In this era of COVID19, proper diagnosis and treatment for pneumonia are very important. Chest X-Ray (CXR) image analysis plays a vital role in the reliable diagnosis of pneumonia. An experienced radiologist is required for this. However, even for an experienced radiographer, it is quite difficult and time-consuming to diagnose due to the fuzziness of CXR images. Also, identification can be erroneous due to the involvement of human judgment. Hence, an authentic and automated system can play an important role here. In this era of cutting-edge technology, deep learning (DL) is highly used in every sector. There are several existing methods to diagnose pneumonia but they have accuracy problems. In this study, an automatic pneumonia detection system has been proposed by applying the extreme learning machine (ELM) on the Kaggle CXR images (Pneumonia). Three models have been studied: classification using extreme learning machine (ELM), ELM with a hybrid convolutional neural network - principle component analysis (CNN-PCA) based feature extraction (ECP), and ECP with the CXR images which are contrast-enhanced by contrast limited adaptive histogram equalization (CLAHE). Among these three proposed methods, the final model provides an optimistic result. It achieves the recall score of 98% and accuracy score of 98.32% for multiclass pneumonia classification. On the other hand, a binary classification achieves 100% recall and 99.83% accuracy. The proposed method also outperforms the existing methods. The outcome has been compared using several benchmarks that include accuracy, precision, recall, etc.

The detection of microorganisms like Pseudomonas are very important as they trigger an infection in human blood, lungs, and different parts of the body causing various ailments. In this paper, a surface plasmon resonance (SPR) biosensor based on photonic crystal fiber (PCF) has been proposed to detect the presence of Pseudomonas bacteria with attractive performance characteristics. The sensor is designed using a simple circular lattice of PCF, coated with a thin chemically stable gold layer. The performance investigation of the sensor is numerically carried out by using a finite element (FE) based simulation tool where the highest wavelength and amplitude sensitivity are found as 20,000 nm/RIU and 1380 RIU ⁻¹ , respectively. The sensor shows an excellent spectral resolution of the highest value of $5.26\times 10^{-6}$ RIU, ensuring the capability of identifying a very small change in analyte refractive index (RI) within the range of 1.33 to 1.42. The performance investigation is also carried out altering the diameter of air holes, pitch, and gold layer thickness to explore the variation in phase matching conditions due to the change in structural parameters. As the sensor is adept at detecting the sample with high sensitivity and sensing resolution, the proposed sensor can be highly efficient in detecting Pseudomonas bacteria as well as other organic compounds, and biological analytes.

As a promising optical method used in a variety of applications surface plasmon resonance (SPR) sensors are employed over a wide range of boundaries. This research proposes a highly sensitive SPR based sensor with a novel hybrid structure using transition metal dichalcogenides (e.g. WSe2, PtSe2) along with black phosphorene (BP) through comprehensive numerical study. To analyze and evaluate the performances of the proposed sensor, the widely used transfer matrix method (TMM) was used. The performances of the sensor were measured in terms of reflectivity, sensitivity, detection accuracy (DA), and figure of merit (FOM). The sensor structure was optimized by changing different structural parameters of the hybrid architecture to obtain better performances. The results revealed that insertion of PtSe2 with WSe2 and BP over a gold layer of the conventional structure improved the performance of the sensor and the maximum sensitivity of the sensor was measured as 200 deg/RIU with a FOM of 17.70 RIU-1. As well, the light penetration through the optimized sensor is investigated using the finite element method (FEM) based software. With this kind of high sensing capabilities, it may be convinced that the proposed sensor can be applied in different fields of biosensing to detect liquid biological and biochemical samples or analytes.

In this paper, a simple controller design with the fractional order calculations applies to unstable cascade processes with a significant time delay. Series cascade control system consists of two loops in which the inner loop is designed using the IMC principle based on the synthesis method. The primary controller is designed with the FOPI-FOPD controller to ensure stable and satisfactory closed-loop performances for the unstable processes. For the primary controller, five tuning parameters are involved, which tuned using the centroid of the convex stability region method. Different examples are given to illustrate the superiority of the proposed design over some existing designs. Results obtained from simulation reveals that with the proposed control design, enhanced closed-loop control performances are obtained for nominal and perturbed conditions.

Rapid increase in scholarly publications on the web has posed a new challenge to the researchers in finding highly relevant and important research articles associated with a particular area of interest. Even a highly relevant paper is sometimes missed especially for novice researchers due to lack of knowledge and experience in finding and accessing the most suitable articles. Scholarly recommender system is a very appropriate tool for this purpose that can enable researchers to locate relevant publications easily and quickly. However, the main downside of the existing approaches is that their effectiveness is dependent on priori user profiles and thus, they cannot recommend papers to the new users. Furthermore, the system uses both public and non-public metadata and therefore, the system is unable to find similarities between papers efficiently due to copyright restrictions. Considering the above challenges, in this research work, a novel hybrid approach is proposed that separately combines a Content Based Filtering (CBF) recommender module and a Collaborative Filtering (CF) recommender module. Unlike previous CBF and CF approaches, public contextual metadata and paper-citation relationship information are effectively incorporated into these two approaches separately to enhance the recommendation accuracy. In order to verify the effectiveness of the proposed approach, publicly available datasets were employed. Experimental results demonstrate that the proposed approach outperforms the baseline approaches in terms of standard metrics (precision, recall, F1-measure, mean average precision, and mean reciprocal rank), indicating that the proposed approach is more efficient in recommending scholarly publications.