This work explores the latest opportunities that machine learning opens up for creating interfaces capable of adapting to users' needs in real-time. Modern machine learning technologies enable developers to create interfaces that respond to user behavior, emotions, gestures, and voice commands. This creates a new level of interactivity for the user interface and an engaging on-site experience, enhancing convenience and effectiveness of interactions.

The article begins with an overview of the basic concepts of machine learning and its role in interface design. It highlights how new algorithms allow developers to train systems based on collected data, making it possible to create intuitive and adaptive UIs. In particular, tools such as Google’s Teachable Machine are discussed, which allow for the quick creation of models to recognize various input data (images, sounds, gestures) without the need for deep programming knowledge.

The article raises the issue of adaptive interfaces' ability to recognize users' emotions. Today, the use of computer vision and natural language processing technologies enables systems to analyze facial expressions and voice. These new capabilities revolutionize the understanding of a classical website, which can operate on fast interactions. Another approach is that in educational platforms, adaptive interfaces can adjust the presentation of material based on the learner's emotional state, significantly increasing the effectiveness of learning. This provides a more engaging experience in a new format of adaptation and interaction. Such adaptive systems can offer personalized content, taking into account users' preferences, making the interaction more individual and meaningful.

The work also examines the advantages of adaptive interfaces for people with disabilities. For example, gesture-responsive interfaces can provide accessibility for users who cannot utilize traditional control methods. This underscores the importance of inclusivity in design, as technologies can help overcome barriers to accessing information and services.

Alongside the powerful possibilities, there are challenges associated with implementing machine learning in adaptive interfaces. Among the main challenges are issues of ethics, data privacy, and model accuracy. Today, there is a need for further research in this area and the development of standards to ensure the safe and effective use of machine learning in adaptive interfaces.

This paper demonstrates the contribution of machine learning algorithms in real-life problem resolutions, taking the example of fraudulent job postings detection. Therefore, the Support Vector Machine (SVM) is associated with a dataset that includes both real job postings and those considered to be fraudulent. Before using this dataset, the content was submitted to preprocessing techniques. Moreover, data mining techniques were relevant to extract essential features from the dataset. The disparity between fraudulent and non-fraudulent classes is addressed by applying the SVM-Synthetic Minority Over-Sampling Technique (SVM-SMOTE) method, considering that synthetic samples from the minority class (fraudulent) are generated to improve the model performance. Also, the model was trained and tuned on processed data to achieve high performance. Additionally, the performance of the model was examined following key factors including accuracy, precision, recall, and F1-score, respectively. The correct or incorrect predictions obtained from the model are exposed by using the confusion matrix.

This paper presents a project (work in progress) where entrepreneurship and higher education in AI (from Master level to postdoc level) are integrated in order to produce a dual effect; helping SMEs to gain insight in how AI can aid in the corporate environment and to expose AI researchers to the real-life situations in the company world. If successful, the companies are made ready for the AI revolution and the researchers more equipped for corporate settings. The project is ongoing, so this paper addresses a work-in-progress project. The paper reflects on the project as well on some aspects that need to be highlighted due to earlier research in the area. There are also some in-sights from the first case study that was presented in December 2024, with 7 more to come during 2025. Finally, some conclusions are drawn and some further research directions is pointed out. The project was funded by Liedon Säästöpankki säätiö and the support is gratefully acknowledged.

Blockchain technology is creating rapid transformations for large-scale fu-ture economies across multiple sectors of finance, health and supply chains. By 2025, the World Economic Forum projects that 10% of the global GDP will be generated by or based on blockchain technology: the demand for an adequately skilled workforce is thus critical. When vocational education and training (VET) programs operate below par or fail to meet the modern-day requirements posited by the blockchain industry, the BCH4VET project should address the gap through the development of an innovative modular curriculum on blockchain technology. This project is set to give three key outputs: A VET framework tailored to blockchain sciences, an AI powered assessment platform and, a gamified e-learning platform. These tools are meant to promote professional development, prepare students for new de-mands in the job market and foster inclusivity through assistance to many underrepresented groups. In these innovations, BCH4VET provides a frame-work for the sustainable incorporation of the emerging technologies into ed-ucation, empowering learners and educators to survive in a digitally oriented world.

In any product development cycle, costs can soar when a project takes longer than anticipated. Because accurately estimating a project’s completion date is not easy. Even in Agile Scrum, where the project is planned and run in short iterations, the risk remains at large. Machine learning can play an essential role in planning and estimating the project schedule to estimate user story efforts. This paper is an effort in that direction, where the effectiveness of Extreme Learning Machines(ELM) in the domain of predicting the effort estimate of user stories (multi-class text classification domain) is studied and compared with some existing techniques like SVM (Support Vector Machine) and LR(Logistic Regression). In this paper, the focus is to highlight the performance of ELM in the field of multi-class text classification, results from other models are studied and analyzed. Some common techniques are studied to improve the accuracy of models, like feature selection and parameter tuning.