Recommendation Systems: A Systematic Review

This article presents a comprehensive and objective systematic review of existing research on recommendation systems with regards to core theory, latest studies, various applications, current attitudes, and potential future applications. The research is mainly based on exploring professional peer-reviewed studies and articles and using their abstracts to create a comprehensive and unbiased review of existing research. The following search terms were used to identify articles and studies for the research: recommendation systems; recommender systems; core theory of recommender systems; current attitudes towards recommendation systems; latest studies on recommendation systems; applications of recommendation systems; potential studies on recommendation systems; and future potential applications of recommendation systems. The research also used the advanced search filter to locate recent studies for comparison by limiting the search by year to find studies published from 2021 onwards. Most literature on this area highlights the importance of recommendation systems in almost all aspects of modern life. Specifically, recommendation systems have become critical components in business, health care, education, marketing, and social networking domains. Additionally, most studies identified reinforcement of learning and deep learning techniques as significant developments in the field. These techniques form the backbone of most modern recommendation systems. The primary concern that could hinder further evolution systems is their consequent filter bubble effects which many studies showed to be problematic. Healthcare is a central area that shows tremendous potential for these systems. Although recommender systems have been implemented in this domain, there remains a lot of untapped potential that, if unleashed, could revolutionize medicine and healthcare. But the problems facing these systems have to be tackled first to establish trust. Keywords: Recommendation systems, Recommender systems, Deep learning, Reinforcement learning DOI: 10.7176/CEIS/13-4-04 Publication date:August 31st 202

Deep Multi-task Learning for Depression Detection and Prediction in Longitudinal Data

Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. However, they are challenged by the relative scarcity of instances of depression in the data. In this work we introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks, namely one-class metric learning and anomaly ranking. The auxiliary tasks introduce an inductive bias that improves the classification model's generalizability on small depression samples. Further, unlike existing studies that focus on learning depression signs from static data without considering temporal dynamics, we focus on longitudinal data because i) temporal changes in personal development and family environment can provide critical cues for psychiatric disorders and ii) it may enable us to predict depression before the illness actually occurs. Extensive experimental results on child depression data show that our model is able to i) achieve nearly perfect performance in depression detection and ii) accurately predict depression 2-4 years before the clinical diagnosis, substantially outperforming seven competing methods.Comment: 9 pages, 3 figures, 3 table

Saprochaete Capitata Infection in an 80–Year Old Chronic Obstructive Pulmonary Disease (COPD) Patient: A Case Report

BACKGROUND: The fungal disease caused by invasive fungus Saprochaete capitata is becoming an increasingly popular infection. Fungal pathogens mainly occur in patients with immunocompromised disorders such as hematologic malignancies, acute myeloid leukemia, transplant patients. CASE REPORT: In this study, we presented a COPD patient infected with S. capitata. At the first check, the patient showed cough, dyspnea, chest pain on both sides. The clinical laboratory test result was characterized with high White blood cell (12.8 G/L), HIV negative. The X ray showed bronchitis and emphysema. Bronchoscopy illustrated bronchial mucositis. CT scanner demonstrated pneumonia with fuzzy nodular lesions and thick interstitial organization in both lungs. The patient was treated with ciprofloxacin 800 mg/day; cefuroxime 2250 mmg/day. However, the fever appeared 2 weeks thereafter. The S. capitata was discovered in the bronchial fluid. The patient was then treated with fluconazole 400 mg/day for 14 days. At the end of treatment, all signs and symptoms of S. capitata infection disappeared and the patient recovered. CONCLUSION: This case study showed that S. capitata infection can occur in the COPD patients and fluconazole is a pertinent drug for treatment of the infection

Why adaptation falters:principles for climate change adaptation policy assessment in Vietnam

PurposeRecognition that not every climate adaptation policy is a good one has shifted attention to new tools and methods to measure the adequacy and effectiveness of adaptation policies. This study aims to propose and apply and applies an innovative adaptation policy assessment framework to identify the extent to which climate adaptation policies in Vietnam exhibit conditions that are likely to ensure a sufficient, credible and effective adaptation.Design/methodology/approachIn total, 21 conditions, categorized under five normative principles and covering critical issue areas in adaptation domain, form the climate adaptation policy assessment framework. The principles were double-checked and tested in case studies through observations and analyses of policy documents to ensure that each condition should be distinct and not overlapping across principles. To see if the principles and attendant conditions were able to capture all relevant aspects of adaptation, the authors used structured expert judgment. In total, 39 policy documents pertaining to climate change adaptation were selected for qualitative document analysis. In-depth interviews with local officials and experts were conducted to address data gaps.FindingsThe study reveals major weaknesses constituting a reasonably worrisome picture of the adaptation policies in Vietnam since several critical conditions were underrepresented. These results shed new light on why some adaptation policies falter or are posing adverse impacts. The findings suggest that a sound policy assessment framework can provide evidence on what effective adaptation policy looks like and how it can be enabled. The framework for climate adaptation policy assessment in this study can be easily adjusted and used for different socio-environmental contexts in which new conditions for policy assessment might emerge.Social implicationsThe findings show underlying weaknesses constituting a reasonably worrisome picture of the adaptation regime in Vietnam. In the absence of mechanisms and measures for accountability and transparency in policy processes, adaptation in Vietnam appears more likely to be prone to maladaptation and corruption. While solving these problems will not be easy for Vietnam, the government needs to evaluate whether the short-term gains in sustaining the existing adaptation policies really make progress and serve its long-term climate-adaptive development goals.Originality/valueAlthough interpretations of adaptation effectiveness may be very divergent in different normative views on adaptation outcomes, the authors argue that a common, agreed-upon effectiveness can be reached if it is clearly defined and measurable in adaptation policies. Thus, the climate adaptation policy assessment framework proposed in this study is critical for policymakers, practitioners, donors and stakeholders dealing with adaptation to better understand the weaknesses in policymaking processes, pinpoint priority areas of action and timely prevent or prepare for possible adverse impacts of policies

A Machine Learning-based Approach to Vietnamese Handwritten Medical Record Recognition

Handwritten text recognition has been an active research topic within computer vision division. Existing deep-learning solutions are practical; however, recognizing Vietnamese handwriting has shown to be a challenge with the presence of extra six distinctive tonal symbols and extra vowels. Vietnam is a developing country with a population of approximately 100 million, but has only focused on digitalization transforms in recent years, and so Vietnam has a significant number of physical documents, that need to be digitized. This digitalization transform is urgent when considering the public health sector, in which medical records are mostly still in hand-written form and still are growing rapidly in number. Digitization would not only help current public health management but also allow preparation and management in future public health emergencies. Enabling the digitalization of old physical records will allow efficient and precise care, especially in emergency units. We proposed a solution to Vietnamese text recognition that is combined into an end-to-end document-digitalization system. We do so by performing segmentation to word-level and then leveraging an artificial neural network consisting of both convolutional neural network (CNN) and a long short-term memory recurrent neural network (LSTM) to propagate the sequence information. From the experiment with the records written by 12 doctors, we have obtained encouraging results of 6.47% and 19.14% of CER and WER respectively

Characterization and utilization of pulp and paper mill sludge digesting thermophilic bacteria in composting process

Pulp and paper mill sludge (PPMS) was found to be poorly colonised with thermophilic microorganisms. However, evidence to support the need for inoculation to facilitate PPMS composting has only been demonstrated in one instance. In this study, we aimed to: screen and identify PPMS digesting thermophilic bacterial strains; investigate effects of the mixture of selected thermophilic bacterial strains on PPMS digestion; and utilize this mixture as start inoculum in PPMS composting and assess the quality of compost product. The results showed that eleven thermophilic bacterial strains were isolated from Bai Bang PPMS by the enrichment culture method. Among these, three strains which reflected high growth rates on the plates of Minimal Media Agar supplemented with Bai Bang PPMS and showed hydrolytic and ligninolytic activities on the agar plates containing appropriate inductive substrates were selected. Based on the morphological, biochemical characteristics and 16S rRNA gene sequencing, they were identified as Bacillus subtilis. The inoculation with the mixture of selected strains enhanced remarkably Bai Bang PPMS digestion. The dry weight decrease, volatile suspended solids removal, dehydrogenase and protease activities in the inoculated sludge were 2.1-, 1.5-, 1.3- and 1.2- fold higher, respectively, compared to the non-inoculated sludge. The assessment of compost quality based on stability using the alkaline trap method and maturity using the germination and root elongation test showed that the inoculated compost was stable and mature while the non-inoculated compost was unstable and immature. These thermophilic bacterial strains therefore have great potential for Bai Bang PPMS composting

Chemical profiles and biological activities of acetone extracts of nine Annonaceae plants

This study investigated the chemical components and bioactivities of acetone leaf extracts of nine Annonaceae plants collected in the Binh Chau-Phuoc Buu Nature Reserve, Vietnam. A total of 182 constituents were identified, with linolenic acid, diaeudesmin, germacrene D, 1-octadecenoic acid, 8-(3-octyl-2-oxiranyl)-1-octanol, oleic acid, and phenylmethyl ester being the major compounds. The antimicrobial activity of the extracts was evaluated using a disc diffusion assay. Eight of the nine extracts, except for the Mitrephora thorelii extract, showed an inhibition effect against Bacillus cereus and Staphylococcus aureus. The antioxidant activity of the extracts was determined using DPPH assay, and the cytotoxic activity was deter mined using SRB assay. The results showed that the acetone extracts of Artabotrys hexapetalus, Uvularia grandiflora, Polyalthia luensis, Xylopia pierrei, Sphaerocoryne affinis, Desmos cochinchinensis, Uvaria littoralis, Mitrephora thorelii, and Goniothalamus touranensis had significant activity with IC50 for the DPPH radical scavenging activity ranging from 18.56 to 702.33 μg/mL, and the IC50 for the cytotoxic effects ranged from 5.39 to 251.77 μg/mL. Overall, the results obtained provide experimental evidence for the potential use of these plants in medicine and other related fields

Chemical components and biological properties from acetone extracts of Conamomum vietnamense

Conamomum vietnamense is an endemic and rare species from Vietnam. The aim of this study is to determine the chemical compositions, antibacterial and antioxidant properties of the acetone extracts obtained from the different organs of this species for the first time. A total of 82 components were identified from the acetone extracts of leaf, flower, and rhizome of C. vietnamense using Gas chromatography–mass spectrometry (GC/MS) technique. Furthermore, the agar disk-diffusion method was also used to determine the antibacterial activity of the C. vietnamense extracts. Accordingly, the leaf extract was found to be effective against eight out of nine bacterial strains while the flower and rhizome extracts displayed activity against four out of nine tested bacteria. In addition, the three organs of C. vietnamense also possessed the high DPPH scavenging properties. The results of this study indicate that C. vietnamense extracts have the potential to be developed into pharmaceutical products in the future