Application Of Recommendations Research Articles

Angiotensin II Type-1 Receptor Antibody in Solid Organ Transplantation – Is It Time to Test?

Angiotensin II type-1 receptor antibody (AT1R-Ab) has been mooted as a potential effector of both acute and chronic antibody mediated rejection (AMR). A growing body of literature on the topic is now coming under scrutiny in the context of the evolving Banff AMR diagnostic classification system and refinement of recommendations for histocompatibility testing by the Sensitization in Transplantation Assessment of Risk (STAR) workgroup. This mini-review discusses the latest understanding of pathophysiological mechanisms, clinical evidence for the pathogenicity of AT1R-Ab, and methods of laboratory testing.

Breast Cancer In Teenager with Neurofibromatosis 1

Breast cancer is highly prevalent amongs t older women but much less common in younger women, especially those under the age of 20 . Here we present a case of an 18-year-old woman with Neurofibromatosis 1 diagnosed with breast cancer. Review of literature demons trates a five-fold increased risk of developing breast cancer before age 50 with a mutation in this tumor suppressor gene. Incorporation of earlier screening recommenda tions for at-risk younger women is warranted in more guide lines.

Élő method based mobile TV recommendation system

AbstractStreaming services spread rapidly. Among these services there are the linear TV, video library or program review system, while the online platform offering these contents is called mobile TV. A recommendation system may not only keep existing clients, but may also generate further turnover, should it introduce new content to the users. In this paper a recommendation system based on the Élő point calculation method is addressed. It is detailed how the programs should be grouped into different dimensions and what type of categories should be considered. Further, the idea of punch cards is introduced. Besides, the user profiles are set. The match system introduced by Élő is applied to the present situation. The system is introduced at a local mobile TV provider with 20,000 users.

Evaluating the use of large language models to provide clinical recommendations in the Emergency Department

The release of GPT-4 and other large language models (LLMs) has the potential to transform healthcare. However, existing research evaluating LLM performance on real-world clinical notes is limited. Here, we conduct a highly-powered study to determine whether LLMs can provide clinical recommendations for three tasks (admission status, radiological investigation(s) request status, and antibiotic prescription status) using clinical notes from the Emergency Department. We randomly selected 10,000 Emergency Department visits to evaluate the accuracy of zero-shot, GPT-3.5-turbo- and GPT-4-turbo-generated clinical recommendations across four different prompting strategies. We found that both GPT-4-turbo and GPT-3.5-turbo performed poorly compared to a resident physician, with accuracy scores 8% and 24%, respectively, lower than physician on average. Both LLMs tended to be overly cautious in its recommendations, with high sensitivity at the cost of specificity. Our findings demonstrate that, while early evaluations of the clinical use of LLMs are promising, LLM performance must be significantly improved before their deployment as decision support systems for clinical recommendations and other complex tasks.

A pragmatic approach to selecting a grading system for clinical practice recommendations in palliative care.

The limited palliative care evidence base is poorly amenable to existing grading schemes utilized in guidelines. Many recommendations are based on expert consensus or clinical practice standards, which are often considered 'low-quality' evidence. Reinforcing provider hesitancy in translating recommendations to practice has implications for patient care. To rationalize the selection of an appropriate grading system for rating evidence to support recommendations made in palliative care clinical practice guidelines. Review of the methodology sections of international palliative care guidelines published in English identified five grading systems comparison: Grading of Recommendations, Assessment, Development and Evaluations (GRADE); the Scottish Intercollegiate Guidelines Network (SIGN); Infectious Diseases Society of America-European Society for Medical Oncology (IDSA-ESMO); Confidence in the Evidence from Reviews of Qualitative research (CERQual) and the National Service Framework for Long Term Conditions (NSF-LTC). There is heterogeneity among grading systems used in published palliative care or terminal symptom management guidelines. GRADE has been increasingly adopted for its methodological rigour and inter-guideline consistency with other medical associations. CERQual has the potential to support recommendations informed by qualitative evidence, but its role in clinical guidelines is less defined. The IDSA-ESMO system has an intuitive typology with the ability to categorize tiers of lower-quality evidence. It is challenging to apply commonly used grading systems to the palliative care evidence base, which often lacks robust randomized controlled trials (RCTs). Adoption of IDSA-ESMO offers a feasible and practical alternative for lower-resourced guideline developers and palliative clinicians without a prerequisite for methodological expertise.

An expert system of recommendations for combating cyber threats using CVSS metrics and game theory

This study is focused on the creation of an expert system for generating recommendations on cyber security. The developed expert system uses a game-theoretic model as a inference engine to transform expert knowledge into recommendations for end-users, who may be chief IT security officers (CISOs), system administrators, or cyber security engineers. Expert knowledge is presented in the form of an estimate of the base group of CVSS metrics - Common Vulnerability Score System, for each type of attack and adjusted values of CVSS in the case that the counterattack strategy is applied. Given a set of attacks and a base of expert attack knowledge, the system generates a game matrix of zero-sum game with a cybercriminal and a cyberdefense expert as players. The inference engine of the expert system is a game-theoretic model responsible for solving the game using the Brown-Robinson iterative method and generating cyber protection recommendations. An experiment was conducted on the convergence of the BrownRobinson algorithm on the 2022 vulnerability dataset from the Cybersecurity and Infrastructure Security Agency database, as a result of which it was determined that the convergence of the algorithm for solving the matrix game is achieved at a number of iterations of 1000. As a result of the work, expert system was designed and implemented along with the Web interface, which provides input by experts of CVSS level assessments of collected threats, threats countermeasures and output of recommendations for combating cyber threats

A Rule-Based System for Admission Recommendations in Nigerian Tertiary Institutions

This research presents a rule-based system to provide admission recommendations in Nigerian tertiary institutions. Unlike many existing studies that employ machine learning classifiers, this research aims to develop a transparent and deterministic system using predefined rules. The rule-based system recommends suitable courses based on candidates’ O’level results, Unified Tertiary Matriculation Examination (UTME) scores, computed post-UTME scores, and specific eligibility criteria. By focusing on a rule-based approach, the system mitigates the limitations and potential misclassifications associated with machine learning, thereby enhancing the efficiency and accuracy of the admissions process. The methodology follows predefined guidelines to ensure a transparent and understandable admissions procedure. This approach avoids the complexities and uncertainties of predictive models, providing an accurate and reliable method for course recommendations. The rule-based recommendation system ultimately aims to support educational institutions and admission offices in making informed decisions regarding candidate qualifications, eligibility, and course recommendations.

A novel CLIPS-based medical expert system for migraine diagnosis and treatment recommendation

Migraines are classified as a neurological disorder defined by recurrent headaches with pain that ranges from mild to severe. Currently, this disorder lacks a permanent cure and definitive diagnostic test. Diagnosis instead requires an assessment of physical and psychological symptoms which differ among patients. To help in the diagnosis process, medical expert systems have been developed and validated since 1960. In this paper, we propose the Migraine Diagnosis and Treatment Expert System (MDATES), a medical expert system for migraine diagnosis and treatment recommendation. The system was designed and implemented using the C Language Integrated Production System (CLIPS) shell. MDATES is able to recognize seven symptoms, two classes of migraines (chronic and episodic), and four subtypes of migraine-classification knowledge (hormonal, aura, hemiplegic, and cluster). A dataset of 300 anonymized patient records with confirmed migraine cases was used to test the system. The diagnoses generated by MDATES were compared against the known diagnoses, and a high level of accuracy was observed, with 67% of the 100 training cases were correctly diagnosed, and 100% of the 200 testing cases were correctly diagnosed. These results highlight the effectiveness and reliability of MDATES and provide valuable assistance to medical professionals in diagnosing migraines. Moreover, we present a literature review that places our proposed system within the broader context of rule-based expert systems for migraine diagnosis and treatment recommendation. This review explores the effectiveness, limitations, and challenges of these systems, and accurately places our system within the current landscape.

Vulnerability of Gen-Z to E-Commerce Deception on Consumer's Belief Categories in Online Product Recommendations Systems

Purpose: Despite the convenience of the Online Product Recommendation (OPR) system, concerns persist regarding deception practices in online shopping, especially among consumers from the Generation Z demographic cohort, for which less attention has been given in the previous literature. Methodology/Approach: The impact of independent variables, namely Perceived Usefulness, Perceived Enjoyment, and Perceived Risk, on Susceptibility to e-commerce fraud among Gen Z consumers has been studied. By conducting a mall intercept survey in four major metropolitan cities in India with a usable sample of 488 responses, the study empirically tested the data using SmartPLS 4.0. Findings: The study concludes that Perceived Usefulness and Enjoyment positively influence continuous usage intention, while Perceived Deception and Perceived Risk are negatively connected with continuous usage intention. It depicts that the Gen Z consumer's belief categories formed during their early years as digital natives sharpen their alertness to deception practices. Research Limitation/Implication: The study may not be generalised to represent Gen Z consumers as it collected data only from those who visited malls in the metropolitan cities of India. Originality/Value of paper: This paper exclusively investigates the interrelation of belief categories to continuous usage intention. Further, the intersection of perceived Deception is proved in light of the E-commerce practices.

Prospects for the development of national logistics under sanctions

The article examines the theoretical background and practical experience of creating a logistics startup in the context of the national logistics infrastructure. The purpose of this scientific article is to develop a system of recommendations for implementing a logistics startup in conditions of economic turbulence. The relevance of the stated topic is due to the fact that due to international instability of economic relations, a significant part of the logistics export and import channels have collapsed, which requires entrepreneurial initiative to create new supply channels taking into account current foreign economic realities. At the same time, in the current conditions, the problems of the Russian logistics system related to its lack of self–sufficiency and the presence of certain links, the existence of which is necessary to ensure its integrity, have become urgent. In particular, these links are represented by structures providing container transportation, structures authorized to carry out international insurance and technology companies used in organizing navigation and dispatch processes. The high probability that Russia will be cut off from global logistics processes for a long time indicates that there are unfilled niches in the country for the creation of domestic logistics start–ups, which requires the implementation of logistics start–ups on conditions other than standard market ones.

Do Perceived Benefits Compensate for Low Provider Trustworthiness in Disclosure Decisions?

Abstract: People regularly receive algorithm-based recommendations (e.g., in social media and online shopping). Today, such algorithmic recommendations cover a wider range of applications, such as finding recipes that match an individual’s needs and food preferences. To give better-fitting recommendations, algorithms incorporate large amounts of personal information about the individual, which raises the issue of privacy concerns. When are people willing to share such information that can then be considered by algorithms? While previous research has indicated that perceived benefits of platform use and provider trustworthiness influence whether people are willing to disclose personal information, these studies often have two major limitations: they are either survey-based – thus, not allowing causal conclusions – or lack realistic usage (i.e., rely on vignettes). Thus, in the present experimental study ( N = 329), we manipulated provider trustworthiness and asked participants about their willingness to disclose information directly after interacting with an algorithm-based system for recipe recommendations. Results indicate that higher perceived benefits and provider trustworthiness are related to more willingness to disclose information. However, these effects are independent of each other. Thus, the present research suggests that provider trustworthiness causally leads to a higher willingness to share information and high benefits do not compensate for low provider trustworthiness.

Doctors' perception on the ethical use of AI-enabled clinical decision support systems for antibiotic prescribing recommendations in Singapore.

The increased utilization of Artificial intelligence (AI) in healthcare changes practice and introduces ethical implications for AI adoption in medicine. We assess medical doctors' ethical stance in situations that arise in adopting an AI-enabled Clinical Decision Support System (AI-CDSS) for antibiotic prescribing decision support in a healthcare institution in Singapore. We conducted in-depth interviews with 30 doctors of varying medical specialties and designations between October 2022 and January 2023. Our interview guide was anchored on the four pillars of medical ethics. We used clinical vignettes with the following hypothetical scenarios: (1) Using an antibiotic AI-enabled CDSS's recommendations for a tourist, (2) Uncertainty about the AI-CDSS's recommendation of a narrow-spectrum antibiotic vs. concerns about antimicrobial resistance, (3) Patient refusing the "best treatment" recommended by the AI-CDSS, (4) Data breach. More than half of the participants only realized that the AI-enabled CDSS could have misrepresented non-local populations after being probed to think about the AI-CDSS's data source. Regarding prescribing a broad- or narrow-spectrum antibiotic, most participants preferred to exercise their clinical judgment over the AI-enabled CDSS's recommendations in their patients' best interest. Two-thirds of participants prioritized beneficence over patient autonomy by convincing patients who refused the best practice treatment to accept it. Many were unaware of the implications of data breaches. The current position on the legal liability concerning the use of AI-enabled CDSS is unclear in relation to doctors, hospitals and CDSS providers. Having a comprehensive ethical legal and regulatory framework, perceived organizational support, and adequate knowledge of AI and ethics are essential for successfully implementing AI in healthcare.

OBLIGATIONS RELATED TO THE APPLICATION OF A PREVENTIVE MEASURE IN THE FORM OF BAIL: PROBLEMS OF IMPLEMENTATION IN THE CONTEXT OF THE CASE LAW OF THE EUROPEAN COURT OF HUMAN RIGHTS

The purpose of the article is to identify the problems in the fulfilment of the obligations related to bail as a preventive measure based on the approaches formulated in the case law of the European Court of Human Rights, as well as to offer recommendations on how to resolve them. Methods. The general philosophical basis of the study was formed by axiological and hermeneutical approaches. In particular, the first one allowed to carry out a value analysis of the fundamental human right to liberty and to assess the impact of the amount of bail to provide an effective alternative for the restriction of this right. Meanwhile, the second made it possible to apply an in-depth study and interpretation of the legal texts of the European Court of Human Rights judgments and the national legislation. When building the system of recommendations, we used the systemic and structural method, as well as the logical research method and the method of legal modelling with. Results and conclusions. The analysis of the legal positions of the ECtHR made it possible to conditionally single out the following standards and recommendations for national judges for ensuring the legality and reasonableness of the determination the amount of bail in criminal proceedings: 1) the main purpose of bail is to ensure the appearance of a person, not to punish him or her or to create conditions for compensation for the damage caused by a criminal offence; 2) the accuracy in determining the amount of bail as an alternative to possible keeping in custody should be equal to the thoroughness in justifying the continued keeping of a person in custody; 3) if the bail is provided by the suspect/accused, the key thing to consider when determining the optimal amount of bail is his/her assets (property status) and solvency. However, if the bail is provided by another person (the bail bondsman), the relationship between the bail bondsman and the suspect/accused should be taken into account to determine the effective bail amount; 4) the extent of the damage caused by the criminal offence may be taken into account as an exception when determining the bail amount - but this cannot be applied as a general rule due to the different purpose of the bail; 5) a suspect/accused person who is being considered the possibility of being granted bail must provide the court with information that may be necessary to adequately determine the amount of potential bail and be verified for this aim; 6) the amount of bail must be substantiated in the decision of the investigating judge or court, which will serve as an important guarantee of preventing arbitrariness; 7) failure of a person to fulfil the obligation to provide bail after it has been set may be a sign that the amount of bail is excessive and disproportionate for the suspect/accused.

Active intrusion detection and prediction based on temporal big data analytics

Computer security consists in protecting access and manipulating system data by several mechanisms. However, conventional protection technologies are ineffective against current attacks. Thus, new tools have appeared, such as the intrusion detection and prediction systems which are important defense elements for network security since they detect the ongoing intrusions and predict the upcoming attacks. Besides, most of conventional protection technologies remain insufficient in terms of actions since they are all passive systems, unable to provide recommendations in order to block or stop the attacks. In this paper, a distributed detection and prediction system, composed of three major parts, is proposed. The first part deals with the detection of intrusions based on the decision tree learning algorithm. The second part deals with intrusions prediction using the chronicle algorithm. The third part proposes an expert system for security recommendations in response to detected intrusions, able to provide appropriate recommendations to stop the attacks. The proposed system gives good results in terms of accuracy and precision in detecting and predicting attacks, and efficiency in proposing the right recommendations to stop the attacks.

Online Medicine Application with Personalized Blockchain Wallet and AI-Based Doctor Consultation System

The "Online Medicine Application with Personalized Blockchain Wallet and AI-based Doctor Consultancy System" presents a visionary approach to address the evolving healthcare landscape by integrating cutting-edge technologies. This initiative encompasses an intuitive online medical application facilitating seamless healthcare service access, alongside a secure and personalized blockchain wallet for confidential health record management. Additionally, it features an AI-driven doctor consultancy system for intelligent diagnosis and treatment recommendations. Emphasizing digital transformation, accessibility, and data security, the project aims to enhance healthcare convenience and patient-centricity. Successful implementation promises to revolutionize healthcare delivery, empowering individuals while addressing regulatory challenges. This report delineates the project methodology, utilized technologies, system architecture, data privacy measures, implementation phases, and testing processes. Moreover, it discusses user training, support strategies, deployment plans, and future directions. By amalgamating these components, the project endeavours to redefine healthcare accessibility and quality, transforming the delivery and experience of healthcare services. Keywords: Blockchain Healthcare, Medical Records Security, Medication Catalog, Personalized Blockchain Wallet, AI Chatbot, Smart Contracts, Patient Privacy

Dual-blend insight recommendation system for e-commerce recommendations and enhance personalization

E-commerce, short for electronic commerce, refers to the buying and selling of goods and services over the internet. This digital transaction model has revolutionized the way businesses operate and consumers shop. In response to the burgeoning complexity of e-commerce datasets, this work addresses the need for advanced recommendation systems. This work introduces the dual-blend insight recommendation system (DIRS) model for personalized e-commerce recommendation system. The DIRS model involves dataset loading, preprocessing, and feature extraction, enabling training with recurrent neural network (RNN) and Bayesian personalized ranking (BPR) models. Recommendations are generated based on user-defined functions, i.e., location and session, and evaluation metrics such as hit rate (HR) and mean reciprocal rate (MRR) highlight DIRS’s superior performance. The model is evaluated using the Tmall dataset. Results reveal DIRS consistently outperforms alternative algorithms, showcasing its effectiveness in 10k and 20k recommendation sets. This study provides valuable insights into optimizing e-commerce recommendations, emphasizing DIRS as a powerful model for enhancing user experience and engagement.

AgriSolutions: Innovations in Crop Disease Prediction, Crop Recommendation, and E Commerce

Abstract: Crop disease prediction and crop recommendation are important steps towards sustainable agriculture. The use of deep learning, EfficientNet_v2 and image processing technologies can help in improve accuracy in these tasks. Identification of plant diseases and recommendation of crops cannot maximize only yield production but can be supportive for varied types of agricultural practices The existing system of crop recommendation employs data analysis to suggest suitable crops based on soil and climate factors, while disease prediction system utilizes predictive models to anticipate and prevent crop diseases. In this Paper, we propose a model that combines these techniques to accurately identify to recommend suitable crops based on the Soil Parameters and climate conditions of the farm. Our proposed model achieved and classify plant diseases, recommend suitable crops based on the soil condition. To achieve this, we trained a deep neural network on a large dataset of images of healthy and diseased plants, and performed image preprocessing.

Digital Technologies to Support Learning

The diversity of technologies and tools in the contemporary digital world is ever rising. They enable teachers to plan and develop their own learning materials with ease - from web pages, wikis and blogs to photos and videos. This becomes the challenge of developing ways in which technology can support learners of this course to effectively create their own learning contexts. The article analyzes modern researches in some theories of learning, with the aim of seeking an answer to the question: how teachers can support the learning of students through teaching materials. As a result, a system of science-based recommendations for teachers in the context of digital resource design to use in teaching is proposed. Analysis of empirical research results shows that a template for a quality distance learning e-course that creates a familiar learning environment is more effective for learning than courses without a template.

Investigation of Bad Credit in P2P Lending Based on Personal Character in Indonesia

This research examines Indonesian P2P lending and bad loan management by analyzing borrower characteristics. The large value of problematic loans is a key signal for this investigation. The Big Five Personality Approach is used to survey P2P lending users. This study solely examines if the Big Five personality traits affect evaluation. On March 5–18, 2023, 17 P2P lending sites gathered data from 197 respondents for two weeks. Data validation continued until 11 respondents were pruned. The remaining 186 respondents' data was evaluated using SPSS. The management process yielded two hypothesis tests, the T-test and the F-test, which showed that the Big Five personality significantly affects present assessment activities. F-Test findings show the Big Five Personalities have a big impact concurrently. These two factors suggest that the Big Five Personalities may evaluate decision support systems for borrower recommendations.

Clinical practice guideline: Interventions for Developmental Language Delay and Disorders.

Approximately 9.9 % of children present with difficulties in language development (DLD), 7.6 % without serious additional impairments and 2.3 % associated with languagerelevant comorbidities, e.g., hearing loss. Notably, in a consensus statement by experts in German-speaking countries, in the guideline presented here, and further in this article, all of these disorders are referred to as "developmental language disorders" (DLD), whereas the international consortium CATALISE only refers to those without comorbidities as DLD. DLDs are among the most commonly treated childhood disorders and, if persistent, often reduce educational and socio-economic outcome. Children in their third year of life with developmental language delay (late talkers, LT) are at risk of a later DLD. This German interdisciplinary clinical practice guideline reflects current knowledge regarding evidence-based interventions for developmental language delay and disorders. A systematic literature review was conducted on the effectiveness of interventions for DLD. The guideline recommends parent training (Hedges g = 0.38 to 0.82) for LTs with expressive language delay, language therapy (Cohen's d = -0.20 to 0.90) for LTs with additional receptive language delay or further DLD risk factors, phonological or integrated phonological treatment methods (Cohen's d = 0.89 to 1.04) for phonological speech sound disorders (SSDs), a motor approach for isolated phonetic SSDs (non-DLD), and for lexical-semantic and morpho-syntactic impairments combinations of implicit and explicit intervention approaches (including input enrichment, modeling techniques, elicitation methods, creation of production opportunities, metalinguistic- approaches, visualizations; Cohen's d = 0.89-1.04). Recom mendations were also made for DLD associated with pragmatic-communicative impairment, bi-/ multilingualism, hearing loss, intellectual disability, autism-spectrum disorders, selective mutism, language- relevant syndromes or multiple disabilities, and for intensive inpatient language rehabilitation. Early parent- and child-centered speech and language intervention implementing evidence-based intervention approaches, frequency, and settings, combined with educational language support, can improve the effectiveness of management of developmental language delay and disorders.