Explore the vast range of titles available.

The rapid advancement of artificial intelligence (AI), coupled with the global rollout of 4G and 5G networks, has fundamentally transformed the Big Data landscape, redefining data management and analysis methodologies. The ability to manage and analyze such vast and varied datasets has exceeded the capacity of any individual or organization. This study introduces an enhanced framework that expands upon the traditional four Vs of Big Data—volume, velocity, volatility, and veracity—by incorporating six additional dimensions: value, validity, visualization, variability, volatility, and vulnerability. This comprehensive framework offers a novel and straightforward approach to understanding and addressing the complexities of Big Data in the AI era. This article further explores the use of ‘Big D’, an AI-driven, RAG-based Big Data analytical bot powered by the ChatGPT-4o model (ChatGPT version 4.0). This article’s innovation represents a significant advance in the field, accelerating and deepening the extraction and analysis of insights from large-scale datasets. This will enable us to develop a more nuanced and comprehensive understanding of intricate data landscapes. In addition, we proposed a framework and analytical tools that contribute to the evolution of Big Data analytics, particularly in the context of AI-driven processes.

In recent years, advances in diagnosing and classifying diseases using machine learning (ML) have grown exponentially. However, due to the many privacy regulations regarding personal data, pooling together data from multiple sources and storing them in a single (centralized) location for traditional ML model training are often infeasible. Federated learning (FL), a collaborative learning paradigm, can sidestep this major pitfall by creating a global ML model that is trained by aggregating model weights from individual models that are separately trained on their own data silos, therefore avoiding most data privacy concerns. This study addresses the centralized data issue with FL by applying a novel DataWeightedFed architectural approach for effective fundus disease diagnosis from ophthalmic images. It includes a novel method for aggregating model weights by comparing the size of each model’s data and taking a dynamically weighted average of all the model’s weights. Experimental results showed a small average 1.85% loss in accuracy when training using FL compared to centralized ML model systems, a nearly 92% improvement over the conventional 55% accuracy loss. The obtained results demonstrate that this study’s FL architecture can maximize both privacy preservation and accuracy for ML in fundus disease diagnosis and provide a secure, collaborative ML model training solution within the eye healthcare space.

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.

In this comprehensive study, a novel MATLAB to Python (M-to-PY) conversion process is showcased, specifically tailored for an intricate image skeletonization project involving fifteen MATLAB files and a large dataset. The central innovation of this research is the adept use of ChatGPT-4 as an AI assistant, pivotal in crafting a prototype M-to-PY converter. This converter’s capabilities were thoroughly evaluated using a set of test cases generated by the Bard bot, ensuring a robust and effective tool. The culmination of this effort was the development of the Skeleton App, adept at image sketching and skeletonization. This live and publicly available app underscores the enormous potential of AI in enhancing the transition of scientific research from MATLAB to Python. The study highlights the blend of AI’s computational prowess and human ingenuity in computational research, making significant strides in AI-assisted scientific exploration and tool development.

With the rapid increase in the amount of big data, traditional software tools are facing complexity in tackling big data, which is a huge concern in the research industry. In addition, the management and processing of big data have become more difficult, thus increasing security threats. Various fields encountered issues in fully making use of these large-scale data with supported decision-making. Data mining methods have been tremendously improved to identify patterns for sorting a larger set of data. MapReduce models provide greater advantages for in-depth data evaluation and can be compatible with various applications. This survey analyses the various map-reducing models utilized for big data processing, the techniques harnessed in the reviewed literature, and the challenges. Furthermore, this survey reviews the major advancements of diverse types of map-reduce models, namely Hadoop, Hive, Pig, MongoDB, Spark, and Cassandra. Besides the reliable map-reducing approaches, this survey also examined various metrics utilized for computing the performance of big data processing among the applications. More specifically, this review summarizes the background of MapReduce and its terminologies, types, different techniques, and applications to advance the MapReduce framework for big data processing. This study provides good insights for conducting more experiments in the field of processing and managing big data.

The study explores the transformative capabilities of Transformers and Large Language Models (LLMs) in the early detection of Acute Lymphoblastic Leukaemia (ALL). The researchers benchmark Vision Transformers with Deformable Attention (DAT) and Hierarchical Vision Transformers (Swin) against established Convolutional Neural Networks (CNNs) like ResNet-50 and VGG-16. The findings reveal that transformer models exhibit remarkable accuracy in identifying ALL from original images, demonstrating efficiency in image analysis without necessitating labour-intensive segmentation. A thorough bias analysis is conducted to ensure the robustness and fairness of the models. The promising performance of the transformer models indicates a trajectory towards surpassing CNNs in cancer detection, setting new standards for accuracy. In addition, the study explores the capabilities of LLMs in revolutionising early cancer detection and providing comprehensive support to ALL patients. These models assist in symptom analysis, offer preliminary assessments, and guide individuals seeking information, contributing to a more accessible and informed healthcare journey. The integration of these advanced AI technologies holds the potential to enhance early detection, improve patient outcomes, and reduce healthcare disparities, marking a significant advancement in the fight against ALL.

Authenticating cultural heritage artifacts such as Matryoshka Nesting Dolls (MNDs) is increasingly complicated by high-fidelity replicas that successfully mimic surface textures and palettes, leading traditional 2D computer vision models to exhibit dangerous overconfidence in false-positive classifications. To address this, we propose an auditable multimodal framework that transitions from appearance-only detection to a robust verification system based on the following three technical pillars: (1) a 2D visual stream utilizing a ConvNeXt-Tiny backbone for fine-grained style recognition; (2) a 3D geometric stream employing a custom 2D-to-3D reconstruction pipeline based on the Blum Medial Axis (BMA) and surfaces of revolution to capture axisymmetric structural fidelity; and (3) a semantic stream leveraging the Qwen3-VL vision-language model to generate human-interpretable evidence cards. To support this framework, we introduce a novel multimodal dataset comprising 168 unique physical MND sets and 27,387 labeled frames, archived for reproducibility. Our experimental results demonstrate that while 2D-only baselines achieve 77.9% authenticity accuracy, they suffer from a high Expected Calibration Error (ECE) of 0.121. The integrated multimodal framework achieves a superior authenticity accuracy of 96.7% and reduces the ECE to 0.041, representing a 66% improvement in calibration reliability. Crucially, the system shifts the mean confidence for incorrect replica classifications from a high-risk 0.82 to a safe 0.45.

Dealing with data scarcity is the biggest challenge faced by Artificial Intelligence (AI), and it will be interesting to see how we overcome this obstacle in the future, but for now, “THE SHOW MUST GO ON!!!” As AI spreads and transforms more industries, the lack of data is a significant obstacle: the best methods for teaching machines how real-world processes work. This paper explores the considerable implications of data scarcity for the AI industry, which threatens to restrict its growth and potential, and proposes plausible solutions and perspectives. In addition, this article focuses highly on different ethical considerations: privacy, consent, and non-discrimination principles during AI model developments under limited conditions. Besides, innovative technologies are investigated through the paper in aspects that need implementation by incorporating transfer learning, few-shot learning, and data augmentation to adapt models so they could fit effective use processes in low-resource settings. This thus emphasizes the need for collaborative frameworks and sound methodologies that ensure applicability and fairness, tackling the technical and ethical challenges associated with data scarcity in AI. This article also discusses prospective approaches to dealing with data scarcity, emphasizing the blend of synthetic data and traditional models and the use of advanced machine learning techniques such as transfer learning and few-shot learning. These techniques aim to enhance the flexibility and effectiveness of AI systems across various industries while ensuring sustainable AI technology development amid ongoing data scarcity.

In the rapidly advancing field of Artificial Intelligence (AI), this study presents a critical evaluation of the resilience and cybersecurity efficacy of leading AI models, including ChatGPT-4, Bard, Claude, and Microsoft Copilot. Central to this research are innovative adversarial prompts designed to rigorously test the content moderation capabilities of these AI systems. This study introduces new adversarial tests and the Response Quality Score (RQS), a metric specifically developed to assess the nuances of AI responses. Additionally, the research spotlights FreedomGPT, an AI tool engineered to optimize the alignment between user intent and AI interpretation. The empirical results from this investigation are pivotal for assessing AI models’ current robustness and security. They highlight the necessity for ongoing development and meticulous testing to bolster AI defenses against various adversarial challenges. Notably, this study also delves into the ethical and societal implications of employing advanced “jailbreak” techniques in AI testing. The findings are significant for understanding AI vulnerabilities and formulating strategies to enhance AI technologies’ reliability and ethical soundness, paving the way for safer and more secure AI applications.

Despite significant advancements in Artificial Intelligence (AI) and Large Language Models (LLMs), detecting and mitigating bias remains a critical challenge, particularly on social media platforms like X (formerly Twitter), to address the prevalent cyberbullying on these platforms. This research investigates the effectiveness of leading LLMs in generating synthetic biased and cyberbullying data and evaluates the proficiency of transformer AI models in detecting bias and cyberbullying within both authentic and synthetic contexts. The study involves semantic analysis and feature engineering on a dataset of over 48,000 sentences related to cyberbullying collected from Twitter (before it became X). Utilizing state-of-the-art LLMs and AI tools such as ChatGPT-4, Pi AI, Claude 3 Opus, and Gemini-1.5, synthetic biased, cyberbullying, and neutral data were generated to deepen the understanding of bias in human-generated data. AI models including DeBERTa, Longformer, BigBird, HateBERT, MobileBERT, DistilBERT, BERT, RoBERTa, ELECTRA, and XLNet were initially trained to classify Twitter cyberbullying data and subsequently fine-tuned, optimized, and experimentally quantized. This study focuses on intersectional cyberbullying and multilabel classification to detect both bias and cyberbullying. Additionally, it proposes two prototype applications: one that detects cyberbullying using an intersectional approach and the innovative CyberBulliedBiasedBot that combines the generation and detection of biased and cyberbullying content.