A Majority Vote Based Classifier Ensemble for Web Service Classification

Service oriented architecture is a glue that allows web applications to work in collaboration. It has become a driving force for the service-oriented computing (SOC) paradigm. In heterogeneous environments the SOC paradigm uses web services as the basic building block to support low costs as well as easy and rapid composition of distributed applications. A web service exposes its interfaces using the Web Service Description Language (WSDL). A central repository called universal description, discovery and integration (UDDI) is used by service providers to publish and register their web services. UDDI registries are used by web service consumers to locate the web services they require and metadata associated with them. Manually analyzing WSDL documents is the best approach, but also most expensive. Work has been done on employing various approaches to automate the classification of web services. However, previous research has focused on using a single technique for classification. This research paper focuses on the classification of web services using a majority vote based classifier ensemble technique. The ensemble model overcomes the limitations of conventional techniques by employing the ensemble of three heterogeneous classifiers: Naïve Bayes, decision tree (J48), and Support Vector Machines. We applied tenfold cross-validation to test the efficiency of the model on a publicly available dataset consisting of 3738 real world web services categorized into 5 fields, which yielded an average accuracy of 92 %. The high accuracy is owed to two main factors, i.e., enhanced pre-processing with focused feature selection, and majority based ensemble classification

Securing Cognitive Radio Networks using blockchains

Due to the increase in industrial applications of Internet of Things (IoT), number of internet connected devices have been increased accordingly. This has resulted in big challenges in terms of accessibility, scalability, connectivity and adaptability. IoT is capable of creating connections between devices on wireless medium but the utilization of scarce spectrum in efficient manner for the establishment of these connections is the biggest concern. To accommodate spectrum allocation problem different radio technologies are being utilized. One of the most efficient technique being used is cognitive radio, which dynamically allocate the unlicensed spectrum for IoT applications. Spectrum sensing being the fundamental component of Cognitive Radio Network (CRN) is threatened by security attacks. Process of spectrum sensing is disturbed by the malicious user (MU) which attacks the primary signal detection and affects the accuracy of sensing outcome. The presence of such MU in system, sending false sensing data can degrade the performance of cognitive radios. Therefore, in this article a blockchain based method is proposed for the MU detection in network. By using this method an MU can easily be discriminated from a reliable user through cryptographic keys. The efficiency of the proposed mechanism is analyzed through proper simulations using MATLAB. Consequently, this mechanism can be deployed for the validation of participating users in the process of spectrum sensing in CRN for IoTs.publishe

ZnO Nano-swirlings for Azo Dye AR183 photocatalytic degradation and antimycotic activity

The sol-gel technique was used to fabricate ZnO Nano-swirlings (ZNsw) at a predetermined agitation rate (of \u3e\u3e 1900 rpm), with around 21.94 gm of zinc acetate dihydrate and 0.2 g cetyltrimethylammoniumbromide (CTAB) and a cationic surfactant (drop-wise). The impact of the predetermined agitation condition on the molecular size and morphology of ZNsw is examined, and the outcomes are dissected by useful characterization tools and techniques viz. XRD, SEM embedded with EDS, TEM, FT-IR and UV–visible. The SEM and TEM results suggest that the product formed into a big cluster of adequate ZNsw, containing a significant quantity of folded long thread-lengths. Each group indicated a fair amount of the volume of these lengths. The photocatalytic process of ZNsw was carried out as a result of the irradiation time due to the deterioration of Azo Dye AR183, resulting in approximately 79 percent dye discoloration following an 80-min UV light irradiation in the presence of ZNsw. Additionally, the synthesized ZNsw was tested for antagonistic activity, and the growth hindrance of two plant pathogenic fungal strains found. Per cent inhibition in growth of Rhizoctonia solani and Alternaria alternata were observed in response to ZNsw

Climate change risk perception and adaptation to climate smart agriculture are required to increase wheat production for food security

Climate change poses a serious risk to wheat farmers in many regions of the world. The present study was conducted in the Sialkot District, Punjab, Pakistan, to investigate climate change trends during the past thirty years and to determine farmers’ knowledge and perceptions about climate change. The study also addresses the impacts of climate change on wheat production, current adaptation strategies, and limitations in adaptations to climate-smart agriculture (CSA) through a questionnaire-based survey. The historical weather data from the past thirty years indicated an increase in the mean annual minimum and maximum temperature and a decrease in annual total precipitation. Wheat productivity during the past thirty years showed an increasing trend but it was inconsistent. The respondents’ perception of climate change indicated that the literate farmers and those with broad farming experience were more knowledgeable about the climatic effects on wheat production. However, the survey results showed that the age of the farmers did not affect their perceptions. The current management practices are primarily based on prior experiences (70%) and traditional practices (30%). The standard management practices to increase farm productivity include an increase in fertilizer use (70%), a decrease in manure use (24%), and intercropping or switching to other crop cultivations (60%). The farmers stated that their reasons for limited adaptation to climate smart farm practices (CSFP) were due to their lack of knowledge and skills (86%), lack of modern technologies (74%), economic constraints (78%), politics (86%), and social influences (74%). Based on the survey results, the study suggests that addressing these gaps can increase farm-level wheat productivity to increase resilience. This can be achieved by introducing stateof- the-art farming practices through farmer training and by providing institutional services with a focus on climate-specific farm consultation services, leading to climate-smart agricultural practices for improved food security. Highlights - Literate farmers are more aware of climate change as compared to illiterate farmers. - The farmers emphasized the increase in both the summer and winter temperature. - Rainfall is identified as a major climate threat in the study area. - The farmers identified that the highest impact of climate change occurred during the harvest phase of wheat. - The farmers stated that the limited adoption of climate smart agricultural practices is due to lack of knowledge and technological, economic, and other gaps

PREDICTIVE POLICING: A Machine Learning Approach to Predict and Control Crimes in Metropolitan Cities

Security is the one of the basic need of human’s life and biggest challenge of history that cannot be diminished at least in metropolitan cities like Karachi. It can only be controlled by efficient resources allocation and effective strategies with forthcoming insight of criminal moves. Big data analytics with the support of machine learning algorithms makes it possible to deals with huge amount of data, extract hidden inter-connection, pattern and meaningful information. This paper, proposed the model for the predictive policing system and built test model using k-means and naïve Bayes methodologies for street crime in Karachi region. The model is then run under R and WEKA environment which produced accuracy around 70%

Toxicopathological effects of endosulfan in female Japanese Quails (Coturnix japonica)

Background: The current study was planned to investigate the toxico-pathological effects of endosulfan in female Japanese quails.Methods: A total of 120 quail of 4 weeks old were divided into six equal groups (A-F) and administered endosulfan in feed at dose rate of 0, 5, 25, 50, 100, and 500 mg/kg feed, respectively for 90 days. Parameters studied included clinical signs, feed intake, body weight and mortality. Hematology, serum biochemistry, hatchability and fertility were also determined. Gross and microscopic changes on different organs were recorded.Results: The quails of the group B did not show any clinical signs and had significantly lower values of feed intake, testes relative weight and leukocyte number than those of the control group A. The quails of group C and D had mild depression while those of the group E and F showed nervous excitation following ingestion of endosulfan. There was a dose related delay in onset of crowing, appearance of foamy material in the droppings. The feed intake, erythrocyte and leukocyte counts, hematocrit values, and serum total proteins of endosulfan fed quails were significantly (p < 0.05) lower than that of the group A. The total egg production in groups A, B and C was significantly higher from group D, E and F.  The hatchability in group A and B was significantly higher from groups C, D, E and F. The difference of dead in shell % and early dead among different groups was nonsignificant. Infertile egg percentage was significantly higher in group E compared with all other groups except group F. The necrotic changes were observed in all parts of oviduct in high dose groups, similarly necrotic changes and vacuolar degeneration was observed in hepatic parenchyma in high dose groups D-F.Conclusion: It may be concluded that endosulfan leads to dose dependent changes in the quails.Keywords: Body weight; Coturnix japonica; Endosulfan; Haematological values; Histopatholog

Thermal Performance Analysis of Various Heat Sinks Based on Alumina NePCM for Passive Cooling of Electronic Components: An Experimental Study

In the modern digital world, electronic devices are being widely employed for various applications where thermal performance represents a significant technical challenge due to continued miniaturization, high heat generated in the system, and non-uniform high-temperature causing failure. Phase change materials (PCMs) owing to the immense heat of fusion are primarily considered for thermal management, but their insulating properties hedge their applications in electronics cooling. Nano-enhanced phase change materials (NePCMs) have the ability to improve the thermal conductivity of PCM, decrease system temperature and escalate the operating time of devices. Accordingly, the current study focused on the experimental investigations for the thermal performance of three heat sinks (HS) with different configurations such as a simple heat sink (SHS), a square pin-fins heat sink (S pfHS), and Cu foam integrated heat sink (Cu fmHS) with various alumina nanoparticles mass concentrations (0.15, 0.20 and 0.25 wt%) incorporated in PCM (RT-54HC) and at heat flux (0.98–2.94 kW/m 2). All HSs reduced the base temperature with the insertion of NePCM compared to the empty SHS. The experimental results identified that the thermal performance of Cu fmHS was found to be superior in reducing base temperature and enhancing working time at two different setpoint temperatures (SPTs). The maximum drop in base temperature was 36.95%, and a 288% maximum working time enhancement was observed for Cu fmHS. Therefore, NePCMs are highly recommended for the thermal management of the electronic cooling system

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Texture classification using rotation-and scale-invariant gabor texture features

This letter introduces a novel approach to rotation and scale invariant texture classification. The proposed approach is based on Gabor filters that have the capability to collapse the filter responses according to the scale and orientation of the textures. These characteristics are exploited to first calculate the homogeneous texture of images followed by the rearrangement of features as a two-dimensional matrix (scale and orientation), where scaling and rotation of images correspond to shifting in this matrix. The shift invariance property of discrete fourier transform is used to propose rotation and scale invariant image features. The performance of the proposed feature set is evaluated on Brodatz texture album. Experimental results demonstrate the superiority of the proposed descriptor as compared to other methods considered in this letter