The following nternational Journal of Diplomacy and Economy article, "Analysis of economic diplomacy between Kazakhstan and Italy: development trends and prospect", 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 Internet Technology and Secured Transactions article, "Enhancing IoT security in the post-quantum era: a hybrid approach to protection from impersonation attacks", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
A study of more than 800 fast-food customers suggests that loyalty to a brand is shaped not only by satisfaction with the product or service but also by the customers’ emotional experiences. The study reported in the International Journal of Internet Marketing and Advertising focused on fast-food consumers in Peru but could have implications for fast-food brands in other emerging markets.
The study was based on responses collected through an online questionnaire and analysed with a statistical technique known as structural equation modelling, which tests the relationships between multiple variables. The researchers found that perceived service quality was perhaps quite obviously associated with the degree of satisfaction with a brand. However, satisfaction, in turn, was linked to the customer’s happiness and their level of brand love and thus to brand loyalty. The research distinguishes between customer satisfaction, which considers whether expectations have been met, and happiness, which reflects a broader positive emotional response to the customer’s experience with the brand.
The findings add to a relatively small body of work that has examined happiness and brand love together and has implications for this particular emerging market where cultural factors may influence how customers assess services and develop relationships with brands.
For fast-food businesses, the results suggest that improving service alone may be insufficient. Managers must also focus on experiences that generate positive emotions, potentially strengthening customers’ attachment to a brand and their willingness to remain loyal.
Pipoli de Azambuja, G.M., Rodríguez Peña, G.A. and Tarazona Vargas, E.G. (2026) ‘The emotional connection between customer happiness and brand love on customer loyalty’, Int. J. Internet Marketing and Advertising, Vol. 25, No. 2, pp.123–148.
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A lightweight artificial-intelligence (AI) system designed to assess rehabilitation exercises in real time could make computer-assisted therapy more practical on low-powered devices, according to research in the International Journal of Business Intelligence and Data Mining.
The researchers developed RMPE Tiny, a human-pose estimation network tailored to rehabilitation. Pose estimation is a computer-vision technique that identifies key points on the body, such as the shoulders, knees and ankles, from images or video. These points can then be used to measure movement, including range, symmetry, and coordination.
The challenge for such systems is that precise pose estimation usually needs a lot of computing power; this limits them to specialist equipment rather than allowing them to be used on tablets or simple, embedded monitoring systems. RMPE Tiny addresses this through several modifications intended to reduce computational demands without substantially compromising accuracy.
The system uses laser triangulation to improve image acquisition and map 3D coordinates onto a 2D image. In tests, it achieved more than 96 per cent overall pose-estimation accuracy, with most samples approaching or exceeding 98 per cent. The system might ultimately be used to support rehabilitation systems that provide immediate feedback outside clinical settings.
Yang, Q. and Zhang, G. (2026) ‘Design of lightweight human pose estimation network for rehabilitation training’, Int. J. Business Intelligence and Data Mining, Vol. 28, No. 10, pp.85–102.
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Drones have many uses in surveillance and surveying, industrial inspection, aerial photography and filmmaking, package delivery, even dynamic light-shows. Their fundamental ability to carry cameras, sensors and other equipment opened up these diverse applications. Now, research in the International Journal of Intelligent Machines and Robotics has explored another: the safe removal of moss and lichen from sloping roofs, an otherwise hazardous job for a worker.
The team used an Arduino-based autopilot system for a quadcopter-type drone capable of operating over horizontal or sloping roofs. It follows a programmed zig-zag, or “staircase”, trajectory designed to cover the roof systematically while avoiding obstacles.
Arduino is a low-cost, programmable electronics platform commonly used to control robots. The proposed system combines it with motors, batteries, electronic speed controllers (ESCs), which regulate motor power, and a flight controller that manages the aircraft’s movement and stability. The researchers point out that this seemingly mundane demonstration opens up possibilities for such machines to be used in other applications that otherwise involve risk to workers.
The prototype remains a development platform. Future work identified by the researchers includes improved obstacle handling and machine learning to better identify patterns from video input to allow the drone to adapt to unexpected situations.
Sanyal, S. (2026) ‘Intelligent autopilot drone robot for de-mossing ramped roofs’, Int. J. Intelligent Machines and Robotics, Vol. 2, No. 1, pp.22–44.
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A machine-learning system can identify fake, or “spoofed”, website addresses, according to research in the International Journal of Electronic Security and Digital Forensics. The work offers a new tool to protect users against phishing attacks, where attackers direct someone to a deceptive website to steal information such as bank logins or personal and private data.
The team combined two deep-learning techniques: a convolutional neural network (CNN), which can identify patterns in data, and a long short-term memory (LSTM) network, which can retain information about sequences in that data. This hybrid CNN-LSTM model achieved accuracy rates of almost 99 per cent on the UCL standard test dataset and almost 97 per cent on the PhishTank dataset.
The findings highlight the potential for automated AI systems in protecting people using online commerce, government services, and other activities. Phishing attacks remain particularly difficult to address as a cybercrime problem because they often simply exploit human behaviour and weaknesses rather than being a technical loophole. If a message is sufficiently convincing and a website realistic, then vulnerable or even simply distracted users might disclose sensitive information without realising they have been duped.
A system to accurately flag spoofed addresses before that happens would be invaluable. For the small percentage that it false-flags, the inconvenience would be negligible compared to what is not lost by blocking spoofed sites.
Santhosh Krishna, B.V., Vidhya, S., Krishnaveni, S. and Ashokkumar, N. (2026) ‘A hybrid deep learning method for URL spoofing in websites’, Int. J. Electronic Security and Digital Forensics, Vol. 18, No. 5, pp.524–537.
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The following International Journal of Entrepreneurship and Small Business article, "Informal entrepreneurship and entrepreneurial ecosystems: a systematic literature review and future research agenda", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
A new approach to modelling vocal fold fatigue could improve detection and monitoring of problems as they arise, particularly among people whose jobs depend on sustained voice use. The research, published in the International Journal of Information and Communication Technology, uses a physics-based framework for tracking changes over time.
The vocal folds, colloquially referred to as vocal cords, are the vibrating muscular structures within the larynx at the top of the trachea that allow us to speak, sing, and make various sounds. Vocal fold fatigue involves declining vocal efficiency, poorer voice quality and greater effort when speaking and can arise in anyone at any time but is common among public speakers, teachers, call centre operators, singers, and others who use their voice more than average or at higher volume.
The new approach, called physics-informed joint simulation and identification (PJSF), treats fatigue as a continuously changing process rather than a fixed condition. It models the depletion and recovery of the vocal system using equations that describe how a system changes over time and combines these with acoustic recordings and laryngovibrography, measurements of vocal-fold vibration to give a diagnosis or predict issues that might arise.
In tests on 20 volunteers, the system was shown to balance between correctly identifying fatigue and avoiding false alarms. The work could lead to a system capable of monitoring vocal strain and supporting personalised voice-use guidance for users whose profession involves constant use of the voice.
Zhang, X., Sun, R., Zhao, C. and Zhao, Y. (2026) ‘Physics-informed modelling of vocal fold fatigue process using acoustic and laryngovibrography data’, Int. J. Information and Communication Technology, Vol. 27, No. 97, pp.89–118.
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A new deep-learning architecture could deflect cyberattacks by combining several methods for analysing network traffic, according to research in the International Journal of Business Intelligence and Data Mining. The approach addresses a weakness in standard intrusion-detection systems, which struggle with the volume and complexity of modern network traffic.
The researchers combined a deep neural network (DNN), which identifies complex patterns in large sets of features, with a bidirectional gated recurrent unit (BiGRU), a type of neural network designed to identify relationships across sequences in both directions. Both components use an attention mechanism, which allows the system to concentrate on the features and points in a sequence that are most relevant to anomaly detection. The outputs are brought together and classified using a multilayer perceptron (MLP), another neural-network architecture used to assign data to categories.
Tests on two well-known benchmark datasets showed that the system could achieve validation accuracies of around 99 per cent. The researchers explain that the system is stable in training and has comparatively strong robustness and generalisation, which means it should be effective on data beyond its training data.
The results suggest that combining static-feature analysis with temporal pattern recognition can harden cybersecurity systems against denial-of-service attacks, network probing, and attempts to gain unauthorised access.
Yan, H., Liu, H., Yu, P., Xu, X., Li, M., Long, Y., Chen, H., Wang, Q. and Long, D. (2026) ‘DNN and BiGRU-based hierarchical attention network for intrusion detection’, Int. J. Business Intelligence and Data Mining, Vol. 28, No. 10, pp.1–24.
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