This research aims to optimize intricate learning models by implementing quantization and bit-depth optimization techniques. The objective is to significantly cut time complexity while preserving model efficiency, thus addressing the challenge of extended execution times in intricate models. Two medical datasets were utilized as case studies to apply a Logistic Regression (LR) machine learning model. Using efficient quantization and bit depth optimization strategies the input data is downscaled from float64 to float32 and int32. The results demonstrated a significant reduction in time complexity, with only a minimal decrease in model accuracy post-optimization, showcasing the state-of-the-art optimization approach. This comprehensive study concludes that the impact of these optimization techniques varies depending on a set of parameters.

The Mar Menor, Europe's largest coastal lagoon, located in Spain, has undergone severe eutrophication crises. Monitoring chlorophyll-a (Chl-a) is essential to anticipate harmful algal blooms and guide mitigation. Traditional in situ measurements are spatially and temporally limited. Satellite-based approaches provide a more comprehensive view, enabling scalable, long-term, and transferable monitoring. This study aims to overcome limitations of chlorophyll monitoring, often restricted to surface estimates or limited temporal coverage, by developing a reliable methodology to predict and map Chl-a across the water column of the Mar Menor. The work integrates Sentinel 2 imagery with buoy-based ground truth to create models capable of high-resolution, depth-specific monitoring, enhancing early-warning capabilities for eutrophication. Nearly a decade of Sentinel 2 images was atmospherically corrected using C2RCC processors. Buoy data were aggregated by depth (0-1 m, 1-2 m, 2-3 m, 3-4 m). Multiple ML and DL algorithms-including RF, XGBoost, CatBoost, Multilater Perceptron Networks, and ensembles-were trained and validated using cross-validation. Systematic band-combination experiments and spatial aggregation strategies were tested to optimize prediction. Results show depth-dependent performance. At the surface, C2X-Complex with XGBoost and ensemble models achieved R2 = 0.89; at 1-2 m, CatBoost and ensemble models reached R2 = 0.87; at 2-3 m, TOA reflectances with KNN performed best (R2 = 0.81); while at 3-4 m, RF achieved R2 = 0.66. Generated maps successfully reproduced known eutrophication events (e.g., 2016 crisis, 2025 surge), confirming robustness. The study delivers an end-to-end, validated methodology for depth-specific Chl-amapping. Its integration of multispectral band combinations, buoy calibration, and ML/DL modeling offers a transferable framework for other turbid coastal systems.

This study explores the use of virtual reality (VR) and artificial intelligence (AI) to predict the presence of dyslexia in Italian and Spanish university students. In particular, the research investigates whether VR-derived data from Silent Reading (SR) tests and self-esteem assessments can differentiate between students that are affected by dyslexia and students that are not, employing machine learning (ML) algorithms. Participants completed VR-based tasks measuring reading performance and self-esteem. A preliminary statistical analysis (t tests and Mann Whitney tests) on these data was performed, to compare the obtained scores between individuals with and without dyslexia, revealing significant differences in completion time for the SR test, but not in accuracy, nor in self esteem. Then, supervised ML models were trained and tested, demonstrating an ability to classify the presence/absence of dyslexia with an accuracy of 87.5 per cent for Italian, 66.6 per cent for Spanish, and 75.0 per cent for the pooled group. These findings suggest that VR and ML can effectively be used as supporting tools for assessing dyslexia, particularly by capturing differences in task completion speed, but language-specific factors may influence classification accuracy.

In industry, software testing is the primary method to verify and validate the functionality, performance, security, usability, and so on, of software-based systems. Test automation has gained increasing attention in industry over the last decade, following decades of intense research into test automation and model-based testing. However, designing, developing, maintaining and evolving test automation is a considerable effort. Meanwhile, AI's breakthroughs in many engineering fields are opening up new perspectives for software testing, for both manual and automated testing. This paper reviews recent research on AI augmentation in software test automation, from no automation to full automation. It also discusses new forms of testing made possible by AI. Based on this, the newly developed taxonomy, ai4st, is presented and used to classify recent research and identify open research questions.

Aura-CAPTCHA was developed as a multi-modal CAPTCHA system to address vulnerabilities in traditional methods that are increasingly bypassed by AI technologies, such as Optical Character Recognition (OCR) and adversarial image processing. The design integrated Generative Adversarial Networks (GANs) for generating dynamic image challenges, Reinforcement Learning (RL) for adaptive difficulty tuning, and Large Language Models (LLMs) for creating text and audio prompts. Visual challenges included 3x3 grid selections with at least three correct images, while audio challenges combined randomized numbers and words into a single task. RL adjusted difficulty based on incorrect attempts, response time, and suspicious user behavior. Evaluations on real-world traffic demonstrated a 92% human success rate and a 10% bot bypass rate, significantly outperforming existing CAPTCHA systems. The system provided a robust and scalable approach for securing online applications while remaining accessible to users, addressing gaps highlighted in previous research.

This paper extends Bayesian probability theory by developing a multidimensional space of events (MDSE) theory that accounts for mutual influences between events and hypotheses sets. While traditional Bayesian approaches assume conditional independence between certain variables, real-world systems often exhibit complex interdependencies that limit classical model applicability. Building on established probabilistic foundations, our approach introduces a mathematical formalism for modeling these complex relationships. We developed the MDSE theory through rigorous mathematical derivation and validated it using three complementary methodologies: analytical proofs, computational simulations, and case studies drawn from diverse domains. Results demonstrate that MDSE successfully models complex dependencies with 15-20% improved prediction accuracy compared to standard Bayesian methods when applied to datasets with high interdimensionality. This theory particularly excels in scenarios with over 50 interrelated variables, where traditional methods show exponential computational complexity growth while MDSE maintains polynomial scaling. Our findings indicate that MDSE provides a viable mathematical foundation for extending Bayesian reasoning to complex systems while maintaining computational tractability. This approach offers practical applications in engineering challenges including risk assessment, resource optimization, and forecasting problems where multiple interdependent factors must be simultaneously considered.

Abstract--This paper describes the development of a costeffective yet precise indoor robot navigation system composed of a custom robot controller board and an indoor positioning system. First, the proposed robot controller board has been specially designed for emerging IoT-based robot applications and is capable of driving two 6-Amp motor channels. Then, working together with the robot controller board, the proposed positioning system detects the robot's location using a down-looking webcam and uses the robot's position on the webcam images to estimate the real-world position of the robot in the environment. The positioning system can then send commands via WIFI to the robot in order to steer it to any arbitrary location in the environment. Our experiments show that the proposed system reaches a navigation error smaller or equal to 0.125 meters while being more than two orders of magnitude more cost-effective compared to off-the-shelve motion capture (MOCAP) positioning systems.

Abstract--This paper details the development of a novel semantic search algorithm utilizing Word2Vec and Annoy Index to efficiently process and retrieve information from large datasets. Addressing traditional search algorithms' limitations, our proposed method demonstrates significant improvements in speed, accuracy, and scalability, validated by rigorous testing on datasets up to 100GB. In the era of big data, efficiently retrieving relevant information from vast, unstructured datasets is crucial across numerous domains such as e-commerce, healthcare, research, and public administration. Traditional search engines, which rely primarily on keyword matching, often struggle with the inherent complexity and ambiguity of natural language. These systems lack the ability to understand the semantic meaning and context of queries, leading to inaccurate results and suboptimal user experiences. The evolution of semantic search technologies aims to address these limitations by focusing on understanding the in high-dimensional space.

Mostofa Akbar Department of CSE Bangladesh University of Engineering & T echnology Dhaka, Bangladesh mostofa@cse.buet.ac.bd Abstract --Emotion artificial intelligence is a field of study that focuses on figuring out how to recognize emotions, especially in the area of text mining. T oday is the age of social media which has opened a door for us to share our individual expressions, emotions, and perspectives on any event. We can analyze sentiment on social media posts to detect positive, negative, or emotional behavior toward society. One of the key challenges in sentiment analysis is to identify depressed text from social media text that is a root cause of mental ill-health. Furthermore, depression leads to severe impairment in day-to-day living and is a major source of suicide incidents. In this paper, we apply natural language processing techniques on Facebook texts for conducting emotion analysis focusing on depression using multiple machine learning algorithms. Preprocessing steps like stemming, stop word removal, etc. are used to clean the collected data, and feature extraction techniques like stylometric feature, TF-IDF, word embedding, etc. are applied to the collected dataset which consists of 983 texts collected from social media posts. In the process of class prediction, LSTM, GRU, support vector machine, and Naive-Bayes classifiers have been used. We have presented the results using the primary classification metrics including F1-score, and accuracy. This work focuses on depression detection from social media posts to help psychologists to analyze sentiment from shared posts which may reduce the undesirable behaviors of depressed individuals through diagnosis and treatment. I NTRODUCTION Text is the most important means of communication in today's world. Popular online social networking sites such as Facebook, Twitter, MySpace, etc. are mainly text-based. The rapid growth of Social Media has created enough opportunities to share information across time and space. Users are now comfortable contributing more to the content of social media websites and posting their own material. The emergence of internet-based media sources has resulted in the availability of substantial user data for the emotional analysis of text and images.

Urban air pollution hotspots pose significant health risks, yet their detection and analysis remain limited by the sparsity of public sensor networks. This paper addresses this challenge by combining predictive modeling and mechanistic approaches to comprehensively monitor pollution hotspots. We enhanced New Delhi's existing sensor network with 28 low-cost sensors, collecting PM2.5 data over 30 months from May 1, 2018, to Nov 1, 2020. Applying established definitions of hotspots to this data, we found the existence of additional 189 hidden hotspots apart from confirming 660 hotspots detected by the public network. Using predictive techniques like Space-Time Kriging, we identified hidden hotspots with 95% precision and 88% recall with 50% sensor failure rate, and with 98% precision and 95% recall with 50% missing sensors. The projected results of our predictive models were further compiled into policy recommendations for public authorities. Additionally, we developed a Gaussian Plume Dispersion Model to understand the mechanistic underpinnings of hotspot formation, incorporating an emissions inventory derived from local sources. Our mechanistic model is able to explain 65% of observed transient hotspots. Our findings underscore the importance of integrating data-driven predictive models with physics-based mechanistic models for scalable and robust air pollution management in resource-constrained settings.