Applied Science and Biotechnology Journal for Advanced Research
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Adversarial for Sequential Recommendation Walking in the Multi-Latent Space
Get PDFRecently, sequential recommendation plays a critical role in our daily life, since it serves as personalized information filters to dis- cover popular users’ preferred products over time. Due to the success of the adversarial learning, a mass of research efforts start to strengthen sequential recommendation by the adversarial learning, which is able to learn complex underlying data distribution.
However, existing adversarial sequential recommendation methods suffer from mode collapse and unexplained prediction. To boost the diversity, performance, and interpretability of sequential recommendation system, we propose a novel temporal-aware adversarial framework, namely TSRGAN.
In principle, the input of traditional adversarial-based recommendation system is a noise variable sampled from normal distribution. We argue that it is hard to generate an item cover complex users’ preferences(e.g. price, brand and item style) using a single latent space. Therefore, our model employs multiple latent space to generate plausible item which matches user’ preferences from multiple views(e.g. Movie style, Movie release date).
Besides, previous adversarial-based recommenders focus on generating active item, but they omits that user’s favour is not in- variable. With GANs terminology, the recommenders only will be rewarded when seeking the peak mode, but it neglects minor mode, in other word mode collapse. In order to alleviate this issue, we design a novel diversity reward function and diversify regularization to encourage the model exploring minor mode over time and guarantee generating diversity item with reasonable.
Concretely, we propose multiple learnable latent codes to generate item matching user’s preferences from different views, then we leverage the diversity reward signal to shape the distribution of multiple latent space over time. It means that the multiple latent space are sampled form different distribution instead of Gaussian distribution. Such a manipulation of the latent space can be treated as walking from plain distribution latent space to diversity distributions latent space. Further, the reward signal is modified over time, therefore, our methods names "Temporal-aware" adversarial framework.
In short, our model has two sequential stages: encode the user’ characteristics and historical behaviours under multiple latent space with the Self Attention-based generator(G), and discriminator(D) try to distinguish the generator’s output item from the ground ruth. Besides, discriminator attempt to apply reward signal to shape the latent space distribution time by time. Extensive experiments demonstrate remarkable performance with interpretability improvement against the state-of-the-art baselines
Development and Characterization of Pili (Canarium ovatum Engl.) Wine
Get PDFThis study aimed to estimate the potential of pili (Canarium ovatum L.) pomace as substrate for the production of fruit wine. The pili fruit wine was characterized in terms of physico-chemical characteristics (pH, TSS and alcohol content) and consumer acceptability level (appearance, taste, aroma, mouthfeel and overall acceptability). It was produced using 5%, 10% and 15% pili pomace as Treatment 1, 2 and 3, respectively. Results showed increase in alcohol content and TSS with increase in concentration of pili pomace while there is decrease in pH as concentration of pili pomace is increased. It was also observed that there is a gradual decrease in total soluble solids and pH and a gradual increase in alcohol content as fermentation time proceeded. Thirty (30) sensory panelists rated pili fruit wine as highly acceptable as commercial wine. Results of the consumer acceptability survey of the pili wine obtained an average rating of 7.71 in overall acceptability which can be interpreted as high liking for the product. Except for appearance, consumer acceptability results of pili fruit wine did not show any significant differences (at 0.05 significance level) in terms of taste, aroma, mouthfeel and overall acceptability when compared to commercial wine
Enzyme Immobilization: Novel Approaches
Get PDFEnzyme enzymes are widely studied and explored areas due to their abundant potential to cater wide application range which makes researchers study more of this area. The high catalytic activity enabling ease in reactions formation of multiple products and byproducts or breakage of complex substances makes it an unavoidable option. However, the cost of pure enzyme increases the economic burden on the operations obstructing its full-fledged use. This leads to making enzyme restrict or immobilize within any inert matrix which can improve the reusability and also improve the tolerance against pH and temperature that can improve the activity of the enzyme. Many different enzyme immobilization approaches have been discussed in the following article
A Study on Consumption of Energy & Health Drinks among Youth with Special Reference to Vapi City
Get PDFHealth drinks are liquids that give nutrition and hydration with minimal calories and sugar. Water is the best option for quenching thirst, however coffee and tea with no added sugars are good. While some studies suggest that energy drinks can improve physical performance and attention, they are also connected with serious health hazards, particularly among teenagers and young adults .So a survey was conducted to study the pattern of the consuming the energy and health drinks among the youth. The majority of the secondary data was gathered from online sources. The main primary source of data was a survey among the young people in Vapi city between the ages of 18 and 25. The aim of the study is to understand the concept & essence of Energy Drink & Health Drink. Also the Researchers aim to conduct the comparative analysis on consumption of energy & health drinks among youth. The study encompasses the domain of Energy & Health Drinks with its consumption among youth. The inferences are limited to the responses of a defined age group and may not be applicable to consumers at large. The conclusions may not apply to all consumers because they are restricted to the replies of a specific age group. The poll was performed using Google Forms, and 111 responses were received. The responses were gathered, processed, and summarized to produce informative data. The data was subsequently used to reach the final conclusion. The results are more indicative in nature rather exhaustive. Researchers have obtained a variety of results and findings regarding the youth and their drinking habits. The organizations in the field of Energy & Health Drinks are the major beneficiaries through this study
Study of Biodiversity Areas for Conservation in India
Get PDFThree broadly accepted claims in conservation biology are that the world\u27s developing tropical countries will see the largest declines in biodiversity in the near future, that these regions are among the least studied globally, and that local community support is particularly important for protection in these regions. In assessing India\u27s protected areas, we evaluate these generalizations. Most ecoregions in India are covered by the 5% of the country that is officially protected, and protected areas have played a significant role in the country\u27s lack of reported species extinctions over the last 70 years. Future chances are improved by India\u27s robust conservation-friendly laws, government investment in its 50 Tiger Reserves, and compensation programs that boost local support. However, connectivity and species utilization in buffer zones are important since many protected areas are too small to support a complete complement of species. The success and difficulties of conservation differ across regions based on their level of development. Protected areas with the greatest biodiversity are found in less developed regions, most notably the biodiverse northeast Himalaya, and are the product of localized efforts by committed individuals. We show that there is much potential for ecotourism to boost local income all around India. Our analysis validates the relevance of local support, growing dangers, and a deficiency of data. Particularly needed are studies on biodiversity in buffer zones, long-term monitoring plans, and an evaluation of the financial and environmental benefits of tourism. The creation of monitoring plans for "eco-sensitive zones" surrounding protected areas and a strong focus on maintaining already-established protected areas should be the two key objectives for policymakers
Trajectory Data to Improve Unsupervised Learning and Intrinsic
Get PDFThe three primary components of machine learning (ML) are reinforcement learning, unstructured learning, and structured learning. The last level, reinforcement learning, will be the main topic of this study. We\u27ll cover a few of the more well-liked reinforcement learning techniques, though there are many more. Reinforcement agents are software agents that make use of reinforcement learning to optimize their rewards within a specific context. The two primary categories of rewards are extrinsic and intrinsic. It\u27s a certain result we obtain after abiding by a set of guidelines and achieving a particular objective. An even better illustration of an intrinsic reward than money is the agent\u27s enthusiasm to learn new skills that could come in handy later on
Financial Fraud Detection in Listed Companies Using Deep Learning and Textual Emotion Analysis
Get PDFFinancial fraud of listed companies refers to the bad faithless behaviour that improperly distorts accounting information, which hurts the company\u27s management, economic development and social interests. At present, the existing research mainly focuses on financial digital data, while the exploration of text information and deep learning algorithms is relatively small. Therefore, this paper proposes a financial fraud identification method for listed companies based on deep learning and integrated text-emotional features. Firstly, the financial index is preprocessed, and then the Bi-LSTM model is used to extract the emotional features of the stock review text. Subsequently, a residual-cross-convolutional (RCC) parallel network structure is used to identify financial fraud. The network simultaneously uses a Residual network, Cross network, Convolutional network and long short-term memory network to extract the characteristics of financial fraud in a parallel way. It obtains the final recognition result through batch standardisation and a full connection layer
Graph Neural Network Recommendation System for Football Formation
Get PDFIn usual, the flow of a football game have different phase, and change from one to another, and the coach is due to observe them, understand and solve the tasks in the game by using appropriate structural strategies.Therefore, it is a critical issues for a coach to decide what kind of structural strategies have been effective for their own team. Therefore, we propose 3 different views to help to the coach to make decisions. First of views, we formulate the passing ball path as a network (passing net- work. More specific, we utilize clustering coeffcient to determine the relations between players. It turnouts that core player will have a strong cluster ability. And our propose network focus not only on local network, but global passing relations.-Final of views, we propose a novel reinforcement learning based Graph-to- Graph framework to decide structure of team. We formulate the positions of players as a graph, and we use the current graph as input, while our deigns return award will effect the structure of team by change the positions step by step. In experiment, we simulate the result of our team versus 3 different level team
Understanding Insulin Mechanisms, Economic Implications, and Future Prospects
Get PDFDiabetes, a persistent metabolic challenge affecting various organs,are of three primary types—Type 1, Type 2, and Gestational—stemming from intricate interplays of genetics and environment. On a global scale, 537 million adults grapple with diabetes, with India experiencing a growing burden. The vital role of insulin in glucose regulation involves a complex biosynthesis process. Economic hurdles, compounded by soaring insulin prices, call for policy interventions to ensure accessible healthcare. Diverse insulin types cater to distinct patient needs, while biosimilars, like the FDA-approved Semglee, offer affordability. Economic analyses underscore the advantages of biosimilars, highlighting the dynamic landscape of diabetes management and treatment costs
A Deep Learning-based Predictive Analytics Model for Remote Patient Monitoring and Early Intervention in Diabetes Care
Get PDFThis paper presents a deep learning-based predictive analytics model for remote diabetes monitoring and early intervention. The proposed method combines photoplethysmography (PPG) signals with population and clinical data by combining LSTM-CNN architecture, achieving the best glucose monitoring results in real time. Manage the inability to care. The system architecture includes a custom-designed wearable device for data acquisition, cloud-based infrastructure, and real-time intervention mechanisms. Validation tests, including 139 subjects (69 diabetics and 70 non-diabetic), showed a 91.2% prediction accuracy over the continuous product to check glucose. The application has achieved 99.7% uptime with a response time of 2.3 seconds, ensuring adequate monitoring time and quick response. The early warning system demonstrated 97.8% accuracy in detecting potential complications through innovative feature extraction methodologies and adaptive learning algorithms. Performance evaluation through Clarke Error Grid analysis indicated clinically acceptable predictions, with all readings falling within zones A and B. The system\u27s cost-effectiveness and reduced invasiveness promote widespread adoption potential, particularly in resource-limited settings. Integrating existing medical systems enables data collection and analysis, facilitating personalized treatment strategies and improving patient outcomes. The research has advanced the level of diabetes management through new contributions to theoretical frameworks and practical applications in remote patient care