An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network

In the area of fuzzy rough set theory (FRST), researchers have gained much interest in handling the high-dimensional data. Rough set theory (RST) is one of the important tools used to pre-process the data and helps to obtain a better predictive model, but in RST, the process of discretization may loss useful information. Therefore, fuzzy rough set theory contributes well with the real-valued data. In this paper, an efficient technique is presented based on Fuzzy rough set theory (FRST) to pre-process the large-scale data sets to increase the efficacy of the predictive model. Therefore, a fuzzy rough set-based feature selection (FRSFS) technique is associated with a Random weight neural network (RWNN) classifier to obtain the better generalization ability. Results on different dataset show that the proposed technique performs well and provides better speed and accuracy when compared by associating FRSFS with other machine learning classifiers (i.e., KNN, Naive Bayes, SVM, decision tree and backpropagation neural network)

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Analysera gemensamma Kriterier Brister att förbättra sin effektivitet

Information security has become a key concern for organizations conducting business in the current electronic era. Rapid technological development continuously creates novel security threats, making IT an uncertain infrastructure. So the security is an important factor for the vendors as well as for the consumers. To fulfill the security needs, IT companies have to adopt some standards to assure some levels that concern with the security in their product. Common Criteria (CC) is one of the standards that maintains and controls the security of IT products. Many other standards are also available to assure the security in products but like these standards CC has its own pros and cons. It does not impose predefined security rules that a product should exhibit but a language for security evaluation. CC has certain advantages due to its ability to address all the three dimensions: a) it provides opportunity for users to specify their security requirements, b) an implementation guide for the developers and c) provides comprehensive criteria to evaluate the security requirements. On the downside, it requires considerable amount of resources and is quite time consuming. Another is security requirements that it evaluates and must be defined before the project start which is in direct conflict with the rapidly changing security threat environment. In this research thesis we will analyze the core issues and find the major causes for the criticism. Many IT users in USA and UK have reservations with CC evaluation because of its limitations. We will analyze the CC shortcomings and document them that will be useful for researchers to have an idea of shortcomings associated with CC. This study will potentially be able to strengthen the CC usage with a more effective and responsive evaluation methodology for IT community.Rana Aamir Raza Ashfaq (0046-76-2473148

Analysera gemensamma Kriterier Brister att förbättra sin effektivitet

Information security has become a key concern for organizations conducting business in the current electronic era. Rapid technological development continuously creates novel security threats, making IT an uncertain infrastructure. So the security is an important factor for the vendors as well as for the consumers. To fulfill the security needs, IT companies have to adopt some standards to assure some levels that concern with the security in their product. Common Criteria (CC) is one of the standards that maintains and controls the security of IT products. Many other standards are also available to assure the security in products but like these standards CC has its own pros and cons. It does not impose predefined security rules that a product should exhibit but a language for security evaluation. CC has certain advantages due to its ability to address all the three dimensions: a) it provides opportunity for users to specify their security requirements, b) an implementation guide for the developers and c) provides comprehensive criteria to evaluate the security requirements. On the downside, it requires considerable amount of resources and is quite time consuming. Another is security requirements that it evaluates and must be defined before the project start which is in direct conflict with the rapidly changing security threat environment. In this research thesis we will analyze the core issues and find the major causes for the criticism. Many IT users in USA and UK have reservations with CC evaluation because of its limitations. We will analyze the CC shortcomings and document them that will be useful for researchers to have an idea of shortcomings associated with CC. This study will potentially be able to strengthen the CC usage with a more effective and responsive evaluation methodology for IT community.Rana Aamir Raza Ashfaq (0046-76-2473148

Multiclass Non-Randomized Spectral–Spatial Active Learning for Hyperspectral Image Classification

Active Learning (AL) for Hyperspectral Image Classification (HSIC) has been extensively studied. However, the traditional AL methods do not consider randomness among the existing and new samples. Secondly, very limited AL research has been carried out on joint spectral–spatial information. Thirdly, a minor but still worth mentioning factor is the stopping criteria. Therefore, this study caters to all these issues using a spatial prior Fuzziness concept coupled with Multinomial Logistic Regression via a Splitting and Augmented Lagrangian (MLR-LORSAL) classifier with dual stopping criteria. This work further compares several sample selection methods with the diverse nature of classifiers i.e., probabilistic and non-probabilistic. The sample selection methods include Breaking Ties (BT), Mutual Information (MI) and Modified Breaking Ties (MBT). The comparative classifiers include Support Vector Machine (SVM), Extreme Learning Machine (ELM), K-Nearest Neighbour (KNN) and Ensemble Learning (EL). The experimental results on three benchmark hyperspectral datasets reveal that the proposed pipeline significantly increases the classification accuracy and generalization performance. To further validate the performance, several statistical tests are also considered such as Precision, Recall and F1-Score

Integrated Fertilizers Synergistically Bolster Temperate Soybean Growth, Yield, and Oil Content

For ensuring food security and imparting sustainability to modern commercial-oriented and highly intensive temperate farming systems, organic wastes from poultry and dairy industries constitute biologically viable strategy to improve crops productivity under changing climate. A field trial was undertaken to appraise the impact of broiler litter (BL = 5 tons ha−1), farm yard slurry (FYS = 10 tons ha−1), and chemical fertilizers including di-ammonium phosphate (DAP = 60 kg ha−1) and single super phosphate (SSP = 60 kg ha−1) applied solely and in conjunction with each other, along with a control treatment (NM). The synergistic fertilization regime encompassing BL+DAP triggered the vegetative growth of soybean as indicated by taller plants having thicker stems and higher leaf area per plant compared to NM. In addition, this fertilization management system improved reproductive yield attributes including pods number and 100-seeds weight which maximized the seed yield, harvest index, seed oil content, and biological yield by 66%, 5%, 31%, and 23% respectively than NM. Moreover, this fertilizers combination was followed by SSP + BL, while BL performed better than FYS and DAP remained superior to SSP. Furthermore, the correlation analyses indicated moderately stronger direct association of seed yield with vegetative growth traits and highly stronger linear relationship with reproductive yield attributes. Thus, co-application of broiler litter (5 tons ha−1) with reduced doses of DAP (60 kg ha−1) might be recommended to temperate soybean growers having access to poultry wastes

Weeds Spectrum, Productivity and Land-Use Efficiency in Maize-Gram Intercropping Systems under Semi-Arid Environment

To ensure food security on sustainable basis, reducing weeds interference and boosting land use efficiency are critical. A field study was conducted at research farm of University of Agriculture Faisalabad, Pakistan, to sort out the most productive maize-gram intercropping system under semi-arid environment. Treatments included sole maize in single row (60 cm apart) (T1) and double rows (90 cm apart) (T2) strips, sole black (T3) and green gram (T4) crops, six single rows (60 cm apart) of maize with twelve double rows (20 cm) of black (T5) and green gram (T6), three double rows (90 cm apart) of maize with three sets of quadratic rows (20 cm apart) of black (T7) and green gram (T8). The experiment was executed in regular arrangement of randomized complete block design with three replications. The results revealed that T1 produced the highest grain yield (6.97 t ha−1) of maize and significantly lower weeds infestation compared to wider row spacing (T2). Among intercropping systems, T8 significantly decreased weeds density (16.33 plants m−2) and their fresh (20.93 g m−2) and dry weights (5.63 g m−2), while the maximum land use efficiency as indicated by unmatched land equivalent ratio and intercropping advantage were recorded by T7 and T8. Interestingly, green gram in intercropping recorded over 58% higher productivity than black gram. We conclude that maize-green gram intercropping hold potential to impart sustainability to maize production by reducing weeds infestation (431% lower than sole maize) and could be a viable option for smallholder farmers in semi-arid environment