Self-paced regularized adaptive multi-view unsupervised feature selection.

Multi-view unsupervised feature selection (MUFS) is an efficient approach for dimensional reduction of heterogeneous data. However, existing MUFS approaches mostly assign the samples the same weight, thus the diversity of samples is not utilized efficiently. Additionally, due to the presence of various regularizations, the resulting MUFS problems are often non-convex, making it difficult to find the optimal solutions. To address this issue, a novel MUFS method named Self-paced Regularized Adaptive Multi-view Unsupervised Feature Selection (SPAMUFS) is proposed. Specifically, the proposed approach firstly trains the MUFS model with simple samples, and gradually learns complex samples by using self-paced regularizer. l2,p-norm (0

Robust capped norm dual hyper-graph regularized non-negative matrix tri-factorization.

Non-negative matrix factorization (NMF) has been widely used in machine learning and data mining fields. As an extension of NMF, non-negative matrix tri-factorization (NMTF) provides more degrees of freedom than NMF. However, standard NMTF algorithm utilizes Frobenius norm to calculate residual error, which can be dramatically affected by noise and outliers. Moreover, the hidden geometric information in feature manifold and sample manifold is rarely learned. Hence, a novel robust capped norm dual hyper-graph regularized non-negative matrix tri-factorization (RCHNMTF) is proposed. First, a robust capped norm is adopted to handle extreme outliers. Second, dual hyper-graph regularization is considered to exploit intrinsic geometric information in feature manifold and sample manifold. Third, orthogonality constraints are added to learn unique data presentation and improve clustering performance. The experiments on seven datasets testify the robustness and superiority of RCHNMTF.

A BERT Framework to Sentiment Analysis of Tweets.

Sentiment analysis has been widely used in microblogging sites such as Twitter in recent decades, where millions of users express their opinions and thoughts because of its short and simple manner of expression. Several studies reveal the state of sentiment which does not express sentiment based on the user context because of different lengths and ambiguous emotional information. Hence, this study proposes text classification with the use of bidirectional encoder representations from transformers (BERT) for natural language processing with other variants. The experimental findings demonstrate that the combination of BERT with CNN, BERT with RNN, and BERT with BiLSTM performs well in terms of accuracy rate, precision rate, recall rate, and F1-score compared to when it was used with Word2vec and when it was used with no variant.

EEG-based emotion recognition using hybrid CNN and LSTM classification.

Emotions are a mental state that is accompanied by a distinct physiologic rhythm, as well as physical, behavioral, and mental changes. In the latest days, physiological activity has been used to study emotional reactions. This study describes the electroencephalography (EEG) signals, the brain wave pattern, and emotion analysis all of these are interrelated and based on the consequences of human behavior and Post-Traumatic Stress Disorder (PTSD). Post-traumatic stress disorder effects for long-term illness are associated with considerable suffering, impairment, and social/emotional impairment. PTSD is connected to subcortical responses to injury memories, thoughts, and emotions and alterations in brain circuitry. Predominantly EEG signals are the way of examining the electrical potential of the human feelings cum expression for every changing phenomenon that the individual faces. When going through literature there are some lacunae while analyzing emotions. There exist some reliability issues and also masking of real emotional behavior by the victims. Keeping this research gap and hindrance faced by the previous researchers the present study aims to fulfill the requirements, the efforts can be made to overcome this problem, and the proposed automated CNN-LSTM with ResNet-152 algorithm. Compared with the existing techniques, the proposed techniques achieved a higher level of accuracy of 98% by applying the hybrid deep learning algorithm.

Cardinality-constrained portfolio selection via two-timescale duplex neurodynamic optimization.

This paper addresses portfolio selection based on neurodynamic optimization. The portfolio selection problem is formulated as a biconvex optimization problem with a variable weight in the Markowitz risk-return framework. In addition, the cardinality-constrained portfolio selection problem is formulated as a mixed-integer optimization problem and reformulated as a biconvex optimization problem. A two-timescale duplex neurodynamic approach is customized and applied for solving the reformulated portfolio optimization problem. In the two-timescale duplex neurodynamic approach, two recurrent neural networks operating at two timescales are employed for local searches, and their neuronal states are reinitialized upon local convergence using a particle swarm optimization rule to escape from local optima toward global ones. Experimental results on four datasets of world stock markets are elaborated to demonstrate the superior performance of the neurodynamic optimization approach to three baselines in terms of two major risk-adjusted performance criteria and portfolio returns.

Decentralized Robust Portfolio Optimization Based on Cooperative-Competitive Multiagent Systems.

This article addresses decentralized robust portfolio optimization based on multiagent systems. Decentralized robust portfolio optimization is first formulated as two distributed minimax optimization problems in a Markowitz return-risk framework. Cooperative-competitive multiagent systems are developed and applied for solving the formulated problems. The multiagent systems are shown to be able to reach consensuses in the expected stock prices and convergence in investment allocations through both intergroup and intragroup interactions. Experimental results of the multiagent systems with stock data from four major markets are elaborated to substantiate the efficacy of multiagent systems for decentralized robust portfolio optimization.

Cardinality-constrained portfolio selection based on collaborative neurodynamic optimization.

Portfolio optimization is one of the most important investment strategies in financial markets. It is practically desirable for investors, especially high-frequency traders, to consider cardinality constraints in portfolio selection, to avoid odd lots and excessive costs such as transaction fees. In this paper, a collaborative neurodynamic optimization approach is presented for cardinality-constrained portfolio selection. The expected return and investment risk in the Markowitz framework are scalarized as a weighted Chebyshev function and the cardinality constraints are equivalently represented using introduced binary variables as an upper bound. Then cardinality-constrained portfolio selection is formulated as a mixed-integer optimization problem and solved by means of collaborative neurodynamic optimization with multiple recurrent neural networks repeatedly repositioned using a particle swarm optimization rule. The distribution of resulting Pareto-optimal solutions is also iteratively perfected by optimizing the weights in the scalarized objective functions based on particle swarm optimization. Experimental results with stock data from four major world markets are discussed to substantiate the superior performance of the collaborative neurodynamic approach to several exact and metaheuristic methods.

Minimax and Biobjective Portfolio Selection Based on Collaborative Neurodynamic Optimization.

Portfolio selection is one of the important issues in financial investments. This article is concerned with portfolio selection based on collaborative neurodynamic optimization. The classic Markowitz mean-variance (MV) framework and its variant mean conditional value-at-risk (CVaR) are formulated as minimax and biobjective portfolio selection problems. Neurodynamic approaches are then applied for solving these optimization problems. For each of the problems, multiple neural networks work collaboratively to characterize the efficient frontier by means of particle swarm optimization (PSO)-based weight optimization. Experimental results with stock data from four major markets show the performance and characteristics of the collaborative neurodynamic approaches to the portfolio optimization problems.

A review of the manufacturing process and infection rate of 3D-printed models and guides sterilized by hydrogen peroxide plasma and utilized intra-operatively.

3D printing in the context of medical application can allow for visualization of patient-specific anatomy to facilitate surgical planning and execution. Intra-operative usage of models and guides allows for real time feedback but ensuring sterility is essential to prevent infection. The additive manufacturing process restricts options for sterilisation owing to temperature sensitivity of thermoplastics utilised for fabrication. Here, we review one of the largest single cohorts of 3D models and guides constructed from Acrylonitrile butadiene styrene (ABS) and utilized intra-operatively, following terminal sterilization with hydrogen peroxide plasma. We describe our work flow from initial software rendering to printing, sterilization, and on-table application with the objective of demonstrating that our process is safe and can be implemented elsewhere. Overall, 7% (8/114 patients) of patients developed a surgical site infection, which was not elevated in comparison to related studies utilizing traditional surgical methods. Prolonged operation time with an associated increase in surgical complexity was identified to be a risk factor for infection. Low temperature plasma-based sterilization depends upon sufficient permeation and contact with surfaces which are a particular challenge when our 3D-printouts contain diffusion-restricted luminal spaces as well as hollows. Application of printouts as guides for power tools may further expose these regions to sterile bodily tissues and result in generation of debris. With each printout being a bespoke medical device, it is important that the multidisciplinary team involved in production and application understand potential pitfalls to ensuring sterility as to minimize infection risk.

A Collaborative Neurodynamic Approach to Multiobjective Optimization.

There are two ultimate goals in multiobjective optimization. The primary goal is to obtain a set of Pareto-optimal solutions while the secondary goal is to obtain evenly distributed solutions to characterize the efficient frontier. In this paper, a collaborative neurodynamic approach to multiobjective optimization is presented to attain both goals of Pareto optimality and solution diversity. The multiple objectives are first scalarized using a weighted Chebyshev function. Multiple projection neural networks are employed to search for Pareto-optimal solutions with the help of a particle swarm optimization (PSO) algorithm in reintialization. To diversify the Pareto-optimal solutions, a holistic approach is proposed by maximizing the hypervolume (HV) using again a PSO algorithm. The experimental results show that the proposed approach outperforms three other state-of-the-art multiobjective algorithms (i.e., HMOEA/D, MOEA/DD, and NSGAIII) most of times on 37 benchmark datasets in terms of HV and inverted generational distance.

Detection of serum neopterin for early assessment of dengue virus infection.

OBJECTIVE: Neopterin is generated and released in increased amounts by macrophages upon activation by interferon-gamma during Th1-type immune response. The potential usefulness of neopterin in early prognostic information of dengue virus infection was investigated. METHODS: Neopterin concentrations were determined in serum samples from 110 dengue fever (DF) patients. The neopterin levels were compared with those in 50 measles and 40 influenza patients; 155 healthy blood donors served as controls. RESULTS: In acute sera of DF patients mean neopterin concentration was 48.2 nmol/L, which was higher than that in patients with measles (mean: 36.3 nmol/L) and influenza (18.8 nmol/L) and in healthy controls (6.7 nmol/L; P<0.001). In the patients with confirmed DF, an early neopterin elevation was detected already at the first day after the onset of symptoms and rose to a maximum level of 54.3 nmol/L 4 days after the onset. Higher increase of neopterin level in DF patients was associated with longer duration of fever and thus predicted the clinical course of the disease. CONCLUSIONS: Neopterin concentrations were found significantly higher in DF patients compared with healthy controls and also with other viral infections (P<0.001) and may allow early assessment of the severity of DF.

Serum neopterin for early assessment of severity of severe acute respiratory syndrome.

Neopterin and C-reactive protein (CRP) concentrations were determined in serum samples from 129 severe acute respiratory syndrome (SARS) patients and 156 healthy blood donors. In the patients with confirmed SARS, an early neopterin elevation was detected already at the day of onset of symptoms and rose to a maximum level of 45.0 nmol/L 3 days after the onset. All SARS patients had elevated neopterin concentrations (>10 nmol/L) within 9 days after the onset. The mean neopterin concentrations were 34.2 nmol/L in acute sera of SARS patients, 5.1 nmol/L in convalescent sera, and 6.7 nmol/L in healthy controls. In contrast, the mean CRP concentrations in both acute and convalescent sera of SARS patients were in the normal range (<10 mg/L). Serum neopterin level in SARS patients was associated with fever period and thus the clinical progression of the disease, while there was no significant correlation between the CRP level and the fever period. Serum neopterin may allow early assessment of the severity of SARS. The decrease of neopterin level was found after steroid treatment, which indicates that blood samples should be collected before steroid treatment for the neopterin measurement.