An Efficient and Reliable Routing Method for Hybrid Mobile Ad Hoc Networks Using Deep Reinforcement Learning.

With the reliance of humans on mobile smart devices that have wireless communication, modules have significantly increased in recent years. Using these devices to communicate with the survivors during a disaster or its aftermath can significantly increase the chances of locating and saving them. Accordingly, a method is proposed in this study to extend the lifetime of the nodes in a Mobile Ad Hoc Network (MANET) while maintaining communications with the nearest base station (BS). Such a methodology allows the rapid establishment of temporary communications with these survivors, as restoring the complex infrastructure is a time-consuming process. The proposed method achieves the longer lifetime of the network by balancing the load throughout the nodes and avoids exhausting those with limited remaining energy. The proposed method has shown significant improvement in the lifetime of the MANET while maintaining similar Packet Delivery Rate (PDR) and route generation time, compared to existing methods.

Classification of Resting-State Status Based on Sample Entropy and Power Spectrum of Electroencephalography (EEG).

An electroencephalogram (EEG) is a significant source of diagnosing brain issues. It is also a mediator between the external world and the brain, especially in the case of any mental illness; however, it has been widely used to monitor the dynamics of the brain in healthy subjects. This paper discusses the resting state of the brain with eyes open (EO) and eyes closed (EC) by using sixteen channels by the use of conventional frequency bands and entropy of the EEG signal. The Fast Fourier Transform (FFT) and sample entropy (SE) of each sensor are computed as methods of feature extraction. Six classifiers, including logistic regression (LR), K-Nearest Neighbors (KNN), linear discriminant (LD), decision tree (DT), support vector machine (SVM), and Gaussian Naive Bayes (GNB) are used to discriminate the resting states of the brain based on the extracted features. EEG data were epoched with one-second-length windows, and they were used to compute the features to classify EO and EC conditions. Results showed that the LR and SVM classifiers had the highest average classification accuracy (97%). Accuracies of LD, KNN, and DT were 95%, 93%, and 92%, respectively. GNB gained the least accuracy (86%) when conventional frequency bands were used. On the other hand, when SE was used, the average accuracies of SVM, LD, LR, GNB, KNN, and DT algorithms were 92% 90%, 89%, 89%, 86%, and 86%, respectively.

Social Touch Gesture Recognition Using Convolutional Neural Network.

Recently, social touch gesture recognition has been considered an important topic for touch modality, which can lead to highly efficient and realistic human-robot interaction. In this paper, a deep convolutional neural network is selected to implement a social touch recognition system for raw input samples (sensor data) only. The touch gesture recognition is performed using a dataset previously measured with numerous subjects that perform varying social gestures. This dataset is dubbed as the corpus of social touch, where touch was performed on a mannequin arm. A leave-one-subject-out cross-validation method is used to evaluate system performance. The proposed method can recognize gestures in nearly real time after acquiring a minimum number of frames (the average range of frame length was from 0.2% to 4.19% from the original frame lengths) with a classification accuracy of 63.7%. The achieved classification accuracy is competitive in terms of the performance of existing algorithms. Furthermore, the proposed system outperforms other classification algorithms in terms of classification ratio and touch recognition time without data preprocessing for the same dataset.

Mammographical mass detection and classification using local seed region growing-spherical wavelet transform (LSRG-SWT) hybrid scheme.

The purpose of this study is to implement accurate methods of detection and classification of benign and malignant breast masses in mammograms. Our new proposed method, which can be used as a diagnostic tool, is denoted Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT), and consists of four steps. The first step is homomorphic filtering for enhancement, and the second is detection of the region of interests (ROIs) using a Local Seed Region Growing (LSRG) algorithm, which we developed. The third step incoporates Spherical Wavelet Transform (SWT) and feature extraction. Finally the fourth step is classification, which consists of two sequential components: the 1st classification distinguishes the ROIs as either mass or non-mass and the 2nd classification distinguishes the masses as either benign or malignant using a Support Vector Machine (SVM). The mammograms used in this study were acquired from the hospital of Istanbul University (I.U.) in Turkey and the Mammographic Image Analysis Society (MIAS). The results demonstrate that the proposed scheme LSRG-SWT achieves 96% and 93.59% accuracy in mass/non-mass classification (1st component) and benign/malignant classification (2nd component) respectively when using the I.U. database with k-fold cross validation. The system achieves 94% and 91.67% accuracy in mass/non-mass classification and benign/malignant classification respectively when using the I.U. database as a training set and the MIAS database as a test set with external validation.

Mammographic mass detection using wavelets as input to neural networks.

The objective of this paper is to demonstrate the utility of artificial neural networks, in combination with wavelet transforms for the detection of mammogram masses as malign or benign. A total of 45 patients who had breast masses in their mammography were enrolled in the study. The neural network was trained on the wavelet based feature vectors extracted from the mammogram masses for both benign and malign data. Therefore, in this study, Multilayer ANN was trained with the Backpropagation, Conjugate Gradient and Levenberg-Marquardt algorithms and ten-fold cross validation procedure was used. A satisfying sensitivity percentage of 89.2% was achieved with Levenberg-Marquardt algorithm. Since, this algorithm combines the best features of the Gauss-Newton technique and the other steepest-descent algorithms and thus it reaches desired results very fast.

Diagnosis of renal failure disease using Adaptive Neuro-Fuzzy Inference System.

Adaptive Neuro-Fuzzy Inference System (ANFIS) is one of the useful and powerful neural network approaches for the solution of function approximation and pattern recognition problems in the last decades. In this paper, the diagnosis of renal failure disease is investigated using ANFIS approach. Totally the raw data of 112 patients is obtained from Istanbul and Cerrahpasa Medical Faculties of Istanbul University, Turkey. Sixty-four of them are related to renal failures and the rest data belong to healthy persons. In ANFIS model, three rules and Gaussian membership functions are chosen, where rules are determined by the subtractive clustering method. Seven parameters of the patients are considered for the input of the system. These are: Blood Urea Nitrogen (BUN), Creatinine, Uric Acid, Potassium (K), Calcium (Ca), Phosphorus (P) and age. We try to decide whether the patient is ill or not. We have reached 100% success in ANFIS and have better results compared to Support Vector Machine (SVM) and Artificial Neural Networks (ANN).

A wavelet-based mammographic image denoising and enhancement with homomorphic filtering.

Breast cancer continues to be a significant public health problem in the world. The diagnosing mammography method is the most effective technology for early detection of the breast cancer. However, in some cases, it is difficult for radiologists to detect the typical diagnostic signs, such as masses and microcalcifications on the mammograms. This paper describes a new method for mammographic image enhancement and denoising based on wavelet transform and homomorphic filtering. The mammograms are acquired from the Faculty of Medicine of the University of Akdeniz and the University of Istanbul in Turkey. Firstly wavelet transform of the mammograms is obtained and the approximation coefficients are filtered by homomorphic filter. Then the detail coefficients of the wavelet associated with noise and edges are modeled by Gaussian and Laplacian variables, respectively. The considered coefficients are compressed and enhanced using these variables with a shrinkage function. Finally using a proposed adaptive thresholding the fine details of the mammograms are retained and the noise is suppressed. The preliminary results of our work indicate that this method provides much more visibility for the suspicious regions.

Colonic polyp detection in CT colonography with fuzzy rule based 3D template matching.

In this paper, we introduced a computer aided detection (CAD) system to facilitate colonic polyp detection in computer tomography (CT) data using cellular neural network, genetic algorithm and three dimensional (3D) template matching with fuzzy rule based tresholding. The CAD system extracts colon region from CT images using cellular neural network (CNN) having A, B and I templates that are optimized by genetic algorithm in order to improve the segmentation performance. Then, the system performs a 3D template matching within four layers with three different cell of 8 x 8, 12 x 12 and 20 x 20 to detect polyps. The CAD system is evaluated with 1043 CT colonography images from 16 patients containing 15 marked polyps. All colon regions are segmented properly. The overall sensitivity of proposed CAD system is 100% with the level of 0.53 false positives (FPs) per slice and 11.75 FPs per patient for the 8 x 8 cell template. For the 12 x 12 cell templates, detection sensitivity is 100% at 0.494 FPs per slice and 8.75 FPs per patient and for the 20 x 20 cell templates, detection sensitivity is 86.66% with the level of 0.452 FPs per slice and 6.25 FPs per patient.

Nodule detection in a lung region that's segmented with using genetic cellular neural networks and 3D template matching with fuzzy rule based thresholding.

OBJECTIVE: The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. MATERIALS AND METHODS: Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset. RESULTS: The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm. CONCLUSION: Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computer-aided detection of lung nodules.

Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching.

A novel fully automated system is introduced to facilitate lesion detection in dynamic contrast-enhanced, magnetic resonance mammography (DCE-MRM). The system extracts breast regions from pre-contrast images using a cellular neural network, generates normalized maximum intensity-time ratio (nMITR) maps and performs 3D template matching with three layers of 12x12 cells to detect lesions. A breast is considered to be properly segmented when relative overlap >0.85 and misclassification rate <0.10. Sensitivity, false-positive rate per slice and per lesion are used to assess detection performance. The system was tested with a dataset of 2064 breast MR images (344slicesx6 acquisitions over time) from 19 women containing 39 marked lesions. Ninety-seven percent of the breasts were segmented properly and all the lesions were detected correctly (detection sensitivity=100%), however, there were some false-positive detections (31%/lesion, 10%/slice).

Estimation of stream temperature in firtina creek (Rize-Turkiye) using artificial neural network model.

Water temperature is one of the most important environmental variables in aquatic ecosystem. Temperature changes may have positive or negative effects on organisms. High water temperatures have caused mortalities in salmonid fishes. Therefore, monitoring and prediction of potential adverse changes in water temperature is very important. Here, we have developed and tested an artificial neural network (ANN) model to predict stream temperature of Firtina Creekin Black Sea region; using local water temperature, dissolved oxygen, pH and other available meteorological data (air temperature, rainfall). Thus, enabling define suitable habitat for native Sea Trout (Salmo trutta labrax, Pallas 1811) under past drought or other adverse envIronmental conditions.

Lung nodule diagnosis using 3D template matching.

In this paper, to utilize the third dimension of Computed Tomography, regions of interest (ROI) slices were combined to form 3D ROI image and a 3D template was determined to find the structures with similar properties of nodules. Convolution of 3D ROI image with the proposed template strengthens the shapes similar to the template and weakens the other ones. False-positive (FP) per nodule and per slice versus diagnosis sensitivity were obtained. The Computer Aided Diagnosis system achieved 100% sensitivity with 0.83 FP per nodule and 0.46 FP per slice, when the nodule thickness was greater than or equal to 5.625 mm.