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The Data hiding concept such as steganography can provide security and facilitate the safe exchange of important information in the digital world. This concept can be used with the various multimedia such as text, image, video, audio acting as cover medium to carry out the one way exchange or two way exchange of secret information. The presented paper focusses only on the kind of steganography that uses audio as a carrier. This concept of steganography is, act of encoding mystery data in invisible manner. Steganography along with cryptography add a step futher in security by hiding cipher text in such a manner that middle man is unaware about the secret message is concealed in it. Cipher text generated by encryption creates suspects but steganography creates environment that looks like normal behavior, hiding suspect nature of cipher text thus making it more secure. Audio steganography is the investigation of concealing a few mystery content or audio data in a host audio. This paper presents execution of two level security of end user information by associating the two fields of system security, cryptography and steganography. Concept of index based chaotic sequence is used for encryption. It carries two approaches which make the cryptanalysis and steganalysis furthermore difficult as compare to simple LSB method. First approach is encrypting the secret text using index based chaotic sequence. The proposed approach is more secure, strong and robust, giving security to our secret information. Second approach is hiding output generated by first approach using the concept of LSB with XOR method. It gives extra level of security by creating minimum distortion in host audio file simultaneously maintaining high embedding capacity. [ABSTRACT FROM AUTHOR]

The Data hiding concept such as steganography can provide security and facilitate the safe exchange of important information in the digital world. This concept can be used with the various multimedia such as text, image, video, audio acting as cover medium to carry out the one way exchange or two way exchange of secret information. The presented paper focusses only on the kind of steganography that uses audio as a carrier. This concept of steganography is, act of encoding mystery data in invisible manner. Steganography along with cryptography add a step futher in security by hiding cipher text in such a manner that middle man is unaware about the secret message is concealed in it. Cipher text generated by encryption creates suspects but steganography creates environment that looks like normal behavior, hiding suspect nature of cipher text thus making it more secure. Audio steganography is the investigation of concealing a few mystery content or audio data in a host audio. This paper presents execution of two level security of end user information by associating the two fields of system security, cryptography and steganography. Concept of index based chaotic sequence is used for encryption. It carries two approaches which make the cryptanalysis and steganalysis furthermore difficult as compare to simple LSB method. First approach is encrypting the secret text using index based chaotic sequence. The proposed approach is more secure, strong and robust, giving security to our secret information. Second approach is hiding output generated by first approach using the concept of LSB with XOR method. It gives extra level of security by creating minimum distortion in host audio file simultaneously maintaining high embedding capacity. [ABSTRACT FROM AUTHOR]

Crop yield prediction (CYP) is a critical challenge for decision-makers of any kind of levels, including regional and national decision-making. Farmers might use effective CYP model to help them determine what to cultivate or when to grow it. The primary aim of precision agriculture (PA) is to increase crop growth and yield thereby decreasing production costs and emissions. Various developmental factors influence potential yield, including soil properties, irrigation, weather, fertilizer maintenance, and topography. CYP is critical to worldwide agricultural production. To improve the global food security, policymakers depend on reliable forecasts to make timely exports and imports decision. The main aim of this paper is to predict the crop yield using machine-learning technique. The collected data are pre-processed using Kalman filter algorithm and then certain features are extracted using the Linear Discriminant Analysis (LDA). The CYP is done using the improved extreme learning machine (IELM). The output weight of the extreme learning machine is improved using the chimp optimization algorithm (COA). The implementation tool used in this method is PYTHON and the dataset used for the CYP is farm yield prediction. The experimental results showed an accuracy of 99.99% which is better when compared to existing methods. [ABSTRACT FROM AUTHOR]

High utility itemset (HUI) mining is a powerful data mining technique to discover profitable patterns. The utility of an item is computed by using two measures named quantity and per-unit profit. All existing HUI mining algorithms consider a single value of external utility (per unit profit) for the entire database. However, the per-unit profit of items might fluctuate over time in many applications. This research introduces three novel strategies to comprise the external utilities of items as input for the HUI mining algorithm. Traditional HUI mining algorithms have been developed for the standalone system and do not fit for big data processing due to the limited computing resources (CPU, memory). Big data are efficiently processed on distributed frameworks like Apache Hadoop, Spark, etc. This paper introduces a distributed HUI mining algorithm named Spark-based Top-k high utility itemset (k-SHUI) miner. We also propose a fair load distribution strategy to divide the search space equally among the cluster nodes. The k-SHUI produces top-k HUIs without the requirement of the minimum utility threshold. We conducted extensive experiments on six real-life datasets to compare the proposed algorithm's performance with the existing algorithm. The experimental results demonstrate that the proposed algorithm outperforms the existing algorithms. [ABSTRACT FROM AUTHOR]

Thread based programs are very much efficient in terms of increasing application response, improving structure of the program, combining with the remote procedure call(RPC) and complete utilization of the computation available. A shared-memory system along with the multithreading, can result in developing a parallel system on which different threads can run in parallel on different processor cores. Even when there are more number of threads than the processor cores, scheduling along with context switching can be done to ensure start of execution of all the threads. Keeping these benefits in mind, present research formulates that in certain cases, the results of multithreading based programming on a single system can be more convincing than making use of the cluster based distributed programming. Proposed work tests all these claims, by decomposing an image having salt and pepper noise into number of small images and then applying median filtering parallely on all these subimages using multithreading approach. Various performance measurement metrics like %Idle Time, %Processor Time, %Maximum Frequency, %Processor Utility, total execution time are used to validate the results generated by the proposed approach. Remote method invocation (RMI) based cluster for distributed programming has also been deployed to perform the same operation on a cluster based architecture with number of nodes working together, to compare the results of both the architectures. [ABSTRACT FROM AUTHOR]

Extensive variability in current climatic conditions necessitates the need for optimal planning of water resources to manage socio-economic and environmental requirements efficiently. The optimal allocation of surface water and groundwater is essential to maximize crop net return due to uncertainty in seasonal rainfall, groundwater, and surface water availability in each region. This study demonstrated a multi-objective model to maximize the crop net return and efficient management of water resources. The multi-objective model comprised three objective functions maximizing the crop net return, minimizing the water deficit, and maximizing the aquifer recharge. The model's practicability was analyzed through the case study of the Pennar-Palar-Cauvery link canal command in India. Three meta-heuristic approaches, particle swarm optimization (PSO), genetic algorithm (GA), and marine predators algorithm (MPA), were employed to solve the presented model. And their performance was evaluated using hypervolume and coverage metrics, indicating MPA's superiority in obtaining well-distributed Pareto-optimal solutions. Thus, decision-makers can choose the best feasible solution based on current resource availability and preferences. The model was executed considering different cropping area deviations (5–25%) in the existing cropping pattern and allowance of groundwater mining from 0% to 70%. The obtained cropping pattern utilizing MPA at 25% cropping deviation achieved an increased net return of 1327.05 million INR. • A multi-objective optimization model was developed for the conjunctive use of water resources. • The sustainable allocation of water resources was evaluated using three approaches. • Particle swarm optimization (PSO), genetic algorithm (GA), and marine predators algorithm (MPA) were used. • The MPA was superior in achieving well-distributed Pareto optimal solutions. [ABSTRACT FROM AUTHOR]

Thread based programs are very much efficient in terms of increasing application response, improving structure of the program, combining with the remote procedure call(RPC) and complete utilization of the computation available. A shared-memory system along with the multithreading, can result in developing a parallel system on which different threads can run in parallel on different processor cores. Even when there are more number of threads than the processor cores, scheduling along with context switching can be done to ensure start of execution of all the threads. Keeping these benefits in mind, present research formulates that in certain cases, the results of multithreading based programming on a single system can be more convincing than making use of the cluster based distributed programming. Proposed work tests all these claims, by decomposing an image having salt and pepper noise into number of small images and then applying median filtering parallely on all these subimages using multithreading approach. Various performance measurement metrics like %Idle Time, %Processor Time, %Maximum Frequency, %Processor Utility, total execution time are used to validate the results generated by the proposed approach. Remote method invocation (RMI) based cluster for distributed programming has also been deployed to perform the same operation on a cluster based architecture with number of nodes working together, to compare the results of both the architectures. [ABSTRACT FROM AUTHOR]

Cloud storage offers users to manage their remote data more efficiently. However, it comes with the threat of severe security concerns, i.e., integrity. Public auditing models have been intensively researched with the growing importance of data integrity. These public auditing models employed a third-party auditor (TPA) to check the integrity of stored data on behalf of users. However, these models are susceptible to procrastinating auditors who may not conduct the scheduled auditing on time. Therefore, this paper introduces a blockchain-based methodology to employ a certificateless public auditing model against malicious and procrastinating auditors with efficient user revocation to address these issues. It enables the storage of immutable time-stamped auditing results as a transaction on the blockchain. So, the users can verify whether TPA performs the auditing task on the scheduled time. This model supports efficient batch auditing in multiple cloud servers without an additional trusted organizer. A formal security analysis guarantees the computational intractability and the performance analysis illustrates the feasibility and practicability of the model. [ABSTRACT FROM AUTHOR]

Now-a-days Massive MIMO (mMIMO) become an attractive technology due to its spectral and energy efficiency by the means of simple signal processing. However, in overloaded scenario, wherein the number of users equipments (UEs) are larger than number of antennas, the spectral effciency (SE) suffers and hence error rate performance, it has been shown recently that use of code domain NOMA in mMIMO can improve the SE performance. Further, interleave division multiple access (IDMA) has been drawn much attention as a suitable code domain non-orthogonal multiple access (NOMA) for future communication standards. This paper proposes the work in two folds, first a massive multiple input and multiple output (MIMO) and IDMA communication system is jointly proposed in which antennas on the base station serves users simultaneously in the same frequency band. Both and are large in numbers. Secondly, the minimum mean square error (MMSE) based beamformer is suggested to combat the propagation loss and effect of multiple access interference (MAI), for massive MIMO-IDMA system under downlink communication constraints. With the help of simulation results, the performance of the proposed system with MMSE beamforming have been investigated in terms of BER vs SNR curve plot. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

With the accelerated process of agricultural modernization, the accurate acquisition of agricultural environmental information has become a major trend. A field microclimate monitoring system based on wireless sensor network is constructed based on the latest concept model of the Internet of things and fuzzy control theory; it mainly composed of data acquisition system, data storage system and visualization platform for big data analysis. The data acquisition system is highly integrated with data acquisition and data transmission to obtain real-time data of farmland environment in different terrain areas, including meteorology, hydrology, soil, growth etc. Wireless transmission will transmit real-time data through the GPRS network to the big data analysis platform. The big data analysis platform presents the site data information and analyzes the rule of historical data through visualization technology, realizes the meteorological disaster early warning and forecast, and provides effective decision-making service information based on the agricultural fuzzy theory. Finally, we analyze all kinds of hardware interference problems and software defects encountered in the debugging process, and propose new solutions through the experimental data obtained from actual production applications. It has been proved that the field microclimate monitoring system runs steadily and meets the demand of agricultural monitoring. [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]

This paper presents a technique of motion planning of robot using Fuzzy Method and Genetic Algorithm along with Three Path concept in a dynamic environment which contains both static and dynamic obstacles. The algorithm is divided into two phases. In the first phase, a path or two is generated by using Three Path Method or Fuzzy-GA while considering static obstacles only. The information about the distances and angles of obstacles from the robot is acquired by using the concept of Three Path. A collision free path is selected by using Three Path concept. When all the three paths are blocked by static obstacles Fuzzy Method is used for obstacles avoidance. Genetic Algorithm is used to find optimal range of the linguistic values of the variables for the membership functions. In the second phase, the moving obstacles are avoided along the path/paths generated in the first phase by considering the velocities of the moving obstacles and the velocity of the robot. Results show that the technique of motion planning of mobile robots using Fuzzy-GA along with Three Path concept is computationally efficient as compared to other hybrid methods for motion planning in dynamic environment. [ABSTRACT FROM AUTHOR]

Diversity and accuracy of classifiers are widely recognized to be two key factors for a successful ensemble. The increase of diversity among classifiers must lead to the decrease of the average accuracy of that, and vice verse. Therefore, finding a tradeoff between the diversity and the accuracy of classifiers can make the ensemble perform the best. Existing ensemble pruning approaches always find the tradeoff using diversity measures and heuristic algorithms separately. Those ensemble pruning approaches based on diversity measures, using different strategies, cannot exactly find the tradeoff; Those approaches based on heuristic algorithms cannot also exhaustively search for that. To address the issue, Combining Weak-link Co-evolution Binary Artificial Fish swarm algorithm and Complementarity measure for Ensemble Pruning (CWCBAFCEP) is proposed using a combination of the proposed Weak-link Co-evolution Binary Artificial Fish Swarm Algorithm (WCBAFSA) and COMplementarity measure (COM). First, the classifiers in a constructed initial pool of classifiers are pre-pruned using COM, which significantly reduce the computational complexity of ensemble pruning. Second, the final ensemble extracted from the remaining classifiers after pre-pruning can be efficiently achieved using the proposed WCBAFSA. Experimental results on 25 datasets from the UCI Machine Learning Repository demonstrate that CWCBAFCEP performs much better than the original ensemble and other state-of-the-art ensemble pruning approaches, and that its effectiveness and efficiency. It provides a new research idea for ensemble pruning. [ABSTRACT FROM AUTHOR]

Failure Modes and Effects Analysis (FMEA) is a common technique used in several manufacturing and service industries for eliminating failures and potential problems using the evaluation of failure modes of a new or an existing product, process, or system. The risk analysis is carried out by calculating the Risk Priority Number (RPN), which is a product of three factors, Occurrence (O), Severity (S), and Detection (D). In the literature, modeling uncertainties are used to improve the FMEA process and overcome the inefficiencies of traditional RPN. One of the common uncertainties in FMEA is the epistemic uncertainty that is essentially modeled using the Dempster-Shafer theory (DST). In this study, a novel risk-based fuzzy evidential approach is proposed by using interval-valued DST and fuzzy axiomatic design (FAD) to assess the risk of failure modes with fuzzy belief structures. The efficiency of the proposed model was investigated with the help of an example and the results are compared with riskless evaluations. Reviewing the results shows the information content of failure modes decrease relatively when risk is taken into account, in fact failure modes become relatively more critical than those in the case where no risk is considered. [ABSTRACT FROM AUTHOR]

Pythagorean fuzzy sets, which is based on intuitionistic fuzzy sets (IFSs), is an important tool to solve problems and has attracted a large number of researchers in different fields. As we know, studies have focused on interval-valued Pythagorean fuzzy set and aggregated operators. However, few studies focus on point operators. This paper introduces and discusses what is the pythagorean fuzzy point operators, study their properties and relationships, which is seen as the extensions of intuitionistic fuzzy sets. The uncertainty regarding to Pythagorean fuzzy set could be decreased if we use the pythagorean fuzzy point operators. In the end, pythagorean fuzzy multi-attributes decision making based on analytic hierarchy procedure is put forward to cope with the complicated MADM (multi-attributes decision making) issues which can be very useful when we face the multi-level analysis. [ABSTRACT FROM AUTHOR]

Microbial activities are the indicators of soil strength. The present study explores the development of efficient predictive modeling systems for the estimation of specific soil microbial dynamics, phosphate solubilization (PS), bacterial population (BP), and 1-aminocyclopropane-1-carboxylate ACC-deaminase activity. More specifically, fuzzy c-means clustering (FCM)-FIS, Wang and Mendel’s (WM) fuzzy inference systems (FIS), adaptive neuro-fuzzy inference system (ANFIS), and subtractive clustering (SC) and have been implemented with the objective to achieve the best estimation accuracy of microbial dynamics. Experimental measurements were performed using controlled pot experiment using minimal salt media. Three experimental parameters, including temperature, pH, and incubation period have been used as inputs of FCM-FIS, SC-FIS, ANFIS, and WM-FIS methods. The SC-FIS method has the best estimation accuracy for the PS (R2 of 0.99) and BP (R2 of 0.94) than the rest three FIS methods. [ABSTRACT FROM AUTHOR]