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Infrastructure service model provides different kinds of virtual computing resources such as networking, storage service, and hardware as per user demands. Host load prediction is an important element in cloud computing for improvement in the resource allocation systems. Hosting initialization issues still exist in cloud computing due to this problem hardware resource allocation takes several minutes of delay in the response process. To solve this issue prediction techniques are used for proper prediction in the cloud data center to dynamically scale the cloud in order for maintaining a high quality of services. Therefore in this paper, we propose a hybrid convolutional neural network long with short-term memory model for host prediction. In the proposed hybrid model, vector auto regression method is firstly used to input the data for analysis which filters the linear interdependencies among the multivariate data. Then the enduring data are computed and entered into the convolutional neural network layer that extracts complex features for each central processing unit and virtual machine usage components after that long short-term memory is used which is suitable for modeling temporal information of irregular trends in time series components. In all process, the main contribution is that we used scaled polynomial constant unit activation function which is most suitable for this kind of model. Due to the higher inconsistency in data center, accurate prediction is important in cloud systems. For this reason in this paper two real-world load traces were used to evaluate the performance. One is the load trace in the Google data center, while the other is in the traditional distributed system. The experiment results show that our proposed method achieves state-of-the-art performance with higher accuracy in both datasets as compared with ARIMA-LSTM, VAR-GRU, VAR-MLP, and CNN models.

Pashto is one of the most ancient and historical languages in the world and is spoken in Pakistan and Afghanistan. Various languages like Urdu, English, Chinese, and Japanese have OCR applications, but very little work has been conducted on the Pashto language in this perspective. It becomes more difficult for OCR applications to recognize handwritten characters and digits, because handwriting is influenced by the writer’s hand dynamics. Moreover, there was no publicly available dataset for handwritten Pashto digits before this study. Due to this, there was no work performed on the recognition of Pashto handwritten digits and characters combined. To achieve this objective, a dataset of Pashto handwritten digits consisting of 60,000 images was created. The trio deep learning Convolutional Neural Network, i.e., CNN, LeNet, and Deep CNN were trained and tested with both Pashto handwritten characters and digits datasets. From the simulations, the Deep CNN achieved 99.42 percent accuracy for Pashto handwritten digits, 99.17 percent accuracy for handwritten characters, and 70.65 percent accuracy for combined digits and characters. Similarly, LeNet and CNN models achieved slightly less accuracies (LeNet, 98.82, 99.15, and 69.82 percent and CNN, 98.30, 98.74, and 66.53 percent) for Pashto handwritten digits, Pashto characters, and the combined Pashto digits and characters recognition datasets, respectively. Based on these results, the Deep CNN model is the best model in terms of accuracy and loss as compared to the other two models.

Infrastructure service model provides different kinds of virtual computing resources such as networking, storage service, and hardware as per user demands. Host load prediction is an important element in cloud computing for improvement in the resource allocation systems. Hosting initialization issues still exist in cloud computing due to this problem hardware resource allocation takes serval minutes of delay in the response process. To solve this issue prediction techniques are used for proper prediction in the cloud data center to dynamically scale the cloud in order for maintaining a high quality of services. Therefore in this paper, we propose a hybrid convolutional neural network long with short-term memory model for host prediction. In the proposed hybrid model, vector auto regression method is firstly used to input the data for analysis which filters the linear interdependencies among the multivariate data. Then the enduring data are computed and entered into the convolutional neural network layer that extracts complex features for each central processing unit and virtual machine usage components after that long short-term memory is used which is suitable for modeling temporal information of irregular trends in time series components. In all process, the main contribution is that we used scaled polynomial constant unit activation function which is most suitable for this kind of model. Due to the higher inconsistency in data center, accurate prediction is important in cloud systems. For this reason in this paper two real-world load traces were used to evaluate the performance. One is the load trace in the Google data center, while the other is in the traditional distributed system. The experiment results show that our proposed method achieves state-of-the-art performance with higher accuracy in both datasets as compared with ARIMA-LSTM, VAR-GRU, VAR-MLP, and CNN models.

In recent years, a lot of metaheuristic algorithms have been successfully used for solving many software engineering problems. In the field of software testing, flower pollination algorithm (FPA) is used for finding minimum t-way tests where it is demonstrated its efficiency for generating sequence t-way tests; however, the existing results highlighting that FPA’s performance is still limited which can lead to poor results. This chapter proposes a new variant of FPA algorithm called adaptive neighbor heuristics flower pollination algorithm (NH-FPA) strategy for generating sequence t-way tests. NH-FPA aims to enhance the intensification capability by introducing neighboring heuristics search operators into original FPA. NH-FPA integrates neighboring heuristics operators such as insert, swap, and scramble, for better search space’s exploitation. Experiments are carried out to evaluate the proposed strategy by, first, comparing NH-FPA’s results with the existing t-way metaheuristics-based strategies. Then NH-FPA will compare with standard FPA to asses the contribution of introducing the neighboring heuristics operators into NH-FPA. Experimental results demonstrate that the proposed strategy outperforms most of the existing sequence t-way strategies in terms of generating minimum tests size and show a fast convergence rate compared with standard FPA.

The Sine-Cosine algorithm (SCA) is a population-based metaheuristic algorithm utilizing sine and cosine functions to perform search. To enable the search process, SCA incorporates several search parameters. But sometimes, these parameters make the search in SCA vulnerable to local minima/maxima. To overcome this problem, a new Multi Sine-Cosine algorithm (MSCA) is proposed in this paper. MSCA utilizes multiple swarm clusters to diversify & intensify the search in-order to avoid the local minima/maxima problem. Secondly, during update MSCA also checks for better search clusters that offer convergence to global minima effectively. To assess its performance, we tested the MSCA on unimodal, multimodal and composite benchmark functions taken from the literature. Experimental results reveal that the MSCA is statistically superior with regards to convergence as compared to recent state-of-the-art metaheuristic algorithms, including the original SCA.

Team formation (TF) in social networks exploits graphs (i.e., vertices = experts and edges = skills) to represent a possible collaboration between the experts. These networks lead us towards building cost-effective research teams irrespective of the geolocation of the experts and the size of the dataset. Previously, large datasets were not closely inspected for the large-scale distributions & relationships among the researchers, resulting in the algorithms failing to scale well on the data. Therefore, this paper presents a novel TF algorithm for expert team formation called SSR-TF based on two metrics; communication cost and graph reduction, that will become a basis for future TF’s. In SSR-TF, communication cost finds the possibility of collaboration between researchers. The graph reduction scales the large data to only appropriate skills and the experts, resulting in real-time extraction of experts for collaboration. This approach is tested on five organic and benchmark datasets, i.e., UMP, DBLP, ACM, IMDB, and Bibsonomy. The SSR-TF algorithm is able to build cost-effective teams with the most appropriate experts–resulting in the formation of more communicative teams with high expertise levels.

Levenberg-Marquardt back-propagation (LMBP) algorithm is one of the most widely used alternative algorithms for training of artificial neural networks (ANN). In some cases, learning in LM can become a challenging machine-learning task owing to poor selection of weights and biases. In this manner, the network may become unstable and cause the search process to get stuck in local minima (i.e. resulting into network stagnancy). Recently, meta-heuristic algorithms have proven themselves to alleviate such problem and improve the convergence slow convergence in ANN. In this paper, Wolf Search (WS) that simulates the hunting behavior of wolves in their natural habitat is implemented with LMBP algorithm to optimize the weight selection in ANN. The performance of the proposed Wolf Search with Levenberg-Marquardt (WSLM) is compared with back-propagation and other stochastic LM algorithms by means of simulations on benchmark datasets. The simulation results show that the proposed WSLM performs superior to BPNN, ABC-BP, Bat-BP, and ABC-LM algorithms in terms of convergence speed and local minima avoidance.

Lungs cancer is a life-taking disease and is causing a problem around the world for a long time. The only plausible solution for this type of disease is the early detection of the disease because at preliminary stages it can be treated or cured. With the recent medical advancements, Computerized Tomography (CT) scan is the best technique out there to get the images of internal body organs. Sometimes, even experienced doctors are not able to identify cancer just by looking at the CT scan. During the past few years, a lot of research work is devoted to achieve the task for lung cancer detection but they failed to achieve accuracy. The main objective of this piece of this research was to find an appropriate method for classification of nodules and non-nodules. For classification, the dataset was taken from Japanese Society of Radiological Technology (JSRT) with 247 three-dimensional images. The images were preprocessed into gray-scale images. The lung cancer detection model was built using Convolutional Neural Networks (CNN). The model was able to achieve an accuracy of 88% with lowest loss rate of 0.21% and was found better than other highly complex methods for classification.

Back propagation (BP) algorithm is a very popular optimization procedure of ANN’s training process. However, traditional BP has some drawbacks such as getting stuck in local minima, and network stagnancy. Recently, some researches proposed the use of Elman Neural Network (ENN) trained with back propagation algorithm to yield faster and more accurate results during learning. Yet, the performance of ENN is still considerably dependent on initial weights in the network. In this paper, a new method known as HAPSOENN which adapts the network weights using Accelerated Particle Swarm Optimization (APSO) was proposed as a mechanism to improve the performance of ENN. The performance of the proposed algorithm is compared with Back-Propagation Neural Network (BPNN) and other similar hybrid variants on benchmarked classification datasets. The simulation results show that the proposed technique performs better and has faster convergence than other algorithms in terms of MSE and accuracy.

Back Propagation (BP) algorithm is one of the oldest learning techniques used by artificial neural networks (ANN). It has successfully been implemented in various practical problems. However, the algorithm still faces some drawbacks such as getting easily stuck at local minima and needs longer time to converge on an acceptable solution. Recently, the introduction of Second Order Methods has shown a significant improvement on the learning in BP but it still has some drawbacks such as slow convergence and complexity. To overcome these limitations, this research proposed a modified approach for BP by introducing the Conjugate Gradient and Quasi-Newton which were Second Order methods together with ‘gain’ parameter. The performances of the proposed approach is evaluated in terms of lowest number of epochs, lowest CPU time and highest accuracy on five benchmark classification datasets such as Glass, Horse, 7Bit Parity, Indian Liver Patient and Lung Cancer. The results show that the proposed Second Order methods with ‘gain’ performed better than the BP algorithm.

At present, several computer systems are available to assist people with special needs (i.e. without hands). However, most of these interfaces are costly or they cause discomfort during prolonged use. In this paper, an algorithm is proposed, that provides a hands-free interface between a user and a computer. It provides effective communication with the computer by interfacing the camera with the user. The head detections movement is based on the AdaBoost algorithm that effectively translates the actions into mouse cursor movements on the screen. During analysis, it was found that the proposed algorithm was highly enhanced with the integration of the AdaBoost algorithm.

Reasoning is commonly understood as a means to increase knowledge and take better decision. That is why the role of arguments has a positive impact in decisions making, as well as in crucial issues discussion. Particularly in selecting one or several alternatives, or to justify an already taken decision. However, much indication illustrates that reasoning often points to epistemic biasness and underprivileged decisions because of uncertain and lack of argumentative context. This paper proposes an argumentative based reasoning framework with rough set theory. The proposed framework is capable of decision making on uncertain data using arguments supporting contrary conclusions. Data of heterogeneous foundations from which the useful information (for arguments construction) was acquired, also changeable ideas of trust on them. As a changed behavior from previous frameworks, the proposed framework accounts for uncertain data because of using rough set theory that is a reliable mathematical approach towards the uncertainty.

Noise-Induced Hearing Loss (NIHL) has become a major health threat to the Malaysian industrial workers in the recent era due to exposure to high frequency noise produced by the heavy machines. Recently, many studies have been conducted to diagnose the NIHL in industrial workers but unfortunately they neglected some factors that can play a major role in speeding-up NIHL. In this paper, a new Hybrid Bat-BP algorithm which is based on the trio combination of BAT based metaheuristic optimization, back-propagation neural network, and fuzzy logic is proposed to diagnose NIHL in Malaysian industrial workers. The proposed Hybrid Bat-BP will use heat, body mass index (BMI), diabetes, and smoking along with the century old audiometric variables (i.e. age, frequency, and duration of exposure) to better predict NIHL in Malaysian workers. The results obtained through Hybrid Bat-BP will be able to help us identify and reduce the NIHL rate in the workers with high accuracy.

The traditional Gradient Descent Back-propagation Neural Network Algorithm is widely used in solving many practical applications around the globe. Despite providing successful solutions, it possesses a problem of slow convergence and sometimes getting stuck at local minima. Several modifications are suggested to improve the convergence rate of Gradient Descent Backpropagation algorithm such as careful selection of initial weights and biases, learning rate, momentum, network topology, activation function and ‘gain’ value in the activation function. In a certain variation, the previous researchers demonstrated that in “feed-forward algorithm”, the slope of activation function is directly influenced by ‘gain’ parameter. This research proposed an algorithm for improving the current working performance of Back-propagation algorithm by adaptively changing the momentum value and at the same time keeping the ‘gain’ parameter fixed for all nodes in the neural network. The performance of the proposed method known as ‘Gradient Descent Method with Adaptive Momentum (GDAM)’ is compared with the performances of ‘Gradient Descent Method with Adaptive Gain (GDM-AG)’ and ‘Gradient Descent with Simple Momentum (GDM)’. The learning rate is kept fixed while sigmoid activation function is used throughout the experiments. The efficiency of the proposed method is demonstrated by simulations on three classification problems. Results show that GDAM is far better than previous methods with an accuracy ratio of 1.0 for classification problems and can be used as an alternative approach of BPNN.

Back Propagation (BP) is commonly used algorithm that optimize the performance of network for training multilayer feed-forward artificial neural networks. However, BP is inherently slow in learning and it sometimes gets trapped at local minima. These problems occur mailnly due to a constant and non-optimum learning rate (a fixed step size) in which the fixed value of learning rate is set to an initial starting value before training patterns for an input layer and an output layer. This fixed learning rate often leads the BP network towrds failure during steepest descent. Therefore to overcome the limitations of BP, this paper introduces an improvement to back propagation gradient descent with adapative learning rate (BPGD-AL) by changing the values of learning rate locally during the learning process. The simulation results on selected benchmark datasets show that the adaptive learning rate significantly improves the learning efficiency of the Back Propagation Algorithm

The back propagation (BP) algorithm is the most popular supervised learning method for multi-layered feed forward Neural Network. It has been successfully deployed in numerous practical problems and disciplines. Regardless of its popularity, BP is still known for some major drawbacks such as easily getting stuck in local minima and slow convergence; since, it uses Gradient Descent (GD) method to learn the network. Over the years, many improved modifications of the BP learning algorithm have been made by researchers but the local minima problem remains unresolved. Therefore, to resolve the inherent problems of BP algorithm, this paper proposed BPGD-A3T algorithm where the approach introduces three adaptive parameters which are gain, momentum and learning rate in BP. The performance of the proposed BPGD-A3T algorithm is then compared with BPGD two term parameters (BPGD-2T), BP with adaptive gain (BPGD-AG) and conventional BP algorithm (BPGD) by means of simulations on classification datasets. The simulation results show that the proposed BPGD-A3T shows better performance and performed highest accuracy for all dataset as compared to other.

Selecting the optimal topology of neural network for a particular application is a difficult task. In case of recurrent neural networks (RNN), most methods only introduce topologies in which their neurons are fully connected. However, recurrent neural network training algorithm has some drawbacks such as getting stuck in local minima, slow speed of convergence and network stagnancy. This paper propose an improved recurrent neural network trained with Cuckoo Search (CS) algorithm to achieve fast convergence and high accuracy. The performance of the proposed Cuckoo Search Recurrent Neural Network (CSRNN) algorithm is compared with Artificial Bee Colony (ABC) and similar hybrid variants. The simulation results show that the proposed CSRNN algorithm performs better than other algorithms used in this study in terms of convergence rate and accuracy.

Metaheuristic algorithm such as BAT algorithm is becoming a popular method in solving many hard optimization problems. This paper investigates the use of Bat algorithm in combination with Back-propagation neural network (BPNN) algorithm to solve the local minima problem in gradient descent trajectory and to increase the convergence rate. The performance of the proposed Bat based Back-Propagation (Bat-BP) algorithm is compared with Artificial Bee Colony using BPNN algorithm (ABC-BP) and simple BPNN algorithm. Specifically, OR and XOR datasets are used for training the network. The simulation results show that the computational efficiency of BPNN training process is highly enhanced when combined with BAT algorithm.

Noise-Induced Hearing Loss (NIHL) has become a major source of health problem in industrial workers due to continuous exposure to high frequency sounds emitting from the machines. In the past, several studies have been carried-out to identify NIHL industrial workers. Unfortunately, these studies neglected some important factors that directly affect hearing ability in human. Artificial Neural Network (ANN) provides very effective way to predict hearing loss in humans. However, the training process for an ANN required the designers to arbitrarily select parameters such as network topology, initial weights and biases, learning rate value, the activation function, value for gain in activation function and momentum. An improper choice of any of these parameters can result in slow convergence or even network paralysis, where the training process comes to a standstill or get stuck at local minima. Therefore, this current study focuses on proposing a new framework on using Gradient Descent Back Propagation Neural Network model with an improvement on the momentum value to identify the important factors that directly affect the hearing ability of industrial workers. Results from the prediction will be used in determining the environmental health hazards which affect the workers health.