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Computers, Volume 13, Issue 7 (July 2024) – 6 articles

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17 pages, 1621 KiB

Open AccessArticle

Modeling Autonomous Vehicle Responses to Novel Observations Using Hierarchical Cognitive Representations Inspired Active Inference

by Sheida Nozari, Ali Krayani, Pablo Marin, Lucio Marcenaro, David Martin Gomez and Carlo Regazzoni

Computers 2024, 13(7), 161; https://doi.org/10.3390/computers13070161 (registering DOI) - 28 Jun 2024

Viewed by 40

Abstract

Equip** autonomous agents for dynamic interaction and navigation is a significant challenge in intelligent transportation systems. This study aims to address this by implementing a brain-inspired model for decision making in autonomous vehicles. We employ active inference, a Bayesian approach that models decision-making processes similar to the human brain, focusing on the agent’s preferences and the principle of free energy. This approach is combined with imitation learning to enhance the vehicle’s ability to adapt to new observations and make human-like decisions. The research involved develo** a multi-modal self-awareness architecture for autonomous driving systems and testing this model in driving scenarios, including abnormal observations. The results demonstrated the model’s effectiveness in enabling the vehicle to make safe decisions, particularly in unobserved or dynamic environments. The study concludes that the integration of active inference with imitation learning significantly improves the performance of autonomous vehicles, offering a promising direction for future developments in intelligent transportation systems. Full article

(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems 2023)

24 pages, 501 KiB

Open AccessArticle

An NLP-Based Exploration of Variance in Student Writing and Syntax: Implications for Automated Writing Evaluation

by Maria Goldshtein, Amin G. Alhashim and Rod D. Roscoe

Computers 2024, 13(7), 160; https://doi.org/10.3390/computers13070160 - 25 Jun 2024

Viewed by 225

Abstract

In writing assessment, expert human evaluators ideally judge individual essays with attention to variance among writers’ syntactic patterns. There are many ways to compose text successfully or less successfully. For automated writing evaluation (AWE) systems to provide accurate assessment and relevant feedback, they must be able to consider similar kinds of variance. The current study employed natural language processing (NLP) to explore variance in syntactic complexity and sophistication across clusters characterized in a large corpus (n = 36,207) of middle school and high school argumentative essays. Using NLP tools, k-means clustering, and discriminant function analysis (DFA), we observed that student writers employed four distinct syntactic patterns: (1) familiar and descriptive language, (2) consistently simple noun phrases, (3) variably complex noun phrases, and (4) moderate complexity with less familiar language. Importantly, each pattern spanned the full range of writing quality; there were no syntactic patterns consistently evaluated as “good” or “bad”. These findings support the need for nuanced approaches in automated writing assessment while informing ways that AWE can participate in that process. Future AWE research can and should explore similar variability across other detectable elements of writing (e.g., vocabulary, cohesion, discursive cues, and sentiment) via diverse modeling methods. Full article

(This article belongs to the Special Issue Natural Language Processing (NLP) and Large Language Modelling)

23 pages, 8049 KiB

Open AccessArticle

Enhanced Security Access Control Using Statistical-Based Legitimate or Counterfeit Identification System

by Aisha Edrah and Abdelkader Ouda

Computers 2024, 13(7), 159; https://doi.org/10.3390/computers13070159 - 22 Jun 2024

Viewed by 370

Abstract

With our increasing reliance on technology, there is a growing demand for efficient and seamless access control systems. Smartphone-centric biometric methods offer a diverse range of potential solutions capable of verifying users and providing an additional layer of security to prevent unauthorized access. To ensure the security and accuracy of smartphone-centric biometric identification, it is crucial that the phone reliably identifies its legitimate owner. Once the legitimate holder has been successfully determined, the phone can effortlessly provide real-time identity verification for various applications. To achieve this, we introduce a novel smartphone-integrated detection and control system called Identification: Legitimate or Counterfeit (ILC), which utilizes gait cycle analysis. The ILC system employs the smartphone’s accelerometer sensor, along with advanced statistical methods, to detect the user’s gait pattern, enabling real-time identification of the smartphone owner. This approach relies on statistical analysis of measurements obtained from the accelerometer sensor, specifically, peaks extracted from the X-axis data. Subsequently, the derived feature’s probability distribution function (PDF) is computed and compared to the known user’s PDF. The calculated probability verifies the similarity between the distributions, and a decision is made with 92.18% accuracy based on a predetermined verification threshold. Full article

(This article belongs to the Special Issue Wireless Sensor Network, IoT and Cloud Computing Technologies for Smart Cities)

15 pages, 606 KiB

Open AccessArticle

Personalized Classifier Selection for EEG-Based BCIs

by Javad Rahimipour Anaraki, Antonina Kolokolova and Tom Chau

Computers 2024, 13(7), 158; https://doi.org/10.3390/computers13070158 - 21 Jun 2024

Viewed by 306

Abstract

The most important component of an Electroencephalogram (EEG) Brain–Computer Interface (BCI) is its classifier, which translates EEG signals in real time into meaningful commands. The accuracy and speed of the classifier determine the utility of the BCI. However, there is significant intra- and inter-subject variability in EEG data, complicating the choice of the best classifier for different individuals over time. There is a keen need for an automatic approach to selecting a personalized classifier suited to an individual’s current needs. To this end, we have developed a systematic methodology for individual classifier selection, wherein the structural characteristics of an EEG dataset are used to predict a classifier that will perform with high accuracy. The method was evaluated using motor imagery EEG data from Physionet. We confirmed that our approach could consistently predict a classifier whose performance was no worse than the single-best-performing classifier across the participants. Furthermore, Kullback–Leibler divergences between reference distributions and signal amplitude and class label distributions emerged as the most important characteristics for classifier prediction, suggesting that classifier choice depends heavily on the morphology of signal amplitude densities and the degree of class imbalance in an EEG dataset. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain 2024)

24 pages, 949 KiB

Open AccessArticle

Advancing Skin Cancer Prediction Using Ensemble Models

by Priya Natha and Pothuraju RajaRajeswari

Computers 2024, 13(7), 157; https://doi.org/10.3390/computers13070157 - 21 Jun 2024

Viewed by 248

Abstract

There are many different kinds of skin cancer, and an early and precise diagnosis is crucial because skin cancer is both frequent and deadly. The key to effective treatment is accurately classifying the various skin cancers, which have unique traits. Dermoscopy and other advanced imaging techniques have enhanced early detection by providing detailed images of lesions. However, accurately interpreting these images to distinguish between benign and malignant tumors remains a difficult task. Improved predictive modeling techniques are necessary due to the frequent occurrence of erroneous and inconsistent outcomes in the present diagnostic processes. Machine learning (ML) models have become essential in the field of dermatology for the automated identification and categorization of skin cancer lesions using image data. The aim of this work is to develop improved skin cancer predictions by using ensemble models, which combine numerous machine learning approaches to maximize their combined strengths and reduce their individual shortcomings. This paper proposes a fresh and special approach for ensemble model optimization for skin cancer classification: the Max Voting method. We trained and assessed five different ensemble models using the ISIC 2018 and HAM10000 datasets: AdaBoost, CatBoost, Random Forest, Gradient Boosting, and Extra Trees. Their combined predictions enhance the overall performance with the Max Voting method. Moreover, the ensemble models were fed with feature vectors that were optimally generated from the image data by a genetic algorithm (GA). We show that, with an accuracy of 95.80%, the Max Voting approach significantly improves the predictive performance when compared to the five ensemble models individually. Obtaining the best results for F1-measure, recall, and precision, the Max Voting method turned out to be the most dependable and robust. The novel aspect of this work is that skin cancer lesions are more robustly and reliably classified using the Max Voting technique. Several pre-trained machine learning models’ benefits are combined in this approach. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain 2024)

21 pages, 4836 KiB

Open AccessArticle

Chef Dalle: Transforming Cooking with Multi-Model Multimodal AI

by Brendan Hannon, Yulia Kumar, J. Jenny Li and Patricia Morreale

Computers 2024, 13(7), 156; https://doi.org/10.3390/computers13070156 - 21 Jun 2024

Viewed by 451

Abstract

In an era where dietary habits significantly impact health, technological interventions can offer personalized and accessible food choices. This paper introduces Chef Dalle, a recipe recommendation system that leverages multi-model and multimodal human-computer interaction (HCI) techniques to provide personalized cooking guidance. The application integrates voice-to-text conversion via Whisper and ingredient image recognition through GPT-Vision. It employs an advanced recipe filtering system that utilizes user-provided ingredients to fetch recipes, which are then evaluated through multi-model AI through integrations of OpenAI, Google Gemini, Claude, and/or Anthropic APIs to deliver highly personalized recommendations. These methods enable users to interact with the system using voice, text, or images, accommodating various dietary restrictions and preferences. Furthermore, the utilization of DALL-E 3 for generating recipe images enhances user engagement. User feedback mechanisms allow for the refinement of future recommendations, demonstrating the system’s adaptability. Chef Dalle showcases potential applications ranging from home kitchens to grocery stores and restaurant menu customization, addressing accessibility and promoting healthier eating habits. This paper underscores the significance of multimodal HCI in enhancing culinary experiences, setting a precedent for future developments in the field. Full article

(This article belongs to the Special Issue Harnessing Artificial Intelligence for Social and Semantic Understanding)

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