Knowledge about fish species with continuous monitoring play a dominant role in determining short and long-term effects on ecosystems and generating ways to manage the problem through specific treatment. Categori...
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Brain tumor is a group of unfamiliar cells present in the cerebrum that may lead to cancer. Brain tumor can be diagnosed by several easy ways but among them MRI imaging is the best way to discover a tumor in the brain...
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Question answering (QA) tasks have been extensively studied in the field of natural language processing (NLP). Answers to open-ended questions are highly diverse and difficult to quantify, and cannot be simply evaluat...
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The Windows Operating System is known for its convenience which tends to breed more and more user information in form of Artifacts. Artifacts are important repository of potential evidence while conducting any compute...
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Knee joint rehabilitation exercise refers to a therapeutic procedure of a patient having dysfunctions in certain abilities to move knee joint due to some medical conditions like trauma or paralysis. The exercise is ba...
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The wireless sensor networks (WSNs) are susceptible to various sorts of security risks and attacks. To identify attacks, a powerful intrusion detection system (IDS) must be employed. Identifying attacks, particularly ...
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in current years, there has been a growing hobby in growing superior surveillance systems for detecting and monitoring anomalous activities. Traditional surveillance systems depend upon handmade policies and features,...
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Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials *** essential component in the process of materials discovery is to know which material(s)will possess desirable ***...
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Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials *** essential component in the process of materials discovery is to know which material(s)will possess desirable *** many materials properties,performing experiments and density functional theory computations are costly and ***,it is challenging to build accurate predictive models for such properties using conventional data mining methods due to the small amount of available *** we present a framework for materials property prediction tasks using structure information that leverages graph neural network-based architecture along with deep-transfer-learning techniques to drastically improve the model’s predictive ability on diverse materials(3D/2D,inorganic/organic,computational/experimental)*** evaluated the proposed framework in cross-property and cross-materials class scenarios using 115 datasets to find that transfer learning models outperform the models trained from scratch in 104 cases,i.e.,≈90%,with additional benefits in performance for extrapolation *** believe the proposed framework can be widely useful in accelerating materials discovery in materials science.
iOS is one of the most broadly used mobile operating systems after Android. In today's era, smartphones are widely used to perform several tasks such as net banking, GPS tracking, ordering products, etc. One of th...
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The incompleteness of multi-view data is a phenomenon associated with real-world data mining applications, which brings a huge challenge for multi-view clustering. Although various types of clustering methods, which t...
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The incompleteness of multi-view data is a phenomenon associated with real-world data mining applications, which brings a huge challenge for multi-view clustering. Although various types of clustering methods, which try to obtain a complete and consensus clustering result from a latent subspace, have been developed to overcome this problem, most methods excessively rely on views-public instances to bridge the connection with view-private instances. When lacking sufficient views-public instances, existing methods fail to transmit the information among incomplete views effectively. To overcome this limitation, we propose an incomplete multi-view clustering algorithm via local and global co-regularization(IMVC-LG). In this algorithm, we define a new objective function that is composed of two terms: local clustering from each view and global clustering from multiple views, which constrain each other to exploit the local clustering information from different incomplete views and determine a global consensus clustering result, ***, an iterative optimization method is proposed to minimize the objective function. Finally, we compare the proposed algorithm with other state-of-the-art incomplete multi-view clustering methods on several benchmark datasets to illustrate its effectiveness.
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