Search

Aromatic rice (Oryza sativa L.) germplasm conserved in global genebanks needs intensive characterization to assess genetic diversity and population structure for crop improvement. We analyzed a panel of 92 accessions of non‐Basmati aromatic rice landrace germplasm with 23 phenotypic traits and 48 microsatellite markers. The landrace accessions displayed a huge phenotypic diversity for traits, namely, culm length, flag‐leaf length, panicle‐bearing tillers (PBT), plant height, panicle length, peduncle length, panicle exsertion, grain length, grain width, hull color, and 1000‐grain weight (TGW). Phenotypic clustering revealed three major clusters and five principal components explained 81.24% of the variance. TGW was associated positively with grain length, grain width, peduncle length, and grain yield but negatively with culm length and PBT. A total of 528 microsatellite alleles were detected, with 11.0 alleles per locus, revealing the presence of high polymorphism. Genetically, four subpopulations were detected based on structure analysis. In the germplasm, high levels of gene flow (Nm = 1.51) and gene diversity (HE = 0.83) were found. Among the subpopulations, we discovered high levels of inbreeding (FIS = 0.92) and moderate genetic differentiation (FST = 0.14). High polymorphism information content (0.806) along with a high level of molecular variance among genotypes (79%) was detected. A large number of multiple alleles were detected in numerous landrace accessions, revealing the presence of heterogeneity and heterozygotes in the landrace germplasm. The results of the present study are vital for the utilization of landrace diversity in the genetic improvement programs designed for rice quality improvement and consumer preference. Core Ideas: We need to characterize aromatic rice landrace accessions conserved in global genebanks to assess their variability, genetic structure, and population differentiation.Molecular and phenotypic characterization revealed a high variance among the genotypes but low variance within genotype and among the subpopulations.We found moderate genetic differentiation with high gene flow between accessions of four subpopulations of aromatic rice landraces.Landraces that displayed huge genetic diversity both at phenotypic and molecular levels could be exploited for rice improvement. [ABSTRACT FROM AUTHOR]

A modified environmental adaptation method (MEAM) is the modified version of environmental adaptation method. The MEAM algorithm is operates based on adaptation and alteration operator to find the solution from the huge search space. The alteration operator in MEAM to evade the incidence of being struck into local minima. To test the performance of on real world application MEAM is applied in test case generation problem and proved to better than PSO--TVAC algorithm. From the results it has been observed that there is the improvement of 4% percent that the PSO-TVAC. [ABSTRACT FROM AUTHOR]

Deformation behaviour of aluminium-zinc-magnesium-copper alloy AA 7075 was investigated at high strain rate using Split Hopkinson Torsional Bar (SHTB). Cylindrical AA 7075 specimens were deformed at different angles of twist, giving rise to strain rate ranging from 700 s −1 to 2000 s −1 . Comprehensive microstructural and textural investigation was carried out using electron back scatter diffraction (EBSD), X-ray line profile analysis and bulk texture measurement using X-rays for all the deformed samples. The high strain rate torsion tested samples showed a characteristic torsion texture for face centre cubic materials with a dominant A { 1 1 ̅ 1 ̅ } < 110 > / { 1 ̅ 11 } < 1 ̅ 1 ̅ 0 > component and the overall texture weakened with increase in strain rate and strain. Intragranular misorientation parameters like local average misorientation and grain orientation spread indicated occurrence of dynamic recovery in the sample deformed at the highest strain rate which was characterized by the maximum fraction of low angle grain boundaries. Microstructural parameters determined from X-ray diffraction using modified Williamson-Hall and Variance method further corroborated these results indicating the onset of dynamic recovery at the highest strain rate. [ABSTRACT FROM AUTHOR]

An Environmental Adaption Method (EAM) has been recently proposed in which, each solution updates its structure on the basis of current environmental fitness and its own fitness. EAM uses a new operator known as adaption operator. In this paper, an improved EAM, henceforth called as IEAM has been proposed and proper tuning of parameters of algorithm has been done to find the optimal solution in minimum time. IEAM has been tested on a set of benchmark functions. Results show the superiority of IEAM over PSO-TVAC, SADE and other optimization algorithms. Even for complex functions, the performance of IEAM is very good. In addition to this, IEAM has also been applied to software testing to generate test cases for white box testing. [ABSTRACT FROM AUTHOR]

Given a prime power q and a positive integer n, let 픽 q n {\mathbb{F}_{q^{n}}} represent a finite extension of degree n of the finite field 픽 q {{\mathbb{F}_{q}}} . In this article, we investigate the existence of m elements in arithmetic progression, where every element is primitive and at least one is normal with prescribed norms. Moreover, for n ≥ 6 {n\geq 6} , q = 3 k {q=3^{k}} , m = 2 {m=2} we establish that there are only 10 possible exceptions. [ABSTRACT FROM AUTHOR]

Eggplant (Solanum melongena L.), an important fruit vegetable exhibits very limited postharvest marketability, particularly due to rapid moisture loss. This study assessed the variation due to chitosan coating on different eggplant cultivars (purple long, purple round, and white long). Fruit were coated with chitosan (1%) and stored at 10 ± 2°C. The chitosan‐coated purple round cultivars (BR‐14 and PR‐5) had minimum weight loss and polyphenol oxidase (PPO) and peroxidase (POD) activity than long cultivars (Kashi Taru, IVBL‐22, and Kashi Himani), while the functional quality (anthocyanins, total phenolics, and flavonoids) of long cultivars was higher. Chitosan was effective in minimizing weight loss by about two fold and retained higher flavonoids by about 1.2–2.3 fold over uncoated fruit in different eggplant cultivars. Chitosan‐coated round (PR‐5 and BR‐14) and long (Kashi Himani, IVBL‐22, and Kashi Taru) cultivars showed an increase in storability by 8 and 6 days, respectively, over uncoated fruit. Practical Applications: The coating of eggplants with chitosan is an easy to use, effective, economical, and biologically safe technique to enhance the postharvest keeping quality of eggplants. It can be employed in the postharvest supply chain to maintain quality and extend marketability of eggplants. [ABSTRACT FROM AUTHOR]

Human Cancer Cell lines have gained a lot of attention since it helps in studying cancer biology and various treatment options. Recently various large-scale drug screening experiments were performed providing access to genomic and pharmacological data. This data helps in predicting drug responses which eventually contributes to the development of personalized cancer treatment. Heterogeneous nature of cancer raises the serious need for therapeutic agents with an essence of personalized treatment. Thus considering the assumption that similar drugs exhibit similar drug responses, we have developed kernelized similarity based regularization matrix factorization framework for predicting anti-cancer drug responses. Drug-Drug chemical structure similarity and Tissue-Tissue similarity (gene expression) are taken as key descriptors to formulate the objective function. The kernel function is used to map non-linear relationships between drugs and tissues. Our aim is to provide an efficient anti-cancer drug response prediction approach to establish the protocol for personalized treatment and new drugs designing. The proposed framework is validated using publicly available tumor datasets: GDSC and CCLE. Proposed KSRMF is further compared with three states of art algorithms using GDSC and CCLE drug screens. We have also predicted missing drug response values in the dataset using KSRMF. KSRMF outperforms other counterparts even though gene mutation data is not incorporated while designing the approach. An average mean square error of 3.24 and 0.504 is achieved using GDSC and CCLE drug screens respectively. The obtained results show that the proposed framework has quite potential to improve anti-cancer drug response prediction. Our analysis showed how data integration can help in achieving the goal of personalized cancer treatment. [ABSTRACT FROM AUTHOR]

This paper models the single and bi-objective brain emotional intelligent controllers for the dual stator winding induction motor (DSWIM) drive. The main purpose of this paper is performance improvement of the DSWIM drive control system and power losses reduction of the inverter in the DSWIM drive at low speeds. In the vector control method, it is difficult to estimate flux at low speeds. To solve the mentioned problem, researchers have used from the free capacity of the two windings of the stator. This paper presents three proposed methods: 1. Using the idea of rotor flux compensation based on classical PI controller at low speeds, the motor works in its standard operating mode; 2. Proposed Method 1 is reformed and improved based on the bi-objective brain emotional controller; and 3. Proposed Method 2 is improved using single-objective brain emotional controller in the speed control loops of the DSWIM drive. The proposed methods are simulated in MATLAB/Simulink software. [ABSTRACT FROM AUTHOR]

Recently, many software companies have shifted to shorter release cycles from the traditional multi-month release cycle. Evolution and transition of release cycles may affect the test effort in the system. This paper analyses 25 traditional releases containing 1210 classes and 69 rapid releases containing 2616 classes of four Open Source Java systems. Correlations between 48 Object Oriented metrics and 2 test metrics were evaluated to identify the best indicators of test effort. The results show that (i) correlation between OO and test metrics remain irrespective of release models, (ii) test effort required in Rapid Release (RR) models (shorter release cycles) is slightly more as compared to Traditional Release (TR) models, (iii) Out of 18 machine learning algorithms instance based machine learning algorithms IBK and K star followed by Multi-Layer Perceptron (MLP) and additive regression are able to predict the test effort accurately in classes. [ABSTRACT FROM AUTHOR]

3D Reconstruction has been an enduring problem in Image understanding and computer vision. There is an increasing interest on 3D information in the general public due to rapid development of 3D imaging techniques, marketing of 3D movies and games, and low cost of depth cameras. Numerous algorithms that have been proposed for performing 3D reconstruction using different variants of Iterative Closest Point(ICP) algorithm focusses mainly on reducing the computation time. The accuracy of 3D reconstruction is not taken in to consideration. An efficient, accurate, real time and active 3D reconstruction method using Kinect sensor is developed in this paper focusing on improving the accuracy of 3D reconstruction in less computation time. An Artificial Bee colony based ICP algorithm is proposed by incorporating several efficient variants of ICP algorithm. The proposed algorithm is intended to improve the accuracy and stability of the standard ICP algorithm. The performance of the proposed algorithm is satisfactory when compared with structured light technique and several ICP variants with respect to accuracy, complexity and computation speed. [ABSTRACT FROM AUTHOR]

Smartphone has been used for recognizing the different motion activities. However, current studies focus on either improving algorithm factor or adjusting neural network structure factor rather than on time cost factor and actual application factor. A novel method to consider these four factors comprehensively enhancing recognition of motion state accuracy is proposed. An architecture of the Bi-LSTM neural network and the TensorFlow machine learning system are used to classify the motion state and evaluate its experimental results. In addition, the Bi-LSTM neural network is compared with other neural network structures. Meanwhile, using the data captured by the accelerometer sensor and gyroscope sensor of the smartphone tests the Bi-LSTM neural network model. Experimental results show that using Bi-LSTM neural network and TensorFlow machine learning system to extract motion state characteristics, this method makes the motion state identification achieve 86.7% accuracy and the Bi-LSTM neural network model is better than other neural network models considering above four factors. The model of Bi-LSTM neural network can be used for other time-series fields such as signal recognition, action analysis, etc. This study provides a new method, which considers the four factors, to enhance the accuracy of the motion state classification. [ABSTRACT FROM AUTHOR]

Software cost estimation is the process of predicting the most realistic and valid amount of effort necessary for the development of any software. The cost estimation of any software is a difficult assignment due to the involvement of many factors that anyhow affect the estimation process. In literature, many cost estimation models have been developed for more than a decade to maintain accuracy in estimation of the cost of software projects. But, it is found that these models are inefficient to estimate the exact cost of software development because of uncertainties and lack of accuracy associated with them. In this paper, Alla F. Sheta models have been taken for optimization, which are the modified versions of the very famous Boehm’s COCOMO model. Parameters of the Sheta models have been tuned enough by the proposed method to estimate and minimize the consequences of different factors that affect the overall software development cost. Experimental work has been carried out in MATLAB environment and analysis of results is performed on the basis of Magnitude of Relative Error (MRE), Prediction (PRED) at 0.25, Value Accounted For (VAF) and Mean Magnitude of Relative Error (MMRE). Estimation accuracy of the proposed work is tested on NASA software project dataset. It is found that the proposed method shows good estimation capabilities over other state-of-the-art cost estimation models. [ABSTRACT FROM AUTHOR]

As the performance of modern multi-core processors is significantly increases, the total energy consumption in the systems also increases drastically. Dynamic Voltage and Frequency Scaling (DVFS) is considered as one of the efficient schemes for achieving the aim of energy saving. In this paper, we consider scheduling a task set, whose release times, deadlines and execution requirements are given, on DVFS-enabled multi-core processor system. Our main aim is to meet the execution requirements of all the tasks, and to minimizethe overall energy consumption on the processor with effective utilization of resources. Instead of seeking optimal solutions with high complexity, we aim to design algorithms suitable for real-time systems, with good performances. We come up with a simple algorithm for task scheduling and energy awareness by considering deadline constraint. We further consider the distribution of deadline and task scheduling, which guarantee that all tasks meet their execution requirements, and tries to minimize the overall energy consumption. Case based simulations for various applications and task characteristics and evaluations using a practical processor’s power configuration indicate that our proposed algorithm has a less energy consumption performance and good resource utilization in terms of saving processor energy, though it has low complexity. Besides, the proposed algorithm is easy to be implemented in practical systems. [ABSTRACT FROM AUTHOR]

Energy storage using batteries is emerging as a fundamental element of standalone power system based on non conventional energy sources like wind and solar to increase the penetration level of these sources. The planning of standalone power system incorporating renewable sources and storage necessitates a vigilant study on modeling of a storage system. In most of the planning study reported in literature pertaining to battery storage, charging efficiency (CE) of a battery is assumed to be fixed at constant value. However, CE and State of charge (SOC) of the battery both are correlated. In this paper, Interval Type(IT)-2 fuzzy logic has been applied for determining CE of battery relative to a specific SOC. For evaluating reliability indices i.e. Expected Energy not served (EENS), probabilistic analysis using analytical method has been applied to the standalone power system, situated near Kandla Port in Gujarat, India. The effect of considering CE of battery as a function of SOC has been compared with the constant value of CE of battery for the different groups consisting of solar-battery storage, wind turbine(WT)-battery storage, and wind-solar battery storage systems. [ABSTRACT FROM AUTHOR]

The augmented growing of visual cryptography in multimedia image transmission or data transmission over unsecured networks leads in safekeeping for confidential information. Generally two techniques are employed to afford secure transmission namely data hiding and cryptography. Cryptography is the main objective of recent research work in which the way of achieving secure transmission over the network be contingent on the interest of data encryption. This encryption process encrypts the constituent of data such as manuscript, image, audial, and audiovisual to make the data unconceivable or incomprehensible during transmission. A novel secret key generation based on Improved Bat Optimized Piecewise Linear Chaotic Map is proposed for image encryption. Our proposed secret key is intended for image encryption owing to the progression of mixing, permutation, double diffusion and confusion with the size of 128 bit to perform secure transmission. The success of our proposed method is revealed by the tentative results and comparison with the existing techniques in terms of sensitivity analysis, Information Entropy, correlation coefficient and, Encryption speed. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has been emerged as one of the most optimistic solution that reducing the gap between physical and cyber world. In IoT, sensors and actuators gather the sensory information; communicate with each other to fulfill application requirements. As sensor nodes are running on batteries, which cannot be replaced, so the development of the green model in IoT for shifting the real world data into the cyber world is challenging task. As IoT is deployed in large area and become more complex, so wireless sensor network current techniques cannot be applied in it directly. To achieve energy efficient network in the field of Internet of Thing, this paper addresses these challenging issues by introducing an efficient deployment scheme. This paper contributes: (1) A hierarchical cluster model for IoT network deployment; (2) An optimal Relay node ( ℝ ℕ ) election technique with respect to node density in the network and their deployment strategy by using their communication area as threshold distance for next ℝ ℕ selection within the cluster; (3) An efficient energy routing algorithm to meet the requirement of proposed model. The analysis of experimental results show that proposed method is more efficient in terms of energy consumption and suits IoT requirements. [ABSTRACT FROM AUTHOR]

The general characteristics observed in Autism is decrease in communication skill, interaction and shows behavioral changes. The reasons for these can be studied by understanding their visual sensory processing. The research work presented here uses image stimuli to study the behavior in children by understanding when and where they look. A Fuzzy based Eye Gaze Point estimation (FEGP) has been proposed which observes the gaze coordinates of the child, analyze the eye gaze parameters to assess the difference in visual perception of an autistic child in comparison to a normal child. The approach helps to identify the visual behavior difference in autistic children with a performance level indicator, visualization and inferences that can be used to tune their learning programs with an attempt to meet their counterparts. [ABSTRACT FROM AUTHOR]

With the construction and application of large-scale datacenters, the issue of resource allocation in cloud computing becomes a serious concern. Although the current static allocation method can make applications get corresponding resources, there still exist some shortcomings such as resource surpluses or shortages. This kind of problem is more crucial in real-time requirements of mobile cloud computing service. Therefore, it is necessary to establish a forecasting model to predict the future resource demands, and then perform on-demand distribution, which can effectively reduce the unnecessary daily network management fees and address the issues mentioned above. This paper focuses on CPU resource forecasting, establishing three forecasting models including Markov chain, weighted Markov chain and stacking weighted Markov chain. By comparing and analyzing the experiment results, the most reasonable forecasting model is found and explained. [ABSTRACT FROM AUTHOR]

The network lifetime often faces great challenges for wireless multi-hop networks since the nodes are typically self-powered in the networks. Intelligent multipoint relays (MPR) selection and energy efficiency have recently been considered to be the major solutions which address to suppress unnecessary messages and reduce energy consumption in the networks, respectively. After extensive studies, a strategy based on Signal to Interference plus Noise Ratio (SINR), together with residual energy aware multipoint relays selection (SRE-MPR) is proposed in this work. In SRE-MPR, a generic model of energy consumption is introduced where SINR is also taken into account. Simulation results demonstrate that outstanding performance can be achieved in comparison with classical Optimized Link State Routing Protocol (OLSR) and Residual Energy-based OLSR (REOLSR2) on the number of alive nodes, throughput and overhead, respectively. [ABSTRACT FROM AUTHOR]

Mobile Ad hoc networks (MANET) are multi hop networks, which are self organized, self configured and their topologies are changed dynamically. The destination node may be out of range of source node and routes are changing with time due to mobility of nodes. So a routing procedure is required to establish a path between them to send packets. Based on mobility pattern of MANET nodes, we have proposed four reactive routing models of MANET, which utilize position information of GPS, enabled nodes to restrict the search of a new stable path in a smaller cone-shape request zone. Among the proposed models, first three are being recommended for open field networks whereas last one is for urban area networks. We have compared our models with LAR, LARDAR and TN-CMAD and have shown the proposed models performed better in terms of overhead packets and energy consumption but a little increase of path-finding error. [ABSTRACT FROM AUTHOR]

Knowledge of extreme meteorological parameters plays an important role, for the selection of any civil structure at nuclear site or to design important nuclear structures to withstand design basis values of meteorological extremes that are likely to occur during the lifetime of these structures. The method used for the evaluation of extreme parameters is least square fit method. In this study, statistical analyses of extreme values of meteorological parameters at Rawatbhata Rajasthan site (RR site) are presented for the period 1980 to 2022 and for rainfall, it is for the period 1964 to 2022. Meteorological parameters such as extreme wind speed and wind gust (5 min) at 10 m and 30 m heights, extreme surface air temperature, extreme atmospheric pressure and extreme rainfall (yearly, monthly, and daily) are evaluated and discussed. From the extreme value analysis, it is observed that the variables for monthly maximum rainfall, annual maximum rainfall, maximum air temperature, maximum atmospheric pressure, maximum hourly wind speed at 10 m and 30 m heights and wind gust (5 min.) at 10 m height obey Fisher-Tippette Type I distribution, whereas daily maximum rainfall, annual minimum rainfall, minimum air temperature, minimum relative humidity, minimum atmospheric pressure and wind gust (5 min.) at 30 m height obey Fisher-Tippette Type II distribution. Distribution function parameters, i.e., location, scale, and shape parameter for each variable, have been determined and design basis values corresponding to return periods of 50 years and 100 years are derived and presented. The derived extreme values are particularly useful for arriving at suitable design values to ensure the safety of civil structures at RR site area with respect to stresses due to weather conditions. The values are also compared with corresponding values at other nuclear reactor sites of Tarapur, Narora, and Kalpakkam. [ABSTRACT FROM AUTHOR]

Cloud computing has become ubiquitous, offers an economical solution for convenient on-demand access to computing resources, which enable the resource-constrained clients to execute extensive computation. However, outsourcing of data and computation to the cloud server is a great cause of concern, such as confidentiality of input/output and verifiability of the result. This paper addresses the problem of designing outsourcing algorithm for linear regression analysis (LR), which is an important data analysis technique and widely applied across multiple domains. The outsourcing framework illustrated by the following scenario: a client is having a large dataset and needs to perform regression analysis, but unable to process due to lack of computing resources. Therefore, the client outsources the computation to the cloud server. In the proposed LR outsourcing algorithm, the client outsources LR problem to the cloud server without revealing to them either the input dataset and the output. The algorithm is a non-interactive solution to the client, it sends only input and receives output along with the proof of verification from the cloud server. The client in the proposed algorithm able to verify the correctness of result with an optimal probability. The analytical analysis shows that the algorithm is successfully meeting the challenges of correctness, security, verifiability, and efficiency. The experimental evaluation validates the proposed algorithm. The result analysis shows that the algorithm is highly efficient and endorses the practical usability of the algorithm. [ABSTRACT FROM AUTHOR]

Cloud computing has become ubiquitous, offers an economical solution for convenient on-demand access to computing resources, which enable the resource-constrained clients to execute extensive computation. However, outsourcing of data and computation to the cloud server is a great cause of concern, such as confidentiality of input/output and verifiability of the result. This paper addresses the problem of designing outsourcing algorithm for linear regression analysis (LR), which is an important data analysis technique and widely applied across multiple domains. The outsourcing framework illustrated by the following scenario: a client is having a large dataset and needs to perform regression analysis, but unable to process due to lack of computing resources. Therefore, the client outsources the computation to the cloud server. In the proposed LR outsourcing algorithm, the client outsources LR problem to the cloud server without revealing to them either the input dataset and the output. The algorithm is a non-interactive solution to the client, it sends only input and receives output along with the proof of verification from the cloud server. The client in the proposed algorithm able to verify the correctness of result with an optimal probability. The analytical analysis shows that the algorithm is successfully meeting the challenges of correctness, security, verifiability, and efficiency. The experimental evaluation validates the proposed algorithm. The result analysis shows that the algorithm is highly efficient and endorses the practical usability of the algorithm. [ABSTRACT FROM AUTHOR]

A new memetic algorithm named EAMDGA is designed by combining the characteristics of Environmental Adaption Method for Dynamic Environment (EAMD) and Genetic Algorithm (GA). This algorithm is highly efficient and robust in solving the unimodal and multimodal problems. It avoids the problems of getting trapped in local optima and premature convergence. Performance of this algorithm is checked over a group of 24 unimodal and multimodal benchmark functions provided by Black Box Optimization Benchmarking (BBOB-2013). It is found that EAMDGA is superior in performance in comparison to the other algorithms. [ABSTRACT FROM AUTHOR]

Data availability ensures efficient data accessibility by the readers anytime and from anywhere. It can be addressed by creating multiple copies of each data file and storing them on well-distributed distinct servers. The more the number of copies, the more is the availability. Considering a distributed cloud scenario with multiple data copies, a file update operation may be performed at any server containing a copy of the data file. Allowing parallel file updates by different users on various servers may incur inconsistent views of the data file among readers. A data replication protocol ensures that a file will remain consistent throughout the network. The existing data replication protocols did not explicitly address the server confidence about when the updated file version will be ready for read. In this work, we propose a consensus-based file replication protocol considering the message passing model that addresses the server confidence issue of the existing protocols. In the proposed protocol, the updated data file will be immediately accessible to the readers without any ambiguity after consensus is made. The proposed protocol is analyzed and compared with the similar protocols. The protocol is implemented, and the experimental results are verified with the analytical results. [ABSTRACT FROM AUTHOR]

An in vitro micropropagation protocol for Nyctanthes arbor-tristis was developed through the culture of seedling-derived nodal explants. The highest direct shoot induction rate, the mean number of shoots per explant and average shoot length was obtained on MS medium supplemented with 5 mg/l 6-benzylaminopurine (BAP). Full-strength MS medium was found better than other concentrations for organogenesis. Their lower or higher concentration from optimum level was inhibitory for shoot regeneration. Among different carbon sources tested for organogenesis, MS medium supplemented with 87.64 mM sucrose was superior to other carbon sources for in vitro morphogenesis. Best rooting of microshoots was achieved on one-quarter strength MS medium supplemented with 2 mg/l indole-3-butyric acid (IBA). Under this treatment, maximum mean number of roots/shoot and mean root length were 9.40 ± 0.70 and 2.47 ± 0.25 cm, respectively. The genetic fidelity among regenerates and donor plant has been confirmed by ISSR markers. [ABSTRACT FROM AUTHOR]

• Nyctanthes arbor-tristis L. is an important member of family Oleaceae. • Efficient shoot proliferation was achieved from cotyledonary node cultured on MS medium supplemented with 22.19 µM BAP. • MS-3 and MS-9 found most suitable media for efficient shoot proliferation. • The genetic-fidelity of regenerated plants was ascertained by ISSR marker. • The ethanol extract of leaves of regenerated plants showed the presence of good amount of polyphenol (phenol and flavonoid) content with strong antioxidant activity. Nyctanthes arbor-tristis L. is an important member of family Oleaceae. An efficient shoot proliferation response (21.53±0.58 shoots/explant) from cotyledonary node was achieved on Murashige and Skoog (MS) medium supplemented with 22.19 µM 6-benzyl amino purine (BAP). To optimize the role of ammonium and nitrate on shoot proliferation, the explants were cultured on modified MS media (MS-1 to MS-12). Among these media, MS-3 and MS-9 were found most suitable for shoot-proliferation than others. The microshoots after indole 3-butyric acid (IBA) pulse treatment (1968.11 µM) for 30 min, when sub-cultured on ½ strength MS basal medium were successfully rooted with a maximum frequency of root induction (77.66±1.45), the maximum number of roots/ shoot (10.66±0.88) and maximum root length (1.60±0.05 cm). In-vitro rooted plants were acclimatized and transplanted outside into field with 80 percent survival-rate after 90 days. The genetic-fidelity was confirmed by monomorphic nature of inter-simple sequence repeat (ISSR) marker pattern. The ethanol extract of leaves of regenerated plants contained good amount of phenol (118.00±0.82 µg/mg gallic acid equivalent) and flavonoid (17.34±0.64 µg/mg rutin equivalent) and possess strong antioxidant activity. The EC 50 value for 2, 2-diphenyl-1-picrylhydrazyl (DPPH) free radical, superoxide radical (SOD) scavenging activity and lipid peroxidation (LPO) inhibition activity was 339.98±3.27, 755.80±75.50 and 689.67±2.46 µg/ml respectively. Hence, these findings suggest that regenerated plants can be used as a therapeutic and inhibitory agent against free radical-induced disease progression. [ABSTRACT FROM AUTHOR]

Conversion of the conventional vehicle (CV) into the plug-in hybrid electric vehicle (PHEV) is one of the promising solutions to improve transport sustainability and reduce outdoor air pollution. Energy management is crucial for the performance of PHEV. The paper presents combine rule based-artificial bee colony optimization algorithm for energy management of converted plug-in hybrid electric vehicle (CPHEV). The diesel operated parallel hybrid topology is considered for study with the designed electric powertrain. NOx and PM are considered as optimization parameters along with specific fuel consumption. The performance based on fuel consumption and emissions (NOx and PM) is analyzed by considering sample Indian urban and highway driving cycle. The complete vehicle is simulated using MATLAB Simulink linked with coding. The results of converted PHEV obtained is compared with conventional one for both driving cycles for analysis of the fuel consumption and emissions considering real-time benchmarking norms. The results indicate that the combine rule based-artificial bee colony strategy keeps pollution under control required as per BSIII norms. [ABSTRACT FROM AUTHOR]

The paper introduces a methodological approach based on genetic algorithms to calibrate microscopic traffic simulation models. The specific objective is to test an automated procedure utilizing genetic algorithms for assigning the most appropriate values to driver and vehicle parameters in AIMSUN. The genetic algorithm tool in MATLAB® and AIMSUN micro-simulation software were used. A subroutine in Python implemented the automatic interaction of AIMSUN with MATLAB®. Focus was made on two roundabouts selected as case studies. Empirical capacity functions based on summary random-effects estimates of critical headway and follow up headway derived from meta-analysis were used as reference for calibration purposes. Objective functions were defined and the difference between the empirical capacity functions and simulated data were minimized. Some model parameters in AIMSUN, which can significantly affect the simulation outputs, were selected. A better match to the empirical capacity functions was reached with the genetic algorithm-based approach compared with that obtained using the default parameters of AIMSUN. Overall, GA performs well and can be recommended for calibrating microscopic simulation models and solving further traffic management applications that practioners usually face using traffic microsimulation in their professional activities. [ABSTRACT FROM AUTHOR]

A linear method based on local statistical parameters of the image to remove the speckles of ultrasound carotid artery medical image is presented in this article. Speckle is the main drawback of medical images and it should be removed before any further processing of images like edge detection and registration. The focus of this article is to filter the speckle efficiently and effectively even at higher density of noise. The filter is designed by keeping in mind that the local statistical parameters are important rather than global statistical parameters. The weighting factor is designed such that it is high for similar areas and thus results into more smoothing without destroying the useful information, whereas it is low at the edges and thus less smoothing will be done. The filter is applied with the help of 5×5 sliding window. The noise ranging from 0.01–0.09 of variance is unnaturally inserted in the medical images through Matlab. The efficiency calculating parameters like Signal to Noise Ratio (SNR), Quality Index (Q), Mean Square Error (MSE), Similarity Index Measure (SSIM) and Edge Preserved Index (EPI) were used to evaluate the proposed technique. The suggested method is also compared with the existing local statistical mean variance filter for the said parameters in order to analyse the performance of the filter. [ABSTRACT FROM AUTHOR]

Emotion is a property by which human beings and machines can be differentiated as machines are emotionless while human beings are not. If the emotion of a speaker is recognized then others can interact accordingly. This paper presents a new approach for recognizing all the six basic emotions (Happy, anger, fear, sadness, boredom and neutral) from the speech signals more effectively. To recognize the emotion of a speaker, pitch value and two wavelet packet feature vectors derived from speech signals are used. Principal Component Analysis (PCA) has been applied to reduce the dimension of feature vectors. Random Forest (RF) and Support Vector Machine (SVM) classifiers are trained separately based on these reduced feature vectors. The experimental results show that the accuracy of emotion recognition with Random Forest classifier is 86.11% while with SVM classifier it is 84.41%. Experimentally, it is also found that clean speech of 1 sec duration is sufficient enough to recognize emotion of the speaker. [ABSTRACT FROM AUTHOR]

Handwritten word recognition is considered as an active research area since long because of its various real life applications. The key obstacle of this research problem is the huge variation of the writing styles of different individuals. In addition to that the complex shapes of alphabet make the recognition process more difficult. A holistic word recognition approach is proposed here in order to classify 80-class handwritten city name images written in Bangla script. Based on the negative refraction property of the light, a novel shape-based feature vector of size 186 is generated from each of the word images. Effectiveness of the feature vector is tested on a database containing total 12000 handwritten word images having equal number of samples from each class. The proposed method achieves a reasonably good recognition accuracy of 87.50% which proves better while comparing with some of the recently published feature vectors used for similar job. The reported result is achieved by combining the classifiers namely Sequential Minimal Optimization (SMO), Simple Logistic and CV Parameter Selection embedded with SMO. To verify the robustness of the present method it is also applied on handwritten word images written in Roman and Devanagari scripts separately and it is found that our method obtains satisfactory result on the both the cases. [ABSTRACT FROM AUTHOR]

Robot path planning is integral to many robotic applications. In this work, three optimization objectives are presented: path length, degree of path smoothness, and degree of security. Due to the lack of local search ability, the optimal solution set is difficult to be obtained with the traditional method especially when the search space is very irregular. And the simple local search algorithm is often trapped into local optimization. A new method with local search is introduced to improve the SPEA2 in this work. The proposed method sets up an external population dedicated to local search, which can increase the local search ability of the method while retaining good global searching ability. In addition, the new crossover operator and the individual update strategy are used for proposed method. The simulation results shows that the proposed method is better than that of SPEA2, NSGA-2 and PESA. It was found that the model proposed in this work is practical for robot path planning. [ABSTRACT FROM AUTHOR]

Even though finding out distance is the central core of k-Nearest Neighbor classification techniques, similarity measures are often favored against distance in various realistic scenarios and situation. Most of the similarity measures, which are used to classify an instance, are based on geometric model. Their effectiveness decreases with the increases in the number of dimensions. This paper establishes an efficient technique called ARSkNN for finding out class of any given instance using a measure based on an unique similarity, that does no longer compute distance, for k-NN classification. Our empirical results show that ARSkNN classification technique is better than the previous established k-NN classifiers. The performance of algorithm was verified and validated on various datasets from different domains. [ABSTRACT FROM AUTHOR]

Contrasted with common obstacle avoidance mode based on single sensor or solo algorithm, this article put forward an intelligent pattern based on Combination from CNN-based Deep Learning Method and liDAR-based Image Processing approach. As for Deep Learning method, a 10-layer Convolutional Neural Network (CNN) is designed which comes to a high recognition accuracy of 97 percent in Tensorflow and success rate of obstacle avoidance is over 90 percent. With regard to liDAR-based Image Processing approach, decision is made by a special method of counting the number of Point Cloud Data (PCD) which is generated by 2D liDAR and a success rate over 90 percent is achieved as well. When two kinds of methods work together, a robust success rate of 100 percent is realized. Meanwhile, Inertial Measurement Unit (IMU) and Xbox360 are taken into consideration for Pose Estimation and Data Collection. Finally, all functions are integrated in Robot Operation System (ROS) on platform of nVidia Jetson TX1. [ABSTRACT FROM AUTHOR]

Due to the limited focus range of optical imaging system, and the locations or focus of different objects in the same scene are different, multiple objects cannot be focused at the same time. In order to solve this problem and make the underwater image clearer, we propose a fusion method based on the sparse matrix in this paper. Firstly, we transform the source image into sparse image by sparse transform and get the clearity of the image based on the sparsity. Then, the clearity image will be segmented into focus regions. After that, the focus regions and non-focus regions are fused respectively based on different fusion algorithms. Finally, the focus regions and non-focus regions are combined to get the enhanced image. The experiments in the end show that the fusion method we proposed in this paper has higher information entropy, correlation entropy, standard deviation, and average gradient, so it can enhance the underwater multi-focus image and can be applied to the underwater object detection. [ABSTRACT FROM AUTHOR]

A hybrid renewable energy scheme comprising of the wind and solar PV electric power systems with appropriate maximum power point tracking is presented in this paper. The maximum power point tracking for the wind generator is carried out using Adaptive Neuro Fuzzy Inference System. The MPPT technique adopted for the photovoltaic power generation system is the Incremental Conductance (IC) algorithm. A power flow control scheme based on fuzzy logic is developed to regulate the power transaction from the wind and solar power sources as well as for the battery charging and discharging. Based on the available velocity of wind and solar insolation and based on the electrical demand different modes of operation are selected automatically using the ANFIS based control strategy. Considering the non linearity’s of the converters and the unpredictable nature of the renewable sources an advanced adaptive controller is necessary. The proposed ANFIS controller performs well and the proposed idea has been validated using MATLAB/Simulink and the simulation results are reported. [ABSTRACT FROM AUTHOR]

With the higher compression ratio, the decoded image produces annoying compression artifacts near block boundaries, ringing artifacts near original edges and corner outliers at block corner. These coding artifacts are caused by quantization and transformation process of discrete cosine transform (DCT). This paper proposes a novel deblocking algorithm that removes block discontinuities by taking into account the ringing, blurring and corner outlier artifacts. The proposed deblocking technique consists of two frequency related modes (smooth and detailed region mode) and corner outlier mode have been proposed and then applied median filter. The proposed technique has been applied to a number of reconstructed images and their performance is compared with conventional methods on the basis of standard metrics such a PSNR-B, BBM and MOS. Experimental simulation results illustrate that proposed technique improves the perceptual quality of reconstructed images and thus outperforms the all existing methods. [ABSTRACT FROM AUTHOR]

Consensus is a significant part that supports the identification of unknown information about animals, plants and insects around the globe. It represents a small part of Deoxyribonucleic acid (DNA) known as the DNA segment that carries all the information for investigation and verification. However, excessive datasets are the major challenges to mine the accurate meaning of the experiments. The datasets are increasing exponentially in ever seconds. In the present article, a memory saving consensus finding approach is organized. The principal component analysis (PCA) and independent component (ICA) are used to pre-process the training datasets. A comparison is carried out between these approaches with the Apriori algorithm. Furthermore, the push down automat (PDA) is applied for superior memory utilization. It iteratively frees the memory for storing targeted consensus by removing all the datasets that are not matched with the consensus. Afterward, the Apriori algorithm selects the desired consensus from limited values that are stored by the PDA. Finally, the Gauss-Seidel method is used to verify the consensus mathematically. [ABSTRACT FROM AUTHOR]

The online smartphone-based human activity recognition (HAR) has a variety of applications such as fitness tracking, healthcare…etc. Currently, the signals generated from smartphone-embedded sensors are used for HAR systems. The smartphone-embedded sensors are utilized in order to provide an unobtrusive platform for HAR. In this paper, we propose a deep convolution neural network (CNN) model that provides an effective and efficient smartphone-based HAR system. For automatic local features extraction from the raw time-series data, we use the CNN while simple time-domain statistical features are used to extract more distinguishable features. Furthermore, we explore the impact of a novel data augmentation on the recognition accuracy of the proposed model. The performance of the proposed method is evaluated using two public data sets (UCI and WISDM) which are collected using smartphones. Experimentally, we show how the proposed model establishes the state-of-the-art performance using these datasets. Finally, to demonstrate the applicability of the proposed model for online smartphone-based HAR, the computational cost of the model is evaluated. [ABSTRACT FROM AUTHOR]

Benefited on the open source software movement, many code search tools are proposed to retrieve source code over the internet. However, the retrieved source code rarely meets user needs perfectly so that it has to be changed manually. This is because the retrieved source code is concretely over-specific to some particular context. To solve this problem, we propose an Abstract Change Pattern Model (ACPM) to ensure the context-specific source code general for various contexts. This model consists of the ACP abstracting and the ACP concretizing algorithms. The former exploits the abstractly context-aware change pattern from the code changes. Based on the change pattern, the latter transforms the context-specific source code into the correct one meeting different user needs. To evaluate ACPM, we extract 7 topics and collect 5-6 code snippets per topic from the Github, while performing 5 different experiments where we explore 2 sensitivity-related rules and use them to raise the accuracy gradually. Our experimental results show that ACPM is feasible and practical with 73.84% accuracy. [ABSTRACT FROM AUTHOR]

Uncovering the potential treatment associations of the drug-disease pairs is a research focus of drug repositioning. However, it is time-consuming and costly to verify the potential treatment relation between a drug and a disease by “wet” experiment methods. Fortunately, along with the accumulation of large amount of data and the development of machine learning methods, lots of computational methods to predict the drug-disease treatment associations have been proposed. In order to build the prediction model based on machine learning techniques, both plenty of positive and negative training samples are required. In the case of biological experiments, however, we can only verify whether a drug cures a disease, yet we are unable to answer whether a drug definitely cannot treat a disease. Correspondently, there are only positive and unlabeled samples in the data. Being lack of validated negative samples, most computational methods assume the unlabeled samples to be negative ones and randomly select some unlabeled samples and positive samples to train the prediction models. Obviously, the unlabeled samples are not necessarily negative, and some of them may be positive just remaining uncovered via experiments. In this paper, we propose a method called PUDrDi which directly make use of the positive and unlabeled samples to train a Biased-SVM classifier. Moreover, we combine the drug and disease features together to represent a drug-disease pair, in which we use chemical substructures and symptoms as the features to represent drugs and diseases respectively. The experiment results demonstrate that PUDrDi outperforms some other methods. The case study further shows the practicality of PUDrDi. [ABSTRACT FROM AUTHOR]

Voice activity detection (VAD) identifies the presence/absence of human speech in a frame of a given speech signal. Presence/Absence of human speech can easily be identified in clean speech signal but its accuracy decreases with decreasing Signal-to-Noise ratio (SNR) value. Robust VAD helps to enhance the efficiency of speech signal based automated applications like speech enhancement, speaker identification, hearing aid devices etc. In this paper, a new feature of speech signal- “Peak of Log Magnitude Spectrum (PLMS)” is introduced and used for VAD. This newly defined feature PLMS along with three existing acoustic features(MFCC;RASTA-PLP and Formant Frequency) are used to train SVM classifier for VAD. Experimentally, it is found that coefficients of PLMS play most prominent role. Experimentally, it is also observed that the accuracy of the trained SVM classifier for VAD is the highest when compared with other state of the art methods (Sohn VAD and VAD G.729). [ABSTRACT FROM AUTHOR]

Decision-making is very important activities in the various applications of science, engineering, and technology. A decision can be derived in three manners by these applications: (1) by developing a mathematical model, (2) taking domain experts advice, (3) developing an expert system. However, accurate mathematical model may not be developed for the domain that might not be completely interpreted. Moreover, the problem with the second method is that the human intervention is not possible all the time and the expenditure of hiring a domain expert may be high. Decision-making, using expert system or controller induces great interest among the researchers and professionals. Expert systems or controllers are capable enough to counter unpredictability, noise, and vagueness. Fuzzy set theory is commonly used in building the expert systems and controllers due to its ease and similarity to human reasoning. Therefore, the proposed approach is based on fuzzy logic for decision making. The proposed model is explained through a case study. The result of the proposed work is compared and judged by the results of earlier studies. The result depicts that the proposed method has a better performance and effectiveness than existing studies. [ABSTRACT FROM AUTHOR]

Sea squirts are cultivated mainly in Korea, Japan, and China. Sea squirt sorting during the harvesting process is labor-intensive and time-consuming as there is no automatic sorting technology for sea squirts. In this study, we developed and evaluated an automatic sea squirt sorting algorithm based on sea squirt color information analyzed using the hue-saturation-value (HSV) color model and the regression equation of the projected area and weight of the sea squirt. The developed algorithm recognizes sea squirts during the sorting process based on the threshold range of sea squirt color values and their weight based on measurements of the projected area. In 100 repeated experiments conducted with mixed products containing sea squirts, mussels, and Styela clava, the average sea squirt recognition rate of the developed algorithm was 98.5%, and the sorting performance based on animal weight and grade was ≥95.5% at an average speed of 1,050 kg/h. [ABSTRACT FROM AUTHOR]

Brain Computer Interface (BCI) enables us to record and process the information generated by the brain and process them. Due to high variability of the Electroencephalogram (EEG) data, multiple trails are recorded for a particular task. The present work aims to improve the accuracy for motor imagery task classification by selecting the most prominent trail from the multiple trails recorded during motor imagery. In this paper, we propose a novel weight optimization algorithm for common spatial filtering (CSP) using evolutionary algorithms (i.e. cuckoo search algorithm (CSA), firefly algorithm (FA) and gravitational search algorithm (GSA)) to select the most prominent trial from the multiple trails recorded for feature extraction. The features extracted from the selected trials were thus used for motor imagery task classification. The performance was evaluated on the extracted features from the selected trials using two classifiers namely linear discriminant analysis (LDA) and support vector machines (SVM). It is observed that FA with band power as a feature gives the best performance in comparison to the earlier reported methods i.e. average, error based and alternating direction method of multipliers (ADMM). [ABSTRACT FROM AUTHOR]

Nowadays, Semi-Supervised Learning lies at the core of the Machine Learning field trying to effectively exploit unlabeled data as much as possible, together with a small amount of labeled data aiming to improve the predictive performance. Depending on the nature of the output class, Semi-Supervised Classification and Semi-Supervised Regression constitute the basic components of Semi-Supervised Learning. Various studies deal with the implementation of Semi-Supervised Classification techniques in many real world problems over the last two decades in contrast with Semi-Supervised Regression, which is deemed to be a more general and slightly touched case. This survey aims to provide a detailed review of Semi-Supervised Regression methods and implemented algorithms in recent years. Our in-depth study reveals the relatively few studies that deal with this specific problem. Moreover, we seek to classify these methods by proposing a schema and categorizing all the related methods that have been developed in recent years according to specific criteria. [ABSTRACT FROM AUTHOR]

A Kriging-based global optimization method is proposed to solve black-box unconstrained design problems in this work. Firstly, the non-convex Kriging optimization problem is converted into the two convex programing problems by the canonical dual transform to quickly get global optimal solution. Then, PSO (Particle Swarm Optimization) algorithm is adopted to find next promising design point by exploring and optimizing the transformed problems. The proposed method not only reduces the computational burden, but also effectively balances local and global search behavior. Some well-known numerical test functions and a real engineering example are investigated to illustrate that the presented method can further enhance the feasibility, validity and robustness of the optimization process in contrast with other global optimization algorithms. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are vulnerable to various localization attacks where attackers intended to provide improper beacons or manipulate the location determination. Attack classification for localization in WSNs is not only the condition, prerequisite and premise of threat analysis, but, more significantly, a vital part of the security anomaly detection. In this paper, a localization attack recognition method using a deep learning architecture was proposed. To enhance the classification performance, a good feature representation was established through combining location features with topological indexes based on the complex network theory. The ability of Stacked Denoising Autoencoder (SDA) to learn the underlying features from input data was exploited. Back-propagation algorithm was performed to update weights through a stochastic gradient descent method. The proposed approach could efficiently distinguish the Sybil attacks, Replay attacks, Interference attacks, Collusion attacks and normal beacons. Extensive experiments demonstrated that the proposed algorithm can achieve an average classification accuracy of 94.39% and was more robust and efficient even in the existent of huge baneful beacons. [ABSTRACT FROM AUTHOR]