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.
DOI: 10.1504/IJRIS.2025.147145

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.
DOI: 10.1504/IJRIS.2025.146930

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.
DOI: 10.1504/IJICS.2025.146881

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.
DOI: 10.1504/IJIMA.2025.146488

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.
DOI: 10.1504/IJWOE.2025.146964

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.
DOI: 10.1504/IJEV.2025.146890

Professional basketball is faster, more physical, and increasingly tactically sophisticated. Even the smallest foul can tilt the balance of a game. Now, research in the International Journal of Computational Systems Engineering shows how artificial intelligence, specifically machine vision, could bring unprecedented precision to one of the sport's most contested areas: the detection of fouls.

Machine vision is a form of AI that enables computers to interpret and analyse visual data. In this study, the researchers applied it to high-level basketball footage, with a specific focus on identifying and categorizing fouls in real time. The technology processes video frame-by-frame, isolating subtle movements, contact points, and spatial dynamics that often escape the naked eye, even that of an experienced referee.

The research centred on analysing games involving the Chinese national basketball team and their international opponents. The study found notable differences in foul patterns between the two sides. Chinese players, for instance, committed more fouls during opponents' shooting attempts, known as shooting-related defensive fouls. In contrast, their opponents were more likely to foul during dribbling plays.

The AI system also tracked subcategories of fouls, such as illegal use of hands or player collisions, revealing behavioural trends that go far beyond standard box-score statistics.

This new level of granularity possible with machine vision will have significant implications for the future of the game if it adopted as a standard. Conventionally, spotting fouls is down to human observation, referees, coaches, and analysts. Each of these characters is subject to the limits of perception, fatigue, and, inevitably, bias. Machine vision, by contrast, offers a consistent, objective view. It sees not just the contact, but the context: where on the court it occurred, at what angle, and how both players were moving in the moments before the foul.

For coaching purposes, this kind of data could be used to diagnose weaknesses or imbalances in a team's defensive or offensive behaviour. A pattern of specific fouls, if spotted early, might point to issues with footwork, positioning, or reaction time. These areas might all be addressed through tailored training. For players, especially those navigating the razor-thin line between aggressive and reckless play, the technology offers feedback with a clarity that a simple post-match video review cannot provide.

Perhaps most importantly, the research could reshape how referees are trained. Accurate, data-driven feedback on decision-making in live situations could improve both consistency and fairness. These are commonly two of the most persistent challenges in officiating high-speed sports.

Machine vision is unlikely to completely replace the human referee, but it may soon become an indispensable tool in their development, helping to reduce errors and improve the flow of the professional game.

Jia, X. and Diao, C. (2025) 'Feature extraction of basketball player's foul action using machine vision', Int. J. Computational Systems Engineering, Vol. 9, No. 10, pp.20–30.
DOI: 10.1504/IJCSYSE.2025.146812

A new data-driven model could improve the accuracy of residential electricity demand forecasting on a daily, or even hourly basis. The model, described in the International Journal of Energy Technology and Policy, might help utility companies better manage power grids as they become more reliant on variable renewable energy sources.

Shan Gao, Xinran Zhang, Lihong Gao, and Yancong Zhou of Tianjin University of Commerce, China, suggest that their research has addressed one of the main problems in the transition from conventional to sustainable electricity production supplying diverse residential settings.

Renewable electricity sources, such as solar and wind, are inherently intermittent. They do not produce a steady electricity supply throughout the year and moreover can drop to zero supply at any time during the day depending on weather conditions. And, of course, solar only works during daylight hours. Such variability makes it difficult for electricity grid operators to ensure that supply meets demand at all times. Accurate forecasting of electricity usage, particularly in residential settings where patterns are highly individual and sensitive to social routines, is now seen as essential for the stability and efficiency of so-called smart grids.

The researchers have developed a hybrid model that is less error-prone than other models. The new approach combines a Convolutional Neural network, which is particularly effective at identifying short-term patterns in datasets, with a Long Short-Term Memory network, a type of recurrent neural network well-suited to tracking longer-term dependencies in time series data.

Such hybrid models have been used successfully in other settings previously, but the team has also incorporated an attention mechanism into theirs. This is a tool borrowed from natural language processing that allows the system to prioritize the most relevant parts of its input data when making predictions. This dynamic filtering process improves the model's ability to respond to variations in household behaviour or external conditions. In addition, in order to account for systematic fluctuations in consumption, the model uses seasonal decomposition, a statistical technique that isolates regular seasonal trends, such as increased winter heating demand or reduced usage during the summer, from the overall dataset.

All of these tools combined allow the new model to recognize and adapt to subtle patterns in electricity demand. For example, it can distinguish between weekday and weekend routines, anticipating delayed morning energy use on Saturdays and Sundays, or adjusting for increased evening usage during colder months. The team has thus demonstrated a mean absolute percentage error as low as 0.76% for daily predictions and just 2.36% for hourly ones. These figures improve on existing models, the team suggests.

Gao, S., Zhang, X., Gao, L. and Zhou, Y. (2025) 'A novel residential electricity load prediction algorithm based on hybrid seasonal decomposition and deep learning models', Int. J. Energy Technology and Policy, Vol. 20, No. 5, pp.1–23.
DOI: 10.1504/IJETP.2025.146888

In the world of high finance, companies are hedging their bets on Artificial Intelligence (AI). Indeed, AI has already been used in algorithmic trading and fraud detection. As the technology grows more powerful and autonomous, so there is an urgent need to put in place checks and balances to ensure ethical and valid use of these tools. Research in the International Journal of Business Information Systems now offers a structured response to the various challenges and proposes a practical framework to help financial institutions implement ethical AI systems grounded in transparency, interpretability, and accountability.

The researchers explain that "explicability" is key to the development and use of AI ethically in finance. Unfortunately, the term itself lacks a clear operational definition. It can be described as encompassing three interrelated dimensions: transparency (the ability to see how a decision was reached), interpretability (the capacity to understand that decision), and accountability (clarity over who is responsible). These ideas are particularly crucial in high-stakes domains such as lending and insurance, where algorithmic decisions can directly affect people's lives as well as company profits.

There are already examples of where opaque AI systems have reinforced existing inequalities so that credit-scoring models and insurance-pricing algorithms, trained on historical data, can disadvantage women and minority groups. This is not necessarily happening deliberately, but simply through the inherent biases in the training data. Whether intentional or not, the outcomes can still be damaging and also become harder to correct once automated systems are embedded in a company's systems.

The research in IJBIS, draws from an interdisciplinary research and expert interviews, introduces to develop a "maturity framework" that is designed to make explicability actionable. Rather than treating ethics as a checkbox exercise or a set of abstract ideals, the framework outlines incremental steps that organisations can, and perhaps should, take, based on their technological sophistication and the complexity of the AI models they employ.

The framework benefits from inherent adaptability, acknowledging from the start that a "one-size-fits-all" solution is neither realistic nor desirable. Instead, it offers a pathway tailored to different institutional contexts, encouraging continuous improvement over time. Among its recommended practices are adopting interpretable AI models, the inner workings of which can be readily understood by humans, creating internal ethics committees, and conducting regular audits for bias and fairness.

Solaimani, S. and Long, P. (2025) 'Beyond the black box: operationalising explicability in artificial intelligence for financial institutions', Int. J. Business Information Systems, Vol. 49, No. 5, pp.1–38.
DOI: 10.1504/IJBIS.2025.146837

Generative artificial intelligence is changing education. The tabloids would have it that students are using it to fulfil their assignments and teachers are using it to grade the papers. It sounds like a serious problem where no one is learning because nobody is teaching. But, the landscape is much more nuanced than that and these new tools could offer us a new model of teaching and learning, according to research in the International Journal of Information and Communication Technology.

The dystopian perspective is of students using generative AI to write their essays, create their artwork, and even carry out lab simulations. The teachers then simply feed this output to another AI to obtain an evaluation,. However, the reality is that generative AIs can offer highly personalized, responsive learning experiences that side-step the one-size-fits-all approach of conventional lessons. And, provided that students, and more importantly their teachers, are made aware of the limitations and the inherent biases of generative AI tools, there is the potential to reduce scholarly administration and allow teachers to engage with their students more directly as well as opening up new ways to learn.

Indeed, generative AI is not simply about faking an assignment, it can be used to produce new educational content, such as custom learning materials, which can be manually validated by the teacher. Such systems can respond dynamically to a learner's progress, interests, and comprehension, tailoring the next part of their instruction in ways previously impractical at scale.

Many language learners are familiar with teaching tools based on conversational agents that adjust to the user's pace and to their specific learning style and any prior knowledge of the language being learned. Feedback is immediate, context-sensitive, and targeted.

The research highlights the need for continued study into how generative AI can best serve as a tool for educational empowerment. As the technology advances, striking the right balance between innovation and human-centred teaching is needed to ensure that the future of education remains equitable, ethical, and genuinely transformative.

Wang, G. and Sun, F. (2025) 'A review of generative AI in digital education: transforming learning, teaching, and assessment', Int. J. Information and Communication Technology, Vol. 26, No. 19, pp.102–127.
DOI: 10.1504/IJICT.2025.146701