Automated medical image captioning translates complex radiological images into diagnostic narratives that can support reporting workflows. We present a Swin-BART encoder-decoder system with a lightweight regional attention module that amplifies diagnostically salient regions before cross-attention. Trained and evaluated on ROCO, our model achieves state-of-the-art semantic fidelity while remaining compact and interpretable. We report results as mean$\pm$std over three seeds and include $95\%$ confidence intervals. Compared with baselines, our approach improves ROUGE (proposed 0.603, ResNet-CNN 0.356, BLIP2-OPT 0.255) and BERTScore (proposed 0.807, BLIP2-OPT 0.645, ResNet-CNN 0.623), with competitive BLEU, CIDEr, and METEOR. We further provide ablations (regional attention on/off and token-count sweep), per-modality analysis (CT/MRI/X-ray), paired significance tests, and qualitative heatmaps that visualize the regions driving each description. Decoding uses beam search (beam size $=4$), length penalty $=1.1$, $no\_repeat\_ngram\_size$ $=3$, and max length $=128$. The proposed design yields accurate, clinically phrased captions and transparent regional attributions, supporting safe research use with a human in the loop.

Understanding the internal mechanisms of large language models (LLMs) is integral to enhancing their reliability, interpretability, and inference processes. We present Constituent-Aware Pooling (CAP), a methodology designed to analyse how LLMs process compositional linguistic structures. Grounded in principles of compositionality, mechanistic interpretability, and information gain theory, CAP systematically intervenes in model activations through constituent-based pooling at various model levels. Our experiments on inverse definition modelling, hypernym and synonym prediction reveal critical insights into transformers' limitations in handling compositional abstractions. No specific layer integrates tokens into unified semantic representations based on their constituent parts. We observe fragmented information processing, which intensifies with model size, suggesting that larger models struggle more with these interventions and exhibit greater information dispersion. This fragmentation likely stems from transformers' training objectives and architectural design, preventing systematic and cohesive representations. Our findings highlight fundamental limitations in current transformer architectures regarding compositional semantics processing and model interpretability, underscoring the critical need for novel approaches in LLM design to address these challenges.

Locating and editing knowledge in large language models (LLMs) is crucial for enhancing their accuracy, safety, and inference rationale. We introduce ``concept editing'', an innovative variation of knowledge editing that uncovers conceptualisation mechanisms within these models. Using the reverse dictionary task, inference tracing, and input abstraction, we analyse the Multi-Layer Perceptron (MLP), Multi-Head Attention (MHA), and hidden state components of transformer models. Our results reveal distinct patterns: MLP layers employ key-value retrieval mechanism and context-dependent processing, which are highly associated with relative input tokens. MHA layers demonstrate a distributed nature with significant higher-level activations, suggesting sophisticated semantic integration. Hidden states emphasise the importance of the last token and top layers in the inference process. We observe evidence of gradual information building and distributed representation. These observations elucidate how transformer models process semantic information, paving the way for targeted interventions and improved interpretability techniques. Our work highlights the complex, layered nature of semantic processing in LLMs and the challenges of isolating and modifying specific concepts within these models.

The use of Electronic Health Records (EHRs) has increased dramatically in the past 15 years, as, it is considered an important source of managing data od patients. The EHRs are primary sources of disease diagnosis and demographic data of patients worldwide. Therefore, the data can be utilized for secondary tasks such as research. This paper aims to make such data usable for research activities such as monitoring disease statistics for a specific population. As a result, the researchers can detect the disease causes for the behavior and lifestyle of the target group. One of the limitations of EHRs systems is that the data is not available in the standard format but in various forms. Therefore, it is required to first convert the names of the diseases and demographics data into one standardized form to make it usable for research activities. There is a large amount of EHRs available, and solving the standardizing issues requires some optimized techniques. We used a first-hand EHR dataset extracted from EHR systems. Our application uploads the dataset from the EHRs and converts it to the ICD-10 coding system to solve the standardization problem. So, we first apply the steps of pre-processing, annotation, and transforming the data to convert it into the standard form. The data pre-processing is applied to normalize demographic formats. In the annotation step, a machine learning model is used to recognize the diseases from the text. Furthermore, the transforming step converts the disease name to the ICD-10 coding format. The model was evaluated manually by comparing its performance in terms of disease recognition with an available dictionary-based system (MetaMap). The accuracy of the proposed machine learning model is 81%, that outperformed MetaMap accuracy of 67%. This paper contributed to system modelling for EHR data extraction to support research activities.

The popularity of Artificial intelligence and machine learning have prompted researchers to use it in the recent researches. The proposed method uses K-Nearest Neighbor (KNN) algorithm for segmentation of medical images, extracting of image features for analysis by classifying the data based on the neural networks. Classification of the images in medical imaging is very important, KNN is one suitable algorithm which is simple, conceptual and computational, which provides very good accuracy in results. KNN algorithm is a unique user-friendly approach with wide range of applications in machine learning algorithms which are majorly used for the various image processing applications including classification, segmentation and regression issues of the image processing. The proposed system uses gray level co-occurrence matrix features. The trained neural network has been tested successfully on a group of echocardiographic images, errors were compared using regression plot. The results of the algorithm are tested using various quantitative as well as qualitative metrics and proven to exhibit better performance in terms of both quantitative and qualitative metrics in terms of current state -of-the-art methods in the related area. To compare the performance of trained neural network the regression analysis performed showed a good correlation.

Dr Hafiz Farooq Ahmad is an Associate Professor at the College of Computer Sciences and Information Technology (CCSIT), King Faisal University, Al Ahsa, Saudi Arabia. He holds a PhD in Distributed Computing from Tokyo Institute of Technology, Tokyo, Japan. He has research interest in semantics systems, machine learning, health informatics and web application security. He is the pioneer for Semantic Web Application Firewall (SWAF) in cooperation with DTS Inc Japan in 2010. He contributed in agent cites project, a European funded research and development project for agent systems.

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations.

The attack comes after Iran exceeded their enriched uranium stockpile limit in the nuclear deal. Secretary of State Mike Pompeo called on the international community to join him Saturday in condemning Iran for drone attacks on two Saudi oil facilities, which he described as "an unprecedented attack on the world's energy supply." "Tehran is behind nearly 100 attacks on Saudi Arabia while [President Hassan] Rouhani and [Foreign Minister Mohammad] Zarif pretend to engage in diplomacy," Pompeo tweeted, referring to the nation's president and foreign affairs minister. There is no evidence the attacks came from Yemen." Iran-backed Houthi rebels in Yemen claimed responsibility for the attack hours before Pompeo's tweet. The world's largest oil processing facility in Saudi Arabia and a major oil field were impacted, sparking huge fires at a vulnerable chokepoint for global energy supplies. "The United States will work with our partners and allies to ensure that energy markets remain well supplied and Iran is held accountable for its aggression," Pompeo concluded. According to multiple news reports that cited unidentified sources, the drone attacks affected up to half of the supplies from the world's largest exporter of oil, though the output should be restored within days. It remained unclear if anyone was injured at the Abqaiq oil processing facility and the Khurais oil field. Sen. Chris Murphy, D-Conn., who sits on the Senate Foreign Relations Committee, denounced Pompeo's description of the attack, calling it an "irresponsible simplification." "The Saudis and Houthis are at war.

Saudi authorities attempt to control a fire at an Aramco factory. The world's largest oil processing facility and a nearby oil field in Saudi Arabia were set ablaze early Saturday morning after reported drone attacks by Iranian-backed Yemeni rebels. The Interior Ministry was quoted by state-run media as saying the fires at the Abqaiq oil processing facility in Buqyaq and the nearby Khurais oil field operated by Saudi Aramco were "targeted by drones." It wasn't immediately clear if there were any injuries, nor what effect it would have on oil production in the kingdom. Smoke is seen following a fire at Aramco facility in the eastern city of Abqaiq, Saudi Arabia, September 14, 2019.

DUBAI, UNITED ARAB EMIRATES – Drones attacked the world's largest oil processing facility in Saudi Arabia and a major oil field operated by Saudi Aramco early Saturday, the kingdom's Interior Ministry said, sparking a huge fire at a processor crucial to global energy supplies. No one immediately claimed responsibility for the attacks in Buqyaq and the Khurais oil field, though Yemen's Houthi rebels previously launched drone assaults deep inside of the kingdom. It wasn't clear if there were any injuries in the attacks, nor what effect it would have on oil production in the kingdom. The attack also likely will heighten tensions further across the wider Persian Gulf amid a confrontation between the U.S. and Iran over its unraveling nuclear deal with world powers. Online videos apparently shot in Buqyaq included the sound of gunfire in the background.