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To prevent falsification, modification, or fraudulent use of paper checks by linking them to a digital register and electronic authorization.

Simulate and estimate parameters for Gamma, Bernoulli, and Poisson distributions, validating results.

To explain the STM32 Serial Peripheral Interface features, operating modes, and usage tips.

—This study presents a comparative analysis of machine learning models for providing an estimate about the maternal risk levels during pregnancy is presented in this particular study. XGBoost, Random Forests, Decision Trees, Gradient Boosting, and Logistic Regression are the models which were used for assessment. These models were trained and tested on a dataset of 1014 entries which included characteristics such as age, blood pressure, blood sugar, body temperature, and heart rate. The accuracy of XGBoost (84.24%) is better than the other models. According to the results, it is closely followed by the Decision Trees Model (81.77%). Decision Trees offer better interpretability, which is essential for clinical decision making, even after XGBoost provides a better predictive performance. By offering individualized risk assessments, this study demonstrates how machine learning can be used to improve maternal health outcomes. It is also crucial to strike a balance between model accuracy, interpretability and practicality which this research also emphasizes on. In order to improve the predictive capabilities, future research directions will examine the hybrid models and incorporate extra clinical features.

To navigate NISQ hardware limitations, realize its potential, and achieve practical computational utility by synthesizing innovations.

It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. While no consensual definition of Artificial Intelligence (AI) exists, AI is broadly characterized as the study of computations that allow for perception, reason and action. Today, the amount of data that is generated, by both humans and machines, far outpaces humans’ ability to absorb, interpret, and make complex decisions based on that data. Artificial intelligence forms the basis for all computer learning and is the future of all complex decision making. This paper examines features of artificial Intelligence, introduction, definitions of AI, history, applications, growth and achievements.

To refine and expand the prioritization of actionable T-cell (neo)epitopes in infection and cancer, enhancing immunogenicity screening.

. In critical emergency situations such as natural disasters, industrial accidents, or medical emergencies, normal speech-based com- munication might not always be reliable. Background noise, damaged devices, or physical injuries can prevent people from speaking or being heard. In such cases, hand gestures offer a reliable alternative to sig- nal for help. We introduce an Emergency Gesture Recognition System (EGRS), a lightweight, high-accuracy gesture recognizer built specifi- callyforemergencyscenarios.Thesystemadaptsaconvolutionalneural network (CNN) originally designed for American Sign Language (ASL) recognitionandrefinesittoasmaller,targetedsetofemergency-related gestures. After addressing dataset duplication artifacts discovered dur- ingvalidation,ouremergency-onlymodelachieves97.42%testaccuracy on a cleaned, held-out emergency test set (736 images). In a separate realistic live-webcam evaluation, the system achieved 51.4% accuracy, demonstrating a substantial training-to-deployment gap that we ana- lyze in detail. A critical contribution of this work is the identification and quantification of this performance gap between laboratory evalua- tionandreal-worlddeployment,emphasizingtheimportanceofrealistic evaluation protocols in gesture recognition research.

To develop a rigorous theoretical framework for emergent intelligence and scaling laws in foundation models using limit theory.

Knowledge Graphs (KGs) store structured factual information as RDF triples, but their util- ity depends on preservation through NLP transformations. This thesis presents QUASAR (Query-oriented System for Multi-hop Knowledge Graph Data Summarisation), a systematic investigation into information loss when KG triples are verbalized into natural language text, optionally summarized, and then reconstructed back into a KG. We implement a complete six-stage round-trip evaluation pipeline on the WebQSP dataset over Freebase, testing 6 pipeline variants formed by combining 2 verbalization methods (rule-based templatesvs.neuralFlan-T5-XL)with3summarizationlevels(extractiveTextRank, abstractive Flan-T5-XL,andnosummarization). EachvariantproducesareconstructedKGviatheREBEL relation extraction model, which is then evaluated by executing the original 1,639 test SPARQL queries and comparing predicted answers against ground truth. Our key (cid:28)ndings are: (1) the reconstruction stage is the dominant information bottleneck, with REBELrecoveringonly∼12%oftriples(ℓ 5 ≈ 0.88);(2)rule-basedverbalizationconsistentlyout- performs neural across all downstream metrics (Answer F1: 0.0295 vs. 0.0275 for best variants); (3) extractive summarization preserves 93% of recoverable information while abstractive loses 44%; (4) 2-hop queries involving CVT intermediate nodes are essentially unrecoverable across all approaches; and (5) even the identity baseline (original 810K-triple KG) can only answer 28.8% of cross-split test queries, establishing a hard ceiling. The cumulative preservation through the best pipeline variant (nosumm_rb) is 7.77%, demon- strating that text remains a highly lossy compression format for structured knowledge. These (cid:28)ndingshaveimplicationsforKGstorage, transfer, andaugmentationvialargelanguagemodels.

To track progress along the WHO Neglected Tropical Diseases Road Map to 2030 using a Gap Assessment Tool (GAT) and provide a guide to its methodology and results.