In making the deep neural network, activation functions play an important role. But the choice of activation functions also affects the network in term of optimization and to retrieve the better results. Several activation functions have been introduced in machine learning for many practical applications. But which activation function should use at hidden layer of deep neural networks was not identified. The primary objective of this analysis was to describe which activation function must be used at hidden layers for deep neural networks to solve complex non-linear problems. The configuration for this comparative model was used by using the datasets of 2 classes (Cat/Dog). The number of Convolutional layer used in this network was 3 and the pooling layer was also introduced after each layer of CNN layer. The total of the dataset was divided into the two parts. The first 8000 images were mainly used for training the network and the next 2000 images were used for testing the network. The experimental comparison was done by analyzing the network by taking different activation functions on each layer of CNN network. The validation error and accuracy on Cat/Dog dataset were analyzed using activation functions (ReLU, Tanh, Selu, PRelu, Elu) at number of hidden layers. Overall the Relu gave best performance with the validation loss at 25th Epoch 0.3912 and validation accuracy at 25th Epoch 0.8320. It is found that a CNN model with ReLU hidden layers (3 hidden layers here) gives best results and improve overall performance better in term of accuracy and speed. These advantages of ReLU in CNN at number of hidden layers are helpful to effectively and fast retrieval of images from the databases.
Image fusion is to converge Multispectral (MS) and Panchromatic (PAN) images into a fused image which is further enlightened. Soft computing based image fusion techniques are fuzzy and neuro-fuzzy are exploited to lessening the severance and vagueness in the output. Fused images achieved after the synthesis is utilized in image analysis, medical applications, armed province, and computer revelation. In this research, we convey iterative image fusion based on fuzzy and neuro-fuzzy methods on source images attained from different sources to improve visualization proficiency. We also compared the proposed techniques with principal component analysis (PCA) and wavelets transform based image fusion. Fused outcomes accomplished from image fusion methods are assessed through typical eminence evaluation parameters. The resulting outcome obtained from iterative fusion is improved in terms of spectral and spatial information when compared to the one-time fused image. Due to neural networks structure, applied sorts of biological neural networks and potentiality of the fuzzy and neuro-fuzzy logic, the proposed method overtakes the conventional methods. The complete investigational consequences formed from anticipated methodology established that the utilization of proposed approaches enhanced image content.
Background: A spiral phase rear mounted masked scheme is proposed based on Gyrator Transform (GT) to enhance the security contribution of second lens of the existing Double Random Phase Encoding (DRPE) system by modulating the phase of the output obtained in output plane. An additional third layer of Spiral Phase Mask (SPM) is included in the output plane in the same 4f system. Objective: To develop a symmetric cryptosystems to enhance the security potential of the second lens and to prevent the comfortable realization of the cipher-image in the transform domain. Methods: The original image is first scrambled using Arnold transform with frequency and then is convoluted with a secret random phase mask, RPM in GT and then obtained result is convoluted with another secret RPM in inverse GT. The obtained result is then finally convoluted with SPM. Results: It verifies the sensitivity and achieves better performance in terms of recovering a high quality image. Results show the security, performance and quality analysis on the basis of correlation coefficient, occlusion attack, key sensitivity and noise attack, entropy and histogram. Conclusion: It enhances the security potential of second lens in DRPE and introduces diffusion in the system. The system is simulated for binary and greyscale image and achieves better performance as compared to existing DRPE variants. Key sensitivity is more secure and cannot recover original image without knowing all the parameters. Correlation coefficient are also weakly correlated and does not reveals relevant information. Simulation result demonstrates the feasibility and robustness of cryptosystem.
Purpose: The aim of this article is to discuss the impact of static load balancing over a set of heterogeneous processors, where tasks are independent and unitary in static environments, by showing how to distribute task in order to optimize both the average response time and the degree of the resources used. Methods: Implementation of a modified scheduling algorithm, the latter is based on two parameters which are the execution time and the failure probability. The algorithm is based on the results of an optimal algorithm that already exists, with only one parameter that is execution time. Results: The obtained results show that the modified scheduling algorithm gives us the good results. Conclusion: The modified algorithm assumes that the processor has smallest execute time. So, the failure probability increases because of it’s frequently use. The results obtained by testing this proposed algorithm are better than the optimal algorithm.
Background: The whale optimization algorithm is one of the popular meta-heuristic algorithms which has successfully been applied in various application areas such as image analysis and data clustering. However, the slow convergence rate and chances of sticking into the local optima due to improper balance of its exploration and exploitation phases are some of its pitfalls. Therefore, in this paper, a new improved whale optimization algorithm has been proposed. Moreover, the proposed method has been used in bag-of-features method for histopathological image classification. Methods: The new algorithm, improved whale optimization algorithm, modifies the encircling phase of original whale optimization algorithm. The proposed algorithm has been used to cluster the extracted features for finding the relevant codewords to be used in the bag-of-features method for histopathological image classification. Results: The efficiency of proposed algorithm has been analyzed on 23 benchmark functions in terms of mean fitness, standard deviation values, and convergence behavior. The performance of the improved whale optimization algorithm based histopathological image classification method has been analyzed on blue histology image dataset and compared with other meta-heuristic based bagof- features methods in terms of recall, precision, F-measure, and accuracy. The experimental results validate that the proposed method outperforms the considered state-of-the-art methods and attains 12% increase in the histopathological image classification accuracy. Conclusion: In this paper, a new improved whale optimization algorithm has been proposed and applied in bag-of-features method for histopathological image classification. The results of proposed method outperform the other existing meta-heuristic methods over standard benchmark functions and histopathological image dataset.
Background: Voice over Internet Protocol (VoIP) has emerged as one of the most significant technology in the field of communication and evolved as a substitute to the conventional communication method as the Public Switched Telephone Network (PSTN). Along with the advantages such as scalability and security, VoIP has some threats such as voice quality and interference that must be dealt with. The voice quality in VoIP is degraded when transmitted over a computer network due to delay, jitter and packet loss etc. Packet loss is one of major reasons for the signal quality degradation. Objective: In this research article, Quadrature Mirror Filter Bank (QMF) has been implemented in wireless VoIP system to enhance the quality of the signals transmitted. Results: The performance has been evaluated under varying network conditions of packet loss. Conclusion: Significant improvement has been observed in the quality of VoIP signal.
Background: Essential proteins are significant for drug design, cell development, and for living organism survival. A different method has been developed to predict essential proteins by using topological feature, and biological features. Objective: Still it is a challenging task to predict essential proteins effectively and timely, as the availability of protein protein interaction data depends on network correctness. Methods: In the proposed solution, two approaches Mean Weighted Average and Recursive Feature Elimination is been used to predict essential proteins and compared to select the best one. In Mean Weighted Average consecutive slot data to be taken into aggregated count, to get the nearest value which considered as prescription for the best proteins for the slot, where as in Recursive Feature Elimination method whole data is spilt into different slots and essential protein for each slot is determined. Results: The result shows that the accuracy using Recursive Feature Elimination is at-least nine percentages superior when compared to Mean Weighted Average and Betweenness centrality. Conclusion: Essential proteins are made of genes which are essential for living being survival and drug design. Different approaches have been proposed to anticipate essential proteins using either experimental or computation methods. The experimental result show that the proposed work performs better than other approaches.
Background: Systems whose requirements change at a rate that necessitates adaptation without human intervention are called self-adaptive systems, and they have the ability to adjust their behavior autonomously at run-time in response to their environment’s evolution. Samples of applications that require self-adaptation include Smart home systems and environmental monitoring. However, self-adaptivity is often constructed in an ad-hoc manner. Methods: In this paper, the authors present a pattern-based specification language for self-adaptive systems. Its semantics are presented in terms of fuzzy logic. Thus, enabling a meticulous processing of requirements, in order to permit the formulation of self-adaptive requirements accurately, thereby facilitates the design of systems that are flexible and responsive to adaptation in a systematic manner. Results: To show the applicability and effectiveness of our language, the authors apply it to two case studies. One case study reviews the Smart fridge in ambient assisted living and the second case study is focused on an ambulance dispatching system using a developed support tool.
Background: Health is the major concern of each and every individual. Being fit both physically and mentally is not an easy task. Yoga and meditation is considered as an optimal solution for the same. In Yoga, Yogi (person who does yoga) performs various asanas (body postures) which energize and refresh their body cells and keep them fit. The real purpose of yoga asanas and breathing techniques is to achieve optimal health possibly the best physical condition based on their life style, environment, age and genetics. Various clinical studies claim that yoga can provide improved mental and physical fitness rather than other physical training or stress management techniques. Objective: Our aim is to increase the performance of the postures of the Yogis, through yoga assistant kit with prediction intelligence which will assist the person to perform suitable yoga postures. This will help the Yogis to achieve more positive results in the practice of Yoga, with highest quality of meditation. The developed IoT kit consists of a hardware module (embedded in wrist band) and a mobile application. The yogi should wear the wrist band while practising yoga. The wrist band consists of various sensors like temperature sensor, pressure sensor, humidity sensor etc. which sense body parameters and store it in a central database. Using neural networks and embedded intelligence our system aims to predict the number of sun salutations a person (yogi) should perform based on the parameters collected from the kit. The results showed that our system works as a virtual trainer which suggests the yogi with the appropriate asanas to be performed based on present body conditions. Methods: It is safe to wear this light weight wrist band as it is made up of a cotton band. The components are embedded inside the band and is safe to use though it uses button cells as a power source. The system is charged by button cells. It is both economical and safe to use it as the kit is designed in such a manner that it doesn’t cause any sort of skin allergies or side effects. Discussion: There is no standard yoga assistant kit available in the market as of now. So our proposed kit can assist the yoga performers to perform yoga in an efficient manner. The intention of our kit is not to improve the health of a yoga person instead it focuses on assisting the yoga person with a set of asanas to be performed at a particular body condition. The smart phone version provides live assistance for the yoga performer with relevant videos. The kit doesn’t consist of any expensive components and hence we can market this product in a nominal price. We performed a clinical study in Amrutha Yoga centre and the results showed that it is non allergic and safe to use for both kids and elder persons. Conclusion: Thus our proposed yoga kit will be an intelligent assistant for every yoga performer to practice yoga efficiently and effectively.
Background: In many modern applications, information filtering is now used that exposes users to a collection of data. In such systems, the users are provided with recommended items’ list they might prefer or predict the rate that they might prefer for the items. So that, the users might be select the items that are preferred in that list. Objective: In web service recommendation based on Quality of Service (QoS), predicting QoS value will greatly help people to select the appropriate web service and discover new services. Methods: The effective method or technique for this would be Collaborative Filtering (CF). CF will greatly help in service selection and web service recommendation. It is the more general way of information filtering among the large data sets. In the narrower sense, it is the method of making predictions about a user’s interest by collecting taste information from many users. Results: It is easy to build and also much more effective for recommendations by predicting missing QoS values for the users. It also addresses the scalability problem since the recommendations are based on like-minded users using PCC or in clusters using KNN rather than in large data sources. Conclusion: In this paper, location-aware collaborative filtering is used to recommend the services. The proposed system compares the prediction outcomes and execution time with existing algorithms.
: Data analytics and data mining systems work on data which stored in files, the files are not store relationships among the data, from such kind of data we compute aggregate values over the set of required attributes for find insights of data, find attributes values which aggregation values greater than threshold such kind of queries called iceberg queries. Computing iceberg queries with average aggregate function is default, because limited memory available. Existing method are suffers with re-computation of candidate. We proposed a Record Traction Algorithm(RTA) ,it use Domain partitioning approach, it avoid re-computation of candidate in during next scan of data set, it use bit vector and bitmap numbers for Domain Partitioning the data, our experiment reveals that our approach generate a candidate only once and input data will reduced in further candidate sets.
Background: In recent years, the cost is becoming one of the biggest obstacles for the effective operation of supply chain. It is increasingly urgent to minimize the cost and optimize the supply chain system. However, importance measures have been little used in it as a research focus in solving the optimization problems. Methods: With respect to the foregoing, this paper combines the reliability, importance measures and cost issues to improve the supply chain. Supply chain costs can be divided into three classes: design cost, manufacturing cost, and inventory cost. Results: Then importance measures based on the costs are given and demonstrated in a case study. Results show that the optimal system and minimal cost can be obtained by focusing on the important parts in supply chain operation. Conclusion: The importance order and the key elements identification are helpful to increase the operational efficiency of the supply chain and provide effective methods for improving the supply chain management.
Background: Most of the common multi-user detection techniques have the shortcomings of large computation and slow operation. For Hopfield neural networks, there are some problems such as high-speed searching ability and parallel processing, but there are local convergence problems. Objective: The stochastic Hopfield neural network avoids local convergence by introducing noise into the state variables and then achieves the optimal detection. Methods: Based on the study of CDMA communication model, this paper presents and models the problem of multi-user detection. Then a new stochastic Hopfield neural network is obtained by introducing a stochastic disturbance into the traditional Hopfield neural network. Finally, the problem of CDMA multi-user detection is simulated. Conclusion: The results show that the introduction of stochastic disturbance into Hopfield neural network can help the neural network to jump out of the local minimum, thus achieving the minimum and improving the performance of the neural network.
Background: Major Depressive Disorder (MDD) in simple terms is a psychiatric disorder which may be indicated by having mood disturbances which are consistent for more than a few weeks. It is considered a serious threat to psychophysiology which when left undiagnosed may even lead to the death of the victim so it is more important to have an effective predictive model. The major Depressive disorder is often termed as comorbid medical condition (medical condition that co-occurs with another), it is hardly possible for the physicians to predict that the victim is under depression, timely diagnosis of MDD may help in avoiding other comorbidities. Machine learning is a branch of artificial intelligence which makes the system capable of learning from the past and with that experience improves the future results even without programming explicitly. As in recent days because of the high dimensionality of features, the accuracy of the predictions is comparatively low. In order to get rid of redundant and unrelated features from the data and improve the accuracy, relevant features must be selected using effective feature selection methods. Objective: This study aims to develop a predictive model for diagnosing the Major Depressive Disorder among the IT professionals by reducing the feature dimension using feature selection techniques and evaluate them by implementing three machine learning classifiers such as Naïve Bayes, Support Vector Machines and Decision Tree. Method: We have used Random Forest based Recursive Feature Elimination technique to reduce the feature dimensions. Results: The results show a considerable increase in prediction accuracy after applying feature selection technique. Conclusion: From the results, it is implied that the classification algorithms perform better after reducing the feature dimensions.
Background: In today’s world it is highly difficult to manage the smart things and fulfill the communication needs without the Internet as it provides ultimate means for human to human (H2H) communication. The ‘things’ could be entities or devices that contribute for the communication. But to enhance and improve such smart communication among the things that involves nonhuman intervention, there is a need to add few more smart capabilities to the Internet. As the world of Internet is on its way of transformation into a new smart world called ‘Internet of Things’ (IoT) where the things should possess the ability of sensing, communication and control to let the things exchange information without complete intervention of humans to provide advanced and qualitative services, which is possible with the help of protocols. Objective: This paper discusses how the transition could be started and being progressed in wired and wireless systems, and how it changes the traditional means of communication among different devices and humans into a smart way. Methods: Transition here refers to how isolated things are being intermingled with each other to generate a smart protocol communication. Results and Conclusion: The aim of such transition is to improve the efficiency, flexibility, adaptability and interoperability. This paper also explores various factors that contribute to IoT.
Background: The scaling up of the MANETs is an important criterion to consider since the nodes have to maintain the updated routing information. Generally, agents are deployed to balance the load. However, a single agent performance may not be satisfactory, if the network has a large set of nodes. Hence, the agents are necessary once the network size increases, but the launching of Agents in the network involves computational complexity, power consumption and in turn, increase network traffic. This paper addresses the impact of multiple agent deployments in MANETs to quantify the favourable number of Agents in MANETs that can balance the computational overhead as well as the performance gain by involving multiple agents in MANETs. Methods: The behavior of a varying number of agents in a dynamic network environment launched by a node is analysed across different network metrics. After that, considering all the constraints affecting the network performance, the optimal number of agents is determined using F-Min- Constrained optimization technique. Result: The Perito-optimal points are generated that shows the approximately near most optimal points to the exact solution. Conclusion: The paper tries to strike a balance between the constraints like power consumption involved in the launch of Multiple Agents in the network.
Background: Active Queue Management (AQM) is a TCP congestion avoidance approach that predicts congestion before sources overwhelm the buffers of routers. Random Early Detection (RED) is an AQM strategy that keeps history of queue dynamics by estimating an average queue size parameter avg and drops packets when this average exceeds preset thresholds. The parameter configuration in RED is problematic and the performance of the whole network could be reduced due to wrong setup of these parameters. Drop probability is another parameter calculated by RED to tune the drop rate with the aggressiveness of arriving packets. Objective: In this article, we propose an enhancement to the drop probability calculation to increase the performance of RED. Methods: This article studies the drop rate when the average queue size is at the midpoint between the minimum and maximum thresholds. The proposal suggests a nonlinear adjustment for the drop rate in this area. Hence, we call this strategy as the Half-Way RED (HRED). Results: Our strategy is tested using the NS2 simulator and compared with some queue management strategies including RED, TD and Gentle-RED. The calculated parameters are: throughput, link utilization and packet drop rate. Conclusion: Each performance parameter has been plotted in a separate figure; then the robustness of each strategy has been evaluated against these parameters. The results suggest that this function has enhanced the performance of RED-like strategies in controlling congestion. HRED has outperformed the strategies included in this article in terms of throughput, link utilization and packet loss rate.
