Abstract One tool for promoting mental health is human stress detection through multitasks of electroencephalography (EEG) recordings. This study proposed a short-term stress detection approach using VGGish as a feature extraction and convolution neural network (CNN) as a classifier based on EEG signals from the SAM 40 dataset. This database was recently available and was collected from 40 patients using 32 channels to identify performance on four tasks including Stroop color-word test (SCWT), answering arithmetic problems, finding mirror-identical images, and relaxing. Each task took 25 s to complete and was then repeated three times to record three trials. This means that the total EEG data contain 480 signals for four tasks recorded using 120 trials per task. The primary objective of this research was to track the amount of short-term stress that patients experienced while they engaged in the four mental tasks. Moreover, the VGGish-CNN model is applied to the SAM 40 dataset using five stages including signal preprocessing, segmentation, filtration, spectrogram, and classification process. We compared the VGGish-CNN model and the VGGish model for stress-based EEG classification to determine the best classification accuracy. The proposed approach for stress detection is the preliminary study that achieved an accuracy of 99.25% using the VGGish-CNN model on the SAM 40 dataset. Next, k-fold cross validation is performed to verify the efficiency of the VGGish-CNN model. This study can advance the application of brain–computer interface (BCI) and its use to identify patterns in EEG data that invoke stress-related inferences to aid in the diagnosis of mental disorders. In the future, investigation of human stress using EEG data will be useful in neurorehabilitation.

Abstract Background Drug combination treatments have proven to be a realistic technique for treating challenging diseases such as cancer by enhancing efficacy and mitigating side effects. To achieve the therapeutic goals of these combinations, it is essential to employ multi-targeted drug combinations, which maximize effectiveness and synergistic effects. Results This paper proposes ‘MultiComb’, a multi-task deep learning (MTDL) model designed to simultaneously predict the synergy and sensitivity of drug combinations. The model utilizes a graph convolution network to represent the Simplified Molecular-Input Line-Entry (SMILES) of two drugs, generating their respective features. Also, three fully connected subnetworks extract features of the cancer cell line. These drug and cell line features are then concatenated and processed through an attention mechanism, which outputs two optimized feature representations for the target tasks. The cross-stitch model learns the relationship between these tasks. At last, each learned task feature is fed into fully connected subnetworks to predict the synergy and sensitivity scores. The proposed model is validated using the O’Neil benchmark dataset, which includes 38 unique drugs combined to form 17,901 drug combination pairs and tested across 37 unique cancer cells. The model’s performance is tested using some metrics like mean square error ( $$MSE$$ MSE ), mean absolute error ( $$MAE$$ MAE ), coefficient of determination ( $${R}^{2}$$ R 2 ), Spearman, and Pearson scores. The mean synergy scores of the proposed model are 232.37, 9.59, 0.57, 0.76, and 0.73 for the previous metrics, respectively. Also, the values for mean sensitivity scores are 15.59, 2.74, 0.90, 0.95, and 0.95, respectively. Conclusion This paper proposes an MTDL model to predict synergy and sensitivity scores for drug combinations targeting specific cancer cell lines. The MTDL model demonstrates superior performance compared to existing approaches, providing better results.

Abstract There is a growing interest in multidisciplinary research in multimodal synthesis technology to stimulate diversity of modal interpretation in different application contexts. The real requirement for modality diversity across multiple contextual representation fields is due to the conflicting nature of data in multitarget sensors, which introduces other obstacles including ambiguity, uncertainty, imbalance, and redundancy in multiobject classification. This paper proposes a new adaptive and late multimodal fusion framework using evidence-enhanced deep learning guided by Dempster–Shafer theory and concatenation strategy to interpret multiple modalities and contextual representations that achieves a bigger number of features for interpreting unstructured multimodality types based on late fusion. Furthermore, it is designed based on a multifusion learning solution to solve the modality and context-based fusion that leads to improving decisions. It creates a fully automated selective deep neural network and constructs an adaptive fusion model for all modalities based on the input type. The proposed framework is implemented based on five layers which are a software-defined fusion layer, a preprocessing layer, a dynamic classification layer, an adaptive fusion layer, and an evaluation layer. The framework is formalizing the modality/context-based problem into an adaptive multifusion framework based on a late fusion level. The particle swarm optimization was used in multiple smart context systems to improve the final classification layer with the best optimal parameters that tracing 30 changes in hyperparameters of deep learning training models. This paper applies multiple experimental with multimodalities inputs in multicontext to show the behaviors the proposed multifusion framework. Experimental results on four challenging datasets including military, agricultural, COIVD-19, and food health data provide impressive results compared to other state-of-the-art multiple fusion models. The main strengths of proposed adaptive fusion framework can classify multiobjects with reduced features automatically and solves the fused data ambiguity and inconsistent data. In addition, it can increase the certainty and reduce the redundancy data with improving the unbalancing data. The experimental results of multimodalities experiment in multicontext using the proposed multimodal fusion framework achieve 98.45% of accuracy.

Abstract This paper presents an accurate model for predicting different payload weights from 3DR SOLO drone acoustic emission. The dataset consists of eleven different payload weights, ranging from 0 to 500 g with a 50 g increment. Initially, the dataset's drone sounds are broken up into 34 frames, each frame was about 5 s. Then, Mel-spectrogram and VGGish model are employed for feature extraction from these sound signals. CNN network is utilized for classification, and during the training phase, the network's weights are iteratively updated using the Adam optimization algorithm. Finally, two experiments are performed to evaluate the model. The first experiment is performed utilizing the original data (before augmentation), while the second used the augmented data. Different payload weights are identified with a potential accuracy of 99.98%, sensitivity of 99.98%, and specificity of 100% based on experimental results. Moreover, a comprehensive comparison with prior works that utilized the same dataset validates the superiority of the proposed model.

Abstract Sustainable Development Goal 7 is dedicated to ensuring access to clean and affordable energy that can be utilized in various applications. Solar panels (SP) are utilized to convert sunlight into electricity, acting as a renewable energy source. It is important to keep SP clean to obtain the required performance, as the accumulation of snow and dust on SP greatly affects the amount of electricity generated. On the other hand, excessive cleaning has some detrimental effects on the SP, therefore cleaning should only be done when necessary and not on a regular basis. Consequently, it is critical to determine whether the cleaning procedure is necessary by automatically detecting the presence of dust or snow on the panels while avoiding inaccurate predictions. Research efforts have been made to detect the presence of dust and snow on SP, but most of the proposed methods do not guarantee accurate detection results. This paper proposes an accurate, reliable, and interpretable approach called Solar-OBNet. The proposed Solar-OBNet can detect dusty SP and snow-covered SP very efficiently and be used in conjunction with the methods used to clean SP. The proposed Solar-OBNet is based on a Bayesian convolutional neural network, which enables it to express the amount of confidence in its predictions. Two measurements are used to estimate the uncertainty in the outcomes of the proposed Solar-OBNet, namely predictive entropy and standard deviation. The proposed Solar-OBNet can express confidence in the correct predictions by showing low values for predictive entropy and standard deviation. The proposed Solar-OBNet can also give an uncertainty warning in the case of erroneous predictions by showing high values of predictive entropy and standard deviation. The proposed Solar-OBNet’s efficacy was verified by interpreting its results using a method called Weighted Gradient-Directed Class Activation Mapping (Grad-CAM). The proposed Solar-OBNet has achieved a balanced accuracy of 94.07% and an average specificity 95.83%, outperforming other comparable methods.

Abstract Discovering the most effective anti-SARS-CoV-2 drugs is the optimal solution to get back to a normal life without COVID-19. Drug repurposing, also known as drug repositioning, has become one of the most important solutions for developing new COVID-19 drugs. However, this alternative requires long-term laboratory experiments to reach the optimal drug that involves the best combination of drug features to resist the COVID-19 virus. In response to this challenge, the COVID-19 drug repurposing (C19-DR) model based on pigeon-inspired optimizer (PIO) and rough sets theory (RST) is proposed. The proposed model presents a new rough set-based feature selection technique that uses a pigeon-inspired optimizer algorithm to find and validate the optimal reduct of drug features to design an effective COVID-19 drug. Moreover, the proposed model can investigate the efficiency of multiple medications against the COVID-19 virus based on the half-maximal inhibitory concentration (IC50) threshold. The effectiveness of the proposed COVID-19 drug repurposing model has been validated using a laboratory drug dataset consisting of 60 medications. The practical results show that the optimized rough set reduct of {hydrogen bonding acceptor (HBA) and number of chiral centers} is the most significant reduct that can be used to design an effective COVID-19 drug. Moreover, the proposed drug design model could verify the efficiency of a selected dataset of drug models based on evaluating the IC50 metric. The verification results proved the high effectiveness of the proposed model in evaluating the predicted IC50 with an accuracy of 91.4% and MSE of 0.034. These findings might be a promising solution that can assist researchers in developing and repurposing novel medications to treat COVID-19 and its new viral mutants.

Abstract Over the years, machine learning (ML) algorithms have proven their ability to make reliable predictions of the toxicity of metal oxide nanoparticles. This paper proposed a predictive ML model of the potential toxicity of metal oxide nanoparticles. A dataset consisting of 79 descriptors including 24 metal oxide nanoparticles (MexOy NPs) and their physicochemical and structural characteristics is adopted. The proposed model comprises of three main phases. The first phase is used to analyze the characteristics of nanoparticles along with their toxicity behavior. In the second phase, the problems associated with the metal oxide nanoparticles dataset are tackled. The first problem namely the class imbalance problem is handled through utilizing synthetic minority over-sampling technique (SMOTE). The second problem namely the outliers is handled through applying a novel feature selection algorithm based on the enhanced binary version of the sine tree-seed algorithm (EBSTSA). The proposed EBSTSA is used to find the relevant features affecting toxicity. The density-based spatial clustering of applications with noise (DBSCAN) is utilized as a tool for identifying outliers in the dataset and for visualizing the impact of the feature selection on the performance of the subsequent classification. Finally, in the third phase, the support vector machine (SVM) supervised machine learning algorithm and k-fold cross-validation method are applied to classify the mode of action of each instance of nanoparticle as toxic or nontoxic. The simulation results showed that the EBSTSA-based feature selection algorithm is reliable and robust across 23 benchmark datasets from the UCI machine learning repository. The results also showed that proposed EBSTSA can effectively find the relevant descriptors for nano-particles. Furthermore, the results demonstrated the efficacy of the proposed ML toxicity prediction model. It is obtained on average 1.02% of error rate, 100% of specificity, 98.87% of sensitivity, and 99.47% of f1-score.

Abstract This paper proposed a novel approach for detecting lung sound disorders using deep learning feature fusion. The lung sound dataset are oversampled and converted into spectrogram images. Then, extracting deep features from CNN architectures, which are pre-trained on large-scale image datasets. These deep features capture rich representations of spectrogram images from the input signals, allowing for a comprehensive analysis of lung disorders. Next, a fusion technique is employed to combine the extracted features from multiple CNN architectures totlaly 8064 feature. This fusion process enhances the discriminative power of the features, facilitating more accurate and robust detection of lung disorders. To further improve the detection performance, an improved CNN Architecture is employed. To evaluate the effectiveness of the proposed approach, an experiments conducted on a large dataset of lung disorder signals. The results demonstrate that the deep feature fusion from different CNN architectures, combined with different CNN Layers, achieves superior performance in lung disorder detection. Compared to individual CNN architectures, the proposed approach achieves higher accuracy, sensitivity, and specificity, effectively reducing false negatives and false positives. The proposed model achieves 96.03% accuracy, 96.53% Sensitivity, 99.424% specificity, 96.52% precision, and 96.50% F1 Score when predicting lung diseases from sound files. This approach has the potential to assist healthcare professionals in the early detection and diagnosis of lung disorders, ultimately leading to improved patient outcomes and enhanced healthcare practices.

Abstract Recently, the population density in cities has increased at a higher pace, so waste generation is on the rise in most societies due to population growth. Given this concern, it would be highly important to manage waste generation. Intelligent city planning is necessary to improve the quality of city life and make cities more livable. This paper presents an intelligent waste management system (IWMS) in smart cities based on Internet of Things components like sensors, detectors, and actuators. IWMS contains three main phases. The first phase of the system is to adapt the low energy adaptive clustering hierarchy approach as an optimization process to better balance the energy consumption of smart waste bins (SBs), thus leading to extending the life of the smart waste network. The second phase is handling the missing values which are retrieved from SBs using an improved version of the k-nearest neighbor algorithm based on artificial hummingbird optimization (AHA), while the third phase presents an optimal energy-efficient route process for the routing of waste trucks that improves fuel efficiency and reduces the time to get an appropriate SB. According to the experimental results, the proposed system has achieved energy savings of 34% for the smart waste bin network. Moreover, compared to other systems, it has a lower mean error rate when generating missing values, and the results related to convergence and running time validate its superiority compared with other metaheuristic algorithms.

Abstract Acute lymphocytic leukemia (ALL) is a common serious cancer in white blood cells (WBC) that advances quickly and produces abnormal cells in the bone marrow. Cancerous cells associated with ALL lead to impairment of body systems. Microscopic examination of ALL in a blood sample is applied manually by hematologists with many defects. Computer-aided leukemia image detection is used to avoid human visual recognition and to provide a more accurate diagnosis. This paper employs the ensemble strategy to detect ALL cells versus normal WBCs using three stages automatically. Firstly, image pre-processing is applied to handle the unbalanced database through the oversampling process. Secondly, deep spatial features are generated using a convolution neural network (CNN). At the same time, the gated recurrent unit (GRU)-bidirectional long short-term memory (BiLSTM) architecture is utilized to extract long-distance dependent information features or temporal features to obtain active feature learning. Thirdly, a softmax function and the multiclass support vector machine (MSVM) classifier are used for the classification mission. The proposed strategy has the resilience to classify the C-NMC 2019 database into two categories by using splitting the entire dataset into 90% as training and 10% as testing datasets. The main motivation of this paper is the novelty of the proposed framework for the purposeful and accurate diagnosis of ALL images. The proposed CNN-GRU-BiLSTM-MSVM is simply stacked by existing tools. However, the empirical results on C-NMC 2019 database show that the proposed framework is useful to the ALL image recognition problem compared to previous works. The DenseNet-201 model yielded an F1-score of 96.23% and an accuracy of 96.29% using the MSVM classifier in the test dataset. The findings exhibited that the proposed strategy can be employed as a complementary diagnostic tool for ALL cells. Further, this proposed strategy will encourage researchers to augment the rare database, such as blood microscopic images by creating powerful applications in terms of combining machine learning with deep learning algorithms.

Abstract Nature-inspired optimization techniques have been applied in various fields of study to solve optimization problems. Since designing a Fuzzy System (FS) can be considered one of the most complex optimization problems, many meta-heuristic optimizations have been developed to design FS structures. This paper aims to design a Takagi–Sugeno–Kang fuzzy Systems (TSK-FS) structure by generating the required fuzzy rules and selecting the most influential parameters for these rules. In this context, a new hybrid nature-inspired algorithm is proposed, namely Genetic–Grey Wolf Optimization (GGWO) algorithm, to optimize TSK-FSs. In GGWO, a hybridization of the genetic algorithm (GA) and the grey wolf optimizer (GWO) is applied to overcome the premature convergence and poor solution exploitation of the standard GWO. Using genetic crossover and mutation operators accelerates the exploration process and efficiently reaches the best solution (rule generation) within a reasonable time. The proposed GGWO is tested on several benchmark functions compared with other nature-inspired optimization algorithms. The result of simulations applied to the fuzzy control of nonlinear plants shows the superiority of GGWO in designing TSK-FSs with high accuracy compared with different optimization algorithms in terms of Root Mean Squared Error (RMSE) and computational time.

Abstract Social computing is a disruptive technology that is changing how we do business and building the enterprises. Mining consumer insights and consumer segmentation are the new drivers that are likely to be the cornerstone of developing new service innovations and social interactions. This special issue was designed to stimulate research on the data mining tools and methods for social computing. In this editorial, we are outlining the broad research framework on social computing, including the papers in this special issue and areas for future research.

This article introduces a new hybrid technique for predicting solar activity. The proposed hybrid technique consists of four phases. The first phase is feature selection, where the most relevant features (variables) are selected based on an artificial bee colony using a neighborhood rough set as the fitness function. The second phase employs the training support vector regression (SVR) using a part of the solar activity data set based on the selected features. Sequential minimal optimization is carried out to determine the optimal parameters of SVR. The third phase tests the regression model based on the second part of the data set. The fourth phase is the process of predicting solar activity. According to the prediction system, the maximum amplitude of cycle 25 is 80 ± 12, and it will occur in 2026. The solution quality is assessed by using three indices called average absolute percent relative error (AAPRE), root-mean-square error (RMSE), and coefficient of determination. These indices prove the high quality of the forecast sunspot number value and the actual ones. In addition, it can be concluded that the proposed system is significantly improved the predicting solar activity performance at acceptable assessment indices.

SCADA (Supervisory Control and Data Acquisition) networks are built to efficiently provide supervisory and control of national and international critical infrastructures. SCADA networks represent a challenging domain for forensic investigators who have the responsibility to discover the main causes of the catastrophic incidents that could happen in these critical mission systems and provide precise and logical evidences supported with comprehensive technical reports to the legal organizations. They urgently need technological tools and frameworks that enable them to effectively do their mission without affecting the running state of SCADA networks which must be sustainable and robust against technical and disruptive incidents. This paper discusses the challenges and opportunities towards achieving that goal and highlights the emerging technological approaches and paradigms that can be considered as promising for the realization of such a framework taking into account the efficient consumption of computational resources. Further, this paper proposes a conceptual framework for automated and secure forensic investigation in modern complex SCADA networks accompanied with a possible realization architecture based on the Multi-Agent Systems (MAS) and Wireless Sensor Networks (WSN) promising technological paradigms. The proposed framework is intentionally designed to be compliant with the currently active motivation towards promoting green computing requirements.

Multi-verse optimization algorithm (MVO) is one of the recent meta-heuristic optimization algorithms. The main inspiration of this algorithm came from multi-verse theory in physics. However, MVO like most optimization algorithms suffers from low convergence rate and entrapment in local optima. In this paper, a new chaotic multi-verse optimization algorithm (CMVO) is proposed to overcome these problems. The proposed CMVO is applied on 13 benchmark functions and 7 well-known design problems in the engineering and mechanical field; namely, three-bar trust, speed reduce design, pressure vessel problem, spring design, welded beam, rolling element-bearing and multiple disc clutch brake. In the current study, a modified feasible-based mechanism is employed to handle constraints. In this mechanism, four rules were used to handle the specific constraint problem through maintaining a balance between feasible and infeasible solutions. Moreover, 10 well-known chaotic maps are used to improve the performance of MVO. The experimental results showed that CMVO outperforms other meta-heuristic optimization algorithms on most of the optimization problems. Also, the results reveal that sine chaotic map is the most appropriate map to significantly boost MVO’s performance.

There are many difficulties facing a handwritten Arabic recognition system such as unlimited variation in human handwriting, similarities of distinct character shapes, interconnections of neighboring characters and their position in the word. This paper presents a handwritten Arabic character recognition system based on BA algorithm. BA algorithm is adopted to reduce the feature set size and to improve the accuracy rate. The proposed system is trained and tested by four well-known classifiers; Bayes Network (BN), artificial neural network (ANN), K-nearest neighbors (KNN), and Random forest (RF) with CENPARMI dataset. The proposed optimization algorithm obtained promising results in terms of classification accuracy as the proposed system is able to recognize 91.59 % of our test set correctly, as well as in terms of computational time reduction. BA algorithm is more efficient in most experiments when comparing with GA and PSO. When compared our results with other related works we find that our result is the highest among other published results.

The security of data over the internet is a crucial thing specially if this data is personal or confidential.The transmitted data can be intercepted during its journey from device to another.For that reason, we are willing to develop a simple method to secure data.Data encryption is one method to secure the messages but the intruders can still try to crack it, in order to overcome this, steganography has been used to hide the data into a cover media(i.e.audio, image or video).Recently steganography attracts many researchers as a hot topic.This paper proposes an advanced technique for encrypting data using Advanced Encryption System (AES) and hiding the data using Haar Discreet Wavelet Transform (HDWT).HDWT aims to decrease the complexity in image steganology while providing less image distortion and lesser detectability.One-forth of the image carrying the details of the image in a region and other three regions carrying a less details of the image then the cipher text is concealed at most two Least Significant Bits (LSB) positions in the less detailed regions of the carrier image, if the message doesn't fit in the first LSB only it will use the second LSB.This proposed algorithm covers almost all type of symbols and alphabets.

This paper presents an approach based on Artificial Bee Colony (ABC) to optimize the parameters of membership functions of Sugeno based Adaptive Neuro-Fuzzy Inference System (ANFIS).The optimization is achieved by Artificial Bee Colony (ABC) for the sake of achieving minimum Root Mean Square Error of ANFIS structure.The proposed ANFIS-ABC model is used to build a system for predicting the wind speed.To ensure the accuracy of the model, a different number of membership functions has been used.The experimental results indicates that the best accuracy achieved is 98% with ten membership functions and least value of RMSE which is 0.39.

Nowadays, a dataset includes a huge number of features with irrelevant and redundant ones. Feature selection is required for a better machine-learning algorithms' performance. A system for feature selection is proposed in this work using a sine cosine algorithm (SCA). SCA is a new stochastic search algorithm for optimization problems. SCA optimization adaptively balances the exploration and exploitation to find the optimal solution quickly. The SCA can quickly explore the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporates both classification accuracy and feature size reduction. The proposed system was tested on 18 datasets and shows an advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators.

This research proposes cultivation-time recommender system for predicting the best sowing dates for winter cereal crops in the newly reclaimed lands in Farafra Oasis, The Egyptian Western Desert. The main goal of the proposed system is to support the best utilization of farm resources. In this research, predicting the best sowing dates for the aimed crops is based on weather conditions prediction along with calculating the seasonal accumulative growing degree days (GDD) fulfillment duration for each crop. Various Machine Learning (ML) regression algorithms have been used for predicting the daily minimum and maximum air temperature based on historical weather conditions data for twenty-five growing seasons (1990/91 to 2014/15). Experimental results showed that using the M5P and IBk ML regression algorithms have outperformed the other implemented regression algorithms for predicting the daily minimum and maximum air temperature based on historical weather conditions data. That has been measured based on the calculated mean absolute error (MAE). Also, obtained experimental results obviously indicated that the best cultivation-time prediction by the proposed recommender system has been achieved by the M5P algorithm, based on the seasonal accumulative GDD fulfillment duration, for the coming five growing seasons (2016/17 to 2019/20).

Commonly, attributes in data sets are originally correlated, noisy and redundant. Thus, attribute reduction is a challenging task as it substantially affects the overall classification accuracy. In this research, a system for attribute reduction was proposed using correlation-based filter model for attribute reduction. The cuckoo search (CS) optimization algorithm was utilized to search the attribute space with minimum correlation among selected attributes. Then, the initially selected solutions, guaranteed to have minor correlation, are candidates for further improvement towards the classification accuracy fitness function. The performance of the proposed system has been tested via implementing it using various data sets. Also, its performance have has been compared against other common attribute reduction algorithms. Experimental results showed that the proposed multi-objective CS system has outperformed the typical single-objective CS optimizer as well as outperforming both the particle swarm optimization (PSO) and genetic algorithm (GA) optimization algorithms.

Optimal search is a major challenge for wrapper-based attribute reduction. Rough sets have been used with much success, but current hill-climbing rough set approaches to attribute reduction are insufficient for finding optimal solutions. In this paper, we propose an innovative use of an intelligent optimisation method, namely the flower search algorithm (FSA), with rough sets for attribute reduction. FSA is a relatively recent computational intelligence algorithm, which is inspired by the pollination process of flowers. For many applications, the attribute space, besides being very large, is also rough with many different local minima which makes it difficult to converge towards an optimal solution. FSA can adaptively search the attribute space for optimal attribute combinations that maximise a given fitness function, with the fitness function used in our work being rough set-based classification. Experimental results on various benchmark datasets from the UCI repository confirm our technique to perform well in comparison with competing methods.

Preserving the content of historic handwritten manuscripts is important for a variety of reasons. On the other hand, digital libraries are rapidly expanding and thus facilitate to store this information directly in digital form. For digitising text documents, a crucial step is to binarise the captured images to separate the text from the background. In this paper, we propose an effective approach for binarisation of handwritten Arabic manuscripts which employs a whale optimisation algorithm, incorporating a fuzzy c-means objective function, to obtain optimal thresholds. Experimental results confirm the effectiveness of the proposed approach compared to earlier methods.

Investigating information credibility is an important problem in online social networks such as Twitter. Since misleading information can get easily propagated in Twitter, ranking tweets according to their credibility can help to detect rumors and identify misinformation. In this paper, we propose a Petri net model to visualise tweet credibility in Twitter. We consider the uniform resource locator (URL) as an effective feature in evaluating tweet credibility since it is used to identify the source of tweets, especially for newsworthy tweets. We perform an experimental evaluation on about 1000 tweets, and show that the proposed model is effective for assigning tweets to two classes: credible and incredible tweets, which each class being further divided into two sub-classes (“credible” and “seem credible” and “doubtful” and “incredible” tweets, respectively) based on appropriate features.

Breast cancer is one from various diseases that has got great attention in the last decades.This due to the number of women who died because of this disease.Segmentation is always an important step in developing a CAD system.This paper proposed an automatic segmentation method for the Region of Interest (ROI) from breast thermograms.This method is based on the data acquisition protocol parameter (the distance from the patient to the camera) and the image statistics of DMR-IR database.To evaluated the results of this method, an approach for the detection of breast abnormalities of thermograms was also proposed.Statistical and texture features from the segmented ROI were extracted and the SVM with its kernel function was used to detect the normal and abnormal breasts based on these features.The experimental results, using the benchmark database, DMR-IR, shown that the classification accuracy reached (100%).Also, using the measurements of the recall and the precision, the classification results reached 100%.This means that the proposed segmentation method is a promising technique for extracting the ROI of breast thermograms.

Attribute reduction approach is proposed in this paper based on a modified version of the flower pollination algorithm optimization (FPA). Flower pollination algorithm (FPA) is one of recently evolutionary computation technique, inspired by the pollination process of flowers. The modified FPA algorithm adaptively balance the exploration and exploitation to quickly find the optimal solution through using local searching with adaptive search diversity. The modified FPA can quickly search the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporate both classification accuracy and feature reduction size. The proposed system is applied on a eight dataset from the UCI machine learning data sets and proves a good performance in comparison with the genetic algorithm (GA) and particle swarm optimization (PSO), that commonly used in this context.

The automated segmentation of the liver area is an essential phase in liver diagnosis from medical images. In this paper, we propose an artificial bee colony (ABC) optimisation algorithm that is used as a clustering technique to segment the liver in CT images. In our algorithm, ABC calculates the centroids of clusters in the image together with the region corresponding to each cluster. Using mathematical morphological operations, we then remove small and thin regions, which may represents flesh regions around the liver area, sharp edges of organs or small lesions inside the liver. The extracted regions are integrated to give an initial estimate of the liver area. In a final step, this is further enhanced using a region growing approach. In our experiments, we employed a set of 38 images, taken in pre-contrast phase, and the similarity index calculated to judge the performance of our proposed approach. This experimental evaluation confirmed our approach to afford a very good segmentation accuracy of 93.73% on the test dataset.

Data sets ordinarily includes a huge number of attributes, with irrelevant and redundant attributes. Redundant and irrelevant attributes might minimize the classification accuracy because of the huge search space. The main goal of attribute reduction is choose a subset of relevant attributes from a huge number of available attributes to obtain comparable or even better classification accuracy than using all attributes. A system for feature selection is proposed in this paper using a modified version of the wolf search algorithm optimization. WSA is a bio-inspired heuristic optimization algorithm that imitates the way wolves search for food and survive by avoiding their enemies. The WSA can quickly search the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporate both classification accuracy and feature reduction size. The proposed system is applied on a set of the UCI machine learning data sets and proves good performance in comparison with the GA and PSO optimizers commonly used in this context.

In this paper, we propose a fuzzy-based multi-spectral (MS) and panchromatic (PAN) image fusion approach which provides a tradeoff solution between spectral and spatial fidelity and is able to preserve more detail in terms of spectral and spatial information. First, we perform principal component analysis on the multi-spectral images and utilise the first principal component to extract matched low and high frequency coefficients. We then apply fuzzy-based image fusion rules to fuse the first principal component with the PAN image, followed by fusing the approximation coefficients. The proposed approach is tested on several satellite images and shown to provide a feasible and effective approach.

This paper proposes a Fuzzy C-Means (FCM) based approach designed for melanoma diagnosis. The methodology comprises the traditional data processing architecture, including pre-processing (contrast stretching), main processing (FCM) and post-processing (morphological erosion). The contrast stretching phase has the purpose of stretching the range of pixel intensities of the input image to occupy a larger dynamic range in the output image. This is followed by the FCM algorithm, which automatically divides the data provided by the contrast stretching phase into two clusters: lesion and skin. This process ends with the morphological erosion of the segmented image, where the structuring element is translated over each pixel of the object, so as to overcome typical irregularities between lesion and skin (e.g., irregular boundaries, dark hair covering the lesions, specular reflections, among others). The proposed approach is evaluated in dermatoscopic images of skin cancer, and results show that it is able to produce accurate identification of lesions.

The road surface condition information is very useful for the safety of road users and to inform road administrators for conducting appropriate maintenance. Roughness features of road surface; such as speed bumps and potholes, have bad effects on road users and their vehicles. Usually speed bumps are used to slow motor-vehicle traffic in specific areas in order to increase safety conditions. On the other hand driving over speed bumps at high speeds could cause accidents or be the reason for spinal injury. Therefore informing road users of the position of speed bumps through their journey on the road especially at night or when lighting is poor would be a valuable feature. This paper exploits a mobile sensor computing framework to monitor and assess road surface conditions. The framework measures the changes in the gravity orientation through a gyroscope and the shifts in the accelerometer's indications, both as an assessment for the existence of speed bumps. The proposed classification approach used the theory of rough mereology to rank the modified data in order to make a useful recommendation to road users.

Falls represent a major cause of fatal injury, especially for the elderly, which accordingly create a serious obstruction for their independent living. Many efforts have been put towards providing a robust method to detect falls accurately and timely. This paper proposes an alerting system for detecting falls of the elderly people that monitors seniors via detecting the elderly faces and their bodies in order to generate an alert on falling detection. The proposed system consists of three phases that are pre-processing, feature extraction, and detecting phases. The integral image-based approach for multi-scale feature extraction developed to characterize the distinctive and robust patterns of different face poses. The histogram of oriented gradient (HOG) of extracted feature is then computed. The experiments were done on the datasets which consists of 191 recorded videos annotated human images with a large range of pose variations and backgrounds. The design of the fall detection system can increase the living time and reduce the rate of death due to the fall and shows the promising performance of the proposed system.

The aim of this paper is to develop an effective classification approach based on Random Forest (RF) algorithm. Three fruits; i.e., apples, Strawberry, and oranges were analysed and several features were extracted based on the fruits' shape, colour characteristics as well as Scale Invariant Feature Transform (SIFT). A preprocessing stages using image processing to prepare the fruit images dataset to reduce their color index is presented. The fruit image features is then extracted. Finally, the fruit classification process is adopted using random forests (RF), which is a recently developed machine learning algorithm. A regular digital camera was used to acquire the images, and all manipulations were performed in a MATLAB environment. Experiments were tested and evaluated using a series of experiments with 178 fruit images. It shows that Random Forest (RF) based algorithm provides better accuracy compared to the other well know machine learning techniques such as K-Nearest Neighborhood (K-NN) and Support Vector Machine (SVM) algorithms. Moreover, the system is capable of automatically recognize the fruit name with a high degree of accuracy.

Accurate segmentation of retinal blood vessels is an important task in computer aided diagnosis of retinopathy. In this paper, we propose an automated retinal blood vessel segmentation approach based on artificial bee colony optimisation in conjunction with fuzzy c-means clustering. Artificial bee colony optimisation is applied as a global search method to find cluster centers of the fuzzy c-means objective function. Vessels with small diameters appear distorted and hence cannot be correctly segmented at the first segmentation level due to confusion with nearby pixels. We employ a pattern search approach to optimisation in order to localise small vessels with a different fitness function. The proposed algorithm is tested on the publicly available DRIVE and STARE retinal image databases and confirmed to deliver performance that is comparable with state-of-the-art techniques in terms of accuracy, sensitivity and specificity.

Automated analysis of retinal vessels is essential for the diagnosis of a wide range of eye diseases and plays an important role in automatic retinal disease screening systems. In this paper, we present an approach to automatic vessel segmentation in retinal images that utilises possibilistic fuzzy c-means (PFCM) clustering to overcome the problems of the conventional fuzzy c-means objective function. In order to obtain optimised clustering results using PFCM, a cuckoo search method is used. The cuckoo search algorithm, which is based on the brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies, is applied to drive the optimisation of the fuzzy clustering. The performance of our algorithm is analysed on two benchmark databases, the DRIVE and STARE datasets, and encouraging segmentation performance is observed.

This paper presents an approach for underdetermined blind source separation in the case of additive Gaussian white noise and pink noise. Likewise, the proposed approach is applicable in the case of separating I + 3 sources from I mixtures with additive two kinds of noises. This situation is more challenging and suitable to practical real world problems. Moreover, unlike to some conventional approaches, the sparsity conditions are not imposed. Firstly, the mixing matrix is estimated based on an algorithm that combines short time Fourier transform and rough-fuzzy clustering. Then, the mixed signals are normalized and the source signals are recovered using modified Gradient descent Local Hierarchical Alternating Least Squares Algorithm exploiting the mixing matrix obtained from the previous step as an input and initialized by multiplicative algorithm for matrix factorization based on alpha divergence. The experiments and simulation results show that the proposed approach can separate I + 3 source signals from I mixed signals, and it has superior evaluation performance compared to some conventional approaches.

This paper proposes two improvements of the ant clustering-based segmentation; first by applying new heuristic function of the ant colony system based on probability theory. Second by selecting any one of new two global update mechanisms of the ant colony system. These improvements evaluation is performed in the domain of automatic retinal blood vessels segmentation. The automatic segmentation of blood vessels in retinal images is vital process for early diagnosis and treatment of many diseases especially diabetic retinopathy since it's the main cause of blindness over the world. The improved ant colony system based segmentation algorithm is tested on publicity available STARE database of retinal images in terms of the sensitivity, specificity and accuracy. The experimental results obtained, show that the proposed improvements are promising.

The fuzzy Euclidean distance clustering algorithm has been well studied and used in information retrieval society for clustering documents. However, the fuzzy logic algorithm poses problems in dealing with large amount of data. In this paper we proposed results for clustering theses documents based on Euclidean distances and cluster-dependent keyword weighting. The proposed approach is based on the Fuzzy Euclidean distance clustering algorithm. The cluster dependent keyword weighting help in partitioning and categorizing the theses documents into more meaningful categories.

Intrusion detection systems (IDS) are well-known research area for the detection of anomalous activities in a system from both inside and outside intruders. In this article, a multi-layer hybrid machine learning intrusion detection system is designed and developed to achieve high efficiency and improve the detection and classification rate accuracy inspired by immune systems with negative selection approach. In the first layer, principal component analysis (PCA) algorithm was used for feature selection. Then, genetic algorithm was applied to generate anomaly detectors, which are able to discriminate between normal and anomalous behaviors in the second layer. It is followed by applying classification using several classifiers including naive bayes, multilayer perceptron neural network, and decision trees to increase the detection accuracy and obtain more information on the detected anomalies. The selected classifiers are trained and applied to label the detected anomalies in both the normal and anomalous traffic. The principle interest of this work is to benchmark the performance of the proposed multi-layer IDS system by using NSL-KDD benchmark data set used by IDS researchers. The obtained results demonstrated that naive bayes classifier has better classification accuracy in the case of lower presented attacks such as U2R and R2L, while the J48 decision tree classifier gives high accuracy up to 82% for DoS attacks and 65.4% for probe attacks in the anomaly traffic.

For every business, where the product escalation of an organization depends on proper distribution of product and services in a region, analyzing customer behavior over time in that region can produce useful results. In this article, we strive to develop a system, which when provided past customer records will predict the forthcoming customer behavior. The proposed system is specially designed to find patterns and predict changes in customer behavior for mobile service providers. The aggregation of comparison and referencing of behavioral patterns, both on off-line and on-line, represents a tremendous opportunity for understanding modeling past behaviors and predicting future behaviors. Several aspects of business and real life trends, both on and off the Web, modified over time from such observable changes, as the augmentation and evolution of contents, to more quasi yet important dynamics. The research effort in this article aimed at exploring the temporal dynamics of consumer behavior, investigating how the behavior can deploy, and predict changes of raised concern about a particular service or brand. Specifically, the informational goals behind the queries, and the search results, will finally affect the business while reducing rate of attrition of customer from particular legacy of product or services.

This article presents an intelligent automatic approach for galaxies images classification based on Artificial Neural Network (ANN) and moment-based features extraction algorithms. The proposed approach consists of three phases; namely, image denoising, feature extraction, and classification phases. For the denoising phase, noise pixels are removed from input images, then input galaxy image is normalized to a uniform scale and Hu seven invariant moment algorithm is applied to reduce the dimensionality of the feature space during the feature extraction phase. Finally, during the classification phase, Self-Organize Feature Maps (SOFMs) and Time Lag Recurrent Networks (TLRNs) algorithms are utilized for classifying the input galaxies images into one of four obtained source catalogue types. Experimental results showed that SOFMs provided better classification results than having TLRNs applied. It is also concluded that a small set of features is sufficient to classify galaxy images and provide a fast classification.

Commonly, aquatic experts use traditional methods such as casting nets or underwater human monitoring for detecting existence and quantities of different species of fish. However, the recent breakthrough in digital cameras and storage abilities, with consequent cost reduction, can be utilized for automatically observing different underwater species. This article introduces an automatic classification approach for the Nile Tilapia fish using support vector machines (SVMs) algorithm in conjunction with feature extraction techniques based on Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) algorithms. The core of this approach is to apply the feature extraction algorithms in order to describe local features extracted from a set of fish images. Then, the proposed approach classifies the fish images using a number of support vector machines classifiers to differentiate between fish species. Experimental results obtained show that the support vector machines algorithm outperformed other machine learning techniques, such as artificial neural networks (ANN) and k-nearest neighbor (k-NN) algorithms, in terms of the overall classification accuracy.

In this paper, a novel approach, called Repeated Reselling Permission based Multiple Reselling (RRP-MR), is proposed. This approach allows a consumer who has bought his license from another consumer to resell this license to a third consumer. This reselling process continues till the license is resold N-times. This multiple reselling process is achieved without compromising neither the content owner's rights nor the consumer's rights. In addition, the RRP-MR approach not only provide secure license reselling process but also fair license reselling between the two involved consumers. Also, the RRP-MR gives the buyer the power to stop the multiple resellings of the license at a given reselling process. Furthermore, it enables the buyer to detect any fake license reselling at the first step of the reselling process. The RRP-MR approach makes use of a License Issuer (LI), License Revocation List (LRL), digital token called Reselling Permission (RP) and a special contract signing protocol known as Reselling Deal Signing (RDS) protocol to achieve fair and secure reselling. An analysis of potential threats has shown that this approach has met its security objectives.

In this paper, Kekre's transform is proposed for protecting fingerprint template and a symmetric bio-hash function is used to improve the protection of the fingerprint biometric template. In the proposed approach; firstly the principal curve approach is used to extract the features from the fingerprint template. Then, we applied a transformation phase using Kekre's transform. It starts by calculating the row and the column mean vectors of fingerprint image to get Kekre transform feature vectors. Finally, we adapt the bio-hash function to calculate the translation, rotation, and the error rate to increase the security performance. Experiments using well know benchmark CASIA fingerprint-V5 data sets show that the obtained results proved that the kekre transform approach is more efficient compared to class distribution preserving (CDP) approach and the error rate is minimized by %20.

Community detection in complex networks has attracted a lot of attention in recent years. Communities play special roles in the structure-function relationship; therefore, detecting communities can be a way to identify substructures that could correspond to important functions. Social networks can be formalized by a statistical model in which interactions between actors are generated based on some assumptions. We adopt the idea and introduce a statistical model of the interactions between social network's actors, and we use Bayesian network (probabilistic graphical model) to show the relation between model variables. Through the use Expectation Maximization (EM) algorithm, we drive estimates for the model parameters and propose a community detection algorithm based on the EM estimates. The proposed algorithm works well with directed and undirected networks, and with weighted and un-weighted networks. The algorithm yields very promising results when applied to the community detection problem.

This article presents a recommender system based on rough mereology for the prediction of water quality in Louisiana, a state located in the southern region of the United States. The proposed system firstly maps the water dataset into a normalized dataset of potential of hydrogen (PH) levels prediction. Then, rough mereology and rough inclusion techniques are applied for clustering and classifying the normalized PH dataset into sets of granules with different radius. Voting by objects approach is subsequently applied in order to select the optimized granules. Finally, normalized rating matrix is acquired, then the predicted PH level will be recommended. The data, tested by the proposed recommender system, was collected from stations of the United states Environmental Protection Agency (EPA). The obtained results demonstrate the effectiveness and the reliability of the proposed recommender system. Based on the data resulted, the average PH level prediction in a certain time is characterized by a mean absolute error of 0.34. In addition, both experimentally resulted and actual dataset values existed in the healthy region of the PH level for drinking water, which is within the range 6.5 to 8.0 according to the World Health Organization (WHO) drinking water guidelines.

This article introduces an improved version of the ant-clustering approach for image segmentation. An application of breast cancer magnetic resonance breas imaging has been chosen and the improved ant-based clustering approach has been applied to see their ability and accuracy to isolate the region of interest in the MRI images. The aim of the proposed ant-based clustering is to identify target objects through an The experimental results obtained, show that the modified ant-based clustering is superior to the classical ant-based clustering and the overall accuracy offered by the improved approach confirm that the effectiveness and performance is 98% in average.

Chemical compounds have a biological activity on animal genes; Mutagenicity and Carcinogenicity are examples of this activity. Both activities are similar except that mutagen compounds induce a heritable change in cells, while carcinogen compounds induce an unregulated growth process in cells. The relation between Mutagenicity and Carcinogenicity is not proved quantitatively yet. In this article, the relation between both activities is discovered based on the machine learning methodology. Feature selection techniques are applied to provide a well defined analysis of the highest discriminating descriptors of the mutagenic or carcinogenic compounds. Molecular charge appears to be a discriminating factor for Carcinogenicity, while Mutagenicity is characterized more by the branching and aromaticity/aliphaticity of the compounds. Electronegativity and Lipophilicity appears to be common factors for both activities. Further analysis and visualization are applied based on rough set and formal concept analysis to check the correlation among these descriptors and the ranges required for each descriptor.

In this paper, we proposed the new system for Hajj event classification in diverse and realistic Hajj videos and image scenes is investigated based on machine learning techniques. This challenging but important subject has mostly been ignored in the past due to several problems one of which is the lack of realistic and annotated video datasets. The main contribution of this work is to address the limitation and investigate the use of video for automatic annotation of human event classification. The proposed system consist of three main phases. Firstly, preprocessing phase which apply shot boundary detection algorithm for Hajj videos. After that feature extraction phase applying sparse coding based on Scale Invariant Feature Transform (SIFT) features. Finally, the event classification phase by applying several machine learning techniques including the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forests (RF) classifiers. Experiments with real data sets revealed the significant performance advantage of the machine learning techniques over the scale invariant feature transform (SIFT) features selection method. Receiver operating characteristics (ROC) analysis is used to compare classifier performance.

Privacy and Security has become an increasingly serious ýproblem for any biometric systems. Template protection, ýwhich mainly prevents from data loss and hacking the stored ýtemplates, is one of the most important issues when considering ýprivacy and security. Cancelable biometrics approach Scheme ýhas been proposed to address this problem. On the other hand ýthe symmetric hash functions might be used to increase the ýbiometric security level by making it is hard to attack the ýtemplate. In this paper, a new hybrid approach based on ýcombining approaches based on transformation and ýcryptosystem on fingerprints is applied. The results are ýcompared to a pre-presented approach in [1] showing that the ýnew approach is more efficient as the security of biometric ýtemplate is increased meanwhile the error rate is minimized.

The scope of this research is to propose a method for determining pairs of corresponding points between medical images, the method is based on the implementation of Particle Swarm Optimization (PSO) used as a function optimizer and Mutual Information used as a similarity measure. Firstly, Landmarks (LMs) were chosen manually specific to Braces Pty Ltd cephalometric analysis and used Thin Plate Spline (TPS) to provide a geometric representation for the relative locations of corresponding landmarks. Secondly, Mutual Information used as a cost-function to determine the degree of similarity between two images. Finally, PSO is used to maximize mutual information function and to improve the correspondence between the LMs. Experimental results demonstrate that the algorithm yields significant improvement in the registration accuracy.

This paper presents and evaluates the performance of two well-known segmentation approaches that were applied on liver computed tomography (CT) images. The two approaches are K-means and normalized cuts. An experiment was applied on ten liver CT scan images, with reference segmentations, in order to test the performance of the two approaches. Experimental results were compared using an evaluation measure that highlights segmentation accuracy. Based on the obtained results in this study, it has been observed that K-means clustering algorithm outperformed normalized cuts segmentation algorithm for cases where region of interest depicts a closed shape, while, normalized cuts algorithm obtained better results with non-circular clusters. Moreover, for K-means clustering, different initial partitions can result in different final clusters.

This paper deals with two dimensional magnetic resonance imaging (MRI) sequence of brain slices which include many objects to identify and estimate the volume of the brain tumors. More than twenty five features based on shape, color and texture was extracted to obtain feature vector for each object to characterize the tumor and identify it. Experimental results show that the accuracy of the estimation of tissue volumes is very high.

This paper presents an automatic image annotation approach for region labeling. The proposed approach is based on multi-class k-nearest neighbor, K-means, and particle swarm optimization algorithms for feature weighting, in conjunction with normalized cuts based image segmentation technique. This hybrid approach refines the output of multi-class classification that is based on the usage of k-nearest neighbor classifier for automatically labeling image regions from different classes. Each input image is segmented using the normalized cuts segmentation algorithm in order to subsequently create a descriptor for each segment. Particle swarm optimization algorithm is employed as a search strategy to identify an optimal feature subset. Experimental results and comparative performance evaluation, for results obtained from the proposed particle swarm optimization based approach and another support vector machine based approach presented in previous work, demonstrate that the proposed particle swarm optimization based approach outperforms the support vector machine based one, regarding annotation accuracy, for the used dataset.

The objective of this paper is to evaluate a new combined approach intended for reliable CT liver image segmentation, to separate the liver from other organs, and segment the liver into a set of regions of interest (ROIs). The approach combines the level set with watershed approach used as post segmentation step to produce a reliable segmentation result. Features of first order statistics and grey-level cooccurrence matrix, are calculated and passed to an artificial neural network, to be trained and to classify infected regions. Filtering is used before the segmentation approach to enhance contrast, remove noise and emphasize certain features, as well as connecting ribs around the liver. To evaluate the performance of presented approach, we performed many tests on different CT liver images. The experimental results obtained, show that the overall accuracy offered by the proposed approach is 92.1% in segmenting CT liver images into set of regions even with noise, and 88.9% average accuracy for neural network classification.

The main purpose of this paper is to show the use of formal concept analysis (FCA) as data mining approach for mining the common hypermethylated genes between breast cancer subtypes, by extracting formal concepts which representing sets of significant hypermethylated genes for each breast cancer subtypes, then the formal context is built which leading to construct a concept lattice which is composed of formal concepts. This lattice can be used as knowledge discovery and knowledge representation therefore, becoming more interesting for the biologists.

The complexity of business and different variations of service providers inspire the sense of matching of the consumers with the most appropriate products and services. This specific attribute initiates the study of recommender systems, which analysis the patterns of user's interest in items or products and services to suggest personalized recommendations for all these verticals, and also to suit a user's taste and satisfaction. High quality recommendation demands perfect decision for classifying manifold options given to user against a particular query. Hence, research challenge remains that whether the decision taken is the optimal at the end of recommended options. In this paper coined Termite Colony Optimization (TCO) is proposed, which provides a decision making model, and it is used by termites to adjust their movement trajectories under the decision tree from web service portal. We strongly advocate that the emerging TCO could be better a choice to be used in recommender system and most importantly on a continuous data stream. The present approach is tested on a brand named as “Big Bazar” (Large Market) of India. Retail recommendation has continuous data and various constraints before achieving optimized suggestions. Empirical investigations demonstrate that Termite behavior and meta-heuristic approach is quite affin to offer optimized recommendations for specifi retail operation. The research also briefs about the potential benefi of such retail recommender model in reality.

Community detection in complex networks has attracted a lot of attention in recent years. Community detection can be viewed as an optimization problem, in which an objective function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. Many single-objective optimization techniques have been used to solve the problem however those approaches have its drawbacks since they try optimizing one objective function and this results to a solution with a particular community structure property. More recently researchers viewed the problem as a multi-objective optimization problem and many approaches have been proposed to solve it. However which objective functions could be used with each other is still under debated since many objective functions have been proposed over the past years and in somehow most of them are similar in definition. In this paper we use Genetic Algorithm (GA) as an effective optimization technique to solve the community detection problem as a single-objective and multi-objective problem, we use the most popular objectives proposed over the past years, and we show how those objective correlate with each other, and their performances when they are used in the single-objective Genetic Algorithm and the Multi-Objective Genetic Algorithm and the community structure properties they tend to produce.

Image segmentation is an essential process for most analysis tasks of medical images. That's because having good segmentation results is useful for both physicians and patients via providing important information for surgical planning and early disease detection. This paper aims at evaluating the performance of the K-means clustering algorithm. To achieve this, we applied the K-means approach on different medical images including liver CT and breast MRI images. Experimental results obtained show that the overall segmentation accuracy offered by the K-means approach is high compared to segmentation accuracy by the well-known normalized cuts segmentation approach.

Colour quantisation algorithms are essential for displaying true colour images using a limited palette of distinct colours. The choice of a good colour palette is crucial as it directly determines the quality of the resulting image. Colour quantisati

Fuzzy C-Means Clustering, FCM, is an iterative algorithm whose aim is to find the center or centroid of data clusters that minimize an assigned dissimilarity function. The degree of being in a certain cluster can be defined in terms of the distance to the cluster-centroid. The domain knowledge is used to formulate an appropriate measure. However the Euclidean distance is considered as a general measure for such value. The calculation of the Euclidean distance doesn't take into consideration the degree of relevance of each feature to the classification model. In this paper, scoring methods like ChiMerge and Mutual information are used in the FCM model to improve the calculation of the Euclidean distance. Experimental results demonstrate the better performances of the improved FCM on UCI benchmark data sets rather than the ordinary FCM, where the ordinary FCM uses in classification either all features or the most important features while the improved FCM uses all the features but the Euclidean Distance will be calculated according to the relevance degree of each feature.

This paper addresses the issue of the weak association between brain MRI intensity value and anatomical meaning of MR image pixels. By investigating the deformation on brain lateral ventricles and compression from tumor, the correlation between them is quantified and utilized. With the proposed feature extraction component, lateral ventricular deformation is transformed into an additional feature for brain tumor segmentation. Some comparative experiments using both supervised and unsupervised pattern recognition segmentation methods show the improved tumor segmentation accuracy in some image cases.

In this paper, we discuss the need for efficient approximate string matching. We present the well-known Aho-Corasick automaton for locating multiple patterns and discuss an approach for fuzzification of this automaton. Along with some motivational examples, we propose and illustrate a novel algorithm for automaton construction.

In this paper, we propose an associative watermarking scheme which is conducted by the concept of Association Mining Rules (AMRs) and the ideas of Vector Quantization (VQ) and Soble operator. Performing associative watermarking rules to the images will reduct the amount of the embedded data, and using VQ indexing scheme can easily recall the embedded watermark for the purpose of image authentication, and establishing the relation between the association rules on both the original image and the watermark image. The Vector Quantization decoding technique is applied to reconstruct the watermarked image from the watermarked index table. The experimental result shows that the proposed scheme is robust. When the watermarked images suffered from various kinds of image-processing procedures, such as Gaussian noise, brightness, blurring, sharpening, cropping, and JPEG lossy compression can be detected without the original images assistance.

Intrusion Detection System (IDS) is an important and necessary component in ensuring network security and protecting network resources and infrastructures. In this paper, we effectively introduced intrusion detection system by using Principal Component Analysis (PCA) with Support Vector Machines (SVMs) as an approach to select the optimum feature subset. We verify the effectiveness and the feasibility of the proposed IDS system by several experiments on NSL-KDD dataset. A reduction process has been used to reduce the number of features in order to decrease the complexity of the system. The experimental results show that the proposed system is able to speed up the process of intrusion detection and to minimize the memory space and CPU time cost.

In the recent years human brain segmentation in three-dimensional magnetic resonance imaging (MRI) has gained a lot of importance in the field of biomedical image processing since it is the main stage for the automatic brain disease diagnosis. In this paper, we propose an image segmentation scheme to segment 3D brain tumor from MRI images through the clustering process. The clustering is achieved using K-mean algorithm in conjunction with the connected component labeling algorithm to link the similar clustered objects in all 2D slices and then obtain 3D segmented tissue using the patch object rendering process.

This paper presents a model of a supervised machine learning approach for classification of a dataset. The model extracts a set of patterns common in a single class from the training dataset according to the rules of the pattern-based subspace clustering technique. These extracted patterns are used to classify the objects of that class in the testing dataset. The user-defined threshold dependence problem in this clustering technique has been resolved in the proposed model. Also this model solve the curse of dimensionality problem without the need of using a separate dimensionality reduction method. Another distinguishing point in this model is its dependence on the variation of the values of relative features among different objects. Experimental results on synthetic and real life datasets show that this approach is more efficient and effective than the existing techniques.

This paper presents a semi-automatic system for home video annotation that searches into the video contents and retrieves video shots for a specific person. The proposed system is composed of four phases; 1) shot detection phase that detects shots boundaries and divides the original video into shots, 2) face detection and recognition phase that detects faces in video shots based on Haar-like features and uses the Principal Component Analysis (PCA) algorithm for features extraction in order to select the eigenvectors with the largest eigenvalues, 3) face clustering and annotation phase that groups faces with similar facial features into the same cluster and apply face annotation on persons' shots (person's faces) using User Interface, and 4) retrieving phase that enables the user to enter a query to search by person's name then retrieves the video shots for this query from the database of the video shots. The proposed system is simple and provides a friendly user interface. It greatly reduces workload and enhances the accuracy of annotating person's faces in home videos.

This paper presents a review of the current literature on rough-set- and near-set-based approaches to solving various problems in medical imaging such as medical image segmentation, object extraction, and image classification. Rough set frameworks hybridized with other computational intelligence technologies that include neural networks, particle swarm optimization, support vector machines, and fuzzy sets are also presented. In addition, a brief introduction to near sets and near images with an application to MRI images is given. Near sets offer a generalization of traditional rough set theory and a promising approach to solving the medical image correspondence problem as well as an approach to classifying perceptual objects by means of features in solving medical imaging problems. Other generalizations of rough sets such as neighborhood systems, shadowed sets, and tolerance spaces are also briefly considered in solving a variety of medical imaging problems. Challenges to be addressed and future directions of research are identified and an extensive bibliography is also included.

Rough set theory offers a novel approach to manage uncertainty that has been used for the discovery of data dependencies, importance of features, patterns in sample data, feature space dimensionality reduction, and the classification of objects. Consequently, rough sets have been successfully employed for various image processing tasks including image segmentation, enhancement and classification. Nevertheless, while rough sets on their own provide a powerful technique, it is often the combination with other computational intelligence techniques that results in a truly effective approach. In this paper we show how rough sets have been combined with various other methodologies such as neural networks, wavelets, mathematical morphology, fuzzy sets, genetic algorithms, Bayesian approaches, swarm optimization, and support vector machines in the image processing domain.

The objective of this research is to illustrate how rough sets can be successfully integrated with mathematical morphology and provide a more effective hybrid approach to resolve medical imaging problems. Hybridization of rough sets and mathematical morphology techniques has been applied to depict their ability to improve the classification of breast cancer images into two outcomes: malignant and benign cancer. Algorithms based on mathematical morphology are first applied to enhance the contrast of the whole original image; to extract the region of interest (ROI) and to enhance the edges surrounding that region. Then, features are extracted characterizing the underlying texture of the ROI by using the gray-level co-occurrence matrix. The rough set approach to attribute reduction and rule generation is further presented. Finally, rough morphology is designed for discrimination of different ROI to test whether they represent malignant cancer or benign cancer. To evaluate performance of the presented rough morphology approach, we tested different mammogram images. The experimental results illustrate that the overall performance in locating optimal orientation offered by the proposed approach is high compared with other hybrid systems such as rough-neural and rough-fuzzy systems.

This paper presents and develops an automated algorithm for segmenting speculated masses of the mammogram images based on pulse coupled neural networks (PCNN) in conjunction with fuzzy set theory. Mammogram image segmentation has proven to be a difficult task due to the low contrast between normal and malignant glandular tissues and the noise in such images that makes it very difficult to segment them. Therefore, the fuzzy histogram hyperbolization (FHH) algorithm is first used as a filter before the segmentation process. Then, the PCNN is applied to segment the images to arrive at the final result. To test the effectiveness of PCNNs on high quality images, a set of mammogram images was chosen. The experimental results show that the proposed algorithm performs well as compared to the fuzzy thresholds and fuzzy C-mean results.

Abstract Rough set theory is a relatively new intelligent technique used in the discovery of data dependencies; it evaluates the importance of attributes, discovers the patterns of data, reduces all redundant objects and attributes, and seeks the minimum subset of attributes. Moreover, it is being used for the extraction of rules from databases. In this paper, we present a rough set approach to attribute reduction and generation of classification rules from a set of medical datasets. For this purpose, we first introduce a rough set reduction technique to find all reducts of the data that contain the minimal subset of attributes associated with a class label for classification. To evaluate the validity of the rules based on the approximation quality of the attributes, we introduce a statistical test to evaluate the significance of the rules. Experimental results from applying the rough set approach to the set of data samples are given and evaluated. In addition, the rough set classification accuracy is also compared to the well‐known ID3 classifier algorithm. The study showed that the theory of rough sets is a useful tool for inductive learning and a valuable aid for building expert systems.

We propose a new and effective image modeling dual technique which is capable of simulating both texture image synthesis and non-texture images like fractals. The technique uses the algebraic approach of graph grammars theory as a new simulation tool for both texture and non-texture image synthesis via its graph production, derivation and double-pushout construction. Validation of our approach is given by discussion and an illustration of some experimental results. An investigation of the relationships between the generated patterns and their corresponding graph grammars is also discussed.

We propose an image morphing algorithm which uses Navier elastic body splines to generate warp functions for interpolating scattered data points. The spline is based on a partial differential equation proposed by Navier that describes the equilibrium displacement of an elastic body subjected to forces. The spline maps can be expressed as the linear combination of an affine transformation and a Navier interpolation spline. The proposed algorithm generates a smooth warp that reflects feature point correspondences. It is efficient in time complexity and smoothly interpolated morphed images with only a remarkably small number of specified feature points. The algorithm allows each feature point in the source image to be mapped to the corresponding feature point in the destination image. Once the images are warped to align the positions of features and their shapes, the in-between facial animation from two given facial images can be defined by cross dissolving the positions of correspondence features and their shapes and colors. We describe an efficient cross dissolve algorithm for generating the in-between images.

The warping of scattered data points is a problem that is appearing in science and engineering. The basic problem involves the construction of a reasonable interpolation function that goes through some of the data points. In this paper, we propose a new spatial image warping algorithm which uses an elastic body spline to generate a warp function that interpolates scattered data points. The spline is based on a partial differential equation of Naiver that describes the equilibrium displacement of the elastic body when subjected to certain forces. The spline maps can be expressed as the linear combination of an affine transformation and a Naiver interpolation spline. The proposed algorithm generates a smooth warp that reflects the feature correspondence points. The in-between facial animation from given true facial images could be defined by interpolating the positions of correspondence features and their shapes and colors.