Comparison of total ozone column observations from space-borne Ozone Monitoring Instrument with ground-based Dobson Ozone Spectrophotometer measurements at an urban location in Indo-Gangetic Basin

This article presents a comparison analysis of OMIT (Ozone Monitoring Instrument retrieved overpass total ozone column (TOC)), and DOST (Dobson Ozone Spectrophotometer observed TOC) over Delhi during a period from October 2004 to June 2011. Megacity Delhi, located in Indo-Gangetic Basin, is an important site for comparison of ground-based and satellite retrieved TOCs due to significant anthropogenic emissions of ozone precursors, large shift in seasons, and large-scale crop residue burning in the region. DOST and OMIT data show an overall bias of 3.07% and significant correlation with coefficient of determination R² = 0.73. Large seasonal fluctuations in the biases and correlations have been observed ranging from 2.46% (winter) to 3.82% (spring), and R² = 0.84 (winter) to R² = 0.09 (summer), respectively. The large biases are attributed to changes in temperature, cloud cover, pollutants emissions from urban area, and crop-residue burning events. We also find notable variations in correlations between the datasets due to the varying burden of absorbing aerosols from open field crop-residue burning. The R² has changed from 0.67 (for aerosol optical depth, AOD 1.5–3.5) to 0.77 (for AOD 0–0.99). The dependence of the bias on solar zenith angle, cloud fraction, and satellite distance is also discussed. A simple linear regression analysis is applied to check the linkage between DOST and OMIT. The influence of atmospheric air temperature and relative humidity on OMIT at different pressure levels between 1000 and 20 hPa has been discussed.     

An Efficient Mechanism for Product Data Extraction from E-Commerce Websites

A large amount of data is present on the web which can be used for useful purposes like a product recommendation, price comparison and demand forecasting for a particular product. Websites are designed for human understanding and not for machines. Therefore, to make data machine-readable, it requires techniques to grab data from web pages. Researchers have addressed the problem using two approaches, i.e., knowledge engineering and machine learning. State of the art knowledge engineering approaches use the structure of documents, visual cues, clustering of attributes of data records and text processing techniques to identify data records on a web page. Machine learning approaches use annotated pages to learn rules. These rules are used to extract data from unseen web pages. The structure of web documents is continuously evolving. Therefore, new techniques are needed to handle the emerging requirements of web data extraction. In this paper, we have presented a novel, simple and efficient technique to extract data from web pages using visual styles and structure of documents. The proposed technique detects Rich Data Region (RDR) using query and correlative words of the query. RDR is then divided into data records using style similarity. Noisy elements are removed using a Common Tag Sequence (CTS) and formatting entropy. The system is implemented using JAVA and runs on the dataset of real-world working websites. The effectiveness of results is evaluated using precision, recall, and F-measure and compared with five existing systems. A comparison of the proposed technique to existing systems has shown encouraging results.     

Controlling the material removal and roughness of Inconel 718 in laser machining

Nickel alloys including Inconel 718 are considered as challenging materials for machining. Laser beam machining could be a promising choice to deal with such materials for simple to complex machining features. The machining accuracy is mainly dependent on the rate of material removal per laser scan. Because of the involvement of many laser parameters and complexity of the machining mechanism it is not always simple to achieve machining with desired accuracy. Actual machining depth extremely varies from very low to aggressively high values with reference to the designed depth. Thus, a research is needed to be carried out to control the process parameters to get actual material removal rate (MRR_act) equals to the theoretical material removal rate (MRR_th) with minimum surface roughness (SR) of the machined surfaces. In this study, five important laser parameters have been used to investigate their effects on MRR and SR. Statistical analysis are performed to identify the significant parameters with their strength of effects. Mathematical models have been developed and validated to predict the machining responses. Optimal set of laser parameters have also been proposed and confirmed to achieve the actual MRR close to the designed MRR (MRR_% = 100.1%) with minimum surface roughness (R_a = 2.67 µm).     

Osteoporosis Prediction for Trabecular Bone using Machine Learning: A Review

Trabecular bone holds the utmost importance due to its significance regarding early bone loss. Diseases like osteoporosis greatly affect the structure of the Trabecular bone which results in different outcomes like high risk of fracture. The objective of this paper is to inspect the characteristics of the Trabecular Bone by using the Magnetic Resonance Imaging (MRI) technique. These characteristics prove to be quite helpful in studying different studies related to Trabecular bone such as osteoporosis. The things that were considered before the selection of the articles for the systematic review were language, research field, and electronic sources. Only those articles written in the English language were selected as it is the most prominent language used in scientific, engineering, computer science, and biomedical researches. This literature review was conducted on the articles published between 2006 and 2020. A total of 62 research papers out of 1050 papers were extracted which were according to our topic of review after screening abstract and article content for the title and abstract screening. The findings from those researches were compiled at the end of the result section. This systematic literature review presents a comprehensive report on scientific researches and studies that have been done in the medical area concerning trabecular bone.     

Selection of solar tracking system using extended TOPSIS technique with interval type-2 pythagorean fuzzy numbers

Optimization and Engineering - The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is considered among the most frequently used techniques to deal with multi-criteria group...     

A comprehensive survey of AI-enabled phishing attacks detection techniques

In recent times, a phishing attack has become one of the most prominent attacks faced by internet users, governments, and service-providing organizations. In a phishing attack, the attacker(s) collects the client’s sensitive data (i.e., user account login details, credit/debit card numbers, etc.) by using spoofed emails or fake websites. Phishing websites are common entry points of online social engineering attacks, including numerous frauds on the websites. In such types of attacks, the attacker(s) create website pages by copying the behavior of legitimate websites and sends URL(s) to the targeted victims through spam messages, texts, or social networking. To provide a thorough understanding of phishing attack(s), this paper provides a literature review of Artificial Intelligence (AI) techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection. This paper also presents the comparison of different studies detecting the phishing attack for each AI technique and examines the qualities and shortcomings of these methodologies. Furthermore, this paper provides a comprehensive set of current challenges of phishing attacks and future research direction in this domain.

     

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.     

Forecasting wheat yield from weather data and MODIS NDVI using Random Forests for Punjab province,Pakistan

Wheat is the staple food of Punjab province of Pakistan, which contributes more than 75% of the total national production. Accurate and timely forecasting of wheat yield is a cornerstone for monitoring food security and planning for agricultural markets, but the efficiency of the current system for near real-time forecasting should be improved. In this research paper, we developed a model to forecast wheat yield before harvest for each of eight individual districts and for Punjab province as a whole. The model uses weather and Moderate Resolution Imaging Spectroradiometer (MODIS)-derived normalized difference vegetation index (NDVI) data for 2001–2014 (14 years) to calculate Random Forest (RF) statistical models using 15 independent variables. Temperature, rainfall, sunshine hours, growing degree days, and MODIS-derived NDVI for each of the eight districts of Punjab province were used to forecast yield for the year 2014. The same independent variables were used to forecast wheat yield of the whole Punjab from 2001 to 2014 by excluding the respective year from training set. Sunshine hour data were not available for all districts and therefore we tested using temperature data and average latitude-based solar radiation as surrogates. The root mean square errors (RMSEs) of the forecast results of the whole of Punjab province were 147.7 kg ha^?1 and 148.7 kg ha^?1 with a mean error of less than 5% using average and generic RFs, respectively. Forecasts for individual districts showed R² of 0.95 with RMSE of 175.6 kg ha^?1 and 5.86% mean error.     

A Dual Ring Connectivity Model for VANET Under Heterogeneous Traffic Flow

Vehicular ad-hoc network (VANET) is characterized as a highly dynamic wireless network due to the dynamic connectivity of the network nodes. To achieve better connectivity under such dynamic conditions, an optimal transmission strategy is required to direct the information flow between the nodes. Earlier studies on VANET’s overlook the characteristics of heterogeneity in vehicle types, traffic structure, flow for density estimation, and connectivity observation. In this paper, we have proposed a heterogeneous traffic flow based dual ring connectivity model to enhance both the message disseminations and network connectivity. In our proposed model the availability of different types of vehicles on the road, such as, cars, buses, etc., are introduced in an attempt to propose a new communication structure for moving vehicles in VANETl under cooperative transmission in heterogeneous traffic flow. The model is based on the dual-ring structure that forms the primary and secondary rings of vehicular communication. During message disseminations, Slow speed vehicles (buses) on the secondary ring provide a backup path of communication for high speed vehicles (cars) moving on the primary ring. The Slow speed vehicles act as the intermediate nodes in the aforementioned connectivity model that helps improve the network coverage and end-to-end data delivery. For the evaluation and the implementation of dual-ring model a clustering routing scheme warning energy aware cluster-head is adopted that also caters for the energy optimization. The implemented dual-ring message delivery scheme under the cluster-head based routing technique does show improved network coverage and connectivity dynamics even under the multi-hop communication system.