This Editor’s Note introduces the December issue of the International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), which gathers contributions applying AI to high-impact domains such as spatio-temporal prediction, education, cultural heritage, medical imaging and intelligent communication networks. The articles cover techniques ranging from clustering, deep learning and multimodal models to attention mechanisms, self-supervised learning and blockchain-based security.

Since Computer Science students are used to applying both General Purpose Programming Languages (GPPLs) and Domain-Specific Languages (DSLs), Generative Artificial Intelligence based on Language Models (LMs) can help them on automatic tasks, allowing them to focus on more creative tasks and higher skills. However, the teaching and evaluation of technical tasks in Computer Science can be inefficient and prone to errors. Thus, the main objective of this article is to explore the performance of LMs compared to that of undergraduate Computer Science students in a specific case study: designing and implementing RESTful APIs DSLs. This research aims to determine if LMs can enhance the efficiency and accuracy of these processes. Our case study involved 39 students and 5 different LMs that must use the two DSLs we also designed for their task assignment. To evaluate performance, we applied uniform criteria to student and LMs-generated solutions, enabling a comparative analysis of accuracy and effectiveness. With a case study comparing performance between students and LMs, this article contributes to checking to what extent LMs are able to carry out software development tasks involving the use of new DSLs specially designed for highly specific settings in a similar way as well-qualified Computer Science students are able to. The results underscore the importance of welldefined DSLs and effective prompting processes for optimal LM performance. Specifically, LMs demonstrated high variability in task execution, with two GPT-based LMs achieving similar grades to those scored by the best of the students for every task, obtaining 0.78 and 0.92 on a normalized scale [0, 1], with 0.23 and 0.14 Standard Deviation for ChatGPT-4 and ChatGPT-4o respectively. After the experience, we can conclude that a well-defined DSL and a proper prompting process, providing the LM with metadata, persistent prompts, and a good knowledge base, are crucial for good LM performance. When LMs receive the right prompts, both large and small LMs can achieve excellent results depending on the task.

This paper reports steps in probing the artistic methods of figurative painters through computational algorithms. We explore a comparative method that investigates the relation between the source of a painting, typically a photograph or an earlier painting, and the painting itself. A first crucial step in this process is to find the source and to crop, standardize and align it to the painting so that a comparison becomes possible. The next step is to apply different low-level algorithms to construct difference maps for color, edges, texture, brightness, etc. From this basis, various subsequent operations become possible to detect and compare features of the image, such as facial action units and the emotions they signify. This paper demonstrates a pipeline we have built and tested using paintings by a renowned contemporary painter Luc Tuymans. We focus in this paper particularly on the alignment process, on edge difference maps, and on the utility of the comparative method for bringing out the semantic significance of a painting.

The proliferation of information disorder in the digital age has sparked a growing concern regarding the credibility of sources disseminating information. This review examines the evolving landscape of source credibility within information disorder. The review synthesizes key findings and trends related to the factors influencing source credibility, including available tools, shared indicators, and existing methods experimented with in calculating source credibility. The analysis highlights that from a more commercial point of view, several tools are aimed at analyzing the content’s credibility and studying the sources’ credibility. However, from a methodological point of view, there is still something more to do. Indicators that can be used to carry out a source credibility assessment focus on the structure and design of the source, excluding others indicating how the page traffic could be. As for the techniques to be used to assess the credibility of a source, it emerged that more innovative techniques, such as deep-learning, are being developed alongside slightly more classical statistical methods. The review analyzes 23 papers from Conferences and 22 from Journals published in recent years. It also identifies avenues for future inquiry and the development of effective strategies to combat the challenges posed by misinformation in the digital era.

Recent crises, recessions and bubbles have stressed the non-stationary nature and the presence of drastic structural changes in the financial domain. The most recent literature suggests the use of conventional machine learning and statistical approaches in this context. Unfortunately, several of these techniques are unable or slow to adapt to changes in the price-generation process. This study aims to survey the relevant literature on Machine Learning for financial prediction under regime change employing a systematic approach.It reviews key papers with a special emphasis on technical analysis. The study discusses the growing number of contributions that are bridging the gap between two separate communities, one focused on data stream learning and the other on economic research. However, it also makes apparent that we are still in an early stage. The range of machine learning algorithms that have been tested in this domain is very wide, but the results of the study do not suggest that currently there is a specific technique that is clearly dominant.

This research introduces a method for predicting where crimes will occur based on clustering activity in the area. Hotspots, or locations with a disproportionately high number of crimes, are located by a combination of spatial and temporal grouping methods employed by this strategy. Crime forecasting models use these hotspots to predict where crimes will occur. The approach's efficacy is tested using actual crime data, and it successfully predicts future crimes in high-crime zones. Law enforcement agencies can use the proposed method to protect the public better, and it shows promise as a tool for crime prediction. Academic research into the topic of foreseeing criminal behavior is a newer development. Researchers in this discipline have discovered that criminal behavior has universal patterns. These patterns may help law enforcement agencies plan for criminal activities. Predictive policing, hotspot analysis, and geographical profiling are examples of when crime forecasting has been useful. Several aspects of the census, such as the average yearly income and literacy rate, are related to the prevalence of crime in a certain area. Indicators of potentially criminal behavior, these characteristics may be seen as markers. This investigation aims to discover if any clues can be gleaned from past criminal behavior that may be utilized to forecast future criminal behavior. Using machine learning and 2-D Hotspot analysis, we propose a method for the spatiotemporal prediction of criminal activity within the scope of this study. Clustering is a method used in 2-dimensional hotspot analysis. Methods of modern categorization, both with and without hotspot analysis, are used to evaluate the suggested model's efficacy. It is found that the model that incorporates hotspot analysis performs better than the one that does not.

This research endeavors to address the persistent dissemination of public opinion within social networks, mitigate the propagation of inappropriate content on these platforms, and enhance the overall service quality of social networks. To achieve these objectives, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) techniques are employed in this research to develop a predictive model for anticipating public opinion crises in social network mining. This model furnishes users with a valuable reference for subsequent decisionmaking processes. The initial phase of this research involves the collection of user behavior data from social networks using IoT technologies, serving as the basis for extensive big data analysis and neural network research. Subsequently, a social network text categorization model is constructed by amalgamating the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture, elucidating the training procedures of deep learning methodologies within CNN and LSTM networks. The effectiveness of this approach is subsequently validated through comparisons with other deep learning techniques. Based on the obtained results and findings, the CNN-LSTM model demonstrates a noteworthy accuracy rate of 92.19% and an exceptionally low loss value of 0.4075. Of particular significance is the classification accuracy of the CNN-LSTM algorithm within social network datasets, which surpasses that of alternative algorithms, including CNN (by 6.31%), LSTM (by 4.43%), RNN (by 3.51%), Transformer (by 40.29%), and Generative Adversarial Network (GAN) (by 4.49%). This underscores the effectiveness of the CNN-LSTM algorithm in the realm of social network text classification.

In India, the demand for fruits and vegetables has been consistently increasing alongside the rising population, making crop production a crucial aspect of agriculture. However, despite the growing demand and potential profitability, farmers have been slow to transition from traditional food grain crops to fruits and vegetables. In this paper, we explore the changing demands of food categories in India, highlighting the shift towards increased consumption of fruits and vegetables. Despite the potential benefits, farmers face various challenges and uncertainties associated with cultivating these crops. To address this, we propose the use of Machine Learning (ML) and Deep Learning (DL) techniques to analyze historical market price data for fruits and vegetables from 2016 to 2021 and predict future prices. This accurate prediction system will aid farmers in deciding which crops to grow and when to harvest, ultimately maximizing profits.

This study aims to devise a traffic electronic information signal acquisition system employing Internet of Things and artificial intelligence technologies, offering a novel approach to address prevailing challenges related to traffic congestion and safety. Initially, the hardware circuit for the high-speed signal acquisition control core is developed, leveraging Field-Programmable Gate Array technology. This facilitates wireless monitoring of signal acquisition. Subsequently, a comprehensive time signal acquisition system is formulated, encompassing modules for communication, acquisition, storage, adaptive measurement, and signal analysis. The geomagnetic acquisition module within this system is utilized for collecting geomagnetic signals, which are then translated into switch signals indicating the presence or absence of vehicles. These signals are subsequently transmitted to the geomagnetic signal processor. Experimental results pertaining to the signal acquisition system reveal a notable peak storage speed of 200KB/s, considering the utilization of one million sampling points. Across a series of tests, the maximum relative error of the obtained results ranges from 2.2% to 2.7%, underscoring the consistency and reliability of the measurements. In comparison to existing testing devices, the system exhibits heightened accuracy in test results, rendering it more apt for traffic signal acquisition applications. In conclusion, this study accomplishes the collection and dissemination of diverse traffic information, furnishing robust support for traffic control and ensuring safe operations.

Probabilistic databases have emerged as an extension of relational databases that can handle uncertain data under possible worlds semantics. Although the problems of creating effective means of probabilistic data representation as well as probabilistic query evaluation have been addressed so widely, low attention has been given to query result explanation. While query answer explanation in relational databases tends to answer the question: why is this tuple in the query result? In probabilistic databases, we should ask an additional question: why does this tuple have such a probability? Due to the huge number of resulting worlds of probabilistic databases, query explanation in probabilistic databases is a challenging task. In this paper, we propose a causal explanation technique for conjunctive queries in probabilistic databases. Based on the notions of causality, responsibility and blame, we will be able to address explanation for tuple and attribute uncertainties in a complementary way. Through an experiment on the real-dataset of IMDB, we will see that this framework would be helpful for explaining complex queries results. Comparing to existing explanation methods, our method could be also considered as an aided-diagnosis method through computing the blame, which helps to understand the impact of uncertain attributes.