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- Managing the climate disaster
Researchers in South Korea discuss how we must adapt our approaches to disaster management to help us cope with the potentially devastating effects of climate change. Writing in the International Journal of Business Continuity and Risk Management, the team uses qualitative content analysis to describe and analyse the three levels of natural disaster management. These three levels – international, national, and local – are considered in the context of three proposed stages of climate change – before climate change, the first half, and the second half.
Kyong-Jin Park of Daegu Haany University in Gyeongsan City, Bong-Woo Lee of Seoul Digital University in Seoul, and Kyoo-Man Ha of Inje University in Gimhae City explain that all "stakeholders" the world over need to address international cooperation, sustainability, education, and training for survival. Their work suggests a history and a chronology where management from 1951 to 1990 was provincial, the period 1991 to 2040 will be seen as patriotic, but the period 2041 to 2100 will be the period of survival-oriented management.
The team alludes to the fact that while there may be denialism and ignorance about climate change, the truth is out there. Moreover, given that we are all culpable, we must all now play our role in the disaster management that is needed if we are to mitigate the impact of climate change on our lives, the lives of future generations, and indeed the future of life on earth. There is an ethical obligation on all of us and on all governments.
As they say, there is no Planet B, we have to work to protect and fix this one before it is too late. Lifestyle changes must take place from the local up to the national and then international level. Climate change is not a natural disaster but it will be disastrous for nature and ourselves unless we have the collective will to address the problems and manage them.
Park, K-J., Lee, B-W. and Ha, K-M. (2022) 'Examining the transition of natural disaster management for climate change', Int. J. Business Continuity and Risk Management, Vol. 12, No. 2, pp.116–130.
- Privacy and data mining
A survey of privacy-preserving data-mining techniques published in the International Journal of Business Intelligence and Data Mining assesses the pros and cons of each approach and offers guidance to potential users.
G. Sathish Kumar of the Department of Computer Science and Engineering at the Sri Krishna College of Engineering and Technology in Coimbatore and K. Premalatha of the Department of Computer Science and Engineering at the Bannari Amman Institute of Technology in Erode, both in Tamil Nadu, India explain how data mining has come to the fore as a powerful way to find patterns and correlations in big data.
However, as with any useful tool it can be mishandled or abused. In the case of big data, there are risks associated with breaches of private and personal information. This is particularly important given that data mining is so widely used with disparate data sets including criminal records, consumer shopping habits, bank transactions, medical information, and much more. Third parties might gain access to the identity of individuals represented in a database and so see associated information regarding that kind of personal and private data. A total breach would represent the worst-case scenario where all information and all individuals in a database is revealed to that third party.
There is therefore a pressing need to have full control of the data being mined so that third parties, malicious or otherwise, cannot compromise that data. The team has reviewed the various approaches and describes the benefits and disadvantages of each, including randomisation, anonymisation, condensation, cryptographic, fuzzy, and statistical methods of privacy preservation in data mining.
It is inevitable that there is always compromise in any approach. Indeed, the team has found that no technique outperforms all the others in all measures. Some work better than others in a given situation but there are trade-offs with each, the team writes. As such, there is still a need, despite recent advances in this area, to develop a system that can solidly preserve privacy while allowing data mining to be carried out.
Kumar, G.S. and Premalatha, K. (2022) 'Privacy preserving data mining – past and present', Int. J. Business Intelligence and Data Mining, Vol. 21, No. 2, pp.149–170.
- Algorithmic pest control
Machine learning has now been used to identify important pests that can ravage vegetable crops, according to work published in the International Journal of Wireless and Mobile Computing.
Changzhen Zhang of Kaili University in Guizhou, Yaowen Ye, Deqin Xiao, Long Qi, and Jianjun Yin of the South China Agricultural University in Guangzhou, China point out that effective pest control requires knowledge of the species affecting the plants and the level of infestation. The team has used a so-called "bag-of-features" model to develop an automatic pest monitoring system has been. They explain that their approach combines remote information processing technology and machine vision technology.
The proposed system can be implemented in a vegetable crop field to monitor four major pests: Phyllotreta striolata (the Striped Flea Beetle, a pest of brassicas), Frankliniella occidentalis (the invasive Western Flower Thrips, feeds on some 500 or more different species of vegetable, fruit, and flower), Bemisia tabaci (the Tobacco White Fly, which affects tomato and other related plants), and Plutella xylostella (the diamond-back moth, a pest of cruciform crops).
The team demonstrated an error rate of less than 10 percent when compared with detection and counting by people trained to spot the pests. Given that B. tabaci can reduce tomato crop yields by 60 percent so the detection of such species is critical to efficient and effective farming. The other species mentioned can all affect a wide variety of crops with devastating consequences when infestation is allowed to run rampant.
The team has demonstrated success in a controlled environment. The next step will be to test the system and improve its abilities in a more complex and realistic vegetable-growing environment.
Zhang, C., Ye, Y., Xiao, D., Qi, L. and Yin, J. (2022) 'Rapid detection and identification of major vegetable pests based on machine learning', Int. J. Wireless and Mobile Computing, Vol. 22, Nos. 3/4, pp.223–235.
- The daily grind of the rumour mill
Research published in the International Journal of Cloud Computing looks at how machine learning might allow us to analyse the nature and characteristics of social media updates and detect which of those updates are adding grist to the rumour mill rather than being factual.
Fake news has been with us ever since the first gossip passed on a rumour back in the day. But, with the advent of social media, it is now so much easier to spread fake news, disinformation, and propaganda to a vast global audience with little constraint. A rumour can make or break a reputation. These days, that might happen the world over through the amplifying echo chamber of social media.
Mohammed Al-Sarem, Muna Al-Harby, Faisal Saeed, and Essa Abdullah Hezzam of Taibah University in Medina, Saudi Arabia have surveyed the different text pre-processing approaches for approaching the vast quantities of data that pour from social media on a daily basis. How well these approaches work in the subsequent rumour detection analysis is critical to how well fake news can be spotted and stopped. The team has tested various approaches on a dataset of political news-related tweets from Saudi Arabia.
Pre-processing can look at the three most relevant characteristics of an update before the text analysis is carried out and silo the different updates accordingly: First, it can look at the use of question marks and exclamation marks and the word count. Secondly, it can look at whether an account is verified or has properties more often associated with a fake or bot account, such as tweet count, replies, retweets, etc. Thirdly, it can look at user-based features, such as the user name and the user's logo or profile picture.
The researchers found that pre-processing can improve analysis significantly when the output is fed to any of support vector machine (SVM), multinomial naïve Bayes (MNB), and K-nearest neighbour (KNN) classifiers. However, those classifiers do react differently depending on what combination of pre-processing techniques is used. For instance, removing stop words, and cleaning out coding tags, such as HTML, stemming, and tokenization.
Al-Sarem, M., Al-Harby, M., Saeed, F. and Hezzam, E.A. (2022) 'Machine learning classifiers with pre-processing techniques for rumour detection on social media: an empirical study', Int. J. Cloud Computing, Vol. 11, No. 4, pp.330–344.
- Finding at-risk students
Traditionally, attendance and exam results have been the main way in which educators can show whether or not a student is struggling with the course. This is done retrospectively. With the advent of cloud-based learning technology and online courses, especially during the COVID-19 pandemic, these metrics are not necessarily the best way to catch at-risk students so that they can be helped.
The converse of that is that this technology can be used to provide and analyse useful data about the students, which can itself highlight those that might be struggling more quickly than can conventional assessment. Moreover, it can do this in a much more timely manner than a retrospective look at attendance and infrequent exam results.
Owen P. Hall Jr. of the Graziadio Business School at Pepperdine University in Malibu, California, USA, describes a machine-learning approach to detecting at-risk students in the International Journal of Social Media and Interactive Learning Environments. "At-risk" is a three-pronged definition alluding to whether a student is considering leaving a course, whether the institution is planning to end the student's place on the course, or whether they are in a probationary period because of problems they have faced or concerns their teachers have about their course work, attendance, and results.
Machine learning has been used to predict examination grades and even attendance in some educational settings for many years. It is also commonly used to group students for study classes and other activities. It has even been used to detect cheating and plagiarism. It is perhaps therefore not such a great leap to picture the use of machine learning in helping students in another way.
Hall suggests that the machine-learning approach can analyse all the data associated with a student, almost continuously, and determine early on whether a student is at-risk or on the verge of being in that position. At this point, teachers and tutors might intervene to help without delay. The lack of delay to the assistance they give will tend to lead to a better outcome for such students.
"Engaging faculty, educational researchers, and administration in the risk mitigation paradigm is essential for ensuring student success," writes Hall. Machine learning offers a novel tool to help with this process, improve student outcomes, and reduce dropout rates in an increasingly pressured educational system.
Hall Jr., O.P. (2022) 'Detecting students at risk using machine learning: applications to business education', Int. J. Social Media and Interactive Learning Environments, Vol. 6, No. 4, pp.267–289.
- An algorithm that lifts the veil
Face-recognition technology is advancing apace and has applications in security and biometrics, marketing, education, criminal investigation, and many other areas, it can now not only recognise the person but can ascertain the expression on their face. Research in the International Journal of Biometrics tackles the limitations of face recognition software when the person's face is partly obscured, by a veil or protective face mask, for instance.
The researchers, based in Hungary, Jordan, Saudi Arabia, the UK, and the USA report a facial recognition accuracy with their deep-learning approach that is 99.95% accurate for facial recognition even for a person wearing a niqab, which most of the face except the eyes. 99.9% accurate for gender recognition, and determination of age. It can recognise that a veiled person or person wearing a covid mask is or is not smiling, through analysis of the eyes, with 80.9% accuracy. Tests were carried out on an image database of 150 people, 41 male and 109 female subjects aged from 8 to 78 years old.
Ahmad B.A. Hassanat of Mutah University in Karak and Abeer Ahmad Albustanji of the Ministry of Environment in Amman, Jordan, Ahmad S. Tarawneh of Eotvos Lorand University in Budapest, Hungary, Malek Alrashidi, Mansoor Alghamdi, and Ibrahim S. Alkhazi of the University of Tabuk, Hani Alharbi of the Islamic University of Madinah, Saudi Arabia, Mohammed Alanazi of Cranfield University, UK, and V.B. Surya Prasath of the University of Cincinnati, Ohio, USA, used a deep convolutional neural network to develop their recognition system. The neural network has 4096 features in each layer of the recognition process.
The team points out that their proof of principle – known as DeepVeil – involved the use of an in-house image database, with face-on images of veiled persons taken at close range. The next step will be to work with a more diverse set of images recorded in a range of settings and the photos taken from different angles. That said, in the early days of conventional facial recognition systems, a clear face-on image was needed to verify a person's identity and that is no longer the case as the algorithms and software have evolved. So, the same will, with the right approach and further development, likely become true for DeepVeil.
Hassanat, A.B.A., Albustanji, A.A., Tarawneh, A.S., Alrashidi, M., Alharbi, H., Alanazi, M., Alghamdi, M., Alkhazi, I.S. and Prasath, V.B.S. (2022) 'DeepVeil: deep learning for identification of face, gender, expression recognition under veiled conditions', Int. J. Biometrics, Vol. 14, Nos. 3/4, pp.453–480.
- Digital fraud in the pandemic world
Writing in the International Journal of Intellectual Property Management, a digital technology leader at telecommunications conglomerate Verizon discusses the impact of the COVID-19 pandemic on digital transformation and digital fraud in the US economy.
Shashidhar Hiremath reiterates just how much of an impact the COVID-19 pandemic has had on the US economy, broadly speaking. Of course, a similar picture is seen across the globe. Tourism, air travel, the housing market, information technology, hospitality, and food industries have perhaps been detrimentally affected the most. However, there have also been some upturns in the fortunes of those companies facilitating working and learning from home and people sharing activities remotely.
An unwanted area of growth during the pandemic was, of course, cybercrime, says Hiremath. The incidence of internet theft, phishing scams, and financial fraud all increased during the pandemic. This was presumably partly due to it being a time when many people were at their most vulnerable and susceptible. Moreover, infrastructure and IT support that would provide checks and assurances were not necessarily in place in the home, or remote-working, environment for countless computer users in the workforce.
Criminals will always find a way to exploit vulnerabilities and even create new ones. The nature of social change that was wrought by the emergence of a lethal coronavirus at the end of 2019 has given us what is euphemistically known as the "new normal". Unfortunately, the new normal has given criminals new opportunities. It is time for a detailed study of how the world has changed in this realm, suggests Hiremath. In the new normal, we may well need new laws and policies to help protect people from the ever-changing landscape of cybercrime and digital fraud, internet theft, and more.
Shashidhar (2022) 'Impact of COVID-19 pandemic on the digital transformation and digital frauds in the US economy', Int. J. Intellectual Property Management, Vol. 12, No. 3, pp.429–448.
- Digital wallets on the move
The number of people using mobile wallets, financial management software, apps, on their smart phone that allow them to make payments, is increasing. Work in the International Journal of Intellectual Property Management looks at the adoption of mobile wallets across India. Uptake there and in other developing nations is significant but there are many challenges that face putative users and those offering the services that are not so apparent in the developed world.
Ravi Kumar Gupta of the Department of Humanities and Management Science at the Madan Mohan Malaviya University of Technology in Gorakhpur, Uttar Pradesh, has collected data from 500 respondents in the Gorakhpur District of Uttar Pradesh to learn more about user and potential user perception of mobile wallets. He used regression, factor analysis and structural equation modelling to process the data.
There are an estimated 500 million smart phone users in India and that number is rising every day. A fraction of those are using a mobile wallet, but that fraction will likely only increase over time. The growth of the middle-income demographic across India is the underlying driver for this increase in technology adoption.
Gupta points out that while people of all ages are using smart phones, much of the growth in mobile wallet use among the younger demographic. That said, he has found from the survey of 500 people that risks associated with security and privacy are serious concerns that dissuade some people from using a mobile wallet. Conversely, ease of use and social influence both correlate positively with adoption of this technology.
Gupta, R.K. (2022) 'Adoption of mobile wallet services: an empirical analysis', Int. J. Intellectual Property Management, Vol. 12, No. 3, pp.341–353.
- The Mediterranean landscape
Anyone familiar with the landscape of the Mediterranean coast would recognise the terraces and stone walls that are an inherent part of farming there and help people intercalate crops between the garigues. The terraces and stone walls are themselves vital to the conservation of biodiversity in these landscapes as well as in farming, cultural heritage and tourism, and have been a key part of the landscape, particularly of the area for centuries if not millennia.
A new study aimed at improving our understanding of the microclimates, the micrometeorology created by this kind of landscape is discussed in the International Journal of Agricultural Resources, Governance and Ecology.
Alexandra Solomou, Nikolaos Proutsos, George Karetsos, Konstantinia Tsagari, and Nikolaos Chatzipavlis of the Institute of Mediterranean Forest Ecosystems part of the Hellenic Agricultural Organisation 'Demeter' (ELGO DIMITRA) in Athens, Greece, have reviewed the research literature on this aspect of the Greek countryside in detail and conclude that it is critical that these micro-landscapes be preserved.
The team points out that Greece is a world biodiversity "hotspot", and its abundance of fauna and flora and the high number of diverse species of fungi as well as its disparate ecosystems and landscapes make it rightly so. It also harbours many species endemic to the region and found nowhere else on the planet. The researchers also explain that the country has a complex terrain, ranging from sea level to quite high mountainous altitudes. It has many islands and a long coastline relative to the total area of the mainland. It thus has a variety of microclimates, which have sustained the rich biodiversity reported for the region. Of course, during recent decades Greece has become more arid as farming practices, water, use, and climate change have their impact.
Based on their review, the team lists the most important benefits of terraces and stone walls as follows:
First, they are an important defence against soil erosion caused by wind and rain and offer protection from extreme events, such as floods and freak winds. Secondly, they provide green infrastructure for island ecosystems, which could help those islands and their inhabitants adapt better to the effects of climate change. Indeed, the microhabitats wrought by this type of traditional manipulation of the landscape will support conservation and protection and even enhance biodiversity.
From the economic perspective, terraces and stone walls can help in the generation of high-value and high-quality agricultural products and other materials of use to industry. Finally, they offer an aesthetic enhancement to the landscape with great cultural value that is enjoyed by locals and tourists alike.
Solomou, A., Proutsos, N., Karetsos, G., Tsagari, K. and Chatzipavlis, N. (2022) 'Micrometeorology of the agricultural terraces and stone walls and impacts on biodiversity in the Mediterranean landscape of Greece', Int. J. Agricultural Resources, Governance and Ecology, Vol. 18, Nos. 1/2, pp.3–21.
- Social media paranoia
Artificial intelligence and text mining techniques can be used to detect paranoia among social media users. Specifically, work published in the International Journal of Computational Science and Engineering, has examined the behaviour of Twitter users in their updates regarding the COVID-19 pandemic in order to detect personality disorders associated with paranoia.
Mourad Ellouze, Seifeddine Mechti, Moez Krichen, and Lamia Hadrich Belguith of the University of Sfax in Tunisia and Vinayakumar Ravi of the Prince Mohammad Bin Fahd University in Khobar, Saudi Arabia, suggest that the behaviour of people towards the pandemic driven by mistrust of authority and fuelled by disinformation has somewhat hindered the way in which we have dealt with this global crisis.
The team suggests that in parallel with this general behaviour among some people there is a more worrying reaction among those with serious mental health problems associated with paranoia. Such conditions, when faced with the existential angst presented by a lethal pandemic, can lead to serious anxiety, grief, and suicidal thoughts.
Ultimately, the team's analysis of Twitter users discussing COVID-19 could allow them to find people who may be suffering unduly and may be entering a personal crisis. In other words, the tools they discuss could be used as a proxy diagnostic that could allow qualified professionals to offer an appropriate intervention for patients with paranoia. Perhaps it might also be used to guide decisions made by Twitter itself and its algorithms that lower risk for its vulnerable users.
Ellouze, M., Mechti, S., Krichen, M., Ravi, V. and Belguith, L.H. (2022) 'A deep learning approach for detecting the behaviour of people having personality disorders towards COVID-19 from Twitter', Int. J. Computational Science and Engineering, Vol. 25, No. 4, pp.353–366.
New Editor for International Journal of Biomedical Engineering and Technology
Prof. Yaodong Gu from Ningbo University in China has been appointed to take over editorship of the International Journal of Biomedical Engineering and Technology.
New Editor for International Journal of Biomechatronics and Biomedical Robotics
Prof. Kean C. Aw from the University of Auckland in New Zealand has been appointed to take over editorship of the International Journal of Biomechatronics and Biomedical Robotics.
International Journal of Economic Policy in Emerging Economies is now an open access-only journal
We are pleased to announce that the International Journal of Economic Policy in Emerging Economies is now an Open Access-only journal. All accepted articles submitted from 14 July 2022 onwards will be Open Access, and will require an article processing charge of US $1500.
Improved impact factors for Inderscience journals
Inderscience's Editorial Office is pleased to report that the following journals have improved the impact factor allocated to them by Clarivate' Science Citation Index Expanded:
International Journal of Ad Hoc and Ubiquitous Computing
International Journal of Environment and Pollution
International Journal of Exergy
International Journal of Materials and Product Technology
International Journal of Vehicle Design
New Editor for International Journal of the Digital Human
Prof. Marina L. Gavrilova from the University of Calgary in Canada has been appointed to take over editorship of the International Journal of the Digital Human.