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  • There are many reasons why someone might wish to know the precise camera that was used to take a digital photo – whether for criminal or fraud investigation, copyright and provenance, and perhaps even for archival purposes. Work published in the International Journal of Computational Vision and Robotics, provides a novel feature-based approach for such an identification using photo-response non-uniformity (PRNU) noise.

    Megha Borole and Satish Kolhe of the School of Computer Sciences at Kavayitri Bahinabai Chaudhari North Maharashtra University in Jalgaon, Maharashtra, India, explain how the pattern of noise in a digital image can act as a "fingerprint" unique to a particular camera. It can even be used to distinguish between the same make and model of camera with the same lens. "PRNU noise exhibits a different noise pattern for each image sensor and if numerous pictures are taken of a similar scene it remains around same," the team explains.

    The team explains that, somewhat paradoxically, they begin by applying a "denoising" procedure to the digital photo of interest. The filter allows them to reveal the PRNU noise pattern. This output is distinct from generic photographic noise and is, the team explains represented by the pixel intensities known as the Hu set of invariant moments. These invariants persist under image scaling, translation, and rotation, unlike many other characteristics of a digital photograph which may be lost when the photo is manipulated. The next step is to feed these features into a fuzzy min-max neural network (FMNN) that has been trained and classified with known digital cameras beforehand.

    The team has demonstrated proof of principle for the approach with seven camera groups and showed that they could identify the specific camera used to take a photo of the same scene as all the others more than nine times out of ten on average. Given that in any real-world situation there may well be other evidence to point to a specific camera in many kinds of investigation where its identity needs to be known. The next step will be to improve the behaviour of the neural network by reducing the impact of inherent random noise.

    Borole, M. and Kolhe, S.R. (2021) 'A feature-based approach for digital camera identification using photo-response non-uniformity noise', Int. J. Computational Vision and Robotics, Vol. 11, No. 4, pp.374–384.
    DOI: 10.1504/IJCVR.2021.116559

  • An analysis of web sales data from the top 100 US online retailers shows that digital sales channels including direct website visits, display ads, e-mail marketing, organic search, paid search, referrals, and social media all play an important role in driving sales. The details of the findings published in the International Journal of Electronic Marketing and Retailing, point to certain sales channels as being more effective in some contexts and so could guide those companies with limited resources to the most appropriate approach to driving web sales effectively. The team carried out their hypothesis testing using a log-log model with a Box-Cox transformation, and the average ticket value is used as a control variable.

    Ravi Narayanaswamy of the School of Business Administration at the University of South Carolina in Aiken and Richard Heiens of the Department of Business Administration at the University of South Carolina Beaufort, Bluffton, USA, open their paper with a quote from the American poet, Maya Angelou, who once famously said, "if you don't know where you've come from, you don't know where you're going." These words could very much apply to the online retail world, the team suggests. They explain that for online retailers, the route a user takes from entry point to shopping basket is a strong predictor of whether the user will ultimately complete the purchase. As such, understanding the path taken and the likely outcome for the vendor is important to guiding their sales and marketing strategy.

    They give an obvious example of a user who accesses user feedback before completing their transaction compared with one who reaches the checkout point without any other interaction between browsing, choosing, and getting ready to pay. With the feedback detour, there is often greater resolve to make the purchase in the end. Conversely, a user that reaches the checkout directly may well be presented with unexpected purchase terms or fees and be dissuaded from committing to the sale.

    The researchers have taken this notion much further to analyse the effect of the detailed route taken and the digital channels used to bring a customer from the point of browsing to the point of buying to allow them to predict how a seller might better guide their customers more effectively to closing a sale. In an age when bricks-and-mortar shopping is becoming less relevant, especially in the present COVID-19 pandemic era, companies need to understand their digital sales channels as clear a way as possible to drive sales.

    Narayanaswamy, R. and Heiens, R.A. (2021) 'The impact of digital sales channels on web sales: evidence from the USA's largest online retailers', Int. J. Electronic Marketing and Retailing, Vol. 12, No. 3, pp.306–322.
    DOI: 10.1504/IJEMR.2021.116505

  • A quick way to identify the "nth" friends of social media users based on spatial data mining of profiles and behaviour on a service such as Twitter is described in the International Journal of Advanced Intelligence Paradigms.

    D. Gandhimathi of the Research and Development Center, Bharathiar University in Coimbatore and John Sanjeev Kumar of Thiagarajar College of Engineering in Madurai, India, explain that Twitter plays an important role in intentional social action. Thus cluster analysis of users based on likes and interests might reveal otherwise latent connections between users and so allow emergent trends to be spotted more effectively and predictions made about the behaviour and actions users might take. Such insights could be of interest to research scientists, companies and their marketing departments, not-for-profit organizations and charities, and perhaps government and law enforcement in many different contexts.

    The team's unconventional quantitative analysis hooks into the geographical metadata of each user's Twitter updates, the geotag, where that is in place and not hidden by the user to provide even richer pickings for the data miners. The team explains that their main focus was on "recommender systems" that would engage a user's "nth" friends in a positive manner by understanding content-based or popularity-based aspects of behaviour and social action on Twitter. The team suggests that their approach could be developed into a useful recommender algorithm. However, it is also a useful tool for community discovery and for answering questions about the large-scale clustering of users.

    Their tests of the approach show it to be relatively low cost in terms of computer resources needed and to provide more accurate results when compared to other approaches.

    Gandhimathi, D. and Kumar, A.J.S. (2021) 'Prediction of Nth friends using spatial data mining in social networks', Int. J. Advanced Intelligence Paradigms, Vol. 19, Nos. 3/4, pp.410–421.
    DOI: 10.1504/IJAIP.2021.116368

  • There is increasing pressure on society to test people in a timely manner for infection by the COVID-19 virus, SARS-CoV-2, but physical testing takes time and effort and requires people to either have a test kit at home or to attend a test centre. The burden on testing equipment and infrastructure might be lessened if there were a simple non-physical way of screening people so that those who are very unlikely to be infected need not have a definitive physical test.

    New work in the International Journal of Intelligent Information and Database Systems has turned to the concept of "fuzzy logic" to "test" people based on their symptoms to determine whether or not they have COVID-19 or not. This, of course, does not provide an answer as to whether a person is an asymptomatic carrier, but it would assist in helping a person or their healthcare worker decide on the next course of action based on their having this or another unrelated illness.

    A fuzzy logic system (FLS) is an expert system that utilises the theory of fuzzy sets that Zadeh laid out in 1965. The application of fuzzy logic allows a probability to be calculated with looser rules than one might assume with a statistical analysis based on different available criteria. It can output a confidence level to a diagnosis with a degree of certainty versus uncertainty.

    The team concedes that at this stage in the research, their fuzzy logic model based on publicly available databases and datasets is very much a prototype. There is no real way to distinguish the symptoms of COVID-19 from those of the common cold, pneumonia, or similar diseases based solely on reported symptoms. In order to boost the test's accuracy to a clinically useful level, additional symptomatic and epidemiological information about the patient's demographic and circumstances is now needed. This could then be fed into the fuzzy logic approach to adjust it based on probabilities. Moreover, in an area of high risk where there are many other confirmed cases, the uncertainty would be low.

    Once the issues of accuracy and false positives and negatives are overcome through additional work, the team anticipates that a website or app might be made available to allow people to carry out a non-physical pre-medical test if they have symptoms to allow them to distinguish with confidence between the overlapping symptoms of other conditions and COVID-19 itself.

    Choudhury, S.H., Aurin, A.J., Mitaly, T.A. and Rahman, R.M. (2021) 'Predicting the possibility of COVID-19 infection using fuzzy logic system', Int. J. Intelligent Information and Database Systems, Vol. 14, No. 3, pp.239–256.
    DOI: 10.1504/IJIIDS.2021.116465

  • Writing in the International Journal of Networking and Virtual Organisations, a team from Finland and the UK has turned to the methods of "criminal profiling" to help them understand the ecosystems of organisations.

    The concept of an ecosystem is commonly associated with biological systems, often at the environmental level, a wetland, a rain forest, a river, an ocean, for instance. However, it is possible to model non-biological systems with a similar perspective to gain insights into how the components of a system are interconnected and how they depend on each other. However, there are also methods of profiling that can be used to invert the question and apply an analytical approach usually reserved for profiling criminals to the ecosystems of organisations to gain new insights.

    The team writes that "In order for companies to survive, grow and maintain competitive advantage in the future, they must systematically monitor and evaluate their business surroundings." They add that organisations cannot exist nor thrive alone, they need others around them and to operate as part of the business ecosystem. Researchers have investigated Microsoft's computing ecosystem and Wal-Mart's retail ecosystem but, the researchers say, there is little that has been done in the way of visualisation of business ecosystems. They explain that "without visualising the collected data, the ecosystem profile would simply be a file full of data without a perspective into the structure of the whole ecosystem."

    Profiling reveals the connections and the connectivity between "actors" in the ecosystem. "The profiling of ecosystems opens up new possibilities for research, supports managerial decision-making, and as a result enables better understanding and management of ecosystems," the team writes. The team has carried out "web farming" and visualisation manually on a case study company using a six-step, three-phase process of building an ecosystem profile following one of the conventional ways that criminals are profiled by law-enforcement investigators.

    The team hopes to develop a tool for future studies so that the web farming and profiling can be done automatically, freeing up time for observations and analysis.

    Ylönen, N., Rissanen, M., Ylä-Kujala, A., Sinkkonen, T., Marttonen-Arola, S., Baglee, D. and Kärri, T. (2021) 'A web of clues: can ecosystems be profiled similarly to criminals?', Int. J. Networking and Virtual Organisations, Vol. 24, No. 4, pp.347–373.
    DOI: 10.1504/IJNVO.2021.116431

  • What has been the role of the COVID-19 pandemic in the digitalisation of the hospitality industry? Domenico Morrone, Nicola Raimo, Annunziata Tarulli, and Filippo Vitolla of the Department of Management, Finance and Technology at LUM University, in Casamassima, Bari, Italy, hope to answer that question in the International Journal Digital Culture and Electronic Tourism.

    The pandemic has pushed many normal activities into the online realm in unprecedented ways leading to the notion of e-tourism or smart tourism. However, the way in which hotels have been affected by the pandemic has not been investigated in detail in terms of the drivers for digitalisation until now. The team hopes to fill this gap through a case study investigation of hotel structures. Digitalisation in other realms might involve the use of information and communications technology (ICT) not only in communication and marketing areas but also in production, sales, customer relations, and beyond.

    The researchers have found that the motivation is mainly concerned with a desire to improve the quality of the hotel structures, to adapt to competitors, and increase financial performance. Digitalisation has had a series of positive effects related to boosting revenues and reducing costs as well as improving corporate image. COVID-19 has significantly accelerated the digitalisation processes, the team writes.

    The team suggests that digitalisation is perhaps the only way forward for hotels during the pandemic and perhaps beyond. "Through digitalisation, in fact, it is possible to guarantee and certify the sanitation of the structures, maintain social distancing, guest traceability and other measures, making people free to enjoy hotel holidays," they write. This implementation will allow tourists to be relatively safe in hotels, allow hoteliers to resume many of their normal activities. Digitalisation will also give the hotel industry a way to face possible future crises with more security.

    Morrone, D., Raimo, N., Tarulli, A. and Vitolla, F. (2021) 'Digitalisation in the hospitality industry: motivations, effects and role of Covid-19', Int. J. Digital Culture and Electronic Tourism, Vol. 3, Nos. 3/4, pp.257–270.
    DOI: 10.1504/IJDCET.2021.116475

  • A lot of entirely unwarranted anti-Asian sentiment in the USA and elsewhere has emerged on social media since the emergence of the COVID-19 pandemic, which had its original source in Wuhan, China, but is a global problem we all must face. Researchers from China and the USA have investigated how this xenophobia can be classified on one particularly prominent social media platform, Twitter, with a view to understanding how it might best be addressed.

    Writing in the International Journal of Society Systems Science, Peng Zhao and Xin Wang of the Big Data and AI Lab, IntelligentRabbit LLC, New Jersey and Xi Chen of the School of Humanity and Law, Beijing University of Civil Engineering and Architecture, suggest that deep learning can be used to investigate public sentiment regarding political opinion and geographical diversity.

    The team has developed a new method to classify those Twitter users posting updates with pandemic-related anti-Asian sentiment. They used a novel dataset for tracking users based on 10 million tweets. It was possible to home utilise known sentiment surrounding the US elections and geolocations. "The empirical result indicates that the political sentiments and the county-level election results make significant contributions to the model building," the team writes. They trained a deep neural network (DNN) model with data from more than 190,000 Twitter users and were able to classify their Twitter activity as "hate" or "non-hate" with 61% accuracy, the team reports.

    Such a classification should be sufficient to guide other classification systems and manual intervention to determine those users expressing xenophobic sentiment. This could then be used to decide whether any given user should be liable for further investigation, suspension, or education. The team points out that anti-Asian sentiment is not confined to the Twitter platform nor is it confined to the USA, it is seen on all platforms, including Facebook, Instagram, YouTube, and others with comments and posts from around the world. As such, the team adds that extracting features from the other platforms – images, voices, and videos will also be helpful in providing a multidimensional understanding of anti-Asian xenophobia and hate online in the COVID-19 context at the global level.

    Zhao, P., Chen, X. and Wang, X. (2021) 'Classifying COVID-19-related hate Twitter users using deep neural networks with sentiment-based features and geopolitical factors', Int. J. Society Systems Science, Vol. 13, No. 2, pp.125–139.
    DOI: 10.1504/IJSSS.2021.116373

  • Research published in the International Journal of Product Lifecycle Management has looked at the concept of obsolescence. A. Sánchez-Carralero and C. Armenta-Déu of the Universidad Complutense de Madrid in Spain explain how they have developed a model to simulate the obsolescence process that leads to the need to replace durable goods.

    The team shows how the benefits of replacement eventually outweigh the various costs of maintaining the original item nudging the user towards replacing the aging item. The model takes into account servicing as well as an irreparable failure that is the end-point of obsolescence in one sense.

    "Prediction of obsolescence is difficult since many factors intervene in the process," the researchers explain, "some depend not on technology or market aspects but on user perception." They add that it is possible to model the obsolescence process and predict when an item may become unusable and so need replacing using sophisticated statistical models such as Bayesian analysis. Such analyses might even be used to optimise the manufacturing process itself. Of course, in a modern, capitalist society, consumerism is key to growth and so obsolescence is necessary if a company is hoping to have repeat sales from users once they and their competitors have saturated the market.

    As such, the much-derided, and the perhaps unethical notion of "planned obsolescence" is prevalent. In this, the manufacturers design their durable goods to essentially have a lifespan limited by factors they might control rather than the lifespan being governed by the way in which a user uses the item. There is an amusing and universal tale of the broom one's grandparent used the same broom throughout their lives bought with their first home, used daily and only having had 6 replacement heads and 7 replacement shanks!

    Obsolescence is essentially entropy, the tendency of a system to move towards disorder and chaos. Understanding the obsolescence process of more sophisticated systems than a broom can help in the marketing of new products as well as perhaps allowing manufacturers and sellers to predict their future profits based on a model of obsolescence for their products and the reliability and replaceabilty of those products. Brooms wear out and have to be replaced, even Grandma will admit that.

    Sánchez-Carralero, A. and Armenta-Déu, C. (2021) 'Modelling and characterisation of the obsolescence process', Int. J. Product Lifecycle Management, Vol. 13, No. 2, pp.140–158.
    DOI: 10.1504/IJPLM.2021.116208

  • There are numerous malware detection and antivirus apps for mobile devices running the Android operating system. However, a team in China introduces a new approach that can detect malicious activity at the source code level. They provide details in the International Journal of Information and Computer Security.

    Junaid Akram, Majid Mumtaz, Gul Jabeen, and Ping Luo of The Key State Laboratory of Information Security at Tsinghua University, explain how their approach is not only scalable but offers self-optimisation of the signature set as it detects malicious apps by reading their source code. The team has developed a prototype of their software, DroidMD. They have tested it against almost 30000 applications of which 3,670 are already identified as malware. It is reliable because it analyses only the code and has a high detection accuracy of 95.5%. The team points out that one of the unique characteristics of their software is that it can detect malware that is a clone or "near-miss" of known viruses and malware. Conventional antivirus and malware detection often fails to detect such malware where the software signature may well be only marginally different from the original virus.

    Given that there are millions of users downloading thousands of apps every day, it is imperative that an effective and reliable approach to controlling malware be found to slow the assimilation of devices into bot nets and other malicious networks and reduce the risk of user data and privacy being compromised by malware.

    "In our future work, we will make DroidMD more resilient for minimising the obfuscation and improving its run time. Meanwhile, we will extend it for other programming languages to detect malware or malicious code fragments from source code to overcome security threats," the team writes.

    Akram, J., Mumtaz, M., Jabeen, G. and Luo, P. (2021) 'DroidMD: an efficient and scalable Android malware detection approach at source code level', Int. J. Information and Computer Security, Vol. 15, Nos. 2/3, pp.299-321.
    DOI: 10.1504/IJICS.2021.116310

  • The industrialised world has responded in disparate ways to the emergence of the novel coronavirus, SARS-CoV-2, and the ensuing pandemic it caused, COVID-19. Technology was repursosed to track and monitor the disease and research and development focused on the development of vaccines and investigated pharmaceutical and physical interventions to treat the disease.

    New research published in the International Journal of Technological Learning, Innovation and Development has looked at the response from a developing nation, Nigeria. This nation has, not unlike many others with fewer resources and less money to spare, not yet contributed in a significant way to R&D into the coronavirus and our response to the pandemic. Through a case study, the team has gleaned lessons that might be applied to lessen the crisis in Nigeria of the next pandemic.

    Morolake Bolaji, John O. Adeoti, and Joshua Adeyemi Afolabi of the Innovation and Technology Policy Department at the Nigerian Institute of Social and Economic Research (NISER), in Ojoo, Ibadan, Nigeria, explain that Nigeria may have the capability but has remained a "laggard in R&D spending as well as R&D activities, particularly in the health sector." One might suggest that the term "developing nation" can only be applied if that country is active in the areas that lead to development.

    The COVID-19 pandemic has, the team suggests, reinforced "the imperative for Nigeria to significantly and urgently increase its R&D spending not only to combat subsequent health challenges but also to facilitate rapid structural transformation and economic development." A country that fails to rise to such crises and challenges by boosting its Sciencebase will inevitably continue to suffer the worst consequences of such a pandemic.

    The team has five recommendations. The first is that the government must increase the nation's R&D budget. Secondly, health infrastructure needs considerable improvement. The third recommendation is that public R&D needs to integrate more effectively with the private sector to improve technological results. Fourthly, the government must improve the transfer of the currently limited R&D "outputs" to the end-users. Finally, education in science and technology must be given a boost through governmental scholarships that focus on problem-solving rather than promotion.

    Bolaji, M., Adeoti, J.O. and Afolabi, J.A. (2021) 'The imperative of research and development in Nigeria: lessons from the COVID-19 pandemic', Int. J. Technological Learning, Innovation and Development, Vol. 13, No. 2, pp.168–189.
    DOI: 10.1504/IJTLID.2021.116342

News

Improved Clarivate Citation Reports and Impact Factors for Inderscience Journals

Inderscience's Editorial Office is pleased to announce that 2021's Journal Citation Reports from Clarivate Analytics have revealed advances in impact factors for many Inderscience journals, including the European Journal of Industrial Engineering, European Journal of International Management, International Journal of Bio-Inspired Computation, International Journal of Exergy, International Journal of Global Warming, International Journal of Mobile Communications, International Journal of Oil, Gas and Coal Technology, International Journal of Shipping and Transport Logistics, International Journal of Surface Science and Engineering, International Journal of Technology Management, International Journal of Web and Grid Services, and Progress in Computational Fluid Dynamics.

The Editorial Office would like to congratulate and thank all editors, board members, authors and reviewers involved, and is pleased to see their endeavours rewarded in these latest Citation Reports.

European Journal of International Management celebrates indexing achievements

We are pleased to announce that the European Journal of International Management has recently improved its indexing scores on several fronts, with a move to Rating 2 in the Chartered ABS Academic Journals Guide, an improved Scopus CiteScore of 3.7 (from 2.7), and a Scimago H index jump to 25 (from 22). EJIM's Editor in Chief and Deputy Editor in Chief, Prof. Ilan Alon and Prof. Włodzimierz, thank their editorial staff, Senior Editors, Editorial and Review Board, reviewers and authors for helping the journal to make such excellent progress.

Inderscience board member Prof. Mohan Munasinghe wins Blue Planet Prize

Inderscience is pleased to announce that Prof. Mohan Munasinghe, an Editorial Board Member for both the International Journal of Global Environmental Issues and International Journal of Global Warming, has been awarded a 2021 Blue Planet Prize. This year marks the 30th awarding of the Blue Planet Prize, an international environmental award sponsored by the Asahi Glass Foundation, chaired by Takuya Shimamura. Every year, the Foundation selects two winners, individuals or organisations who have made significant contributions to the resolution of global environmental problems.

Prof. Munasinghe made the following statement:

"I am deeply grateful and honoured to receive the 2021 Blue Planet Prize, the premier global environmental sustainability award, symbolizing the outstanding commitment of the Asahi Glass Foundation of Japan, to a better future. I am indebted also to many who have contributed generously to my intellectual development and emotional intelligence, including teachers, mentors, colleagues, family and friends. Social ties have been invaluable to survive the pressures of COVID-19.

It is encouraging to learn that the award committee has specifically acknowledged several key concepts I developed and their practical application worldwide, during almost 5 decades, including the Sustainomics framework, sustainable development triangle (economy, environment, society), balanced inclusive green growth (BIGG), and Millennium Consumption Goals (MCGs).

My research interests have evolved, from basic disciplines like engineering, physics and economics, to application sectors like energy, water, transport, ICT, and environmental resources, and finally to multidisciplinary topics like poverty, disasters, climate change and sustainable development. This eclectic experience helped me develop Sustainomics, as an integrative, trans-disciplinary methodology. Drawing on my past work and the global platform provided by the prestigious Blue Planet Prize, I will continue my modest efforts to make our planet more sustainable for all."

Inderscience's Editorial Office sends its sincere congratulations to Prof. Munasinghe for this outstanding and significant achievement.

International Journal of Sustainable Agricultural Management and Informatics indexed by Clarivate Analytics' Emerging Sources Citation Index

Inderscience is pleased to announce that the International Journal of Sustainable Agricultural Management and Informatics has been indexed by Clarivate Analytics' Emerging Sources Citation Index.

Prof. Basil Manos, Editor in Chief of the journal, says, "Getting IJSAMI into the Emerging Sources Citations Index is the outcome of our persistent and methodical efforts to ensure the highest quality of papers, to use competent reviewers, and to have fast email exchanges with our authors and reviewers. I am very pleased and excited with this acknowledgment of our work, and I remain committed to providing the international scientific community with a journal of the highest quality."

International Journal of Hydromechatronics indexed by Clarivate Analytics' Emerging Sources Citation Index

Inderscience is pleased to announce that the International Journal of Hydromechatronics has been indexed by Clarivate Analytics' Emerging Sources Citation Index.

Prof. Yimin Shao, Editor in Chief of the journal, says, "I am very glad that IJHM has been included in the Emerging Sources Citations Index. It is a recognition of the academic achievements and editorial work of the journal. I would like to express our sincerest gratitude to all those who have contributed to this journal. We will continue to adhere to our publishing policy, and to publish high-quality papers to promote academic exchange and development within the fluid power and electromechanical control fields."