Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

With the increasing and rapid growth rate of COVID-19 cases, the healthcare scheme of several developed countries have reached the point of collapse. An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients, in such a way that positive patient can be treated and isolated. A chest radiology image-based diagnosis scheme might have several benefits over traditional approach. The accomplishment of artificial intelligence (AI) based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems. This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System (IFFA-DTLMS). The proposed IFFA-DTLMS model majorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs. To attain this, the presented IFFA-DTLMS model primarily applies densely connected networks (DenseNet121) model to generate a collection of feature vectors. In addition, the firefly algorithm (FFA) is applied for the hyper parameter optimization of DenseNet121 model. Moreover, autoencoder-long short term memory (AE-LSTM) model is exploited for the classification and identification of COVID19. For ensuring the enhanced performance of the IFFA-DTLMS model, a wide-ranging experiments were performed and the results are reviewed under distinctive aspects. The experimental value reports the betterment of IFFA-DTLMS model over recent approaches.     

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.     

Enhancing encoding capacity of combinational test stimulus decompressors

While scan-based compression is widely utilized in order to alleviate the test time and data volume problems,the overall compression level is dictated not only by the chain to channel ratio but also the ratio of encodable patterns.Aggressively increasing the number of scan chains in an effort to raise the compression levels may reduce the ratio of encodable patterns,degrading the overall compression level.In this paper,we present various methods to improve the ratio of encodable patterns.These methods are b...     

Thermodynamic studies on the complete phase diagram of aqueous two phase system containing polyethylene glycol dimethyl ether 2000 and di-potassium hydrogen phosphate at different temperatures

The liquid-liquid equilibrium of polyethylene glycol dimethyl ether 2000 (PEGDME₂₀₀₀)+K₂HPO₄+H₂O system has been determined experimentally at T=(298.15,303.15,308.15 and 318.15) K. The liquid-solid and complete phase diagram of this system was also obtained at T=(298.15 and 308.15) K. A nonlinear temperature dependent equation was successfully used for the correlation of the experimental binodal data. Furthermore, a temperature dependent Setschenow-type equation was successfully used for the correlation of the tie-lines of the studied system. Moreover, the effect of temperature on the binodal curves and the tie-lines for the investigated aqueous two-phase system have been studied. Also, the free energies of cloud points for this system and some previously studied systems containing PEGDME₂₀₀₀ were calculated from which it was concluded that the increase of the entropy is the driving force for formation of aqueous two-phase systems. Additionally, the calculated free energies for phase separation of the studied systems were used to investigate the salting-out ability of the salts having different anions. Furthermore, the complete phase diagram of the investigated system was compared with the corresponding phase diagrams of previously studied systems, in which the PEGDME₂₀₀₀ has been used, in order to obtain some information regarding the phase behavior of these PEGDME₂₀₀₀+salt+water systems.     

Using cross-layer metrics to improve the performance of end-to-end handover mechanisms

Network centric handover solutions for all IP wireless networks usually require modifications to network infrastructure which can stifle any potential rollout. This has led researchers to begin looking at alternative approaches. Endpoint centric handover solutions do not require network infrastructure modification, thereby alleviating a large barrier to deployment. Current endpoint centric solutions capable of meeting the delay requirements of Voice over Internet Protocol (VoIP) fail to consider the Quality of Service (QoS) that will be achieved after handoff. The main contribution of this paper is to demonstrate that QoS aware handover mechanisms which do not require network support are possible. This work proposes a Stream Control Transmission Protocol (SCTP) based handover solution for VoIP called Endpoint Centric Handover (ECHO). ECHO incorporates cross-layer metrics and the ITU-T E-Model for voice quality assessment to accurately estimate the QoS of candidate handover networks, thus facilitating a more intelligent handoff decision. An experimental testbed was developed to analyse the performance of the ECHO scheme. Results are presented showing both the accuracy of ECHO at estimating the QoS and that the addition of the QoS capabilities significantly improves the handover decisions that are made.     

A training-free nose tip detection method from face range images

Nose tip detection in range images is a specific facial feature detection problem that is highly important for 3D face recognition. In this paper, we propose a nose tip detection method that has the following three characteristics. First, it does not require training and does not rely on any particular model. Second, it can deal with both frontal and non-frontal poses. Finally, it is quite fast, requiring only seconds to process an image of 100-200 pixels (in both x and y dimensions) with a MATLAB implementation. A complexity analysis shows that most of the computations involved in the proposed algorithm are simple. Thus, if implemented in hardware (such as a GPU implementation), the proposed method should be able to work in real time. We tested the proposed method extensively on synthetic image data rendered by a 3D head model and real data using FRGC v2.0 data set. Experimental results show that the proposed method is robust to many scenarios that are encountered in common face recognition applications (e.g., surveillance). A high detection rate of 99.43% was obtained on FRGC v2.0 data set. Furthermore, the proposed method can be used to coarsely estimate the roll, yaw, and pitch angles of the face pose.     

SimClus: an effective algorithm for clustering with a lower bound on similarity

Clustering algorithms generally accept a parameter k from the user, which determines the number of clusters sought. However, in many application domains, like document categorization, social network clustering, and frequent pattern summarization, the proper value of k is difficult to guess. An alternative clustering formulation that does not require k is to impose a lower bound on the similarity between an object and its corresponding cluster representative. Such a formulation chooses exactly one representative for every cluster and minimizes the representative count. It has many additional benefits. For instance, it supports overlapping clusters in a natural way. Moreover, for every cluster, it selects a representative object, which can be effectively used in summarization or semi-supervised classification task. In this work, we propose an algorithm, SimClus, for clustering with lower bound on similarity. It achieves a O(log n) approximation bound on the number of clusters, whereas for the best previous algorithm the bound can be as poor as O(n). Experiments on real and synthetic data sets show that our algorithm produces more than 40% fewer representative objects, yet offers the same or better clustering quality. We also propose a dynamic variant of the algorithm, which can be effectively used in an on-line setting.     

A generalized least absolute deviation method for parameter estimation of autoregressive signals

This paper proposes a generalized least absolute deviation (GLAD) method for parameter estimation of autoregressive (AR) signals under non-Gaussian noise environments. The proposed GLAD method can improve the accuracy of the estimation of the conventional least absolute deviation (LAD) method by minimizing a new cost function with parameter variables and noise error variables. Compared with second- and high-order statistical methods, the proposed GLAD method can obtain robustly an optimal AR parameter estimation without requiring the measurement noise to be Gaussian. Moreover, the proposed GLAD method can be implemented by a cooperative neural network (NN) which is shown to converge globally to the optimal AR parameter estimation within a finite time. Simulation results show that the proposed GLAD method can obtain more accurate estimates than several well-known estimation methods in the presence of different noise distributions.     

Three-dimensional face recognition in the presence of facial expressions: an annotated deformable model approach

In this paper, we present the computational tools and a hardware prototype for 3D face recognition. Full automation is provided through the use of advanced multistage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact metadata. We present our results on the largest known, and now publicly available, face recognition grand challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, this is the highest performance reported on the FRGC v2 database for the 3D modality     

Reinforcement Learning Based Algorithms for Average Cost Markov Decision Processes

This article proposes several two-timescale simulation-based actor-critic algorithms for solution of infinite horizon Markov Decision Processes with finite state-space under the average cost criterion. Two of the algorithms are for the compact (non-discrete) action setting while the rest are for finite-action spaces. On the slower timescale, all the algorithms perform a gradient search over corresponding policy spaces using two different Simultaneous Perturbation Stochastic Approximation (SPSA) gradient estimates. On the faster timescale, the differential cost function corresponding to a given stationary policy is updated and an additional averaging is performed for enhanced performance. A proof of convergence to a locally optimal policy is presented. Next, we discuss a memory efficient implementation that uses a feature-based representation of the state-space and performs TD(0) learning along the faster timescale. The TD(0) algorithm does not follow an on-line sampling of states but is observed to do well on our setting. Numerical experiments on a problem of rate based flow control are presented using the proposed algorithms. We consider here the model of a single bottleneck node in the continuous time queueing framework. We show performance comparisons of our algorithms with the two-timescale actor-critic algorithms of Konda and Borkar (1999) and Bhatnagar and Kumar (2004). Our algorithms exhibit more than an order of magnitude better performance over those of Konda and Borkar (1999).