The following International Journal of Information and Communication Technology article, "Optimisation of cross border export e-commerce supply chain network based on machine learning and random programming", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Design of AI-enhanced hybrid storage engine for multimodal data management", 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 method of student learning behaviour based on machine learning and data mining", is freely available for download as an open access article.
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As carbon neutrality become an increasingly urgent issue, the challenge of managing increasingly complex and renewable-heavy power systems remains. Work in the International Journal of Reasoning-based Intelligent Systems looks at power system optimization and presents a new approach to reducing both emissions and operating costs using artificial intelligence, AI.
The context for this innovation is the dual carbon goal, a policy framework adopted by many countries aiming to reach carbon neutrality sooner, rather than later. As electricity grids evolve from centralized, fossil-fuel-based structures to more decentralized systems dominated by intermittent renewable sources like wind and solar, they become harder to control in real-time. Grid operators must now balance not only power supply and demand, but also the economic cost of power generation and its environmental impact.
The researchers have used a refined version of a computational technique known as Particle Swarm Optimization (PSO). Originally inspired by the coordinated movement of animals, PSO seeks out optimal solutions from complex systems by simulating swarming behaviour. The researchers have extended PSO with a more advanced variant called Multi-Strategy Adaptive PSO (MAPSO), which incorporates a dynamic “reward mechanism” to improve how the AI searches for optimal outcomes. The researchers have then taken this yet another step forward, with MOMAPSO (Multi-Objective MAPSO). It operates within a dual-objective framework that simultaneously minimizes fuel costs and pollutant emissions, while respecting essential operational constraints such as power balance and generation limits.
Traditional mathematical optimization methods, while theoretically accurate, often struggle under the weight of modern grid complexity. They tend to be computationally demanding and less effective at navigating trade-offs between competing objectives. Heuristic algorithms like PSO and the extended version MAPSO, by contrast, offer a more flexible and efficient alternative.
Yao, L. (2025) ‘Optimisation of operation algorithms based on artificial intelligence in power system control’, Int. J. Reasoning-based Intelligent Systems, Vol. 17, No. 9, pp.34–43.
The following International Journal of Information and Communication Technology article, "Performance optimisation of a normalised operational assessment system using hybrid population intelligence 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 Information and Communication Technology article, "The evaluation method of English teaching quality incorporating students' cognitive transfer", is freely available for download as an open access article.
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In an obvious departure from conventional wisdom regarding tourism, research in the International Journal of Business Innovation and Research shows that domestic travellers in Indonesia are prioritising safety over traditional markers of quality, such as service excellence. The study, based on data from 300 tourists across Jakarta, Banten, and West Java, suggests that the post-pandemic traveller is guided less by indulgence and more by psychological comfort and trust in a destination.
The researchers draw on the Theory of Planned Behaviour, a social psychology model commonly used to explain how individuals form intentions based on attitudes, social norms, and perceived control. They then examined how a tourist’s perception of a destination and the risks associated with a visit to that place are now shaping travel decisions. The findings indicate a fundamental shift in the Indonesian tourism landscape: travellers are no longer primarily attracted by high-end service offerings. Instead, they favourable a strong destination image that encompasses traveller safety, emotional appeal, cultural assets, and perceived value.
Where such positives might encourage travel to a particular tourist spot. Any perceived risk to personal safety and wellbeing acts as a strong deterrent. Fears regarding health and safety risks, as well as concerns about financial loss or the reliability of infrastructure and local services feed into this deterrent, the research shows. Even destinations with traditionally high service standards are seeing waning interest if they cannot address these apparently new psychological barriers.
One of the most notable demographic findings from the study is the emergence of middle-class women aged 30 to 40 as the most influential segment of the domestic market. For this group, affordability, consistency, and emotional well-being rank higher than novelty or luxury. Their preferences are shaping a broader trend toward more cautious, purpose-driven travel.
Ultimately, the research has implications beyond Indonesia if the same personal risks assessments are being applied to other destinations in South East Asia. For tourism operators and policymakers across the globe, these insights suggest the industry needs to recalibrate its marketing and operational strategies to take into account changes in the attitudes of today’s tourists.
Ambarwati, M.F.L., So, I.G., Abdinagoro, S.B. and Pradipto, Y.D. (2025) ‘Recalibrating the compass: what truly matters to travellers after uncertainty’, Int. J. Business Innovation and Research, Vol. 37, No. 6, pp.1–21.
The following International Journal of Information and Communication Technology article, "Extraction of beat features for piano teaching performance based on improved autoencoder", 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, "Recalibrating the compass: what truly matters to travellers after uncertainty", 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, "Optimisation of operation algorithms based on artificial intelligence in power system control", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Research in the International Journal of Vehicle Performance discusses a new integrated sensing and positioning system that can improve how autonomous locomotives navigate underground mining tunnels. This is one of the most challenging environments for vehicle automation, given the potential for hazards. The team has combined advanced laser-based mapping with wireless positioning and demonstrates that their system promises safer and more reliable navigation in settings where traditional sensing technologies often fail.
Underground mining poses distinct problems for autonomous vehicles. Tunnels are narrow, poorly lit, and often filled with dust and smoke, conditions that render conventional cameras almost useless and reduce the effectiveness of radar. This study addresses these challenges with a hybrid approach that pairs Light Detection and Ranging (LiDAR) sensors with Ultra Wide Band (UWB) wireless positioning.
LiDAR works by emitting laser pulses and measuring the time it takes for the light to return after bouncing off nearby objects. Unlike cameras, LiDAR does not depend on ambient light and can generate detailed 3D maps even in visually obscured environments. But the raw data it produces is often noisy and difficult to interpret, especially in cluttered or uneven spaces like those found in mining tunnels.
To refine this data, the researchers employed a technique known as multi-iteration plane fitting, which systematically isolates the ground surface from the rest of the environment. This step is essential for identifying true obstacles rather than confusing irregularities in the terrain for actual hazards. Once the ground is mapped, remaining data points, representing non-ground objects, are grouped into clusters.
Clustering in irregular environments presents its own difficulties. Traditional algorithms can misinterpret the scene, either by merging separate objects into one or splitting a single object into several false positives. The research team addressed this by analysing the scan line distribution from the LiDAR sensors, that is, the geometric pattern in which the laser sweeps across the environment. This allowed them to group points that belong to the same physical object more effectively. This approach not only improves obstacle recognition accuracy but also speeds up data processing by more than a fifth, which is important for systems operating in real time.
Wang, H., Wang, Y. and Shen, Y. (2025) ‘Identification of intrusion obstacles for underground locomotives based on the fusion of LiDAR and wireless positioning technology’, Int. J. Vehicle Performance, Vol. 11, No. 3, pp.253–277.
The following International Journal of Reasoning-based Intelligent Systems article, "Adaptive convolutional network and transfer learning-based dance movement recognition for realistic subjects", is freely available for download as an open access article.
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The following International Journal of Reasoning-based Intelligent Systems article, "Exploration on the construction of colleges general education credit bank based on blockchain technology", is freely available for download as an open access article.
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The following International Journal of Reasoning-based Intelligent Systems article, "Evaluation of teaching quality in accounting smart education classrooms driven by student expression feature recognition", is freely available for download as an open access article.
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The following International Journal of Shipping and Transport Logistics article, "The dynamic coupling relationship between port and city from the perspective of port container traffic and the economy of port city", is freely available for download as an open access article.
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As artificial intelligence (AI) continues to transform how images and video are created, concerns about the manipulation of digital content have grown. Research in the International Journal of Forensic Engineering discusses how AI can be used to fight back and find the fakes.
Deepfakes, videos or images that have been manipulated using AI to depict events that never occurred and words that were never uttered, pose a growing threat to public discourse, politics, criminal investigations, and scientific integrity. These convincing forgeries are typically generated by a form of AI known as Generative Adversarial Networks (GANs). In a GAN, two neural networks compete: one creates realistic fake images, while the other attempts to identify the deception. As they evolve, so the system becomes adept at producing forgeries that are difficult even for experts to distinguish from authentic content.
Such technologies have not only led to blurred lines between real and artificial but also raised critical questions for journalism, law enforcement, and scientific publishing, where the authenticity of visual evidence can have serious consequences. As the manipulation of media becomes more sophisticated, traditional detection methods, such as spotting anomalies in shadows or unnatural features, have proven inadequate.
The work in IJFE has repurposed a relatively simple machine-learning model to detect deepfakes with high accuracy. The researchers tested three convolutional neural network (CNN) architectures, ResNet-50, AlexNet, and LeNet-5, to determine which was most effective in distinguishing genuine images from AI-generated fakes.
CNNs are particularly good at analysing visual data. They function by identifying patterns, textures, and other features that may not be visible to the human eye. Although newer models like ResNet-50 are often assumed to be more powerful due to their increased sophistication, the researchers found that LeNet-5, a comparatively basic model developed in the 1990s, initially performed best.
The team was able to enhance this CNN through architectural fine-tuning and parameter optimisation, so that their modified version of the LeNet-5 model could reach almost 96 percent accuracy. It also had a demonstrable Area Under Curve (AUC) score of almost 99 percent, meaning that statistically it performed well across a range of test scenarios. It is worth noting that this system does not rely on prior knowledge about an image’s content nor the embedded metadata.
Of course, as researchers are developing the tools to detect the fakes, so those who develop the tools to create them in the first place will respond with increasing sophistication to outwit the detectors.
Pawade, V.S. and Mathur, S. (2025) ‘Comparative study of CNN models for detecting altered and manipulated images’, Int. J. Forensic Engineering, Vol. 5, No. 3, pp.216–227.
The following International Journal of Management and Enterprise Development article, "Strategic planning, ambidexterity and exit strategy: dynamics in SMEs", is freely available for download as an open access article.
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The following International Journal of Data Mining and Bioinformatics article, "Data-driven forecasting of pharmaceutical sales: distinguishing promotional vs. daily scenarios", is freely available for download as an open access article.
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The following International Journal of Economics and Business Research article, "Extracting the Federal Reserve's priorities and transparency from FOMC statements using textual data mining tools", is freely available for download as an open access article.
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The following International Journal of Economics and Business Research article, "PRISMA-based systematic review: exploring the dimensions of digital transformation", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Electronic component fault diagnosis based on cross-domain features and deep contrastive learning", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Harmonising code and composition: computational design strategies for multimedia classical concerts", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Deep learning and layered architecture-based anomaly detection method for IoT", is freely available for download as an open access article.
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A newly developed performance evaluation system can assess energy storage power plants (ESPPs) in terms of investment decisions and public policy, as well as their place in sustainable energy infrastructure. The research is discussed in the International Journal of Power and Energy Conversion.
A longstanding and well-known bottleneck in sustainable energy integration is how to reliably and comprehensively evaluate the complex trade-offs involved in operating electrochemical energy storage systems, rechargeable batteries, in other words. Energy storage is critical to stabilizing supply when the power sources are unpredictable, such as solar and wind.
To investigate the issues, the researchers have used a refined version of a computational tool known as NSGA-II, or Non-dominated Sorting Genetic Algorithm II. NSGA-II is a well-established multi-objective optimization algorithm commonly used in engineering and operations research. It is particularly suited to problems with multiple, often conflicting goals, for example, maximizing system reliability while minimizing cost. The novelty in this research is that the team has overcome some of the limitations of standard versions of NSGA-II. In their approach they have extended the algorithm so that it does not settle prematurely on suboptimal solutions, local optima and instead explores all possible outcomes.
This enhancement was achieved first by introducing a new method for selecting the algorithm’s starting conditions, its initial population, and secondly by replacing the conventional crossover mechanism with a more exploratory alternative. These enhancements led to an algorithm that was up to five times faster than its predecessor and demonstrably more effective at identifying better solutions across a wider range of scenarios.
The team was able to demonstrate improvements over earlier algorithms in terms of technical performance and economic and investment viability. For instance, one scenario modelled in the study suggested that under specific operational conditions, annual returns on investment in ESPPs could be as high as 13 percent. These findings suggest that, with careful system design and coordination, ESPPs can offer compelling financial incentives in addition to their environmental benefits.
Wu, J., Dong, C., Liu, B., Weng, Z. and Zhu, J. (2025) ‘Design of performance evaluation system for electrochemical energy storage power plants based on NSGA-II‘, Int. J. Power and Energy Conversion, Vol. 16, No. 5, pp.1-20.
As Europe’s ageing population triggers a wave of business ownership transitions, research in the International Journal of Management and Enterprise Development has looked at one of the often-overlooked aspects of business, the factors influencing whether small and medium-sized enterprises (SMEs) can survive such transitions in terms of exit strategies and succession planning.
The researchers studied more than 1200 SMEs and their owner-managers in Finland, all aged 55 or older. They found that planning for the future plays a decisive role in whether firms remain innovative and adaptive as their founders prepare to step down. The research examines how different exit strategies, particularly family succession versus selling to a third party, relate to what ambidexterity. This two-handed ability to exploit existing capabilities while also exploring new opportunities.
Indeed, ambidexterity is widely regarded as essential for a company’s long-term viability, particularly in a rapidly changing business landscape. However, achieving it is particularly challenging for SMEs, which often depend heavily on the personal involvement and judgement of a single owner-manager. Without deliberate planning, many small businesses risk stagnation, or even closure, when the boss steps down.
The study’s key finding is that firms intending to hand over to a family member are more likely to be ambidextrous, but only if they also engage in strategic planning. In this context, strategic planning refers to a structured process of anticipating and preparing for future developments, including shifts in markets, competition, and internal capacity. Simply having the intention to keep the business in the family does not in itself translate into innovation or preparedness. It is the act of planning, systematically and proactively, that can unlock the firm’s ability to balance continuity with change.
In contrast, for businesses with low or no strategic planning, the chosen exit route, whether a family succession or selling to a third party, had little effect on forward-looking or adaptable the business was. This underscores the researchers’ argument that the mere expectation of a transition is not enough; it must be accompanied by intentional efforts to chart a path for the firm’s future.
Viljamaa, A., Joensuu-Salo, S. and Varamäki, E. (2025) ‘Strategic planning, ambidexterity and exit strategy: dynamics in SMEs’, Int. J. Management and Enterprise Development, Vol. 24, No. 6, pp.1–18.
The following International Journal of Information and Communication Technology article, "Research on the application of large-scale convolution kernels and multi-scale fusion networks in landslide remote sensing image", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Optimisation and application of management accounting system based on generative artificial intelligence", is freely available for download as an open access article.
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The following International Journal of Power and Energy Conversion article, "Design of performance evaluation system for electrochemical energy storage power plants based on NSGA-II", is freely available for download as an open access article.
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The following International Journal of Postharvest Technology and Innovation article, "Knowledge, attitude and practices relevant to food loss reduction along the bean supply chain of the home-grown school meal program in Kajiado and Kitui counties, Kenya", 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 social English learning behaviour based on federated knowledge graph and privacy preservation", is freely available for download as an open access article.
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Operational efficiency often leads to environmental compromise in solid waste management. An approach discussed in the International Journal of Applied Decision Sciences proposes an innovative approach that could improve the collection and transportation of municipal waste. The researchers have turned to the Plant Propagation Algorithm (PPA), a nature-inspired computational method, for a different perspective on routing waste collection vehicles.
Urban sanitation services need vehicles to traverse the city as efficiently as possible. But, working out the schedules and routes many vehicles need to take is no simple planning matter. Vehicles obviously have limited waste capacity, traffic conditions vary, and there are collection time windows with which to contend in terms of access. Moreover, all vehicles need to offload their waste several times on a single shift.
As urban populations grow and waste volumes rise, solving this logistical puzzle becomes increasingly difficult, especially using conventional logistics-planning methods. Many of these traditional approaches are heuristic in nature, which means they are essentially rule-of-thumb. Metaheuristics on the other hand used advanced algorithms based on biological or physical processes. These can efficiently search large sets of possible solutions to find the most viable ones.
The Plant Propagation Algorithm stands out among these tools. It draws its logic from the way strawberry plants reproduce: by sending out runners, some explore nearby soil, others venture further afield, to find ideal growing locations for new plants to grow. This metaphor is converted into a computational search strategy that can balance local and global exploration in the space of potential vehicle routes.
The team created benchmark scenarios using the algorithm and determined performance against criteria such as total distance travelled, fuel usage, the number of vehicles required, and driver working hours. The algorithm was able to generate feasible solutions despite the complexities of urban waste systems anc could handle variable service schedules, diverse waste types, and geographically irregular service zones.
Mat, N.A., Benjamin, A.M., Abdul-Rahman, S., Ku-Mahamud, K.R. and Ramli, M.F. (2025) ‘Adaptation of plant propagation algorithm for waste collection vehicle routing problem’, Int. J. Applied Decision Sciences, Vol. 18, No. 4, pp.383–407.
The following International Journal of Information and Communication Technology article, "Deep learning based automated generation method for fashion design", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Landscape perception analysis and design optimisation of urban parks supported by multimodal data", 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 EFL learners' academic anxiety based on R language and graph neural network", is freely available for download as an open access article.
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The following International Journal of Shipping and Transport Logistics article, "An empirical study of Sino-European trade geographic regional competition under the belt and road and Arctic routes based on transportation cost equilibrium line model", is freely available for download as an open access article.
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The following International Journal of Vehicle Design article, "An obstacle avoidance path selection for autonomous vehicles based on multi-dimensional data mining", is freely available for download as an open access article.
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Research in the International Journal of Postharvest Technology and Innovation has demonstrated that the knowledge, attitudes, and practices of farmers and traders play a central role in determining how much food is lost after harvest in Kenya’s bean supply chains. The insights from the research could have an important effect on food security initiatives across Sub-Saharan Africa.
The research, conducted in Kajiado and Kitui counties in Kenya, examined the supply chains serving the country’s Home-Grown School Meals Programme (HGSMP), which provides essential nutrition to schoolchildren using locally sourced food. The focus was on common beans, a crop grown by more than three million small-scale farmers in Kenya and a vital source of protein and nutrients in the region.
While food loss in Sub-Saharan Africa is often discussed in terms of inadequate infrastructure or the impact of pests and diseases, this study turns its attention to human behaviour and specifically knowledge, attitudes, and practices. These include basic yet essential actions such as drying beans properly prior to storage, turning grains during drying, and covering them at night to protect from moisture. Each of these practices can make a measurable difference in preserving food quality and quantity.
The study found that farmers who knew and applied good post-harvest practices saw losses reduced by up to 15%. For traders, proper drying techniques led to nearly 30% less spoilage. Even attitudes, such as the willingness to adopt such drying practices, had a significant effect. The evidence suggests that behavioural change, informed by targeted education and training, could have as much impact as technical interventions such as improved storage facilities.
This shift in focus has implications across Sub-Saharan Africa. As governments and development agencies invest in reducing food loss to address hunger and improve food security, this research shows that equal weight must be given to human behaviour as to infrastructure or technology. In contexts where resources are limited, improving what people know and do may offer a more scalable and cost-effective solution than simply ploughing in more money where it is available.
Okumu, C.A., Mburu, J., Mujuka, E., Ambuko, J. and Klug, I.L.F. (2025) ‘Knowledge, attitude and practices relevant to food loss reduction along the bean supply chain of the home-grown school meal program in Kajiado and Kitui counties, Kenya’, Int. J. Postharvest Technology and Innovation, Vol. 10, No. 6, pp.1–24.
The following International Journal of Mining and Mineral Engineering article, "A framework for safety performance indicators: the case of the Swedish mining industry", is freely available for download as an open access article.
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The following International Journal of Accounting, Auditing and Performance Evaluation article, "Silence is golden? Disclosure of critical audit matters and auditors' perception of due professional care", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Research in the International Journal of Data Mining and Bioinformatics discusses a new approach to demand forecasting for the pharmaceutical retail sector based on an artificial intelligence model. The findings hold promise for improving accuracy in one of the industry’s most persistent logistical challenges: managing sales that swing sharply during promotional periods. The new system works better than traditional models by distinguishing between routine demand and the short-term surges driven by marketing campaigns.
The team has built their forecasting system using a machine-learning framework known as the Temporal Fusion Transformer. This deep-learning model is designed specifically to analyse time-series data, such as daily sales figures or seasonal illness rates. Where conventional systems might smooth over the spikes and troughs in this kind of data, the new model can interpret such fluctuations and offer a more nuanced analysis for more reliable forecasting.
One of the underlying factors leading to this improved approach is the model’s use of multivariate feature construction. This method can be used to integrate diverse types of data into a single analytical framework. Rather than relying solely on historical sales figures, the model can use public health trends, seasonal disease prevalence, and promotional calendars. By working with such an enriched dataset, the model can detect complex patterns and anticipate demand with much greater precision.
In addition, the system uses a knowledge-guided attention mechanism. This process allows the system to prioritize the most relevant data depending on the sales scenario. For example, during an outbreak of influenza, the model may focus more heavily on regional health reports, whereas during a promotion, it shifts emphasis toward marketing schedules and in-store behaviour. This flexibility allows it to treat routine and promotional demand as fundamentally distinct processes, and so make better predictions about demand.
The researchers have tested their system on data from over 1.2 million retail transactions. The model reduced forecasting errors by almost a quarter compared to traditional methods. When tested in a commercial setting, it led to an almost one-third improvement in medication stock availability and just over a quarter reduction in excess inventory. Such improvements are not merely operational gains. Both outcomes are central to ensuring access to essential medicines while minimising waste in pharmaceutical supply chains.
Zeng, Z., Guo, Y., Ji, Y., Shi, Y. and Feng, T. (2025) ‘Data-driven forecasting of pharmaceutical sales: distinguishing promotional vs. daily scenarios’, Int. J. Data Mining and Bioinformatics, Vol. 29, No. 5, pp.1–26.
The following International Journal of Reasoning-based Intelligent Systems article, "A diagnostic model of students' civic education achievement based on multi-feature cognitive diagnosis in digital perspective", is freely available for download as an open access article.
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The following International Journal of Reasoning-based Intelligent Systems article, "Fault-tolerant control of intelligent transportation vehicles based on instant learning and heuristic dynamic planning", is freely available for download as an open access article.
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The following International Journal of Reasoning-based Intelligent Systems article, "Objective evaluation of English teaching in colleges and universities based on textual analysis in multi-element perspective", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
A new data-driven technique for obstacle avoidance in autonomous vehicles is reported in the International Journal of Vehicle Design. The approach might overcome many of the longstanding challenges in the development of self-driving navigation.
Obstacle avoidance refers to the ability of a vehicle to detect and manoeuvre around objects in its path. Despite years of development, many systems still struggle with this core capability, often producing inefficient routes, reacting slowly to sudden changes, or failing altogether in complex or unpredictable environments. The new method addresses these shortcomings by integrating advanced data mining and optimization algorithms into the vehicle’s navigation process.
The researchers explain that multidimensional data mining is key to their approach. This involves extracting patterns from a wide array of data sources, including visual input from cameras, spatial measurements from LIDAR (light detection and ranging, a laser-based system for mapping distances), location data from GPS (Global Positioning System), and real-time traffic information.
This data is processed using K-means clustering, a machine-learning algorithm that groups similar data points without needing prior labels. The purpose is to allow the vehicle to interpret its surroundings more intelligently, recognizing patterns such as obstacle types, road features, or the movement of nearby objects.
Once the environment is processed, the vehicle builds what researchers call a target function. This is a mathematical model that balances the goals of safety, speed, and efficiency. To optimize this function, the team applies the Whale Optimization Algorithm (WOA), a method inspired by the foraging behaviour of killer wales, Orcinus orca. WOA excels at quickly identifying optimal solutions in complex spaces, making it well suited to the high-speed demands of on-road decision-making.
In their simulations, the team demonstrated an obstacle-avoidance success rate of almost 99 percent, with reaction times as fast as 0.44 seconds. These results represent a marked improvement over many existing techniques, which often require longer processing times and produce less direct or more conservative paths.
Wang, A., Yao, Y. and Shang, Z. (2025) ‘An obstacle avoidance path selection for autonomous vehicles based on multi-dimensional data mining’, Int. J. Vehicle Design, Vol. 97, No. 5, pp.1–21.
The following International Journal of Reasoning-based Intelligent Systems article, "Logistic regression mathematical algorithms based on alternating direction method of multipliers in higher education 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, "AI recognition of art sculpture styles based on seagull optimisation machine learning", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Acoustic noise recognition of ancient buildings based on space time joint processing and deep learning", is freely available for download as an open access article.
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A new approach that might improve the evaluation of college-level English teaching by incorporating a broader range of data sources could address longstanding concerns about the subjectivity of traditional assessment methods. Conventional evaluations often rely predominantly on surveys and test scores, which provide limited insight into the complexities of teaching quality. The new approach, discussed in the International Journal of Reasoning-based Intelligent Systems, instead integrates diverse forms of data, multimodal elements, to produce a more objective appraisal.
Multimodal elements encompass different types of information beyond text, including images and audio recordings related to the teaching process and student feedback. The study’s main innovation lies in its use of an advanced computational framework that simultaneously analyses these varied data types, thereby capturing a more complete picture of the teaching environment.
A sophisticated algorithm, known as Cross-modal attention mechanism (CMAM), is used to identify meaningful relationships across modalities. For instance, it can link emotional expressions found in written comments with corresponding tones in audio or facial cues in images. This cross-referencing allows the system to interpret feedback in context, rather than treating each factor in isolation.
To bring information from the different modalities together, the system employs a gating mechanism. This regulates the contribution of each data source, ensuring that the most pertinent details have the most influence on the final evaluation. The combined data is then processed through a Transformer model, a machine-learning system that can understand complex patterns in language and context.
The model then performs sentiment analysis, which automatically detects the emotional tone behind the evaluations, whether positive, negative, or neutral. Unlike basic scoring, this method captures the subtleties of attitudes toward teaching quality, offering a richer understanding of student perspectives. A weighted naïve Bayes algorithm is then applied to come up with an overall evaluation score. Overall, this approach ensures the final assessment reflects not only what is said but also how it is expressed emotionally.
Chen, X. (2025) ‘Objective evaluation of English teaching in colleges and universities based on textual analysis in multi-element perspective’, Int. J. Reasoning-based Intelligent Systems, Vol. 17, No. 8, pp.11–20.
The following International Journal of Information and Communication Technology article, "English text classification model based on graph neural networks and contrastive learning", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "A machine classification learning model based on factor space mathematical theory in higher vocational education", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Optimisation of gradient descent algorithm and resource scheduling in big data environment", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Automatic summarisation of digital media news based on the transformer model", is freely available for download as an open access article.
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The Swedish mining industry, previously associated with a relative high accident rate, has seen a notable shift in its approach to safety in recent years. The lessons it has learned may help other high-risk sectors around the world improve their safety record. Research in the International Journal of Mining and Mineral Engineering has used internal records from six Swedish mining companies to help develop a new framework for evaluating safety performance. The new approach prioritizes everyday safety practices over retrospective accident statistics.
Traditionally, safety performance in mining has been assessed reactively, by counting incidents and injuries. While such metrics are clear and easy to track, they provide a limited view. They tell us what went wrong, but not necessarily what was done to prevent it. The study argues that this reactive model overlooks crucial indicators of risk and resilience, particularly in complex environments where threats may be hidden until a failure occurs. A proactive model is needed.
The proposed framework focuses on the organisational and human systems that shape safety on a daily basis. These include how companies structure safety responsibilities, how they educate their workers and managers, and how they engage employees in identifying and addressing risks. By examining all of these elements, the study suggests, organisations might gain a much clearer picture of their safety readiness, before accidents happen.
Safety management structures and leadership roles are not just bureaucratic arrangements, the researchers assert, they are active mechanisms that influence how risks are perceived and managed on the ground. Effective safety leadership requires clear communication channels, defined responsibilities, and a culture of trust, where employees feel empowered to speak up about unsafe conditions or procedural flaws.
One of the study’s central insights is that safety is not purely a technical problem. Rather, it is deeply social. The ability of a company to adapt to new risks, whether posed by emerging technologies or changing work patterns, depends heavily on its internal culture. Do employees feel they have a voice? Is safety treated as a shared responsibility? Are there mechanisms in place to learn from near-misses or unexpected disruptions?
This emphasis on the “social fabric” of safety represents a significant departure from older models that treat safety management as little more than a checklist or compliance exercise. The researchers point out that while social factors are harder to quantify than, whether everyone is wearing their hard-hat and safety boots, they are no less critical. In fact, they are often what determine whether technical measures are followed, or whether they can be adapted quickly under changing conditions.
The study also highlights a broader challenge facing industries that rely on complex technical systems: the detection of latent risks—problems that may not surface until multiple small failures interact in unexpected ways. As mining operations become more automated and digitised, traditional safety metrics struggle to capture these hidden vulnerabilities. In this context, the researchers suggest that companies need more adaptive tools that can account for the dynamic nature of modern work environments.
Nygren, M. and Sundström, E. (2025) ‘A framework for safety performance indicators: the case of the Swedish mining industry’, Int. J. Mining and Mineral Engineering, Vol. 16, No. 5, pp.1–17.
The following International Journal of Information and Communication Technology article, "Evaluation of marketing effectiveness based on fuzzy comprehensive evaluation method in the perspective of generalised virtual 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 Information and Communication Technology article, "Evaluation of teaching quality in database courses based on domain-adaptive transfer 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 Information and Communication Technology article, "AI-driven framework for enhancing vocational college teacher development: a natural language processing approach to professional growth and skill enhancement", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Using deep learning algorithms to identify diverse types of art designs", is freely available for download as an open access article.
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Research in the International Journal of Accounting, Auditing and Performance Evaluation raises important questions about how auditors communicate complex professional judgements. A team at Shandong University of Finance and Economics in China found that subtle differences in disclosure style can significantly influence perceptions of auditors’ diligence and competence.
This study follows recent reforms in international auditing standards: the mandatory inclusion of Critical Audit Matters (CAMs) in audit reports. CAMs are meant to highlight areas of greatest complexity or judgement during an audit, issues requiring scrutiny or posing reconciliation challenges. However, while auditors must describe these matters, current standards don’t require them to offer a conclusive evaluation or opinion within CAMs. That decision is left to the auditor’s discretion.
Drawing on psychological theory and professional norms, the research explored whether including a conclusive evaluation in CAMs affects how others perceive an auditor’s professional care, a core principle involving diligence, prudence, and competence.
Through a controlled experiment with auditors at different career stages, the researchers observed a consistent pattern: CAMs that avoided definitive conclusions were viewed as demonstrating higher professional care. Conversely, including conclusive evaluations, such as stating a complex valuation was sound, was seen, especially by experienced auditors, as a lapse in cautious professionalism.
The explanation lies in social norms theory, which holds that individuals are judged not just on actions, but on how well those actions align with expectations. In auditing, restraint and prudence are valued over assertiveness, particularly amid ambiguity. This preference for discretion is especially strong in China, where indirect expression is a cultural norm.
Experienced auditors appeared particularly attuned to these norms. Their scepticism toward definitive CAMs suggests that professional maturity brings heightened awareness of how even slight deviations from tradition can affect credibility. Novice auditors, less familiar with these unwritten rules, showed milder reactions.
The findings have global implications. They reveal tension between formal standards and informal expectations. While conclusive CAMs are technically allowed, the profession may view them unfavourably. Regulators may need to clarify when such assertiveness is appropriate or best avoided.
For companies, the study highlights the need for technically skilled auditors who also grasp the communicative nuances of their role. As audit quality faces growing scrutiny, how findings are framed may matter as much as the findings themselves.
Han, D. and Guan, Y. (2025) ‘Silence is golden? Disclosure of critical audit matters and auditors’ perception of due professional care’, Int. J. Accounting, Auditing and Performance Evaluation, Vol. 21, No. 5, pp.1–17.
The following International Journal of Information and Communication Technology article, "Enhancing English teaching effectiveness in vocational colleges: a data-driven approach using machine learning and adaptive learning models", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Assessing chorus size effects on art music performance quality using hesitant bipolar fuzzy multi-criteria decision-making", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Artificial intelligence-based automatic identification and classification of diverse sports using advanced deep learning models", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "A review of AI-driven art education: enhancing creativity through deep learning and digital image processing", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Using artificial intelligence based models for strategy design of rural landscape", is freely available for download as an open access article.
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A new approach to safety and reliability of intelligent vehicles tackles the challenges caused by actuator failures, sensor anomalies, and unpredictable driving conditions. The work builds on improved fault tolerance, the ability of a system to maintain proper function despite component faults. Details are discussed in the International Journal of Reasoning-based Intelligent Systems.
Conventional fault-tolerant methods typically rely on static or pre-trained models that do not adjust quickly to sudden failures or rapidly changing environments. To address this, the new research develops a control framework that integrates Just-In-Time Learning (JITL) and Heuristic Dynamic Programming (HDP).
JITL allows the system to create and update lightweight, localized models in real time using continuous data from vehicle sensors such as speed, steering angle, and environmental inputs. This enables rapid fault detection and classification by identifying unusual data patterns indicative of malfunctions.
The HDP system employs a dual-layer optimization structure with evaluation and execution networks. This design adjusts the vehicle’s control strategy dynamically to compensate for faults and disturbances such as strong crosswinds or uneven road surfaces. Unlike traditional control methods that focus primarily on resisting faults, the HDP controller balances resilience with responsiveness, leading to smoother and more accurate vehicle trajectory tracking.
In their tests using the widely recognized Next Generation Simulation (NGSIM) traffic dataset, the researchers demonstrated notable improvements on previous systems. They were able to reduce lateral tracking errors and heading deviations by 30 to over 50 percent. They were also able to improve control smoothness by a third. The system maintained real-time responsiveness with minimal processing delays, demonstrating its practical viability.
Beyond technical performance, the paper also highlights some of the ethical considerations necessary for autonomous vehicle deployment. Transparency, ensuring automated decisions align with human logic and traffic laws, and accountability, clearly defining the roles of human drivers and automated systems and how they interact. Only by emphasizing such matters can public trust be built, and such vehicles meet regulatory standards.
Sun, Z. (2025) ‘Fault-tolerant control of intelligent transportation vehicles based on instant learning and heuristic dynamic planning’, Int. J. Reasoning-based Intelligent Systems, Vol. 17, No. 8, pp.21–28.
Research in the International Journal of Reasoning-based Intelligent Systems has shown how artificial intelligence can be used to make personal recommendations on stopping-off places when one is travelling. The approach could optimise the route one takes and so reduce travel times and fuel consumption if travelling by road, for instance, while at the same time offering surprising and interesting places one might visit that reflect one’s individual preferences and practical constraints. The system moves away from conventional travel planning, which typically suggests only the most popular destinations and largely ignores user needs or schedules.
At the centre of the research is a novel method for identifying and ranking points of interest, a term that encompasses landmarks, museums, scenic areas, and other tourist sites. While popularity remains a factor in the algorithm, the new method also accounts for how long visitors usually spend at a location and how these visits fit into a tourist’s available travel time. The system can thus generate a travel itinerary that is not only appealing but achievable.
The researchers introduce a “point-of-interest correlation diagram”. This tool maps how tourists interact with different attractions over time based on geotagging data from a well-known photo-sharing website. It spots patterns in preferences that go beyond surface-level interest. Then, the algorithm, based on principles from content-based recommendation systems, offers the user potentially the most interesting destinations that will fit their schedule.
In tests, tourist satisfaction with the suggested routes scored well over 90 percent. As mobile apps increasingly replace traditional guidebooks and group tours, travellers are always on the look out for tools that can recommend new places to visit but can also adapt to their behaviour. The present system could help make better use of limited vacation time, improve the quality of experiences, and even ease the pressure on heavily trafficked tourist sites by encouraging exploration of under-visited areas.
Ge, D., Wu, Q. and Lai, Z. (2025) ‘Study on intelligent travel route recommendation method based on popularity of interest points’, Int. J. Reasoning-based Intelligent Systems, Vol. 17, No. 2, pp.83–89.
The following International Journal of Information and Communication Technology article, "Dynamic path transformer network for regional economic forecasting and resource allocation", is freely available for download as an open access article.
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The following International Journal of Information and Communication Technology article, "Early warning system of cigarette process quality combined with intelligent sensing technology", is freely available for download as an open access article.
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The following International Journal of Economics and Business Research article, "Development of a financial capability model: a hybrid dual-stage partial least square structural equation modelling and artificial neural network analysis", is freely available for download as an open access article.
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Millions of us use social media and online messaging every day. It’s convenient but as with every tool, there are risks, such as the threat of crime such as spam and scam messages and phishing attacks that have the potential to cost the victims dearly. Malicious messages disrupt the user experience and pose serious security threats by attempting to steal personal data, distribute malware, and compromise digital systems. Standard detection tools often lag behind the continually evolving tactics employed by attackers, leaving users vulnerable to increasingly sophisticated scams.
Research in the International Journal of Information and Computer Security, introduces a new approach that could improve the detection of spam on digital platforms. Central to the research is the use of a recurrent neural network, which can analyse text, and not only detect patterns but recognise those same patterns when it subsequently encounters them.
The approach also embeds a so-called soft attention mechanism, which enables the neural network to prioritize parts of a message that are most relevant to identifying spam or malicious intent. This mechanism mimics the way humans naturally focus on keywords or suspicious phrases when quickly scanning content.
In addition, the system also uses self-adaptive windowing to detect content drift, where the scammers and spammers continually change the type of language they use to try to avoid detection. The system can through windowing update its learning progressively as new types of spam and scam messages emerge. This avoids the need for complete and frequent reboots, instead the system undergoes on-the-job training. This adaptability, the researchers suggest, is important for maintaining detection accuracy over time.
Tests showed the model could achieve 99.3 percent accuracy, which surpasses traditional methods such as decision trees and even naïve Bayes classifiers. The number of false negatives and false positives was negligible even when the system was tested on realistic, imbalanced datasets, where spam is far less common than legitimate messages, to ensure that it would work well in real-world conditions.
Patil, S.M., Mhatre, S., Dakhare, B. and Chavan, G.T. (2025) ‘Prevention of cyber attacks and real-time social media spam detection and sentiment analysis using recurrent self-adaptive windowing approach’, Int. J. Information and Computer Security, Vol. 27, No. 2, pp.261–284.
The following International Journal of Economics and Business Research article, "Supplier selection strategies evaluation: a multi-agent based simulation", is freely available for download as an open access article.
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The following International Journal of Education Economics and Development article, "An analysis of how peer mentors at university adjusted their mentoring strategy to facilitate better mentoring practice during COVID-19 from 2019-2021", is freely available for download as an open access article.
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Creating emotional bonds between consumers and brands has long been a marketing goal. But what happens when that emotional bond turns negative? Research in the International Journal of Internet Marketing and Advertising explores this darker side of brand relationships: brand hate.
Unlike brand loyalty, which drives repeat purchases and glowing recommendations, brand hate can spark direct complaints and damaging word-of-mouth. To better understand this phenomenon, researchers surveyed almost 500 young consumers who expressed strong aversion to brands they had used. The goal was to identify what motivates negative reactions and how these are expressed, whether in person or online.
Brand hate, defined as an intense, emotionally charged dislike, isn’t as simple as it sounds. The study found two main motivations behind it: concern for others and the need to vent personal frustration. Each leads to very different kinds of behaviour.
The first, concern for others, is rooted in altruism. People who feel wronged by a brand often try to warn their friends and family, usually through offline conversations. These low-profile interactions may seem minor, but they can quietly damage a brand’s reputation within close-knit communities, where trust matters a lot.
The second motivation, venting, is more self-focused and plays out online. Angry customers take to social media or review platforms not just to complain, but to release frustration, seek validation, or provoke a reaction. The internet’s reach and speed make it an ideal outlet for these emotionally charged responses. A single angry post can go viral, rallying others with similar experiences and potentially catching the attention of media or competitors.
Both forms of brand hate can hurt businesses, but online negativity presents a particularly fast-moving threat. Public posts are visible, easily shared, and often permanent. Brands can’t afford to ignore them. To manage this, researchers recommend agile digital customer service teams trained not just in quick problem-solving, but also in emotional intelligence. Responding empathetically, not just with facts, can help de-escalate frustration. While brand love may not be the outcome, moving a hater to a place of neutrality could be a realistic and valuable goal.
Francioni, B., Curina, I., Hegner, S.M., Cioppi, M. and Savelli, E. (2025) ‘Brand hate and consumers’ responses: an analysis in the offline and online environment’, Int. J. Internet Marketing and Advertising, Vol. 22, No. 4, pp.460–483.
The following Journal of Business and Management article, "Authentic leadership, gender, and emotional labour: a study", is freely available for download as an open access article.
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The following Journal of Business and Management article, "The intersection of gender and generation: new insights into drivers of job satisfaction", is freely available for download as an open access article.
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Workplace bullying is being increasingly acknowledged as a serious problem associated with poor employee well-being and reduced organizational health. A study in the International Journal of Work Organisation and Emotion focusing on the issue in Brunei shows how cultural norms can redefine what counts as bullying. The researchers surveyed 160 employees across various sectors and analysed the responses, finding that there is a regionally specific understanding of workplace mistreatment.
In Brunei, employees commonly identify gossip, public insults, false accusations, and the denial of professional respect as bullying behaviour. However, overwork, commonly flagged as a form of abuse in many Western contexts, is rarely perceived as such. The researchers suggest this perception is different because of different attitudes to authority in a place where expectations of loyalty to superiors are culturally reinforced.
The researchers suggest that Brunei as a high power-distance society, a term used in social sciences to describe cultures where inequalities in power are accepted and rarely challenged. In such a working environment, behaviour that might otherwise be seen as unreasonable, such as excessive workloads or being asked to perform the duties of others, tends to be interpreted as a normal professional obligation. As a result, individuals subjected to this form of pressure often do not name it as bullying, let alone report it.
The study also complicates assumptions about who becomes a target. While conventional narratives often depict passive or underachieving employees as the most vulnerable, the Bruneian data suggests that even diligent, outgoing, or simply emotionally sensitive individuals are susceptible. Their perceived emotional openness or social visibility may, paradoxically, make them more noticeable, and thus more exposed, to hostile behaviour.
The problems this causes can become particular apparent when humour, serving as a social lubricant, is misused to create harmful dynamics. Jokes at the expense of newer employees may begin in jest, but often develop into persistent mockery or exclusion. When employees feel unable to object, especially when their boss is making the joke at their expense, the line between camaraderie and cruelty becomes dangerously blurred.
The researchers suggest that despite its prevalence, workplace bullying remains an unspoken issue in Brunei. Many victims choose not to report mistreatment, deterred by fears of retaliation or being labelled as troublemakers. This silence is compounded by an institutional absence of clear reporting mechanisms and the lack of legal protections specific to workplace harassment. In an increasingly globalised world, we need culturally informed approaches to organizational policy, so that problems like bullying can be eradicated in a culture-sensitive manner.
Zamree, F.M., Sumardi, W.A., Sumardi, W.H. and Anshari, M. (2025) ‘Workplace bullying: its causes, consequences and coping strategies’, Int. J. Work Organisation and Emotion, Vol. 16, No. 2, pp.111–143.
The following Journal of Business and Management article, "Engineering of a proactive stakeholder culture", is freely available for download as an open access article.
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The following Journal of Business and Management article, "Navigating the digital frontier: thriving in remote work through AI and human connection", is freely available for download as an open access article.
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The following International Journal of Automotive Technology and Management paper, "Between geoeconomic competition and local embeddedness - how Chinese investors influence digitalisation in acquired German manufacturing companies", is freely available for download as an open access article.
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Received wisdom suggests that business closure equates to business failure. However, research in the International Journal of Entrepreneurial Venturing has challenged this assumption, suggesting that entrepreneurial exit is frequently a deliberate, strategic decision, particularly among those with the greatest resources at their disposal.
The researchers drew on data from more than 28000 individuals across 48 countries via the Global Entrepreneurship Monitor. Their work shifts the focus from the beginnings and growth of startups to their ends and attempts to answer a question that is rarely addressed in business research: why do entrepreneurs choose to exit their businesses?
Fundamentally, rather than being a marker of defeat or failure, the researchers found that exit often represents a rational choice, especially for individuals endowed with high levels of entrepreneurial capital. This term refers to three broad categories of resources: human capital (an entrepreneur’s skills and experience), social capital (their professional networks and relationships), and financial capital (access to funding and liquidity). Indeed, those with greater resources were often more likely to exit their ventures than their less well-equipped peers. One might assume that access to greater resources would bolster the survival chances of a business, but the team’s findings suggest that having more can also mean having more to gain elsewhere.
This idea is associated with opportunity recognition, the ability of an entrepreneur to spot viable or promising new ventures in the wider market. The study found that entrepreneurs with both financial capital and high levels of opportunity recognition are more likely to shut down a given startup and move on to new ventures – we often talk of serial entrepreneurs, after all. The logic is clear: if one sees a better opportunity on the horizon and has the means to pursue it, holding on to a current, less promising venture may represent a suboptimal use of resources.
Bogatyreva, K. and Laskovaia, A. (2025) ‘Should I stay or leave? The role of human, financial and social capital in entrepreneurial exit’, Int. J. Entrepreneurial Venturing, Vol. 17, No. 1, pp.73–87.
