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Metamaterial-based double-slit complementary split rectangular resonator sensor is proposed for the characterisation of dielectric properties of the materials under test (MUTs). The proposed sensor is designed and simulated on the CST microwave studio software using a low-cost substrate FR4. An array of three identical resonators is etched in the ground plane of the sensor to achieve a single and deep notch of −58.7 dB in the transmission coefficient (S 21) at the resonant frequency of 7.01 GHz, which is the novelty of the proposed sensor. A deep and single resonant frequency band has a significant role in the precise measurement of the dielectric properties of the MUTs. The effective constitutive parameters are extracted from the S-parameters. An equivalent circuit model is suggested that describes the overall behaviour of the sensor. The sensor is fabricated on the FR4 substrate and measured through the vector network analyser (N5224B) by placing the standard materials. The parabolic equation for the proposed sensor is formulated to approximate the permittivity of the MUTs. A very small percentage of error, 0.77, is found which shows high accuracy of the sensor. This methodology is efficient, simple in fabrication, and reduces cost and computational time also.

Haemorrhagic septicaemia (HS) caused by Pasteurella multocida serotype B:2 is an economically important disease of cattle and buffalo, causes heavy economic losses due to sudden death of animals in developing countries like Pakistan. In this country, animals are vaccinated by alum (adjuvant) precipitated vaccine twice a year. Immunity induced through this prophylactic measure lasts for 3-4 months only. Two new HS oil based vaccines were prepared by using two new oil adjuvants such as Eolane-150 and Eolane-170. The ratio of bacterial antigen and oil adjuvants was 1:1 while bacterial dry weight was adjusted to 2 mg/ml. The addition of enrichments and aeration resulted in dense bacterial growth of Pasteurella multocida. Both new vaccines passed sterility, safety and potency tests as per OIE, 2017. Active and passive Mouse Protection Tests were performed to evaluate its potency. Indirect Haem-Agglutination (IHA) test was conducted on serum samples of two rabbits, groups each of which was vaccinated with HS oil based vaccines adjuvanted with Eolane-150 and Eolane-170. IHA indicated that immune response was higher (GMT=32) initially on 45th day to 75th day post vaccination and then declined (GMT=16) in the rabbits vaccinated with HS vaccine adjuvanted with Eolane-150, while protective immune response remained constant (GMT=16) up to ninety days post vaccination in the rabbits vaccinated with HS vaccine adjuvanted with Eolane-170. Vaccines were easy to inject with no side effects, including swelling at the injection site and longer protection as well. That would hopefully motivate the livestock owners and farmers to use this new product to protect their animals against this fatal HS disease.

One of the most significant problems in cluster analysis is to determine the number of clusters in unlabeled data, which is the input for most clustering algorithms. Some methods have been developed to address this problem. However, little attention has been paid on algorithms that are insensitive to the initialization of cluster centers and utilize variable weights to recover the number of clusters. To fill this gap, we extend the standard fuzzy $k$ k -means clustering algorithm. It can automatically determine the number of clusters by iteratively calculating the weights of all variables and the membership value of each object in all clusters. Two new steps are added to the fuzzy $k$ k -means clustering process. One of them is to introduce a penalty term to make the clustering process insensitive to the initial cluster centers. The other one is to utilize a formula for iterative updating of variable weights in each cluster based on the current partition of data. Experimental results on real-world and synthetic datasets have shown that the proposed algorithm effectively determined the correct number of clusters while initializing the different number of cluster centroids. We also tested the proposed algorithm on gene data to determine a subset of important genes.

Aim: This research investigated the effectiveness of an intervention for improving the prescribing and patient safety behaviour of Foundation Year doctors. The intervention consisted of simulated clinical encounters with subsequent personalised, structured, video-enhanced feedback and deliberate practice, undertaken at the start of four-month sub-specialty rotations. Method: Three prospective, non-randomised control intervention studies were conducted, within two secondary care NHS Trusts in England. The primary outcome measure, error rate per prescriber, was calculated using daily prescribing data. Prescribers were grouped to enable a comparison between experimental and control conditions using regression analysis. A breakeven analysis evaluated cost effectiveness. Results: There was no significant difference in error rates of novice prescribers who received the intervention when compared with those of experienced prescribers. Novice prescribers not participating in the intervention had significantly higher error rates (p=0.026, 95% CI Wald 0.093 to 1.436; p=0.026, 95% CI 0.031 to 0.397) and patients seen by them experienced significantly higher prescribing error rates (p=0.007, 95% CI 0.025 to 0.157). Conversely, patients seen by the novice prescribers who received the intervention experienced a significantly lower rate of significant errors compared to patients seen by the experienced prescribers (p=0.04, 95% CI-0.068 to -0.001). The break-even analysis demonstrates cost effectiveness for the intervention. Conclusion: Simulated clinical encounters using personalised, structured, video-enhanced feedback and deliberate practice improves the prescribing and patient safety behaviour of junior doctors in their Foundation Training. The intervention is cost-effective with potential to reduce avoidable harm.

Introduction: Medical school graduates in the UK consistently report feeling underprepared for the task of prescribing when embarking on practice. The effective application of self-regulated learni...

Novel graphitic carbon nitride (g-C3N4) microrods were prepared by chemical method via pre-treatment of melamine and molar solution of HNO3 at a lower temperature. The phase and morphological structure was examined by XRD and SEM, while elemental composition was found by performing EDX spectroscopy. SEM results revealed that the as-prepared g-C3N4 materials has rods like morphology with average length 3–5 µm. Furthermore, the vibrational modes were studied by FTIR, and optical properties were analyzed by UV–Vis and PL spectroscopy respectively. The optical measurements showed that the band gap of g-C3N4 microrods was 2.70 eV which make them useful for photocatalytic process under visible light. The blue PL emission at 455 nm was ascribed to band-to-band transition which also confirms the activation of g-C3N4 microrods in visible spectrum range. Finally, g-C3N4 microrods were utilized as photocatalyst for the decomposition of hazardous Methyl Orange (MO) dye, and the results showed that they exhibited excellent performance (k = 0.026 min−1) visible light irradiation. Keywords: Graphitic carbon nitride, Melamine, Microrods, Photocatalyst

Evolutionary Algorithms are bio-inspired optimization problem-solving approaches that exploit principles of biological evolution. , such as natural selection and genetic inheritance. This review paper provides the application of evolutionary and swarms intelligence based query optimization strategies in Distributed Database Systems. The query optimization in a distributed environment is challenging task and hard problem. However, Evolutionary approaches are promising for the optimization problems. The problem of query optimization in a distributed database environment is one of the complex problems. There are several techniques which exist and are being used for query optimization in a distributed database. The intention of this research is to focus on how bio-inspired computational algorithms are used in a distributed database environment for query optimization. This paper provides working of bio-inspired computational algorithms in distributed database query optimization which includes genetic algorithms, ant colony algorithm, particle swarm optimization and Memetic Algorithms.

Background: Laparoscopic surgery is associated with shorter hospital stay, less post-operative pain and excellent cosmesis. Post-operative pain is lesser in laparoscopic procedures but in some cases, it is not absolutely painless. The aim of different methods to reduce the post-operative pain is to avoid the use of opioids by using anti-inflammatory drugs and infiltration of local anesthesia either intra-peritoneal or in the wound. Theoretically peripheral blockage of pain stimuli with local anesthetic agent is more effective than treating pain. For this purpose, Bupivacaine has been recently used to be injected at port sites. Objective: To evaluate the pain and requirement of injectable anti-inflammatory drug (inj. Ketorolac 30mg) after port site infiltration of inj. bupivacaine in laparoscopic cases. Study Design: Prospective randomized study of elective laparoscopic procedures. Settings: Surgical Unit-IV, DHQ Teaching Hospital Faisalabad, Medical University Faisalabad Pakistan. Duration: November 2015 to May 2017. Methodology: Two hundred and sixty patients included in the study were divided into two groups with equal number in each group. Results: In the study group(A), 103(79.3%) patients were females of 27(20.7%) patients were males. All female patients in the study group underwent LC and among males, 26(96.3%) patients underwent TAPP and 1(3.7%) patient underwent LC. In the control group(B) 97(74.6%) patients were females, 33(25.4%) patients were male. All female patients (100%) underwent LC and among the males, 30 patients (90.9%) underwent TAPP, 3(9.1%) patients underwent LC. In the study group, the mean VAS in the study group at 2 hours, 6 hours, 12 hours after surgery was 1.6, 1.7, 1.6 with standard deviation 1.6, 1.7, 1.8 respectively while in the control group VAS was 2.9, 3.1, 2.6 with standard deviation 2.1, 1.8, 1.9 respectively. In the study group the mean of anti-inflammatory drug injections (Ketorolac) needed was 1.0000 with standard deviation 0.7 and in control group, the same was 1.3 with standard deviation 70.8. The previous was less than 0.05. Conclusion: Infiltration of local anesthetic agent i.e., Bupivacaine results in almost total painless procedure in laparoscopic surgery, the timing and anatomical site of injection should be investigated further.

Automatic Speech Recognition Systems (ASR) have significantly improved in recent years, where deep learning is playing an important role in the development of end to end ASR's. ASR is the task of converting spoken language into computer readable text. ASRs are becoming ever more prevalent way to interact with technology, thereby significantly closing the gap in terms of how humans interact with computers, making it more natural. Urdu is an under resourced language, for which training such a system requires a huge amount of data that is not readily available. In this paper we present improvements to the architecture of a statistical automatic speech recognition system for which the components involved in a statistical ASR have been explored in great detail. We also present the results on various statistical models that are trained for Urdu language. We choose the Kaldi toolkit for training the Urdu ASR using approximately 100 hours of transcribed data. The refined Subspace Gaussian Model gives a word error rate of 9% on the test set.

This paper highlights recent developments and challenges on beam steering characteristics of reconfigurable transmitarray antennas. It introduces the operating principle of beam forming/beam steering high gain transmitarray antennas to enable the user to opt for economical and high performance solutions. A transmitarray antenna typically consists of a source antenna and a phase transformation structure. The incident waves generated from the source antenna is tilted using the phase transformation structure in a desired direction to steer the beam. Moreover, the phase transformation structure alters the incident wavefront to a plane wavefront using phase change characteristics. In order to steer a beam to a specific desired angle, it can be divided into two methods. There is a method of applying a transmitarray with a variable transmission phase change or a method of changing the shape of the wavefront of the source antenna. This type of beam forming/beam steering high gain antenna has been mainly studied from the point of view of high efficiency, low profile, and low cost. Several solutions of transmitarray unit cells have been presented in the literature, using PIN diodes, varactors, MEMS switches, and microfluids enable electronics to realize reconfigurable characteristics of transmitarray antennas. This paper analyzes the characteristics of various beam steering high gain reconfigurable transmitarrays (RTA) and highlights the future opportunities and challenges of the structure design for transmitarray antennas. This paper also highlights the challenges and gaps in terahertz and optical frequencies related to future work due to the structure complexity and lack of components’ availability. Moreover, the challenges and limitations related to multi-bit structures and dual-band requirements are presented.

Intelligent control of powered upper limb prosthetic devices through pattern recognition of surface electromyography (sEMG) signals is an active area of research. The objective is to develop a prosthetic device controller that decodes multi-channel sEMG activity to recognize distinct grasp patterns. A number of supervised and semi-supervised classification techniques have been developed with more than 90% classification accuracy. However, the realization of a clinically viable prosthetic controller for a multi-degree of freedom bionic hand is yet to be achieved. One of the reasons for this disparity between academic excellence and clinical reality is the limited number of taxonomically-opposite hand motion classes used for classifier evaluation in academic studies and the diverse set of motion classes encountered during daily life activities. In this study we have investigated the effect of motion class taxonomy on the performance of classifiers based on linear discriminant analysis (LDA) and support vector machine (SVM). The results of the study have shown a significant dependence of motion class taxonomy on classifier performance. The SVM classifier showed 1.8% average improvement while the LDA classifier resulted in an improvement of 4.3% for decoding taxonomically-distant motion classes as compared to the taxonomically-close classes.

Dual-Slit Complementary Split Rectangular Resonator (DS-CSRR) based microwave sensor is suggested for characterization of dielectric properties of materials under test (MUTs). Microwave sensor is designed and simulated on HFSS. Economical FR4 substrate is used in designing and fabrication of suggested sensor. A deep notch of transmission coefficient (S 21 ) at -32dB is achieved at resonance frequency of 8.56 GHz. Negative electromagnetic properties of electric permittivity ($\varepsilon$) and magnetic permeability ($\mu$) are extracted from magnitude of scattering parameters which are the basic criterion of metamaterials/Left Handed Materials (LHM)/Double Negative Group (DNG). Suggested sensor is fabricated by using photolithography technique. Scattering parameters of fabricate sensor are measured by using VNA (N5224B). A good agreement is shown between the simulated and measured results. Parabolic and transcendental equations are formulated to estimate the resonance frequency and the permittivity of unknown materials through suggested sensor. This method requires only calculating resonant frequency and the magnitude response, thus achieving a substantial reduction in cost, computation time, ease in fabrication and integration with other microwave devices.

Dual split ring resonator based Metamaterial sensor materials is presented in this paper. This complementary split ring resonator (CSRR) is etched in the ground plane on back of the Microstrip line. Full wave electromagnetic simulation is done to verify the resonance phenomenon. Presented metamaterial sensor shows good result around 6GHz. Simulation results show S21 with −31 dB notch depth at resonant frequency. Electromagnetic properties like effective electric permittivity ( $\varepsilon$ ) and magnetic permeability ( $\mu$ ) of the sensor are derived from S-parameters simulation. Standard photolithography technique is used in fabrication of this metamaterial sensor using low cost FR4 substrate. S-parameters are measured by using Keysight technologies Vector Network Analyzer N5224B. S-parameters simulated and measured results show good agreement. By measurement of only resonant frequency (GHz) and the magnitude (dB) response of the sensor with material under test (MUT) the dielectric constant of the material can be calculated numerically. This nondestructive measurement technique has many applications in material testing for security and bio-sensing.

Metamaterial (MTM) sensor based on Twin-Slit Complementary Split Rectangular Resonator (TS-CSRR) is proposed for measurement of dielectric properties of the materials under test (MUT). Array of two TS-CSRR structures on single board is designed and simulated on CST Microwave Studio by using low cost FR4 substrate. Very sharp response with notch depth of −49.9 dB is obtained at resonance frequency of 7.9 GHz. Array of multi structured CSRR on single board with single band and very sharp response is the novelty. DNG (Double Negative) and NIM (Negative Indexed) properties are extracted from scattering parameters. The transcendental equation is derived to calculate relative permittivity and resonance frequency of unknown MUTs. The response of proposed sensor is like a narrow stop band filter. This approach is very modest, simple in fabrication and decreases cost and computational time. Proposed sensor can be used effectively in numerous fields like satellite communications, radar systems, bio-sensing and liquid characterization.

New technologies are adapted to made progress in healthcare especially for independent livings. Medication at distance is leading to integrate technologies with medical. Machine learning methods in collaboration with wearable sensor network technology are used to find hidden patterns in data, detect patient movements, observe habits of patient, analyze clinical data of patient, find intention of patients and make decision on the bases of gathered data. This research performs comparative study on non-clinical systems in healthcare for independent livings. In this study, these systems are sub-divided w.r.t their working into two types: single purpose systems and multi-purpose systems. Systems that are built for single specific purpose (e.g. detect fall, detect emergent state of chronic disease patient) and cannot support healthcare generically are known as single purpose systems, where multi-purpose systems are built to serve for multiple problems (e.g. heart attack etc.) by using single system. This study analyzes usages of machine learning techniques in healthcare systems for independent livings. Answer Set Programming (ASP), Artificial Neural Networks, Classification, Sampling and Rule Based Reasoning etc. are some state of art techniques used to determine emergent situations and observe changes in patient data. Among all methods, ASP logic is used most widely, it is due to its feature to deal with incomplete data. It is also observed that system using ANN shows better accuracy than other systems. It is observed that most of the systems created are for single purpose. In this work, 10 single purpose systems and 5 multi-purpose systems are studied. There is need to create more generic systems that can be used for patients with multiple diseases. Also most of the systems created are prototypical. There is need to create systems that can serve healthcare services in real world., Comment: 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE)

In this work, the design of microstrip bandpass filter (BPF) at the millimeter-wave band is investigated using a method that is based on dual-mode ring resonator and folded half-wavelength resonators. Earlier such structure has been reported to realize BPF in quasi-millimeter waveband. The filter design parameters are optimized using the EM simulation tool. A couple of filter structures are designed by deploying this method with a fractional bandwidth of 18% at a center frequency of 34 GHz and fractional bandwidth of 15% at a center frequency of 40 GHz and subsequently manufactured on Rogers 5880 10-mil substrate. The measured insertion losses of these two filters are $2.27\pm 0.35\text{dB}$ and $2.50\pm 0.4\ \text{dB}$ respectively. The filters exhibited flat response in the passband, sharp roll-off skirt, and good stopband suppression over a wide frequency range. The sizes of core structures of filters are 5.4×2.95mm and 4.7x2.66 mm, while connecting transmission lines were extended to fit the circuit in mechanical housing having a size of 13.8x7.6mm. Measured parameters of physical circuits are in close agreement with simulation results.

Complementary Split Rectangular Resonators based Metamaterial sensor is designed and simulated on HFSS for the non-destructive evaluation of dielectric substrates. S-parameters measurements are made to extract the complex permittivity and permeability of the structures. Proposed metamaterial sensor operating in the S-band are fabricated and tested for verification. Comparative analysis is also performed among four structures namely Rectangular, Circular, Square and Triangular. Proposed sensor is a two port structure. Top side of all the sensors are microstrip line while aforementioned structures are etched in the bottom of the substrate. After the analysis, rectangular structure is found better in respect of resonant frequency and sharp notch depth which is −19.6 dB at 3.78 GHz. Rectangular structure based microwave sensor is then fabricated. Electromagnetic properties of effective permittivity (e) and permeability (µ) of the proposed sensor is calculated numerically. Proposed sensor is fabricated by using low cost FR4 material. The dimensions of FR4 substrate are (w = 24, l = 30, h = 0.8) Dimensions of the proposed rectangular structure is w = 4, l = 6 and h = 0.035. Transmission coefficient magnitude (S 21 ) is measured by using Vector Network Analyzer (E8363B). The response of the rectangular sensor is like a narrow band rejects filter. This methodology is very simple and needs only to calculate the resonant frequency which reduces not only the cost but also computation time. Ease in fabrication, and integration with other microwave devices are the main benefits of complementary metamaterial microwave sensors. Proposed sensor can be applicable in the fields of bio-sensing, security and designing of 2D sensor array for surface imaging.

Considering the requirements of amplifiers for modern communication systems and having different topology variants with different advantages or disadvantages over each other, this paper presents experimental comparison between Class AB and High efficiency Class F−1 power amplifiers using GaN based Transistors. The results show that Class F−1 power amplifier exhibit higher output power and higher efficiency but degraded linearity response as compared to class AB power amplifier. For comparison, the inverse class F and class AB power amplifiers were designed and implemented at 2.14 GHz using GaN Hemt. Measured performance showed 7% higher power added efficiency along with 0.3 dB increase in output power for inverse class F power amplifier.

Language assets, like corpora, are essential for different natural language processing tasks. There are many useful applications of emotion analysis of text such as dialog systems, smart agents, mental disorder clinical diagnoses. In this research, an emotion labeled corpus for Roman Urdu is presented for evaluation of emotions in short text. We collected a sizable corpus with 10,000 manually annotated sentences in Roman Scripted Urdu to facilitate the development and evaluation of emotion detection systems for Roman Urdu. This corpus is the first of its kind to be created for the Roman Urdu.

Reversible watermarking has gained importance due to increased involvement of digital media in sensitive fields, such as medical and law enforcement. We propose a prediction error expansion-based watermarking scheme that allows embedding reversible watermark in the image with low distortion. Research work proposes four-phase representation of image which allows exploitation of larger prediction context. We have also proposed a hybrid predictor that helps enhance the prediction accuracy. To reduce image distortion at lower capacity payloads, we use sorting of estimated prediction errors through sorting of prediction context variances. For improvement at higher capacity payloads, adaptive embedding is used to determine whether to embed single or two bits in a given prediction error. The results are compared against some state-of-the-art techniques in the field and show promising results.

Labeled data is the main ingredient for classification tasks. Labeled data is not always available and free. Semi-supervised learning solves the problem of labeling the unlabeled instances through heuristics. Self-training is one of the most widely-used comprehensible approaches for labeling data. Traditional self-training approaches tend to show low classification accuracy when the majority of the data is unlabeled. A novel approach named Self-Training using Associative Classification using Ant Colony Optimization (ST-AC-ACO) has been proposed in this article to label and classify the unlabeled data instances to improve self-training classification accuracy by exploiting the association among attribute values (terms) and between a set of terms and class labels of the labeled instances. Ant Colony Optimization (ACO) has been employed to construct associative classification rules based on labeled and pseudo-labeled instances. Experiments demonstrate the superiority of the proposed associative self-training approach to its competing traditional self-training approaches.

Discretization is the process of converting numerical values into categorical values. Contemporary literature study reveals that there are many techniques available for numerical data discretization. The performance of classification method is dependent on the exploitation of the data discretizing method. In this article, we investigate the effect of discretization methods on the performance of associative classifiers. Most of the classification approaches work on the discretized databases. There are various approaches exploited for the discretization of the database to compare the performance of the classifiers. The selection of the discretization method greatly influences the classification performance of the classification method. We compare the performance of associative classifiers namely CBA and CBA2 on the selective discretizing methods i.e. 1R Discretizer (1R-D), Ameva Discretizer (Ameva-D), Bayesian Discretizer (Bayesian-D), Discretization algorithm based on Class-Attribute Contingency Coefficient (CACC-D), Class-Attribute Dependent Discretizer (CADD-D), Distribution-Index-Based Discretizer (DIBD-D), Cluster Analysis (ClusterAnalysis-D), Chi-Merge Discretizer (ChiMerge-D) and Chi2 Discretizer (Chi2-D) in terms of accuracy. The main object of this study is to investigate the impact of discretizing method on the performance of the Associative Classifier by keeping constant other experimental parameters. Our experimental results show that the performance of the Associative Classifier significantly varies with the change of data discretization method. So the accuracy rate of the classifier is highly dependent on the selection of the discretization method. For this comparative performance study, we use the implementation of these methods in KEEL data mining tool on public datasets.

In this paper, a novel approach for discovering emerging patterns has been proposed. Majority of the existing algorithms for the discovery of emerging patterns are tree-based which involve growth and shrinking of trees for this purpose. These algorithms follow greedy search approach for discovery of emerging patterns. The proposed approach utilizes the diversity of ant colony optimization and avoids complexity and greedy search of tree-based algorithms for discovery of emerging patterns. The experiments show that the proposed approach provides higher accuracy than existing state of the art classifiers as well as emerging pattern-based classifiers.

This article represents the focused performance analysis of the statistical Pattern Recognition methods. There is a variety of emerging pattern recognition problems for different data representations like multimedia, spatial, temporal and textual data. Statistical Pattern Recognition approaches are extensively applied for pattern recognition and classification purposes. There are various statistical pattern-recognition methods proposed in the contemporary literature. For the selection of more appropriate statistical pattern recognition, method demands comprehensive performance evaluation of contemporary methods. However, this empirical study is focused on the performance evaluation of statistical methods for pattern discovery in terms of accuracy and error rate. The studied methods include Naive-Bayes (NB-C), Linear Discriminant Analysis (LDA-C), Kernel Classifier (Kernel-C), Least Mean Square Linear Classifier (LinearLMS-C), Least Mean Square Quadratic classifier(PolQuadraticLMS-C), Multinomial logistic regression model with a ridge estimator(Logistic-C) and Particle Swarm Optimization - Linear Discriminant Analysis (PSOLDA-C). The implementation of mentioned methods in KEEL, a data mining tool, is applied on public datasets for the comparative analysis. Experimental results reveal that the performance of PSOLDA-C is promising than other methods in terms of accuracy.

Ant-Miner, a rule-based classifier, is a powerful algorithm used to extract classification rules. It has the ability to classify the complex data accurately but it has limitations of high selective pressure, and pre-mature convergence or very slow convergence. One of the big limitations is the correct prediction based evaluation of the rules that has not yet been addressed. In this paper, we propose a new ant-miner (named as AMclr) based on a new quality function, rank based term selection and on rule rejection thresholds. The new quality function is based on rule length, coverage and correct prediction. The rule rejection thresholds are based on coverage and quality of the rule. The proposed algorithm has been tested on various benchmark datasets and compared with other state of the art algorithms. The experimental results showed that the proposed approach achieved better results in terms of accuracy, convergence speed, and comprehensibility.

Acceleration coefficients are the key parameters of particle swarm optimization (PSO) algorithm used to control the movement of particles by modifying its cognitive and social components. Several variants have been proposed that modify the acceleration coefficients to improve the convergence speed of PSO in continuous search space. In this regard, a few attentions have been paid to improve the convergence speed of binary particle swarm optimization (BPSO). Moreover, in presence of distinct position of particles by ignoring the dispersion of particles in a search space, BPSO deals all particles equally. To address this issue, we have proposed a fitness-based acceleration coefficients Novel BPSO, called FAC-NBPSO. In the proposed algorithm, the fitness of each particle is used to modify the cognitive and social components of each particle. The performance of the proposed algorithm is tested on four benchmark test functions. The experimental results show that the proposed algorithm performs better than the compared algorithm with improved convergence speed.

INTRODUCTION: Although medication errors may cause significant morbidity and mortality, the true cost of avoidable harm from such errors is unclear. While studies describe different methods for calculating a financial cost from an error, there remains variability in the way calculations are conducted depending on the clinical context. This review aimed to investigate the range of approaches for calculating medication error costs across healthcare settings. METHODS: A systematic review was carried out with a duplicate data extraction approach and mixed methods data synthesis. Medline, Embase and Web of Science were searched for studies published between 1993 and 2015. Studies that explicitly described a method for calculating medication error cost were included. The variables used for the calculations and a description of the approach for calculating errors were reported. RESULTS: 21 studies were included in the final review. There was wide variation in the way calculations were undertaken, with some calculations using a single variable only and others using several variables in a multistep approach. Few calculations included indirect costs, such as loss of earnings for the patient, and only one calculation considered opportunity cost. The majority of studies presented direct medication error costs whereas others approximated error costs from the savings made following an intervention. CONCLUSIONS: There are a wide range of methods used for calculating the cost of medication errors. The diversity arises from the number of variables used in calculations, the perspective from which the calculation is conducted from, and the degree of economic rigour applied by researchers.

Clustering plays a major role in data mining. It helps in identifying patterns and distribution of data. In this paper, we propose ant colony optimization (ACO) based clustering algorithm for clustering the social network data. The proposed technique takes advantage of Ants cemetery and allows each ant to play a role. In every iteration, the ants produce a cluster for the data, and the pheromone values are updated after every iteration of all the ants. The experiment results show that proposed technique discovered the clusters which reveal the truthfulness of the network. We have also compared the proposed technique with another state of the art clustering algorithm, and experimental result demonstrated that proposed technique find out the better clusters.

Ant-Miner, a rule-based classification algorithm, has been successfully applied for classification tasks but it has some limitations such as getting stuck in local optima, high selective pressure, fixed exploration and exploitation rate, and premature convergence. In this paper, we have proposed a novel Ant-Miner based technique based on new Pheromone update method, Rule Rejection threshold, Adaptive gamma, and altered Tournament selection (PRRAT_AM) that caters to these limitations. The proposed algorithm introduced an adaptive gamma parameter to avoid fixed exploration and exploitation rate. To decrease the selective pressure, pheromone is updated by weighted average of rule length, rule quality and heuristic of the path. Ants are selected using improved tournament selection strategy to update the pheromone. Rules that covered less than one percent of the training examples are rejected to generate generic rules. These improvements aid PRRAT_AM in avoiding premature convergence and high selective pressure. We have tested the proposed approach on eight publicly available data-sets on standard benchmark performance measures that include accuracy and F1-score. The proposed approach has been compared with state of the art versions of Ant-Miner and with various data mining algorithms. The experimental results showed that the proposed approach achieved better results when compared with other techniques in terms of standard performance measures and convergence speed.

This article presents the empirical performance analysis of the Associative Classification approaches on imbalanced datasets. The imbalanced dataset is a dataset in which ratio of an instance of one class drastically differs from the other one. The ratio difference in class instances, imbalanced dataset, highly affects the performance of the classifiers. An associative Classification is a hybrid technique which combines the classification rules discovery and association rules discovery both are important tasks of Knowledge Discovery. We investigate the performance of selective associative classifiers namely CBA, CBA2, CMAR-C, CPAR-C, and Fuzzy- FARCHD-C by using the methods implemented in KEEL data mining tool on public imbalanced datasets. The experimental results show that the performance of the Fuzzy-FARCHD-C is promising with respect other methods in terms of accuracy.

Clustering is a popular machine learning technique used to segregate the number of objects into different classes. Locating multiple peaks of a multimodal problem is a challenging task. In this regard, clustering is considered to be the best technique that divides the population into a number of clusters. In literature, a number of clustering techniques have been suggested with the particle swarm optimization (PSO) algorithm. Among different clustering techniques, partition-based clustering is widely used with the PSO to solve multimodal optimization problems. This paper provides a brief survey of clustering-based PSO techniques for solving multimodal optimization problems. Open problems have been discussed at the end of the paper as well.

An active area of research in data mining and machine learning is dimensionality reduction. Feature subset selection is an effective technique for dimensionality reduction and an essential step in successful data mining applications. It reduces the number of features, removes irrelevant, redundant, or noisy features, and enhances the predictive capability of the classifier. It provides fast and cost-effective predictors and leading to better model comprehensibility. In this paper, we proposed a hybrid approach for feature subset selection. It is a filter based method in which a classifier ensemble is coupled with Ant colony optimization algorithm to enhance the predictive accuracy of filters. Extensive experimentation has been carried out on eleven data sets over four different classifiers. All of the data sets are available publically. We have compared our proposed method with numerous filter and wrapper based methods. Experimental results indicate that our method has remarkable ability to generate subsets with reduced number of features. Along with it, our proposed method attained higher classification accuracy.

Objectives Errors in prescribing can cause avoidable harm to patients. Establishing the extent of prescribing errors across medical specialties is critical. This research explores the frequency and types of prescribing errors made by healthcare professionals prescribing for patients with renal disease where prescribing problem-solving and decision-making is complex due to comorbidity. Methods All prescriptions and errors made by prescribers were captured over a 4-month period in a UK renal unit. Data were recorded concerning the medicine associated with the error, the type and severity of the error, and the prescriber9s occupational grade. Results 10 394 prescribed items were captured and 3.54% had associated prescribing errors. While Foundation Year 1 doctors made almost one error each week (mean 15.13) and Foundation Year 2 doctors one every 2 weeks (mean 8.00), other prescribers made one error per month (mean 3.94). The medicines most frequently associated with errors for Foundation doctors were paracetamol (6.51%), calcium acetate (5.33%), meropenem (3.55%), alfacalcidol (3.55%) and tazocin (3.55%), while for all other prescribers they were meropenem (6.15%), alfacalcidol (4.62%), co-amoxiclav (4.62%) and tacrolimus (4.62%). The most common types of error for both groups were omitting the indication, using the brand name inappropriately, and prescribing inaccurate doses. Conclusions The range of errors made by multi-professional healthcare prescribers confirms the complexity of prescribing on a renal unit for patients with kidney disease and multimorbidity. These findings have implications for the types of educational interventions required for reducing avoidable harm and overcoming human factors challenges to improve prescribing behaviour.

In this paper, we will address the important question of how quality improvement science (QIS) and human factors and ergonomics (HFE) can work together to produce safer solutions for healthcare. We suggest that there will be considerable advantages from an integrated approach between the two disciplines and professions which could be achieved in two phases. First, by identifying people trained in HFE and those trained in QIS who understand how to work together and second, by developing opportunities for integrated education and training. To develop this viewpoint we will: 1. Discuss and explore how QIS and HFE could be integrated by building on existing definitions, scope of practice, knowledge, skills, methods, research and expertise in each discipline. 2. Outline opportunities for a longer-term integration through training, and education for healthcare professionals. The disciplines and professions of QIS and HFE developed from similar origins in the 20th century to engage workers in the identification of problems and development of solutions.1 ,2 They diverged with QIS focussing more on process issues (eg, production quality control) and HFE focussing on wellbeing (occupational health and safety) and performance. Both have been used in healthcare for many years, with several recent papers discussing confusion about jargon in one or both disciplines.3–7 We will offer a simple outline of our perspectives for each before suggesting an approach for integrated working. We are using the term QIS to include both quality improvement and improvement science.8 QIS is used, defined and explained in the literature in many different ways, for example, ‘the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge’;9 ‘better patient experience and outcomes achieved through changing provider behaviour and organisation through using a systematic change method and strategies’ …

From last three decades, the relational databases are being used in many organizations of various natures such as Education, Health, Business and in many other applications. Traditional databases show tremendous performance and are designed to handle structured data with ACID (Atomicity, Consistency, Isolation, Durability) property to manage data integrity. In the current era, organizations are storing more data i.e. videos, images, blogs, etc. besides structured data for decision making. Similarly, social media and scientific applications are generating large amount of semi-structured data of varied nature. Relational databases cannot process properly and manage such large amount of data efficiently. To overcome this problem, another paradigm NoSQL databases is introduced to manage and process massive amount of unstructured data efficiently. NoSQL databases are divided into four categories and each category is used according to the nature and need of the specific problem. In this paper we will compare Oracle relational database and NoSQL graph database using optimized queries and physical database tuning techniques. The comparison is two folded: in the first iteration we compare various kinds of queries such as simpler query, database tuning of Oracle relational database such as sub databases and perform these queries in our desired environments. Secondly, for this comparison we will perform predictive analysis for the results obtained from our experiments.