The following International Journal of Environment and Pollution article, "Research on emergency monitoring methods for landslide disasters based on improved atmospheric correction GB-SAR and multi-source data geocoding", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Thick cloud cover can completely obscure the surface of the earth from satellite view, while thinner haze and shadows distort the image of rural and urban regions. As such, many remote sensing images for monitoring climate, crops, and urban growth are only partially usable.
Research in the International Journal of Bio-Inspired Computation offers a way for satellites to see through clouds using a hybrid artificial intelligence system. The system essentially removes clouds from the images sent back by the satellite and reconstructs the land surface beneath with greater fidelity than is possible with earlier techniques. Almost all optical satellite images are affected by clouds to some degree, so improvements in AI cloud removal could expand the reliability of high-resolution Earth observation data.
Traditional approaches have relied either on physical models of atmospheric light scattering or on image-processing techniques that compare multiple images through time or across different wavelengths of light. Those methods are useful but struggle with varying cloud thickness or large, fully obscured areas. More recent machine learning systems, in which algorithms learn patterns from large datasets, have improved results, but they need clear reference images, without them, they simply produce blurred areas where the landscape was obscured by clouds.
The new approach is a deep denoising application known as SenseNet. It treats those image pixels with clouds or haze as being structured noise that can be removed. The system uses a model inspired by nature called a hybrid Coyote Fox Optimisation algorithm, which works by modelling the social, cooperative behaviour in canines to take the input data and process it to find the optimal solution. In computational terms, it helps tune the network’s internal parameters so that training does not stall on suboptimal solutions that would otherwise confound the learning algorithm.
Compared with existing denoising approaches, the system improved signal-to-noise ratios by more than two decibels and reduced residual errors. An improvement of just 2 dB is an almost 60 per cent improvement.
By clearing the clouds away, the system can more readily delineate agricultural boundaries and map road networks and bodies of water so that phenomena such as deforestation, crop yields, and infrastructure can be viewed with more detail. In persistently cloudy regions, including much of the tropics, more reliable cloud removal could reduce data gaps, supporting climate adaptation and disaster response strategies that increasingly depend on near-real-time satellite intelligence.
Gound, R.S. and Thepade, S.D. (2026) ‘SenseNet: satellite image enhancement using optimised deep denoiser for cloud removal’, Int. J. Bio-Inspired Computation, Vol. 27, No. 1, pp.45–59.
The following International Journal of Environment and Pollution article, "Empowering sustainable growth: the transformative impact of environmental protection inspections on heavy polluters", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Environment and Pollution article, "Promoting the transformation of digital economy structure based on artificial intelligence in the low carbon economy environment", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Environment and Pollution article, "Evaluation and trend prediction of the relationship between carbon emissions, energy, and sustainable growth based on neural networks", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Economics and Business Research article, "Financial deepening and fiscal capacity: new empirical evidence from the association of Southeast Asian nations", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Economics and Business Research article, "Validating the rational and intuitive decision-making styles - RIDMS scale across nine countries", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Economics and Business Research article, "Exploring the impact of older knowledge workers' career capital on career success: with self-efficacy and job crafting as mediators and perceived organisational support as a moderator", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Computer Applications in Technology article, "Anomaly detection and pattern recognition methods for high-dimensional data", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
As cities build upwards to accommodate growing populations, the safety of deep excavation, the process of digging large foundation pits to anchor high-rise buildings, has become a significant challenge in the construction industry. These pits must withstand the problem of shifting of the underlying earth, changes in groundwater pressure, and the heavy machinery while remaining stable enough to protect workers and nearby structures. Failures at this stage can trigger collapses, flooding or structural damage.
Work in the International Journal of Critical Infrastructures discusses an AI (artificial intelligence) system designed to improve safety monitoring at deep foundation pit support sites. The system aims to identify abnormal behaviour, such as unsafe actions, improper equipment use, or entry in restricted zones without protective gear, in close to real time so that warnings can be sounded in a timely manner.
Construction sites have traditionally relied on manual supervision and earlier generations of automated monitoring. But these approaches often struggle to detect unsafe behaviour quickly and accurately. Many systems record high false acceptance rates, meaning they mistakenly classify dangerous actions as safe. Others process video feeds too slowly to intervene effectively in rapidly changing environments.
The new system combines several advanced AI techniques to address those weaknesses. It begins by extracting key frames from surveillance footage using the fractional Fourier transform. This is a mathematical method that analyses data across different domains. By identifying the most informative frames rather than scanning every second of video, the system reduces computational load but still retains critical information.
The system then uses a spatiotemporal graph convolutional network, a form of deep learning that analyses both space and time data. The spatial analysis examines how workers and machinery are positioned relative to one another, while the temporal analysis tracks how movements change over time. Unlike conventional image-recognition models that treat frames in isolation, this approach captures sequences of actions and interactions. This is vital for working out what is happening moment to moment on the construction site.
The final step is to use a hybrid model that combines a convolutional neural network (CNN) with a so-called long short-term memory network (LSTM). The CNN can recognise visual features such as body posture or equipment shape. The LSTM can detect patterns in sequences of data. Working together, those two tools allow the system to determine not only what is happening in a single frame, but whether a series of movements constitutes a safety violation.
In their tests on active deep excavation sites, the researchers got a minimum false acceptance rate of 2.43 per cent and a peak abnormal behaviour recognition accuracy of 99.12 per cent. Processing time was as low as 0.19 seconds per analysis cycle, allowing near real-time monitoring.
Qi, W. (2026) ‘An adaptive recognition of abnormal behaviour in deep excavation support construction site of high-rise buildings’, Int. J. Critical Infrastructures, Vol. 22, No. 7, pp.1–17.
The following International Journal of Computer Applications in Technology article, "DAFPN: a lightweight multi-objective framework for small object detection in remote sensing images", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Computer Applications in Technology article, "Cloud computing based construction and empirical evaluation of the security risk early warning evaluation system of digital economy", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Computer Applications in Technology article, "Pose estimation technology of electronic components based on point cloud segmentation algorithm", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Computer Applications in Technology article, "Mean-shift-based moving target tracking algorithm in complex industrial environments", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The International Journal of Computer Applications in Technology has published an Open Access special issue: "Machine Learning for Cloud-Edge Emerging Network Architectures: Part 1".
All of the issue’s papers can be downloaded via the full-text links available here.
The following International Journal of Computer Applications in Technology article, "Digital media video image data processing based on computer vision", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Reasoning-based Intelligent Systems article, "Deep learning-based virtual screening system for drug molecules", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Reasoning-based Intelligent Systems article, "Research on modified Biaffine method for Chinese semantic role labelling", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Reasoning-based Intelligent Systems article, "Identification and long-term temporal sequential change analysis of urban VOCs high-value areas based on GIS and remote sensing", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
A large-scale study published in the International Journal of Business Innovation and Research has looked at what factors lead to sustained gains in the construction industry. The team looked at 226 nationally registered firms and found that operational efficiency and collaboration, long seen as the sector’s primary remedies for underperformance, are insufficient on their own to lead to sustained gains. Instead, the decisive factor is whether companies fundamentally rethink how they create, deliver, and capture value.
The research used a statistical tool known as Partial Least Squares Structural Equation Modelling to analyse information from the 226 companies and to look for any relationships between various organisational factors. The approach allowed them to look at how lean construction practices and strategic partnerships affect performance. It was also possible to discern whether business model innovation acts as a bridge between these strategies and measurable outcomes such as profitability, operational efficiency and competitive position.
Lean construction is a systematic project management approach designed to eliminate waste and maximise value throughout a project’s lifecycle. Waste includes excess materials, redundant labour, delays, reworking, and poor coordination between contractors. Unlike simple cost-cutting, lean methods emphasise continuous improvement, integrated workflows, and delivering greater value to clients.
The study confirms that those companies that adopt lean practices do tend to perform better. However, the most significant improvements did not stem solely from streamlining their processes. Instead, lean thinking proved most powerful when it also prompted broader strategic change.
That broader shift is captured in the concept of business model innovation. A business model defines how a company creates value for customers, how it delivers that value, and how it generates revenue. Innovation in this context involves reconfiguring those core elements. For example, this might include moving from one-off, project-based contracts to long-term integrated service models, adopting digital coordination platforms, redesigning revenue structures, and embedding sustainability into what the company offers to clients.
Business model innovation was found to have a strong and direct positive effect on performance. More importantly, it amplified the impact of lean construction. When lean methods were embedded within a redesigned business model, performance gains were significantly greater than when lean was treated as a stand-alone efficiency tool. The research found that partnerships boosted performance only when it allowed companies to innovate in their business models. Access to shared knowledge, resources, and trust-based relationships yielded gains only if companies used them to reconfigure how they compete and deliver value.
Arifin, J., Prabowo, H., Hamsal, M. and Elidjen, E. (2026) ‘Innovating for performance: the role of lean construction and strategic partnerships in construction firms’, Int. J. Business Innovation and Research, Vol. 39, No. 6, pp.1–25.
The following International Journal of Reasoning-based Intelligent Systems article, "Adaboost algorithm-based cost risk assessment for university laboratory construction", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Reasoning-based Intelligent Systems article, "Region-specific multi-scale meteorological forecasting based on data assimilation and reinforcement learning", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Business and Emerging Markets article, "Building organisational strategic resilience through leadership, design thinking, and business modelling", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Information and Communication Technology article, "Spatial domain semantic collaborative recognition model for complex emotions in artistic images", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Information and Communication Technology article, "Virtual reality and actual technology in psychology: intermediate research and analysis", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Real-time AI-regulated animation-user interaction system in virtual reality environments", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
In the age of global branding, instantaneous communication, and generative AI images, the symbols that we see in our daily lives circulate at an unprecedented rate. A study in the International Journal of Information and Communication Technology argues that if the symbols we share are to foster understanding rather than confusion, designers must treat them as carriers of cultural meaning, not mere decoration.
The team has used communication science, design theory, and semiotics, the study of signs and how they create meaning, to propose a systematic, evidence-based framework to identify, refine and test traditional cultural symbols. Their concept echoes an insight by Ferdinand de Saussure that suggests that a sign is not simply a form but a form bound to shared content. A flower or mythical creature, in this view, evokes memories, values and beliefs as much as it depicts the object it illustrates.
As digital platforms accelerate the circulation and mutation of images, we experience the fragmentation of symbols and signs. Moreover, in the age of generative artificial intelligence, almost all content is being cannibalised and regurgitated as derivative works, visual motifs are thus losing their inherited symbolism or, at best, being misappropriated or diluted. In the face of these changes, the researchers suggest that semiotics has now become a necessary part of creativity and perhaps the only hope of our conserving our symbols and their significance.
In their paper, the researchers discuss a five-step process beginning with systematic data collection and identification of culturally significant symbols. They followed this with a cross-cultural analysis, design refinement, and empirical testing. Statistical analysis together with expert review allowed them to look at specific symbols, such as the blue-and-white porcelain motifs featuring the lotus, peony, and plum blossom. As a good example of symbolic art, these patterns scored highly for clarity, adaptability, and perceived authenticity. The lotus is widely associated in East Asia with purity and renewal, the peony with prosperity and honour, and the plum blossom with resilience in adversity. Their visual simplicity combined with layered symbolism appears to aid translation into contemporary branding, the analysis found. More complex imagery failed to ignite the imagination of general audiences, although it was recognised as culturally significant by the experts.
Quantitative evaluation thus shows the different priorities associated with authenticity and meaning, challenging assumptions of universal interpretation for even familiar symbols that might be used in marketing and branding.
Li, A. (2026) ‘Research on the identification and optimisation of traditional cultural symbols from the perspective of cross-cultural communication’, Int. J. Information and Communication Technology, Vol. 27, No. 9, pp.18–38.
The following International Journal of Information and Communication Technology article, "Construction of digital art knowledge graph based on deep recurrent neural network", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Information and Communication Technology article, "AI-powered recommendation and task assignment mechanism for interactive vocational English teaching", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Information and Communication Technology article, "Development of an AI-assisted spoken language assessment system for Japanese language teaching", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Innovative practice of AI-driven intelligent assessment system in university course teaching reform", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Federated learning-enabled personalised delivery and student privacy protection in universities", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Mental health problems are among the most pressing of public health challenges, affecting millions across different age groups and societies. Depression, anxiety, and stress-related conditions rank among the leading causes of diminished quality of life worldwide. They exact a heavy social toll and economic cost. Yet diagnosis still relies largely on self-reported symptoms and intermittent clinical interviews, which means diagnosis is vulnerable to memory lapse, stigma, and limited access to trained professionals.
Research in the International Journal of Networking and Virtual Organisations discusses an artificial intelligence (AI) diagnostic system that can spot early signs of various mental health conditions by analysing how people write online. The model, known as a Fossa-based Graph Neural Network (FbGNN), examines language patterns in text drawn from social media platforms and online forums. Instead of relying solely on questionnaires, it studies sentiment-driven textual information, the emotional tone, word choices and behavioural cues embedded in a person’s online writing.
The researchers explain that their system combines two advanced computational techniques. The first is the Fossa optimisation, a feature-selection method based on search strategies seen in nature. In machine learning, features are identifiable pieces of information, specific words, phrases or emotional markers. By applying Fossa optimisation, the system can filter out any irrelevant data from those features and identify pertinent indicators of mental distress.
The second component is a Graph Neural Network, a GNN. A GNN analyses relationships by representing information as a network of nodes and connections. Here, nodes correspond to features, and the connections are the interactions between them. This allows the model to detect complex patterns, such as recurring combinations of emotional expression and behavioural signals.
By training the system to classify text based on categories such as depression, anxiety, stress, bipolar disorder, suicidal ideation, and personality disorders, the team was able to then test its accuracy against known sample data. It was able to predict a person’s mental health status with an accuracy of almost 99 per cent in the trials. Such accuracy would be useful in screening for mental health problems among a cohort of users, such as students, employees, or any other group. It would allow healthcare follow-ups to be directed at those most likely to have problems that might be addressed and would only miss one in a hundred. Further refinements of the system could bring that accuracy closer to 100 per cent.
Shobitha, G.S., Kataksham, V.S., Nagalaxmi, T., Spandana, V., Sreelatha, G. and Radha, V. (2025) ‘A smart intelligent Internet of Things framework for predicting mental health’, Int. J. Networking and Virtual Organisations, Vol. 33, No. 3, pp.251–278.
The following International Journal of Information and Communication Technology article, "Evaluation of teaching effectiveness in data analysis courses using a behavioural big data model", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Information and Communication Technology article, "Construction of mental health analysis model based on multi-modal feature learning and fusion network", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Information and Communication Technology article, "Generative music composition teaching system based on mobile interaction", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Information and Communication Technology article, "Temporal convolutional networks with language models for decoding music preferences in mental health profiling", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Information and Communication Technology article, "Application of an AI-driven visual aesthetic scoring system for style calibration in art works", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Information and Communication Technology article, "Research on the identification and optimisation of traditional cultural symbols from the perspective of cross-cultural communication", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Managerial and Financial Accounting article, "Bibliometric insights into green accounting research: analysing trends, impact, and theoretical foundations", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Critical Infrastructures article, "An adaptive recognition of abnormal behaviour in deep excavation support construction site of high-rise buildings", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Services and Operations Management article, "How lighthouse companies are pioneering Indonesia's Industry 4.0 and 5.0 revolution? A soft system methodology approach", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Business Innovation and Research article, "Innovating for performance: the role of lean construction and strategic partnerships in construction firms", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Digital payments are a routine part of daily life for many people. As such, the risk of online fraud is rising alongside this convenience. Identity theft, email compromise, scams, and misleading investment schemes all exploit technological weaknesses and often user naivety and can lead to big financial losses.
Research in the American Journal of Finance and Accounting has looked at technological threat avoidance theory (TTAT), a framework used to understand how individuals respond to technology-related risks. The study sheds new light on what motivates users to protect themselves from online financial threats, if they do at all. It considers user attitudes towards fraud and the perception of potential financial loss with the aim of identifying the specific influences that lead to a user taking protective action.
The team surveyed users of online payment platforms and found that rather than an abstract fear of fraud, the decisive factor in whether or not people took preventative measures was simply the perceived financial loss. This finding suggests that awareness campaigns focused on general threats may be less effective than approaches that point out the direct financial consequences of online fraud.
Online fraud costs us roughly US$1 trillion per annum, and it is likely that figure is rising year on year. There are millions of reported cases and probably many more that are never reported. The losses that people bear when a victim of online fraud erodes overall trust in the digital systems on which we rely. Moreover, widespread, organised fraud can disrupt financial infrastructure, threatening broader economic stability and making it almost impossible for regulators to maintain oversight and control.
Facing such problems, the digital economy needs technological innovation in payment systems to incorporate effective strategies to influence user behaviour. Such strategies need to make it difficult for users to compromise themselves through technological naivety. Policymakers, platform developers, and financial educators also need to help in the design of interventions that align perceived risk with actual behaviour and so strengthen the individual against threats as well as help maintain trust in digital financial systems.
Peswani, R. and Vijay, P. (2026) ‘Minimising exposure to cyber frauds in digital finance: perspectives from technology threat avoidance theory’, American J. Finance and Accounting, Vol. 9, No. 1, pp.76–98.
The following International Journal of Computational Systems Engineering article, "RBF neural network model construction for enterprise financial big data analysis", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following Journal of Business and Management article, "The effect of in-store employee retail technology on employee well-being: based on the job demands-resources model", is freely available for download as an open access article.
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The following Journal of Business and Management article, "The COVID-19 pandemic effects on FDI in select emerging economies", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Personalised learning path generation mechanism based on RL and knowledge graph", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Analysis of tourist emotions and behaviour patterns using deep learning", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
