Empowering recommender systems using automatically generated Knowledge Graphs and Reinforcement Learning

Personalized recommendations have a growing importance in direct marketing, which motivates research to enhance customer experiences by knowledge graph (KG) applications. For example, in financial services, companies may benefit from providing relevant financial articles to their customers to cultivate relationships, foster client engagement and promote informed financial decisions. While several approaches center on KG-based recommender systems for improved content, in this study we focus on interpretable KG-based recommender systems for decision making.To this end, we present two knowledge graph-based approaches for personalized article recommendations for a set of customers of a large multinational financial services company. The first approach employs Reinforcement Learning and the second approach uses the XGBoost algorithm for recommending articles to the customers. Both approaches make use of a KG generated from both structured (tabular data) and unstructured data (a large body of text data).Using the Reinforcement Learning-based recommender system we could leverage the graph traversal path leading to the recommendation as a way to generate interpretations (Path Directed Reasoning (PDR)). In the XGBoost-based approach, one can also provide explainable results using post-hoc methods such as SHAP (SHapley Additive exPlanations) and ELI5 (Explain Like I am Five).Importantly, our approach offers explainable results, promoting better decision-making. This study underscores the potential of combining advanced machine learning techniques with KG-driven insights to bolster experience in customer relationship management.Comment: Accepted at KDD (OARS) 2023 [https://oars-workshop.github.io/

The orthodontic-oral surgery interface. Part one: A service evaluation and overview of the diagnosis and management of common anomalies

Introduction: Complex dental anomalies often require multidisciplinary management in secondary care. These anomalies may present to general dental practitioners in the first instance, therefore it is important that all clinicians are aware of such anomalies and their management. / Aims: This service evaluation aimed to profile the dental anomalies presenting to the multidisciplinary joint dento-alveolar clinic at a district general hospital and to outline the management of the common anomalies. / Method: This two-part series details a service evaluation and profiles the dental anomalies presenting to the joint dento-alveolar clinic at a district general hospital. In addition, the features and management of the common dental anomalies are outlined. / Results: Fifty-four percent of patients were female, with a mean age of 17 years. The most common anomalies were impacted canines (49%), followed by supernumerary teeth (16%) and impacted premolars (10%). Other anomalies accounted for only a small percentage of referrals. / Conclusion: A wide range of anomalies presented to the joint dento-alveolar clinic. It is important for all clinicians to be aware of such anomalies, their diagnosis and management. Part one and two of this article therefore provide an overview of the common anomalies and highlight their management

Voice Analysis System for Detection of Vishing Using Deep Learning

For many years, telecom fraud has caused significant financial harm to Indian telecommunications users. Traditional approaches for identifying telecom fraud frequently center on boycotting of faux phone numbers. Attackers, on the other hand, could merely escape discernment by modifying their phone numbers, which is fairly straightforward with VoIP (Voice over IP).To address this issue, this method detects telecommunication fraud supporting the substance of a spoken language rather than merely the caller's sign. This paper collects chronicles of telecommunication deceit, above all, from press sources and social platforms. To make datasets, our planned model utilizes machine learning techniques to look at knowledge and opt for high-quality descriptions from antecedently gathered knowledge. After that, Natural language processing is employed to draw out characteristics from the text- based data. Then, for extra telecommunication fraud detection, criteria to acknowledge identical material within the same call are formed. To spot telecommunication fraud online, the system provides an associate degree android application that will be loaded on a customer's smartphone. Once an associate degree incoming fraud decision is answered, the program will dynamically measure the call's contents to sight fraud. Our findings demonstrate that the system will effectively safeguard clients

Voice analysis system for detection of vishing using deep learning

For many years, telecom fraud has caused significant financial harm to Indian telecommunications users. Traditional approaches for identifying telecom fraud frequently center on boycotting of faux phone numbers. Attackers, on the other hand, could merely escape discernment by modifying their phone numbers, which is fairly straightforward with VoIP (Voice over IP).To address this issue, this method detects telecommunication fraud supporting the substance of a spoken language rather than merely the caller's sign. This paper collects chronicles of telecommunication deceit, above all, from press sources and social platforms. To make datasets, our planned model utilizes machine learning techniques to look at knowledge and opt for high-quality descriptions from antecedently gathered knowledge. After that, Natural language processing is employed to draw out characteristics from the text- based data. Then, for extra telecommunication fraud detection, criteria to acknowledge identical material within the same call are formed. To spot telecommunication fraud online, the system provides an associate degree android application that will be loaded on a customer's smartphone. Once an associate degree incoming fraud decision is answered, the program will dynamically measure the call's contents to sight fraud. Our findings demonstrate that the system will effectively safeguard clients

Opinion of dentists and gynecologists on the link between oral health and preterm low birth weight: “Preconception care - treat beyond the box”

Background: Periodontal diseases are known to set an inflammatory response at the systemic level which can adversely affect the pregnancy outcomes, and many patients are unaware of this association. Health-care providers play a vital role in educating patients toward maintaining good oral health; hence, our study was planned to evaluate the knowledge, attitude, and practices of dentists' and gynecologists' concerning this link. In addition, referral to the dentists' during the preconception period was also emphasized. Materials and Methods: This was a cross-sectional questionnaire survey conducted among dentists and gynecologists' in the city of Mysore. The participants were divided into two groups and were asked specific questions about the association between oral health and preterm low birth weight (PTLB). Group I constituted the dentists possessing both graduate and postgraduate degree and Group II were the gynecologists'. Results: Seventy-nine percent of the total participants agreed about the link between periodontal health and PTLB. Dentists were more aware of the periodontal changes during pregnancy and stressed on frequent dental checkup during pregnancy. Preconception care was a neglected part of the protocol by the gynecologists', and only 12% of them referred patients to a dentist in the preconception period. Factors significantly associated with knowledge in logistic regression analyses were older age, dentists and the years of experience (>5 years). Conclusion: Knowledge about oral health is important, to maintain good general health; hence, health workers should not miss opportunities to contribute to this health promotion. An integrated work by the dentists and gynecologists may reduce the adverse pregnancy outcomes associated with periodontal diseases

Inadvertent Right Ventricle Perforation by Temporary Pacemaker Lead: A Case Report Requiring Surgical Exploration

Right ventricle perforation by pacemaker lead is rare but a life-threatening complication. It may present acutely within few hours of pacemaker implantation as hemopericardium, dreaded cardiac tamponade, leading to acute hemodynamic deterioration; even death, if not address immediately. At times, it presents gradually with progressive decline in hemodynamic and requires surgical exploration. The authors report a case of 51-year old female whose hemodynamic worsens gradually after temporary pacemaker implantation, requiring surgical exploration

Acute Rupture of Sinus of Valsalva into Right Atrium: An Echocardiographic Halftone

A ruptured sinus of Valsalva is a rare and emergent cardiac abnormality, which can be acute and rapidly expanding to cause hemodynamic deterioration. Rupturing into right-sided heart chambers causes severe left-to-right shunt and sudden volume overload. Echocardiography especially transesophageal echocardiography (TEE) is the gold standard facility for early perioperative diagnosis and also to guide surgical technique. Medical stabilization followed by prompt surgical intervention is the treatment of choice. We hereby report a case of sinus of Valsalva rupturing into right atrium leading to severe left-to-right shunt and the role of TEE in its perioperative management