paper attempts to develop an understanding of what happens when AI and big data are combined in businesses.


Findings
AI and big data have tremendous bearing on modern businesses. Because big data comprises enormous information of diverse sorts, AI-assisted machines, tools and devices help modern businesses process it quickly, efficiently and meaningfully. Therefore, business leaders and entrepreneurs need to focus heavily on ontological and communicative perspectives to deal with diverse range of challenges and problems particularly in the context of recent crises caused by COVID-19 pandemic.


Research limitations/implications
There is hardly any arena of human activity wherein AI and big data are not relevant. The implication of this paper is that of combining both well so that we may find answers to the difficult and challenging multi-sectoral scenarios concerning not just businesses but life at large. Moreover, automated tools based on AI such as natural language processing and speech to text also facilitate meaningful communication at various levels not just in business organizations but other fields of human activities as well.


Social implications
This paper has layered social implications, as it conceptually works out as to how strategically we may combine AI and big data to benefit modern business scenarios dealing with service providers, manufacturers, entrepreneurs, business leaders, customers and consumers. All the stakeholders are socio-culturally and contextually rooted/situated, and that is how this study becomes socially relevant.


Originality/value
This paper is an original piece of research and has been envisioned in view of the challenging business scenarios across the world today. This paper underscores the importance of strategically combining AI and big data, as they have enormous bearing on modern businesses. The insights arrived at in this paper have implications for business leaders and entrepreneurs across the globe who could focus more on ontological and communicative perspectives of AI combined with Big Data to deal with diverse range of challenges and problems that modern businesses have been facing particularly in recent times.


—Regulatory and technological changes have recently transformed the digital footprint of credit card transactions, providing at least ten times the amount of data available for fraud detection practices that were previously available for analysis. This newly enhanced dataset challenges the scalability of traditional rule-based fraud detection methods and creates an opportunity for wider adoption of artificial intelligence (AI) techniques. However, the opacity of AI models, combined with the high stakes involved in the finance industry, means practitioners have been slow to adapt. In response, this paper argues for more researchers to engage with investigations into the use of Explainable Artificial Intelligence (XAI) techniques for credit card fraud detection. Firstly, it sheds light on recent regulatory changes which are pivotal in driving the adoption of new machine learning (ML) techniques. Secondly, it examines the operating environment for credit card transactions, an understanding of which is crucial for the ability to operationalise solutions. Finally, it proposes a research agenda comprised of four key areas of investigation for XAI, arguing that further work would contribute towards a step-change in fraud detection practices.

/pdf/opportunities-in-real-time-fraud-detection-an-explainable-3emxi66b.pdf

Opportunities in Real Time Fraud Detection: An Explainable Artificial Intelligence (XAI) Research Agenda

Purpose – The best algorithm that was implemented on this Brazilian dataset was artificial immune system (AIS)
algorithm. But the time and cost of this algorithm are high. Using asexual reproduction optimization (ARO) algorithm,
the authors achieved better results in less time. So the authors achieved less cost in a shorter time. Their framework
addressed the problems such as high costs and training time in credit card fraud detection. This simple and effective
approach has achieved better results than the best techniques implemented on our dataset so far. The purpose of this
paper is to detect credit card fraud using ARO.
Design/methodology/approach – In this paper, the authors used ARO algorithm to classify the bank transactions
into fraud and legitimate. ARO is taken from asexual reproduction. Asexual reproduction refers to a kind of production in
which one parent produces offspring identical to herself. In ARO algorithm, an individual is shown by a vector of variables.
Each variable is considered as a chromosome. A binary string represents a chromosome consisted of genes. It is supposed
that every generated answer exists in the environment, and because of limited resources, only the best solution can remain
alive. The algorithm starts with a random individual in the answer scope. This parent reproduces the offspring named bud.
Either the parent or the offspring can survive. In this competition, the one which outperforms in fitness function remains
alive. If the offspring has suitable performance,it will be the next parent, and the current parent becomes obsolete.Otherwise,
the offspring perishes, and the present parent survives. The algorithm recurs until the stop condition occurs.
Findings – Results showed that ARO had increased the AUC (i.e. area under a receiver operating characteristic (ROC)
curve), sensitivity, precision, specificity and accuracy by 13%, 25%, 56%, 3% and 3%, in comparison with AIS,
respectively. The authors achieved a high precision value indicating that if ARO detects a record as a fraud, with a high
probability, it is a fraud one. Supporting a real-time fraud detection system is another vital issue. ARO outperforms AIS
not only in the mentioned criteria, but also decreases the training time by 75% in comparison with the AIS, which is a
significant figure.
Originality/value – In this paper, the authors implemented the ARO in credit card fraud detection. The authors
compared the results with those of the AIS, which was one of the best methods ever implemented on the benchmark
dataset. The chief focus of the fraud detection studies is finding the algorithms that can detect legal transactions from
the fraudulent ones with high detection accuracy in the shortest time and at a low cost. That ARO meets all these
demands.

/pdf/credit-card-fraud-detection-using-asexual-reproduction-4560zxirko.pdf

Nila Bahrambeik

Papers

Credit card fraud detection using asexual reproduction optimization