Offline printed Sindhi optical text recognition: survey

Optical Charter Recognition (OCR) applications are becoming more intensive than before and show great prospective for rapid data entry, but has limited success when applied to the Sindhi language. This paper summarize the general topic of optical character recognition and highlights the characteristics of Sindhi script. It also presents an historical review of the Sindhi text recognition systems. More this paper underlines the capabilities of different OCT=R systems, and then introduce a five stage model for off-line printed Sindhi text recognition system and classify research work according to this mode

DAWM: cost-aware asset claim analysis approach on big data analytic computation model for cloud data centre.

The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches

Character Segmentation of Sindhi, an Arabic Style Scripting Language, using Height Profile Vector,

Abstract: In this paper, a problem of sub-word segmentation of printed Sindhi, an Arabic style scripting language, into characters is addressed. Printed or handwritten Sindhi text is cursive in nature. In the cursive writing, mostly the subsequent characters in a word are joined with each other. In the proposed segmentation algorithm, first of all, Height Profile Vector (HPV) of thinned primary stroke of a sub-word is calculated and analyzed for the segmentation into its constituent characters. The number and locations of possible segmentation points (PSP) are determined. The number of PSPs gives a rough estimation of the number of characters in the sub-word. The data around the last PSP is further analyzed to determine the exact number of characters in the sub-word. As the characters' set of Sindhi is the superset set of Arabic characters' set hence the proposed segmentation algorithm may be used for the segmentation of text written in other Arabic scripting languages

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Multiband Circular Polarizer Based on Fission Transmission of Linearly Polarized Wave for X-Band Applications

A multiband circular polarizer based on fission transmission of linearly polarized wave for x-band application is proposed, which is constructed of 2 × 2 metallic strips array. The linear-to-circular polarization conversion is obtained by decomposing the linearly incident x-polarized wave into two orthogonal vector components of equal amplitude and 90° phase difference between them. The innovative approach of “fission transmission of linear-to-circular polarized wave” is firstly introduced to obtain giant circular dichroism based on decomposition of orthogonal vector components through the structure. It means that the incident linearly polarized wave is converted into two orthogonal components through lower printed metallic strips layer and two transmitted waves impinge on the upper printed strips layer to convert into four orthogonal vector components at the end of structure. This projection and transmission sequence of orthogonal components sustain the chain transmission of electromagnetic wave and can achieve giant circular dichroism. Theoretical analysis and microwave experiments are presented to validate the performance of the structure. The measured results are in good agreement with simulation results. In addition, the proposed circular polarizer exhibits the optimal performance with respect to the normal incidence. The right handed circularly polarized wave is emitted ranging from 10.08 GHz to 10.53 GHz and 10.78 GHz to 11.12 GHz, while the left handed circular polarized wave is excited at 10.54 GHz–10.70 GHz and 11.13 GHz–11.14 GHz, respectively

DeepLabv3+-Based Segmentation and Best Features Selection Using Slime Mould Algorithm for Multi-Class Skin Lesion Classification

The development of abnormal cell growth is caused by different pathological alterations and some genetic disorders. This alteration in skin cells is very dangerous and life-threatening, and its timely identification is very essential for better treatment and safe cure. Therefore, in the present article, an approach is proposed for skin lesions’ segmentation and classification. So, in the proposed segmentation framework, pre-trained Mobilenetv2 is utilised in the act of the back pillar of the DeepLabv3+ model and trained on the optimum parameters that provide significant improvement for infected skin lesions’ segmentation. The multi-classification of the skin lesions is carried out through feature extraction from pre-trained DesneNet201 with N × 1000 dimension, out of which informative features are picked from the Slim Mould Algorithm (SMA) and input to SVM and KNN classifiers. The proposed method provided a mean ROC of 0.95 ± 0.03 on MED-Node, 0.97 ± 0.04 on PH2, 0.98 ± 0.02 on HAM-10000, and 0.97 ± 0.00 on ISIC-2019 datasets

SUSIC:A Secure User Access Control mechanism for SDN-enabled IIoT and Cyber Physical Systems

The integration of thriving information and communications technology (ICT) and cyber-physical systems (CPSs) has spawned several innovative applications, such as remote healthcare, smart and intelligent transportation, smart logistics, smart grids, and public safety. An emerging software-defined networks (SDNs) technology further enabled to optimize the communication among Industrial IoT (IIoT) and CPS entities. Nonetheless, the communication on public channel among different IIoT entities in an SDN-enabled environment may be exposed to various security threats due to wireless and insecure communication channels. To counter these security challenges in the way of wider CPS or IIoT adoption, we propose a novel three-factor authenticated key exchange mechanism (SUSIC) for SDN-enabled IIoT ecosystem. The SUSIC enables a registered user to access real-time data from physical IIoT environment directly after having mutual authentication performed through SDN-enabled controller node. The scheme is proved to be secure under rigorous formal and informal security analysis. Moreover, the simulation results and performance evaluation signifies toward achieving a better tradeoff between security functionalities and computational overheads comparatively

A low-cost privacy preserving user access in mobile edge computing framework

The computational offloading from conventional cloud datacenter towards edge devices sprouted a new world of prospective applications in pervasive and Mobile Edge Computing (MEC) paradigm, leading to substantial gains in the form of increased availability, bandwidth with low latency. The MEC offers real-time computing and storage facility within the proximity of mobile user-access network, hence it is imperative to secure communication between end user and edge server. The existing schemes do not fulfill real time processing and efficiency requirements for using complex crypto-primitives. To this end, we propose a novel two-factor biometric authentication protocol for MEC enabling efficient and secure combination of Physically Unclonable Functions (PUFs) with user-oriented biometrics employing fuzzy extractor-based procedures. The performance analysis depicts that our scheme offers resistance to known attacks using lightweight operations and supports 30% more security features than comparative studies. Our scheme is provably secure under Real-or-Random (ROR) formal security analysis model

DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre

The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches

An Improved Particle Swarm Optimization Algorithm for Data Classification

Optimisation-based methods are enormously used in the field of data classification. Particle Swarm Optimization (PSO) is a metaheuristic algorithm based on swarm intelligence, widely used to solve global optimisation problems throughout the real world. The main problem PSO faces is premature convergence due to lack of diversity, and it is usually stuck in local minima when dealing with complex real-world problems. In meta-heuristic algorithms, population initialisation is an important factor affecting population diversity and convergence speed. In this study, we propose an improved PSO algorithm variant that enhances convergence speed and population diversity by applying pseudo-random sequences and opposite rank inertia weights instead of using random distributions for initialisation. This paper also presents a novel initialisation population method using a quasi-random sequence (Faure) to create the initialisation of the swarm, and through the opposition-based method, an opposite swarm is generated. We proposed an opposition rank-based inertia weight approach to adjust the inertia weights of particles to increase the performance of the standard PSO. The proposed algorithm (ORIW-PSO-F) has been tested to optimise the weight of the feed-forward neural network for fifteen data sets taken from UCI. The proposed techniques&rsquo; experiment result depicts much better performance than other existing techniques