COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

The purpose of this research is the segmentation of lungs computed tomography (CT) scan for the diagnosis of COVID-19 by using machine learning methods. Our dataset contains data from patients who are prone to the epidemic. It contains three types of lungs CT images (Normal, Pneumonia, and COVID-19) collected from two different sources; the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur, Pakistan, and the second one is a publicly free available medical imaging database known as Radiopaedia. For the preprocessing, a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest (ROIs) and acquire 52 hybrid statistical features for each ROIs. Also, 12 optimized statistical features are selected via the chi-square feature reduction technique. For the classification, five machine learning classifiers named as deep learning J4, multilayer perceptron, support vector machine, random forest, and naive Bayes are deployed to optimize the hybrid statistical features dataset. It is observed that the deep learning J4 has promising results (sensitivity and specificity: 0.987; accuracy: 98.67%) among all the deployed classifiers. As a complementary study, a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.     

A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects—voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds (575 h) if it was annotated manually. For the validation of the proposed technique, a sub-dataset—Voice, is annotated manually as well as with the proposed technique. Cohen's Kappa statistics is used to evaluate the degree of agreement between the two annotations. The high Kappa value (i.e., 0.9571%) shows the high level of agreement between the two. This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost. This research also contributes in consolidating the guidelines for the manual annotation process.     

Ultrafast Sodium/Potassium‐Ion Intercalation into Hierarchically Porous Thin Carbon Shells

The large‐scale application of sodium/potassium‐ion batteries is severely limited by the low and slow charge storage dynamics of electrode materials. The crystalline carbons exhibit poor insertion capability of large Na⁺/K⁺ ions, which limits the storage capability of Na/K batteries. Herein, porous S and N co‐doped thin carbon (S/N@C) with shell‐like (shell size ≈20–30 nm, shell wall ≈8–10 nm) morphology for enhanced Na⁺/K⁺ storage is presented. Thanks to the hollow structure and thin shell‐wall, S/N@C exhibits an excellent Na⁺/K⁺ storage capability with fast mass transport at higher current densities, leading to limited compromise over charge storage at high charge/discharge rates. The S/N@C delivers a high reversible capacity of 448 mAh g^‐1 for Na battery, at the current density of 100 mA g^‐1 and maintains a discharge capacity up to 337 mAh g^‐1 at 1000 mA g^‐1. Owing to shortened diffusion pathways, S/N@C delivers an unprecedented discharge capacity of 204 and 169 mAh g^‐1 at extremely high current densities of 16 000 and 32 000 mA g^‐1, respectively, with excellent reversible capacity for 4500 cycles. Moreover, S/N@C exhibits high K⁺ storage capability (320 mAh g^‐1 at current density of 50 mA g^‐1) and excellent cyclic life.     

Exsolution of Ru Nanoparticles on BaCe0.9Y0.1O3-δ Modifying Geometry and Electronic Structure of Ru for Ammonia Synthesis Reaction Under Mild Conditions

Green ammonia is an efficient, carbon-free energy carrier and storage medium. The ammonia synthesis using green hydrogen requires an active catalyst that operates under mild conditions. The catalytic activity can be promoted by controlling the geometry and electronic structure of the active species. An exsolution process is implemented to improve catalytic activity by modulating the geometry and electronic structure of Ru. Ru nanoparticles exsolved on a BaCe_0.9Y_0.1O_3-δ support exhibit uniform size distribution, 5.03 ± 0.91 nm, and exhibited one of the highest activities, 387.31 mmol_NH3 g_Ru⁻¹ h⁻¹ (0.1 MPa and 450 °C). The role of the exsolution and BaCe_0.9Y_0.1O_3-δ support is studied by comparing the catalyst with control samples and in-depth characterizations. The optimal nanoparticle size is maintained during the reaction, as the Ru nanoparticles prepared by exsolution are well-anchored to the support with in-plane epitaxy. The electronic structure of Ru is modified by unexpected in situ Ba promoter accumulation around the base of the Ru nanoparticles.     

Analysis and design of a stacked power amplifier with 196% fractional bandwidth using equivalent circuit model

Stacked structure is a good solution to overcome the low output voltage swing provided by a single device. When several devices are stacked, the bandwidth and output power are multiple times higher. This article analyzes the small‐signal voltage gain of the stacked structure, deriving the gain expression of the high‐frequency model and simplified model. Based on the specific device parameter, the different small‐signal voltage gains between the two models are compared and the designed stacked structure is proved to obtain a flat gain at low frequencies below about 3 GHz. To our best knowledge, this is the first article to analyze the gain flatness of stacked structure with two equivalent circuit models. To verify the stacked theory, a pseudomorphic high‐electron‐mobility transistor（PHEMT） power amplifier (PA) is implemented using 0.25 μm Gallium arsenide (GaAs) technology. The PA achieves an ultra‐high bandwidth of 30 MHz to 3 GHz and a linear gain of 21 dB ± 1.5 dB. At a 16‐V drain bias voltage, a saturated output power of higher than 2 W and a peak power‐added efficiency (PAE) of 44.1% are attained.     

A WLAN band‐notched compact four element UWB MIMO antenna

A compact four‐element multiple‐input‐multiple‐output (MIMO) antenna for ultra‐wideband (UWB) applications with WLAN band‐notched characteristics is proposed here. The proposed antenna has been designed to operate from 2 to 12 GHz while reject the frequencies between 4.9 to 6.4 GHz. The four antenna elements are placed orthogonal to attain the polarization diversity and high isolation. A thin stub connected to the ground plane is deployed as a LC notch filter to accomplish the rejected WLAN band in each antenna element. The mutual coupling between the adjacent elements is at least 17 dB while it has low indoor and outdoor envelop correlation (<0.45) and high gain with compact size of two boards, each measuring 50 × 25 mm². To validate the concept, the prototype antenna is manufactured and measured. The comparison of the simulation results showed good agreement with the measured results. The low‐profile design and compact size of the proposed MIMO antenna make it a good candidate for diversity applications desired in portable devices operating in the UWB region.     

A passive technique for detecting copy-move forgeries by image feature matching

Multimedia Tools and Applications - Due to the recent evolutions in the technologies various digital devices and image processing tools are available in the market. Consequently, crime rates are...     

Steps Towards Redesigning Cryptosystems by a Non-associative Algebra of IP-Loops

Wireless Personal Communications - In block ciphers, the substitution boxes (S-boxes) are used with the purpose to induce confusion in cryptosystems. For the last three decades most of algebraic...     

CDCSS: cluster-based distributed cooperative spectrum sensing model against primary user emulation (PUE) cyber attacks

In cognitive radio network, the secondary users (SUs) use the spectrum of primary users for communication which arises the security issues. The status of SUs as legitimate users is compulsory for the stability of the system. This paper addresses the issue of delay caused by a band-selection decision process that directly affects the security and performance. The model cluster-based distributed cooperative spectrum sensing is proposed. In this model, cluster heads (CHs) exchange control information with other CHs and ordinary nodes. This model significantly reduced the delay, sensing, convergence, routing, in band-selection process. This also reduces the energy consumption while sensing the spectrum which seriously leads to performance upgradation. The simulated results show the improved performance of cognitive radio networks in terms of delay, packet loss ratio and bandwidth usage as compared to cluster-based cooperative spectrum sensing model. The opportunity for primary user emulation attacker is minimized as the overall delay is reduced.     

A fault‐tolerant workflow management system with Quality‐of‐Service‐aware scheduling for scientific workflows in cloud computing

Cloud computing provides solutions to many scientific and business applications. Large‐scale scientific applications, which are structured as scientific workflows, are evaluated through cloud computing. In this paper, we proposed a Quality‐of‐Service‐aware fault‐tolerant workflow management system (QFWMS) for scientific workflows in cloud computing. We have considered two real‐time scientific workflows, i.e., Montage and CyberShake, for an evaluation of the proposed QFWMS. The results of the proposed QFWMS scheduling were evaluated through simulation environment WorkflowSim and compared with three well‐known heuristic scheduling policies: (a) minimum completion time (MCT), (b) Maximum‐minimum (Max‐min), and (c) Minimum‐minimum (Min‐min). By considering Montage scientific workflow, the proposed QFWMS reduces the make‐span 8.86%, 8.94%, and 5.53% compared with existing three heuristic policies. Similarly, the proposed QFWMS reduces the cost 6.19%, 3.52%, and 3.60% compared with existing three heuristic policies. Likewise, by considering CyberShake scientific workflow, the proposed QFWMS reduces the make‐span 19.54%, 21.41%, and 25.71% compared with existing three heuristic policies. Similarly, the proposed QFWMS reduces the cost 8.78%, 8.40%, and 8.61% compared with existing three heuristic policies. More so, for QFWMS, SLA is neither violated for time constraints nor for cost constraints. While for MCT, Max‐min and Min‐min scheduling policies, SLA is violated 32, 37, and 23 times, respectively. Conclusively, the proposed QFWMS scheduling and management system is one of the significant workflow management systems for execution and management of scientific workflows in cloud computing.